Professor Prashant Kumar

Traditional wood stoves emit high levels of particulate matter ≤ 2.5 μm (PM2.5), prompting the development of improved models to reduce emissions. However, these stoves may unintentionally increase ultrafine particle ≤ 100 nm (UFP) emissions, which can penetrate biological barriers and pose health risks. This study evaluates the impacts of four solid fuel types on indoor air quality (IAQ) in five non-smoking households in Guildford, United Kingdom, using different wood stoves (eco-design, multifuel eco-design, clear skies stage (v), and open fireplace) during winter. Indoor UFP, PM10, PM2.5, black carbon (BC), and carbon monoxide (CO) levels were measured using handheld monitors in living areas. Fuel type, room volume, stove type, and burning duration significantly influenced IAQ, exacerbated by inadequate ventilation. Open fireplaces had the highest exposure levels, followed by multifuel eco-design, eco-design, and clear skies stage (v) stoves. During burning periods, median (interquartile range) indoor pollutant concentrations were UFP: 3.6 (5.8) ×10⁴ # cm⁻³, PM2.5: 38.4 (65.5) µg m⁻³, PM10: 89.6 (89.0) µg m⁻³, and BC: 1.7 (3.6) µg m⁻³ for open fireplaces. Among improved stoves, multifuel eco-design had the highest exposure; UFP: 2.2 (4.9) ×10⁴ # cm⁻³, PM2.5: 14.2 (16.9) µg m⁻³, PM10: 37.9 (45.9) µg m⁻³, and BC: 1.5 (2.3) µg m⁻³ followed by eco-design, and clear skies stage (v) stoves. Wood briquettes produced the highest pollutant levels, followed by smokeless coal, kiln-dried wood, and seasoned wood. Burning manufactured fuels (wood briquettes) increased PM2.5 and UFP by 4- and 1.5-times, respectively, compared to seasoned wood. The mean CO concentration for open fireplaces was 3.1 ppm, below the World Health Organisation’s (WHO) 24-hour exposure limit guideline (3.49 ppm). Smaller rooms ( 50 m³). Low air changes per hour (ACH) (

Hydrogen possesses the ability to produce energy with minimal greenhouse gas emissions when sustainably produced, making it a promising renewable energy carrier. Moreover, recent advancements in Artificial Intelligence (AI) can further enhance cleaner hydrogen production in a more optimised way. The main objective of this review paper is to comprehensively examine the current state-of-the-art in the integration of AI techniques with Renewable Energy Sources (RES), such as biomass, solar, algae power, geothermal, and wind to advance various hydrogen production methods, including electrolysis, biological, and photovoltaic processes. Furthermore, we aim to explore how AI optimisation can enhance sustainability, reliability, and commercial viability of Green Hydrogen (GH2) systems. These processes are crucial for reducing greenhouse gas emissions and meet the world's growing energy needs. The integration of RES with hydrogen production technologies has been recognised as a key strategy to attain a sustainable and environmentally friendly energy future, and the incorporation of AI can optimise efficiency and cost-effectiveness. This review found that there is a growing interest in the development of AI techniques to optimise GH2 production. While most of the studies focus on utilising wind and solar energy sources, this review found minimal existing research applying AI to GH2 production from algae, ocean, intermittency, and hybrid RES. Moreover, no works exploring AI to optimise GH2 production from sources like tidal and hydropower were found. Thus, prioritising AI-enabled system development to integrate and optimise these resources for GH2 production can help progress renewable generation capabilities towards a more sustainable, cleaner, carbon-free future for industry, transport, and societal sectors. Further extensive research is essential to fully harness the promise of AI in transforming diverse RES for clean hydrogen.

Climate change endangers approximately 3.6 billion human lives. To limit climate impacts and reduce loss of life, existing urban settlements require proactive transformation, to become climate resilient and sustainable. The review identifies urban design principles that address regional climate change hazards, specified by the Intergovernmental Panel on Climate Change. The objective of this review is to create a framework to inform design of climate resilient, sustainable urban settlements, specifically neighbourhoods. Following PRISMA-SCR guidance, existing knowledge is integrated to propose a framework comprised of urban design principles at varying urban scales, that resolve common climate hazards for multiple regions. This review proposed a resilient urbanism framework, a set of spatial metrics for climate resilient sustainable neighbourhoods and indicators to assess the success of a design proposal or intervention. The metrics and indicators may have potential for improving urban design practice and the regulatory frameworks influencing where and what is built. Finally, the review highlights knowledge gaps on climate change hazards in the records for Africa, Asia, Australasia, North, Central and South America, and Europe, requiring further research. Small Islands are unrepresented, so related climate hazards are highlighted for academic researcher consideration.

Background: Antibiotic resistance increasingly threatens the interconnected health of humans, animals, and the environment. While misuse of antibiotics is a known driver, environmental factors also play a critical role. A balanced One Health approach—including the environmental sector—is necessary to understand the emergence and spread of resistance. Methods: We systematically searched English-language literature (1990–2021) in MEDLINE, Embase, and Web of Science, plus grey literature. Titles, abstracts, and keywords were screened, followed by full-text reviews using a structured codebook and dual-reviewer assessments. Results: Of 13,667 records screened, 738 met the inclusion criteria. Most studies focused on freshwater and terrestrial environments, particularly associated with wastewater or manure sources. Evidence of research has predominantly focused on Escherichia coli and Pseudomonas spp., with a concentration on ARGs conferring resistance to sulphonamides (sul1–3), tetracyclines (tet), and beta-lactams. Additionally, the People’s Republic of China has produced a third of the studies—twice that of the next country, the United States—and research was largely domestic, with closely linked author networks. Conclusion: Significant evidence gaps persist in understanding antibiotic resistance in non-built environments, particularly in marine, atmospheric, and non-agricultural set65 tings. Stressors such as climate change and microplastics remain notably under-explored. There is also an urgent need for more research in low-income regions, which face higher risks of antibiotic resistance, to support the development of targeted, evidence-based interventions.

A smart city is an urban area that uses emerging technologies to enhance city operations and citizen services. With over 250 smart city initiatives worldwide and a market projected to reach $97.9 billion by 2026, the focus is shifting from efficiency and technology toward prioritizing citizen health and equity. Recent research underscores the profound impact of external environments on health, highlighting the need to assess the multidimensional environmental factors. Smart monitoring technologies create promising opportunities for innovative solutions in smart cities. This research systematically reviews technologies for environmental and health monitoring in smart cities, examining trends, challenges, and opportunities for creating healthier urban environments. Our analysis of 64 studies identifies twelve distinct monitoring domains categorized by spatial dimension (home, neighborhood, city) and target (behavioral vs. environmental). Key findings reveal that residential-level monitoring primarily focuses on individual health behaviors, while neighborhood- and city-level technologies enable broader applications including real-time air quality mapping, noise assessment, and integrated urban monitoring. These technologies facilitate early detection of community-level disease risks, efficient environmental pollutant management, and faster emergency responses. However, implementation challenges persist, including technical limitations, marketability concerns, usability barriers, privacy issues, and health equity considerations. As monitoring capabilities show growing disparity between developed and developing regions, addressing these challenges is crucial for implementing these technologies equitably to reduce health disparities and promote community health across all populations.

Nitrogen dioxide (NO2) is a major air pollutant in urban areas, prompting the development of numerous analytical methods for its monitoring. Among these, the chemiluminescence method stands out as the most commonly used and is widely regarded as a reference method. In recent years, the development of low-cost sensor (LCS) technology has facilitated the outdoor measurement of NO2 using different analytical methods. Nevertheless, the performance of these methods needs to be evaluated against reference methods. This review aims to identify studies that utilise both LCS and reference methods for measuring NO2 in urban environments and to assess the performance of LCS. For this purpose, we conducted a search across four different scientific databases (Scopus, Web of Science, PubMed, and ScienceDirect). For detailed analysis, 65 primary studies were selected based on criteria such as real-case applications using measured data and the requirement for reference instruments and sensors to be measured together outdoors. The results clearly indicate that the majority of studies were conducted in the USA (n=14), the UK (n=7), and China (n=5). Electrochemical (EC) LCS were used in 95% of the studies, while metal oxide semiconductor (MOS) LCS were utilized in only 17%, with EC LCS outperforming MOS sensors. Among sensor performance evaluation methods, machine learning techniques were the most commonly employed (68 applications), followed by linear regression and multiple linear regression methods (38 and 36 applications, respectively). Additionally, 79% of studies measured NO2 alongside ozone. Ambient temperature and humidity were found to influence LCS measurements significantly. Enhancing LCS to minimize external interference and interaction with other pollutants could improve the performance and reliability of NO2 measurements, facilitating higher-performance applications. The adoption of LCS can offer policymakers detailed insights for source identification, pollution hotspot detection, and trend analysis.

Urban greening plays a crucial role in enhancing climate resilience, environmental quality, public health, and societal wellbeing. Policy makers at all levels are increasingly embracing greening and other nature-based solutions; however, successful implementation of these approaches requires a multidisciplinary strategy involving collaboration, community engagement, and adaptive interventions. This paper synthesizes key insights from an expert panel, comprising representatives of government agencies, research institutions, private sector, and local authorities, forming an international panel of experts, convened by the RECLAIM Network Plus (https://www.reclaim-network.org). Funded by the United Kingdom Research and Innovation (UKRI), this network project brings together over 650 members from 40 plus countries. It provides national leadership in urban greening, serving as a “one-stop-shop” for towns and cities to access green and blue infrastructure support, resources, and peer connections. The paper highlights grand challenges, priorities, successful case studies and opportunities in urban greening initiatives. The importance of strategic planning is also addressed, together with technological advancements and access to suitable data for maximizing the impact of green infrastructure as well as innovative funding models, such as corporate social responsibility and green finance. This work highlights the importance of an integrated, inclusive, and forward-thinking approach to urban greening for more resilient, sustainable, and equitable cities. Further, it emphasizes the need for cross-sector collaboration among local authorities, researchers, and businesses, as well as community involvement in successful planning for both short- and long-term outcomes. Ultimately, urban greening strategies must be informed by future climate scenarios while prioritizing equity and social justice to ensure adaptation options benefitting all communities.

In naturally ventilated classrooms, the air quality and ventilation conditions rely heavily upon window and door opening behaviour of the class teacher. This study aimed to examine the impact of an Internet of Things (IoT)-based CO₂ self-surveillance display system (SAVE unit) on classroom air quality and thermal comfort. The visual-acoustic signalling of classroom ventilation conditions notified the class teacher for opening/closing of classroom doors and windows. The air quality data were collected across baseline (no alarm), S1 (visual alarm) and S2 (visual-acoustic alarm) scenarios. The alarm was triggered to notify a class teacher when CO₂ exceeded 1000 and 1500 ppm with different alarm schemes. Results showed a 19.5 % reduction in CO₂ with visual alarms and 19 % with visual-acoustic alarms. However, PM concentrations (PM₁₀, PM₂.₅, PM₁, PM₀.₁) increased due to window openings, though a daily average of PM₁₀ and PM₂.₅ remained within WHO safe limits. Teacher's decision to open or close windows was primarily influenced by classroom temperature. Higher CO2 concentration was observed during colder days (S2) due to windows kept closed to avoid uncomfortable classroom temperature and excessive use of heaters. The SAVE unit helped to effectively lowered CO₂, but it also led to higher energy consumption due to heat loss from open windows. While natural ventilation improved air quality, it highlighted the need for balancing energy efficiency and thermal comfort A holistic signalling system that integrates temperature, air quality, and ventilation parameters would better guide teachers in managing classroom ventilation. Additionally, a school-level ventilation protocol based on IoT-based signalling is recommended to ensure consistent and effective air quality management. This study underscores the importance of real-time data-driven ventilation strategies to optimise indoor air quality, reduce exposure to pollutants, and maintain a comfortable learning environment in classrooms. [Display omitted] •Ventilation behaviour induced by the visual alarm reduced classroom CO₂ by 19.5 %.•Ventilation behaviour induced by the visual-acoustic alarm reduced classroom CO₂ by 19 %.•Classroom PM increased due to window opening but remained under WHO limits.•Visual alarm is preferred in classrooms as it causes less distraction than visual-acoustic alarm.•Higher energy use could hinder large-scale adoption of such systems in natural-ventilated schools.

The COVID-19 pandemic demonstrated a profound inability of pre-pandemic passenger ship policies implemented by both ship operators and governmental authorities to detect and address newly emerging diseases. The essentiality of maritime transport puts into focus the risk of approach to address known and new emerging airborne infectious diseases that, due to increasing capacity, are likely to occur on passenger ships. In order to enhance the passenger experience, prepare shipping for pandemics like COVID-19, and improve the resilience and safety of the industry, this review critically synthesises existing literature on (1) monitoring ventilation conditions and aerosol dispersion, linking them to airborne transmission risk using airborne aerosols and ventilation performance as input parameters for computational fluid dynamics (CFD) simulations, and (2) modelling airborne disease transmission risk in controlled passenger ship environments. This review analysed 39 studies on aerosol monitoring, thermal comfort, and infection risk modelling on passenger ships (2000–2023). Additionally, 55 papers on CFD modelling of airborne pathogen dispersion were reviewed: 22 included validation, with most focused on built environments and only four specifically addressing ship environments. Two major challenges relate to the complexity and poorly characterised ventilation boundary conditions on passenger ships, and the other is the lack of suitable validation data. For this reason, ship experimental studies are required for CFD model validation. Only a handful of studies were found that have measured aerosol concentrations on board passenger ships. To the best of our knowledge, there have been no studies conducted on aerosol mass or airborne transmission sampling on board passenger ships or other types of vessels. The results of this review have the potential to create synergistic connections between experimental and modelling studies to inform, characterise and improve the development of numerical models that can accurately estimate infection risk on ships for prevention, mitigation and management of outbreaks.

We developed and evaluated an AI-based surrogate model to simulate Hydrogen (H2) production via Proton Exchange Membrane Water Electrolysis (PEMWE). A variety of Machine Learning (ML) and Deep Learning (DL) models were tested and fine-tuned using real-world PEMWE datasets from multiple sources, ensuring model robustness and accuracy. The models included ML algorithms such as k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Category Boosting (CB), Light Gradient Boosting (LGB), and Gradient Boosting (GB), with DL models Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and one-dimensional (1D) Convolutional Neural Networks (CNN). Among these, the 1DCNN model demonstrated superior performance, achieving an R-squared (R2) of 0.998944, a Mean Squared Error (MSE) of 488.82, a very low Normalised Root Mean Square Error (NMSE) of 0.001055, and Pearson correlation of 0.999472, having MLP also performing exceptionally well. Conversely, the LSTM model performed the poorest. Unlike many prior studies, we employed cross-validation techniques to rigorously validate model performance and the Wilcoxon Signed-Rank Test for statistical validation, establishing the robustness and reliability of 1DCNN predictions. Comparatively, the MLP model performed well but failed to pass the Wilcoxon test, indicating its predictions were not statistically different from any other models. These findings underscore the novelty of the proposed 1DCNN-based surrogate model, which stands out in its ability to accurately simulate PEMWE behaviour for H2 production. This model can simulate PEMWE processes in a fraction of the time required by traditional methods, providing valuable operational insights and advancing technologies across H2 production, energy sectors, transportation, and sustainable energy systems.

Older people are more susceptible to health risks from indoor air pollution, even at low pollution levels. This study aimed to improve ventilation and air quality of densely occupied multipurpose rooms in elderly care centers (ECCs). The specific objectives were to investigate the impact of different ventilation types on ventilation performance and air quality in the ECC’s multipurpose room, including mixing (MV), displacement (DV), zone (ZV), stratum (SV), and underfloor ventilation (UV); analyze the influence of ventilation on CO2 concentration; and discuss appropriate ventilation design comprehensively considering air velocity, CO2 level, air change efficiency (ACE), mean age of air (MAA), contaminant removal effectiveness (CRE), and predicted mean vote (PMV). First, an experimental study was conducted, and then 11 potential optimization models were proposed based on the experiment’s results. Finally, quantitative results were obtained through computational fluid dynamics (CFD) analysis. Analysis revealed that model ZV2 with wall outlets at 1.1 m and ceiling inlets proved to be the optimal ventilation type. It had fewer air circulation and stagnation areas, and it maintained its effectiveness regardless of furniture configurations or occupant positioning (caregivers and elderly people), factors that could potentially compromise airflow in other models. Compared to model ZV2, CO2 concentrations in the sitting breathing zone of other models increased from a minimal increase of 0.2% to a substantial increase of 38.9%. Analysis also showed that the seats in the first row consistently maintained low pollutant concentration environments. These new results offer valuable insights for ECC stakeholders by assessing ventilation and air quality in crowded spaces for older people at two different breathing heights (sitting and standing).

Delhi is one of the world’s most polluted cities, and hence pollution control is a very high priority. A key aspect of a cost-effective strategy is to differentiate emissions from local sources from pollution arising from regional transport. Detailed chemical characterisation of atmospheric fine particulate matter (PM2.5) has been made at two sites in three seasons with a view to source attribution. Chloride ion (Cl-) concentrations make an exceptional contribution at both sites in winter, reaching a maximum of 76 and 97 μg m-3 during the winter campaign at the central and suburban sites, respectively. Differing diurnal patterns of Cl- were observed between the seasons, indicating contrasts in sources and secondary chemical processes between seasons. Inter-site comparisons show high correlation between secondary inorganics (NO3-, SO42-, and NH4+) in the winter and pre-monsoon, indicating the influence of regional processes for their formation at both sites, while for Cl- and other measured ions (Na+, Ca2+, Mg2+, PO42- and K+), local influences were found to be dominant through inter-site comparisons and ventilation coefficient analyses. A significant contribution of local sources to secondary inorganic compounds and Organic Matter mass within PM2.5 in the post-monsoon period was observed, implying that the regional sources (such as crop residue burning, often indicted as the cause of poor air quality) are not the sole driver for high PM2.5 loadings in Delhi in autumn, contrary to previous studies.

Cities must be prepared to improve the quality of life, including exposure to air pollutants, for their residents as urban populations continue to grow. Green infrastructure and green areas have proven increasingly important in tackling this challenge. This study examined the relationship between urban green spaces and air pollution in the São Paulo Metropolitan Area. Using PM10 and PM2.5 as indicators of atmospheric pollutants and NDVI and NDBI to evaluate green areas and building density, statistical tools such as cluster analysis and Sen's Slope test were applied, complemented by visual analyses with temporal heat maps. The results indicated a general reduction in PM2.5 and PM10 concentrations from 2014 to 2023, with NDVI increasing until 2021 and then decreasing, possibly due to real estate speculation. Regions with higher NDVI showed 50 % lower PM concentrations compared with industrial and high-traffic areas. The Sen's Slope analysis outlined the influence of tree-planting public policies on reducing pollutant levels. These findings the ongoing importance of research on the interaction between green areas and urban air quality to inform decision-makers.

An airshed concept has been widely practiced in developed countries as a tool for air quality mitigation, but its application in developing countries is still evolving. The air pollution challenges in developing countries are complex and cannot be solved merely through a city-centric approach, requiring a suitable framework for regional airshed approach to better comprehend the sources, impacts and design adequate response, rather than localised action within administrative boundaries of a city. The implementation of the airshed approach in developing countries may encounter challenges due to various constraints, including limited resources, specifically in terms of finance, and a shortage of trained researchers, such as modellers. Additionally, the lack of high-performance computational facilities and institutional networking further adds to the difficulties faced by these countries. Thus, the main objective of this review paper is to critically analyse the various airshed approaches that are commonly used in the developed countries. By doing so, the review identify gaps in air pollution mitigation strategies specifically in developing countries and proposes a cost-effective and practical airshed management framework that can be implemented in developing countries. Airshed delineation should be based on scientific assessment of air pollution transport and accumulation through representative stations in an airshed and robust source apportionment combining meteorological factors. The domain falling in the spatial extent of airshed may be classified as nonattainment areas for maintaining the uniformity in control actions and effective implementation. Building on city-specific airshed framework, an institutional framework for regional airshed management has also been suggested for planning, monitoring and implementing the participatory approach with financial autonomy and extant regulatory backup. The suggested framework can useful for the policy makers to analyse the air pollution mitigation strategies on a regional scale.

Understanding of the various sources of indoor air pollution requires indoor air quality (IAQ) data that is usually lacking. Such data can be obtained using unobtrusive, low-cost sensors (LCS). The aim of this review is to examine the recent literature published on LCS for IAQ measurements and to determine whether these studies employed any methods to identify or quantify sources of indoor air pollution. Studies were reviewed in terms of whether any methods of source apportionment were employed, as well as the microenvironment type, geographical location, and several metrics relating to the contribution of outdoor pollutant ingress versus potential indoor pollutant sources. We found that out of 60 relevant studies, just four employed methods for source apportionment, all of which utilised receptor models. Most studies were undertaken in residential or educational environments. There is a lack of data on IAQ in other types of microenvironments and in locations outside of Europe and North America. There are inherent limitations with LCS in terms of producing data which can be utilised in source apportionment models. This applies to external pollution data, however IAQ can be even more challenging to measure due to its characteristics. The indoor environment is heterogeneous, with significant variability within the space as well as between different microenvironments and locations. Sensor placement, occupancy, and activity reports, as well as measurements in different microenvironments and locations, can contribute to understanding this variability. Outdoor pollutants can ingress into the space via the building envelope, however measurement of external pollution and environmental conditions, as well as recording details on the building fabric and ventilation conditions, can help apportion external contributions. Whether or not source apportionment models are employed on indoor data from LCS, there are parameters which, if carefully considered during measurement campaigns, can aid in source identification of pollutants.

There is ongoing and rapid advancement in approaches to modelling the fate of exhaled particles in different environments relevant to disease transmission. It is important that models are verified by comparison with each other using a common set of input parameters to ensure that model differences can be interpreted in terms of model physics rather than unspecified differences in model input parameters. In this paper, we define parameters necessary for such benchmarking of models of airborne particles exhaled by humans and transported in the environment during breathing and speaking.

Green-blue-grey infrastructure (GBGI) offers environmental benefits in urban areas, yet its impact on air pollution is under-researched, and the literature fragmented. This review evaluates quantitative studies on GBGI's capability to mitigate air pollution, compares their specific pollutant removal processes, and identifies areas for further investigation. Of the 51 GBGI types reviewed, only 22 provided quantitative pollution reduction data. Street trees and mixed-GBGI are the most studied GBGIs, with efficacy influenced by wind, GBGI type vegetation characteristics, and urban morphology. Negative percentages denote worsening air quality, while positive reflect improvement. The 22 different GBGI grouped into eight main categories provide an average (±s.d.) reduction in air pollution of 16±21% , with substantial reduction shown by linear features (23±21%), parks (22±34%), constructed GI (14±25%), and other non-sealed urban areas (14±20%). Other individual GBGI reducing air pollutants include woodlands (21±38%), hedges (14±25%), green walls (14±27%), shrubland (12±20%), green roofs (13±23%), parks (9±36%), and mixed-GBGI (7±23%). On average, GBGI reduced PM1, PM2.5, PM10, UFP and BC by 13±21%, 1±25%, 7±42%, 27±27% and 16±41%, respectively. GBGI also lowered gaseous pollutants CO, O3 and NOx by 10±21%, 7±21% and 12±36%, on average, respectively. Linear (e.g., street trees and hedges) and constructed (e.g., green walls) features can impact local air quality positively or negatively, based on the configuration and density of the built environment. Street trees generally showed adverse effects in street canyons and beneficial outcomes in open-road conditions. Climate change could worsen air pollution problems and impact GBGI effectiveness by shifting climate zones. In Europe and China, climate shifts are anticipated to affect 8of the 22 GBGIs, with the rest expected to remain resilient. Despite GBGI's potential to enhance air quality, the meta-analysis highlights the need for standardised reporting structure or to enable meaningful comparisons and effectively integrate findings into urban pollution and climate strategies.

•Daily maximum temperature (Tmax) and mortality were associated using the DLNM model.•Minimum mortality temperature is 2oC higher for SE England than Aberdeenshire.•Despite 1oC rise in mean Tmax, mortality is nearly constant for the two sites.•Relative risk of extreme temperature has increased by 7% in SE England.•Although the risk in Aberdeenshire is mixed, the harvested lagged risk persists. Heatwaves pose a protracted health risk depending on its intensity and exposure time. Not only cities but countryside areas are also exposed to risk of summertime heat which has not been recently updated at the bucolic scale. This study aims to associate temperature and mortality and explore its temporal variation. A Poisson regression model combined with a distributed lag non-linear model was applied over daily mortality and maximum temperature data from 1981 to 2018 to formulate the lagged response of summer temperature. The relative risk (RR) and mortality attributable fraction (AF) with respect to minimum mortality temperature (MMT) in Southeast England and Aberdeenshire, UK was calculated. The RR and AF for high and extreme (95th and 99th percentile) temperature with respect to MMT have increased (RR– 1% and 7%; AF– 1.33 and 1.9 times, respectively) in Southeast England but reduced in Aberdeenshire (RR– 2% and 6%; AF– 0.49 and 0.15 times, respectively) in last two decades. However, lagged risk persists for very extreme temperature after several days of exposure at both sites and the hazard cannot be underestimated and neglected. Hence, action is needed to update the heat action plan for extreme temperature management formulating appropriate heat-mitigation strategies focused on vulnerable populations. Graphical Abstract [Display omitted] .

Young children are a vulnerable population cohort. They receive higher exposure to particulate matter than adults in outdoor roadside environments, necessitating research on an unexplored area of exposure to young children in electric bike trailers. We simulated the exposure profiles of an adult cyclist and young children sitting in a bike-trailer attached to it for multiple air pollutants – particulate matter ≤10µm in aerodynamic diameter (PM10), ≤2.5µm (PM2.5; fine particles), ≤1µm (PM1), BC, and CO2 – during the school run in the morning and afternoon hours. We assessed the differences in their exposure concentrations and analysed the impact of trailer covers and COVID-19 lockdown restrictions via simultaneous measurements under six settings forming three scenarios: (i) bike-trailer versus adult cyclist height; (ii) bike-trailer with and without the cover; and (iii) exposure during the lockdown and eased-lockdown periods. We carried out a total of 82 single runs covering a length of 172 km. These runs were repeated on a 2.1 km long predefined route between an origin (University campus) and destination (a local school) to simulate morning drop-off (08:00-10:00h; local time) and afternoon pick-up (15:00-17:00h) times of school children. Substantial variability was observed in concentrations of measured pollutants within each run (e.g., up to 97% for BC) and between different runs (e.g., ~93% for PM2.5 during morning versus afternoon) in bike-trailer. Compared with cyclist height, the average bike-trailer concentration of fine and coarse particles was higher by up to 14% and18%, respectively, during both morning and afternoon runs. The lockdown restrictions when schools were closed led to a reduction in bike-trailer PM2. concentrations by up to 91% compared with eased lockdown period when schools re-opened in March 2021. Trailer covers led up to 50% (fine particles) and 24% (BC; a component of PM2.5) reductions in concentrations compared with trailers without cover. Young children carried in bike trailers are exposed to higher air pollution concentrations compared with the cyclist, particularly during peak morning periods at urban pollution hotspots such as traffic lights.

16 Heavy metals are persistent and bio-accumulative, and pose potential risk to human health and 17 ecosystem. We reviewed the current state of heavy metals contamination, the ecotoxicological 18 and human health risk of heavy metals reported in urban road dust from various cities in different 19 continents (Asia, Europe, Africa, America, and Australia). We compared and synthesized the 20 findings on the methods related to sample collection, extraction, analytical tools of heavy metals, 21 their concentrations, level of contamination, ecological risk, non-carcinogenic risk, and 22 carcinogenic risk in road dust. Concentrations of Pb, Zn, Cu, Ni, Cd, Cr, Mn, and Fe were found 23 to be higher than their background values in soil. As expected, the contamination levels of the 24 heavy metals varied extensively among cities, countries, continents, and periods. A high level of 25 contamination is observed for Pb and Cd in road dust due to operating leaded gasoline and the old 26 vehicle population. The highest Zn contamination was observed from road dust in Europe, 27 followed by Asia, Africa, Australia, and America (North America and South America). Cu 28 contamination and the pollution load index (PLI) is found to be the highest in Europe and lowest 29 in Africa, with in-between values of PLI in American and African cities. The potential ecological 30 risk on different continents was observed highest in Asia, followed by Europe, Australia, America, 31 and Africa. A comparative assessment of non-carcinogenic risk for children indicated that 32 Australia is a most susceptible country due to high heavy metals exposure in road dust, followed 33 by Asia. However, there is no susceptible risk in European, African and American cities. We did 34 not observe any potential risk to adults due to non-carcinogenic metals. Carcinogenic risk to all 35 age groups was within the threshold limit range for all the regions worldwide. 36

Ventilation and indoor air quality are important factors that affect the health of the elderly. The purpose of this study was to find effective ventilation design measures for improving ventilation and air quality in typical two-bed bedrooms in elderly care centres (ECCs). Mixing ventilation (MV), displacement ventilation (DV), zone ventilation (ZV) and stratum ventilation (SV) were analysed with twelve scenarios to find the most effective ventilation design solutions including six scenarios with curtains between the beds and six scenarios without curtains between the beds. Airflow distribution, CO2 concentration, ventilation efficiency and health risk assessment were adopted for discussion. SV was found to be an effective method for improving air quality in the ECC bedroom while also taking into account the needs and rights of elderly residents, such as privacy. Comparing scenarios with and without curtains between beds under same types of ventilation, scenarios without curtains showed a slight (≤8%) decrease in CO2 concentration in the pillow area. However, this could increase virus transmission risk and compromise elderly privacy, so it is not recommended. Regarding the scenarios with curtains between the beds, the contaminant removal efficiency (CRE) of scenarios using SV was increased by 2.58, 3.22 and 2.12 times compared to the scenarios using MV, DV, and ZV respectively. Additionally, the health ratio (HR) of SV was reduced by 46.3%, 53.7%, and 41.7%. Hence, it is recommended to install curtains between the beds and apply SV in ECC bedrooms. This study can be used as a guide for systematically designing ventilation systems in ECC bedrooms. Furthermore, collaboration among environmental engineers, designers, policymakers, and the wider community is essential to develop sustainable indoor environments for the elderly.

The world's population is shifting to the cities, and consequently, cities worldwide are growing in number and in size. Cities are complex systems, making it extremely difficult to build and run cities in a way that all the elements of the system operate in harmony. Recently a concept of urbanome, the genome of the city was proposed to address this complexity. Here we first explore this concept and analogy, taking advantage of the potential of other ‘omics, modern data collection techniques, Big Data analysis methods and a transdisciplinary approach. Then, we propose a theoretical approach to build the urbanome as a means of quantifying and qualifying population outcomes, being a function of the form of an urban area including the built environment, the physical and social services it provides, and the population density.

The study investigates the influence of indoor plants on relative humidity (RH) and the subsequent reduction in particulate matter (PM) in a naturally ventilated office room at CSIR- NPL, Delhi. PM concentrations were compared under two conditions: control (without plants) and experimental (with eight potted Epipremnum aureum plants). The comparison was conducted under two distinct cases: background PM, and induced PM from incense burning. The presence of plants resulted in an average RH increase of 13.55% and a temperature decrease of 4.1%. Plant-induced RH elevation led to a sixfold reduction in PM I/O ratios. RH values (>60%) were negatively correlated with ultrafine, fine, and coarse particles. Plant-induced RH accelerates the deposition loss rate of all sized particles by ∼44% and reduces the infiltration rate by ∼78%. During pre-emission, in addition to PM dry deposition, plant-induced RH contributed to a substantial reduction of fine PM by 6.53% and coarse PM by 26.45% respectively. During incense burning, in the presence of plants, ultrafine PM concentrations dropped by 23.41%, fine PM by 72.39%, and coarse PM by 71.49%. It demonstrates that PM chemical composition significantly influences PM reduction, as it alters particle hygroscopicity. There was a decrease in the mass percentage of elements like Na, Mg, Al, Si, Cl, and K by 1.87, 1.23, 2.26, 5.48, 0.66, and 0.91 percent respectively. It can be inferred that to achieve a 13% increase in the average RH, plants with a leaf area size equivalent to ∼6% of the room surface would be required. [Display omitted] •At RH>60%, negative correlations with different-sized PM observed•Indoor plants-induced RH reduced the I/O ratio of PM concentration by 6-times•Indoor plant-induced RH observed to increase PM deposition loss rate•Maintaining RH ≥60% should be the criteria for selection of number of plants required in a room•To improve RH by 13%, plants with leaf area equivalent to only 6% of room surface is required

This study simulates an unusual extreme rainfall event that occurred in Salvador City, Bahia, Brazil, on December 9, 2017, which was named subtropical storm Guará and had precipitation of approximately 24 mm within less than 1 h. Numerical simulations were conducted using the Weather Research and Forecasting (WRF) model over three domains with horizontal resolutions of 9, 3, and 1 km. Different combinations of seven microphysics, three cumulus, and three planetary boundary layer schemes were evaluated based on their ability to simulate the hourly precipitation during this rainfall event. Statistical indices (MB = -0.69; RMSE = 4.11; MAGE = 1.74; r = 0.55; IOA = 0.66, FAC2 = 0.58) and 47 time series plots showed that the most suitable configuration for this weather event were Mellor-Yamada-Janjić, Grell- Freitas, and Lin for the planetary boundary layer, cumulus, and microphysics schemes, respectively. The results were compared with the data measured at meteorological stations located in Salvador City. The WRF model simulated well the arrival and occurrence of this extreme weather event in a tropical and coastal region, considering that the region already has intense convective characteristics and is constantly influenced by sea breezes, which could interfere in the model results and compromise the performance of the simulations.

During the Covid-19 pandemic and resulting lockdowns, road traffic volumes reduced significantly leading to reduced pollutant concentrations and noise levels. Noise and the air pollution data during the lockdown period and loosening of restrictions through five phases in 2021 are examined for a school site in the United Kingdom. Hourly and daily average noise level as well as the average over each phase, correlations between noise and air pollutants, variations between pollutants, and underlying reasons explaining the temporal variations are explored. Some strong linear correlations were identified between a number of traffic-sourced air pollutants, especially between the differently sized particulates PM , PM , and PM (0.70 < r

With the electrification of road vehicles leading to a reduction in tailpipe emissions, the relative contribution of non-exhaust emissions (NEEs) has become increasingly prominent. NEEs, particularly nanoparticles smaller than 100 nm in aerodynamic diameter (PM0.1), present significant health and environmental risks. A comprehensive understanding and strategic management of these emissions are urgently required to mitigate their impact. This article reviews existing studies and reveals that nanoparticles in NEEs are generated from brake and tyre wear under critical temperature conditions, while road wear and resuspension do not directly produce nanoparticles but contribute to larger particles. Common methodologies in studying these emissions include laboratory experiments (with brake dynamometers, tyre dynamometers, chassis dynamometers, and simulators), field tests (tunnel and real road emission tests), and source apportionments. The emission rate of PM0.1, calculated based on particle number concentration, ranges from 1.2% to 98.9%, depending on driving conditions. Extreme driving conditions result in high nanoparticle generation. Emission inventories reveal that PM0.1 emission levels have remained stable since 2020, without an observable reduction. Moreover, emissions attributable to brake wear are found to surpass those from tyre wear. Current mitigation strategies focus on material improvements for brake pads and tyres, better road maintenance, and regulatory measures. Mitigating the environmental and health impacts of nanoscale particulate matter requires additional research and regulations to control it at the source.

Volatile organic compounds (VOCs) include a wide range of chemicals, some of which act as precursors to tropospheric ozone and secondary organic aerosols. These compounds are released into the atmosphere from a wide range of sources. We conducted a source apportionment analysis of the hourly concentrations of 16 VOCs with the highest potential to contribute to O3 and SOA formation. VOCs were monitored over an 18-month period in an urbanized and industrialized coastal region in Brazil. A novel approach not previously utilized in the literature, incorporating the Positive Matrix Factorization (PMF) model while accounting for intraday VOC patterns and their dependence on wind direction was used. This method enabled us to identify source contributions during specific times of the day when certain sources had stronger impact. The analysis revealed five factors representing contributions from solvent usage (6-13%), industrial processes (11-13%), fuel evaporation (4-13%), vehicular exhaust (43-46%), and coke ovens (25-26%). Some VOCs exhibited hourly variations influenced by both time of day and wind direction, and their respective lifetimes, with long-lived species such as benzene, n-pentane, and ethylbenzene being prevalent between 00:00 and 11:59, correlating with aged air masses, while short-lived species like 1,2,4-trimethylbenzene showed greater directional variability, linked to prevailing winds, indicating recent contributions, suggesting the likely locations of sources. This intraday variation resulted in unique source impacts, highlighting a time dependence that influences source apportionment throughout the day. As a result, source apportionment based on these periodic VOC variations provided valuable insights for developing targeted mitigation strategies.

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM2.5 and PM10) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellin (Colombia); Sao Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with similar to 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM2.5 (PM10) concentrations were in Dhaka 185 +/- 48 (220 +/- 58) mu g m(-3) owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellin 10 +/- 3 (14 +/- 2) mu g m(-3). Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM2.5 and PM10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (

Volatile organic compounds (VOCs) concentrations vary considerably across different times and locations, making high-resolution monitoring crucial for accurately representing the formation processes of secondary pollutants. In this study, we assessed the impact of hourly concentrations of VOCs and nitrogen oxides (NOx) on ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) at two sites in an urban-industrialized region of Brazil. The aim was to understand the effects of VOCs on SOA formation and identify optimal strategies for controlling their environmental impacts. An analysis of the urban-industrial and suburban sites revealed significant differences in pollution patterns. The region is influenced by local anthro-pogenic emissions such as residential/commerce (39%), vehicular exhaust (28%), industrial processes (18%), fuel evaporation (10%), and solvent usage (3%). The ES site showed higher and more varied VOC concentrations, elevated NOx, and distinct ozone production dynamics indicative of a VOC-limited regime. In contrast, the MO site exhibited lower VOC levels, suggesting a suburban pattern mainly influenced by aged air masses. Key VOCs like trimethylbenzene isomers and ethylbenzene dominate OFP calculated by measured VOCs (OFP M) at the ES site, while a more even distribution among VOC species is observed at MO. SOAFP analysis highlights substantial contributions from benzene and ethylbenzene at both sites, with ES showing a closer match between SOAFP and measured PM 2.5 concentrations. The findings emphasize the necessity for site-specific air quality management strategies targeting major VOC contributors to effectively reduce ozone and PM 2.5 levels, thereby improving environmental health outcomes.

Aeolian-driven sand dune erosion is an escalating environmental and socioeconomic challenge in arid and semi-arid deserts, particularly in the Thar Desert, which faces intensified dune migration due to climate change and increased anthropogenic activities. This article provides a comprehensive review of sand dune fixation (SDF) strategies in the Thar Desert, evaluating their effectiveness in mitigating the adverse impacts of dune erosion. The review begins by examining globally adopted SDF measures, followed by a focused analysis of the Thar Desert's geomorphology, dune evolution, and the reactivation of dunes driven by climatic shifts and anthropogenic pressures. It also reviews aeolian soil dynamics, dune soil characteristics, and the limitations of existing SDF strategies in the region. Western Rajasthan districts including Jaisalmer, Barmer, Bikaner, and Jodhpur are highlighted, where wind-driven dune mobility has reached alarming levels. A field-based pilot study at a solar park in western Rajasthan suggests that surface treatments like stone mulching with cement mortar improve dune stability, particularly on sloped dunes. Additionally, Khejri tree plantations are identified as a sustainable long-term solution due to their deep root systems and minimal water requirements. Despite these promising findings, the review identifies significant gaps in current research, including a lack of field-based investigations, aerodynamic considerations, scalable mechanical solutions, and grain-scale or dune morphology studies. Furthermore, the integration of climate change models in evaluating dune dynamics remains insufficient. This article advocates for a holistic, multidisciplinary approach combining geomorphology, wind engineering, and climate science to develop effective dune migration control strategies and calls for urgent research to address these critical gaps.

Urbanization and urban overheating present significant heat exposure risks to city dwellers, underscoring the urgent need to enhance the thermal resilience of social-ecological-technological systems. However, understanding how humans interact with the urban environment to design thermally comfortable adaptation spaces and formulate effective resilience strategies remains unclear. Therefore, the overarching aim of this study is to investigate critical factors and generate actionable insights that can inform the development of effective strategies for enhancing thermal resilience in urban environments. An integrative review approach was conducted combining a scoping review and a bibliometric analysis. The principal findings highlight the significance of green infrastructure, urban canyons, and building stock scenarios in designing comfortable microclimates. Behavioural adaptation for access to the design scenarios plays an important role in achieving thermal comfort. Key driving forces influencing design scenarios are identified, including social vulnerability protection and economic resource feasibility in the social system, air pollutant reduction and biodiversity protection in the ecological system, and cooling effectiveness and energy efficiency in technological systems. Stakeholder and expert involvement are necessary to develop interventions and strategies for strengthening thermal resilience. The proposed frameworks attempt to provide a comprehensive understanding of human-urban interaction and adaptation mechanisms and offer a general approach to developing interventions and strategies for thermal resilience. The findings contribute to providing planning and design strategies by improving the ability of the built environment and humans to adapt to escalating climate warming and the rise in extreme heatwave events, thereby supporting the sustainability and well-being of urban populations. •Previous reviews addressing thermal resilience are fragmented•An integrative approach merges a scoping review and a bibliometric analysis•Key elements of design scenarios interconnect driving forces for thermal resilience•Human-urban interaction and adaptation requires involving stakeholders•Integrating qualitative and quantitative methods is recommended

Ambient air pollution is the most important environmental factor impacting human health. Urban landscapes present unique air quality challenges, which are compounded by climate change adaptation challenges, as air pollutants can also be affected by the urban heat island effect, amplifying the deleterious effects on health. Nature-based solutions have shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study aims to demonstrate potential applications of nature-based solutions in urban environments for climate stressors and air pollution mitigation by analyzing two distinct scenarios with and without green infrastructure. Utilizing low-cost sensors, we examine the relationship between green infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, our study employs low-cost sensors in tropical urban environments. Through this novel approach, we are able to obtain highly localized data that demonstrates this mitigating relationship. In this study, as a part of the NERC-FAPESP-funded GreenCities project, four low-cost sensors were validated through laboratory testing and then deployed in two locations in São Paulo, Brazil: one large, heavily forested park (CIENTEC) and one small park surrounded by densely built areas (FSP). At each site, one sensor was located in a vegetated area (Park sensor) and one near the roadside (Road sensor). The locations selected allow for a comparison of built versus green and blue areas. Lidar data were used to characterize the profile of each site based on surrounding vegetation and building area. Distance and class of the closest roadways were also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM1, PM2.5 and PM10) parameters. Particulate matter concentrations were lower for the sensors located within the forest site. At both sites, the road sensors showed higher concentrations during the daytime period. These results further reinforce the capabilities of green–blue–gray infrastructure (GBGI) tools to reduce exposure to air pollution and climate stressors, while also showing the importance of their design to ensure maximum benefits. The findings can inform decision-makers in designing more resilient cities, especially in low-and middle-income settings.

Landfilling of municipal solid waste (MSW) is a common waste management practice in municipalities of developing countries, including Cameroon, but contributes significantly to greenhouse gas (GHG) emissions and climate change. However, methane gas generated can be turned into renewable energy through waste-to-energy processes and address the energy demands simultaneously. In Cameroon, there is no data on the gas capacity of the Nkolfoulou landfill in Yaound & eacute;. This paper evaluates landfill gas, methane, and energy capacities at the Nkolfoulou sanitary landfill. It also investigates waste composition and the benefits of GHG emission reduction. A waste-to-energy method, namely, sanitary landfills, is used to assess the energy potential from MSW under three scenarios. Methane gas was estimated with the LandGEM model v3.03 using the determined values of the methane generation constant and potential methane generation capacity. The results demonstrated that the total methane gas generated represents 50% of the landfill gas from 1990 to 2030, with a peak methane generation of 7.482x1007\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.482 \times 10{07}$$\end{document} m3/year in 2030. The electricity that could be generated from that landfill in 2030 is estimated at 155.26 GWh and would contribute to 11% of Yaound & eacute; City's overall electricity demands. Among the three scenarios proposed, landfill gas-to-energy technology has the potential to reduce GHG emissions by an average of 72% significantly compared to the literature. These findings provide for the first time the energy potential, and waste composition of the Nkolfoulou landfill, and its GHG emissions, and align with results from Nigeria with the same method, and other East African countries.

Atmospheric fine particulate matter (PM2.5, near-surface concentrations when size is ≤ 2.5 μm) affects global climate and human health. India alone accounts for a quarter of the global PM2.5-related health burden. Studies in India, mostly in urban areas, have reported significant declines in PM2.5 concentrations because of COVID-19 lockdown. These studies did not consider the long-term PM2.5 patterns and time-lag effect (inter-yearly variations in PM2.5 concentrations carried forward from one period to another due to interannual shifts in meteorological conditions). Since the studies focused primarily on urban areas, not covering rural/remote areas where pollution may rise during lockdown, it is still unclear what impact lockdown had on statewide pollution levels in India. Here, we examine whether significant changes in fine-mode aerosol optical depth (AODf: columnar PM2.5) occurred statewide across India because of lockdown after excluding the confounding variables. We found a substantial decrease in AODf in a few (28%) states/territories. The declines were significant (ANCOVA; α = 0.05) in some Northeastern states/territories: Sikkim (29%), Arunachal Pradesh (24%), Nagaland (5%), Mizoram (4%) and Uttarakhand (3%). However, in most states/territories, AODf increased significantly because of lockdown. The lockdown-associated hardship caused more people to rely on polluting cooking fuels, thereby increasing residential emissions, particularly in rural areas. At city-level, we found significant reductions in near-surface PM2.5 concentrations due to lockdown. These declines were comparable or greater than previously reported. Also, there were significant reductions in AODf (PM2.5 concentrations) at state (city) levels resulting from previous environmental intervention measures. If not accounted, previous environmental intervention measures can significantly bias lockdown effect estimates in India.

Air pollution is a global issue, and its health impacts are discussed at the major level. Among different types of air pollutants, Particulate Matter (PM) is a primary pollutant that causes serious health issues related to pulmonary functions. India, one of the rapidly developing countries propelled by intense urbanization and industrial growth, faces escalating emissions of air pollutants in its major urban cities. This study estimates the air quality and associated respiratory deposition doses (RDD) of PM2.5 and PM10 in five major cities of India: Delhi, Mumbai, Kolkata, Chennai, and Bangalore from 2019 to 2022. The study collectes air quality data from the air quality monitoring station, data are analysed using R software and the visualizations are done using Origin software. The study period experienced different emission patterns due to restrictions imposed on anthropogenic sources due to the widespread pandemic. The study helps to estimate the role of anthropogenic sources on urban air quality and found that reducing sources improves air quality and leads to less exposure. The PM2.5 concentration in the cities ranges from 17 to 65 µg/m3, and PM10 ranges from 41 to 178 µg/m3 for four consecutive years. The walking mode RDD for PM2.5 ranged from 0.52 to 1.42 µg/min and 1.21 to 3.73 µg/min for PM10. Similarly, the RDD ranged for sitting mode from 0.18 to 0.51 µg/min for PM2.5 and 0.45 to 1.34 µg/min for PM10. In general, over the four years of study period, Delhi city experienced the highest pollution load, and Mumbai experienced the lowest. The maximum reduction of RDD values was found in Kolkata, with a 41% reduction. The study outcomes revealed the role of anthropogenic emissions in urban air quality and emphasize the need to adopt mitigation measures to improve air quality and human health.

Green-blue urban infrastructures potentially offer win-win benefits for people and nature in urban areas. Given increasing evidence of widespread declines of insects, as well as their ecological importance, there is need to better understand the potential role of green-blue urban infrastructure for insect conservation. In this review, we evaluated 201 studies about the ability of green-blue infrastructure to support insect diversity. Most studies were focused on the role of local and landscape-level characteristics of green-blue infrastructure. Fewer studies explicitly compared one type of infrastructure to another, and even fewer compared insect communities between green-blue infrastructure and traditional infrastructure. Overall, the body of research highlights the importance of plant diversity and reduced intensity of management (e.g., mowing) for most insect taxon groups. While local characteristics seem to be generally more important than landscape factors, insect communities within green-blue infrastructures can also depend on their connectivity and landscape context. Some infrastructure types are generally more beneficial than others; for instance, ground-level habitats tend to support more insects than green roofs. Few studies simultaneously studied synergies or trade-offs with other services provided by green-blue infrastructure, but environmental variables, such as tree cover and plant diversity, that affect insects are likely to also affect the provision of other services such as improving thermal comfort and the wellbeing of people. Our review offers some initial evidence for how green-blue infrastructure could be designed for multifunctionality with insects in mind.

Vitamin D plays an important role in maintaining human health. Its deficiency has been associated with an increased risk of several chronic diseases. Sun exposure, particularly UV-B radiation, accounts for greater than 90% of vitamin D production in humans. The aim of this study was to examine the relationship between dust and UV index and its effect on vitamin D concentrations. Data on the UV index and the number of dusty days measured at ≤1000 m, ≤3000 m, and ≤5000 m altitudes in the period January 2017 to June 2022 were collected. Dust particles (PM2.5 and PM10) and vitamin D values were also gathered. No correlation was observed between UV index and PM2.5 (r = −0.013, p = 0.947) and between UV index and PM10 (r = 0.251, p = 0.165) due to numerous factors, such as unavailable data on UV-B and particle concentrations at a maximum of 1000 m rather than 20 to 30 km. A positive correlation was observed between the number of dusty days at all altitudes and PM10 (p < 0.001), whereas no correlation was found between the number of dusty days at all altitudes and PM2.5. A positive correlation was found between vitamin D-deficient patients and PM2.5 (r = 0.529, p = 0.005) and between vitamin D-deficient patients and PM10 (r = 0.399, p = 0.024). The PM 2.5 and PM10 concentrations exceeded both the WHO guidelines and the Environmental Protection Agency’s recommended levels during most months of the study period. The average range of the PM2.5/PM10 ratio was low (0.24–0.35), indicating dust pollution. The results indicate a strong relationship between PM10 dust particles and the number of vitamin D-deficient patients, indicating high levels of dust air pollution, which might have an influence on the high levels of vitamin D deficiency in Bahrain. This study hypothesized that dust events may reduce UV-B levels, leading to vitamin D deficiency (VDD). However, the results of the study supported this hypothesis only partially due to several limitations, including the unavailability of data on UV-B, dusty days, and dust particles (PM2.5 and PM10) at higher altitudes (20–30 Km).

Large passenger ships are characterised as enclosed and crowded indoor spaces with frequent interactions between travellers, providing conditions that facilitate disease transmission. This study aims to provide an indoor ship CO2 dataset for inferring thermal comfort, ventilation and infectious disease transmission risk evaluation. Indoor air quality (IAQ) monitoring was conducted in nine environments (three cabins, buffet, gym, bar, restaurant, pub and theatre), on board a cruise ship voyaging across the UK and EU, with the study conducted in the framework of the EU HEALTHY SAILING project. CO2 concentrations, temperature and relative humidity (RH) were simultaneously monitored to investigate thermal characteristics and effectiveness of ventilation performance. Results show a slightly higher RH of 68.2 ± 5.3 % aboard compared to ASHRAE and ISO recommended targets, with temperature recorded at 22.3 ± 1.4 °C. Generally, good IAQ (20 L s−1 person−1) were highly over-ventilated. Dining areas including the pub and restaurant recorded high CO2 concentrations (>2000 ppm) potentially due to higher footfall (0.6 person/m−2 and 0.4 person/m−2) and limited ACH (2.3 h−1 and 0.8 h−1), indicating a potential risk of infection; these areas should be prioritised for improvement. The IAQ and probability of infection indicate there is an opportunity for energy saving by lowering hotel load for the theatre and cabins and achieving the minimum acceptable VR (10 L s−1 person−1) for occupants' comfort and disease control. Our study produced a first-time dataset from a sailing cruise ship's ventilated areas and provided evidence that can inform guidelines about the optimisation of ventilation operations in large passenger ships, contributing to respiratory health, infection control and energy efficiency aboard.

Green infrastructure (GI) is known to reduce road air pollution exposure, but their implementation in schools and associated benefits remain under-researched. In this study, two GI solutions, green screen and green gate, were co-designed and installed at a primary school in Guildford using collaborative and participatory methods. By assessing changes in air pollution levels, noise, and public perception before and after GI installation, we aimed to understand their impact on reducing children's exposure and evaluate other co-benefits. Without considering wind direction's effect, a maximum reduction of up to 32 %, 10 % and 12 % in the average daily concentration of PM10 (green gate), PM2.5 (green screen) and PM1 (green gate), respectively, when compared with in-front concentration. The decay in concentration decreases with distance from the GI, and different wind directions result in varying percentage reductions in PM concentration. For the green screen, ‘parallel to the screen’ and for the green gate, ‘away from the gate’ wind directions provided the highest PM reduction. The horizontal abatement efficiency of GI varied with PM size, with the highest being PM10. Continuous monitoring behind the green screen revealed a decrease in PM concentration after installation, and this relative concentration varied from 0.29 to 0.90 compared to before installation. The green gate effectively lowered noise by 5 dB(A), and the green screen did not report a noticeable impact on noise levels. Most parents perceived the installation of GI in school as significantly decreasing air pollution exposure and slightly reducing noise levels, resembling the changes in their levels observed in monitoring. The successful co-creation and co-implementation of GI interventions and resulting co-benefits underscore the importance of community engagement and participatory approaches in urban planning and environmental management. This study paves the way for the wider-scale application of innovative strategies involving local communities, stakeholders, and policymakers in implementing GI projects to ensure their sustainability and effectiveness. [Display omitted] •This co-designed and participatory study installed the first living green gate and hedges around the school perimeter.•Green gate reduced PM10, PM2.5, PM1 and noise by 32 %, 19 %, 12 % and 5 dB(A), respectively.•Green screen reduced PM10, PM2.5, and PM1 by 31 %, 10 %, and 6 %, respectively.•PM reduction decreased with distance and had no impact from GI after 25-36 m.•PM reduced by 44 % (wind flowing away from green gate) and 42 % (wind flowing parallel to green screen).

Low-cost air quality sensors are a promising supplement to current reference methods for air quality monitoring but can suffer from issues that affect their measurement quality. Interferences from environmental conditions such as temperature, humidity, cross-sensitivities with other gases and a low signal-to-noise ratio make them difficult to use in air quality monitoring without significant time investment in calibrating and correcting their output. Many studies have approached these problems utilising a variety of techniques to correct for these biases. Some use physical methods, removing the variability in environmental conditions, whereas most adopt software corrections. However, these approaches are often not standardised, varying in study duration, measurement frequency, averaging period, average concentration of the target pollutant and the biases that are corrected. Some go further and include features with no direct connection to the measurement such as the level of traffic nearby, converting the initial measurement into a modelled value. Though overall trends in performance can be derived when aggregating the results from multiple studies, they do not always match observations from individual studies, a phenomenon observed across many different academic fields and known as “Simpson’s Paradox”. The preference of performance metrics which utilise the square of the error, such as root mean squared error (RMSE) and r2, over ones which use the absolute error, such as mean absolute error (MAE), makes comparing results between models and studies difficult. Ultimately, comparisons between studies are either difficult or unwise depending on the metrics used, and this literature review recommends that efforts are made to standardise the reporting of calibration and correction studies. By utilising metrics which do not use the square of the error (e.g., MAE), models can be more easily compared within and between studies. By not only reporting the raw error but also the error normalised by multiple factors (including the reference mean and reference absolute deviation), the variabilities induced by environmental factors such as proximity to pollution sources can be minimised.

Fine particulate matter (PM2.5) poses significant health risks, prompting public health organizations to recommend the use of respirators and facemasks (RFMs) to mitigate exposure. Consequently, interest in their usage has increased, leading to several studies assessing the efficiency of these personal-level interventions against various fractions of ambient particulate matter (PM). We conducted a comprehensive literature search across PubMed, Web of Science, and Scopus to identify relevant studies and address the following objectives: (1) explore the efficiency of RFMs in reducing ambient PM; (2) discuss discrepancies in efficiencies reported; (3) critique the experimental setups used to evaluate the efficiency of RFMs; and (4) propose recommendations for future research. Five relevant studies we reviewed reported significantly lower RFM effectiveness against ambient PM, with a size-dependent efficiency that decreases for smaller PM fractions. Variations in the reported efficiencies were primarily attributed to design-related factors, resulting in poor facial fit. Therefore, it is crucial to consider standardizing and properly designing these products. These studies overlooked essential factors, such as using dummy heads with flexible textures that mimic human skin. The use of rigid-textured dummy heads, as seen in previous studies, may fail to accurately represent real-world conditions. We recommend researchers take into account diverse facial profiles in their experiments. Moreover, it is essential to consider facial characteristics in the design of RFMs. We believe the evidence supports the increasing need for the adoption of appropriate guidelines and regulations to govern RFM suppliers at both national and international levels.

Extreme temperature events such as heatwaves are becoming increasingly severe and frequent because of climate change, posing significant challenges to public health and energy infrastructure. This study explored the impacts of extreme temperature events leading to heat-/cold waves on public health and energy consumption in Kazakhstan from 1959 to 2021. The most striking trends in heatwave-related indices emerged in the western and southwestern regions. Conversely, despite heightened coldwave intensity, a decline was noted in their frequency and number. The impact of heatwaves on various health conditions, notably consistent and statistically significant rises in all-cause and cardiovascular mortalities, was observed. Shifts in energy demand were also unveiled with a noticeable spike in cooling-degree days and a reduction in heating-degree days. The mean total energy consumption stood at 552 kWh across the country with an average annual energy generation of approximately 8.76 kWh. To gauge the environmental implications, the mean CO2 emissions were estimated at 464 kg per kWh for both heating and cooling purposes. With climate change set to escalate heatwaves, the need for comprehensive health planning was underscored to mitigate their adverse health impacts. Furthermore, transitioning from fossil fuels to green energy sources is crucial to reduce our environmental footprint.

Soybeans, a vital source of protein for animal feed and an essential industrial raw material, are the most traded agricultural commodity worldwide. Accurate price forecasting is crucial for maintaining a resilient global food supply chain and has significant implications for agricultural economics and policymaking. This review examines over 100 soybean price forecast models published in the last decade, evaluating them based on the specific markets they target—futures or spot—while highlighting how differences between these markets influence critical model design decisions. The models are also classified into AI-powered and traditional categories, with an initial aim to conduct a statistical analysis comparing the performance of these two groups. This process unveiled a fundamental gap in best practices, particularly regarding the use of common benchmarks and standardised performance metrics, which limits the ability to make meaningful cross-study comparisons. Finally, this study underscores another important research gap: the lack of models forecasting soybean futures prices in Brazil, the world’s largest producer and exporter. These insights provide valuable guidance for researchers, market participants, and policymakers in agricultural economics.

As climate change intensifies, the frequency and intensity of heatwaves are rising to pose significant health risks. Population vulnerability, influenced by socio-economic and demographics factors, is a widespread concern. We analysed heat vulnerability by demonstrating usefulness of principal component analysis on recent, localised census data at lower super output scale for vulnerability factors such as poverty, access to cooling facilities, age, and gender for a non-city yet highly heat risk vulnerable case study of Surrey, UK. Four major factors (poverty, elderly population, unemployed students, daily commute) were identified, creating a cumulative Heat Vulnerability Index, aiding in prioritising interventions and mapping vulnerable areas. Mapping revealed most areas had a moderate vulnerability level of 3 out of 6 for individual major factors, with cumulative scores ranging from 11 to 12 out of 20. The study emphasises the interconnectedness of vulnerability factors and highlights the applicability of the approach beyond Surrey. The demonstrated methodology provides a valuable template for vulnerability assessments in regions facing similar challenges and have its up-to-date effective heat action plan underlining the importance of tailored strategies for comprehensive heat risk management (e.g. cooling centres, transport aid, multilingual risk communication and home visits). Policymakers can utilise the insights gained to develop targeted measures for vulnerable populations and manage heat-related issues effectively on a global scale.

The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation. This work was undertaken as a contribution to the Rapid Assistance in Modelling the Pandemic (RAMP) initiative, coordinated by the Royal Society.

Urban heat island (UHI) and urban pollution island (UPI) effects are two major challenges that affect the liveability and sustainability of cities under the circumstance of climate change. However, existing studies mostly addressed them separately. Urban green infrastructure offers nature-based solutions to alleviate urban heat, enhance air quality and promote sustainability. This review paper provides a comprehensive synthesis of the roles of urban green spaces, street trees, street hedges, green roofs and vertical greenery in mitigating UHI and UPI effects. These types of green infrastructure can promote the thermal environment and air quality, but also potentially lead to conflicting impacts. Medium-sized urban green spaces are recommended for heat mitigation because they can provide a balance between cooling efficiency and magnitude. Conversely, street trees pose a complex challenge since they can provide cooling through shading and evapotranspiration while hindering pollutant dispersion due to reduced air ventilation. Integrated research that considers simultaneous UHI and UPI mitigation using green infrastructure, their interaction with building features, and the urban geographical environment is crucial to inform urban planning and maximize the benefits of green infrastructure installations. [Display omitted] •Synergies and conflicts of green infrastructure on UHI and UPI control are evaluated.•Small to medium-sized urban green spaces are preferred for UHI mitigation.•Street trees post complex challenges to UHI and UPI mitigation.•Mitigating UHI and UPI using green infrastructure requires more research effort.

Cycling is a popular means of transport in cities, this experimental wind tunnel study models groups of cyclists' exposure to road vehicle emissions on a typical London street. Transport for London state that polluting vehicles are responsible for half of London's air pollution and research shows a direct link between poor air quality and increased rates of respiratory diseases. Cyclists are particularly at risk due to their increased inhalation rates and proximity to traffic, therefore expressing the importance and significance of this research. This is the first study to specifically look at the implications of cycling in groups, often the case in congested cycle lanes at peak hours. The results of this project, carried out in the Environmental Flow wind tunnel, confirm that pollutant concentration decreases rapidly with increased separation distance from an exhaust when a rider and vehicle are in line. However, cyclists at the front of a group of in-line riders are subjected to the least pollution when adjacent to polluting vehicles, regardless of their separation distance. Following other riders may therefore increase exposure to air pollution. The increased pollutant concentration observed in groups of riders is likely linked with the complex aerodynamic field generated by upstream cyclists, trapping the vehicle exhaust fumes among the riders. This is combined with the reduced wind speed within groups which is less effective at sweeping the pollutants away. These findings suggest policy makers should construct wider cycle paths, or even better, separate riders from the road. Meanwhile, cyclists should distance themselves from both vehicles and other riders to minimise exhaust emission exposure. Drivers should also be advised to maximise the space they leave cyclists on the road.

Scientific evidence sustains PM2.5 particles' inhalation may generate harmful impacts on human beings' health; therefore, their monitoring in ambient air is of paramount relevance in terms of public health. Due to the limited number of fixed stations within the air quality monitoring networks, development of methodological frameworks to model ambient air PM2.5 particles is primordial to providing additional information on PM2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM2.5 data measured by 84 fixed monitoring stations and meteorological data from ERA5 (ECMWF Reanalysis v5) reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach. Data from January 2017 to December 2020 were employed to build a mathematical expression that related the dependent variable (PM2.5) to predictor ones (sea-level pressure, planetary boundary layer height, temperature, precipitation, wind direction and speed), while 2021 data tested the model. Evaluation indicators evidenced a good performance of model (maximum values of RMSE, MAE and MAPE: 1.80 µg/m³, 3.24 µg/m³, and 20.63%, respectively), compiling the current legislation's requirements for modelling ambient air PM2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM2.5 concentrations from 2000 to 2021. The highest PM2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations.

Exposure to air pollutants like sulfur dioxide (SO2), nitrogen oxides (NOx), and ozone (O3) is associated with adverse health effects, particularly with exacerbations of asthma symptoms and new asthma cases in both children and adults. While fixed-site monitoring (FSM) stations are commonly used in air pollutant exposure studies, they may not fully capture personal exposures due to limitations such as inadequate consideration of daily routines and indoor/outdoor concentration variations. In this study, to enhance the accuracy of personal exposure calculated by using FSM data, individual's daily activity routine, encompassing both indoor and outdoor environments, were incorporated by using indoor-to-outdoor concentration ratios. Three methodologies were compared to assess the accuracy of exposure calculations: (i) direct exposure determination employing passive samplers (PS), (ii) personal exposure calculated using FSM data alone, and (iii) personal exposure calculated using FSM data refined by integrating local average individual daily activity routines and indoor-to-outdoor ratios. The results demonstrate that the refined method (iii) yields substantial improvements in estimated exposure levels, reducing the average error from 1.4% to 0.4% for NO2, from 72.1% to 12.7% for SO2, and from 323.4% to 24.9% for O3.

A guide for home occupants, owners, builders and local councils to reduce exposure to cooking emissions in low-middle income homes.

A particulate matter (PM) control device known as aspiration efficiency reducer (AER) has been developed as an attachment to the fresh air intake in building ventilation systems to reduce building energy consumption and improve the fresh air intake’s quality. Ambient particle laden air is drawn into the fresh air inlet of a mechanically ventilated building via the air handling unit (AHU). The ventilation system particle filters become loaded and clogged with PM, increasing the load on the fan’s motor. Three novel AER devices were tested against an AHU inlet rainhood, and their long-term energy performance assessed. Furthermore, an AER attached to an AHU incorporating single-stage filtration (SSF) was compared against an AHU fitted with rainhoods employing two-stage filtration (TSF). The findings showed AER technology resulted in a 6.6–11.4 % reduction in the AHU’s energy consumption. Finally, the impact of the AER with SSF compared to a rainhood with TSF led to a lowering of the system pressure throughout the entire testing period, reduced filter and labour costs resulting in a 36.5 % reduction in the total costs. AER technology and a ventilation filtration system design tailored to the local environment will result in lower building energy consumption and CO2 emissions.

Cities are constantly evolving and so are the living conditions within and between them. Rapid urbanization and the ever-growing need for housing have turned large areas of many cities into concrete landscapes that lack greenery. Green infrastructure can support human health, provide socio-economic and environmental benefits, and bring color to an otherwise grey urban landscape. Sometimes, benefits come with downsides in relation to its impact on air quality and human health, requiring suitable data and guidelines to implement effective greening strategies. Air pollution and human health, as well as green infrastructure and human health, are often studied together. Linking green infrastructure with air quality and human health together is a unique aspect of this article. A holistic understanding of these links is key to enabling policymakers and urban planners to make informed decisions. By critically evaluating the link between green infrastructure and human health via air pollution mitigation, we also discuss if our existing understanding of such interventions is enabling their uptake in practice. Both the natural science and epidemiology approach the topic of green infrastructure and human health very differently. The pathways linking health benefits to pollution reduction by urban vegetation remain unclear and that the mode of green infrastructure deployment is critical to avoid unintended consequences. Strategic deployment of green infrastructure may reduce downwind pollution exposure. However, the development of bespoke design guidelines is vital to promote and optimize greening benefits and measuring green infrastructure’s socio-economic and health benefits are key for their uptake. Greening cities to mitigate pollution effects is on the rise and these needs to be matched by scientific evidence and appropriate guidelines. We conclude that urban vegetation can facilitate broad health benefits, but there is little empirical evidence linking these benefits to air pollution reduction by urban vegetation, and appreciable efforts are needed to establish the underlying policies, design and engineering guidelines governing its deployment.

Air pollution exposure to in-pram babies poses a serious threat to their early childhood development, necessitating a need for effective mitigation measures. We reviewed the scientific and grey literature on in-pram babies and their personal exposure to traffic generated air pollutants such as particulate matter ≤10 μm (PM10), ≤2.5 μm (PM2.5), ≤0.10 μm (ultrafine particles) in size, black carbon and nitrogen oxides and potential mitigation pathways. In-pram babies can be exposed up to ~60% higher average concentrations depending on the pollutant types compared with adults. The air within the first few meters above the road level is usually most polluted. Therefore, we classified various pram types based on criteria such as height, width and the seating capacity (single versus twin) and assessed the breathing heights of sitting babies in various pram types available in the market. This classification revealed the pram widths between 0.56 and 0.82 m and top handle heights up to ~1.25 m as opposed to breathing height between 0.55 and 0.85 m, suggesting that the concentration within the first meter above the road level is critical for exposure to in-pram babies. The assessment of flow features around the prams suggests that meteorological conditions (e.g., wind speed and direction) and traffic-produced turbulence affect the pollution dispersion around them. A survey of the physicochemical properties of particles from roadside environment demonstrated the dominance of toxic metals that have been shown to damage their frontal lobe as well as cognition and brain development when inhaled by in-pram babies. We then assessed a wide range of active and passive exposure mitigation strategies, including a passive control at the receptor such as the enhanced filtration around the breathing zone and protection of prams via covers. Technological solutions such as creating a clean air zone around the breathing area can provide instant solutions. However, a holistic approach involving a mix of innovative technological solutions, community empowerment and exposure-centric policies are needed to help limit personal exposure of in-pram babies.

Poor environmental quality in school classrooms can have a detrimental impact on children’s health, nevertheless, the association between air pollutants and physical features of classrooms is poorly understood. We monitored particulate matter (PM), carbon dioxide (CO2) and thermal comfort in sixty classrooms across ten London primary schools using similar equipment to produce a comparable dataset. The overall research objective was to understand the association of classroom air quality with occupancy levels, floor types, classroom locations, classroom volume, ventilation types and different year groups. Average in-classroom PM10 (29±20), PM2.5 (10±2) and PM1 (5±2 μg m-3) during occupied hours were ∼150% (PM10) and 110% (PM2.5) higher compared to non-occupied hours. PM10 concentration was reduced by 30% for dual (mechanical+natural) compared to natural ventilation only; the corresponding reduction was slightly lower for PM2.5 (28%) and PM1 (20%). PM10 almost doubled for wooden floored classrooms compared with those having carpets. During high occupancy (>26 occupants), the average CO2 (935±453 ppm) was ∼140% higher than non-occupancy. The average CO2 in classrooms occupied by younger children (reception and year one) was ∼190% higher than those with older children (years eight and nine). 68% of classrooms exceeded the recommended levels of 40% relative humidity. Low PM10 concentrations coincided with low CO2 concentrations in classrooms across all schools. These findings highlight the importance of simultaneously addressing both thermal comfort and the resuspension of PM10 to achieve comprehensive improvements in classroom air quality. Classroom settings where indoor environment is likely to be compromised can also be identified and addressed.

The use of nature-based solutions (NbS) to address the risks posed by hydro-meteorological hazards have not yet become part of the mainstream policy response, and one of the main reasons cited for this, is the lack of evidence that they can effectively reduce disaster risk. This paper addresses this issue, by providing model-based evidence from five European case studies which demonstrate the effectiveness of five different NbS in reducing the magnitude of the hazard and thus risk, in present-day and possible future climates. In OAL-Austria, the hazard is a deep-seated landslide, and the NbS analysed is afforestation. Modelling results show that in today's climate and a landcover scenario of mature forest, a reduction in landslide velocity of 27.6% could be achieved. In OAL-Germany, the hazard is river flooding and the NbS analysed is managed grazing with removal of woody vegetation. Modelling results show that the NbS could potentially reduce maximum flood water depth in the near-future (2031-2060) and far-future (2070-2099), by 0.036m and 0.155m, respectively. In OAL-Greece, the hazard is river flooding, and the NbS is upscaled natural storage reservoirs. Modelling results show that in a possible future climate the upscaled NbS show most potential in reducing the total flooded area by up to 1.26 km2. In OAL-Ireland, the hazard is surface and river flooding, and the NbS is green roofs. Results from a modelled upscaling analysis under two different climate scenarios show that both maximum flood water depth, and total flooded area were able to be reduced. In OAL-UK, the hazard is shallow landslides, and the NbS is high-density planting of two different tree species. Modelling results under two different climate scenarios show that both tree species were able to improve slope stability, and that this increased over time as the NbS matured. The significance of these results is discussed within the context of the performance of the NbS over time, to different magnitude type events, impact with stakeholders in engendering wider support for the adoption of the NbS in the OALs, and the uncertainty in the modelling analyses.

We designed a novel experimental set-up to pseudo-simultaneous measure size-segregated filtration efficiency (ηF), breathing resistance (ηP) and potential usage time (tB) for 11 types of face protective equipment (FPE; four respirators; three medical; and four handmade) in the submicron range. As expected, the highest ηF was exhibited by respirators (97±3%), followed by medical (81±7%) and handmade (47±13%). Similarly, the breathing resistance was highest for respirators, followed by medical and handmade FPE. Combined analysis of efficiency and breathing resistance highlighted trade-offs, i.e. respirators showing the best overall performance across these two indicators, followed by medical and handmade FPE. This hierarchy was also confirmed by quality factor, which is a performance indicator of filters. Detailed assessment of size-segregated aerosols, combined with the scanning electron microscope imaging, revealed material characteristics such as pore density, fiber thickness, filter material and number of layers influence their performance. ηF and ηP showed an inverse exponential decay with time. Using their cross-over point, in combination with acceptable breathability, allowed to estimate tB as 3.2-9.5hours (respirators), 2.6-7.3hours (medical masks) and 4.0-8.8hours (handmade). While relatively longer tB of handmade FPE indicate breathing comfort, they are far less efficient in filtering virus-laden submicron aerosols compared with respirators. [Display omitted] •FFP3 respirators showed highest filtration efficiency and breathing resistance.•Multi-layered micro/nano-scale fibres of medical masks offer ηF comparable to respirators.•Highest quality factor was obtained for respirators while the lowest for handmade masks.•FFP3 showed maximum potential usage time and quality factor at acceptable breathability.•SEM images revealed dense aerosol layers deposited on facemasks with thinner fibres.

The COVID-19 pandemic elicited a global response to limit associated mortality, with social distancing and lockdowns being imposed. In India, human activities were restricted from late March 2020. This ‘anthropogenic emissions switch-off’ presented an opportunity to investigate impacts of COVID-19 mitigation measures on ambient air quality in five Indian cities (Chennai, Delhi, Hyderabad, Kolkata, and Mumbai), using in-situ measurements from 2015 to 2020. For each year, we isolated, analysed and compared fine particulate matter (PM2.5) concentration data from 25 March to 11 May, to elucidate the effects of the lockdown. Like other global cities, we observed substantial reductions in PM2.5 concentrations, from 19 to 43% (Chennai), 41–53 % (Delhi), 26–54 % (Hyderabad), 24–36 % (Kolkata), and 10–39 % (Mumbai). Generally, cities with larger traffic volumes showed greater reductions. Aerosol loading decreased by 29 % (Chennai), 11 % (Delhi), 4% (Kolkata), and 1% (Mumbai) against 2019 data. Health and related economic impact assessments indicated 630 prevented premature deaths during lockdown across all five cities, valued at 0.69 billion USD. Improvements in air quality may be considered a temporary lockdown benefit as revitalising the economy could reverse this trend. Regulatory bodies must closely monitor air quality levels, which currently offer a baseline for future mitigation plans.

Data assimilation is a method of combining physical observations with prior knowledge (for instance, a computational simulation) in order to produce an improved estimate of the state of a system; that is, improved over what the physical observations or the computational simulation, alone, could offer. Recently, machine learning techniques have been deployed in order to address the significant computational burden that is associated with the procedures involved in data assimilation. In this paper we propose an approach that uses a non-intrusive reduced-order model (NIROM) as a surrogate for a high-resolution model thereby saving computational effort. The mismatch between observations and the surrogate model is propagated forwards and backwards in time in a manner similar to 4D-variational data assimilation methods. The observations and prior are reconciled in a new way which takes full advantage of the neural network used in the NIROM and also means that there is no need to form the sensitivities explicitly when propagating the mismatch. Instead, the observations are part of the input and output of the network. The key advantages of this data assimilation approach are its simplicity (as we can avoid differentiating the forward model and bypass the use of an optimisation method), and its ability to integrate with control and uncertainty quantification methodologies. Modelling the air quality in a school classroom is the test case for our demonstration. After comparing the proposed data assimilation approach with 4D Variational Data Assimilation, we investigate two scenarios. The first of these is a dual-twin type experiment, for which the proposed approach is shown to perform very well, and the second is a test case which assimilates predictions from the NIROM with observations collected from a classroom in Houndsfield Primary School.

Globally, the deteriorating Urban Heat Island (UHI) effect poses a significant threat to human health and undermines ecosystem stability. UHI mitigation strategies have been investigated and utilized extensively within cities by the provision of green, blue or gray infrastructures. However, urban land is precious and limited for these interventions, making it challenging to address this issue. Neighboring rural land cover may serve as a cooling source and have a great potential to mitigate UHI through processes such as heat absorption and circulation. This study aims to address the following questions: (1) what is the location of neighboring rural land cover to effectively mitigate UHI for the entire city and (2) what are the key parameters of the landscape. We investigated the quantitative and qualitative relationships between rural land cover and UHI, drawing on geographical and environmental data from 30 Chinese cities between 2000 and 2020. We found that the rural land cover extending outward from the urban boundary, approximately half of the equivalent diameter of city, had the most pronounced impact on UHI mitigation. The number and adjacency of landscape patches (a patch is a homogeneous and nonlinear basic unit of a landscape pattern, distinct from its surroundings) emerged as two key factors in mitigating UHI, with their individual potential to reduce UHI by up to 0.5 °C. The proposed recommendations were to avoid fragmentation and enhance shape complexity and distribution uniformity of patches. This work opens new avenues for addressing high-temperature urban catastrophes from a rural perspective, which may also promote coordinated development between urban and rural areas.

Green infrastructure (GI) offers a promising solution for mitigating the adverse effects of climate change, but evaluating its effectiveness necessitates a comprehensive understanding of how that has been quantified in the literature. This study aims to review the methods (monitoring, remote sensing, and modelling) employed to assess the effectiveness of GI in urban areas for three ecosystem services: heat mitigation (cooling of air temperature), thermal comfort control, and air quality mitigation. The objectives include evaluating the suitability of these approaches across diverse scales, categorising the essential parameters, and identifying the strengths and limitations inherent in each method. Through a literature review, 126 research papers were selected for detailed analysis. Modelling was the dominant method for heat mitigation (45.6%), thermal comfort (70%), and air pollution (51.9%). The main inputs for assessing these three ecosystem services by GI were: meteorological parameters used in monitoring or modelling, morphological parameters (describing vegetation, surface, and built-up area conditions), specified parameters depending on the evaluated benefit such as landscape metrics (for heat mitigation), personal factors (for thermal comfort), pollutant measures (for air pollution), and other parameters (e.g. building and traffic heat emissions). The application scale of each method was dependent on the instruments, satellite data, and simulation tools utilised. Monitoring methods were employed in studies ranging from street-scale to neighbourhood-scale, remote sensing methods covered city-scale to regional-scale assessments, and modelling studies spanned from street-scale to regional-scale analyses. These diverse methods used to assess the GI benefits each have individual strengths and limitations which need to match the context and objectives of the study.

In the context of global warming, urban heat island (UHI) effect is increasing with the rapid urbanization. As a major driver of urbanization, urban transportation sector emits tremendous heat, which intensifies the UHI and further increases the health risks. Meanwhile, under the impact of UHI, emissions of nitrogen oxides and other gases react to generate tropospheric ozone and other harmful pollutants, further degrading urban air quality and posing immense health threats. Therefore, real-time assessment of urban traffic related heat and pollution coupled health risks are imperative, further providing reasonable suggestions of mitigation measures. This study aims to develop a coupled hazard-vulnerability-exposure framework to obtain the hourly spatiotemporal patterns and real-time zoning of health risks from urban traffic. Four districts in Suzhou city are selected as study area, including Wu Zhong, Hu Qiu, Gu Su, and Xiang Cheng districts. Targeted risk alleviation strategies are proposed by reducing vulnerabilities based on blue and green infrastructures, medical infrastructures, and real time shade. Results showed that two peak risk hours were 11:00 and 15:00, with high risks of 49.1% across the study area. Peak risks in central zones were 1.8 times of those in peripheral regions. Medical infrastructure had the largest influence on vulnerability mitigation, followed by green infrastructure. The impact of real-time shading on reducing health hazards substantially increased during sunrise and sunset periods, thus the shading structures or tall trees within traffic zones can be augmented. This work can provide insights into developing urban health risk management and regulation strategies.

A series of wind tunnel experiments were conducted in the University of Surrey's Environmental Flow wind tunnel with a 1:50 scale of a typical London street canyon to assess the exposure of cyclists riding in a group to the emissions of polluting vehicles. A propane source emitted from an Ahmed body was used to model a car exhaust and a fast flame ionisation detector was used to measure pollutant concentration around four cyclists for multiple configurations of the source, cyclists, and wind directions. Two cases were investigated with a vehicle driving in front of a line of cyclists and adjacent to them (as if it were overtaking them). In the first case, for small wind incidence, findings confirm that the cyclists exposure decreases exponentially with their distance from the source with a small dependence on wind direction but largely independently of the riders position within the group. For large wind incidences, typical of urban canyons, the rider position within the group becomes more important. For the second set of experiments, with the vehicle positioned adjacent to the riders, it was found to be preferable for a rider to be in front of the group regardless of the distance from the source, as this results in lower exposure to pollutants. This is likely linked with the complex aerodynamic field generated by the group of riders that can trap the vehicle exhaust fumes amongst the cyclists, hence increasing the exposure. This research suggests that group riding should be considered when designing mitigation strategies to minimise cyclists exposure to road traffic pollution within urban environments, where busy and narrow cycle lanes often results in cyclists riding in line.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the “black-box” nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration. [Display omitted] •What does AI bring to geoscience? AI has been accelerating and deepening our understanding of Earth Systems in an unprecedented way, including the atmosphere, lithosphere, hydrosphere, cryosphere, biosphere, anthroposphere and the interactions between spheres.•What are the noteworthy challenges of AI in geoscience? As we embrace the huge potential of AI in geoscience, several challenges arise including reliability and interpretability, ethical issues, data security, and high demand and cost.•What is the future of AI in geoscience? The synergy between traditional principles and modern AI-driven techniques holds immense promise and will shape the trajectory of geoscience in upcoming years.

This paper presents an analysis of the effects of the COVID-19 pandemic on the air quality of the Metropolitan Region of Sao Paulo (MRSP). The effects of social distancing are still recent in the society; however, it was possible to observe patterns of environmental changes in places that had adhered transportation measures to combat the spread of the coronavirus. Thus, from the analysis of the traffic volumes made on some of the main access highways to the MRSP, as well as the monitoring of the levels of fine particulate matter (PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2), directly linked to atmospheric emissions from motor vehicles-which make up about 95% of air polluting agents in the region in different locations-we showed relationships between the improvement in air quality and the decrease in vehicles that access the MRSP. To improve the data analysis, therefore, the isolation index parameter was evaluated to provide daily information on the percentage of citizens in each municipality of the state that was effectively practicing social distancing. The intersection of these groups of data determined that the COVID-19 pandemic reduced the volume of vehicles on the highways by up to 50% of what it was in 2019, with the subsequent recovery of the traffic volume, even surpassing the values from the baseline year. Thus, the isolation index showed a decline of up to 20% between its implementation in March 2020 and December 2020. These data and the way they varied during 2020 allowed to observe an improvement of up to 50% in analyzed periods of the pollutants PM2.5, CO and NO2 in the MRSP. The main contribution of this study, alongside the synergistic use of data from different sources, was to perform traffic flow analysis separately for light and heavy duty vehicles (LDVs and HDVs). The relationships between traffic volume patterns and COVID-19 pollution were analyzed based on time series.

Hydro-meteorological hazards (HMHs) have had a strong impact on human societies and ecosystems. Their impact is projected to be exacerbated by future climate scenarios. HMHs cataloguing is an effective tool to evaluate their associated risks and plan appropriate remediation strategies. However, factors linked to HMHs origin and triggers remain uncertain, which pose a challenge for their cataloguing. Focusing on key HMHs (floods, storm surges, landslides, droughts, and heatwaves), the goal of this review paper is to analyse and present a classification scheme, key features, and elements for designing nature-based solutions (NBS) and mitigating the adverse impacts of HMHs in Europe. For this purpose, we systematically examined the literature on NBS classification and assessed the gaps that hinder the widespread uptake of NBS. Furthermore, we critically evaluated the existing literature to give a better understanding of the HMHs drivers and their interrelationship (causing multi-hazards). Further conceptualisation of classification scheme and categories of NBS shows that relatively few studies have been carried out on utilising the broader concepts of NBS in tackling HMHs and that the classification and effectiveness of each NBS are dependent on the location, architecture, typology, green species and environmental conditions, as well as interrelated non-linear systems. NBS are often more cost-effective than hard engineering approaches used within the existing systems, especially when taking into consideration their potential co-benefits. We also evaluated the sources of available data for HMHs and NBS, highlighted gaps in data, and presented strategies to overcome the current shortcomings for the development of the NBS for HMHs. We highlighted specific gaps and barriers that need to be filled since the uptake and upscaling studies of NBS in HMHs reduction is rare. The fundamental concepts and the key technical features of past studies reviewed here could help practitioners to design and implement NBS in a real-world situation.

Urban parks play an important role in alleviating the negative impacts of global climate change and benefit urban thermal resilience. A well-designed thermal environment in urban parks contributes to people's health and attracts more individuals to engage in outdoor activities. However, the application of thermal comfort evaluation methods to urban parks and the effect of influencing factors on thermal comfort have not been deeply explored. This study aims to provide a comprehensive review of the evaluation and influencing factors of thermal comfort in urban parks. A total of 72 relevant articles were selected through screening. The results indicate that PET (Physiological Equivalent Temperature) and UTCI (Universal Thermal Climate Index) are commonly used for evaluating thermal comfort in urban parks. However, the reference ranges used by PET and UTCI do not quite match the actual neutral temperature ranges well. The combination of different landscape factors such as trees, water bodies, and grass can provide varying impacts on thermal comfort. Compared to winter, most people are more sensitive to temperature changes, and the neutral ranges of PET and UTCI are relatively narrow in summer. Moreover, people often adopt related adaptive behaviors (such as increasing activity intensity, moving away from sunny or warm areas, and drinking water) to alleviate thermal discomfort. This literature review emphasizes the calibrations of PET and UTCI reference ranges considering the landscapes, climate, and personal characteristics of urban parks. It provides insights for the evaluation, design, and service, aiming to develop the full potential of thermal comfort in urban parks.

Under climate change scenarios, it is important to evaluate the changes in recent behavior of heavy precipitation events, the resulting flood risk, and the detrimental impacts of the peak flow of water on human well-being, properties, infrastructure, and the natural environment. Normally, flood risk is estimated using the stationary flood frequency analysis technique. However, a site’s hydroclimate can shift beyond the range of historical observations considering continuing global warming. Therefore, flood-like distributions capable of accounting for changes in the parameters over time should be considered. The main objective of this study is to apply non-stationary flood frequency models using the generalized extreme value (GEV) distribution to model the changes in flood risk under two scenarios: (1) without nature-based solutions (NBS) in place and; (2) with NBS i.e. wetlands, retention ponds and weir/low head dam implemented. In the GEV model, the first two moments i.e. location and scale parameters of the distribution were allowed to change as a function of time-variable covariates, estimated by maximum likelihood. The methodology is applied to OPEn-air laboRAtories for Nature baseD solUtions to Manage hydro-meteo risks, which is in Europe. The time-dependent 100-year design quantiles were estimated for both the scenarios. We obtained daily precipitation data of climate models from the EURO-CORDEX project dataset for 1951–2020 and 2022–2100 representing historical and future simulations, respectively. The hydrologic model, HEC-HMS was used to simulate discharges/flood hydrograph without and with NBS in place for these two periods: historical (1951-2020) and future (2022-2100). The results showed that the corresponding time-dependent 100-year floods were remarkably high for the without NBS scenario in both the periods. Particularly, the high emission scenario (RCP 8.5) resulted in dramatically increased flood risks in the future. The simulation without NBS also showed that flooded area is projected to increase by 25% and 40% for inundation depth between 1.5 and 3.5 m under RCP 4.5 and RCP 8.5 scenarios, respectively. For inundation depth above 3.5 m, the flooded area is anticipated to rise by 30% and 55% in both periods respectively. With the implementation of NBS, the flood risk was projected to decrease by 20% (2022–2050) and 45% (2071–2100) with a significant decrease under RCP 4.5 and RCP 8.5 scenarios. This study can help improve existing methods to adapt to the uncertainties in a changing environment, which is critical to develop climate-proof NBS and improve NBS planning, implementation, and effectiveness assessment.

This study investigated influences of leaf traits on particulate matter (PM) wash-off and (re)capture (i.e., net removal) over time. Leaf samples were taken before and after three rainfall events from a range of 10 evergreen woody plants (including five different leaf types), which were positioned with an optical particle counter alongside a busy road. Scanning electron microscopy was used to quantify the density (no./mm2), mass (μg/cm2), and elemental composition of deposited particles. To enable leaf area comparison between scale-like leaves and other leaf types, a novel metric (FSA: foliage surface area per unit branch length) was developed, which may be utilised by future research. Vehicle-related particles constituted 15 % of total deposition, and there was a notable 50 % decrease in the proportion of tyre wear particles after rainfall. T. baccata presented the lowest proportion (11.1 %) of vehicle-related particle deposition but the most consistent performance in terms of net PM removal. Only four of the 10 plant specimens (C. japonica, C. lawsoniana, J. chinensis, and T. baccata) presented effective PM wash-off across all particle size fractions and rainfall intensities, with a generally positive relationship observed between rainfall intensity and wash-off. Mass deposition was more significantly determined by particle size than number density. Interestingly, larger particles were also less easily washed off than smaller particles. Some traits typically considered to be advantageous (e.g., greater hairiness) may in fact hinder net removal over time due to retention under rainfall. Small leaf area is one trait that may promote both accumulation and wash-off. However, FSA was found to be the most influential trait, with an inverse relationship between FSA and wash-off efficacy. This finding poses trade-offs and opportunities for green infrastructure design, which are discussed. Finally, numerous areas for future research are recommended, underlining the importance of systems approaches in developing vegetation management frameworks.

Infectious diseases (e.g., coronavirus disease 2019) dramatically impact human life, economy and social development. Exploring the low-cost and energy-saving approaches is essential in removing infectious virus particles from indoors, such as in classrooms. The application of air purification devices, such as negative ion generators (ionizers), gains popularity because of the favorable removal capacity for particles and the low operation cost. However, small and portable ionizers have potential disadvantages in the removal efficiency owing to the limited horizontal diffusion of negative ions. This study aims to investigate the layout strategy (number and location) of ionizers based on the energy-efficient natural ventilation in the classroom to improve removal efficiency (negative ions to particles) and decrease infection risk. Three infected students were considered in the classroom. The simulations of negative ion and particle concentrations were performed and validated by the experiment. Results showed that as the number of ionizers was 4 and 5, the removal performance was largely improved by combining ionizer with natural ventilation. Compared with the scenario without an ionizer, the scenario with 5 ionizers largely increased the average removal efficiency from around 20% to 85% and decreased the average infection risk by 23%. The setup with 5 ionizers placed upstream of the classroom was determined as the optimal layout strategy, particularly when the location and number of the infected students were unknown. This work can provide a guideline for applying ionizers to public buildings when natural ventilation is used.

Rapid growth in the global population requires expansion of building stock, which in turn calls for increased energy demand. This demand varies in time and also between different buildings, yet, conventional methods are only able to provide mean energy levels per zone and are unable to capture this inhomogeneity, which is important to conserve energy. An additional challenge is that some of the attempts to conserve energy, through for example lowering of ventilation rates, have been shown to exacerbate another problem, which is unacceptable indoor air quality (IAQ). The rise of sensing technology over the past decade has shown potential to address both these issues simultaneously by providing high–resolution tempo–spatial data to systematically analyse the energy demand and its consumption as well as the impacts of measures taken to control energy consumption on IAQ. However, challenges remain in the development of affordable services for data analysis, deployment of large–scale real–time sensing network and responding through Building Energy Management Systems. This article presents the fundamental drivers behind the rise of sensing technology for the management of energy and IAQ in urban built environments, highlights major challenges for their large–scale deployment and identifies the research gaps that should be closed by future investigations.

The World Health Organisation declared the infectious spread of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) an epidemic during its initial outbreak in Wuhan (China) and has since declared it a pandemic and, more recently, an endemic infection that may remain in our communities. A vaccine for COVID-19 is expected to take several months, meaning that the spread may continue in future, in the absence of the most effective measures of social distancing and self-isolation. While these measures have worked well under lockdowns, the potential of airborne transmission of COVID-19 under the eased restrictions has not been considered important enough. We discuss the need to acknowledge the airborne spread of COVID-19 inside built spaces under eased movement restrictions and the potential steps that can be taken to control it.

There are two kind of software that runs in our system. They are system software and application software. Operating System is system software that is responsible for handling all other processes that runs on our system. At a time there are various processes that demands for CPU and resources, since job executed only when the job is allotted to CPU. The order of assigning job to CPU is called CPU scheduling. Assigning of CPU to many jobs depends on various factors like their service time, waiting time, priority, response time etc. Once the job has assigned CPU, it do not leave CPU till it's completion in Non-Preemptive scheduling. In Preemptive there is relaxation to leave CPU after assigning it to any job. Generally CPU leaves when it demands for some resources or wants some others task to be done before it, than it resides in main memory or in secondary memory and go in to waiting state or suspend state. There are five states of any job i.e. new, ready, running, waiting and terminating state.

Cars are a commuting lifeline worldwide, despite contributing significantly to air pollution. This is the first global assessment on air pollution exposure in cars across ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Portable laser particle counters were used to develop a proxy of car-user exposure profiles and analyse the factors affecting particulate matter ≤2.5 μm (PM2.5; fine fraction) and ≤10 μm (PM2.5–10; coarse fraction). Measurements were carried out during morning, off- and evening-peak hours under windows-open and windows-closed (fan-on and recirculation) conditions on predefined routes. For all cities, PM2.5 and PM10 concentrations were highest during windows-open, followed by fan-on and recirculation. Compared with recirculation, PM2.5 and PM10 were higher by up to 589% (Blantyre) and 1020% (São Paulo), during windows-open and higher by up to 385% (São Paulo) and 390% (São Paulo) during fan-on, respectively. Coarse particles dominated the PM fraction during windows-open while fine particles dominated during fan-on and recirculation, indicating filter effectiveness in removing coarse particles and a need for filters that limit the ingress of fine particles. Spatial variation analysis during windows-open showed that pollution hotspots make up to a third of the total route-length. PM2.5 exposure for windows-open during off-peak hours was 91% and 40% less than morning and evening peak hours, respectively. Across cities, determinants of relatively high personal exposure doses included lower car speeds, temporally longer journeys, and higher in-car concentrations. It was also concluded that car-users in the least affluent cities experienced disproportionately higher in-car PM2.5 exposures. Cities were classified into three groups according to low, intermediate and high levels of PM exposure to car commuters, allowing to draw similarities and highlight best practices.

Realistic natural ventilation potential estimation is critical for energy-efficient building design. For buildings in urban areas, evaluations using design expectations tend to overestimate potential. This study presents a comprehensive framework and corresponding method to predict the natural ventilation potential of buildings, integrating multiple key environmental factors of climate features, local wind status, and air pollution level. It addresses their complex interplay. Climate impacts are evaluated through simulating using a pre-set natural ventilation rate. Computational fluid dynamics models typical neighbourhood wind fields, giving empirical wind pressure coefficient distributions by layout and direction to enable real-time ventilation rate calculation. Substituting real-time for pre-set rates, the natural ventilation potential considering the community layout can be obtained. Based on the data-driven rapid pollutant prediction model established in the previous study, the predictions of the temporal and spatial distributions of particulate matter can be obtained. Filtering excessive pollution periods yields air quality-integrated potentials. Natural ventilation potentials are compared under different analysis scenarios for a case-study building. Hotter climates and more frequent heatwaves lower the potential. Morphology-based potentials are significantly lower than design expectations using constant ventilation rates. Deteriorating urban air quality also directly limits ventilation use, substantially decreasing potentials when filtering for poor conditions. By capturing complex environmental interactions, this research provides a robust platform to reveal localization potentials and limitations.

The aim of this study is to assess particle number concentrations (PNCs) and distributions (PNDs) in a car cabin while driving. Further objectives include the determination of the influence of particle transformation processes on PNCs, PNDs and estimation of PNC related exposure. On-board measurements of PNCs and PNDs were made in the 5–560 nm size range using a fast response differential mobility spectrometer (DMS50), which has a response time of 500 ms. Video records of the traffic ahead of the experimental car were also used to correlate emission events with measured PNCs and PNDs. A total of 30 return trips was made on a 2.7 km route during morning and evening rush hours, with journey times of 7 ± 2 and 10 ± 3 min, respectively. The average PNC for the set of morning journeys, 5.79 ± 3.52 × 104 cm−3, was found to be nearly identical to the average recorded during the afternoon, 5.95 ± 4.67 × 104 cm−3. Average PNCs for individual trips varied from 2.42 × 104 cm−3 to 2.18 × 105 cm−3, mainly due to changes in the emissions affecting the experimental car (e.g. when the experimental car was following another vehicle). The largest one second averaged PNC during a specific event, 1.85 × 106 cm−3, was found to be over 30-times greater than the overall average of 5.87 ± 4.06 × 104 cm−3. Correlation of video records and concentration data indicated that close proximity to a preceding vehicle led to a clear increase in PNCs of freshly emitted nucleation mode particles. The evolution of normalised PNDs demonstrated that dilution was the dominant transformation process in the car cabin. The deposition of inhaled particles in the lung was estimated on the basis of either the size-resolved distribution or the total PNC. In general, the two methods yielded similar results but differences up to 30% were noted in some cases, with the latter method giving the lower values. Overall, the results reflect the importance of size-resolved measurements for deriving accurate evaluations of exposure rates, as well as identifying emissions from nearby traffic as the cause of short-term elevations of PNCs and hence dose rates.

This study aims to assess the long-term trend of fine particles (PM2.5; ≤2.5 μm) at two urban sites of Lahore during 2007-2011. These sites represent two distinct areas: commercial (Townhall) and residential cum industrial (Township). The highest daily mean concentrations of PM2.5 were noted as 389 μg m–3 and 354 μg m–3 at the Townhall and Township sites, respectively. As expected, the annual seasonal mean of PM2.5 was about 53% and 101% higher during winter compared with the summer and monsoon/post-monsoon seasons, respectively. On contrary to many observations seen in developing cities, the annual mean PM2.5 during the weekends was higher than weekdays at both monitoring sites. For example, these were 100 (142) μg m–3 and 142 (148) μg m–3 during the weekdays (weekends) at the Townhall and Township sites, respectively. The regression analysis showed a significant positive correlation of PM2.5 with SO2, NO2, and CO as opposed to a negative correlation with O3. The bivariate polar plots suggested a much higher influence of localised sources (e.g., road vehicles) at the Townhall site as opposed to industrial sources affecting the concentrations at the Township site. The imageries from the MODIS Aqua/Terra indicated long-range transport of PM2.5 from India to Pakistan during February to October whereas from Pakistan to India during November to January. This study provides important results in the form of multi-scale relationship of PM2.5 with its sources and precursors, which are important to assess the effectiveness of pollution control mitigation strategies in Lahore and similar cities elsewhere.

The distributions of nanoparticles (below 300 nm in diameter) change rapidly after emission from the tail pipe of a moving vehicle due to the influence of transformation processes. Information on this time scale is important for modelling of nanoparticle dispersion but is unknown because the sampling frequencies of available instruments are unable to capture these rapid processes. In this study, a fast response differential mobility spectrometer (Cambustion Instruments DMS500), originally designed to measure particle number distributions (PNDs) and concentrations in engine exhaust emissions, was deployed to measure particles in the 5–1000 nm size range at a sampling frequency of 10 Hz. This article presents results of two separate studies; one, measurements along the roadside in a Cambridge (UK) street canyon and, two, measurements at a fixed position (20 cm above road level), centrally, in the wake of a single moving diesel-engined car. The aims of the first measurements were to test the suitability and recommend optimum operating conditions of the DMS500 for ambient measurements. The aim of the second study was to investigate the time scale over which competing influences of dilution and transformation processes (nucleation, condensation and coagulation) affect the PNDs in the wake of a moving car. Results suggested that the effect of transformation processes was nearly complete within about 1 s after emission due to rapid dilution in the vehicle wake. Furthermore, roadside measurements in a street canyon showed that the time for traffic emissions to reach the roadside in calm wind conditions was about 45 6 s. These observations suggest the hypothesis that the effects of transformation processes are generally complete by the time particles are observed at roadside and the total particle numbers can then be assumed as conserved. A corollary of this hypothesis is that complex transformation processes can be ignored when modelling the behaviour of nanoparticles in street canyons once the very nearexhaust processes are complete.

Abstract The Integrated Gasification Combined Cycle coupled with chemical looping combustion (IGCC-CLC) is one of the most promising technologies that allow generation of cleaner energy from coal by capturing carbon dioxide (CO2). It is essential to compare and evaluate the performances of various oxygen carriers (OC), used in the CLC system; these are crucial for the success of IGCC-CLC technology. Research on OCs has hitherto been restricted to small laboratory and pilot scale experiments. It is therefore necessary to examine the performance of OCs in large-scale systems with more extensive analysis. This study compares the performance of five different OCs – copper, cobalt, iron, manganese and nickel oxides – for large-scale (350–400 MW) IGCC-CLC processes through simulation studies. Further, the effect of three different process configurations: (i) water-cooling, (ii) air-cooling and (iii) air-cooling along with air separation unit (ASU) integration of the CLC air reactor, on the power output of IGCC-CLC processes – are also investigated. The simulation results suggest that iron-based OCs, with 34.3% net electrical efficiency and ~100% CO2 capture rate lead to the most efficient process among all the five studied OCs. A net electrical efficiency penalty of 7.1–8.1% points leads to the IGCC-CLC process being more efficient than amine based post-combustion capture technology and equally efficient to the solvent based pre-combustion capture technology. The net electrical efficiency of the IGCC-CLC process increased by 0.6–2.1% with the use of air-cooling and ASU integration, compared with the water- and air-cooling cases. This work successfully demonstrates a correlation between the reaction enthalpies of different OCs and power output, which suggests that the OCs with higher values of reaction enthalpy for oxidation (ΔHr, oxidation) with air-cooling are more valuable for the IGCC-CLC.

Vehicular pollution is one of the major sources of air pollution in urban locales that have reportedly elevated concentrations of air pollutants. This study aims to examine the performance of two air quality dispersion models, STREET and CALINE 4 to predict pollutant concentrations for an urban monitoring location that is en route to the high traffic volumes in Shimla, Himachal Pradesh, India. This study will compare the predicted and observed concentrations (for the urban monitoring location) using both quantitative and statistical methods for the 2 years of the study. The pollutant selected for the study is PM10. It was observed from the modeling studies that the performance of CALINE4 was slightly better than the STREET model. The models selected to a certain extent are defined by the available parameters for successful run completions. The application of detailed modeling studies is the first of its kind for the study location, to the best of the authors' knowledge. Hence, the application of basic and simplistic models and the examination of their performance could potentially find the best fit model to predict approximately precise concentrations. Further scope of this study should include the use of advanced air quality dispersion models for the improved prediction of concentrations. (c) 2020 American Society of Civil Engineers.

Students spend nearly one third of their typical day in the school environment, where they may be exposed to harmful air pollutants. A consolidated knowledge base of interventions to reduce this exposure is required for making informed decisions on their implementation and wider uptake. We attempt to fill this knowledge gap by synthesising the existing scientific literature on different school-based air pollution exposure interventions, their efficiency, suitability, and limitations. We assessed technological (air purifiers, HVAC - Heating Ventilation and Air Conditioning etc.), behavioural, green infrastructure, structural, school-commute and policy and regulatory interventions. Studies suggest that the removal efficiency of air purifiers for PM2.5, PM10, PM1 and BC can be up to 57 %, 34 %, 70 % and 58 %, respectively, depending on the air purification technology compared with control levels in classroom. The HVAC system combined with high efficiency filters has BC, PM10 and PM2.5 removal efficiency up to 97 %, 34 % and 30 %, respectively. Citizen science campaigns are effective in reducing the indoor air pollutants' exposure up to 94 %. The concentration of PM10, NO2, O3, BC and PNC can be reduced by up to 60 %, 59 %, 16 %, 63 % and 77 %, respectively as compared to control conditions, by installing green infrastructure (GI) as a physical barrier. School commute interventions can reduce NO2 concentration by up to 23 %. The in-cabin concentration reduction of up to 77 % for PM2.5, 43 % for PNC, 89 % for BC, 74 % for PM10 and 75 % for NO2, along with 94 % reduction in tailpipe emission of total particles, can be achieved using clean fuels and retrofits. No stand-alone method is found as the absolute solution for controlling pollutants exposure, their combined application can be effective in most of the scenarios. More research is needed on assessing combined interventions, and their operational synchronisation for getting the optimum results. [Display omitted] •Air purifiers can effectively remove PM10 and PM2.5 by up to 34 % and 57 % in classrooms.•HVAC system with MERV 11-16 filter can remove PM10 up to 34 % and BC up to 97 %.•Policy interventions for clean fuel can reduce PM2.5 concentration up to 62 % inside school buses and 94 % in tailpipe emission.•Green barriers can reduce PM10, PM2.5 and NO2 up to 60 %, 44 % and 59 % respectively.•Combination of interventions may work effectively against PM and gaseous pollutants.

The last decade in India has seen a rapid deterioration in the air quality in its major cities. This has led to increased interest from the general public to their exposure to ambient air quality primarily because of the effects of such air pollutants on human health. In this context, the air quality indices (AQI) is often used by the local authorities to signify the levels of the seriousness of air pollution to the common public. The use of air quality indexing for assessment of existing air quality standards has been widely used for different cities in India and the world. The paper presents the application of air quality indices for assessing the existing air quality standards in an Indian city, Shimla. The indices have been calculated using the methodology described by the US Environmental Protection Agency (USEPA), which is adopted by the Central Pollution Control Board (CPCB) in India. An alternative method for determination of air quality indices is also utilized (referred to as AQIam for the Indian context. The estimates air quality indices are applied to two monitoring sites (Tekka Bench, Ridge and ISBT bus stand) in Shimla city over the study period (2004-2015) on the pollutants: Sulphur dioxide (SO2), oxides of nitrogen (NOx), suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM). The annual air quality indices results for the study period showed that the air quality was 'good' for Tekka Bench monitoring station for the entire study period and for the ISBT bus stand for all the years, except 2011 when it was in ‘moderate' category. The annual air quality indices predicted using the alternative methodology indicated the level of air quality to be 'good' for the entire study period, except 2013 when it was classified as ‘satisfactory' for the monitoring site at Tekka Bench. Similarly, the annual air quality was classified as 'moderate’ for the years 2011, 2013-2015 for the monitoring station at ISBT bus stand site with the remaining years of the study period being classified as 'good'. These categorizations of existing air quality interpret the expected health effects of exposure to surrounding ambient air. Higher the value of air quality indices more severe is the categorization and thereby more harmful are the human health effects being exposed to ambient air conditions. Similar such seasonal variations of air quality indices were also observed during the study period at both the monitoring sites.

Natural and human activities generate a significant amount of PM2.5 (particles ≤2.5 µm in aerodynamic diameter) into the surrounding atmospheric environments. Because of their small size they can remain suspended for a relatively longer time in the air than coarse particles and thus can travel long distances in the atmosphere. PM2.5 is one of the key indicators of pollution and known to cause numerous types of respiratory and lung related diseases. Due to poor implementation of regulations and a time lag in introducing the vehicle technology, levels of PM2.5in most Asian cities are much worse than those in European environments. Dedicated reviews on understanding the characteristics of PM2.5in Asian urban environments are currently missing but much needed. In order to fill the existing gaps in the literature, the aim of this review article is to describe dominating sources and their classification, followed by current status and health impact of PM2.5, in Asian countries. Further objectives include a critical synthesis of the topics such as secondary and tertiary aerosol formation, chemical composition, monitoring and modelling methods, source apportionment, emissions and exposure impacts. The review concludes with the synthesis of regulatory guidelines and future perspectives for PM2.5 in Asian countries. A critical synthesis of literature suggests a lack of exposure and monitoring studies to inform personal exposure in the household and rural areas of Asian environments.

Green infrastructure (GI) can reduce air pollutant concentrations via coupled effects of surface deposition and aerodynamic dispersion, yet their magnitudes and relative effectiveness in reducing pollutant concentration are less studied at the urban scale. Here, we develop and apply an integrated GI assessment approach to simulate the individual effects of GI along with their combined impact on pollutant concentration reduction under eight GI scenarios. These include current for year 2015 (2015-Base); business-as-usual for year 2039 (2039-BAU); three alternative future scenarios with maximum possible coniferous (2039-Max-Con), deciduous (2039-Max-Dec) trees, and grassland (2039-Max-Grl) over the available land; and another three alternative future scenarios by considering coniferous (2039-NR-Con), deciduous (2039-NR-Dec) trees, and grassland (2039-NR-Grl) around traffic lanes. A typical UK town, Guildford, is chosen as study area where we estimated current and future traffic emissions (NOx, PM10 and PM2.5), annual deposited amount and pollutants concentration reductions and percentage shared by dispersion and deposition effect in concentration reduction under above scenarios. The annual pollutant deposition was found to vary from 0.27-2.77 t.yr–1.km–2 for NOx, 0.46-1.03 t.yr–1.km–2 for PM10 and 0.08-0.23 t.yr–1.km–2 for PM2.5, depending on the percentage share of GI type and traffic emissions. The 2039-Max-Dec showed the aerodynamic effect of GI can reduce the annual pollutant concentration levels up to ~10% in NOx, ~1% in PM10 and ~0.8% in PM2.5. Furthermore, the total reductions can be achieved, via GI’s coupled effects of surface deposition and aerodynamic dispersion, up to ~35% in NOx, ~21% in PM10 and ~8% in PM2.5 with ~75% GI cover in modelled domain under 2015-Base scenario. Coniferous trees (2039-Max-Con) were found to promote enhanced turbulence flow and offer more surface for deposition. Moreover, planting coniferous trees near traffic lanes (2039-NR-Con) was found to be a more effective solution to reduce annual pollutant concentration.

Airborne transmissions of infectious disease (e.g. SARS-CoV-2) in indoor environments may induce serious threat to public health. Air purification devices are necessary to remove and/or inactivate airborne biological species from indoor air environment. Corona discharge in an electrostatic precipitator is capable of removing particulate matter and disinfecting biological aerosols to act as electrostatic disinfector (ESD). The ions generated by ESD can effectively inactivate bacteria/viruses. However, the available research rarely investigated disinfection effect of ESD, and it is urgent to develop quantitative ESD design methods for building mechanical ventilation applications. This study developed an integrated numerical model to simulate disinfection performance of ESD. The numerical model considers the ionized electric field, electrohydrodynamic flow, and biological disinfection. The model prediction was validated with the experimental data (E. coli): Good agreement was observed. The validated model then was used to study the influences of essential design parameters (e.g. voltage, inlet velocity) of ESD on disinfection efficiency. The effects of modeling of electrophoretic force and EHD (electrohydrodynamic) flow patterns on disinfection efficiency and computing time were also analyzed. The disinfection efficiency of well-designed ESD (with space charge density of 3.6 × 10−06 C/m3) could be as high as 100%. Compared with HEPA, ESD could save 99% of energy consumed by HEPA without sacrificing disinfection efficiency.

The number of daily commuters in Greater Cairo has exceeded 15 million nevertheless personal exposure studies in transport microenvironments are limited. The aim of this study is to quantify PM2.5 exposure during peak hours in four transport modes of Greater Cairo - car (windows-open, windows-closed with recirculation and AC-on), microbus (windows-open), cycling and walking - and understand its underlying drivers. Data was collected using a pDR-1500 monitor and analysed to capture concentration variations, spatial variability, exposure doses, commuting costs versus inhaled doses, health burden and economic losses. Car with recirculation resulted in the least average PM2.5 concentrations (32±6 μg/m3), followed by walking (77±35 μg/m3), car with windows-open (82±32 μg/m3), microbus with windows-open (96±29 μg/m3) and cycling (100±28 μg/m3). Evening hours observed average PM2.5 concentrations by 26-58% lesser than morning. Spatial variability analysis showed that 75th-90th percentile PM2.5 concentrations coincided with congested spots. Cycling and walking lanes are rare hence commuters are exposed to surges in PM2.5 concentrations when passing near construction and solid waste burning sites. Cycling and walking also resulted in inhaling 40-times and 32-times higher PM2.5 dose per kilometer than for car with recirculation. Commuting by microbus cost (with windows-open) ~45% of car cost (with recirculation) but it resulted in 4-times higher inhaled PM2.5 dose. As expected due to the lowest PM2.5 exposure concentrations, health burden resulting from car travel (with recirculation) caused the least death rates of 0.07 (95% CI 0.07-0.08) prematures deaths per 100,000 commuters/year while microbus with windows-open resulted in the highest death rates; 0.52 (95% CI 0.49-0.56). Microbus deaths represent 57% of national economic losses due to PM2.5 exposure amongst the four transport modes. This study provides real-time exposure data and analyses its implications on commuter health as a first step in informed decision-making and better urban planning.

This study aims to assess the physicochemical characteristics of the particulate matter ≤10 µm (PM10) at both congested and non-congested areas of Lahore, the second-largest city of Pakistan. PM10 samples from 10 urban sites in Lahore were analysed for source apportionment. The techniques of scanning electron microscopy/energy dispersive spectrometry (SEM/EDX) and inductively coupled plasma-optical emission spectroscopy (ICP-OES) were used to determine the morphology and the chemical composition of PM10. Thirteen elements including toxic metals were consequently detected and quantified: Ca (48.1%), Zn (17.0%), Fe (13.3%), Al (8.2%), Mg (6.6%), Pb (5.5%), Mn (0.4%), Cu (0.3%), Ba (0.17%), Cd (0.15%), Ni (0.04%), Cr (0.01%) and Co (0.008%). The results showed that the daily PM10 concentration was 6%–9% higher than the World Health Organization’s guideline values at all urban sites of Lahore. The congested sites showed higher contents than the non-congested areas for most of the elements, including Cd (41.8%), Cr (35.0%), Zn (19.7%), Cu (12.7%), Ni (6.2%), Ca (3.4%), Ba (1.2%), Mg (1.2%) and Al (0.07%). The non-congested areas showed higher contents only for Pb (0.07%) and Co (4.3%). The principal component analysis indicated that 72% of PM10 originates from road dust and vehicular sources, and 38% from industrial sources.

Street canyons are generally highly polluted urban environments due to high traffic emissions and impeded dispersion. Green infrastructure (GI) is one potential passive control system for air pollution in street canyons, yet optimum GI design is currently unclear. This review consolidates findings from previous research on GI in street canyons and assesses the suitability of different GI forms in terms of local air quality improvement. Studies on the effects of various GI options (trees, hedges, green walls, green screens and green roofs) are critically evaluated, findings are synthesised, and possible recommendations are summarised. In addition, various measurement methods used for quantifying the effectiveness of street greening for air pollution reduction are analysed. Finally, we explore the findings of studies that have compared plant species for pollution mitigation. We conclude that the influences of different GI options on air quality in street canyons depend on street canyon geometry, meteorological conditions and vegetation characteristics. Green walls, green screens and green roofs are potentially viable GI options in existing street canyons, where there is typically a lack of available planting space. Particle deposition to leaves is usually quantified by leaf washing experiments or by microscopy imaging techniques, the latter of which indicates size distribution and is more accurate. The pollutant reduction capacity of a plant species largely depends on its macromorphology in relation to the physical environment. Certain micromorphological leaf traits also positively correlate with deposition, including grooves, ridges, trichomes, stomatal density and epicuticular wax amount. The complexity of street canyon environments and the limited number of previous studies on novel forms of GI in street canyons mean that offering specific recommendations is currently unfeasible. This review highlights a need for further research, particularly on green walls and green screens, to substantiate their efficacy and investigate technical considerations.

Job accessibility and environmental quality are rarely equally distributed in spatial and/or social dimensions within metropolitan regions. Availability of these affects the quality of residential locations, and can be expected to be capitalised into house prices. For prospective house owners, their options will be limited to sub housing markets within certain price bands depending on their available housing budgets. Availability and marginal prices of job accessibility and environmental quality, as well as trade-offs between them, might be different between these submarkets. Using Greater London as the case metropolitan region, this study explored such differences, to shed light on the role of housing market in equity and/or inequity in job accessibility, environmental quality and their interactions. Results of this study show that lower-price submarkets have advantages in job accessibility in terms of marginal price, but are disadvantaged in terms of availability. Differences are more mixed in marginal price and availability between the submarkets for environmental quality. When balancing job accessibility and environmental quality within constrained housing budgets, households in lower-price submarkets would find it relatively easier to gain job accessibility with less sacrifice on environmental quality as compared to those searching in higher-price submarkets, but hard to reach the higher levels of job accessibility that are mainly reserved for the higher-price submarkets. •Sub housing markets are defined by price band to reflect housing affordability.•Lower-price submarkets hardly reach most accessible places.•Lower-price submarkets only offer flats and no houses in moderately accessible places.•Trading off environmental quality for job accessibility is more rewarding in lower-price submarkets.

Abstract This work examines long-term measurements of major criteria pollutants concentrations in an urban station in South-Eastern Mediterranean, in Nicosia – Cyprus, which is susceptible both to transboundary air pollution transport from Sahara-dust events as well as to evaporative transport of sea-sprays. The work investigates in particular the role of such multi-scale contributions in the urban air quality measurements, which are important considerations in the assessment of the effectiveness of any mitigation policies implemented by regulatory authorities. Attention is drawn in the regional-scale component of the particulate matter concentrations (PM10; ≤10 μm in diameter) and its contribution in the local measurements. Hourly averaged data of CO, NOx and PM10 concentrations as well as of meteorological parameters were collected from the Air Quality Monitoring Station (AQMS) of the University of Cyprus over a period of more than 5 years (2008–13) and were analysed. Scanning Electron Microscope (SEM) was used to identify chemical characteristics of PM10 and to attribute it to possible sources. A total of 321 days over the entire period were found to exceed the daily limit value of 50 μg/m3 for PM10 concentrations which corresponds to ∼19% of the actual monitored time. Numerical simulations using the Dust REgional Atmospheric Model from Barcelona Supercomputing Center (BSC/DREAM) gave a strong indication that PM10 exceedances were associated with the high regional background dust concentrations during westerly winds. It was also found that despite the implementation of tighter regulations for vehicular and industrial emissions in Europe, the monthly average concentration values of criteria pollutants do not exhibit any falling trend.

Global warming induced climate change is bringing periods of extremely hot summer days called heatwaves across the world. Its frequency, intensity and magnitude have escalated multifold in recent decades and have been predicted to keep intensifying. Many past studies have only focused on cities for heatwave risk assessment overlooking the risks in suburban and rural areas. The aim of this work is to form a scientific framework for preparing and managing the human-health impacts of heatwaves in more pastoral regions. We associated the extreme temperature with mortality to evaluate its risk using recent data on daily-deaths and maximum temperature from nine counties of southeast England for the period of 1981-2014. The reproduced methodology will also be applied to OPERANDUM project’s test regions called open-air laboratories across Europe. The relationship between temperature and daily-deaths has been examined using a poisson regression model combined with a distributed-lag nonlinear model (DLNM). We computed the absolute excess (numbers) and relative excess (fraction) deaths owed to temperature or relative risk (RR) of mortality by comparing the extremely hot temperature (99th percentile) with the minimum mortality temperature (MMT). Total heat ascribed mortality is given by the sum of the contributions from all the days of the time-series, and its ratio with the total number of deaths. Significant and non-linear associations between temperature and daily-deaths were noticed. The overall cumulative RR at the extremely hot vs. MMT was 1.292 (95% CI: 1.251–1.333). The results of this study can help in location-centric heat management action plans to certain areas at most risk.

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

The fast-growing sensing technology means there is an extensive range of low-cost indoor air quality (IAQ) devices or sensor modules readily available on the commercial market. However, less attention has been paid to exploring people’s perceptions and responses to indoor air pollution. Such knowledge is deemed important for improving the design of future IAQ sensors, enhancing public awareness and providing guidance on ways to improve indoor environmental conditions. This study aimed to explore people’s perceptions and responses to IAQ under different quasi-experimental environmental conditions and levels of information. The study is reported from the perspective of 16 staff and students at a UK university, held within a prototype smart and modular home built on campus. It compares questionnaire survey responses and focus group interview discussions with actual IAQ concentrations and other influential indoor parameters (indoor air temperature and relative humidity). Study outcomes revealed the importance of understanding contextual factors when developing feedback and communication strategies to best improve people’s awareness, acceptance, and behavioural responses to IAQ. Findings highlight the usefulness of focus group discussions in the design of future IAQ sensors.

The present study aims to evaluate the long-term trends of PM10 at two monitoring stations (an urban and a background station) in Shimla city, in India during the period 2011-2017. The highest daily mean concentrations were determined to be 176μg/m³ and 152μg/m³ respectively at the urban and the background monitoring locations. Similarly, the annual mean concentrations at the monitoring locations were determined to be 59μg/m³ and 45μg/m³ respectively for urban and background concentrations. Exceedance factors determined showed that at the urban monitoring location the ranges varied between ‘moderate to high’ while at the background monitoring station it remained at ‘moderate’ levels. Seasonal analysis study carried out revealed that higher concentrations were observed during summer in comparison to winter with the least concentrations occurring during the monsoon season. A regression analysis was carried out to test the interdependency of the PM10 with other pollutants and a positive correlation was observed between PM10 and NO₂ and SO₂. Similarly, correlation of PM10 with meteorological parameters such as wind speed and temperature were found to be positive while for parameters like precipitation and relative humidity it was negative. The paper also presents a critical discussion on the outcomes of the trend analysis study. This includes design and location of additional monitoring sites to adequately represent the actual ambient air quality conditions in Himachal Pradesh.

The health and academic performance of children are significantly impacted by air quality in classrooms. However, there is a lack of understanding of the relationship between classroom air pollutants and contextual factors such as physical characteristics of the classroom, ventilation and occupancy. We monitored concentrations of particulate matter (PM), CO2 and thermal comfort (relative humidity and temperature) across five schools in London. Results were compared between occupied and unoccupied hours to assess the impact of occupants and their activities, different floor coverings and the locations of the classrooms. In-classroom CO2 concentrations varied between 500 and 1500 ppm during occupancy; average CO2 (955 ± 365 ppm) during occupancy was ∼150% higher than non-occupancy. Average PM10 (23 ± 15 μgm-3), PM2.5 (10 ± 4 μgm-3) and PM1 (6 ± 3 μg m-3) during the occupancy were 230, 125 and 120% higher than non-occupancy. Average RH (29 ± 6%) was below the 40–60% comfort range in all classrooms. Average temperature (24 ± 2 °C) was >23 °C in 60% of classrooms. Reduction in PM10 concentration (50%) by dual ventilation (mechanical + natural) was higher than for PM2.5 (40%) and PM1 (33%) compared with natural ventilation (door + window). PM10 was higher in classrooms with wooden (33 ± 19 μg m-3) and vinyl (25 ± 20 μgm-3) floors compared with carpet (17 ± 12 μgm-3). Air change rate (ACH) and CO2 did not vary appreciably between the different floor levels and types. PM2.5/PM10 was influenced by different occupancy periods; highest value (∼0.87) was during non-occupancy compared with occupancy (∼0.56). Classrooms located on the ground floor had PM2.5/PM10 > 0.5, indicating an outdoor PM2.5 ingress compared with those located on the first and third floors (300 m3) classroom showed ∼33% lower ACH compared with small-volume (100–200 m3). These findings provide guidance for taking appropriate measures to improve classroom air quality.

National Highways (NH) are the major road networks linking cities but exposure studies during long commutes on highways are limited. We assessed exposure concentrations of fine particles ≤2.5 μm in diameter (PM2.5) and carbon monoxide (CO) inside bus, ac (air-conditioned) and non-ac car and on an Indian NH over 200 km length. A total of nine round journeys were made in three modes. Analysis of variance (ANOVA) and generalized linear model (GLM) were applied to quantify the contribution of determinants that may explain the variability of exposure concentrations and their association with in-vehicle temperature and relative humidity (RH). The highest and lowest exposures concentrations to PM2.5 were observed in non-ac car (89 ± 32 μg m−3) and the ac car (55 ± 19 μg m−3). Exposures concentrations in non-ac car were higher during in-city travel (113 ± 36 μg m−3). The average CO exposure concentrations were highest in ac car (2.0 ± 0.9 ppm). Results of GLM analysis suggested that travel mode, highway segments (in/out-city) and the journey times are key determinants of personal exposure concentrations. Travel mode for PM2.5 (15%) and NH segments for CO (21%) explained maximum variability. Altogether, these explained 33% and 57% of the variability in PM2.5 and CO exposure concentrations, respectively. PM2.5 consists of soot, mineral and fly ash that are a proxy of fresh exhaust emissions, re-suspended road dust and industrial emissions, respectively. Additionally, EDX analyses revealed an abundance of Si, Al, Ca and Pb, confirming re-suspension, brake/tire wear and construction dust as important sources.

Rapid urbanization has contributed to urban heat islands, which can potentially lead to increased energy consumption and carbon emissions, further worsening global warming. The U-shaped street canyon is one of the leading causes of urban heat islands, which may block air circulation and lead to urban heat accumulation. The canyon heat issues can be usually mitigated by nature-based solutions, such as street trees. It is important to increase the greenery space benefits (e.g., cooling effect of trees) with limited canyon space. However, there is an absence of refined greenery space design strategy in various street canyons. This work explored the quantitative design of greenery space (e.g., tree spacing) in different street canyons with complex morphological characteristics, in order to effectively improve co-benefits of trees and mitigate urban heat islands. Eighteen morphological types were considered, including symmetrical & asymmetrical shallow, ideal, and deep street canyons. Co-benefit considering cost of different tree spacings were analyzed, to maximize the benefits of cooling effect and carbon sequestration at minimal nurturing cost. Compared with street canyons without trees, ideal street canyon with tree spacing of 0.2W (W is canyon width) achieved the maximum temperature reduction of 6 oC. The positive correlation between tree spacing and co-benefits was found. The maximum co-benefits of street canyon trees occurred at tree spacing of less than 0.7W, which was largely increased by about 14% compared with 0.2W. This work can provide the guideline for efficient greenery space design, which is crucial for mitigating urban heat islands by nature-based solutions.

Smoke particles ejected into the atmosphere from biomass burning can modify the atmospheric composition around and even 30 far from the sources. In late winter and early spring, biomass burning emissions from inland regions can be efficiently 31 transported to urban areas in south-eastern South America, thus affecting air quality in those areas. In this study, the Weather 32 Research and Forecasting with Chemistry (WRF-Chem) model was applied in order to investigate the impact of biomass 33 burning sources on aerosol loadings and properties over the São Paulo Metropolitan Area (SPMA), in south-eastern Brazil, 34 during the period from 19 August to 3 September 2014. The model performance was evaluated using available aerosol 35 measurements from the Narrowing the Uncertainties on Aerosol and Climate Change in São Paulo State (NUANCE-SPS) 36 project. The combined application of aerosol data and WRF-Chem simulations made it possible to represent some of the most 37 important aerosol properties, such as particle number concentration (PNC) and cloud condensation nuclei (CCN) activation, in 38 addition to evaluation of the impact of biomass burning by analysing a five-day transport event, from 22 August to 26 August 39 2014. During this transport event, differences in the average predicted PM2.5 concentration reached 15 μg m−3 (peaking at 20 40 μg m−3 during the night-time hours) over the SPMA, compared with 35 μg m−3 over inland areas northwest and north of the 41 SPMA. Biomass burning accounted for up to 20 % of the baseline PNC- and CCN-weighted relative differences over the 42 SPMA (2300 cm−3 and 1400 cm−3, respectively).

Air pollution and climate change are a deadly duo for Africa, and must be tackled together. Air pollutants and greenhouse gases often share the same sources and can be even more dangerous when combined. Africa is particularly vulnerable to climate change, and currently, an estimated 1 million people per year die prematurely from air pollution on the continent. But there is a way to improve the situation: preventing emissions from short-lived climate pollutants, like methane and black carbon, is crucial for the world to stay below 1.5°C. Reducing SLCPs will help both save lives and protect the environment. Africa has a huge opportunity to continue developing sustainably, improve human well-being, and protect nature by investing in solutions to fight climate change and air pollution together. A new Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa from the African Union Commission, the Climate and Clean Air Coalition, and the UN Environment Programme, developed by African scientists in a process led by the Stockholm Environment Institute, shows how African leaders can act quickly across 5 key areas—transport, residential, energy, agriculture, and waste—to fight climate change, prevent air pollution, and protect human health. 

While the crushing of concrete gives rise to large quantities of coarse dust, it is not widely recognized that this process also emits significant quantities of ultrafine particles. These particles impact not just the environments within construction activities but those in entire urban areas. The origin of these ultrafine particles is uncertain, as existing theories do not support their production by mechanical processes. We propose a hypothesis for this observation based on the volatilisation of materials at the concrete fracture interface. The results from this study confirm that mechanical methods can produce ultrafine particles (UFP) from concrete, and that the particles are volatile. The ultrafine mode was only observed during concrete fracture, producing particle size distributions with average count median diameters of 27, 39 and 49 nm for the three tested concrete samples. Further volatility measurements found that the particles were highly volatile, showing between 60 and 95% reduction in the volume fraction remaining by 125 °C. An analysis of the volatile fraction remaining found that different volatile material is responsible for the production of particles between the samples.

Aerosol particles scatter and absorb solar radiation and affects earth's radiation budget. The aerosol particles are usually non-spherical in shape and inhomogeneous in chemical composition. For simplicity, these particles are approximated as homogeneous spheres/spheroids in radiative models and in retrieval algorithms of the ground and spaceborne observations. The lack of information on particle morphology (especially shape), chemical composition (that govern their spectral refractive indices) and most importantly internal structure (three Dimensional, 3D spatial distribution of chemical species) lead to uncertainty in the numerical estimation of their optical and radiative properties. Here, we present a comprehensive assessment of the particles' volumetric composition. The particles were collected from typical arid and urban environments of India and subjected to Focused Ion-Beam (FIB) coupled with Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscope (EDS). Particles from the arid environment were observed to be composed of Fe, Ca, C, Al, and Mg rich shell with Si and O rich core opposed to those from urban environment consisting Hg, Ag, C, S and N rich shell with Cu and S rich core. Based on the homogeneous sphere/spheroid assumption, conventional SEM-EDS and FIB-SEM-EDS results, different particle model shapes [single species homogeneous sphere (S1) and spheroid (SPH1); multiple species homogeneously mixed sphere (S2) and spheroid (SPH2); and core-shell (CS)] were considered for simulating their respective optical properties; SSA (Single Scattering Albedo) and g (Asymmetric parameter). The effect of internal structure on SSA was found to be prominent in particles having low value of the imaginary part of refractive index (k). While the same was observed to be low (nearly negligible) for the particle with the high value of “k”. The particles rich in copper are found to be highly absorbing in nature which causes positive radiative forcing.

In the post-pandemic era, Signaled Manual Airing (SMA, i.e. windows opening driven by real-time IAQ measurements) is still expected to play a role in schools, either independently or in support of mechanical ventilation (MV) systems, to meet more stringent regulatory requirements for indoor CO₂ levels. The present study in-depth evaluates the impact of SMA on improved levels of CO₂ but also its thermal impact in schools via a dedicated IoT-IAQ monitoring platform equipped with fully tuneable multi-threshold alarm schemes. An aged middle-school building, representative of a large amount of old existing school buildings in Italy (and Europe) and a Northern Italy location, were selected to investigate cold and high humidity winter conditions as the most critical for SMA. The mean indoor CO₂ difference with reference classrooms without alarms ranged from 20% to an impressive nearly 70%. We demonstrate a clear dependence on signalling mode and level of system usage learning. The best results were in fact obtained with acoustically enhanced signalling (acu-SMA). As downside effect, operating SMA at even moderately cold latitudes caused also thermal discomfort during the coldest days, with worsened response to the alarms. The observed mean CO₂ levels maintained by classroom groups appear to be correlated with the average indoor temperature and its inverse product [CO₂*Tindoor]-1 to a cumulative fatigue function with memory effect, referred as " thermal behavioural fatigue". Bounds of generalization, further developments towards smart hybrid ventilation systems and most suitable Italian regions for pure SMA retrofitting are finally discussed.

Atmospheric aerosol is the most important source of cloud condensation nuclei (CCN). Microphysics and chemical composition of aerosols can affect cloud development and precipitation process. Only a few studies in Latin American have reported the impact of urban aerosol on CCN activation parameters such as activated ratio (AR) and activation diameter (Dact). Sao Paulo Metropolitan Area (SPMA) is the biggest megacity of South America with over 20 million inhabitants. This is the first study in a megacity on South America to assess the impact of remote sources and new particle formation (NPF) events on CCN activation properties. The measurements were conducted at São Paulo city from August to September 2014. The CCN were measured within the 0.2–1.0% range of supersaturation, simultaneous with particle number concentration (PNC) and distribution (PND), trace elemental concentrations (TEC) and black carbon (BC). The NPF events were identified during 35% of the sampling days. Combination of TEC and BC associated with aerosol profile from Lidar analysis and Hysplit trajectories allowed to identify sea-salt and biomass burning contribution from remote regions as 28% and 21% of total number of days, respectively. The AR and Dact parameters presented a clearly different pattern for diurnal and nocturnal periods. The diurnal periods presented lower CCN activation than the nocturnal durations and this pattern was found to be associated mainly with local vehicular traffic emissions. NPF events showed a negative feedback to CCN activation. Weak effects of sea-salt and biomass burning aerosols could be observed on activation parameters as sea-salt showed a positive feedback. The results of this study show that particulate matter from local traffic emissions has the main effect on activation parameters compared with remote sources

Roadside vegetation barriers are used in many urban areas to restrict air and noise pollution from reaching roadside pedestrians, but their effectiveness in limiting the movement of nanoparticles is not yet known. This study investigates the influence of a roadside vegetation barrier on particle number distribution (PND) and concentration (PNC) and associated exposure under different wind directions. Size-resolved particles in the 5–560 nm size range were measured along a busy roadside in Guildford (Surrey, UK) using a fast response differential mobility spectrometer (DMS50). A custom-built solenoid switching system, together with the DMS50, was used to make sequential measurements at the front (L2), middle (L3) and back (L4) of the vegetation barrier; L1 was in parallel to L2 at a vegetation-free location. Measured data were divided into the three predominant wind directions: cross-road (NW–SW), cross-footpath (NE–SE) and along-road (NW–NE). The consistency in the shape of PNDs and the corresponding geometric mean diameters at the three sites (L2, L3, L4) indicate an identical removal effect of vegetation barrier for all sizes of particles. Comparison of the PNCs at two parallel locations (with and without the vegetation barrier) showed ∼11% higher PNCs (1.99 ± 1.77 × 105 cm−3) at L2 than those at L1 during cross-road winds, showing the impeding effect of the vegetation barrier. Such differences were insignificant during the remaining wind directions. Cross-road winds indicate the effect of vegetation barrier; the PNCs were decreased by 14 and 37% at L3 and L4, respectively, compared with L2. During cross-footpath winds, particles were carried away by the wind from the sampling location. Significant decrease in PNCs were consequently seen at L3 (1.80 ± 1.01 × 104 cm−3) and L4 (1.49 ± 0.91 × 104 cm−3) compared with L2 (6.26 ± 3.31 × 104 cm−3). The PNCs at these locations showed modest differences during the cross-footpath and along-road winds. Respiratory deposited doses (RDD) at L4 were found to be the lowest during all wind directions compared with the L1–L3. The vegetation barrier efficiently reduced the RDD by ∼36% during cross-road winds. Our results show the mitigation potential of vegetation barriers in limiting near-road nanoparticles exposure and the measured data can facilitate performance evaluation of theoretical models.

The microalgae biomass is emerging as a potential source of energy and bioproducts with several advantages over conventional crops in terms of its ability to produce ~300-times more renewable oil. Microalgae also have a high photosynthetic response, product accumulation rate and biomass production rate compared with other energy crops. Microalgae have the ability to grow on nonagricultural soil using wastewater instead of drinking water. Furthermore, microalgae have high capability to fix carbon dioxide from the environment. Microalgae-based bioproducts have different applications in pharmaceuticals, food and feed industries, and agricultural and transportation sectors. The key objectives covered in this review pertain to the role of microalgae to (i) maintain food chain, (ii) conservation of land and water resources in the environment with sequestration of CO2, (iii) production of energy in the form of biodiesel with zero waste, and (iv) simultaneous release of higher oxygen to the environment compared with other energy crops.

We examined the trade-offs between in-car aerosol concentrations, ventilation and respiratory infection transmission under three ventilation settings: windows open (WO); windows closed with air-conditioning on ambient air mode (WC-AA); and windows closed with air-conditioning on recirculation (WC-RC). Forty-five runs, covering a total of 324 km distance on a 7.2-km looped route, were carried out three times a day (morning, afternoon, evening) to monitor aerosols (PM2.5; particulate matter WC-AA>WC-RC) due to the ingress of polluted outdoor air on urban routes. A clear trade-off, therefore, exists for the in-car air quality (icAQ) versus ventilation, where WC-RC showed the least aerosol concentrations (i.e. four-times lower compared with WO), but corresponded to elevated CO2 levels (i.e. five-times higher compared with WO) in 20 mins. We considered COVID-19 as an example of respiratory infection transmission. The probability of its transmission from an infected occupant in a five-seater car was estimated during different quanta generation rates (2-60.5 quanta hr-1) using the Wells-Riley model. In WO, the probability with 50%-efficient and without facemasks under normal speaking (9.4 quanta hr-1) varied only by upto 0.5%. It increased by 2-fold in WC-AA (

This study describes the process of deriving integrated water vapor (IWV) from (a) a set of 18 GPS receivers that were installed at different airports across India and (b) a pair of GPS receivers located in Ahmedabad situated around 8 km apart. The Zenith Tropospheric Delay was estimated from the GPS observations using the GAMIT software. Further, IWV was estimated from the ZTD values using surface temperature and pressure from ERA-I reanalysis as additional inputs. The IWV estimates for 1 year-March 2013 to February 2014-were compared with ECMWF Reanalysis Interim (ERA-I) reanalysis as well as radiosonde soundings. The Root Mean Squared Error (RMSE) was & AP;6 mm or better for most stations. The IWV estimates for July 2013 were assimilated into the WRF model and had a positive impact on model analysis of IWV. The forecasted rain improved by up to 3-4 mm/day in some regions as a result of GPS-derived IWV estimates. For the Ahmedabad receivers, the GPS-derived IWV was compared with IWV from ERA-I reanalysis and was found to have a RMSE of & AP;7.7 mm which is < 20% of the mean value. The study demonstrates that the observed IWV variation is consistent with rainfall patterns over Ahmedabad. The rise and dips in the IWV correlate well with the active-break cycle in the monsoon rain. The study demonstrates the value of local measurements of IWV with high temporal frequency, as they are more likely to respond to fast-moving weather phenomena such as rainfall. Thus, the GPS-derived IWV measurements are likely to have significant value in the short-term forecasts of precipitation.

Cadherin-mediated adhesion plays a crucial role in multicellular organisms. Dysfunction within this adhesion system has major consequences in many pathologies, including cancer invasion and metastasis. However, mechanisms controlling cadherin recognition and adhesive strengthening are only partially understood. Here, we investigated the homophilic interactions and mechanical stability of the extracellular (EC) domains of E-cadherin and cadherin 7 using atomic force microscopy and magnetic tweezers. Besides exhibiting stronger interactions, E-cadherin also showed more efficient force-induced self-strengthening of interactions than cadherin 7. In addition, the distributions of the unbinding forces for both cadherins partially overlap with those of the unfolding forces, indicating that partial unfolding/deformation of the cadherin EC domains may take place during their homophilic interactions. These conformational changes may be involved in cadherins physiology function and contribute to the significant differences in adhesive strength mediated by type I and type II cadherins. •At the molecular level, the bond between E-cad is stronger than Cad 7.•Force-strengthening of homophilic binding of E-cad, but absent in case of Cad 7.•Unbinding forces of cadherins overlap with unfolding forces of their EC domains.•At a stretching force ∼5 pN, EC domains of E-cad unfold in ∼30 s.•Forced-deformation of EC domains is expected to help the strengthening of binding.

Modelling photochemical pollutants, such as ground level ozone (O3), nitric oxide (NO) and nitrogen dioxide (NO2), in urban terrain was proven to be cardinal, chronophagous and complex. We built linear regression and random forest regression models using 4-years (2015–2018; hourly-averaged) observations for forecasting O3, NO and NO2 levels for two scenarios (1-month prediction (for January 2019) and 1-year prediction (for 2019)) — with and without the impact of meteorology. These flexible models have been developed for, both, localised (site-specific models) and combined (indicative of city-level) cases. Both models were aided with machine learning, to reduce their time-intensity compared to models built over high-performance computing. O3 prediction performance of linear regression model at the city level, under both cases of meteorological consideration, was found to be significantly poor. However, the site-specific model with meteorology performed satisfactorily (r = 0.87; RK Puram site). Further, during testing, linear regression models (site-specific and combined) for NO and NO2 with meteorology, show a slight improvement in their prediction accuracies when compared to the corresponding equivalent linear models without meteorology. Random forest regression with meteorology performed satisfactorily for indicative city-level NO (r = 0.90), NO2 (r = 0.89) and O3 (r = 0.85). In both regression techniques, increased uncertainty in modelling O3 is attributed to it being a secondary pollutant, non-linear dependency on NOx, VOCs, CO, radicals, and micro-climatic meteorological parameters. Analysis of importance among various precursors and meteorology have also been computed. The study holistically concludes that site-specific models with meteorology perform satisfactorily for both linear regression and random forest regression. [Display omitted] •Site-specific models with meteorology have better performance over the indicative city level model.•Lower degrees of polynomial transformation on pollutant prediction show smaller error.•Hourly averaged observations are well-suited for the prediction of NO, NO2 and O3.•Random forest regression approach produces better models than linear regression for O3.•O3 prediction shows the highest dependence on solar radiation, NO2, wind speed and NO respectively.

This study aims to assess the impacts of assimilating the clear-sky radiances from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite on 72-h forecasts. First, we compare the water vapour (WV) brightness temperature (T B ) in July 2018 from the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses and Weather Research and Forecasting (WRF) forecasts with the clear-sky AGRI WV channel observations. The results suggest that the NCEP GDAS analyses are more consistent with the AGRI observations than the WRF forecasts, and the AGRI observations can be utilized to improve the initial conditions of the WRF model by data assimilation, especially in water vapour and surface temperature. After the preliminary verification, two identical cycling assimilation experiments are performed with and without the AGRI WV T B assimilation for a month. The results reveal that the WRF forecasts with AGRI T B assimilation are closer to satellite observations than the first guess in both WV channels. The validation with WV channel data of the Microwave Humidity Sounder (MHS) and SAPHIR sensors indicates that after the AGRI data assimilation, the errors are smaller than in the control experiment (without AGRI assimilation). The assimilation of the T B observed by AGRI WV channels shows a remarkable positive impact on moisture forecasts at middle and upper levels. Comparison of the T B from the WRF forecasts with the MHS observations suggests that the AGRI WV channel assimilation can improve the predictions. Overall, assimilating AGRI WV observations can positively influence the WRF analyses and forecasts.

Currently, there are no air quality regulations in force in any part of the world to control number concentrations of airborne atmospheric nanoparticles (ANPs). This is partly due to a lack of reliable information on measurement methods, dispersion characteristics, modelling, health and other environmental impacts. Because of the special characteristics of manufactured (also termed engineered or synthesised) nanomaterials or nanoparticles (MNPs), a substantial increase is forecast for their manufacture and use, despite understanding of safe design and use, and health and environmental implications being in its early stage. This article discusses a number of underlining technical issues by comparing the properties and behaviour of MNPs with anthropogenically produced ANPs. Such a comparison is essential for the judicious treatment of the MNPs in any potential air quality regulatory framework for ANPs.

In buildings, energy is primarily consumed by mechanical air conditioning systems. Low energy alternatives, such as natural ventilation, are needed. However, they need to be able to cope with increasing heatwaves and pollution, particularly in warm climates. This review paper looked at the ability of natural ventilation to provide adequate thermal comfort, resilience against heatwaves, and good Indoor Air Quality in warm climates. Single-sided ventilation demonstrates the poorest ability to provide thermal comfort, while cross ventilation highlights better performance in terms of reducing indoor air temperatures compared to outdoor. However, windcatchers and solar chimneys displayed even better performance by producing relatively high ventilation rates. During heatwaves and future climatic scenarios, natural ventilation, by cross-ventilation, was not able to meet internal thermal comfort standards. A potential low energy solution could be combining solar chimneys or windcatchers with water evaporation cooling. A critical synthesis of the literature suggests that these systems can generate high ventilation rates and keep indoor temperatures around 8 °C cooler than outdoor temperatures in warm weather (>35 °C). However, no studies were found testing these systems against future climate scenarios, and further studies are recommended. The literature supported natural ventilation being effective in removing pollution generated indoors due to adequate ventilation rates. However, using unfiltered natural ventilation for areas with high outdoor pollution can increase the indoor deposition of harmful particulate matter. With increasing air pollution, further studies are urgently required to investigate filter enabled natural ventilation, particularly with solar chimney/windcatcher incorporated. •Single-sided or cross ventilation won't meet thermal comfort in future warm climate.•Windcatcher and solar chimney show promise for good Indoor Air Quality in hot climate.•Natural ventilation with evaporative cooling can be resilient to heatwaves.•Further studies required on heatwave resilient natural ventilation in warm climates.•Research on filter enabled natural ventilation for pollution control is lacking.

The deterioration in air quality is a challenging problem worldwide. There is a need to raise awareness among the people and support informed decision making. Over the years, citizen science activities have been implemented for environmental monitoring and raising awareness but most of such works are contributory in nature, i.e. task design, planning and analysis are performed by professional researchers and citizens act as participants. Our objective is to demonstrate that citizen science can be used as a ‘tool’ to enhance public understanding of air pollution by engaging communities and local stakeholders. We present a co-creation based citizen science approach which incorporates the ideas of inclusion, where citizens are involved in most of the steps of the scientific process; collaboration, where the citizen scientists define research problems and methodologies, and reciprocation, where citizen scientists share their observations through storytelling. We integrate the use of interactive air quality quizzes, offline questionnaires and low-cost air quality monitoring sensors. The results show that such methods can generate insightful data which can assist in understanding people’s perception and exposure levels at a fine-grained level. It was also observed that community engagement in air quality monitoring can enhance partnerships between the community and research fraternity.

Nature-based solutions (NBS) are increasingly being implemented as suitable approaches for reducing vulnerability and risk of social-ecological systems (SES) to hydro-meteorological hazards. Understanding vulnerability and risk of SES is crucial in order to design and implement NBS projects appropriately. A systematic literature review was carried out to examine the suitability of, or gaps in, existing frameworks for vulnerability and risk assessment of SES to hydro-meteorological hazards. The review confirms that very few frameworks have been developed in the context of NBS. Most of the frameworks have emphasised social systems over ecological systems. Furthermore, they have not explicitly considered the temporal dimension of risk reduction measures. The study proposes an indicator-based vulnerability and risk assessment framework in the context of NBS (VR-NBS) that addresses both the above limitations and considers established NBS principles. The framework aims to allow for a better consideration of the multiple benefits afforded by NBS and which impact all the dimensions of risk. A list of 135 indicators is identified through literature review and surveys in NBS project sites. This list is composed of indicators representing the social sub-system (61% of total indicators) and the ecological sub-system (39% of total indicators). The list will act as a reference indicator library in the context of NBS projects and will be regularly updated as lessons are learnt. While the proposed VR-NBS framework is developed considering hydro-meteorological hazards and NBS, it can be adapted for other natural hazards and different types of risk reduction measures.

Dispersion of particles, as evidenced by changes in their number distributions (PNDs) and concentrations (PNCs), in urban street canyons, is still not well understood. This study compares measurements by a fast-response particle spectrometer (DMS500) of the PNDs and the PNCs (5–1000 nm, sampled at 1 Hz) at street and rooftop levels in a Cambridge UK street canyon, and examines mixing, physical and chemical conversion processes, and the competing influences of traffic volume and rooftop wind speed on the PNDs and the PNCs in various size ranges. PNCs at street level were ≈6.5 times higher than at rooftop. Street-level PNCs followed the traffic volume and decreased with increasing wind speed, showing a larger influence of wind speed on 30–300 nm particles than on 5–30 nm particles. Conversely, rooftop PNCs in the 5–30 nm size range increased with wind speed, whereas those for particles between 30 and 300 nm did not vary with wind speed.

Vehicles are one of the most significant sources of air pollutant emissions in urban areas, and their real contribution always needs to be updated to predict impacts on air quality. Radar databases and traffic counts using statistical modeling is an alternative and low-cost approach to produce traffic activities data in each urban street to be used as input to predict vehicular emissions. In this work, we carried out a spatial statistical analysis of local radar data and calculated traffic flow using local radar data combined with different statistical models. Future scenarios about vehicle emission inventory to define public policies were also proposed and analyzed for Belo Horizonte (BH), a Brazilian State capital, with the third-largest metropolitan region in the country. The Normal-Neighborhood Model (i.e., the mixed effect model with random effect in the neighborhood, radar type, and in the regional area) was used to calculate traffic flow in each urban street. Results showed average reductions in CO (4.5%), NMHC (3.0%), NOx (3.0%) and PM2.5 (6.2%) emissions even with an increase in fleet composition (25% in average). The decrease is a result of the implementation of emission control programs by the government, improvements vehicles technologies, and the quality of fuels. Prediction of traffic data from radar databases has proven to be useful for avoiding the high costs of performing origin-destination surveys and traffic modeling using commercial software. Radar databases can provide many potential benefits for research and analysis in environmental and transportation planning. These findings can be incorporated in future investigations to implement public policies on vehicular emission reduction in urban areas and to advance environmental health effects research and human health risk assessment. [Display omitted] •The model Normal-Neighborhood was more suitable to perform a spatial distribution of vehicle flow.•Different fleet reduction combinations generate up to a 40% reduction in vehicle emissions.•Mobility and transportation solutions can be proposed using radar data.

We measured size–resolved PNCs in the 5–560 nm range at two different types (4– and 3–way) of TIs in Guildford (Surrey, UK) at fixed sites (~1.5 m above the road level), sequentially at 4 different heights (1, 1.5, 2.5 and 4.7 m), and along the road at five different distances (10, 20, 30, 45 and 60 m). The aims were to: (i) assess the differences in PNCs measured at studied TIs, (ii) identify the best fit probability distribution curves for the PNCs, (iii) determine vertical and horizontal decay profiles of PNCs, (iv) estimate particle number emission factors (PNEFs) under congested and free–flow traffic conditions, and (v) quantify the pedestrian exposure in terms of respiratory deposition dose (RDD) rates at the TIs. Daily averaged particle number distributions at TIs reflected the effect of fresh emissions with peaks at 5.6, 10 and 56nm. Despite the relatively high traffic volume at 3–way TI, average PNCs at 4–way TI were about twice as high as at 3–way TI, indicating less favourable dispersion conditions. Generalised extreme value distribution fitted well to PNC data at both TIs. Vertical PNC profiles followed an exponential decay, which was much sharper at 4–way TI than at 3–way TI, suggesting ~60% less exposure for people at first floor (4.7 m) to those at ground floor around 4-way TI. Vertical profiles indicated much sharper (~132–times larger) decay than in horizontal direction, due to close vicinity of road vehicles during the along-road measurements. Over an order of magnitude higher PNEFs were found during congested, compared with free–flow, conditions due to frequent changes in traffic speed. Average RDD rate at 4–way TI during congested conditions were up to 14–times higher than those at 3–way TI (1.20×1011 h˗1). Findings of this study are a step forward to understand exposure at and around the TIs.

Understanding the transformation of nanoparticles emitted from vehicles is essential for developing appropriate methods for treating fine scale particle dynamics in dispersion models. This article provides an overview of significant research work relevant to modelling the dispersion of pollutants, especially nanoparticles, in the wake of vehicles. Literature on vehicle wakes and nanoparticle dispersion is reviewed, taking into account field measurements, wind tunnel experiments and mathematical approaches. Field measurements and modelling studies highlighted the very short time scales associated with nanoparticle transformations in the first stages after the emission. These transformations strongly interact with the flow and turbulence fields immediately behind the vehicle, hence the need of characterising in detail the mixing processes in the vehicle wake. Very few studies have analysed this interaction and more research is needed to build a basis for model development. A possible approach is proposed and areas of further investigation identified.

We made fast response measurements of size-resolved particle number concentrations (PNCs) and distributions (PNDs) in the 5-1000 nm range close to a busy roadside, continuously for 31 days, in Kuwait. The aims were to understand their dispersion characteristics during summertime and dust events, and association with trace pollutants (NOx, O3, CO, SO2, and PM10) and meteorological parameters. PNCs were found up to ∼19-times higher (5.98 × 10(5) cm(-3)) than those typically found in European roadside environments. Size distributions exhibited over 90% of PNCs in ultrafine size range (

Nature-based solutions are increasingly implemented to tackle disaster risk reduction and climate change adaptation. Their rising popularity over grey solutions is partially explained by their number of additional benefits (so called co-benefits) for the socio-ecological system (SES). Frameworks are available to monitor and assess co-benefits, however, these frameworks are lacking clear guidance and ex-ante quantification of co-benefits and potential disbenefits of NBS. Another limitation is the accessibility and quality (representativeness) of data for computing indicators, especially, going towards larger scales (regional, pan-European). To develop a comprehensive framework and method for assessing and estimating possible side effects in advance, this paper aligns to existing frameworks but goes beyond those by providing practical guidance on data sourcing (including possible proxy variables) and quantification of both co-benefits and disbenefits. The resulting framework will support decision-making on area specific suitability of NBS for disaster risk reduction. Furthermore, it will enhance the planners’ knowledge and understanding of linked processes which can lead to potential positive and negative side effects; thus, this guidance will build a base for selecting suitable locations and NBS interventions.

Modelling of ambient particle number concentrations (PNC) has been implemented in the Danish air quality modelling system DEHM/UBM/AirGIS and evaluated with long-term measurements. We implemented particle dynamical processes in the regional scale model DEHM using the M7 aerosol dynamics module (presented in the accompanying article by Frohn et al. 2021), and we developed models for PNC at the local scale (UBM) and street scale (OSPM), in a first approximation without including the particle dynamics as presented in this article. Outdoor concentration estimates are provided at the front door of all residential address locations in Denmark for the past 40 years (1979 – 2018) with a spatial resolution of 1 km x 1 km taking all emission sectors in Denmark into account and additionally at the street location, with significant traffic (> 500 vehicles / day). We evaluated our model with up to 18-year long measurement time series of particle number size distributions (PNSD) at Danish street, urban and rural background stations. Two particle size ranges were used for evaluation: PNC>10 (count of particles with diameter larger than 10 nm) and PNC30_250 (diameter range 30 to 250 nm), in order to exclude nucleation events from the measurements and to obtain a consistent long-term measured time series. When comparing our model estimates with PNC30_250 measurements, we obtain Pearson correlation coefficients (Rp) in the range 0.39-0.95 depending on station location (street, urban background, rural) and averaging time (hour, day, month, year). The highest correlations were found for yearly averages at a monitoring station located at a street with dense traffic (Rp=0.95) whereas shorter time averages and comparisons with monitoring stations at urban and rural background locations provided lower correlations. The model performance for PNC in terms of correlation coefficients with respect to measurements is comparable to the performance for other pollutants such as NOX , PM2.5 and better than the performance for PM10. The model generally overestimated the observed concentrations, Normalised Mean Bias (NMB) was in the range 6% to 190% compared to PNC>10 and 90% to 290% compared to PNC30_250. These relatively high NMBs are probably caused by uncertainties in the modelling process, especially the estimation of particle number emissions, which largely determine the ambient concentrations of PNC. Furthermore, uncertainties might as well originate from the complexity of modelling particle dynamical processes accurately and the great challenges in performing long-term PNC measurements. The presented model can estimate PNC at all Danish addresses over the last 40 years with a 1-hour time resolution. The data seem to provide a good indication of the relative differences in PNC at Danish addresses.

Observation of air pollution at high spatio-temporal resolution has become easy with the emergence of low-cost sensors (LCS). LCS provide new opportunities to enhance existing air quality monitoring frameworks but there are always questions asked about the data accuracy and quality. In this study, we assess the performance of LCS against industry-grade instruments. We use linear regression (LR), artificial neural networks (ANN), support vector regression (SVR) and random forest (RF) regression for development of calibration models for LCS, which were Smart Citizen (SC) kits developed in iSCAPE project. Initially, outdoor colocation experiments are conducted where ten SC kits are collocated with GRIMM, which is an industry-grade instrument. Quality check on the LCS data is performed and the data is used to develop calibration models. Model evaluation is done by testing them on 9 SC kits. We observed that the SVR model outperformed other three models for PM2.5 with an average root mean square error of 3.39 and average R2 of 0.87. Model validation is performed by testing it for PM10 and SVR model shows similar results. The results indicate that SVR can be considered as a promising approach for LCS calibration.

Dust samples were collected from 38 naturally ventilated houses for 12 weeks. Effects of three variables in two groups each were evaluated: proximity to traffic density (main- and side-roads), cigarettes smoking (smoking and no-smoking), and houses’ age (old and new). No significant differences were identified between the two groups for all variables (p = 0.227–0.247). The average dust loading rate for the entire group was 66.7 ± 30.9 mg m–2 week–1. The average metal concentrations (µg g–1) for the entire group were 58.7 ± 17.4 for V, 53.8 ± 12.7 (Cr), 473 ± 137 (Mn), 9.68 ± 2.83 (Co), 130 ± 52.1 (Cu), 241 ± 65.3 (Sr), 0.827 ± 0.552 (Cd), 324 ± 143 (Ba), and 58.9 ± 28.9 for Pb. Likewise, the average metal loading rates (µg m–2 week–1) for the entire group were: 4.01 ± 2.41 for V, 3.62 ± 1.97 (Cr), 31.9 ± 18.3 (Mn), 0.662 ± 0.387 (Co), 8.57 ± 5.30 (Cu), 16.3 ± 9.23 (Sr), 0.051 ± 0.034 (Cd), 21.1 ± 12.4 (Ba), and 3.97 ± 2.74 for Pb. We noticed enrichment factors (EF) of less than 2 and strong correlations between V, Cr, Mn, Co, and Sr indicating their crustal origin. Conversely, Pb, Cu, and Cd showed low to moderate correlations together with moderate to significant EF suggesting anthropogenic pollution of non-crustal origins. Despite the scarcity of rain fall and arid environment in the studied area, our dust and metal loading rates can be considered as intermediate when compared to some international cities. Such a finding could be attributed to the absence of major industries and the relatively low traffic density in our study area.

The aim of this study is to assess personal exposure to Particle Number Concentrations (PNC) in four size ranges between 0.3 and 10 μm, and particulate matter (PM1; PM2.5; PM4; PM10) in order to evaluate possible genotoxic effects through a comet assay in buccal cells. A convenience cohort of 30 individuals from a Brazilian medium-sized city was selected. These individuals aged between 20 and 61 and worked in typical job categories (i.e., administrative, commerce, education, general services and transport). They were recruited to perform personal exposure measurements during their typical daily routine activities, totaling 240 hours of sampling. The 8-hour average mass concentrations in air for volunteers ranged from 2.4 to 31.8 mg m-3 for PM1, 4.2 to 45.1 mg m-3 for PM2.5, 7.9 to 66.1 mg m-3 for PM4 and from 23.1 to 131.7 mg m-3 for PM10. The highest PNC variation was found for 0.3-0.5 range, between 14 to 181 particles cm- 3, 1 to 14 particles cm-3 for the 0.5-1.0 range, 0.2 to 2 particles cm-3 for the 1.0-2.5 range, and 0.06 to 0.7 particles cm-3 for the 2.5-10 range. Volunteers in the ‘education’ category experienced the lowest inhaled dose of PM2.5, as opposed to those involved in ‘commercial‘ activities with the highest doses for PM10 (1.63 μg kg-1 h-1) and PM2.5 (0.61 μg kg-1 h-1). The predominant cause for these high doses was associated with the proximity of the workplace to the street and vehicle traffic. The comet assay performed in buccal cells indicated that the volunteers in ‘commerce’ category experienced the highest damage to their DeoxyriboNucleic Acid (DNA) compared with the control category (i.e. ‘education’). These results indicate the variability in personal exposure of the volunteers in different groups, and the potential damage to DNA was much higher for those spending time in close proximity to the vehicle sources (e.g. commercial services) leading to exposure to a higher fraction of fine particles. This study builds understanding on the exposure of people in different job categories, and provide policy makers with useful information to tackle this neglected issue.

The study was conducted to assess the performance of improved and traditional cookstoves using wood as a fuel and three combinations of other fuel mixes – (i) wood and cow dung, (ii) wood and mustard stalks, and (iii) cow dung and mustard stalks). Energy and emission parameters such as specific energy consumption (SEC), emission factors (EFs) of carbon monoxide (CO), particulate matter (PM) and black carbon (BC) were used to compare four different types of cookstoves. These included top-feed forced draft (TF-FD), top-feed natural draft (TF-ND), front-feed natural draft (FF-ND) and front-feed traditional (FF-TR) cookstoves. Controlled cooking test (CCT) was used as the test protocol. The results showed the performance of improved cookstove technologies can vary based on the fuel used for cooking. It was observed that emission factors for PM and CO increased by 67–96% and 45–90% respectively when all three improved cookstoves were tested with three fuel combinations against wood as cooking fuel. Among the tested cookstoves, a marked difference was observed between performance of forced draft and natural draft cookstoves. Forced draft cookstoves emitted higher amount of all pollutant emissions compared to natural draft cookstoves when used with mustard stalks in combination with either wood or cowdung. The results are of critical importance given that forced draft cookstoves have been promoted in geographical regions where fuel mix use is prevalent. Therefore, forced draft cookstove might not be the right choice when the goal is climate mitigation and reduction in impact on human health. It is imperative to study comprehensively the influence of various field variables on performance of cookstoves, which have severe implications on the performance of cookstoves.

Experiments were conducted in an UK inter-city train carriage with the aim of evaluating the risk of infection to the SARS-CoV-2 virus via airborne transmission. The experiments included in-service CO2 measurements and the measurement of salt aerosol concentrations released within the carriage. Computational fluid dynamics simulations of the carriage airflow were also used to visualise the airflow patterns, and the efficacy of the HVAC filter material was tested in a laboratory. Assuming an infectious person is present, the risk of infection for a 1-h train journey was estimated to be 6 times lower than for a full day in a well-ventilated office, or 10–12 times lower than a full day in a poorly ventilated office. While the absolute risk for a typical journey is likely low, in the case where a particularly infectious individual is on-board, there is the potential for a number of secondary infections to occur during a 1-h journey. Every effort should therefore be made to minimize the risk of airborne infection within these carriages. Recommendations are also given for the use of CO2 sensors for the evaluation of the risk of airborne transmission on train carriages.

Nanotechnology is currently a key area of research with both useful applications and environmental concerns. The aim of this chapter is to evaluate both the positive and negative aspects of applying nanotechnology within concrete materials and structures. The chapter begins with information on current applications of nanotechnology enabled products within the construction industry, followed by a summary of recent research and review articles related to the application of nanomaterials in concrete technology. The broader application of nanotechnology integrated products in the construction industry for the creation of lighter and stronger structural composites, low maintenance surface coatings, and enhancing the properties of cementitious materials are considered. The application of nanosensors in the construction industry is also discussed. The subsequent sections present an overview of the potential environmental impacts of nanotechnologies in terms of the release of particulate pollutants, including nanoparticles, during the construction, demolition and refurbishment activities. The chapter finally concludes by highlighting some areas for future work.

Providing children with a clear understanding of climate change drivers and their mitigation is crucial for their roles as future earth stewards. To achieve this, it will be necessary to reverse the declining interest in STEM (Science, Technology, Engineering and Mathematics) education in schools in the UK and other countries, as STEM skills will be critical when designing effective mitigation solutions for climate change. The ‘Heat-Cool Initiative’ was co-designed and successfully implemented in five primary/secondary UK schools, as a playful learning tool to unleash student interest in STEM subjects. 103 students from two cohorts (years 5-6 and 7-9) participated in five Heat-Cool activity sessions where they used infrared cameras to explore the issue of urban heat. Their learning was evaluated using a multi-functional quantitative assessment, including pre- and post-session quizzes. Climate change literacy increased by 9.4% in primary school children and by 4.5% in secondary school children. Analyses of >2000 infrared images taken by students, categorised into 13 common themes, revealed age-related differences in children’s cognitive development. At primary school age, images of the ‘self dominated; secondary school children engaged more with their physical environment. This novel approach demonstrated the importance of developing tailored technology-enhanced STEM education programmes for different age cohorts, leading to a high capacity for improving learning outcomes regarding climate change. Such programmes, embedded in school curricula nationally and internationally, could become a much-needed positiv contribution to reaching the United Nation’s Sustainable Development Goals, especially Goals 4 (Quality Education) and 13 (Climate Action).

The impact of traffic pollution on the health and safety of residents that live in roadside residential buildings has been a major concern for governments. This study investigated the spatial distributions of PM2.5 concentration due to road traffic emissions and put forward a spatial distribution model for the estimation of PM2.5 concentration (SDC) based on Machine Learning. Meanwhile, based on SDC model, the decrease in life expectancy (DLE) of residents was assessed. On-site monitoring of PM2.5 concentration was conducted on different floors of a typical residential building situated by the roadside. Computational Fluid Dynamics (CFD) simulation was conducted for the spatial distribution analysis of PM2.5 concentration, which was verified by measurements. The findings show the PM2.5 concentration was decreased from 74 μg/m^3to 43 μg/m^3within 0 to 120 m distance from the road, and was decreased from 73 μg/m^3 to 42 μg/m^3 within 0 to 60 m height. The DLE in these locations was up to 5.11 years. The concentration of PM2.5 was stabilized within 40 to 45 μg/m³ when the building height was above 60 m (roughly the 17th floor from the ground) and the distance was 120 m away from the road. The DLE in these locations was stabilized within 0.62 years to 0.91years. The SDC model was established to efficiently predict DLE of residents and PM2.5 concentration along roadside. These findings would facilitate the precaution guidelines making of urban pollution as well as the future planning of urban health and safety buildings.

The adoption of Nature-Based Solutions (NBSs) represents a novel means to mitigate natural hazards. In the framework of the OPERANDUM project, this study introduces a methodology to assess the efficiency of the NBSs and a series of Open-Air Laboratories (OALs) regarded as a proof-of-concept for the wider uptake of NBSs. The OALs are located in Finland, Greece, UK, Italy, and Ireland. The methodology is based on a wide modeling activity, incorporated in the context of future climate scenarios. Herein, we present a series of models’ chains able to estimate the efficiency of the NBSs. While the presented models are mainly well-established, their coupling represents a first fundamental step in the study of the long-term efficacy and impact of the NBSs. In the selected sites, NBSs are utilized to cope with distinct natural hazards: floods, droughts, landslides, salt intrusion, and nutrient and sediment loading. The study of the efficacy of NBSs to mitigate these hazards belongs to a series of works devoted to the implementation of NBSs for environmental purposes. Our findings prove that land management plays a crucial role in the process. Specifically, the selected NBSs include intensive forestry; the conversion of urban areas to grassland; dunes; marine seagrass; water retention ponds; live cribwalls; and high-density plantations of woody vegetation and deep-rooted herbaceous vegetation. The management of natural resources should eventually consider the effect of NBSs on urban and rural areas, as their employment is becoming widespread.

Background: Air pollution exposure has a detrimental effect on children who spend more than 17% of their weekdays inside a school building. The purpose of this study is to look into the effects of particulate matter (PM) and toxic gases on health of the school children. Method: To evaluate the impact of air pollution, 250 students (on average 20 students from each school) aged from 9 to 12 were selected from ten schools. Automatic monitors (AEROCET 531S, USA) were employed to measure PM1.0, PM2.5, and PM10 concentrations. NO2, TVOC, and CO2 concentrations were measured using an AEROQUAL (500S, New Zealand), and the respiratory rate is measured by BSMI Peak Flow Meter (Made: BSMI, Origin: China). Monitors were placed at about 2.0 meters above the floor at breathing height and no student wore the sensors. The ANOVA test was conducted to see the statistical significance between air quality parameters and peak flow meter readings. Results: The mean ± standard deviation of PM1.0, PM2.5, and PM10 concentrations were 19.1±3.6, 34.2±10.1, and 131.3±58.6 μgm-3, respectively. PM2.5 and PM10 concentrations exceeded WHO standards (15 and 45 μgm-3 of 24 hours) by 2.3 and 2.9 times. The highest concentrations of toxic gases were found on school campuses where vehicle densities (measured manually) were high. The mean Hazard Quotient (HQ) for PM10 (2.5±2.2 indoor; 3.6±2.6 outdoor) and PM2.5 (1.8±0.8 indoor; 1.9±1.0 outdoor) among all participating students was >1 indicating an unacceptable risk for human health. Lung function associated with the PEF value has a negative correlation with PM1.0 and PM2.5 concentrations in most cases. Conclusions: The findings of this study are useful in gaining a general understanding of the school environment in Dhaka. It aimed to understand how children were personally exposed in school and to develop effective control strategies to mitigate negative effects.

Nature-based solutions (NBS) are being deployed around the world in order to address hydrometeorological hazards, including flooding, droughts, landslides and many others. The term refers to techniques inspired, supported and copied from nature, avoiding large constructions and other harmful interventions. In this work the development and evaluation of an NBS applied to the Spercheios river basin in Central Greece is presented. The river is susceptible to heavy rainfall and bank overflow, therefore the intervention selected is a natural water retention measure that aims to moderate the impact of flooding and drought in the area. After the deployment of the NBS, we examine the benefits under current and future climate conditions, using various climate change scenarios. Even though the NBS deployed is small compared to the rest of the river, its presence leads to a decrease in the maximum depth of flooding, maximum velocity and smaller flooded areas. Regarding the subsurface/groundwater storage under current and future climate change and weather conditions, the NBS construction seems to favor long-term groundwater recharge.

We critically assessed numerous aspects such as vehicle fleet, type of fuel used in road vehicles, their emissions and concentrations of particulate matter ≤2.5 µm (PM2.5) and ≤10 µm (PM10) in three of the most polluted metropolitan areas of Brazil: the Metropolitan areas of São Paulo (MASP), Rio de Janeiro (MARJ) and Belo Horizonte (MABH). About 90% of the Brazilian LDVs run on ethanol or gasohol. The HDVs form a relatively low fraction of the total fleet but account for 90% of the PM from road vehicles. Brazilian LDVs normally emit 0.0011g (PM) km-1 but HDVs can surpass 0.0120g (PM) km-1. The emission control programs (e.g., PROCONVE) have been successful in reducing the vehicular exhaust emissions, but the non-exhaust vehicular sources such, as evaporative losses during refueling of vehicles as well as wear from the tyre, break, and road surface have increased in line with the increase in the vehicle fleet. The national inventories show the highest annual mean PM2.5 (28.1μg m–3) in the MASP that has the largest vehicle fleet in the country. In general, the PM10 concentrations in the studied metropolitan areas appear to comply with the national regulations but were up to ~3-times above the WHO guidelines. The current Brazilian air quality standards are far behind the European standards. There has been a progress in bringing more restrictive regulations for air pollutants including PM10 and PM2.5 but such steps also require suitable solutions to control PM emissions from motor vehicles and mechanical processes.

Informational interventions are considered important to bring positive changes in attitudes and perception about pro-environmental life styles among individuals. In relation to mobility aspects, it is vital to identify relatively easier changes that have potential to reduce negative impacts of mobility on environment and individual health. This paper provides a comprehensive methodological framework and developed a computation algorithm that helps identify such an easy changes in the travel behavior of an individual. The development of algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data. A variety of rules that are part of the computational algorithm are taken from the transport modelling literature, where constraints and factors were examined for various activity-travel decisions. Three major aspects of activity-travel behavior such as lesser car use, cold start of car engines and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to data collected, using citizens from Hasselt and their pro-environmental potential is determined, which has been found significant.

Like many countries, the Central Pollution Control Board (CPCB), Delhi, in India evaluates exceedences of air pollution levels against the National Ambient Air Quality Standards (NAAQS). One of the mandatory requirements for NAAQS compliance is that the probability of non-exceedence should be at least 0.98, meaning that the formulated framework of NAAQS is essentially statistical. The current practice for assessing the compliance criterion is based on simple computation of the count of number of exceedences in a given year, without giving any consideration to the distribution function followed by different pollutants in the ambient air. This becomes even more important for monitoring stations where continuous monitoring is not done for all 365 days, but assessment is based on a minimum sample of 104 readings recorded in a year. The proper method for evaluating the compliance is the foreknowledge of the population distribution and computation of non-exceedence (or exceedence) probability of NAAQS from the probability density function (pdf). The study proposes an integrated and scientifically robust methodology that is generic in nature and could well be used for assessing the air quality compliance criteria laid out by the NAAQS for India, besides suggesting percent reduction in source emissions to those pollutants that exceed the NAAQS. The usefulness of proposed methodology is exhibited by a case study conducted on four criteria air pollutants – sulphur dioxide (SO2), nitrogen dioxide (NO2), suspended particulate matter (SPM), and particulate matter less 10 micron in size (PM10) – monitored in the ambient air of megacity Delhi at six monitoring stations. The collected data at all these sites underwent statistical analysis for the: (i) identification and estimation of the best-fit distributions, (ii) computation of probability of exceedence of the NAAQS for the non-complying pollutants, (iii) determination of return period of NAAQS violation, and (iv) estimation of percentage source emission reduction to meet the NAAQS criteria for the non-complying pollutants using the statistical rollback theory. It was concluded that the knowledge of pdf is a basic and essential requirement for realistically evaluating the compliance of NAAQS.

Particulate matter (PM) is a crucial health risk factor for respiratory and cardiovascular diseases. The smaller size fractions, ≤2.5 μm (PM2.5; fine particles) and ≤0.1 μm (PM0.1; ultrafine particles), show the highest bioactivity but acquiring sufficient mass for in vitro and in vivo toxicological studies is challenging. We review the suitability of available instrumentation to collect the PM mass required for these assessments. Five different microenvironments representing the diverse exposure conditions in urban environments are considered in order to establish the typical PM concentrations present. The highest concentrations of PM2.5 and PM0.1 were found near traffic (i.e. roadsides and traffic intersections), followed by indoor environments, parks and behind roadside vegetation. We identify key factors to consider when selecting sampling instrumentation. These include PM concentration on-site (low concentrations increase sampling time), nature of sampling sites (e.g. indoors; noise and space will be an issue), equipment handling and power supply. Physicochemical characterisation requires micro- to milli-gram quantities of PM and it may increase according to the processing methods (e.g. digestion or sonication). Toxicological assessments of PM involve numerous mechanisms (e.g. inflammatory processes and oxidative stress) requiring significant amounts of PM to obtain accurate results. Optimising air sampling techniques are therefore important for the appropriate collection medium/filter which have innate physical properties and the potential to interact with samples. An evaluation of methods and instrumentation used for airborne virus collection concludes that samplers operating cyclone sampling techniques (using centrifugal forces) are effective in collecting airborne viruses. We highlight that predictive modelling can help to identify pollution hotspots in an urban environment for the efficient collection of PM mass. This review provides guidance to prepare and plan efficient sampling campaigns to collect sufficient PM mass for various purposes in a reasonable timeframe.

Abstract Gaussian process regression is used to predict ultrafine particle (UFP) number concentrations. We infer their number concentrations based on the concentrations of NO, NO2, CO and O3 at half hour and 5 min resolution. Because UFP number concentrations follow from a dynamic process, we have used a non-stationary kernel based on the addition of a linear and a rational quadratic kernel. Simultaneous measurements of UFP and gaseous pollutants were carried out during one month at three sampling locations situated within a 1 km2 area in a Belgian city, Antwerp. The method proposed provides accurate predictions when using NO and NO2 as covariates and less accurate predictions when using CO and O3. We have also evaluated the models for different training periods and we have found that a training period of at least seven days is suitable to let the models learn the UFP number concentration dynamics in different typologies of traffic.

Vegetation can form a barrier between traffic emissions and adjacent areas, but the optimal configuration and plant composition of such green infrastructure (GI) are currently unclear. We examined the literature on aspects of GI that influence ambient air quality, with a particular focus on vegetation barriers in open-road environments. Findings were critically evaluated in order to identify principles for effective barrier design, and recommendations regarding plant selection were established with reference to relevant spatial scales. As an initial investigation into viable species for UK urban GI, we compiled data on 12 influential traits for 61 tree species, and created a supplementary plant selection framework. We found that if the scale of the intervention, the context and conditions of the site, and the target air pollutant type are appreciated, the selection of plants that exhibit certain biophysical traits can enhance air pollution mitigation. For super-micrometre particles, advantageous leaf micromorphological traits include the presence of trichomes and ridges or grooves. Stomatal characteristics are more significant for sub-micrometre particle and gaseous pollutant uptake, although we found a comparative dearth of studies into such pollutants. Generally advantageous macromorphological traits include small leaf size and high leaf complexity, but optimal vegetation height, form and density depend on planting configuration with respect to the immediate physical environment. Biogenic volatile organic compound and pollen emissions can be minimised by appropriate species selection, although their significance varies with scale and context. While this review assembled evidence-based recommendations for practitioners, several important areas for future research were identified.

A link between outdoor pollution of particulate matter (PM) and the mortality from COVID-19 disease has been reported. The potential interaction of SARS-CoV2 emitted from an infected subject in the form of droplets or as an aerosol with PM[Formula: see text] (PM of 2.5 [Formula: see text]m or less in aerodynamic diameter) may modulate SARS-CoV2 replication and infectivity. This may represent an important airborne route of transmission, which could lead to pneumonia and a poor outcome from COVID-19. Further studies are needed to assess the potential infectivity and severity of such transmission.

Cars are a dominant mode of transport in megacity Cairo, yet there is a scarcity of personal exposure data on air pollution. This is the first car users exposure study that investigates the underlying factors affecting particulate matter (PM); with aerodynamic diameter ≤2.5 μm (PM2.5) and ≤10 μm (PM10), nitrogen oxides (NO2) and carbon monoxide (CO) concentrations across Greater Cairo. Data is collected using a portable monitor during morning and evening peak hours for three car settings (open window; closed window; AC On). Open window consistently resulted in highest PM10 and PM2.5 concentrations (65 % and 48 % higher than AC On). However, most Cairo commuters do not have AC and are exposed to levels of PM10 and PM2.5 as high as 227 and 119 μg/m3. Evening peak hours experience higher pollution compared to morning peak. Zones with construction activities have 64 % higher PM10 concentrations. PM2.5 concentrations in cross-city routes are 3.6-times those in high-activity zones. The derived PM2.5/PM10 ratios are relatively low (

We have modeled the transmission of coronavirus 2019 in the isolation room of a patient suffering from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the Royal Brompton Hospital in London. An adaptive mesh computational fluid dynamics model was used for simulation of three-dimensional spatial distribution of SARS-CoV-2 in the room. The modeling set-up is based on data collected in the room during the patient stay. Many numerical experiments have been carried out to provide an optimal design layout of the overall isolation room. Our focus has been on (1) the location of the air extractor and filtration rates, (2) the bed location of the patient, and (3) consideration of the health and safety of the staff working in the area.

Traditional pottery manufacturing involves firing of the ceramics in kilns, a process that leads to high concentrations of airborne particles that are harmful to human health. In order to assess the associated exposure levels and the involved risks, here, for the first time, we investigate the size, the concentration and the elemental composition of the particles emitted during the different stages of the ceramic firing process. Number size distributions of the emitted particles, having diameters in the range from 10 nm to 20 [small mu ]m, were measured in a traditional small-sized pottery studio using a Scanning Mobility Particle Sizer (SMPS) and an Optical Particle Counter (OPC). The measurements showed dominance of the nanoparticle mode (i.e., particles smaller than 100 nm) when the kiln reached temperatures above 600 [degree]C. The mean size of the particles ranged from 30 to 70 nm and their peak number concentration was 6.5 [times] 105 cm-3 during the first stage of the firing process where the ceramics were unpainted and unglazed. During the second stage of the firing process, where the ceramics were painted and glazed, the mean particle size ranged from 15 to 40 nm and their number concentration peaked at 1.2 [times] 106 cm-3. Elemental analysis of individual particles collected during the two firing stages and studied by Energy-Dispersive X-ray (EDX) spectroscopy showed that the emitted nanoparticles contain significant amounts of lead. These findings provide new information for understanding the health impacts of traditional pottery manufacturing, and underline the need for adopting adequate measures to control nanoparticle emissions at the source.

Abstract Over 150 research articles relating three multi-disciplinary topics (air pollution, climate change and civil engineering structures) are reviewed to examine the footprints of air pollution and changing environment on the sustainability of building and transport structures (referred as built infrastructure). The aim of this review is to synthesize the existing knowledge on this topic, highlight recent advances in our understanding and discuss research priorities. The article begins with the background information on sources and emission trends of global warming (CO2, CH4, N2O, CFCs, SF6) and corrosive (SO2, O3, NOX) gases and their role in deterioration of building materials (e.g. steel, stone, concrete, brick and wood) exposed in outdoor environments. Further section covers the impacts of climate- and pollution-derived chemical pathways, generally represented by dose-response functions (DRFs), and changing environmental conditions on built infrastructure. The article concludes with the discussions on the topic areas covered and research challenges. A comprehensive inventory of DRFs is compiled. The case study carried out for analysing the inter-comparability of various DRFs on four different materials (carbon steel, limestone, zinc and copper) produced comparable results. Results of another case study revealed that future projected changes in temperature and/or relatively humidity are expected to have a modest effect on the material deterioration rate whereas changes in precipitation were found to show a more dominant impact. Evidences suggest that both changing and extreme environmental conditions are expected to affect the integrity of built infrastructure both in terms of direct structural damage and indirect losses of transport network functionality. Unlike stone and metals, substantially limited information is available on the deterioration of brick, concrete and wooden structures. Further research is warranted to develop more robust and theoretical DRFs for generalising their application, accurately mapping corrosion losses in an area, and costing risk of corrosion damage.

Air pollutants influence the morphological, physiological, and biochemical status of plants, and their impacts vary substantially among different species and cultivars. Current review synthesises published literature on the assessment of air pollution impacts on vegetation, with a specific focus on chronicling and summarizing scientific methods that quantify those impacts. Investigations carried out globally on pollutant-plant exposure-response, and articles that describe impact of air pollutants on plants and pollutant abatement using green infrastructure (GI) were systematically reviewed. 273 articles reviewed indicated that a substantial number of past explorations were on a small spectrum of certain species, mainly wheat, rice, soybean and maize; and fewer on non-crop plant species, which cover most of the urban areas and are part of GI. Furthermore, in lower middle-income countries which face significant pollution loads, even studies on crop species are limited. Most studies either use Air Pollution Tolerance Index, which is not pollutant dependent or concentrate on either Ozone or Particulate Matter (PM) and rarely investigate the impact of multiple pollutants in the atmosphere. Also, very few studies differentiate the effect of PM on plants based on its composition. Subsequently, the best possible experimental set ups and wide array of plant health parameters for determining and understanding the effects of different air pollutants on a variety of plant species has been emphasized. While this review compiled literature-based commendations for academic federations wanting to study and quantify air pollutant impacts on vegetation, numerous pertinent vital topics for future research were identified.

Simultaneous measurements of ultrafine particles (UFPs) were carried out at four sampling locations situated within a 1 km2 grid area in a Belgian city, Borgerhout (Antwerp). All sampling sites had different orientation and height of buildings and dissimilar levels of anthropogenic activities (mainly traffic volume). The aims were to investigate: (i) the spatio-temporal variation of UFP within the area, (ii) the effect of wind direction with respect to the volume of traffic on UFP levels, and (iii) the spatial representativeness of the official monitoring station situated in the study area. All sampling sites followed similar diurnal patterns of UFP variation, but effects of local traffic emissions were evident. Wind direction also had a profound influence on UFP concentrations at certain sites. The results indicated a clear influence of local weather conditions and the more dominant effect of traffic volumes. Our analysis indicated that the regional air quality monitoring station represented the other sampling sites in the study area reasonably well; temporal patterns were found to be comparable though the absolute average concentrations showed differences of up to 35%.

© 2015 Springer Science+Business Media Dordrecht This work investigates the role of materials selected for different urban surfaces (e.g. on building walls, roofs and pavements) in the intensity of the urban heat island (UHI) phenomenon. Three archetypal street-canyon geometries are considered, reflecting two-dimensional canyon arrays with frontal packing densities (λf) of 0.5, 0.25 and 0.125 under direct solar radiation and ground heating. The impact of radiative heat transfer in the urban environment is examined for each of the different built packing densities. A number of extreme heat scenarios were modelled in order to mimic conditions often found at low- to mid-latitudes dry climates. The investigation involved a suite of different computational fluid dynamics (CFD) simulations using the Reynolds-Averaged Navier–Stokes equations for mass and momentum coupled with the energy equation as well as using the standard k-ε turbulence model. Results indicate that a higher rate of ventilation within the street canyon is observed in areas with sparser built packing density. However, such higher ventilation rates were not necessarily found to be linked with lower temperatures within the canyon; this is because such sparser geometries are associated with higher heat transfer from the wider surfaces of road material under the condition of direct solar radiation and ground heating. Sparser canyon arrays corresponding to wider asphalt street roads in particular, have been found to yield substantially higher air temperatures. Additional simulations indicated that replacing asphalt road surfaces in streets with concrete roads (of different albedo or emissivity characteristics) can lead up to a ~5 °C reduction in the canyon air temperature in dry climates. It is finally concluded that an optimized selection of materials in the urban infrastructure design can lead to a more effective mitigation of the UHI phenomenon than the optimisation of the built packing density.

Global warming due to anthropogenic emission of green-house gases has induced climate change which is disturbing and will continue to impact the ecology and energy balance of our earth environment. The duration, frequency and intensity of extreme hot days in summers called heatwaves have increased with the beginning of the 21st century worldwide and have been projected to increase. Associated human health loss or damage can be managed or mitigated by planning proper management strategies, such as nature-based green and/or blue solutions in advance, along with proper evaluation of the risk of heat. Since heat stress is more pronounced in urban and built areas, most studies for heatwave risk assessment have been limited to big cities. The risk variation in semi-urban, sub-urban and rural areas has not been much investigated. The heat risk develops with time because of changing climate and socio-demographics, and risk assessment is needed to be done utilising recent data on climate and population characteristics. In this study, the heatwave or extreme hot (99 percentile) temperature risk has been estimated by using statistical approach on summer daily temperature and mortality data from Aberdeenshire and South East (SE) England, UK for the duration 1981-2018. A distributed-lag nonlinear model from Poisson regression family was applied to model the relationship between daily temperature and mortality. We calculated relative risk (RR) and mortality attributable fraction (AF) due to high temperature by comparing the extreme heat with the minimum mortality temperature. AF was calculated by dividing the number of excess deaths due to heat from all the days of the time-series by the total number of deaths. The overall risk in SE England was noted 56 % higher (RR 1.067) than Aberdeenshire (RR 1.043), with 36% more excess death in SE England (AF 0.15% and 0.11% respectively) due to different levels of people’s adaptation and resilience to different climate conditions. The outcome of this study can help in site focused mitigation strategies to certain areas at most risk and develop a scientific framework for early warning, planning and managing the health impacts of heatwave in more rustic regions.

Nature-Based Solutions (NBS) refer to the sustainable management, protection and use of nature to preserve the ecosystem and prevent the loss of biodiversity. Given the multiple environmental, social, and economic benefits they provide to society, NBS have been increasingly promoted and implemented in cities, especially for air pollution mitigation and the improving of human thermal comfort and well-being. Several databases and web platforms already exist, which document these beneficial impacts of NBS in our cities by collecting and exposing existing NBS case studies and projects from around globe. However, the effort of cataloging and storing NBS data according to common and harmonized principles and standards seems yet sporadic and uncoordinated at the global and European level, especially in the context of natural hazard-related disasters. Nature-based solutions have been indeed recently emerged as viable and effective measures to mitigate the impacts of hydro-meteorological phenomena such as floods, landslide, etc. in both urban and rural environments, an aspect not often emphasized in the existing databases. Driven by the ambition of overcoming these two main gaps, an innovative geo-catalogue of existing NBS has been developed within the framework of GeoIKP, the NBS web-platform newly created by the EU H2020 project OPERANDUM. The geo-catalogue represents a comprehensive, geo-referenced, database of NBS case studies which are specifically designed to mitigate the risk and impacts of hydro-meteorological hazards, under a variety of environmental setting and hazard categories. It therefore represents a novel and open-access data source to learn about, and explore, the usability of NBS in fulfilling climate mitigation and adaptation objectives over a wide range of hydro-meteorological hazards. Case studies collected from various resources (NBS platforms, scientific literature, technical reports, OPERANDUM living labs, etc.) are revised, classified and harmonized according to internationally recognized standard and classification schemes (e.g., INSPIRE legislation, MAES classification, etc.) which allow to characterize each NBS through a comprehensive set of parameters, including the type of hazard and ecosystem, the societal challenges and driving policies linked to it, the type of intervention and its spatial coverage, among many others. The highly structured and comprehensive data model adopted here enables to query the database and/or filter the results based on a multitude of individual parameters which encompass all different dimensions of NBS (e.g. geophysical, societal, environmental, etc.). This not only allows for a straightforward and automatic association to one or more thematic aspects of NBS, but also enhances standardization, discoverability and interoperability of NBS data.

Background Children spend significant amounts of time at school, making the school environment a potentially important contributor to air quality exposure. Aim The SAMHE initiative has a dual aim: 1) to develop and test a bespoke citizen science framework for collecting environment and indoor air quality data in classrooms, alongside contextual data capable of enriching analysis, at an unprecedented scale; and, 2) to simultaneously use these methods to raise awareness among communities regarding their exposure to air pollution in the school environment. Methodology To achieve this dual aim, the SAMHE project was initiated to deploy more than 2 000 low-cost indoor air quality monitors in school classrooms. A Web App has been co-designed with schools to support collecting a large comprehensive dataset (including school buildings’ characteristics, operation and behavioural patterns) and to enable students and teachers to interact with the data gathered in their school. Results and outlook We present the design of the interface and visuals that have been co-designed with 20+ schools and tested with 120+ schools. Within one week of the SAMHE launch week, 537 schools had registered to join the project, and at the time of writing (just seven weeks later) this number had grown to around 800 schools. This highlights the potential for this novel initiative to provide a step-change in the way that indoor air quality datasets are gathered at a national and, potentially, international level while simultaneously enabling schools to better manage their indoor environment and empowering students and teachers to reduce their environmental health risks.

Human health is the driving force for setting the Ambient Air Quality Standards for the country. As per Auto Fuel Policy released by Govt. of India, Air Quality Monitoring and Source Apportionment Studies were initiated in six cities. Apart from determining emission data from other sources, the assessment of automotive emission inventory was done by conducting the emission testing on vehicles of different categories and vintages following a driving cycle. India has been following Modified Indian Driving Cycle (MIDC) adopted from European driving cycle which may not give a realistic assessment of vehicular emissions in laboratory as compared to on-road emissions. The variation could be due to different traffic density, land-use patterns, road infrastructure and traffic management encountered in India as compared to Europe. This paper presents the evolution of Driving Cycle developed for passenger cars in Delhi. The entire exercise was divided into 5 major components including selection of representative routes, vehicles, measuring instruments, data analysis methodology and validation of final driving cycle. Seven routes were selected considering Home-to-Work trips covering major landmarks and traffic zones across Delhi. Traffic monitoring was conducted for 3 days (16 hrs/day) at 21 sites on selected routes. The data acquisition was carried out on 4 passenger cars covering 224 trips and spanning 120 days. The driving patterns generated from each trip were statistically analysed following the micro-trip approach on the basis of different traffic conditions like congested, semi-urban, urban and extra-urban with the help of specifically designed software based on in-house developed algorithm. Suitable boundary conditions depending upon the traffic conditions in Delhi were incorporated for identifying and eliminating the redundant data in order to derive a realistic speed time sequence. The finalised cycle was validated on the chassis dynamometer based on the on-road fuel consumption.

Urban civilization has a high impact on the environment and human health. The pollution level of indoor air can be 2–5 times higher than the outdoor air pollution, and sometimes it reaches up to 100 times or more in natural/mechanical ventilated buildings. Even though people spend about 90% of their time indoors, the importance of indoor air quality is less noticed. Indoor air pollution can be treated with techniques such as chemical purification, ventilation, isolation, and removing pollutions by plants (phytoremediation). Among these techniques, phytoremediation is not given proper attention and, therefore, is the focus of our review paper. Phytoremediation is an affordable and more environmentally friendly means to purify polluted indoor air. Furthermore, studies show that indoor plants can be used to regulate building temperature, decrease noise levels, and alleviate social stress. Sources of indoor air pollutants and their impact on human health are briefly discussed in this paper. The available literature on phytoremediation, including experimental works for removing volatile organic compound (VOC) and particulate matter from the indoor air and associated challenges and opportunities, are reviewed. Phytoremediation of indoor air depends on the physical properties of plants such as interfacial areas, the moisture content, and the type (hydrophobicity) as well as pollutant characteristics such as the size of particulate matter (PM). A comprehensive summary of plant species that can remove pollutants such as VOCs and PM is provided. Sources of indoor air pollutants, as well as their impact on human health, are described. Phytoremediation and its mechanism of cleaning indoor air are discussed. The potential role of green walls and potted-plants for improving indoor air quality is examined. A list of plant species suitable for indoor air phytoremediation is proposed. This review will help in making informed decisions about integrating plants into the interior building design.

Severe episodic air pollution blankets entire cities and regions and have a profound impact on humans and their activities. We compiled daily fine particle (PM2.5) data from 100 cities in five continents, investigated the trends of number, frequency, and duration of pollution episodes, and compared these with the baseline trend in air pollution. We showed that the factors contributing to these events are complex; however, long-term measures to abate emissions from all anthropogenic sources at all times is also the most efficient way to reduce the occurrence of severe air pollution events. In the short term, accurate forecasting systems of such events based on the meteorological conditions favouring their occurrence, together with effective emergency mitigation of anthropogenic sources, may lessen their magnitude and/or duration. However, there is no clear way of preventing events caused by natural sources affected by climate change, such as wildfires and desert dust outbreaks.

The increasing industrial activities, number of vehicles on road and population in large cities causes the contamination of air in urban environment, and eventually affect human health. Therefore, the aim of this study was to collect soil and dust samples from twelve roadside academic institutions in Dhaka City, Bangladesh. One of the twelve sites is control site for this study. The elemental (Ca, Fe, K, Ti, Sr, Zn, Zr, Rb, Cr, Ni, Pb and Cu) concentration in soil and dust samples were analyzed by XRF technique. The metals concentration in dust and soil samples followed the following order: Fe > Ti > Sr > Zn > Zr > Rb > Pb > Cu, and Fe > Ti >Zr > Sr > Rb > Zn > Cu > Pb > As, respectively. As expected, the most elemental concentration at the control site, which was situated inside a village and ~ 1 km far away from the road, was lower compared with those in soil and dust samples. Average As concentration (16.52 mg/kg) in soil was observed to be three times higher than its background value. The concentration of Pb in the dust sample of a school at Sadarghat (136.04 mg/kg) was significantly higher than the other sites. We observed that the mean concentration for most of the metals had a higher concentration than the background values set by Chinese Environmental Protection Administration (CEPA), except for K and Zr. Soil samples were analyzed to determine the percentage of organic matter by dry combustion technique, and the average amount of organic matter in soil samples was 1.42%. Conversely, the contamination levels of heavy metals were assessed based on the geo-accumulation index (Igeo), enrichment factor (EF) and contamination factor (CF). Subsequently non-carcinogenic health risk was determined using lifetime average daily dose (LADD). The non-carcinogenic health risk was found to be more prominent for children than that for adults. No significant carcinogenic health risk was found in the study area.

Air pollution is a major cause of premature death in Greater Cairo, but studies on emission control are limited. We used local and international data to predict the impact of transport emission control measures on sector parameters including congestion. The International Vehicle Emission model accordingly estimated quantities of criteria, toxic and global warming emissions produced by on-road vehicles. Emissions were estimated for 2019 base case (2019-BC) and projected for 2030 under the ‘do nothing’ scenario (2030-DNS) and five scenarios: fuel subsidy removal (2030-FSR), road expansions (2030-RE), public transport improvements (2030-PTI), inspection and maintenance (I/M) programs (2030-I/MP), and fuel enhancements (2030-FE). The 2030-FSR would reduce emissions by 11.2% versus 2030-DNS. The 2030-RE resulted in an average increase of 37% in emissions compared with 2030-DNS since it induces more traffic. The 2030-PTI provides alternatives to car travel; hence, cars result in an average drop of 32.8% for all emission types compared with 2030-DNS. The 2030-I/MP exhibited reductions in PM10 and toxic pollutants, of 35–54.8% compared with 2030-DNS. The 2030-FE reduced SOx, benzene and N2O emissions by 91.8%, 81% and 39.1%, respectively, compared with 2030-DNS. The 2030-I/MP is most effective in reducing health damaging pollutants while 2030-PTI positively impacts commuters’ lifestyle.

Protecting the health of growing urban populations from air pollution remains a challenge for planners and requires detailed understanding of air flow and pollutant transport in the built environment. In recent years, the work undertaken on passive methods of reducing air pollution has been examined to address the question: "how can the built environment work to alter natural dispersion patterns to improve air quality for nearby populations?" This review brings together a collective of methods that have demonstrated an ability to influence air flow patterns to reduce personal exposure in the built environment. A number of passive methods exists but, in the context of this paper, are split into two distinct categories: porous and solid barriers. These methods include trees and vegetation (porous) as well as noise barriers, low boundary walls and parked cars (solid); all of which have gained different levels of research momentum over the past decade. Experimental and modelling studies have provided an understanding of the potential for these barriers to improve air quality under varying urban geometrical and meteorological conditions. However, differences in results between these studies and real-world measurements demonstrate the challenges and complexities of simulating pollutant transport in urban areas. These methods provide additional benefits to improving air quality through altering dispersion patterns; avenue trees and vegetation are aesthetically pleasing and provides cooling and shade from direct sunlight. Additionally, real-world case studies are considered an important direction for further verification of these methods in the built environment. Developing design guidelines is an important next stage in promoting passive methods for reducing air pollution and ensuring their integration into future urban planning strategies. In addition, developing channels of communication with urban planners will enhance the development and uptake of design guidelines to improve air quality in the built environment. (C) 2015 Elsevier Ltd. All rights reserved.

This study provides an insight into the dominant negotiation processes that occur between the authors of research articles and academic reviewers at the peer reviewing stage. Data of reviewers comments and authors responses on 32 science and engineering based journal articles covering four decision categories (accept as is, accept with minor revisions, major revisions and reject) were collected. A commonly practised peer-review approach in teaching was applied to analyse the data and to identify the key negotiation attributes, their frequency of occurrence, authors' reaction and approach to negotiate with the reviewers. Six main negotiation attributes were identified. Technical quality was the most frequent (31% of all instances) attracting mixed reactions from the authors. The remaining attributes constituted suggestion (20%), explanation (20%), restatement (15%), grammar (13%) and structure (~1%). With the exception of `explanation' where authors had to counteract to clear misunderstood concepts or contents by the reviewers, the other attributes were of highly collaborative nature and were willingly accepted by the authors. All these negotiations were found to help authors in improving the overall quality, clarity and readability of their manuscripts, besides forcing them to rethink about unclear contents. The negotiation trends emerged here can help the academic researchers to improve the quality of their articles before submission to the peer-reviewed journals. It can also provide a link through which their classroom teaching experience involving supervision of peer review negotiations among students can be utilised in writing their research articles and negotiating with academic reviewers.

Wind tunnel measurements downwind of reduced scale car models have been made to study the wake regions in detail, test the usefulness of existing vehicle wake models, and draw key information needed for dispersion modelling in vehicle wakes. The experiments simulated a car moving in still air. This is achieved by (i) the experimental characterisation of the flow, turbulence and concentration fields in both the near and far wake regions, (ii) the preliminary assessment of existing wake models using the experimental database, and (iii) the comparison of previous field measurements in the wake of a real diesel car with the wind tunnel measurements. The experiments highlighted very large gradients of velocities and concentrations existing, in particular, in the near-wake. Of course, the measured fields are strongly dependent on the geometry of the modelled vehicle and a generalisation for other vehicles may prove to be difficult. The methodology applied in the present study, although improvable, could constitute a first step towards the development of mathematical parameterisations. Experimental results were also compared with the estimates from two wake models. It was found that they can adequately describe the far-wake of a vehicle in terms of velocities, but a better characterisation in terms of turbulence and pollutant dispersion is needed. Parameterised models able to predict velocity and concentrations with fine enough details at the near-wake scale do not exist.

A number of Nature Based Solutions (NBS) are being used around the world in order to address various hydrometeorological hazards as a more environmentally friendly alternative to hard structures. Such a solution has been created in the Spercheios river basin in Central Greece, which is susceptible to heavy rainfall and river bank overflow due to flood water from upstream, in order to mitigate flood and drought impacts under current and future climate conditions. Here a first attempt is made to use actual measurements taken from various sources in the river, including in-situ and satellite data, in order to establish early experimental evidence of the NBS efficiency in the area. The measurements include data from automated hydrological stations from the OpenHi network, satellite remote sensing data and field measurements performed along the Spercheios River basin. For each measurement used, different analysis has been performed based on data availability and pertinence to the NBS efficiency. Preliminary results presented here show that the NBS functions as designed and provides protection against flooding in the area, and can potentially diminish the risk of drought. The results are in agreement with the numerical outputs already presented in our previous work.

The application of regional-scale air quality models is an important tool in air quality assessment and management. For this reason, the understanding of model abilities and performances is mandatory. The main objective of this research was to investigate the spatial and temporal variability of background particulate matter (PM) concentrations, to evaluate the regional air quality modelling performance in simulating PM concentrations during statically stable conditions and to investigate processes that contribute to regionally increased PM concentrations with a focus on Eastern and Central Europe. The temporal and spatial variability of observed particulate matter (PM) was analysed at 310 rural background stations in Europe during 2011. Two different regional air quality modelling systems (offline coupled EMEP and online coupled Weather Research and Forecast-Chem) were applied to simulate the transport of pollutants and to further investigate the processes that contributed to increased concentrations during high pollution episodes. Background PM measurements from rural background stations and wind speed, surface pressure and ambient temperature data from 920 meteorological stations across Europe, classified according to the elevation, were used for the evaluation of individual model performance. Among the sea-level stations (up to 200 m), the best modelling performance, in terms of meteorology and chemistry, was found for both models. The underestimated modelled PM concentrations in some cases indicated the importance of accurate assessment of regional air pollution transport under statically stable atmospheric conditions and the necessity of further model improvements.

We compared the effect of ambient temperature observed in two different seasons on the size distribution and particle number concentration (PNC) as a function of distance (up to ~ 250 m) from a major traffic road (25% of the vehicles are heavy-duty diesel vehicles). The modal particle diameter was found between 10 and 30 nm at the roadside in the winter. However, there was no peak for this size range in the summer, even at the roadside. Ambient temperature affects both the atmospheric dilution ratio (DR) and the evaporation rate of particles, thus it affects the decay rate of PNC. We corrected the DR effect in order to focus on the effect of particle evaporation on PNC decay. The decay rate of PNC with DR was found to depend on the season and particle diameter. During the winter, the decay rate for smaller particles (< 30 nm) was much higher (i.e., the concentration decreased significantly against DR), whereas it was low during the summer. In contrast, for particles > 30 nm in diameter, the decay rate was nearly the same during both seasons. This distinction between particles less than or greater than 30 nm in diameter reflects differences in particle volatility properties. Mass-transfer theory was used to estimate evaporation rates of C20–C36 n-alkane particles, which are the major n-alkanes in diesel exhaust particles. The C20–C28 n-alkanes of 30-nm particles completely evaporate at 31.2 °C (summer), and their lifetime is shorter than the transport time of air masses in our region of interest. Absence of the peak at 10–30 nm and the low decay rate of PNC < 30 nm in diameter in the summer were likely due to the evaporation of compounds of similar volatilities comparable to the C20–C36 n-alkanes from particles near the exhaust pipes of vehicles, and complete evaporation of semivolatile materials before they reached the roadside. These results suggest that the lifetime of particles < 30 nm in diameter depends on the ambient temperature, which differs between seasons. This leads us to conclude that these particles show distinctly different spatial distributions depending on the season.

Abstract We propose three estimation strategies (local, remote and mixed) for ultrafine particles (UFP) at three sites in an urban air pollution monitoring network. Estimates are obtained through Gaussian process regression based on concentrations of gaseous pollutants (NOx, O3, CO) and UFP. As local strategy, we use local measurements of gaseous pollutants (local covariates) to estimate UFP at the same site. As remote strategy, we use measurements of gaseous pollutants and UFP from two independent sites (remote covariates) to estimate UFP at a third site. As mixed strategy, we use local and remote covariates to estimate UFP. The results suggest: UFP can be estimated with good accuracy based on NOx measurements at the same location; it is possible to estimate UFP at one location based on measurements of NOx or UFP at two remote locations; the addition of remote UFP to local NOx, O3 or CO measurements improves models' performance.

The impact of weather- and climate-related hydro-meteorological hazards (HMHs) is amongst the greatest global challenges society is facing today. The concept of nature-based solution (NBS) is becoming popular for HMH management but the lack of knowledge on NBS designing and effectiveness is hindering its wider acceptance. This work discusses HMH risk analysis, relevant data, the role of NBS and its operationalisation by bringing co-design concept and testing them in OPERANDUM project’s open-air laboratories (OALs). HMH risk assessment employs different methodologies with respect to exposure, vulnerability and adaptation interaction of the elements at risk. The classification and effectiveness of any NBS depend on its location, design, typology and environmental conditions. OALs, via the collaboration of researchers and end-users, can foster increasing uptake, upscaling, replication and implementation of NBS projects as compared to traditional grey infrastructure approach. Multi-hazard risk analysis and inclusion of NBS into policy plans can foster NBS operationalisation processes across all sectors and at levels by fostering participatory processes such as co-design, co-creation and co-management among municipalities, researches, policy-makers, funding agencies and other stakeholders; and can inspire more effective use of skills, knowledge, manpower, as well as economic, social and cultural resources. NBS data monitoring, its standardisation, accessible storage and compliance with existing standard metadata is needed. The monitoring and evaluation manuals and guidelines are needed to decrease uncertainty about performance and overall cost-effectiveness of NBS and overcome potential hurdles to create long-term stability and enhance the wider uptake of NBS.

The high demand for generation and development in wind electrical power competitiveness has gained significant popularity in wind energy and speed forecasting models. It is also an essential method for planning the wind power plant system. Several models were created in the past to forest the speed and energy of the Wind. However, results have very low prediction accuracy due to their nonlinear and irregular characteristics. Therefore, a novel Modular Red Deer Neural System (MRDNS) was developed in this research to forecast wind speed and energy effectively. Primarily, the system accepted the data from the wind turbine SCADA database and preprocessed it to remove the training flaws. Further, the relevant features are extracted. The complexity of the prediction process was reduced by processing the relevant features. By analysing these features, the wind speed and energy were predicted in accordance with the fitness function of the MRDNS. The model obtained higher prediction accuracy. The recommended strategy was checked in the Python platform and the robustness metrics including RMSE, MSE, and precision were computed. The model scored 99.99% prediction accuracy; also gained 0.0017 MSE value, 0.0422 RMSE value for wind power forecasting and 0.0003 MSE, 0.0174 RMSE for wind speed forecasting.

The present study aims to evaluate the long-term trends of PM10 at two monitoring stations (an urban and a background station) in Shimla city, in India during the period 2011–2017. The highest daily mean concentrations were determined to be 176 μg/m³ and 152 μg/m³ respectively at the urban and the background monitoring locations. Similarly, the annual mean concentrations at the monitoring locations were determined to be 59 μg/m³ and 45 μg/m³ respectively for urban and background concentrations. Exceedance factors determined showed that at the urban monitoring location the ranges varied between ‘moderate to high’ while at the background monitoring station it remained at ‘moderate’ levels. Seasonal analysis study carried out revealed that higher concentrations were observed during summer in comparison to winter with the least concentrations occurring during the monsoon season. A regression analysis was carried out to test the interdependency of the PM10 with other pollutants and a positive correlation was observed between PM10 and NO₂ and SO₂. Similarly, correlation of PM10 with meteorological parameters such as wind speed and temperature were found to be positive while for parameters like precipitation and relative humidity it was negative. The paper also presents a critical discussion on the outcomes of the trend analysis study. This includes design and location of additional monitoring sites to adequately represent the actual ambient air quality conditions in Himachal Pradesh.

People living in urban and industrialized societies, which are expanding globally, spend more than 90% of their time in the indoor environment, breathing indoor air (IA). Despite decades of research and advocacy, most countries do not have legislated indoor air quality (IAQ) performance standards for public spaces that address concentration levels of IA pollutants (1). Few building codes address operation, maintenance, and retrofitting, and most do not focus on airborne disease transmission. But the COVID-19 pandemic has made all levels of society, from community members to decision-makers, realize the importance of IAQ for human health, wellbeing, productivity, and learning. We propose that IAQ standards be mandatory for public spaces. Although enforcement of IAQ performance standards in homes is not possible, homes must be designed and equipped so that they could meet the standards.

This research work presents a study on the relationship between stress & related events with meditation practice and other socio-demographic variables during COVID 19 pandemic among healthy adults. In this cross-sectional survey design, healthy adults with and without practice of Raja yoga meditation completed stress, anxiety & depression related questions (Depression Anxiety & stress Scale, DASS 21) and its impact (Impact of Event Scale-Revised, IES-R) along with other socio-demographic including COVID infection or contact related information. Data was assessed for difference in DASS 21 scores and IES-R scores between Raja yoga meditators (n = 802) & non-meditators (n = 357). An analysis was performed to study the predictors of DASS 21 and IES-R scores. We conclude that healthy Raja yoga meditation practitioners differ from non-meditators in terms of stress/anxiety/depression and its impact during COVID 19 pandemic and meditation practice predicts mental health better along with other sociodemographic variables.

Atmospheric Boundary Layer (ABL) characteristics are investigated using a Ceilometer Lidar over Ahmedabad, a semi-arid region in western India. Strong diurnal variations of ABL are observed during 2019, the observation period. There is a stark winter-summer difference in ABL, with summer Boundary Layer Height (BLH) exceeding winter BLH by 1-1.5 km. ABL usually collapses during monsoon and is equivocal due to the presence of thick clouds on top of ABL. The ABL is thicker during the onset of monsoon in contrast to active monsoon, rises again during the withdrawal of monsoon. Lidar observed ABL has been compared with satellite, radiosonde, and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) dataset. ERA5 shows good agreement with differences within 500 m; radiosonde observations have under-estimated ground-based measurements, especially during summer. Satellite observations highly overestimated BLH. This comparative study reveals the importance of ground-based lidars in continuous monitoring of ABL at high resolution because radiosonde, satellite, and reanalysis datasets have coarser resolutions and sparse observations. Such quantitative evaluation of ABL is formerly unavailable over this region, which can now be used to improve the representation in numerical models and thereby estimates of radiative and climate effects due to ABL.

Four geometrical configurations of a real street canyon in Barreiro city (Portugal) are considered to study their influence on the dispersion of PM. These configurations include actual architectural layout of the street (Option 1), and three virtual cases (Options 1-3). Option 2 includes the modification of real geometry by including 4 m gaps between the buildings situated on the southern part of the street canyon. Option 3 considers 6 m gaps between buildings as opposed to 4 m gaps in Option 2. Option 4 assumes the same height for all buildings on the southern part of the street canyon, with no gaps between buildings. Computational fluid dynamics code (CFD), FLUENT, is used to simulate the detailed flow and turbulence characteristics in three-dimensional domain of chosen street canyon, together with the PM dispersion for both the summer and winter seasons. The modelled PM concentrations were then compared with the measured data at seven different locations in the street canyon. Our results indicate up to 23% lower PM concentrations at 1.5 m above the road level during the along-canyon wind direction due to the channelling of flow, compared with those observed during the cross-canyon wind direction. Detailed inspection of the results obtained from the Options 1-3 indicated that the spacing between the buildings tend to increase particle dilution during the cross-canyon winds, resulting in up to 20, and 22% reduced concentrations for options 2, and 3 respectively, compared with the actual configuration (Option 1). The largest improvement (∼7%) in the PM concentrations was given by Option 2, while other options showed modest changes. Possible reasons for these changes under varying meteorological conditions are explained in the context of changing building configurations and their implications in city planning. © 2013 Elsevier Ltd. All rights reserved.

As cities continue to grow, developing mitigation strategies is crucial to minimize the corresponding increase in air pollutants. One source of potentially controllable air pollution is the emissions from residential buildings. We conducted a literature review to systematically examine air pollution emissions from residential buildings in urban areas, identifying pollutants and their sources; investigated mitigation-aimed intervention types by field of application or study, and finally listed and discussed strategies to reduce the concentration of air pollutants in residential buildings. Our compilation shows that among the nature-based solutions, green walls offered the highest relative reduction of air pollution (−15 % NO₂ and −23 % PM₁₀). Of the construction-based solutions, already-available photocatalytic paint can achieve reductions of 25 % NO, 23 % NOₓ and 19 % NO₂ as is. Industrial-based solutions promise high levels of reduction, but these must be adapted to residential buildings. The integration of various existing and potentially adapted mitigation solutions may achieve even higher pollution reduction rates in urban areas.

There is a lack of clear guidance regarding the optimal configuration and plant composition of green infrastructure (GI) for improved air quality at local scale. This study aimed to co-develop (i.e. with feedback from end-users) a public engagement and decision support tool, to facilitate effective GI design and management for air pollution abatement. The underlying model uses user-directed input data (e.g. road type) to generate output recommendations (e.g. plant species) and pollution reduction projections. This model was computerised as a user-friendly tool named HedgeDATE (Hedge Design for Abatement of Traffic Emissions). A workshop generated feedback on HedgeDATE, which we also discuss. We found that data from the literature can be synthesised to predict air pollutant exposure and abatement in open road environments. However, further research is required to describe pollutant decay profiles under more diverse roadside scenarios (e.g. split-level terrain) and to strengthen projections. Workshop findings validated the HedgeDATE concept and indicated scope for uptake. End-user feedback was generally positive, although potential improvements were identified. For HedgeDATE to be made relevant for practitioners and decision-makers, future iterations will require enhanced applicability and functionality. This work sets the foundation for the development of advanced GI design tools for reduced pollution exposure.

Abstract Quantification of disproportionate contribution made by signalised traffic intersections (TIs) to overall daily commuting exposure is important but barely known. We carried out mobile measurements in a car for size–resolved particle number concentrations (PNCs) in the 5–560 nm range under five different ventilation settings on a 6 km long busy round route with 10 TIs. These ventilation settings were windows fully open and both outdoor air intake from fan and heating off (Set1), windows closed, fan 25% on and heating 50% on (Set2), windows closed, fan 100% on and heating off (Set3), windows closed, fan off and heating 100% on (Set4), and windows closed, fan and heating off (Set5). Measurements were taken sequentially inside and outside the car cabin at 10 Hz sampling rate using a solenoid switching system in conjunction with a fast response differential mobility spectrometer (DMS50). The objectives were to: (i) identify traffic conditions under which TIs becomes hot–spots of PNCs, (ii) assess the effect of ventilation settings in free–flow and delay conditions (waiting time at a TI when traffic signal is red) on in–cabin PNCs with respect to on–road PNCs at TIs, (iii) deriving the relationship between the PNCs and change in driving speed during delay time at the TIs, and (iv) quantify the contribution of exposure at TIs with respect to overall commuting exposure. Congested TIs were found to become hot–spots when vehicle accelerate from idling conditions. In–cabin peak PNCs followed similar temporal trend as for on–road peak PNCs. Reduction in in–cabin PNC with respect to outside PNC was highest (70%) during free–flow traffic conditions when both fan drawing outdoor air into the cabin and heating was switched off. Such a reduction in in–cabin PNCs at TIs was highest (88%) with respect to outside PNC during delay conditions when fan was drawing outside air at 25% on and heating was 50% on settings. PNCs and change in driving speed showed an exponential–fit relationship during the delay events at TIs. Short–term exposure for ∼2% of total commuting time in car corresponded to ∼25% of total respiratory doses. This study highlights a need for more studies covering diverse traffic and geographical conditions in urban environments so that the disparate contribution of exposure at TIs can be quantified.

With the rapid urbanization, traffic volume and road density continue to increase. Urban residents are encountering high exposure risks to roadside particulate matters emitted from traffic (e.g., PM2.5), especially at bus stations where vulnerable passenger groups like children are at higher risks. Green infrastructure such as green hedge is an effective strategy to mitigate exposure risks to PM2.5. However, rare studies have explored the effectiveness of such strategy at bus stations. This study investigated the influence of different hedge heights (0.5, 1.0, and 1.5m) on PM2.5 deposition and passengers’ health risk at bus stations, under different wind directions. Field surveys were conducted to analyze the passengers’ demographics and waiting time. Using the simulation method, green hedges at the heights between 1.0m and 1.5m proved more effective in blocking PM2.5 compared with that of 0.5m. Without green hedges, passengers faced average daily exposure risks between 10-4 and 10-6. After using green hedges, risks were largely decreased by approximately 62%, reaching the safety threshold below 10-6. Strategies including optimizing passengers’ locations, entrance and exit of station, and integration of green hedges and belts were proposed. This study can provide valuable insights into effective roadside greening initiatives for creating a healthy urban environment.

The increase of temperature attributed to anthropogenic emissions is projected to continue in future climate scenarios. Protocols and policies are being put in place in several European countries to reduce both emissions and impact of human activities on climate. The Irish Reforestation policy is a good example of such protocols. Nevertheless often contemplated policies do not take into account their potential effects on the atmospheric variables. This study aims to assess the influence of the increase of vegetation cover over Ireland, on surface temperature, livestock and human heat comfort, using the Weather Research Forecast (WRF-ARW 3.7.1) model. Multi-scale numerical simulations are performed under two scenarios: (i) a “control scenario” con- sidering no change in vegetation cover with respect to the prescribed one and (ii) a “green scenario” with increased tree cover based on the introduced Irish Reforestation policy. To simulate this policy, the cropland and vegetative mo- saic is substituted with evergreen broad leaf forest, increasing the total forest area from 19.7% to 36.2% of the land in the analyzed domain. This change in vegetation cover increases the temperature over the simulated domain up to 0.7oC and, moreover, it enhances both human and livestock heat discom- fort during the day-time, with different magnitude all over the domain. It is concluded that the reforestation policy, which is introduced to mitigate the climate warming and greenhouse gas emissions, causes a further increase in temperature along with heat discomfort to both human and livestock.

The health of the city depends on how well all the elements of this system are interconnected and operating in harmony. Here the authors introduced the concept of urbanome which is analogous to the human genome that can be used to characterise the form and functioning of cities.

Lockdown was imposed by the Indian government in the month of March 2020 as an early precaution to the COVID-19 pandemic which obstructed the socio-economic growth globally. The main aim of this study was to analyse the impact of lockdown (imposed in March and continued in April 2020) on the existing air quality in three megacities of India (Delhi, Mumbai and Kolkata) by assessing the trends of PM and NO concentrations. A comparison of the percentage reduction in concentrations of lockdown period with respect to same period in year 2019 and pre-lockdown period (February 14-March 24) was made. It was observed from the study that an overall decrease of pollutant concentrations was in the ranges of 30-60% and 52-80% of PM and NO , respectively, in the three cities during lockdown in comparison with previous year and pre-lockdown period The overall decrease in concentrations of pollutants at urban sites was greater than the background sites. Highest decline in concentrations of PM were observed in Kolkata city, followed by Mumbai and Delhi, while decline in NO was highest in Mumbai. Results also highlighted that capital city Delhi had the worst air quality amongst three cities, with particulate matter (PM ) being the dominant pollutant. Although COVID-19 has significantly affected the human life considering the mortality and morbidity, lockdowns imposed to control the pandemic had significantly improved the air quality in the selected study locations, although for the short amount of period.

Green streets are increasingly being used as a stormwater management strategy to mitigate stormwater runoff at its source while providing other environmental and societal benefits, including connecting pedestrians to the street. Simultaneously, human exposure to particulate matter from urban transportation is of major concern worldwide due to the proximity of pedestrians, drivers, and cyclists to the emission sources. Vegetation used for stormwater treatment can help designers limit the exposure of people to air pollutants. This goal can be achieved through the deliberate placement of green streets, along with strategic planting schemes that maximize pollutant dispersion. This communication presents general design considerations for green streets that combine stormwater management and air quality goals. There is currently limited guidance on designing green streets for air quality considerations; this is the first communication to offer suggestions and advice for the design of green stormwater streets in regards to their effects on air quality. Street characteristics including (1) the width to height ratio of the street to the buildings, (2) the type of trees and their location, and (3) any prevailing winds can have an impact on pollutant concentrations within the street and along sidewalks. Vegetation within stormwater control measures has the ability to reduce particulate matter concentrations; however, it must be carefully selected and placed within the green street to promote the dispersion of air flow.

Hydro-meteorological hazards (HMHs) have had a strong impact on human societies and ecosystems. Their impact is projected to be exacerbated by future climate scenarios. HMHs cataloguing is an effective tool to evaluate their associated risks and plan appropriate remediation strategies. However, factors linked to HMHs origin and triggers remain uncertain, which poses a challenge for their cataloguing. Focusing on key HMHs (floods, storm surge, landslides, droughts, and heatwaves), the goal of this review paper is to analyse and present a classification scheme, key features, and elements for designing nature-based solutions (NBS) and mitigating the adverse impacts of HMHs in Europe. For this purpose, we systematically examined the literature on NBS classification and assessed the gaps that hinder the widespread uptake of NBS. Furthermore, we critically evaluated the existing literature to give a better understanding of the HMHs drivers and their interrelationship (causing multi-hazards). Further conceptualisation of classification scheme and categories of NBS shows that relatively few studies have been carried out on utilising the broader concepts of NBS in tackling HMHs and that the classification and effectiveness of each NBS are dependent on the location, architecture, typology, green species, environmental conditions as well as interrelated non-linear systems. NBS are often more cost-effective than hard engineering approaches used within the existing systems, especially when taking into consideration their potential co-benefits. We also evaluated the sources of available data for HMHs and NBS, highlighted gaps in data, and presented strategies to overcome the current shortcomings for the development of the NBS for HMHs. We highlighted specific gaps and barriers that need to be filled since the uptake and upscaling studies of NBS in HMHs reduction is rare. The fundamental concepts and the key technical features of past studies reviewed here could help practitioners to design and implement NBS in a real-world situation.

A “call to action” has been issued for scholars in landscape and urban planning, natural science, and public health to conduct interdisciplinary research on the human health effects of spending time in or near greenspaces. This is timely in light of contemporary interest in municipal tree planting and urban greening, defined as organized or semi-organized efforts to introduce, conserve, or maintain outdoor vegetation in urban areas. In response to injunctions from scholars and urban greening trends, this article provides an interdisciplinary review on urban trees, air quality, and asthma. We assess the scientific literature by reviewing refereed review papers and empirical studies on the biophysical processes through which urban trees affect air quality, as well as associated models that extend estimates to asthma outcomes. We then review empirical evidence of observed links between urban trees and asthma, followed by a discussion on implications for urban landscape planning and design. This review finds no scientific consensus that urban trees reduce asthma by improving air quality. In some circumstances, urban trees can degrade air quality and increase asthma. Causal pathways between urban trees, air quality, and asthma are very complex, and there are substantial differences in how natural science and epidemiology approach this issue. This may lead to ambiguity in scholarship, municipal decision-making, and landscape planning. Future research on this topic, as well as on urban ecosystem services and urban greening, should embrace epistemological and etiological pluralism and be conducted through interdisciplinary teamwork.

Metrology of Airborne nanoparticles, Standardisation and Applications, NPL Teddington, UK

This methods paper describes a new UK-wide citizen science project, the Schools’ Air Quality Monitoring for Health and Education (SAMHE) project, which is exploring indoor air quality (IAQ) in schools. Central to the project is a Web App, where school teachers and pupils can see air quality and environmental data from their classroom, learn about the significance of the data that their monitor collects, enter important contextual information to support data analysis by researchers, and are supported to do their own experiments related to air quality. School use of the SAMHE Web App is essential to the project’s aims to 1) improve understanding of air quality in schools; 2) empower teachers and pupils to make informed decisions about management of their classroom environment, including ventilation; and 3) support the UK’s next generation to think differently about air quality. Therefore, it is critical that the SAMHE Web App was co-designed with schools, to maximise its acceptability within schools, and to ensure that teachers and pupils engage with it. This paper describes the co-design process used within SAMHE, how co-design has helped shape the web app (including overall theme, visualisation of data, and supporting materials), and some lessons learned from the process that will be useful for future software development and citizen science projects with schools.

A key challenge in controlling Delhi’s air quality is a lack of clear understanding of the impacts of emissions from the surrounding National Capital Region (NCR). Our objectives are to understand the limitations of publicly available data, its utility to determine pollution sources across Delhi-NCR and establish seasonal profiles of chemically active trace gases. We obtained the spatiotemporal characteristics of daily-averaged particulate matter (PM10 and PM2.5) and trace gases (NOX,O3,SO2, and CO) within a network of 12 air quality monitoring stations located over 2000 km2 across Delhi-NCR from January 2014 to December 2017. The highest concentrations of pollutants, except O3, were found at Anand Vihar compared with lowest at Panchkula. A high homogeneity in PM2.5 was observed among Delhi sites as opposed to a high spatial divergence between Delhi and NCR sites. The bivariate polar plots and k-means clustering showed that PM2.5 and PM10 concentrations are dominated by local sources for all monitoring sites across Delhi-NCR. A consequence of the dominance of local source contributions to measured concentrations, except to one site remote from Delhi, is that it is not possible to evaluate the influence of regional pollution transport upon PM concentrations measured at sites within Delhi and the NCR from concentration measurements alone.

Green infrastructure (GI) is effective in reducing PM concentrations in near-road environments, but how such reductions in concentration compared with relative respiratory deposition doses (RDDs) is rarely discussed. We quantified variations in RDD in the presence of three GI types (trees, hedges and tree-hedge combinations), and compared them with PM reduced by the GI under different wind directions and seasons through the assessment of data collected during multiple field campaigns. We also studied three scenarios (sitting, walking, running) to investigate RDD in adults and children during different possible activities in the presence of GI at public parks or gardens or in front of houses. Finally, we illustrated particle mass distribution before and after different GI configurations to explore the reasons for variations in RDD. Changes in RDD displayed a trend of %ΔRDDPM10 > %ΔRDDPM2.5 = %ΔRDDPM1, compared to the changes in PM concentrations of %ΔPM1 > %ΔPM10 > %ΔPM2.5. A maximum reduction (25%) in RDD was observed for PM10 in the presence of the tree-hedge combination, and this combination emerged as the most effective GI type in lowering the RDD. The changes in ratios of mass median diameter and deposition fraction of roughly ±0.2 before and after the GI led to differences between %∆PM and %∆RDD. Cross-winds (perpendicular to road direction) led to greater variations between %∆PM and %∆RDD, whereas parallel winds (along the road) led to similar variations in %∆RDD and %∆PM. Particle mass distributions revealed the absence of a peak around particle diameter 2.5 μm in the presence of GI. The highest difference in RDD behind GI was observed in the presence of a hedge-tree combination during different physical activities.

Study of indoor air quality (IAQ) has received attention of the researchers and policy makers over the last several years due to its affiliation with the adverse health effects and occupants’ discomfort. This article focuses on the importance and need of IAQ studies in Indian rural and urban indoor environments. A number of questions in this context are posed and addressed, together with identifying the allied research gaps and missing links in the existing literature. Also discussed are the technical challenges to carry out the IAQ studies in India, and the initiatives and future road map required to overcome them.

Mitigating the impact of pollution on human health worldwide is important to limit the morbidity and mortality arising from exposure to its effect. The level and type of pollutants vary in different urban and rural settings. Here, we explored the extent of air pollution and its impacts on human health in the megacity of Delhi (India) through a review of the published literature. The study aims at describing the extent of air pollution in Delhi, the magnitude of health problems due to air pollution and the risk relationship between air pollution and associated health effects. We found 234 published articles in the PubMed search. The search showed that the extent of air pollution in Delhi has been described by various researchers from about 1986 onwards. We synthesized the findings and discuss them at length with respect to reported values, their possible interpretations and any limitations of the methodology. The chemical composition of ambient air pollution is also discussed. Further, we discuss the magnitude of health problem with respect to chronic obstructive pulmonary diseases (COPD), bronchial asthma and other illnesses. The results of the literature search showed that data has been collected in last 28 years on ambient air quality in Delhi, though it lacks a scientific continuity, consistency of locations and variations in parameters chosen for reporting. As a result, it is difficult to construct a spatiotemporal picture of the air pollution status in Delhi over time. The number of sites from where data have been collected varied widely across studies and methods used for data collection is also nonuniform. Even the parameters studied are varied, as some studies focused on particulate matter

Global climate change, demographic change and advancing mechanization of everyday life will go along with new ways of living. Temperature extremes, an ageing society and higher demands on a comfortable life will lead to the implementation of sensor based networks in order to create acceptable and improved living conditions. Originally, the idea of the smart home served primarily the efficient use of energy and the optimization of ventilation technology connected with new ways of constructing buildings (low-energy and passive houses, respectively). Today the term 'smart home' is also linked with the networking of home automation systems, home appliances and communications and entertainment electronics. Living in a smart home often makes also significant demands on the occupants who are required to drastically change some of their living habits. This review summarizes current findings on the effect of measured environmental parameters on indoor air quality, individual thermal comfort and living behavior in smart homes with focus on central Europe. A critical evaluation of available sensor technologies, their application in homes and data security aspects as well as limits and possibilities of current technologies to control particles and gaseous pollutants indoors is included. The review also considers the acceptance of smart technologies by occupants in terms of living habits, perceived indoor air quality and data security.

Air pollution and climate change are a deadly duo for Africa, and must be tackled together. Air pollutants and greenhouse gases often share the same sources and can be even more dangerous when combined. Africa is particularly vulnerable to climate change, and currently, an estimated 1 million people per year die prematurely from air pollution on the continent. But there is a way to improve the situation: preventing emissions from short-lived climate pollutants, like methane and black carbon, is crucial for the world to stay below 1.5°C. Reducing SLCPs will help both save lives and protect the environment. Africa has a huge opportunity to continue developing sustainably, improve human well-being, and protect nature by investing in solutions to fight climate change and air pollution together. A new Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa from the African Union Commission, the Climate and Clean Air Coalition, and the UN Environment Programme, developed by African scientists in a process led by the Stockholm Environment Institute, shows how African leaders can act quickly across 5 key areas—transport, residential, energy, agriculture, and waste—to fight climate change, prevent air pollution, and protect human health. 

São Paulo in Brazil has relatively relaxed regulations for ambient air pollution standards and often experiences high air pollution levels due to emissions of airborne particles from local sources and long-range transport of biomass burning-impacted air masses. In order to evaluate the sources of particulate air pollution (PM) and related health risks, a year-round sampling was performed for PM2.5 (≤ 2.5 μm) and PM10 (≤ 10 μm) in 2014 through intensive (every day sampling in wintertime) and extensive campaigns (once a week for the whole year) with 24 h of sampling. This year was characterized to have lower average precipitation comparing to meteorological data, and high pollution episodes were observed all year round, with a significant increase of pollution level in the intensive campaign, which was performed during wintertime. Different chemical constituents, such as carbonaceous species, polycyclic aromatic hydrocarbons (PAHs) and derivatives, water-soluble ions and biomass burning tracers were identified in order to evaluate health risks and to apportion sources. The species such as PAHs, inorganic and organic ions and monosaccharides were determined by chromatographic techniques and carbonaceous species by thermal-optical analysis. The associated risks to particulate matter exposure based on PAH concentrations were also assessed, along with indexes such as the benzo[a]pyrene equivalent (BaPE) and lung cancer risk (LCR). High BaPE and LCR were observed in most of the samples, rising to critical values in the wintertime. Also, biomass burning tracers and PAHs were higher in this season, while secondarily formed ions presented low variation throughout the year. Meanwhile, vehicular tracer species were also higher in the intensive campaign suggesting the influence of lower dispersion conditions in that period. Source apportionment was done by Positive Matrix Factorization (PMF), which indicated five different factors: road dust, industrial emissions, vehicular exhaust, biomass burning and secondary processes. The results highlighted the contribution of vehicular emissions and the significant input from biomass combustion in wintertime, suggesting that most of the particulate matter is due to local sources, besides the influence of pre-harvest sugarcane burning.

This chapter discusses the emission, transformation, and fate of incidental airborne nanoparticles. It starts with the up‐to‐date summary of recent review articles covering various aspects of both the incidental nanoparticles and ENPs. Transformation processes play an important role in influencing the characteristics of nanoparticles both spatially and temporally. A common method to represent the atmospheric size distributions of atmospheric particles is through various modes. A typical size distribution in atmospheric environments shows the presence of the following modes:nucleation, Aitken, accumulation, and coarse. Nucleation mode particles are those generally formed by the gas‐to‐particle conversion after rapid cooling and dilution of exhaust emissions. Understanding the different transformation processes (i.e., nucleation, coagulation, condensation, evaporation, and dry deposition) is important in order to study the temporal and spatial changes occurring to nanoparticles in the atmospheric environment. The detection of ENP concentrations is necessary for determining human exposure in both the indoor factory environment and ambient non‐workplace atmosphere.

A major source of airborne pollution in arid and semi-arid environments (i.e. North Africa, Middle East, Central Asia, and Australia) is the fugitive particulate matter (fPM), which is a frequent product of wind erosion. However, accurate determination of fPM is an ongoing scientific challenge. The objective of this study is to examine fPM emissions from the loose Calcisols (i.e. soils with a substantial accumulation of secondary carbonates), owing to construction activities that can be frequently seen nowadays in arid urbanizing regions such as the Middle East. A two months field campaign was conducted at a construction site, at rest, within the city of Doha (Qatar) to measure number concentrations of PM over a size range of 0.25–32 μm using light scattering based monitoring stations. The fPM emission fluxes were calculated using the Fugitive Dust Model (FDM) in an iterative manner and were fitted to a power function, which expresses the wind velocity dependence. The power factors were estimated as 1.87, 1.65, 2.70 and 2.06 for the four different size classes of particles ≤2.5, 2.5–6, 6–10 and ≤10 μm, respectively. Fitted power function was considered acceptable given that adjusted R2 values varied from 0.13 for the smaller particles and up to 0.69 for the larger ones. These power factors are in the same range of those reported in the literature for similar sources. The outcome of this study is expected to contribute to the improvement of PM emission inventories by focusing on an overlooked but significant pollution source, especially in dry and arid regions, and often located very close to residential areas and sensitive population groups. Further campaigns are recommended to reduce the uncertainty and include more fPM sources (e.g. earthworks) and other types of soil.

The diversity of ambient particle size and chemical composition considerably complicates pinpointing the specific causal associations between exposure to particles and adverse human health effects, the contribution of different sources to ambient particles at different locations, and the consequent formulation of policy action to most cost-effectively reduce harm caused by airborne particles. Nevertheless, the coupling of increasingly sophisticated measurements and models of particle composition and epidemiology continue to demonstrate associations between particle components and sources (and at lower concentrations) and a wide range of adverse health outcomes. This article reviews the current approaches to source apportionment of ambient particles and the latest evidence for their health effects, and describes the current metrics, policies and legislation for the protection of public health from ambient particles. A particular focus is placed on particles in the ultrafine fraction. The review concludes with an extended evaluation of emerging challenges and future requirements in methods, metrics and policy for understanding and abating adverse health outcomes from ambient particles.

Exposure to high levels of formaldehyde is known as both acute and chronic health problems, but the studies analyzing ambient concentrations of formaldehyde, especially in Middle East cities such as Tehran, are still rare. The aim of this study is to survey the variations in the concentration of formaldehyde in several areas with a high traffic volume of Tehran city during different seasons. The other objectives include understanding the influence of carbon monoxide, ozone and nitrogen dioxide concentrations, ambient temperature, relative humidity, and air pressure on the variation of formaldehyde concentration. Measurements were carried out during the period of 6 months between 2013 (December 22 to February 14) and 2014 (April 27 to June 20 at five different locations within the city, together with a background site. One hundred and eight samples, each averaged over 3 hours from 11 AM to 2PM, were taken from the sampling locations. The average concentration of formaldehyde in the spring (22.7±5.3 ppb) was found about 1.31 times higher than winter (17.3±4.2ppb). Formaldehyde concentrations demonstrated a significant correlation with the changes in air temperature (in the range of 0.46 to 0.66 for different locations) but not having any strong correlation with humidity and pressure. Carbon monoxide and nitrogen dioxide showed a significant coefficient of determination with formaldehyde concentrations with R2 as 0.80 and 0.67 during the winter, respectively, whereas the corresponding R2 values during spring were 0.39 and 0.41. Ozone showed a significant correlation with formaldehyde (R2=0.64) during the spring and has not such the significant correlation during the season winter (R2=0.23). Overall, it concluded that Road vehicles were recognized as main contributor of formaldehyde production during both the seasons, especially in the winter, also photochemical oxidation was another important and considerable contributor producing formaldehyde during the spring.

Building activities are recognised to produce coarse particulate matter but less is known about the release of airborne ultrafine particles (UFPs; those below 100 nm in diameter). For the first time, this study has investigated the release of particles in the 5-560 nm range from three simulated building activities: the crushing of concrete cubes, the demolition of old concrete slabs, and the recycling of concrete debris. A fast response differential mobility spectrometer (Cambustion DMS50) was used to measure particle number concentrations (PNC) and size distributions (PNDs) at a sampling frequency of 10 Hz in a confined laboratory room providing controlled environment and near-steady background PNCs. The sampling point was intentionally kept close to the test samples so that the release of new UFPs during these simulated processes can be quantified. Tri-modal particle size distributions were recorded for all cases, demonstrating different peak diameters in fresh nuclei (

Urban pedestrian-level air quality is a result of an interplay between turbulent dispersion conditions, background concentrations and heterogeneous local emissions of air pollutant and their transformation processes. Still, the complexity of these interactions cannot be resolved by the commonly used air quality models. By embedding the sectional aerosol module SALSA to the large-eddy simulation model PALM, a novel, high-resolution, urban aerosol modelling framework has been developed. The first model evaluation study on the vertical variation of aerosol number concentration and size distribution in a simple street canyon without vegetation in Cambridge, UK, shows excellent agreement with measurements. Dispersion conditions and local emissions govern the pedestrian-level aerosol number concentrations. Out of different aerosol processes, dry deposition is shown to decrease the total number concentration by over 20 %, while condensation and dissolutional increase the total mass by over 10 %. Following the model development, the application of PALM can be extended to local- and neighbourhood-scale air pollution and aerosol studies that require a detailed solution of the ambient flow field.

Indoor, airborne, transmission of SARS-CoV-2 is a key infection route. We monitored fourteen different indoor spaces in order to assess the risk of SARS-CoV-2 transmission. PM2.5 and CO2 concentrations were simultaneously monitored in order to understand aerosol exposure and ventilation conditions. Average PM2.5 concentrations were highest in the underground station (261 ± 62.8 μgm−3), followed by outpatient and emergency rooms in hospitals located near major arterial roads (38.6 ± 20.4 μgm−3), the respiratory wards, medical day units and intensive care units recorded concentrations in the range of 5.9 to 1.1 μgm−3. Mean CO2 levels across all sites did not exceed 1000 ppm, the respiratory ward (788 ± 61 ppm) and the pub (bar) (744 ± 136 ppm) due to high occupancy. The estimated air change rates implied that there is sufficient ventilation in these spaces to manage increased levels of occupancy. The infection probability in the medical day unit of hospital 3, was 1.6-times and 2.2-times higher than the emergency and outpatient waiting rooms in hospitals 4 and 5, respectively. The temperature and relative humidity recorded at most sites was below 27 °C, and 40% and, in sites with high footfall and limited air exchange, such as the hospital medical day unit, indicate a high risk of airborne SARS-CoV-2 transmission.

Green infrastructure (GI) in urban areas may be adopted as a passive control system to reduce air pollutant concentrations. However, current dispersion models offer limited modelling options to evaluate its impact on ambient pollutant concentrations. The scope of this review revolves around the following question: how can GI be considered in readily available dispersion models to allow evaluation of its impacts on pollutant concentrations and health risk assessment? We examined the published literature on the parameterisation of deposition velocities and datasets for both particulate matter and gaseous pollutants that are required for deposition schemes. We evaluated the limitations of different air pollution dispersion models at two spatial scales – microscale (i.e. 10–500 m) and macroscale (i.e. 5–100 km) - in considering the effects of GI on air pollutant concentrations and exposure alteration. We conclude that the deposition schemes that represent GI impacts in detail are complex, resource-intensive, and involve an abundant volume of input data. An appropriate handling of GI characteristics (such as aerodynamic effect, deposition of air pollutants and surface roughness) in dispersion models is necessary for understanding the mechanism of air pollutant concentrations simulation in presence of GI at different spatial scales. The impacts of GI on air pollutant concentrations and health risk assessment (e.g., mortality, morbidity) are partly explored. The i-Tree tool with the BenMap model has been used to estimate the health outcomes of annually-averaged air pollutant removed by deposition over GI canopies at the macroscale. However, studies relating air pollution health risk assessments due to GI-related changes in short-term exposure, via pollutant concentrations redistribution at the microscale and enhanced atmospheric pollutant dilution by increased surface roughness at the macroscale, along with deposition, are rare. Suitable treatments of all physical and chemical processes in coupled dispersion-deposition models and assessments against real-world scenarios are vital for health risk assessments.

This work presents an assessment of geochemical toxic metal stocking in top-soil within the area of a limestone quarry in Gombe State. Samples of topsoil from the area of a limestone quarry in Gombe (North-eastern Nigeria) were collected to analyse levels of hazardous substances such as of Hg, Fe, Zn, Ni, Mn, Cu, Cr, Cd and Pb. A total of 24 topsoil samples were collected around the radius of 0.5 km from the blasting arena. Additionally, six background samples were also collected from an unexploited reserved area that was ~6 km far from the main sampling location. Two rocks of limestone samples from blasting area were also collected and analysed for heavy metals as a reference. All the samples were processed and extracted with nitrate acid solution and analysed using smart spectrophotometer methods. The results suggested varying organic contents in soil, sand, silt, clay and pH. All these parameters are correlated with those of unexploited samples. Limestone rocks samples displayed a high concentration of Fe and Mn improvement. Toxic metals concentrations (mg/kg) in top-soil with background levels were discovered in Hg, Fe, Mn, Ni, Zn, Cd, Cu, Cr and Pb. Residual phases exhibited the lowest enrichment for most metals possibly, because of high loamy sand content. The situated enrichment advocates influence from mining activities. The results especially geoaccumulation index assessment exhibit below detected limit to 0.20 mg/kg for Pb which is uncontaminated by Lead when compared with the USA threshold limit of particulate metal concentration. Conversely, the other hazardous metals ranged from 1 to 2, indicating the area is contaminated moderately. The exposure to dust containing high silica in quarry workers leads to deterioration of pulmonary function and hence suggesting a need for protective measures of the quarry workers.

F ine particulate matter (PM 2.5 ; ≤ 2.5μm in aerodynamic diameter) stands out among all pollutants as more directly responsible for long - term health problems . T h is work aims to evaluate the public health benefits of improved air quality in Brazil , based on the estimated reduction in mortality from PM 2.5 , a pollutant commonly related to all causes mortality including non - accidental, cardiovascular , ischemic heart diseases and lung cancer. Annual PM 2.5 concentrations we re obtained from 50 monitoring stations spread across 24 Brazil ian cities between the years 2000 and 2017 , which constituted the baseline scenario . The control scenario was represented by the annual PM 2.5 guideline values (10 μ g m - 3 ) of the World Health Organization ( WHO ) . The relationship between the change in baseline and control scenarios with health effects was estimated using the BenMAP - CE program and the application of exposure - response functions. São Paulo city showed the highest number of avoidable deaths, with values ranging from 2 8 , 874 ± 9,769 and 8 2 , 720 ± 24,549 for all causes from 2000 to 2017. In 20 09 , just three Brazilian cities were monitoring PM 2.5 . Between 877 ± 295 and 2,497 ± 719 a ll causes avoidable deaths related to PM 2.5 were estimated under the scenario when the WHO guideline w as applied. In 2017, the 15 cities with representative annual PM 2.5 data account for between 2,378 ± 801 and 6,282 ± 1 , 818 avoidable deaths due to all - cause PM 2.5 mortality, between 2,974 ± 376 and 10,397 ± 516 avoidable deaths due non - accidental causes, between 1,373 ± 230 and 3,428 ± 265 avoidable deaths due cardiovascular disease , between 927 ± 162 and 2,514 ± 156 avoidable deaths due ischemic heart diseases and the lowest between 101 ± 45 and 264 ± 88 avoidable deaths due to lung cancer.

Construction activities are common across cities; however, the studies assessing their contribution to airborne PM10 (≤10 μm) and PM2.5 (≤2.5 μm) particles on the surrounding air quality are limited. Herein, we assessed the impact of PM10 and PM2.5 arising from construction works in and around London. Measurements were carried out at 17 different monitoring stations around three construction sites between January 2002 and December 2013. Tapered element oscillating microbalance (TEOM 1400) and OSIRIS (2315) particle monitors were used to measure the PM10 and PM2.5 fractions in the 0.1-10 μm size range along with the ambient meteorological data. The data was analysed using bivariate concentration polar plots and k-means clustering techniques. Daily mean concentrations of PM10 were found to exceed the European Union target limit value of 50 μg m(-3) at 11 monitoring stations but remained within the allowable 35 exceedences per year, except at two monitoring stations. In general, construction works were found to influence the downwind concentrations of PM10 relatively more than PM2.5. Splitting of the data between working (0800-1800 h; local time) and non-working (1800-0800 h) periods showed about 2.2-fold higher concentrations of PM10 during working hours when compared with non-working hours. However, these observations did not allow to conclude that this increase was from the construction site emissions. Together, the polar concentration plots and the k-means cluster analysis applied to a pair of monitoring stations across the construction sites (i.e. one in upwind and the other in downwind) confirmed the contribution of construction sources on the measured concentrations. Furthermore, pairing the monitoring stations downwind of the construction sites showed a logarithmic decrease (with R(2) about 0.9) in the PM10 and PM2.5 concentration with distance. Our findings clearly indicate an impact of construction activities on the nearby downwind areas and a need for developing mitigation measures to limit their escape from the construction sites.

We compared various pollutant concentrations (PM1, PM2.5, PM10, PNC, BC) at four different urban microenvironments (MEs) in London (Indoor, IN; Traffic Intersection, TI; Park, PK; and Street Canyon, SC). The physico-chemical characteristics of particles were analysed, and the respiratory deposition doses (RDD) were estimated. Field measurements were conducted over a period of 121 days. The mean PM2.5 (PNC) concentrations were found to be 9.47 ± 7.05 (16366 ± 11815), 8.09 ± 4.57 (10951 ± 6445), 5.11 ± 2.96 (7717 ± 4576), 3.88 ± 3.06 (5672 ± 2934) μg m−3 (# cm−3) at TI, SC, PK and IN, respectively. PM2.5, PM10 and PNC exhibited a trend of TI > SC > PK > IN; higher concentrations for PM1 and BC were observed at IN than PK due to the emissions from printers, producing a trend of TI > SC > IN > PK. We observed 12%–30% higher fine PM concentrations at TI and SC sites during morning peak (07:00–09:30) than the evening peak hours (16:00–19:00); while IN showed a smaller variation in fine PM concentrations compared with outdoor TI, PK and SC sites owing to their prevalence in the IN for a longer time. Fine and ultrafine PM containing potentially toxic trace transition metals including Fe, Ti, Cr, Mn, Al and Mg were detected by high resolution electron microscopy at all sites. There was a similar relative abundance of different elements at the TI, IN and PK sites, which suggests a transport of PM between MEs. RDD for PM1 was highest (2.45 ± 2.27 μg h−1) at TI for females during running; PM2.5 and PM10 were highest at SC (11.23 ± 6.34 and 37.17 ± 20.82 μg h−1, respectively). The results show that the RDD variation between MEs does not follow the PM concentration trend. RDD at PK was found to be 39%–53% lower than TI and SC during running for all the PM fractions. Overall, the study findings show the air quality variation at different MEs and reveals the exposure inequalities around the city, which enable the management of personal exposure by selecting appropriate MEs for different activities.

The use of Nature-based Solutions (NBS), designed and implemented with participatory approaches, is rapidly increasing. Much use is being made of the Living Lab (LL) concept to co-create innovative NBS with stakeholders in a certain societal and environmental, real-life context. Most of the current research revolves around urban LLs, thus overlooking specificities of rural areas. Furthermore, the influence of the context itself on co-creation processes is insufficiently recognised, leaving challenges associated with co-creation such as stakeholder engagement unresolved. By exploring the co-creation processes in the LLs of the OPERANDUM project, this study identifies eighteen contextual factors shaping the co-creation processes of NBS within rural territories and provides associated recommendations. In addition, based on lessons learnt in the OPERANDUM project, we discuss the value of a relational place-based approach in LLs, suggesting that the co-creation process should be approached as a dynamic confluence of many interconnected contextual factors. We conclude that acknowledging the interconnections in co-creation in the real-life context of rural territories may increase the success and impact of the LL approach, and ultimately, the benefits of NBS. •Better understanding of the essence and dynamics of “real life context” in the co-creation in the living labs is needed.•Real life context of a living lab is composed of factors that refer to ecological-physical, socio-economic, institutional, research and NbS context.•Co-creation of NbS in rural living labs differs from urban living labs.•Effective and inclusive co-creation for NbS requires relational and place-based approach with understanding of interrelated and dynamic contexts.

Localised vehicular fine particulate matter (PM2.5) emissions in an urban canyon can influence the energy performance of a building ventilation system at roof level. This paper examines the energy demands of air filtration through an air handling unit (AHU) located in different positions and orientations on a building rooftop. A series of 3D numerical simulations examined the impact of aspiration efficiency (AE) on filter loading rates as the distance from the source increases and AHU orientation relative to ambient wind direction. The ventilation PM2.5 concentration was equal to the ambient when positioned near the windward wall of the target building. A decrease of 33% and 60% in the filter loading rate occurred at a wind speed of 7.5 m/s and 2.5 m/s at the leeward wall. There was no energy savings when the AHU is positioned on the windward side of the target building but a reduction in energy consumption of 9.8% at the centre and 19.4% at the leeward side. Comparing the effect of wind orientation for identical AHU positions on the rooftop centre resulted in a 26–35% reduction in AE when the AHU inlet is not facing into the particle laden wind and incurred energy savings of 25.8%.

Deterioration of air quality in Indian megacities (Delhi, Mumbai or Kolkata) is much more significant than that observed in the megacities of developed countries. Densely packed high-rise buildings restrict the self-cleaning capabilities of Indian megacities. Also, the ever growing number of on-road vehicles, resuspension of the dust, and anthropogenic activities exacerbate the levels of ambient air pollution, which is in turn breathed by urban dwellers. Pollution levels exceeding the standards on a regular basis often result in a notable increase in morbidity and mortality. This article discusses the challenges faced by Indian megacities in their quest for sustainable growth, without compromising the air quality and urban way of life.

Children are exposed to outdoors and indoor air pollution. They are more vulnerable than adults to the effects of exposure to air pollutants made up of harmful chemicals. Assessing the exposure of children is complex because indoor air pollution is affected by many factors - types of cooking fuel and cookstoves, indoor ventilation, geographical and meteorological conditions, and exposure time. The most addressed health outcomes in literature are respiratory and birth outcomes. However, most of them reported difficulty in performing a meta-analysis due to the few studies on the personal exposure of children. The limited effectiveness of specific interventions at the household level points out the need for integrated and sustained public policies over time, associated with regulatory measures on pollutants emissions.

Severe air pollution and its associated health impacts have become one of the major concerns in China. A detailed analysis of PM2.5 chemical compositions is critical for optimizing pollution control measures. In this study, daily 24-h bulk filter samples were collected and analyzed for totally 21 field campaigns at 17 sites in China between 2008 and 2013. The 17 sites were classified into four groups including six urban sites, seven regional sites, two coastal sites in four fast developing regions of China (i.e. Beijing-Tianjin-Hebei region, Yangtze River Delta, Pearl River Delta and Sichuan Basin), and two ship cruise measurements covered the East China Sea and Yellow Sea of China. The high average concentrations of PM2.5 and the occurrences of extreme cases at most sites imply the widespread air pollution in China. Fine particles were largely composed of organic matter and secondary inorganic species at most sites. High correlation between the temporal trends of PM2.5 and secondary species of urban and regional sites highlights the uniformly distributed air pollutants within one region. Secondary inorganic species were the dominant contributors to the high PM2.5 concentration in Northern China. However in Southern China, the relative contributions of different chemical species kept constant as PM2.5 increased. This study provides us a better understanding of the current state of air pollution in diversified Chinese cities. Analysis of chemical signatures of PM2.5 could be a strong support for model validation and emission control strategy.

In the twenty-first century, megacities around the world are vulnerable to climate change, and its effect on urban mortality is exacerbated by extreme heat events (EHE) and urban heat islands (UHI). Since climate projections tend to exclude the EHE and UHI, their impact on urban health and urban mortality could be underestimated. This present study aims to provide a systematic synthesis of the available evidence of the impact of UHI on urban mortality across the globe. A literature survey was performed on research articles published by Web of Science and Google Scholar, and relevant peer-reviewed articles were included to investigate the relationship between all-cause mortality with UHI episodes in the megacities around the world. The evidence pertaining to the all-cause mortality based on field survey, retrospective time series analysis, and models were extracted for expert judgement. The results suggest that the UHI contributed to the total heat-related mortality during the major heatwave episodes in the world. Effects were found to vary with the cause of death, age, gender, geographical settings, and sociodemographic status in the reported studies. Comprehension of the main determinants of heat-related mortality and the projected trend of this association in the rapidly expanding urban regions is prudential to inform preparedness and targeted interventions across the globe.

Groundwater vulnerability maps are useful for decision making in land use planning and water resource management. This paper reviews the various groundwater vulnerability assessment models developed across the world. Each model has been evaluated in terms of its pros and cons and the environmental conditions of its application. The paper further discusses the validation techniques used for the generated vulnerability maps by various models. Implicit challenges associated with the development of the groundwater vulnerability assessment models have also been identified with scientific considerations to the parameter relations and their selections.

In-kitchen air pollution is a leading environmental issue, attributable to extensive cooking, poor ventilation and the use of polluting fuels. We carried out a week-long monitoring of CO2, temperature and relative humidity (RH) in five low-income residential kitchens of 12 global cities (Dhaka, Chennai, Nanjing, Medellín, São Paulo, Cairo, Sulaymaniyah, Addis Ababa, Nairobi, Blantyre, Akure and Dar-es-Salaam). During cooking, the average in-kitchen CO2 concentrations were 22.2% higher than the daily indoor average. Also, the highest CO2 was observed for NVd (natural ventilation-door only; 711 ± 302 ppm), followed by NVdw (natural ventilation-door + window; 690 ± 319 ppm) and DVmn (dual ventilation-mechanical + natural; 677 ± 219 ppm). Using LPG and electric appliances during cooking exhibited 32.2% less CO2 than kerosene. Larger kitchens (46–120 m3) evinced 28% and 20% less CO2 than medium (16–45 m3) and small (4–15 m3) ones, respectively. In-kitchen CO2 with >2 occupants during cooking was 7% higher than that with one occupant. 87% of total kitchens exceeded the ASHRAE standard (RH >40%, temperature >23 °C) for thermal comfort. Considering the ventilation type, both the ACH (air change rate per hour) and ventilation rate followed the order: NVdw > NVd > DVmn, while the trend for weekly average CO2 concentration was NVd > DVmn > NVdw. Larger kitchens presented 22% and 28% less ACH, and 82% and 190% higher ventilation rate than medium- and small-volume ones, respectively. Forty-three percent kitchens had ACH

Abstract This work aims to develop an emission inventory of methane (CH4), nitrous oxide (N2O), ammonia (NH3), nitrogen oxides (NO, NO2) and CO2 from various agricultural activities and wetlands in Delhi area using an emission factor and activity based approach between the years 2001 and 2011. Among all agricultural activities, livestock enteric fermentation (LEF) was found to be the main source, contributing up to 90% of total CH4. This is followed by livestock manure management (LMM) (6–7%), paddy field (3–5%) and burning of crop residue (0.6–0.9%). It was also found that LMM practices alone contributed ∼99.8% of total N2O emissions and ∼106–141 Gg of NH3 during 2001–2011. Crop residue burning was responsible for ∼41 Gg of annual average emissions of NOx over the period 2001–2011. Annual CH4 emissions from rice cultivation practices were found to be in the 560–634 Gg range during same period. N2O emission from crop residue burning and fertilizer were insignificant when compared with LMM practices. About 54 Gg, 1.5 Gg and 14 Mg of CO2, CH4 and N2O, respectively, were released by natural and manmade wetlands in Delhi during 2009 while manmade wetlands were found to be responsible for 48–49% of total GHG (CO2,CH4,N2O) emissions.

The Southeast Asian (SEA) region is no stranger to forest fires - the region has been suffering from severe air pollution (known locally as ‘haze’) as a result of these fires, for decades. The fires in SEA region are caused by a combination of natural (the El Niño weather pattern) and manmade (slash-and-burn and land clearing for plantations) factors. These fires cause the emissions of toxic aerosols and pollutants that can affect millions of people in the region. Thus, this study aims to identify the impact of the SEA haze on the Southern region of the Malaysian Peninsula and Borneo region of East Malaysia using the entire air quality observation data at surface level in 2015. Overall, the concentration of PM10 was about two-fold higher during the haze period compared to non-haze period. The concentrations of CO, flux of CO and flux of BC were aligned with PM10 during the entire observation period. The wind field and cluster of trajectory indicated that the Southern Malaysian Peninsula and Borneo were influenced mainly from the wildfires and the combustion of peat soil in the Indonesian Borneo. This study finds that wildfires from Borneo impacted the Southern Malaysian Borneo more seriously than that from Sumatra region.

Hand-tools, such as sledgehammer, are widely used in refurbishment activities, nonetheless there is very little knowledge on nanoparticle generation. We measured particle number size distributions (PSD) and concentrations (PNC) in the 10-420 nm using a NanoScan Scanning Mobility Particle Sizer (SMPS) during the use of hand-tools (i.e., sanding and removal of wall) in a real indoor refurbishment environment. Results indicated that refurbishment activities from removal of wall increased average PNCs by ~6 times over the background, while was ~1.5 times higher than sanding. Highest total PNC was 1.9 × 105 particles cm30 −3, that corresponded to removal of wall activities. For sanding activities, PNC was lower as the coat of the plaster was probably slightly wet. Moreover, comparison between the two principal activities showed a similar peak in the accumulation mode (~65 nm), with a monomodal pattern. Results suggest that removal of wall activities emitted nanoparticles with a 59% of contribution in the Aitken mode. According to these data, can be inferred that the application of hand-tools in refurbishment activities generates lower total PNC than using electromechanical equipment. This study may contribute to our understanding of nanoparticle generation in refurbishment activities

Atmospheric nanoparticles are a pollutant currently unregulated through ambient air quality standards. The aim of this chapter is to assess the environmental and health impacts of atmospheric nanoparticles in European environments. This chapter begins with the conventional information on the origin of atmospheric nanoparticles, followed by their physical and chemical characteristics. A brief overview of recently published review articles on this topic is then presented to guide those readers interested in exploring any specific aspect of nanoparticles in greater detail. A further section reports a summary of recently published studies on atmospheric nanoparticles in European cities. This covers a total of about 45 sampling locations in 30 different cities within 15 European countries for quantifying levels of roadside and urban background particle number concentrations (PNCs). Average PNCs at the reviewed roadside and urban background sites were found to be 3.82 ± 3.25 × 10 4 and 1.63 ± 0.82 × 10 4 cm −3 , respectively, giving a roadside to background PNC ratio of ~2.4. Engineered nanoparticles are one of the key emerging categories of airborne nanoparticles, especially for the indoor environments. Their ambient concentrations may increase in future due to widespread use of nanotechnology integrated products. Evaluation of their sources and probable impacts on air quality and human health are briefly discussed in the following section. Respiratory deposition doses received by the public exposed to roadside PNCs in numerous European locations are then estimated. These were found to be in the 1.17–7.56 × 10 10 h −1 range over the studied roadside European locations. The following section discusses the potential framework for airborne nanoparticle regulations in Europe and, in addition, the existing control measures to limit nanoparticle emissions at source. The chapter finally concludes with a synthesis of the topic areas covered and highlights important areas for further work.

Nanoparticle emissions from road vehicles have been studied extensively in the recent past due to their dominant contribution towards the total airborne particle number concentrations (PNCs) found in the urban atmospheric environment. In view of upcoming tighter vehicle emission standards and adoption of cleaner fuels in many parts of the world, the contribution to urban nanoparticles from non-vehicle exhaust sources (NES) may become more pronounced in future. As of now, only limited information exists on nanoparticle emissions from NES through the discretely published studies. This article presents critically synthesised information in a consolidated manner on 11 NES (i.e. road–tyre interaction, construction and demolition, aircraft, ships, municipal waste incineration, power plants, domestic biomass burning, forest fires, cigarette smoking, cooking, and secondary formation). Source characteristics and formation mechanisms of nanoparticles emitted from each NES are firstly discussed, followed by their emission strengths, airborne concentrations and physicochemical characteristics. Direct comparisons of the strengths of NES are not straightforward but an attempt has been made to discuss their importance relative to the most prominent source (i.e. road vehicles) of urban nanoparticles. Some interesting comparisons emerged such as 1 kg of fast and slow wood burning produces nearly the same number of particles as for each km driven by a heavy duty vehicle (HDV) and a light duty vehicle, respectively. About 1 min of cooking on gas can produce the similar particle numbers generated by ∼10 min of cigarette smoking or 1 m travel by a HDV. Apportioning the contribution of numerous sources from the bulk measured airborne PNCs is essential for determining their relative importance. Receptor modelling methods for estimation of source emission contributions are discussed. A further section evaluates the likely exposure risks, health and regulatory implications associated with each NES. It is concluded that much research is needed to provide adequate quantification of all nanoparticle sources, and to establish the relative toxicity of nanosize particles from each.

The depletion of fossil fuels is a major issue in energy generation; hence, biomass and renewable energy sources, especially bioenergy, are the solution. The dependence on bioenergy has many benefits to mitigate environmental pollution. It is imperative that the global society adopts these alternative, sustainable energy sources in order to mitigate the constant growth of climate change. Biomass and Bioenergy Solutions for Climate Change Mitigation and Sustainability highlights the challenges of energy conservation and current scenarios of existing fossil fuel uses along with pollution potential of burning fossil fuel. It further promotes the inventory, assessment, and use of biomass, pollution control, and techniques. This book provides the solution for climate change, mitigation, and sustainability. Covering topics such as biofuel policies, economic considerations, and microalgae biofuels, this premier reference source is an essential resource for environmental scientists, environmental engineers, government officials, business leaders, politicians, librarians, students and faculty of higher education, researchers, and academicians. The depletion of fossil fuels is a major issue in energy generation; hence, biomass and renewable energy sources, especially bioenergy, are the solution. The dependence on bioenergy has many benefits to mitigate environmental pollution. It is imperative that the global society adopts these alternative, sustainable energy sources in order to mitigate the constant growth of climate change. Biomass and Bioenergy Solutions for Climate Change Mitigation and Sustainability highlights the challenges of energy conservation and current scenarios of existing fossil fuel uses along with pollution potential of burning fossil fuel. It further promotes the inventory, assessment, and use of biomass, pollution control, and techniques. This book provides the solution for climate change, mitigation, and sustainability. Covering topics such as biofuel policies, economic considerations, and microalgae biofuels, this premier reference source is an essential resource for environmental scientists, environmental engineers, government officials, business leaders, politicians, librarians, students and faculty of higher education, researchers, and academicians.

Surface water monitoring is a necessary component of researching ecological and hydrological processes. Recent improvements in satellite-based remote sensors have ushered in a new era in the area of surface water monitoring. The assessment and mapping of water resources in the semi-arid regions are one of the challenging tasks for the research domain of hydrogeologists and decision-makers. In this study, an inventory of existing ancillary data and remotely sensed data, i.e., Landsat 7 ETM+, Landsat 8 OLI/TIRS, and DEM data used for monitoring and mapping the surface water resources in the semi-arid regions of south India. For monitoring and mapping the water resources, twenty years of land cover changes were assessed using the Modification of Normalized Difference Water Index (MNDWI) techniques. Each MNDWI land cover has been classified into five categories viz., fallow land, vegetation land, developed land, moisture soils, and surface water bodies, based on the pixel values varying from +1 to -1. The results of the research reveal that the surface water bodies vary from year to year over the past two decades. The fallow land (36.20 %), vegetation lands (35.37 %) are decreased, and developed land (22.56 %), moisture soils (4.81 %), and surface water bodies (1.06 %) are increased over the past two decades from 2000 to 2020. Most land cover categories differed statistically from one another, however, when defining vegetation with varying water content, there were some commonalities. MNDWI is found to be efficient in identifying water bodies.

Knowledge of the driving cycle is an important requirement in the evaluation of exhaust emissions. Data were collected from trips performed on five routes between the home addresses in the surrounding areas and place of work at Napier University in Edinburgh. A real world Edinburgh motorcycle driving cycle (EMDC) is developed for each of the urban and rural roads, using this data. Forty-four trips were made on the routes in both urban and rural areas. We assess motorcycle speed, percentage time spent in cruise, accelerations, decelerations and idling and their statistical validity over trip lengths. The results show that EMDC has a cycle length of 770 and 656s for urban and rural trips, which are higher than those of the European Commission's driving cycle for cars used for emission estimations of motorcycles. Time spent in acceleration and deceleration modes of EMDC are found to be significantly higher than in other driving cycle studies, reflecting diverse driving conditions in Edinburgh.

The present study focuses on investigating the impacts of a sudden dust storm on the atmospheric boundary layer (ABL) over Ahmedabad (23.02 degrees N, 72.57 degrees E), an urban site located in the western region of India. The accumulation of dust particles in the atmosphere during the dust storm, originating from the Thar Desert in Rajasthan, led to the decrease in surface temperature as a consequence of dust-radiation interaction. Ambient particulate matter data obtained from Air Quality (AQ) station at Ahmedabad showed a spike of 118.5% and 44.5% in PM10 and PM2.5 concentrations, respectively, during the event in comparison with the previous control day. Sudden exposure to an anomalous increase in the particulate matter may cause severe impacts on human health. These surface forcings have been reflected in the stable nocturnal ABL. Backscatter signals recorded by ground-based Ceilometer Lidar at Physical Research Laboratory (PRL) showed that ABL was shallow and collapsed during the dust storm episode. Turbulence has been detected in the ABL during the event which further assisted in the vertical mixing of dust particles in the ABL. These dust particles got trapped within the residual layer, preventing further percolation in the free atmosphere. Such sub-grid scale changes in the ABL during the dust storm were not reflected in the boundary layer height (BLH) obtained from the ERA-5 reanalysis dataset. A significant association between the ABL and the local radiative budget has been found. Coupled Ocean-Atmosphere Radiative Transfer Model (COART) simulations substantiated or showed a cooling event of the surface during the dust storm. This study is important as it can be taken as feedback to improve local climate models with respect to dust storm meteorology.

Exposure to particulate matter, carbon dioxide, and carbon monoxide inside a car during commuting were determined during the period October–November 2017 in Nur-Sultan, Kazakhstan. We choose to follow five bus routes (#10, 18, 19, 37 and 53) that cover the majority of the city's area. CO (ppm), CO2 (ppm) and PM1, PM2.5, PM4, and PM10 mass concentration (μg/m3) were measured in this study. PM11 was found to be the largest fraction of all sizes of PM. The mean PM1 concentrations along the forward (backward) paths for each of the five bus routes were measured as 11 ± 14 (11 ± 7), 14 ± 8 (16 ± 6), 25 ± 11 (21 ± 14), 23 ± 8 (15 ± 6) and 76 ± 26 (99 ± 55) μg/m3, respectively. Average CO concentrations among five bus routes (#10, 18, 19, 37 and 53) along the forward (backward) paths were 0.67 ± 0.16 (0.78 ± 0.17), 0.7 ± 0.16 (0.53 ± 0.32), 1.04 ± 0.01 (2.3 ± 0.95), 2.67 ± 1.3 (2.03 ± 0.41), 3.54 ± 3.57 (2.17 ± 0.37) ppm. The mean PM1/PM2.5 and PM2.5/PM10 ratios were 0.96 and 0.91, respectively. Nur-Sultan could be an example for those cities that are under major developments and candidates to be green cities by showing the exposures to atmospheric pollutants across the city. Those cities that are developing themselves as tourist attractions should create maps of PM exposures along major urban routes, and route traffic to exclude tourist areas from being hotspots.

In this paper, we use origin and destination mobility surveys and high-resolution assignment and emission models to study air quality and short-lived climate pollutant impacts related to on-road transportation in the Metropolitan Area of São Paulo (MASP). To begin with, we calculate transport carbon dioxide (CO2) emissions from fossil fuel-driven vehicles (light and heavy-duty) at spatial and temporal resolutions of 500 m and one hour, by means of traffic demand forecasting. These estimates, carry out for 2007 and 2012, are based on passenger and freight trips and the height of the atmospheric boundary layer, among other variables. These proxies depend also on ancillary parameters as particulate matter concentrations and dilution rates. In the second place, we evaluate the changes in CO2 emissions from the MASP (3%/year). Transport emission inventories combine mobility surveys and road network assignments with air quality data. In spite of using different methodologies, bottom-up road link assignments versus top-down vehicle activity-based and fuel consumption approaches, the estimated CO2 emissions are consistent with the Official São Paulo State's Inventory. This work found that the CO2 emissions in MASP were 10,044 and 11,503 t Ceq./day in 2007 and 2012 (73% light and 27% heavy vehicles), respectively. On-road emission patterns agree with the spatial-temporal variation of transportation journeys and corresponding passenger and freight network assignments. Temporal patterns, diurnal, weekly and monthly, were determined using traffic counts and congestion surrogates. The patterns were also crosschecked with average CO2 measurements, available for 2014 at the road (western area of MASP) and background sites (Jaraguá Peak). Kerbside road measurements showed two prominent peaks associated to the morning (437 ± 45 ppm) and night rush hours (435 ± 49 ppm), coupled to low values of boundary layer height (313 m) and dilution rate (329 m2/h). Background values (414 ± 2 ppm) were subtracted from on-road measurements to estimate excess CO2 (12 ± 8 ppm) directly attributed to vehicles. The inventory reflects the relationships between traffic patterns and emissions, and the developed methodology could be used to evaluate the impacts of forthcoming urban transport and emission control policies. In the future, our estimates will be verified with ground measurements of CO2 concentrations over a bigger monitoring network in the MASP.

The international community, particularly across the European Union (EU), is increasingly recognizing and promoting Nature-based Solutions (NBS) as long-term and sustainable measures against hydro-meteorological hazards such as flooding, coastal erosion, heat waves and landslides. Yet, scaled implementation of NBS at EU and global level presently remains a challenge due to often complex and lengthy permitting procedures. While efforts have been made to highlight the explicit and implicit role of NBS in major global and European policy frameworks, uncertainty remains when it comes to the level of coherence across government levels, from international to national and local scale. This paper attempts to address this gap by introducing an open-access online policy catalogue pertaining to the implementation of 740 NBS projects globally to mitigate the impact of hydro-meteorological phenomena. Based on a policy screening of 88 NBS projects in Europe and an in-depth analysis of the NBS permitting paths across seven Open-Air Laboratories in European countries, we examine the linkages between European and national legislation and policies. Understanding these linkages will help promote NBS mainstreaming as the NBS agenda is set at EU and global level while implementation is heavily dependent on national-scale governance. We identify a common permitting path for NBS paved by the EU in several directives, as well as some divergence in the implementation of these directives at national level which can pose significant challenges to the uptake of NBS. The NBS policy catalogue provides a valuable resource for further analysis of the NBS policy context from local to global levels towards increasing uptake and acceptance of NBS in Europe and beyond.

This study assesses the ambient air concentration of and heavy metals at six different sites (including three sub-urban and three rural) in Roorkee, India. Monthly measurements were carried out continuously between January and March 2007 at all sites. PM concentrations at the rural sites ranged from 37-959 μg/m compared with 151-422 μg/m at sub-urban sites. These concentrations were well above the CPCB (Central Pollution Control Board, Delhi) standards during all sampling months except February. Conversely, lowest concentration during February was the result of removal of particles by heavy rain before the sampling days. In the case of heavy metals, highest concentrations for Cr, Fe, Mn, Zn, and Al were 2.04, 30, 0.80, 7.13, and 15.6 ng/m , respectively, at rural sites compared with 0.28, 0.37, and 0.02 ng/m for Ni, Cu, and Cd, respectively, at an industrial site. Main sources of PM and heavy metals at sub-urban sites were road dust, traffic exhaust, tire abrasion, industrial emissions, and oil lubricants use at vehicle-servicing centers. Heavy metals and PM at the rural sites originate from coal and wood burning, sugar mill and brick furnace emissions, fertilizers use in farming, agricultural activity, road construction activity, and the dust from long-range transport along with naturally occurring resuspended dust. Among all, wood burning was identified as the most significant source of elevated PM concentrations at rural sites. As opposed to the PM that remains a concern, concentrations of all heavy metals were found to be far below the standard limits prescribed by the World Health Organization (WHO) and the United States Environmental Protection Agency (EPA). An integrated assessment of air pollution and health risk is believed to be required to be carried out to draw better conclusions about air quality conditions in study areas. © 2012 American Society of Civil Engineers.

Low-cost sensors (LCS) for indoor and ambient air quality monitoring have gained significant attention in recent decades due to their affordability, availability, and potential for broad usage in various environments. LCS are available in diverse types, each with its own specifications. These sensors present a cost-effective solution for air quality monitoring, enabling individuals, communities, agencies, and organizations to obtain real-time concentrations of gaseous and particulate matter (PM). Numerous studies have examined the pollutants that can be monitored by LCS, including different size fractions of PM such as PM2.5 and PM10 (with an aerodynamic diameter equal to or less than 2.5 and 10μm, respectively), carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2), and total volatile organic compounds (TVOC). Monitoring pollution levels provides valuable insights into potential sources and aids in implementing appropriate mitigation strategies. LCS-based indoor air quality (IAQ) monitoring allows occupants of residences, schools, and workplaces to assess the IAQ within their premises. Similarly, LCS-based ambient air quality monitoring complements the existing network of monitoring stations and improves spatial coverage. With advancements in sensing technology, LCS units can be utilized in stationary, mobile, and wearable applications, either as stand-alone devices or as part of wirelessly-connected networks to fulfill their designated roles. Today, LCS-based units are capable of remotely collecting, storing, transmitting, processing, analyzing, and visualizing data as required. However, challenges associated with LCS, such as data accuracy, calibration, validation, and quality assurance, persist. Efforts are ongoing to enhance the reliability and usability of this technology in all aspects. LCS units serve as valuable tools for monitoring air pollution, raising awareness, promoting community-driven initiatives, and driving policy changes in air quality management and public health. In this chapter, we elaborate on the aforementioned elements to equip readers with comprehensive knowledge of LCS.

While concrete recycling is practiced worldwide, there are many unanswered questions in relation to ultrafine particle (UFP; Dp < 100 nm) emissions and exposure around recycling sites. In particular: (i) Does recycling produce UFPs and in what quantities? (ii) How do they disperse around the source? (iii) What impact does recycling have on ambient particle number concentrations (PNCs) and exposure? (iv) How effective are commonly used dust respirators to limit exposure? We measured size-resolved particles in the 5–560 nm range at five distances between 0.15 and 15.15 m that were generated by an experimentally simulated concrete recycling source and found that: (i) the size distributions were multimodal, with up to ∼93% of total PNC in the UFP size range; and (ii) dilution was a key particle transformation mechanism. UFPs showed a much slower decay rate, requiring ∼62% more distance to reach 10% of their initial concentration compared with their larger counterparts in the 100–560 nm size range. Compared with typical urban exposure during car journeys, exposure decay profiles showed up to ∼5 times higher respiratory deposition within 10 m of the source. Dust respirators were found to remove half of total PNC; however the removal factor for UFPs was only ∼57% of that observed in the 100–560 nm size range. These findings highlight a need for developing an understanding of the nature of the particles as well as for better control measures to limit UFP exposure.

Under climate change scenarios, it is important to evaluate the changes in recent behavior of heavy precipitation events, the resulting flood risk, and the detrimental impacts of the peak flow of water on human well-being, properties, infrastructure, and the natural environment. Normally, flood risk is estimated using the stationary flood frequency analysis technique. However, a site’s hydroclimate can shift beyond the range of historical observations considering continuing global warming. Therefore, flood-like distributions capable of accounting for changes in the parameters over time should be considered. The main objective of this study is to apply non-stationary flood frequency models using the generalized extreme value (GEV) distribution to model the changes in flood risk under two scenarios: (1) without nature-based solutions (NBS) in place and; (2) with NBS i.e. wetlands, retention ponds and weir/low head dam implemented. In the GEV model, the first two moments i.e. location and scale parameters of the distribution were allowed to change as a function of time-variable covariates, estimated by maximum likelihood. The methodology is applied to OPEn-air laboRAtories for Nature baseD solUtions to Manage hydro-meteo risks, which is in Europe. The time-dependent 100-year design quantiles were estimated for both the scenarios. We obtained daily precipitation data of climate models from the EURO-CORDEX project dataset for 1951–2020 and 2022–2100 representing historical and future simulations, respectively. The hydrologic model, HEC-HMS was used to simulate discharges/flood hydrograph without and with NBS in place for these two periods: historical (1951-2020) and future (2022-2100). The results showed that the corresponding time-dependent 100-year floods were remarkably high for the without NBS scenario in both the periods. Particularly, the high emission scenario (RCP 8.5) resulted in dramatically increased flood risks in the future. The simulation without NBS also showed that flooded area is projected to increase by 25% and 40% for inundation depth between 1.5 and 3.5 m under RCP 4.5 and RCP 8.5 scenarios, respectively. For inundation depth above 3.5 m, the flooded area is anticipated to rise by 30% and 55% in both periods respectively. With the implementation of NBS, the flood risk was projected to decrease by 20% (2022–2050) and 45% (2071–2100) with a significant decrease under RCP 4.5 and RCP 8.5 scenarios. This study can help improve existing methods to adapt to the uncertainties in a changing environment, which is critical to develop climate-proof NBS and improve NBS planning, implementation, and effectiveness assessment.

This chapter introduces the aim of the NBS Impact Evaluation Handbook as a reference for evaluating the impacts of nature-based solutions (NBS). It provides a general framework on the value of NBS to the community, investors, and policy makers, and illustrates how the NBS impact evaluation framework can be used. Chapter 1 describes the global context in which NBS operate. Two infographics help visualise the definition of NBS and provide an in-depth explanation of the concept’s origin and evolution. Another infographic describes the full life cycle of NBS including monitoring, evaluation, and cost-benefit analysis. The chapter concludes by describing the content of each section of the handbook.

The transport sector is the dominant source of nanoparticles in the urban atmosphere. It is also responsible for about 20-25% of current global CO2 emissions, a figure that is expected to grow to about 30-50% by 2050 (Fuglestvedt et al., 2008). One option to counter this trend and contribute to the attainment of carbon emission reduction targets is the use of biofuels in road vehicles. This leads to a reduction in CO, CO2 and particle mass emissions, though particle number emissions may increase. This article discusses the potential impact of the particle number concentrations derived from biofuel vehicles on existing regulatory concerns over atmospheric nanoparticles. Copyright (C) 2010 Royal Meteorological Society

Particulate matter (PM) resuspension from indoor surfaces is one of the leading causes of occupant exposure to PM. It has been recognized as a main source of indoor air pollution by many field studies. This review article explores the mechanisms and effects of single and multiple influential factors on PM resuspension, distribution characteristics, spatial and temporal PM distribution characteristics and interactions of human activities and mitigation measures. A roadmap is proposed to provide a strong basis for subsequent research into the dynamic characteristics of PM resuspension. The synthesis of existing literature suggests that the mechanism behind the opposite effect caused by multiple influential factors deserves further exploration. Microscopic simulations of motion over short periods and richer activity features based on various scenarios are needed to get more accurate results. More advanced strategies should be considered to change the control process and enhance the efficiency of the purification devices. Collaboration between multiple disciplines is required to get the complete picture of PM resuspension.

The use of cars for drop-off and pick-up of pupils from schools is a potential cause of pollution hotspots at school premises. Employing a joint execution of smart sensing technology and citizen science approach, a primary school took an initiative to co-design a study with local community and researchers to generate data and provide information to understand the impact on pollution levels and identify possible mitigation measures. This study was aimed to assess the hotspots of vehicle-generated particulate matter ≤2.5 μm (PM2.5) and ≤ 10 μm (PM10) at defined drop-off/pick-up points and its ingress into a nearby naturally ventilated primary school classroom. Five different locations were selected inside school premises for measurements during two peak hours: morning (MP; 0730-0930 h; local time) and evening (EP; 1400-1600 h) peak hours, and off-peak (OP; 1100-1300 h) hours for comparison. These represent PM measurements at the main road, pick-up point at the adjoining road, drop-off point, a classroom, and the school playground. Additional measurements of carbon dioxide (CO2) were taken simultaneously inside and outside (drop-off point) the classroom to understand its build-up and ingress of outdoor PM. The results indicate nearly a three-fold increase in the concentrations of fine particles (PM2.5) during drop-off hours compared to off-peak hours indicated the dominant contribution of car queuing in the school premises. Coarse particles (PM2.5–10) were prevalent in the school playground, while the contribution of fine particles as a result of traffic congestion became more pronounced during drop-off hours. In the naturally ventilated classroom, the changes in indoor PM2.5 concentrations during both peak hours (0.58 

Studies on the natural human exposures to fine particulate matter (PM⁠2.5) and their elements composition are practically non-existent in South America. In order to understand the natural exposure of the typical Brazilian population to PM⁠2.5 and their trace element composition, we measured PM⁠2.5 concentrations and collected mass on filters for nine continuous hours during a typical workday of volunteers. In addition, bus routes were performed at peak and non-peak periods , mimicking the routine activity of the population. Mean concentrations of PM⁠2.5 in the bus and car groups were similar while the fraction of BCe was higher for the bus group. For all routes, mean PM⁠2.5 concentrations were higher during peak than non-peak hours, with an average of 43.5±33.1μgm⁠−3 and 14.3±10.2μgm⁠−3, respectively. The trace elements S, K and Na originated mainly from vehicle emissions; Na was associated with the presence of biofuel in diesel. Toxic elements (Pb, Cr, Cu, Ni, Zn, Mn) were found at low levels as evident by the total hazard index that ranged from 2.15×10⁠−03 to 1.38 for volunteers. For all routes, the hazard index ranged from 2.25×10⁠−03 to 5.03. Average PM⁠2.5 respiratory deposition dose was estimated to be 0.60μg/kg-hour for peak hours. Potential health damages to people during their movements and at workplaces close to the traffic were identified. Improvements in the design of the building to reduce the entrance of air pollutants as well as the use of filters in the buses could help to limit population exposure.

Transfer of people and transportation of goods is an indispensable part of our daily lives. Choosing the most environmentally friendly alternative will have the least impact on human health, ecosystem, and the materials. This study aims to carry out a comparative assessment of various emission scenarios from highway and railway transportation between Kırşehir and Niğde-Ulukışla in the middle Anatolian Peninsula, in Turkey, to allow making an optimum decision from an environmental viewpoint. Currently, the transportation is sustained through highway, which has 232.6 km length between the cities used as a case study. High-speed railway construction is projected on the same route. We formed different capacity alternatives as alternative scenarios and life cycle assessment approach was applied to these scenarios. Environmental damage ratio decreased with the increasing utilization ratio of the railway. The greatest change was seen in ecosystem quality. We also assessed emissions during the construction activities of both railway and highway. A social cost-benefit analysis suggested that damage cost in the current situation was €562,000. The scenario with 100% replacement of highway with railway transportation showed the lowest damage cost (€157,000) while the highest damage cost was due to NOx emissions.

The vertical variation of particle number distributions (PNDs) and concentrations in a street canyon is the result of the competing influences of meteorology, traffic and transformation processes overall and for various particle size ranges. A recently developed instrument, the ‘fast-response differential mobility spectrometer DMS500’, measured PNDs in the 5–2738nm range, pseudo-simultaneously, at four different heights (z/H ¼ 0.09, 0.19, 0.40 and 0.64) on the leeward side of an 11.6-m-deep street canyon which had a height-to-width ratio of near unity. Measurements were made in Cambridge, UK, between 20 and 21 March 2007. The PNDs were bimodal with the same shape at each height, and with similar values of both the peak and geometric mean particle diameters in each mode. This suggested that transformation processes were not important. Coagulation and condensation time scales were comparable and large, and these processes should have had a negligible effect on the PNDs. The particle number concentrations (PNCs) changed significantly with height from a maximum at z/H ¼ 0.19 and decreasing towards both the lowest (z/H ¼ 0.09) and highest (z/H ¼ 0.64) sampling points. The decrease in PNCs with height in the upper part of the canyon was attributed to the removal of particles as a result of mass exchange between street canyon and the wind above while the reduction in the PNC towards street level was thought to be due to dilution and dry deposition. Over 99% of the PNCs were found in 10–300nm range whereas the particle mass concentrations were almost equally distributed between the 10–1000nm and 1000–2738nm size range at each height. The PNCs in the 10–30nm and the 30–300nm size range were linearly correlated with the traffic volume but poorly correlated with the rooftop wind speed.

Background Poor air quality is associated with poor health. Little attention is given to the complex array of environmental exposures and air pollutants that affect mental health during the life course. Aims We gather interdisciplinary expertise and knowledge across the air pollution and mental health fields. We seek to propose future research priorities and how to address them. Method Through a rapid narrative review, we summarise the key scientific findings, knowledge gaps and methodological challenges. Results There is emerging evidence of associations between poor air quality, both indoors and outdoors, and poor mental health more generally, as well as specific mental disorders. Furthermore, pre-existing long-term conditions appear to deteriorate, requiring more healthcare. Evidence of critical periods for exposure among children and adolescents highlights the need for more longitudinal data as the basis of early preventive actions and policies. Particulate matter, including bioaerosols, are implicated, but form part of a complex exposome influenced by geography, deprivation, socioeconomic conditions and biological and individual vulnerabilities. Critical knowledge gaps need to be addressed to design interventions for mitigation and prevention, reflecting ever-changing sources of air pollution. The evidence base can inform and motivate multi-sector and interdisciplinary efforts of researchers, practitioners, policy makers, industry, community groups and campaigners to take informed action. Conclusions There are knowledge gaps and a need for more research, for example, around bioaerosols exposure, indoor and outdoor pollution, urban design and impact on mental health over the life course.

The combination of urbanisation and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate change. Yet, the risk of urban overheating can be mitigated by urban green-blue-grey infrastructures (GBGI), such as parks, wetlands, and engineered greening, which have the potential to effectively reduce summer air temperatures. Despite many reviews, the evidence bases on quantified GBGI cooling benefits remains partial and the practical recommendations for implementation are unclear. This systematic literature review synthesises the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximise their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorised under 10 main divisions. Certain GBGI (green walls, parks, street trees) have been well-researched for their urban cooling capabilities. However, several other GBGI have received negligible (zoological garden, golf course, estuary) or minimal (private garden, allotment) attention. The most efficient air cooling was observed in botanical gardens (5.0±3.5°C), wetlands (4.9±3.2°C), green walls (4.1±4.2°C), street trees (3.8±3.1°C), and vegetated balconies (3.8±2.7°C). Under changing climate conditions (2070-2100) with consideration of RCP8.5, there is a shift in climate subtypes, either within the same climate zone (e.g., Dfa to Dfb and Cfb to Cfa) or across other climate zones (e.g., Dfb (continental warm-summer humid) to BSk (dry, cold semi-arid) and Cwa (temperate) to Am (tropical)). These shifts may result in lower efficiency for the current GBGI in the future. Given the importance of multiple services, it is crucial to balance their functionality, cooling performance, and other related co-benefits when planning for the future GBGI. This global GBGI heat mitigation inventory can assist policymakers and urban planners in prioritising effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience. [Display omitted] •This review focuses on how to mitigate the risk of urban overheating by green-blue-grey infrastructure (GBGI)•51 GBGI types in 10 key categories assessed by monitoring>modelling>remote sensing>mixed methods.•Highest cooling efficiency: botanical garden>wetland>green wall>street trees.•New GBGI implementation should consider future climate impact, multifunctional co-benefits, and unintended consequences.

Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (

Nature-based solutions (NBS) such as green (vegetation) and blue (waterbodies) infrastructure are being promoted as cost-effective and sustainable strategies for managing the heatwaves risks, but long-term monitoring evidence is needed to support their implementation. This work aims to conduct a comparative assessment of the cooling efficiency of green (woodland and grassland) and blue (waterbody) NBS in contrast to a built-up area. Over a year of continuous fixed monitoring showed that the average daily maximum temperatures at NBS locations were 2–3 °C (up-to 15%) lower than the built-up area. Woodland showed the maximum temperature reduction in almost all seasons, followed by waterbody and grassland. NBS performed the best during the summers, peak sunshine, and heatwave hours (up to ∼ 6 °C cooler than built-up area). Using an e-bike for mobile monitoring, the areas where green–blue NBS were combined showed the highest spatial cooling extent, followed by waterbody, woodland, and grassland areas. The database generated can validate city-scale environmental models and assist city planners to incorporate NBS into urban dwellings based on the opportunity, need and scope, aligning with Sustainable Development Goals 11 (sustainable cities and communities) and 13 (climate action).

Abstract Building activities generate coarse (PM10 ≤ 10 μm), fine (PM2.5 ≤ 2.5 μm) and ultrafine particles (<100 nm) making it necessary to understand both the exposure levels of operatives on site and the dispersion of ultrafine particles into the surrounding environment. This study investigates the release of particulate matter, including ultrafine particles, during the mixing of fresh concrete (incorporating Portland cement with Ground Granulated Blastfurnace Slag, GGBS or Pulverised Fuel Ash, PFA) and the subsequent drilling and cutting of hardened concrete. Particles were measured in the 5–10,000 nm size range using a GRIMM particle spectrometer and a fast response differential mobility spectrometer (DMS50). The mass concentrations of PM2.5–10 fraction contributed ∼52–64% of total mass released. The ultrafine particles dominated the total particle number concentrations (PNCs); being 74, 82, 95 and 97% for mixing with GGBS, mixing with PFA, drilling and cutting, respectively. Peak values measured during the drilling and cutting activities were 4 and 14 times the background. Equivalent emission factors were calculated and the total respiratory deposition dose rates for PNCs for drilling and cutting were 32.97 ± 9.41 × 108 min−1 and 88.25 ± 58.82 × 108 min−1. These are a step towards establishing number and mass emission inventories for particle exposure during construction activities.

The elderly population spend relatively more time indoors and is more sensitive to air pollution–related health risks but there is limited information on the quality of the air they breathe inside their residences. The objectives of this work are to (i) characterise mass of size–segregated particulate matter (PM) in elderly residences in Metropolitan Area of Sao Paulo (MASP) in Brazil, (ii) assess the impact of the meteorological parameters on the behaviour of indoor PM concentrations, (iii) evaluate the indoor and outdoor relationship of PM mass concentration, and (iv) estimate the respiratory deposition doses (RDD). To achieve these objectives, we measured mass concentrations of size–segregated particles in 59 elderly residences in MASP. The measurements were made in the 0.25–10 μm size range in 5 size bins using a Personal Cascade Impactor Sampler. We evaluated the mass concentration of particles using a gravimetric method and compared our PM10 (sum of all size bins) and PM2.5 (sum of all size bins, except PM10–2.5) concentrations against the 24 h mean guidelines recommended by World Health Organization (WHO). Our results show the mean PM10 and PM2.5 measured in elderly residences in MASP as 35.2 and 27.4 μg m−3, respectively. PM2.5 and PM

Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

The air quality in classrooms significantly impacts school children's health and learning performance. It has been reported worldwide that classroom air quality does not meet the required standard and actions are pledged for improvement. However, it poses a challenge for decision-making in terms of prioritising taking-up measures. The aim of this study is to propose a method of identifying the action measures for improving classroom air quality and prioritising them. Case studies in the UK and China were conducted, and the key measures were identified through literature studies, open-ended questionnaire surveys, and workshop discussions, which are classified into three categories: B1, policy; B2, technology; and B3, information sharing. The analytical hierarchy process (AHP) is applied in the prioritisation of the action measures. A total of 138 teachers and parents from China and the UK participated in this case study. The genetic algorithm-optimised Hadamard product (GAOHP) method is applied to justify the consistency ratio (CR) within the required threshold value in order to ensure the consistency of the subjective perception and the accuracy of comparative weights. The results show that item B2, technology, is the most desired measure by both Chinese and British parents and teachers, despite the deviation from the optimal choice in China and the UK. Among the proposed action measures, the UK respondents strongly expected air purifiers with natural ventilation as opposed to their Chinese counterparts preferring to share the real-time status of classroom air quality. Our work will provide strong support for the subsequent selection of indoor air quality improvement strategies for schools.

Motivated by public interest, the Clean Air and Urban Landscapes (CAUL) hub deployed instrumentation to measure air quality at a roadside location in Sydney. The main aim was to compare concentrations of fine particulate matter (PM2.5) measured along a busy road section with ambient regional urban background levels, as measured at nearby regulatory air quality stations. The study also explored spatial and temporal variations in the observed PM2.5 concentrations. The chosen area was Randwick in Sydney, because it was also the subject area for an agent-based traffic model. Over a four-day campaign in February 2017, continuous measurements of PM2.5 were made along and around the main road. In addition, a traffic counting application was used to gather data for evaluation of the agent-based traffic model. The average hourly PM2.5 concentration was 13 µg/m3, which is approximately twice the concentrations at the nearby regulatory air quality network sites measured over the same period. Roadside concentrations of PM2.5 were about 50% higher in the morning rush-hour than the afternoon rush hour, and slightly lower (reductions of ˂30%) 50 m away from the main road, on cross-roads. The traffic model under-estimated vehicle numbers by about 4 fold, and failed to replicate the temporal variations in traffic flow, which we assume was due to an influx of traffic from outside the study region dominating traffic patterns. Our findings suggest that those working for long hours outdoors at busy roadside locations are at greater risk of suffering detrimental health effects associated with higher levels of exposure to PM2.5. Furthermore, the worse air quality in the morning rush hour means that, where possible, joggers and cyclists should avoid busy roads around these times.

Nature-based solutions (NBS) are being promoted as adaptive measures against predicted increasing hydrometeorological hazards (HMHs), such as heatwaves and floods which have already caused significant loss of life and economic damage across the globe. However, the underpinning factors such as policy framework, end-users' interests and participation for NBS design and operationalisation are yet to be established. We discuss the operationalisation and implementation processes of NBS by means of a novel concept of Open-Air Laboratories (OAL) for its wider acceptance. The design and implementation of environmentally, economically, technically and socio-culturally sustainable NBS require inter- and transdisciplinary approaches which could be achieved by fostering co-creation processes by engaging stakeholders across various sectors and levels, inspiring more effective use of skills, diverse knowledge, manpower and resources, and connecting and harmonising the adaptation aims. The OAL serves as a benchmark for NBS upscaling, replication and exploitation in policy-making process through monitoring by field measurement, evaluation by key performance indicators and building solid evidence on their short- and long-term multiple benefits in different climatic, environmental and socio-economic conditions, thereby alleviating the challenges of political resistance, financial barriers and lack of knowledge. We conclude that holistic management of HMHs by effective use of NBS can be achieved with standard compliant data for replicating and monitoring NBS in OALs, knowledge about policy silos and interaction between research communities and end-users. Further research is needed for multi-risk analysis of HMHs and inclusion of NBS into policy frameworks, adaptable at local, regional and national scales leading to modification in the prevalent guidelines related to HMHs. The findings of this work can be used for developing synergies between current policy frameworks, scientific research and practical implementation of NBS in Europe and beyond for its wider acceptance.

https://eac2021.co.uk/

Sulfur dioxide (SO2) is considered the most widespread pollutant that threatens environmental and human health. The purpose of this study is to propose a new method for evaluating the spatial variation of SO2 levels in the Metropolitan Area of Porto Alegre (MAPA). This method included use of Chi-square test to better identify the origin of SO2 sources. Additionally, results of the different methods applied allowed to analyze the temporal SO2 levels and their association with meteorological parameters. SO2 at five sampling sites (Esteio, Canoas, Charqueadas, Triunfo, and Gravataí) were measured during 2010–2015; using fluorescence SO2 automated analyzers. Results showed that Charqueadas had the highest average concentration (~ 15 μg m−3), followed by Triunfo (13 μg m−3), Esteio (6 μg m−3), Canoas (3 μg m−3), and Gravataí (2 μg m−3). Chi-square test applied to SO2, and wind direction quadrants showed significant contribution of local emission sources. Seasonal variation revealed higher SO2 levels on cold days for most of the studied sites, except for Esteio site. Day-wise variations revealed higher SO2 concentration on weekdays than weekends for Esteio and Canoas sites, indicating traffic influence especially during the rush-hours. Annual averages analysis identified an increasing trend in SO2 concentrations, implying that applied emission control systems and technological improvement of engines and fuels were not sufficient and thus points out a need for better subsidies mechanisms to pollutant control and effective emission reduction strategies that decision makers, including environmental agencies, must make priority by considering the local realities.

The study investigated inter-species variation in particulate matter (PM) accumulation, wash-off, and retention on green wall plants, with a focus on leaf characteristics. Ten broadleaf plant species were studied in an experimental green wall. Ambient PM concentrations remained relatively stable throughout the measurement period: PM1: 16.60 ± 9.97 μgm−3, PM2.5: 23.27 ± 11.88 μgm−3, and PM10: 39.59 ± 25.72 μgm−3. Leaf samples were taken before and after three rainfall events, and PM deposition was measured using Scanning Electron Microscopy (SEM). Leaf micromorphological traits, including surface roughness, hair density, and stomatal density, exhibited variability among species and leaf surfaces. Notably, I.sempervirens and H.helix had relatively high PM densities across all size fractions. The study underscored the substantial potential of green wall plants for atmospheric PM removal, with higher Wall Leaf Area Index (WLAI) species like A.maritima and T.serpyllum exhibiting increased PM accumulation at plant level. Rainfall led to significant wash-off for smaller particles, whereas larger particles exhibited lower wash-off rates. Leaf micromorphology impacted PM accumulation, although effects varied among species, and parameters such as surface roughness, stomatal density, and leaf size did not consistently affect PM deposition. The composition of deposited particles encompassed natural, vehicular, salt, and unclassified agglomerates, with minimal changes after rainfall. Air Pollution Tolerance Index (APTI) assessments revealed that I.sempervirens displayed the highest air pollution tolerance, while O.vulgare had the lowest. APTI showed a moderate positive correlation with PM deposition across all fractions. The study concluded that the interplay of macro and micromorphology in green wall plant species determines their PM removal potential. Further research is needed to identify the key leaf characteristics for optimal green wall species selection for effective PM removal. [Display omitted] •Study aimed to understand the inter-species variation in PM accumulation and wash-off.•I.sempervirens and H.helix had high PM densities across all size fractions.•Rainfall resulted in higher wash-off for smaller particles, than larger particles.•Trichome density is moderately correlated with PM1 density on leaves.•APTI showed a moderate positive correlation with PM deposition across all fractions.

The likely health and environmental implications associated with atmospheric nanoparticles have prompted considerable recent research activity. Knowledge of the characteristics of these particles has improved considerably due to an ever growing interest in the scientific community, though not yet sufficient to enable regulatory decision making on a particle number basis. This review synthesizes the existing knowledge of nanoparticles in the urban atmosphere, highlights recent advances in our understanding and discusses research priorities and emerging aspects of the subject. The article begins by describing the characteristics of the particles and in doing so treats their formation, chemical composition and number concentrations, as well as the role of removal mechanisms of various kinds. This is followed by an overview of emerging classes of nanoparticles (i.e. manufactured and bio-fuel derived), together with a brief discussion of other sources. The subsequent section provides a comprehensive review of the working principles, capabilities and limitations of the main classes of advanced instrumentation that are currently deployed to measure number and size distributions of nanoparticles in the atmosphere. A further section focuses on the dispersion modelling of nanoparticles and associated challenges. Recent toxicological and epidemiological studies are reviewed so as to highlight both current trends and the research needs relating to exposure to particles and the associated health implications. The review then addresses regulatory concerns by providing an historical perspective of recent developments together with the associated challenges involved in the control of airborne nanoparticle concentrations. The article concludes with a critical discussion of the topic areas covered.

Formaldehyde is categorized as a definitive carcinogen by the International Agency for Research on Cancer. To the best of our knowledge, no study has assessed the health risks of occupational exposure of workers in carpet manufacturing plants to formaldehyde. Therefore, this study assesses the health risks of the occupational exposure to formaldehyde of 67 male workers in carpet manufacturing plants in Iran in 2022. Exposure to formaldehyde was quantitatively determined after collecting personal exposure samples from the workers’ respiratory zone and spectrophotometric analysis based on method number 3500 of the National Institute of Occupational Safety and Health. In the next step, the carcinogenic and noncarcinogenic risks based on personal exposure to formaldehyde were evaluated. Sensitivity analyses were employed using the Monte Carlo simulation method. The mean inhalation exposure of workers to formaldehyde was 0.636 mg m−3. The inhalation cancer risk value based on the integrated risk information system for formaldehyde was 4.06×10-4 ± 3.17×10−5 (mean ± standard deviation), which exceeded the value reported by the US Environmental Protection Agency. An unacceptable carcinogenic risk level was found in 75.6% of workers. The highest mean inhalation cancer risk was 6.74×10−4 (i.e., 6.74 additional cases per 10,000 employees exposed) was found in sizing post employees. The hazard quotient of formaldehyde was 0.311±0.024. The formaldehyde concentration had a considerable effect on the health risk. The findings of this study provide valuable scientific information that supports the development of future policies to enhance the health status of employees in carpet manufacturing plants.

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models and tools that are utilised for the optimum allocation, design and efficiency evaluation of NBS for five HMRs (flooding, droughts, heatwaves, landslides, and storm surges and coastal erosion). We found that methods to assess the complex issue of NBS efficiency and cost-benefits analysis are still in the development stage and they have only been implemented through the methodologies developed for other purposes such as fluid dynamics models in micro and catchment scale contexts. Of the reviewed numerical models and tools MIKE-SHE, SWMM (for floods), ParFlow-TREES, ACRU, SIMGRO (for droughts), WRF, ENVI-met (for heatwaves), FUNWAVE-TVD, BROOK90 (for landslides), TELEMAC and ADCIRC (for storm surges) are more flexible to evaluate the performance and effectiveness of specific NBS such as wetlands, ponds, trees, parks, grass, green roof/walls, tree roots, vegetations, coral reefs, mangroves, sea grasses, oyster reefs, sea salt marshes, sandy beaches and dunes. We conclude that the models and tools that are capable of assessing the multiple benefits, particularly the performance and cost-effectiveness of NBS for HMR reduction and management are not readily available. Thus, our synthesis of modelling methods can facilitate their selection that can maximise opportunities and refute the current political hesitation of NBS deployment compared with grey solutions for HMR management but also for the provision of a wide range of social and economic co-benefits. However, there is still a need for bespoke modelling tools that can holistically assess the various components of NBS from an HMR reduction and management perspective. Such tools can facilitate impact assessment modelling under different NBS scenarios to build a solid evidence base for upscaling and replicating the implementation of NBS. [Display omitted]

Household air pollution is ranked the 9th largest Global Burden of Disease risk (Forouzanfar et al., The Lancet 2015). People, particularly urban dwellers, typically spend over 90% of their daily time indoors, where levels of air pollution often surpass those of outdoor environments. Indoor air quality (IAQ) standards and approaches for assessment and control of indoor air require measurements of pollutant concentrations and thermal comfort using conventional instruments. However, the outcomes of such measurements are usually averages over long integrated time periods, which become available after the exposure has already occurred. Moreover, conventional monitoring is generally incapable of addressing temporal and spatial heterogeneity of indoor air pollution, or providing information on peak exposures that occur when specific indoor sources are in operation. This article provides a review of the new air pollution sensing methods to determine IAQ and discusses how real-time sensing could bring a paradigm shift in controlling the concentration of key air pollutants in billions of urban houses worldwide. However, we also show that besides the opportunities, challenges still remain in terms of maturing technologies, or data mining and their interpretation. Moreover, we discuss further research and essential development needed to close gaps between what is available today and needed tomorrow. In particular, we demonstrate that awareness of IAQ risks and availability of appropriate regulation are lagging behind the technologies.

The combined application of technological intervention and natural ventilation were studied by using air purifier and window opening to investigate their effect on classroom air quality (CAQ) in two classrooms of an infant school. One classroom was located adjacent to the highway and occupied by younger students, and the other was away from the highway and occupied by older students. Four ventilation scenarios along with the use of HEPA air purifier were studied; (1) partial ventilation (single window open); (2) continuous ventilation (all windows open); (3) no ventilation (all windows closed) and air purifier in operation and (4) scheduled ventilation (windows opened during non-occupancy period) and using air purifier during occupancy period. Partial ventilation has up to 27%, 18% and 9% improvement in PM10, PM2.5 and CO2 concentration respectively in classroom B, with no improvement in classroom A. Continuous ventilation resulted in 15%, 6% and 21% reduction in PM10, PM2.5 and CO2 concentration, respectively, in near-highway classroom and no improvement in the classroom located away from highway. Using air purifier with windows closed has no effect on CAQ in near-highway classroom and 18%, 17% reduction in PM10 and PM2.5 concentration, respectively, in other classroom. Scheduled ventilation using air purifier resulted in reduction of up to 36%, 14% and 28% in PM10, PM2.5 and CO2 concentration, respectively, depending upon the classroom. No change found in PM1 concentration in both the classrooms under all ventilation scenarios. The CAQ and the effectiveness of the interventions depend on children’s activities, classroom layout and teachers’ ventilation behaviour which is affected by classroom’s proximity to highway. The CAQ and thermal comfort can be improved significantly in a more cost-efficient way by strategizing the application of technological and natural interventions in a naturally ventilated classroom.

The burning of biomass in pizza ovens can be an important source of air pollution. Fine particulate matter represents one of the most aggressive pollutants to human health, besides the potential to interfere with global radiative balance. A study in real-world condition was performed in three pizzerias in São Paulo city. Two of the pizzerias used eucalyptus timber logs and one used wooden briquettes. The results from the three pizzerias revealed high average concentrations of PM2.5: 6171.2 μg/m3 at the exit of the chimney and 68.2 μg/m3 in indoor areas. The burning of briquette revealed lower concentrations of PM2.5. BC represented approximately 20% and 30% of the PM2.5 mass concentration in indoor and at chimney exhaust, respectively. Among the trace elements, potassium, chlorine and sulphur were the most prevalent in terms of concentration. Scanning electron microscopy (SEM) analysis revealed particles with an individual and spherical morphology, i.e. the conglomeration of spherical particles, flattened particles in the formation of fibres, the overlapping of layers and the clustering of particles with sponge-like qualities. The average emission factors for PM2.5 and BC due to the burning of logs were 0.38 g/kg and 0.23 g/kg, respectively. The total emissions of PM2.5 and BC were 116.73 t/year and 70.65 t/year, respectively, in the burning of timber logs.

Evolving Anthropocene epoch wields significant influence in altering atmospheric carbon, which affects the carbon cycle, leading to climate change. Understanding the carbon stock, fate, and transport across ecosystems is essential in determining India's carbon budget, hitherto, unavailable. In this study, we have analysed the stock, source, distribution, flux, and the relationship between terrestrial and aquatic black carbon over a high-altitude mountainous area in the Western Ghats region using the data collected from September 2019 to February 2021. Soil Organic Carbon (SOC) and Black Carbon (BC) are highest in the forest region (SOC:23 ± 3 g of C/kg (dry weight (dw)), BC:6 ± 3 g/kg) and are lowest in the urban region (SOC: 13 ± 2 g of C/kg (dw), BC:2 ± 1 g/kg). SOC is labile, whereas BC is non-labile. The BC/SOC ratio represents soil lability. Topsoil BC/SOC ratios vary by land use and land cover, with urban areas having greater labile carbon pools than the forests. Dissolved BC (DBC) concentrations were most strongly correlated with bulk dissolved Organic Carbon (DOC) concentrations in midstream (R = 0.6, p 

The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.

Clean air is a fundamental necessity for human health and well-being. Anthropogenic emissions that are harmful to human health have been reduced substantially under COVID-19 lockdown. Satellite remote sensing for air pollution assessments can be highly effective in public health research because of the possibility of estimating air pollution levels over large scales. In this study, we utilized both satellite and surface measurements to estimate air pollution levels in 20 cities across the world. Google Earth Engine (GEE) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) application were used for both spatial and time-series assessment of tropospheric Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) statuses during the study period (1 February to May 11, 2019 and the corresponding period in 2020). We also measured Population-Weighted Average Concentration (PWAC) of particulate matter (PM2.5 and PM10) and NO2 using gridded population data and in-situ air pollution estimates. We estimated the economic benefit of reduced anthropogenic emissions using two valuation approaches: (1) the median externality value coefficient approach, applied for satellite data, and (2) the public health burden approach, applied for in-situ data. Satellite data have shown that ~28 tons (sum of 20 cities) of NO2 and ~184 tons (sum of 20 cities) of CO have been reduced during the study period. PM2.5, PM10, and NO2 are reduced by ~37 (μg/m3), 62 (μg/m3), and 145 (μg/m3), respectively. A total of ~1310, ~401, and ~430 premature cause-specific deaths were estimated to be avoided with the reduction of NO2, PM2.5, and PM10. The total economic benefits (Billion US$) (sum of 20 cities) of the avoided mortality are measured as ~10, ~3.1, and ~3.3 for NO2, PM2.5, and PM10, respectively. In many cases, ground monitored data was found inadequate for detailed spatial assessment. This problem can be better addressed by incorporating satellite data into the evaluation if proper quality assurance is achieved, and the data processing burden can be alleviated or even removed. Both satellite and ground-based estimates suggest the positive effect of the limited human interference on the natural environments. Further research in this direction is needed to explore this synergistic association more explicitly.

Increasing emissions from sources such as construction and burning of biomass from crop residues, roadside and municipal solid waste have led to a rapid increase in the atmospheric concentrations of fine particulate matter (≤2.5 μm; PM2.5) over many Indian cities. Analyses of their chemical profiles are important for receptor models to accurately estimate the contributions from different sources. We have developed chemical source profiles for five important pollutant sources - construction (CON), paved road dust (PRD), roadside biomass burning (RBB), solid waste burning (SWB), and crop residue burning (CPB) - during three intensive campaigns (winter, summer and post-monsoon) in and around Delhi. We obtained chemical characterisations of source profiles incorporating carbonaceous material such as organic carbon (OC) and elemental carbon (EC), water-soluble ions (F−, Cl−, NO2−, NO3−, SO42−, PO43−, Na+ and NH4+), and elements (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Br, Rb, Sr, Ba, and Pb). CON was dominated by the most abundant elements, K, Si, Fe, Al, and Ca. PRD was also dominated by crustal elements, accounting for 91% of the total analysed elements. RBB, SWB and CPB profiles were dominated by organic matter, which accounted for 94%, 86.2% and 86% of the total PM2.5, respectively. The database of PM emission profiles developed from the sources investigated can be used to assist source apportionment studies for accurate quantification of the causes of air pollution and hence assist governmental bodies in formulating relevant countermeasures. [Display omitted]

The biogenic aerosol contribution to atmospheric particulate matter (PM) mass concentration is usually neglected due to the difficulty in identifying its components, although it can be significant. In the Metropolitan Area of São Paulo (MASP)-Brazil, several studies have been performed to identify sources for PM, revealing vehicular emissions and soil re-suspension as the main identified sources. The organic fraction has been related primarily to biomass burning (BB) and fuel combustion, although there is significant presence of green areas in the city which render biogenic emissions as an additional source of organic carbon (OC). The objectives of this work are to (i) estimate the relative mass contribution of fungal spores to PM concentrations with sizes smaller than 10μm (PM10) in MASP, (ii) assess the main sources of PM10, and (iii) characterise the composition of the PM10. To achieve these objectives, we measured markers of biogenic sources and BB, during the fall-winter transition, which along with other constituents, such as ions, organic/elemental carbon, elemental composition and fungal spore concentrations, help assess the PM10 sources. We used receptor models to identify distinct source-related PM10 fractions and conversion factors to convert biomarker concentrations to fungal mass. Our results show the mean contributions of fungal aerosol to PM10 and OC mass were 2% and 8%, respectively, indicating the importance of fungal spores to the aerosol burden in the urban atmosphere. Using specific rotation factor analysis, we identified the following factors contributing to PM: soil re-suspension, biogenic aerosol, secondary inorganic aerosol, vehicular emissions and BB/isoprene-related secondary organic aerosol (I-SOA) markers. BB/I-SOA markers are the main source representing 28% of the PM10 mass, while biogenic aerosol explained a significant (11%) fraction of the PM10 mass as well. Our findings suggest that primary biogenic aerosol is an important fraction of PM10 mass, yet not considered in most studies.

Good quality ambient air is recognized as an important factor of social justice. In addition, providing access to high-capacity public transportation in big cities is known to be a good practice of social equity, as well as economic and environmental sustainability. However, the health risks associated with air pollution are not distributed equally across cities; the most vulnerable people are more exposed to ambient air as they commute to work and wait for buses or trains at the stations. The overall goal of this work was to assess the determinants of human exposure to particulate matter (PM) during commuting time spent inside bus terminals in the Metropolitan Area of Sao Paulo (MASP), in Brazil. Fine and coarse particles were collected at four bus terminals in the MASP. The concentrations of PM and its harmful constituents (black carbon and metals) were used in order to estimate potential doses and the associated health risk during the time spent at bus terminals in the MASP. Our findings show that bus commuters travelling through the bus terminal in the MASP on weekdays inhaled up to 128% higher doses of coarse particles than did those travelling outside the terminal; even on weekends, that difference was as high as 56%. Our risk assessment indicated that time spent inside a bus terminal can result in an intolerable health risk for commuters, mainly because of the Cr present in fine particles. Although bus commuters are exposed to fine particle concentrations up to 2 times lower than the worldwide average, we can affirm that inhalable particles in the MASP bus terminals pose a high carcinogenic risk to the daily users of those terminals, mainly those in the most susceptible groups, which include people with heart or lung disease, older adults and children.

Significant spatial and temporal variation in ultrafine particle (UFP;

Background: Spread of SARS-CoV2 by aerosol is considered an important mode of transmission over distances >2 m, particularly indoors. Objectives: We determined whether SARS-CoV2 could be detected in the air of enclosed/semi-enclosed public spaces. Methods and analysis: Between March 2021 and December 2021 during the easing of COVID-19 pandemic restrictions after a period of lockdown, we used total suspended and size-segregated particulate matter (PM) samplers for the detection of SARS-CoV2 in hospitals wards and waiting areas, on public transport, in a university campus and in a primary school in West London. Results: We collected 207 samples, of which 20 (9.7%) were positive for SARS-CoV2 using quantitative PCR. Positive samples were collected from hospital patient waiting areas, from hospital wards treating patients with COVID-19 using stationary samplers and from train carriages in London underground using personal samplers. Mean virus concentrations varied between 429 500 copies/m3 in the hospital emergency waiting area and the more frequent 164 000 copies/m3 found in other areas. There were more frequent positive samples from PM samplers in the PM2.5 fractions compared with PM10 and PM1. Culture on Vero cells of all collected samples gave negative results. Conclusion: During a period of partial opening during the COVID-19 pandemic in London, we detected SARS-CoV2 RNA in the air of hospital waiting areas and wards and of London Underground train carriage. More research is needed to determine the transmission potential of SARS-CoV2 detected in the air.

Human civilization is currently facing two particular challenges: population growth with a strong trend towards urbanization and climate change. The latter is now no longer seriously questioned. The primary concern is to limit anthropogenic climate change and to adapt our societies to its effects. Schools are a key part of the structure of our societies. If future generations are to take control of the manifold global problems, we have to offer our children the best possible infrastructure for their education: not only in terms of the didactic concepts, but also with regard to the climatic conditions in the school environment. Between the ages of 6 and 19, children spend up to 8 h a day in classrooms. The conditions are, however, often inacceptable and regardless of the geographic situation, all the current studies report similar problems: classrooms being too small for the high number of school children, poor ventilation concepts, considerable outdoor air pollution and strong sources of indoor air pollution. There have been discussions about a beneficial and healthy air quality in classrooms for many years now and in recent years extensive studies have been carried out worldwide. The problems have been clearly outlined on a scientific level and there are prudent and feasible concepts to improve the situation. The growing number of publications also highlights the importance of this subject. High carbon dioxide concentrations in classrooms, which indicate poor ventilation conditions, and the increasing particle matter in urban outdoor air have, in particular, been identified as primary causes of poor indoor air quality in schools. Despite this, the conditions in most schools continue to be in need of improvement. There are many reasons for this. In some cases, the local administrative bodies do not have the budgets required to address such concerns, in other cases regulations and laws stand in contradiction to the demands for better indoor air quality, and sometimes the problems are simply ignored. This review summarizes the current results and knowledge gained from the scientific literature on air quality in classrooms. Possible scenarios for the future are discussed and guideline values proposed which can serve to help authorities, government organizations and commissions improve the situation on a global level.

Outdoor air pollution was responsible for approximately 4.2 million deaths around the world in 2016, with the emissions from road vehicles being the main source of air pollution in urban areas. To fulfill the need to identify the contribution of pollutants emitted by on-road vehicles and examine the limitations of various air quality models (boundary conditions, wind behavior representations, chemical mechanisms and reactions), a systematic review of the main traffic variables used in emissions and air quality modeling was performed. The discussion of their relationships, connections, and relevance showed a consistent sequence to generate traffic data using different traffic models. A list of key traffic variables to use as input data for vehicle emissions modeling and consequently to improve the accuracy of air quality modeling was proposed. A revision over 125 published articles was realized approaching methods to integrate traffic, emissions, air quality models, and detailing how these data can improve the results generated by the air quality model. Traffic models (macroscopic, mesoscopic, and microscopic) require variables at different levels of detail, such as traffic flow, speed, fuel consumption, and fleet composition. The emissions models (static and dynamic) are the key inputs to regional air quality models, but there is a tradeoff between the accuracy in emission estimates and the level of detail in model inputs. Meteorological data also influence the results. The conclusions showed that gaps remain on consistent emissions factors, spatial and temporal distributions, allocations of emissions on grid cells, and performance of the meteorological models. The average link-based traffic parameters are a persistent limitation. The proposed key traffic variables list point to flow per vehicle type as the most important variable. There is a need for scientific efforts to integrate traffic engineering data into emissions models to improve air quality modeling results using better traffic flow representations. Uncertainties in traffic data must first be analyzed, and accordingly a guidance with an accuracy reference for distinctive applications in different regions should be proposed.

Residential solid fuel combustion has increased because of rising energy costs but little is known about the emission characteristics of unregulated pollutants such as ultrafine particles (UFPs). This review aims to characterise the emissions and chemical composition of UFPs, build an understanding of the particle number size distribution (PSD), assesses the factors affecting pollutants emission, and the efficacy of pollutants mitigation strategies. A systematic appraisal of literature suggests that the pollutants emissions from domestic solid fuel combustion are influenced by the quality and type of fuels, stove types, and combustion conditions. Low volatile matter content fuels such as smokeless fuels emit lesser PM2.5, NOX, SO2 than high volatile matter content fuels such as wood. However, CO emissions does not directly correlate with volatile matter content, but depend on air supply, combustion temperature, and fuel particle size. Majority of UFPs are emitted during the coking and flaming phases of combustion. Since UFPs have a large surface area, they adsorb significant amounts of hazardous metals and chemicals such as PAHs, As, Pb, and NO3 in addition to minor amounts of C, Ca and Fe. Emission factor of solid fuel based on the particle number concentration (PNC) can range from 0.2-2×1015#kg-1of fuel. UFP cannot be reduced by improved stoves, mineral additives, or small-scale electrostatic precipitators (ESP). In fact, improved cook stoves can increase UFP emissions by a factor of 2 compared with conventional stoves. However, they have demonstrated a 35–66% reduction in PM2.5 emissions. Using a domestic stove within a home puts occupants at risk of being exposed to significant concentrations of UFPs in a short period of time. As there are limited studies on the topic area, further research on different improved heating stoves is required to better understand their emissions of unregulated pollutants such as the UFPs.

The global spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has challenged most countries worldwide. It was quickly recognized that reduced activities (lockdowns) during the Coronavirus Disease of 2019 (COVID-19) pandemic produced major changes in air quality. Our objective was to assess the impacts of COVID-19 lockdowns on ground-level PM2.5, NO2, and O3 concentrations on a global scale. We obtained data from 34 countries, 141 cities, and 458 air monitoring stations on 5 continents (few data from Africa). On a global average basis, a 34.0% reduction in NO2 concentration and a 15.0% reduction in PM2.5 were estimated during the strict lockdown period (until April 30, 2020). Global average O3 concentration increased by 86.0% during this same period. Individual country and continent-wise comparisons have been made between lockdown and business-as-usual periods. Universally, NO2 was the pollutant most affected by the COVID-19 pandemic. These effects were likely because its emissions were from sources that were typically restricted (i.e., surface traffic and non-essential industries) by the lockdowns and its short lifetime in the atmosphere. Our results indicate that lockdown measures and resulting reduced emissions reduced exposure to most harmful pollutants and could provide global-scale health benefits. However, the increased O3 may have substantially reduced those benefits and more detailed health assessments are required to accurately quantify the health gains. At the same, these restrictions were obtained at substantial economic costs and with other health issues (depression, suicide, spousal abuse, drug overdoses, etc.). Thus, any similar reductions in air pollution would need to be obtained without these extensive economic and other consequences produced by the imposed activity reductions.

The study of ultrafine particles (those below 100 nm in diameter) is of great interest to the scientific community and policy makers due to their likely impacts on human health and the environment. Understanding the behaviour of ultrafine particles from their number concentrations and size distribution point of view in the ambient air will help to expedite the development of regulatory controls. Vegetation barriers are used in many places to reduce the pollution generated by the road traffic from reaching to the people living in urban areas, especially close to the road, where the ultrafine particles are expected to be in high concentrations. Limited information currently exist that could reveal detailed understanding about the effectiveness of near road vegetation barriers in removing concentrations of ultrafine particles. A fast response differential mobility spectrometer (DMS50) is used for the pseudo-simultaneous measurements of number and size distributions in the 5-560 nm size range. The measurements were made at four different points along the side of a busy highway. These points were at the front, middle and back of the vegetation barrier, and at a point without any vegetation; all these points were at the same height above the road level. The data was collected at 10 Hz sampling rate, with T10-90% equal to 500 milliseconds, during a weekday (7 August 2012) and a weekend (11 August 2012). Analysis of the data was performed to investigate the influence of near road vegetative barriers on the number concentration and size distributions. Further analysis will be carried out to develop understanding about the effect of wind direction on the efficiency of the vegetation barrier and an indication about the dispersion of particles as they move away from source (vehicle tailpipe) through the vegetation barriers to roadside footpath. Preliminary results based on the weekday data shows that the concentrations of particles gradually decrease while passing through the vegetation barrier. No clear trend was found from the weekend data due to winds being parallel to road and low traffic density. Detailed analysis of the data is currently underway.

Intensifying the proportion of urban green infrastructure has been considered as one of the remedies for air pollution levels in cities, yet the impact of numerous vegetation types deployed in different built environments has to be fully synthesised and quantified. This review examined published literature on neighbourhood air quality modifications by green interventions. Studies were evaluated that discussed personal exposure to local sources of air pollution under the presence of vegetation in open road and built-up street canyon environments. Further, we critically evaluated the available literature to provide a better understanding of the interactions between vegetation and surrounding built-up environments and ascertain means of reducing local air pollution exposure using green infrastructure. The net effects of vegetation in each built-up environment are also summarised and possible recommendations for the future design of green infrastructure are proposed. In a street canyon environment, high-level vegetation canopies (trees) led to a deterioration in air quality, while low-level green infrastructure (hedges) improved air quality conditions. For open road conditions, wide, low porosity and tall vegetation leads to downwind pollutant reductions while gaps and high porosity vegetation could lead to no improvement or even deteriorated air quality. The review considers that generic recommendations can be provided for vegetation barriers in open road conditions. Green walls and roofs on building envelopes can also be used as effective air pollution abatement measures. The critical evaluation of the fundamental concepts and the amalgamation of key technical features of past studies by this review could assist urban planners to design and implement green infrastructures in the built environment.

Informational interventions are important to bring positive changes in attitudes and perception among individuals. In relation to the individual’s mobility behavior, habits, attitudes, and perceptions are difficult to change. Therefore, it is vital to identify relatively soft aspects of travel behavior with a potential to reduce the negative impacts of mobility on the environment and individual health. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. A variety of rules that are part of the algorithm are derived from the transport modeling literature, where constraints and factors were examined for activity-travel decisions. Three major aspects of activity-travel behavior, such as reduced car use, cold start of car engines, and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to collected small datasets from citizens of Hasselt (Belgium), Bologna (Italy), and Guildford (UK). A significant replaceable potential for car trips within 3 km to cycling and car trips to public transport has been found. The replaceable potential of excessive cold starts and participation in non-mandatory outdoor activities were also found, to some extent, to bring positive changes in the environment. In future research, these identified potentials are reported back to individuals with their consequence as part of a mobility-based informational intervention.

Air pollution is a major environmental health problem around the world, which needs to be monitored. In recent years, a new generation of low-cost air pollution sensors has emerged. Poor or unknown data quality, resulting from the intrinsic properties of the sensor as well as the lack of a consensus on data processing methodologies for these sensors, has, among other factors, prevented widespread adoption of these sensors. To contribute to the creation of this consensus, we reviewed the available methodologies for quality control, outlier detection and gap filling and applied two outlier detection methodologies and five gap filling methodologies to a case study (consisting of an 11-month long air quality data set from a low-cost sensor). We showed that erroneous data can be detected in a fully automated way, that point and contextual outlier detection methodologies can be applied to low-cost air pollution data and yield meaningful results, and that linear interpolation has the best performance for gap filling for low-cost air pollution sensors. In conclusion, data cleaning procedures are important, and the presented methods can form part of a generalised data processing methodology for low-cost air pollution sensors.

Commuters are regularly exposed to short-term peak concentration of traffic produced nanoparticles (i.e. particles <300 nm in size). Studies indicate that these exposures pose adverse health effects (i.e. cardiovascular). This study aims to obtain particle number concentrations (PNCs) and distributions (PNDs) inside and outside a car cabin whilst driving on a road in Guildford, a typical UK town. Other objectives are to: (i) investigate the influences of particle transformation processes on particle number and size distributions in the cabin, (ii) correlate PNCs inside the cabin to those measured outside, and (iii) predict PNCs in the cabin based on those outside the cabin using a semi-empirical model. A fast response differential mobility spectrometer (DMS50) was employed in conjunction with an automatic switching system to measure PNCs and PNDs in the 5–560 nm range at multiple locations inside and outside the cabin at 10 Hz sampling rate over 10 s sequential intervals. Two separate sets of measurements were made at: (i) four seats in the car cabin during ∼700 min of driving, and (ii) two points, one the driver seat and the other near the ventilation air intake outside the cabin, during ∼500 min of driving. Results of the four-point measurements indicated that average PNCs at all for locations were nearly identical (i.e. 3.96, 3.85, 3.82 and 4.00 × 104 cm−3). The modest difference (∼0.1%) revealed a well-mixed distribution of nanoparticles in the car cabin. Similar magnitude and shapes of PNDs at all four sampling locations suggested that transformation processes (e.g. nucleation, coagulation, condensation) have minimal effect on particles in the cabin. Two-point measurements indicated that on average, PNCs inside the cabin were about 72% of those measured outside. Time scale analysis indicated that dilution was the fastest and dominant process in the cabin, governing the variations of PNCs in time. A semi-empirical model was proposed to predict PNCs inside the cabin as a function of those measured outside. Performance evaluation of the model against multiple statistical measures was within the recommended guidelines for atmospheric dispersion modelling. Trip average PNCs obtained using the model demonstrate a reasonably good correlation (i.e. R2 = 0.97) with measured values.

Noise and air pollutants share many common sources including traffic volume. Noise pollution causes annoyance and disturbs sleep and it is the second risk factor, after air pollution, to the estimated environmental burden of disease in Europe. It can also act as a proxy for some of the air pollutants, to allow building of holistic view of environmental pollution. During the pandemic and the resulting lockdowns in cities across the world, traffic volumes reduced significantly, leading to reduced pollutant concentrations and noise levels. In this work, we present an analysis of the multiple pollutants (e.g., fine particulate matter, nitrogen oxide) and noise data that are monitored continuously during the lockdown at 15-minute resolution at a school site in the UK, which is situated next to a busy road. This talk will present trends of noise and the air pollutants during the lockdown period, explore possible relationship of noise as a proxy for air pollutants; variations between pollutants and the underlining reasons explaining the temporal variations.

Three pedestrian trajectories are considered to study the influence of PM10 concentrations on children exposure, in a high-traffic street canyon. Two types of exposure were calculated: daily exposure for each wind condition and cumulative annual exposure considering all wind conditions. FLUENT was used to simulate the flow, turbulence, and PM10 dispersion in the street canyon. Our results indicate that exposure is influenced by the chosen walking trajectory and wind direction. When considering daily exposure, the highest value is achieved for the trajectory on the south side of the street, under westerly wind conditions, 13% higher than the baseline that assumes no traffic. The results indicate that a particular trajectory can be better for one specific wind direction but can represent the worst for a different wind direction. A difference of 3% to 13% higher exposure was achieved by choosing the best and worst trajectories. When computing cumulative annual exposure, trajectory on the north side of the street shows better results, 8.4% higher than the baseline value. Northerly and westerly winds result in the lowest and the highest exposure value for every studied trajectory. Careful selection of the best pedestrian paths can help reduce the exposure in busy street canyons.

The concern about air pollution in urban areas has substantially increased worldwide. One of its main components, particulate matter (PM) with aerodynamic diameter of ≤2.5 µm (PM2.5), can be inhaled and deposited in deeper regions of the respiratory system, causing adverse effects on human health, which are even more harmful to children. In this sense, the use of deterministic and stochastic models has become a key tool for predicting atmospheric behavior and, thus, providing information for decision makers to adopt preventive actions to mitigate air pollution impacts. However, stochastic models present their own strengths and weaknesses. To overcome some of disadvantages of deterministic models, there has been an increasing interest in the use of deep learning, due to its simpler implementation and its success on multiple tasks, including time series and air quality forecasting. Thus, the objective of the present study is to develop and evaluate the use of four different topologies of deep artificial neural networks (DNNs), analyzing the impact of feature augmentation in the prediction of PM2.5 concentrations by using five levels of discrete wavelet transform (DWT). The following types of deep neural networks were trained and tested on data collected from two living lab stations next to high-traffic roads in Guildford, UK: multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolutional neural network (1D-CNN) and a hybrid neural network composed of LSTM and 1D-CNN. The performance of each model in making predictions up to twenty-four hours ahead was quantitatively assessed through statistical metrics. The results show that wavelets improved the forecasting results and that discrete wavelet transform is a relevant tool to enhance the performance of DNN topologies, with special emphasis on the hybrid topology that achieved the best results among the applied models.

Fine Particulate Matter (PM₂․₅) has become a major issue in cities around the world as it adversely affects human health and the environment. This study aims to develop a deeper understanding of the impact of bicycle lanes designs on cyclist exposure to air pollution in a developing-country city. PM₂․₅ concentrations were measured along a predefined route with different bicycle lane designs in the city of Medellín, Colombia. The measurement campaign was made between October and December 2020 during peak and off-peak hours on weekdays, where a total of 29 bicycle trips were carried out. To obtain accurate measurements, we used a laser-based particle monitoring system. The study's findings reveal that the bicycle route section without dedicated bicycle lanes had the highest PM₂․₅ exposure and inhaled dose per kilometer traveled. The next highest exposure was observed in bike lanes that were separated from the road by a sidewalk, while the lowest exposure was in lanes separated by a road. Furthermore, the mean PM₂․₅ exposure for cyclists during the morning peak hours was higher (33.8 μg/m³) compared to the evening peak (16.1 μg/m³) and off-peak hours (11.1 μg/m³). The inhaled PM₂․₅ dose was three times higher during morning peak hours than during off-peak hours and twice as high during evening peak hours. These results show that segregated cycling lanes on the sidewalk can considerably lower PM₂․₅ exposure and inhaled doses for cyclists when compared to other lane designs, highlighting the significance of infrastructure development in supporting sustainable transportation and public health.

This study aims to explain the role of local emission sources to PM2.5 mass concentration in a tropical coastal-urban area, highly influenced by industrial and urban emissions, located in the Southeast of Brazil. The Integrated Source Apportionment Method (ISAM) tool was coupled with the chemistry and transport Community Multiscale Air Quality (CMAQ) model (CMAQ-ISAM) to quantify the contribution of ten emission sectors of PM2.5. The simulations were performed over five months between spring 2019 and summer 2020 using a local inventory, which was processed by the Sparse Matrix Operator Kernel Emission (SMOKE). The meteorological fields were provided by the Weather Research and Forecasting (WRF-Urban) model. The boundary and initial conditions to the CMAQ-ISAM were performed by the GEOS-Chem model. The simulations results show that the road dust resuspension (36%) and point (17%) emissions sources were the major contributors to PM2.5 mass in the Metropolitan Region of Vitória (MRV). The boundary conditions (BCON), representing the transport contribution from sources outside the domain, were also a dominant contributor in the MRV (20% on average). Furthermore, the primary atmospheric pollutants emitted by the point (14%) and shipping (7%) sectors in the MRV also affected the cities located in the south region of the domain, strengthened by the wind fields that mostly come from the northeast direction.

At present, there is insufficient data to understand the processes driving emissions and fluxes of greenhouse gases (GHGs) from tropical peatlands in Southeast Asia (SEA). In this review, we discuss fundamental factors controlling emissions of major GHGs (CO₂, CH₄, and N₂O) from tropical peatlands and their contribution to global climate change. Classifying peatlands in tropical and subtropical regions can aid in understanding their emission characteristics. The applicability of existing GHG emission factors to land use categories in SEA is discussed. We find that rewetting peatland can increase CH₄ emissions, and therefore more studies are needed to establish whether peatlands act as a net sink or net sources of GHGs. Few studies have investigated the effectiveness of liming toward reducing peat soil acidity. The review also finds that there is limited data on CO₂ concentrations in drainage and wildfire areas, N₂O fluxes in agriculture areas, and the impact and reduction of CH₄ in tropical peatlands. Addressing these research gaps could support the development of a framework for GHG emission measurements and abatement in tropical peatlands.

A series of experiments was undertaken on an intercity train carriage aimed at providing a “proof of concept” for three methods in improving our understanding of airflow behaviour and the accompanied dispersion of exhaled droplets. The methods used included the following: measuring CO2 concentrations as a proxy for exhaled breath, measuring the concentrations of different size fractions of aerosol particles released from a nebuliser, and visualising the flow patterns at cross-sections of the carriage by using a fog machine and lasers. Each experiment succeeded in providing practical insights into the risk of airborne transmission. For example, it was shown that the carriage is not well mixed over its length, however, it is likely to be well mixed along its height and width. A discussion of the suitability of the fresh air supply rates on UK train carriages is also provided, drawing on the CO2 concentrations measured during these experiments,

A significant fraction of daily personal exposure to air pollutants occurs during commuting in transport microenvironments (TMEs). We carried out systematic mobile monitoring on a pre-defined route to assess personal exposure levels of particulate matter (PM) in four TMEs (bus, car, cycle and walk). Measurements were made during morning peak (MP), afternoon off-peak (OP) and evening peak (EP) hours in a typical UK town, Guildford. The objectives were to quantify the real-time exposure to fine and coarse particles, identify the factors influencing their spatiotemporal variation and estimate the respiratory deposition doses (RDD). The mean PM10 concentrations were 90±63, 23±9, 14±17, and 63±76 μg m-3 for bus, car, cycle and walk modes, respectively. The average ratios of PM2.5/PM10 were 0.32, 0.90, 0.67, and 0.36 for bus, car, cycle, and car journeys, respectively. The mean concentrations of coarse particles (PM2.5-10) followed the trend: bus >walk >cycle >car. In contrast, mean concentrations of submicron (PM1) and fine particles (PM2.5) were usually high in the car while lowest for cyclists. RDD depend on the physical activity, particle size distribution and thus deposited fraction are not always proportional to the ambient concentration. RDD for coarse particles was largest for the walk mode (56±14 μg h-1), followed by buses (31±2 μg h-1), cycle (12±3 μg h-1) and cars (1.2±0.3 μg h-1). The corresponding RDD of fine particles were comparable for both the walk (5.5±0.3 μg h-1) and cycle (5.1±1.2 μg h-1), followed by bus (4.1±0.7 μg h-1) and car (2.0±0.2 μg h-1). Car mode experienced both the least concentrations and RDD for coarse particles. It also had the lowest RDD for fine particles despite high concentrations. Physical activity of car commuters is modest compared with walking and cycling, which makes the rank ordering of RDD different than those of exposure concentrations. Hence the management of commuting exposures should consider potential dose and not just exposure concentration for curtailing adverse health effects related to commuting. RDD for pedestrian and cycle modes were not the lowest among the measured modes but opportunities such an increased distance between the heavily trafficked roadways and pedestrians/cyclists should be considered in urban planning to reduce potential doses.

Abstract Estimation of zone of influences (ZoI) at signalised traffic intersections (TI) is important to accurately model particle number concentrations (PNCs) and their exposure to public at emission hotspot locations. However, estimates of ZoI for PNCs at different types of TIs are barely known. We carried out mobile measurements inside the car cabin with windows fully open for size–resolved PNCs in the 5–560 nm range on a 6 km long busy round route that had 10 TIs. These included four–way TIs without built–up area (TI4w-nb), four–way TIs with built–up area (TI4w-wb), three–way TIs without built–up area (TI3w-nb) and three–way TIs with built–up area (TI3w-wb). Mobile measurements were made with a fast response differential mobility spectrometer (DMS50). Driving speed and position of the car were recorded every second using a global positioning system (GPS). Positive matrix factorisation (PMF) modelling was applied on the data to quantify the contribution of PNCs released during deceleration, creep–idling, acceleration and cruising to total PNCs at the TIs. The objectives were to address the following questions: (i) how does ZoI vary at different types of TIs in stop– and go–driving conditions?, (ii) what is the effect of different driving conditions on ZoI of a TI?, (iii) how realistically can the PNC profiles be generalised within a ZoI of a TI?, and (iv) what is the share of emissions during different driving conditions towards the total PNCs at a TI? Average length of ZoI in longitudinal direction and along the road was found to be the highest (148 m; 89 to −59 m from the centre of a TI) at a TI3w-wb, followed by TI4w-nb (129 m; 79 to −42 m), TI3w-nb (86 m; 71 to −15 m) and TI4w-wb (79 m; 46 to −33 m) in stop– and go–driving conditions. During multiple stopping driving conditions when a vehicle stops at a TI more than once in a signal cycle due to oversaturation of vehicles, average length of ZoI increased by 55, 22 and 21% at TI4w-nb, TI3w-nb and TI3w-wb, respectively, compared with stop– and go–driving conditions. Within average length of ZoI in stop– and go– driving conditions, PNCs followed a three degree polynomial form at all TIs. Dimensional analysis suggested that coefficients of polynomial equations at both four–way and three–way TIs were mainly influenced by delay time, wind speed and particle number flux. The PMF analysis suggested that deceleration contributed the most to total PNCs at all TIs, except TI4w-wb. Findings of this study are a step forward to understand the contribution of different driving conditions towards the total PNCs and their exposure at the TIs.

This study investigates and proposes emission factors (EFs) and models for vehicle-induced exhaust (VEX) and fugitive (VfPM) particulate matter emissions representative of areas with arid climates. Particle number (PNC) and mass (PMC) concentrations and their integrated samples were collected for a period of three months for both PM10 and PM2.5 next to a trafficked road in the city of Doha, Qatar. Using Positive Matrix Factorization (PMF) on the elemental data of the samples, six distinct PM sources were identified: traffic exhaust, dust resuspension, fresh and aged sea salt, secondary aerosols, and fuel oil/shipping. Dispersion modelling and regression analysis were combined to derive EFs (linear analysis) and models (non-linear analysis) for the total traffic fleet (heavy and light duty). The estimated EFs were between 620 and 730 mg VKT−1 (VKT; Vehicle Kilometer Travelled) (adj. R2 ~ 0.84) and between 1080 and 1410 mg VKT−1 (adj. R2 ~ 0.70) for VEX and VfPM, respectively. The integration of field measurements, chemical characterization, and dispersion modelling presented herein is one of the first similar studies conducted in the wider region, identifies the importance of fugitive PM (fPM), and marks the need for further studies to improve emissions modelling of VfPM in arid desert climates.

18th International conference on Modelling, Monitoring and Management of Air Pollution, 21-23 June 2010 Kos, Greece

Based on the Final Operational Global Analysis (FNL) data from the National Centers for Environmental Prediction and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts, the all-sky brightness temperatures of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A satellite (FY-4A) are simulated, which are then used to investigate Typhoon In-Fa (2021). The results show that the all-sky simulations based on ERA5 and FNL for FY-4A GIIRS channels 27 (716.25 cm(-1)), 90 (755.625 cm(-1)), and 417 (960 cm(-1)) can reproduce well the structure and intensity of Typhoon In-Fa. But the FNL simulations overestimate the typhoon intensity with more high ice clouds around the typhoon center. Fourier analysis of Typhoon In-Fa at severe tropical storm (STS) and typhoon (TY) stages is conducted. The results reveal that the dominant features of Typhoon In-Fa are primarily large-scale, with a relatively small proportion of observations and simulations dedicated to small-scale features at the STS stage. However, the proportion of large-scale features decreases while the amount of small-scale information increases during the TY stage. The purpose of this study is to assess the performance of FY-4A GIIRS all-sky simulations based on the ERA5 and FNL data, as well as to prepare for future all-sky data assimilations.

Aerodyne Research, Inc., Billerica, Massachusetts, USA

Air quality is directly associated with the health of society. So, it becomes essential to forecast air pollution, which assumes an imperative part in air pollution warnings and control. A time-series simulation approach was adapted for the forecasting of monthly mean ambient air pollutants (PM2.5, O3, NO2) concentration and Aerosol Optical Depth (AOD) at an urban traffic site (Mathura Road, CSIR-CRRI) in New Delhi, India. Satellite-based aerosol loading (AOD550) retrieved from the Terra MODIS (Collection 6) enhanced Deep Blue (DB) algorithm was used for further analysis. The analysis considered the average monthly mean concentration of air pollutants and AOD between 2012-2017 and, simulates the concentrations of PM2.5, O3, NO2, and AOD for the same period and then forecasts air quality for the years 2020-2023. The forecasted results were validated with 24 months of in-situ and satellite data from 2018-to and 2019. In the year 2020, observed and simulated results are in lower agreement due to the shutdown of anthropogenic activities to combat pandemic situations. Otherwise, modeled and forecasted results are in good harmony with the in-situ and satellite observations. The results also signify that the time series Autoregressive Integrated Moving Average (ARIMA) modeling approach can be an effective and simple tool for air pollution simulation and future forecast. The results are evocative concerning the forecast of near future aerosol loading information and will also be profound to address the problems.

The present study focuses on the middle atmospheric ozone variability using 14 (2002-2015) years of Sounding of the Atmosphere using Broadband Emission Radiometry onboard Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite observations over the mid-latitude regions of northern and southern hemispheres. It is noted that ozone buildup starts late winter, and peaks during the springtime and gradually decreases in summer to autumn transitional period in both the hemispheres. The time series of ozone indicates the dominant annual and semi-annual oscillations in the middle atmosphere. The annual oscillation (AO) is found to be dominant over both the hemispheres, while the semi-annual oscillation (SAO) peaks at two different altitude regions: 30-60 km and 80-100 km. Further, the amplitude of AO is much significant than SSAO and MSAO. It is also noted another significant oscillation that peaks at 4 months in the altitude range 60-80 km. The strength of these oscillations at different sites is studied by comparing it with the zonal mean spectrum to assess the longitudinal asymmetry. It is found that the longitudinal asymmetry is more significant in the northern hemisphere than the southern hemisphere. This can be attributed to the differences in the land (elevated topographies in the northern hemisphere) and primarily ocean (in southern hemisphere) contrast that further contributes to the differences in the strength of the vertically propagating planetary-scale waves modulating the middle atmospheric ozone.

Selecting appropriate indicators of NBS performance and impact can be challenging, and is context-dependent. In this chapter, we present case studies from a variety of NBS demonstrations across Europe and Asia that illustrate the application of the NBS indicators and methods presented in Chapter 4 and thoroughly described in Evaluating the Impact of Nature-Based Solutions: Appendix of Methods. Each case study presents a brief NBS description, reasons for the selection of specific indicators for that particular NBS and a brief overview of the ways the indicators are applied and/or monitored. The case studies describe the stakeholders involved in co-design and co-monitoring of NBS and discuss the barriers and lessons learned during or after the process. Each case study provides key references for further reading. The case studies in this chapter focus on the selection of recommended indicators for NBS performance and impact, which are generally of primary importance when creating NBS monitoring and evaluation plans. The case studies further demonstrate how and why additional indicators can be selected to reflect particular objectives of projects and local challenges.

Urban pedestrian-level air quality is a result of an interplay between turbulent dispersion conditions, background concentrations and heterogeneous local emissions of air pollutant and their transformation processes. Still, the complexity of these interactions cannot be resolved by the commonly used air quality models. By embedding the sectional aerosol module SALSA to the large-eddy simulation model PALM, a novel, high-resolution, urban aerosol modelling framework has been developed. The first model evaluation study on the vertical variation of aerosol number concentration and size distribution in a simple street canyon without vegetation in Cambridge, UK, shows excellent agreement with measurements. Dispersion conditions and local emissions govern the pedestrian-level aerosol number concentrations. Out of different aerosol processes, dry deposition is shown to decrease the total number concentration by over 20%, while condensation and dissolutional increase the total mass by over 10%. Following the model development, the application of PALM can be extended to local- and neighbourhood-scale air pollution and aerosol studies that require a detailed solution of the ambient flow field.

City dwellers are regularly exposed to nanoparticle (i.e. particles < 300 nm in diameter), emitted by fossil fuelled vehicles, whilst commuting by transport modes such as taxis and buses. Exposure to these nanoparticles can lead to significant adverse effects on human health. This study aims to investigate spatial distribution of particle number concentrations (PNCs) and distributions (PNDs) in and outside a car cabin during driving. Possible influences of particle transformation processes on PNC and PNDs in the car cabin are also investigated. Another objective is to predict the PNCs in the car cabin using those measured outside. Measurements of particles in the 5-560 nm size range were conducted using a fast response differential mobility spectrometer (DMS50) in conjunction with an automated switching system. The DMS50 was used to measure size-resolved sequential distributions at: (i) four seats in the car cabin during about 700 minutes of driving, and (ii) two points at the driver's seat, inside and the front bonnet outside the cabin, during about 500 minutes of driving. The emission penetration and spatial distribution in the car cabin through (i) the ventilation system (Vent), and (ii) door/window sealing (CG) was simulated by means of three-dimensional computational fluid dynamics (CFD) using the Fluent code. Standard k-ε turbulence model was employed to simulate turbulence flow in the cabin. Vent/CG emission ratio was altered for the two different scenarios (0.9/0.1 and 0.7/0.3), indicating; (i) no filter fitted Vent and high vehicle sealing efficiency, (ii) filter fitted Vent and reduced sealing efficiency. Four-point measurements indicated that the average PNCs at the front seats (3.96 and 3.85 × 104 cm-3) were almost identical to those found at the rear seats (3.82 and 4.00 × 104 cm-3). The very small differences (∼0.1%) suggest that the car cabin is very close to a well-mixed microenvironment. Two-point measurements revealed that the ratio of average PNCs in (2.72 ± 1.03 × 104 cm-3) and outside the car cabin (3.75 ± 1.62 × 104 cm-3) was about 0.72. A semi-empirical box mode model was introduced to predict PNCs in the car cabin as a function of those measured outside and cabin air exchange rate. Performance evaluation of the box model against statistical measures was within the recommended guidelines for urban air quality modelling. Overall, PNCs calculated by the model demonstrate a satisfactory correlation with the measured values. CFD simulations indicate that away from the Vent, emission is dispersed almost uniformly in the car cabin. Vent / CG ratios indicated that despite changes of emission filtering into the cabin, the dispersion characteristics remained almost identical at passengers' breathing height (i.e. 1.2 m from the floor).

There is substantial evidence that air pollution exposure is associated with asthma prevalence that affects millions of people worldwide. Air pollutant exposure can be determined using dispersion models and refined with receptor models. Dispersion models offer the advantage of giving spatially distributed outdoor pollutants concentration while the receptor models offer the source apportionment of specific chemical species. However, the use of dispersion and/or receptor models in asthma research requires a multidisciplinary approach, involving experts on air quality and respiratory diseases. Here, we provide a literature review on the role of dispersion and receptor models in air pollution and asthma research, their limitations, gaps and the way forward. We found that the methodologies used to incorporate atmospheric dispersion and receptor models in human health studies may vary considerably, and several of the studies overlook features such as indoor air pollution, model validation and subject pathway between indoor spaces. Studies also show contrasting results of relative risk or odds ratio for a health outcome, even using similar methodologies. Dispersion models are mostly used to estimate air pollution levels outside the subject's home, school or workplace; however, very few studies addressed the subject's routines or indoor/outdoor relationships. Conversely, receptor models are employed in regions where asthma incidence/prevalence is high or where a dispersion model has been previously used for this assessment. Road traffic (vehicle exhaust) and NOx are found to be the most targeted source and pollutant, respectively. Other key findings were the absence of a standard indicator, shortage of studies addressing VOC and UFP, and the shift toward chemical speciation of exposure.

Car microenvironments significantly contribute to the daily pollution exposure of commuters, yet health and socioeconomic studies focused on in-car exposure are rare. This study aims to assess the relationship between air pollution levels and socioeconomic indicators (fuel prices, city-specific GDP, road density, the value of statistical life (VSL), health burden and economic losses resulting from exposure to fine particulate matter ≤2.5µm; PM2.5) during car journeys in ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Data collected by portable laser particle counters were used to develop a proxy of car-user exposure profiles. Hotspots on all city routes displayed higher PM2.5 concentrations and disproportionately high inhaled doses. For instance, the time spent at the hotspots in Guangzhou and Addis Ababa was 26% and 28% of total trip time, but corresponded to 54% and 56%, respectively, of the total PM2.5 inhaled dose. With the exception of Guangzhou, all the cities showed a decrease in per cent length of hotspots with an increase in GDP and VSL. Exposure levels were independent of fuel prices in most cities. The largest health burden related to in-car PM2.5 exposure was estimated for Dar-es-Salam (81.6±39.3 μg m-3), Blantyre (82.9±44.0) and Dhaka (62.3±32.0) with deaths per 100,000 of the car commuting population per year of 2.46 (2.28-2.63), 1.11 (0.97-1.26) and 1.10 (1.05-1.15), respectively. However, the modest health burden of 0.07 (0.06-0.08), 0.10 (0.09-0.12) and 0.02 (0.02-0.03) deaths per 100,000 of the car commuting population per year were estimated for Medellin (23±13.7 μg m-3), São Paulo (25.6±11.7) and Sulaymaniyah (22.4±15.0), respectively. Lower GDP was found to be associated with higher economic losses due to health burdens caused by air pollution in most cities, indicating a socioeconomic discrepancy. This assessment of health and socioeconomic parameters associated with in-car PM2.5 exposure highlights the importance of implementing plausible solutions to make a positive impact on peoples’ lives in these cities.

Dust storms are a common phenomenon that occurs in many dry and arid areas, demonstrates very high levels of particulate matter (PM), can spread significantly further than its origin, affects both outdoor and indoor air quality, and can cause serious health problems although it is a low frequency event. Focus of this study is the prediction of PM (PM₂.₅ and PM₁₀) infiltration at typical commercial and office building environments during severe dust storms. Therefore, a two-month field campaign was conducted to capture such an event in Doha, Qatar, and a modelling methodology is proposed based on the one-way coupling of a multi-zone and a computational fluid dynamics software. The predicted levels are in fair agreement with the measurements for both the dust storm and typical days, attributed to the accurate estimation of the external wind pressure and representation of the building envelope. The agreement further improves when the efficiency of the ventilation filters is estimated, from the measuremetns, rather than being extracted from specification sheets. Finally, predictions are found to conform with physical reality and to offer useful insights into PM building infiltration during dust storm events when cross examined with measurements.

Low-cost sensors (LCS) for measuring air pollution are increasingly being deployed in mobile applications but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and higher-quality instruments? Can data from LCS be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions we deployed low-cost PM 2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM 2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements, and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with corrected DustTrak data. In general, corrections developed using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM 2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements 1 agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM 2.5 are appropriate use-cases for mobile LCS data.

Ambient air quality is the most important environmental factor affecting human health, estimated by the WHO to be responsible for 4.2 million deaths annually. Having timely estimates for air quality is critical for implementing public policies that can limit anthropogenic emissions, reduce human exposure and allow for preparation and interventions in the health sector. In Brazil, wildfires constitute an important source of particulate matter emission, particularly in the country’s northern and midwestern regions, areas that are under-served in terms of air quality monitoring infrastructure. In the absence of regulatory-grade monitoring networks, low-cost sensors offer a viable alternative for generating real-time, publicly available estimates of pollutant concentrations. Here, we examine data from two low-cost sensors deployed in Brasília, in the Federal District of Brazil, during the 2022 wildfire season and use NOAA’s HYSPLIT model to investigate the origin of a particulate matter peak detected by the sensors. There was high agreeability of the data from the two sensors, with the raw values showing that daily average PM2.5 concentrations reached peak values of 46 µg/m3 and 43 µg/m3 at the school and park sites, respectively. This study demonstrates the value of low-cost sensors and their possible application in real-time scenarios for environmental health surveillance purposes.

This study presents the pilot-scale production of highly efficient real respiratory masks enhanced by bacterial cellulose nanofibers (BCNFs). The BCNFs suspension was deposited onto tissue paper substrates using fog spray technique with three BCNFs grammage levels of 0.5, 1, and 2 g/m2, followed by freeze drying. Also, two continuous and batch welding processes have been used to construct the core structure of the masks. Field emission scanning electron microscopy (FE-SEM) confirmed the uniform distribution and size of fog-sprayed BCNFs and their pore networks. With increase in BCNFs grammage, the adsorption efficiency of masks increased in both continuous and batch production methods. The mask produced through batch processing showed the highest efficiency of 99.2 % (N99) for the particulate matter of 0.3 μm, while the maximum corresponding efficiency value in continuous processing was 95.4 % (N95). The pressure drops of the masks increased with the increase in BCNFs grammage in both methods. The maximum pressure drops of N95 and N99 masks obtained were 112 ± 10 Pa and 128 ± 8 Pa, respectively. Notably, the filtration efficacy of masks was preserved when subjected to relative humidity fluctuations ranging from 30 % to 70 %. The successful findings of this study offer significant promise for future air filtration applications.

Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM1, PM2.5, PM10), gaseous pollutants (NO2, O3 and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021 when, owing to new year celebration fireworks, the daily average PM2.5 was around three-times the World Health Organisation limit. PM2.5 and NO2 exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM2.5 and NO2, respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO2 dataset that performed moderately (R2, 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised.

A naturally-ventilated operational classroom was instrumented at 18 locations to assess spatial variations of classroom air pollution (CRAP), thermal comfort and ventilation indicators under 10 different scenarios (base scenario without air purifier (AP); three single AP scenarios; three scenarios with two APs at same locations; three scenarios with two APs at different locations). Unlike PM2.5, monitored PM10 and CO2 concentrations followed diurnal occupancy profile. Highest vertical variation (38%) in CO2 was at the classroom entry zone at 40–300 cm height. CO2 increased until 225 cm before stratifying further. PM10 increased to highest levels at children sitting height (100 cm) before decreasing to adult breathing height (150 cm). Highest horizontal variations in CO2 (PM10) were 29% (22%) at 40 cm height between the entry and occupied zones. Teachers' exposure to CO2 (PM10) in breathing zone varied by up to 6% (3%); the corresponding variations across monitored locations were up to 14% (19%). Teachers' exposure to CO2 was up to 13% higher than that of children and 18% lower for PM10. Traffic emissions (PM2.5 and NOx), secondary pollutants (VOCs and O3), thermal comfort parameters and noise level in the classroom varied insignificantly among scenarios. PM10 reduction was not doubled by using two air purifiers, which were most effective when placed within the highest PM concentration zone. Cross-comparisons of scenarios showed: use of AP reduced classroom's spatial average PM10 up to 14%; PM10 was reduced by increasing the AP's filtration capacity; and AP had insignificant impact on spatial average CO2. PM10 showed a maximum reduction of 46% (teacher zone), 62% (occupied zone) and 50% (entry zone) at children's breathing height, depending on usage scenario. This study produced high-resolution data for validating the detailed numerical models for classrooms and informing decision-making on AP's placement to minimise children's exposure to CRAP and re-breathed CO2.

The World Health Organization estimates 3.7 million deaths in 2012 in low- and middle-income Asian countries due to outdoor air pollution. However, these estimates do not account for the higher exposures of specific particulate matter (PM) components – including fine particles (PM2.5), ultrafine particles (UFP) and black carbon (BC) – typical of transport microenvironments (TMEs). With the rapidly growing number of on-road vehicles in Asia, human exposure to PM is an increasing concern. The aim of this review article is to comprehensively assess the PM2.5, UFP, and BC related studies in Asian TMEs to understand the extent of exposure, the underlying factors leading to such exposure, and how Asian exposures compare to those found in Europe and the United States of America (USA). Pollutants considered and their health impacts are identified, along with the key factors that influence personal exposure in TMEs. We also characterised the human exposure to PM2.5, UFP, and BC in TMEs (walk, cycle, car, and bus) in cities of Asia, Europe, and the USA. Instrumentation and measurement methods, exposure modeling techniques, and regulation are reviewed for PM2.5, UFP, and BC. Relatively few studies have been carried out in urban Asian TMEs (i.e., walk, cycle, car, and bus) where PM2.5, UFP, and BC had generally higher concentrations compared to Europe and USA. Based on available data, PM2.5 concentrations while walking were 1.6 and 1.2 times higher in Asia (average 42 μg m−3) compared to Europe (26 μg m−3) and the USA (35 μg m−3), respectively. Likewise, average PM2.5 concentrations in car (74 μg m−3) and bus (76 μg m−3) modes in Asia were approximately two to three times higher than in Europe and the USA. UFP exposures in Asia were twice as high for pedestrians and up to ∼9-times as high in cars than in Europe or the USA. Asian pedestrians were exposed to ∼7-times higher BC concentrations compared with pedestrians in the USA. Stochastic population-based models have yet to be applied widely in Asia but can be used to quantify inter-individual and inter-regional variability in exposures and to assess the contribution of TMEs to total exposures for multiple pollutants. The review also highlights specific gaps in the data set that need to be filled by future research as UFP and BC studies were rare as were studies of pedestrian and cyclist exposure in Asian TMEs.

Abstract Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, while addressing the major challenges for their effective implementation.

In-pram babies are more susceptible to air pollution effects, yet studies assessing their exposure are limited. We measured size-resolved particle mass (PMC; 0.25-32 μm) and number (PNC; 0.2–1 μm) concentrations on a 2.7 km route. The instruments were placed inside a baby pram. The route passed through 4 traffic intersections (TIs) and a bus stand. A total of ~87 km road length was covered through 64 trips, made during school drop-in (morning) and pick-up (afternoon) hours. The objectives were to assess PMC and PNC exposure to in-pram babies at different route segments, understand their physicochemical characteristics and exposure differences between in-pram babies and adults carrying them. Over 5-fold variability (14.1–78.2 μg m-3) was observed in PMCs. Small-sized particles, including ultrafine particles, were always higher by 66% (PM1), 29% (PM2.5) and 31% (PNC) during the morning than afternoon. Coarse particles (PM2.5-10) showed an opposite trend with 70% higher concentration during afternoon than morning. TIs emerged as pollution hotspots for all the particle types. For example, PM2.5, PM2.5-10 and PNCs during the morning (afternoon) at TIs were 7 (10)%, 19 (10)% and 68 (62)% higher, respectively, compared with the rest of the route. Bus stand was also a section of enhanced exposure to PNC and PM2.5, although not so much for PM2.5-10. EDX analyses revealed Cl, Na and Fe as dominant elements. Road salt might be a source of NaCl due to de-icing during the measurements while Fe contributed by non-exhaust emissions from brake abrasion. The respiratory deposition rates imitated the trend of PMC, with higher doses of coarse and fine particles during the afternoon and morning runs, respectively. Special protection measures during conveyance of in-pram babies, especially at pollution hotspots such as traffic intersections and bus stands, could help to limit their exposure.

This paper explores the relationships between commuting times, job accessibility, and commuting satisfaction based on a large-scale survey applied in the Greater London Area (GLA), the municipality of São Paulo (MSP) and the Dutch Randstad (NLR). Potential accessibility to jobs is estimated under 3 different scenarios: reported actual commuting times (ACT), ideal commuting times (ICT), and maximum willingness to commute (MCT). Additionally, Binary Logistic Regression models, estimated using generalized linear modeling (GLM), are performed to assess the impact of these temporal preferences on the likelihood of being satisfied with commuting. As expected, ideal and maximum commuting preferences strongly impact the volume and spatial distribution of the measured accessibility to jobs. In the selected case studies, estimated ICT-based job accessibility significantly decreases total measured accessibility (60 to 100 percent), with those living in the lowest accessibility zones impacted the most. Furthermore, although specific results varied between regions, the overall findings show an association between ACT and satisfaction. Additionally, commuting mode was found to be a strong predictor of travel satisfaction. Those actively traveling in all three metropolitan regions tend to be more satisfied with their commutes. Potential job accessibility is found to be only weakly associated with travel satisfaction.

Particulate matter (PM) is one of the primary pollutants produced from surface mining operations. Health related studies indicate a strong association of airborne PM with adverse impacts such as restricted airways, reduced lung capacity, reduced lung function, increased cardiovascular disease, pneumoconiosis, cancer, and neurotoxic effects. A review of the existing studies to estimate the emission of PM from surface mining indicates empirical relationships among a wide range of parameters including silt content and moisture content of the PM, vehicle speed, drop height, weight of the vehicle, size of loader, area of the exposed surface, frequency of loading and unloading, number of dry days. Mitigation strategies are needed to determine the PM exposure level to human health inside opencast mines where production based operations take place. Synthesis of available studies suggests that while the empirical relationships explain the emission estimates, there is yet no established theoretical basis to explain the movement of fine particles inside the mine. A few recent studies on modeling PM concentration profile across the benches are reviewed. It is felt that there is a need of detailed studies for assessment of fate of the PM from mining operations for better understanding of its health impact on miners and people around the mining sites and to improve the local air quality. To this effect, need of studies focusing on wind field modeling and vertical transport of PM in surface mines is emphasized. Particle size analysis and PM inhalation model can help in better understanding the health impact of PM emitted from different surface mining activities.

Atmospheric quasi-ultrafine particles (qUFP; PM

The water quality analysis has been an intriguing subject in the recent years because of the issues related to water resources. The work presents the application of the genetic algorithm to water samples containing contaminants for the optimal selection of electrode and the corresponding frequency for the better classification of various contaminants. We have used 24 water samples containing 8 different heavy metal ions (Cd, Co, Zn, Ni, Cu, Cr, Ar and Pb) for our experiment. The electrodes used were Gold, Platinum, Glassy Carbon and Silver Nanoparticle electrode. The impedance values of these four electrodes are recorded as Single-Electrode Multi-Frequency (SEMF), Single-Frequency Multi-Electrode (SFME) and Multi-Electrode Multi-Frequency (MEMF). The impedance values are subjected to Principal Component Analysis. Further, the optimal classification of various metal ions present in the water samples is done using Genetic Algorithm and this is validated by the application of Davis Bouldin index. The results show that DBI value may be enhanced by choosing electrode with optimum frequency.

The transportation of ambient particulate matter (PM) from outdoor air into the inlet of a mechanical building ventilation system is poorly understood. No studies have examined the effect commonly used commercial air handling unit (AHU) inlet designs have upon the migration of PM from the ambient environment into the building ventilation system, and implications of this on energy consumption and indoor air quality (IAQ). Through the numerical analysis of commercial AHU inlets, the differences in concentration of PM in ambient air and that within AHUs was determined, more commonly referred to as Aspiration Efficiency (AE %). A 20-50% difference in particle concentrations between ambient air and the in-AHU concentration was observed between forward and rear-facing AHUs relative to ambient wind direction and speed, and at the maximum ventilation flow rate. Furthermore, a decrease in the ventilation flow rates resulted in a significant reduction in PM concentrations entering the rear-facing AHU. Increasing the Stoke number led to lower AE as a continuous decrease was observed for both rear-facing inlets. The findings of this paper show that AHU 2 inlet design has significant implications on IAQ and building energy consumption, and scope exists to design these inlets to impact both aspects positively.

Global climate change will go along with changes in climatic conditions such as temperature, water balance and direct sunshine. As this will lead to a worsening of air quality, we have to look intensively at the matter of how we can create healthy living environments in areas with extreme air conditions. In order to serve primarily the efficient use of energy and the optimization of ventilation technology connected with new ways of constructing buildings (low energy and passive houses), “smart home technology” was introduced also in private homes. Facing demographic change, higher demands on a comfortable life and advancing mechanization of everyday life, sensor technology is increasingly implemented in order to create acceptable and improved living conditions. Thus, the term “smart home” is also linked today with the networking of home automation systems, home appliances and communications and entertainment electronics. In general, low-energy houses, which as a rule have a system of artificial ventilation, require special technologies to achieve good indoor air quality. Using modern sensor technology, it is possible to monitor not only the climatic parameters, but also the concentrations of air polluting substances, such e.g. carbon dioxide, sum parameters of volatile organic compounds (VOCs) and particles and record them in Home Energy Management Systems (HEMS). Indoor air quality and air hygiene are taken now as important aspects of smart home technology. Nevertheless, living in a smart home often puts demands on the occupants who are required to change some of their living habits. Due to the significant impact of smart home technology on everyday life in the near future, the authors have summarized the actual state-of-the-art of housing technology on indoor air quality, individual thermal comfort and living behaviour for the temperate climate zone. The main findings will be presented at the conference.

It is of great significance to control the air quality of underground metro stations, especially considering their poorly ventilated environments. Existing literature mainly focuses on the removal and control of underground pollutants and seldom pay attention to the ingress effects of atmospheric pollutants (e.g., from the burning of fossil fuels, exhaust gas of vehicles, etc.) diffusion through the entrances/exits. In this study, we aim to study the reducing effects of integrated air curtain and exhaust systems on atmospheric airborne particles entering into the metro stations. Validated turbulent models were adopted for numerical simulations. The associated effects of air curtain velocity, exhaust velocity, and atmospheric wind speed were investigated. Four different installation locations of the air curtain were considered. Subsequently, monitoring, Artificial Neural Network, and air purification methods were proposed for the optimal design of integrated air curtain and exhaust system. It is concluded that an integrated air curtain and exhaust system can improve the interception efficiency on particles around 40% compared with no measure taken at the subway entrance, or it can be increased about 20% compared with only using an air curtain system (no exhaust vent) at the entrance.

The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.

The knowledge derived from successful case studies can act as a driver for the implementation and upscaling of nature-based solutions (NBS). This work reviewed 547 case studies to gain an overview of NBS practices and their role in reducing the adverse impact of natural hazards and climate change. The majority (60 %) of case studies are situated in Europe compared with the rest of the world where they are poorly represented. Of 547 case studies, 33 % were green solutions followed by hybrid (31 %), mixed (27 %), and blue (10 %) approaches. Approximately half (48 %) of these NBS interventions were implemented in urban (24 %), and river and lake (24 %) ecosystems. Regarding the scale of intervention, 92 % of the case studies were operationalised at local (50 %) and watershed (46 %) scales while very few (4 %) were implemented at the landscape scale. The results also showed that 63 % of NBS have been used to deal with natural hazards, climate change, and loss of biodiversity, while the remaining 37 % address socio-economic challenges (e.g., economic development, social justice, inequality, and cohesion). Around 88 % of NBS implementations were supported by policies at the national level and the rest 12 % at local and regional levels. Most of the analysed cases contributed to Sustainable Development Goals 15, 13, and 6, and biodiversity strategic goals B and D. Case studies also highlighted the co-benefits of NBS: 64 % of them were environmental co-benefits (e.g., improving biodiversity, air and water qualities, and carbon storage) while 36 % were social (27 %) and economic (9 %) co-benefits. This synthesis of case studies helps to bridge the knowledge gap between scientists, policymakers, and practitioners, which can allow adopting and upscaling of NBS for disaster risk reduction and climate change adaptation and enhance their preference in decision-making processes. [Display omitted] •~60 % of NBS case studies were from the EU, limited application in other regions.•Most case studies were implemented to address natural hazards and climate change.•Half of NBS is used in urban and river settings; green approach is the most used.•Of 547 case studies, ~88 % of NBS implementations are supported by national policies.•~60 % of NBS supported SDGs (15, 13, 6) and 68 % aided biodiversity goals (B, D).

The ongoing COVID-19 pandemic has brought into focus how poor indoor air quality can amplify the effects of airborne viruses. Rather than promoting health and wellbeing, our built environment often worsens air quality through inadequate ventilation, air recirculation, material specification and the additional pollution load from mechanical heating and cooling. In this thinkpiece, we introduce a selection of interrelated building design strategies to improve indoor air quality and reduce the spread and impact of airborne disease. We also highlight the need for interdisciplinary collaboration, targeted policy change and leadership on air quality to build resilience against future airborne viral outbreaks.

Shifting urban commuters to public transport can be an effective strategy to deal with the energy and environmental problems associated with the transport sector. In order to enhance public transport the mode of choice for urban commuters, public expectations and requirements should be at the centre of the policy-making process. This study uses pair-wise weighing method (i.e. Analytical Hierarchy Process) to derive priorities for different criteria for shifting urban commuters to the public transport system based on their opinion. The primary survey has been conducted to collect the data for identifying public preferences for public transport characteristics under four parent criteria: reliability, comfort, safety and cost, identified based on literature review and expert opinion. This information was collected using questionnaire based surveys between January 2013 and July 2013 from nearly 50 locations using a stratified random sampling technique from nine districts of Delhi. Our results suggest safety as the most important criteria (36% of total) for encouraging the urban commuters to shift from private vehicles to public transport and then reliability (27%), cost (21%) and comfort (16%). Based on above four criteria, commuters were found to be happy with Delhi metro services compared to buses and other mode of public transport due to more frequency, adherence to schedule, less travel time, comfort and safety. Commuters were willing to pay more for better public transport service since the travel cost was not considered to be one of the important criteria. The results also showed that 96% commuters are willing to shift to public transport if above criteria or services are considered for providing an efficient public transport system. These results can assist transport planners to integrate public preferences with the available technical alternatives for the wise allocation of the available resources.

Over the years the surface water quality of Indian rivers has been degrading. There are various reasons for the degradation of quality of river waters in Indian conditions. The pollution potential of river water involves various factors such as pH, Conductivity, Dissolved Oxygen, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Coliform and Fecal Coliform. The paper presents an approach to develop an empirical equation to predict the pollution potential of river water. The empirical equation developed uses aforementioned factors as variables. These variables have been assigned various ratings on a scale of 1-3 according to standard pollution potential charts. The pollution potential predicted using this empirical equation is in congruence with the current potential pollution of Indian rivers.

People with low-income often experience higher exposures to air pollutants. We compared the exposure to particulate matter (PM1, PM2.5 and PM10), Black Carbon (BC) and ultrafine particles (PNC; 0.02-1 µm) for typical commutes by car, bus and underground from 4 London areas with different levels of income deprivation (G1 to G4, from most to least deprived). The highest BC and PM concentrations were found in G1 while the highest PNC in G3. Lowest concentrations for all pollutants were observed in G2. We found no systematic relationship between income deprivation and pollutant concentrations, suggesting that differences between transport modes are a stronger influence. The underground showed the highest PM concentrations, followed by buses and a much lower concentrations in cars. BC concentrations in the underground were overestimated due to Fe interference. BC concentrations were also higher in buses than cars because of a lower infiltration of outside pollutants into the car cabin. PNCs were highest in buses, closely followed by cars, but lowest in underground due to the absence of combustion sources. Concentration in the road modes (car and bus) were governed by the traffic conditions (such as traffic flow interruptions) at the specific road section. Exposures were reduced in trains with non-openable windows compared to those with openable windows. People from lower income deprivation areas have a predominant use of car, receiving the lowest doses (RDD

This work examined the efficacy of passive air pollution mitigation measures (green infrastructure) in elderly care centres (ECCs) located around heavily-trafficked roads. We measured indoor and outdoor PM2.5 concentrations of three ECCs near major roads. We found that the highest PM2.5 concentrations were usually occurred during peak traffic. ECC3 (295 ± 67 μg m−3) exhibited the highest concentrations, presumably because of the shortest width of tree space (10 m) between the ECC and the road, followed by ECC2 (184 ± 38 μg m−3) and ECC1 (60 ± 16 μg m−3) having a distance of 40 and 105 m, respectively, covered by the park with dense trees in between. These results suggest that a wide space of trees between the ECC and the road was conducive to improving air quality. There is a strong positive correlation between traffic volume and outdoor PM2.5 concentrations of ECCs. Additionally, higher wind speed and temperature, along with lower relative humidity (RH), were linked to favourable conditions for reducing concentrations. Furthermore, measured indoor and outdoor concentrations were strongly correlated. The indoor/outdoor ratio decreased as the distance between the ECC and the road increased. The hazard ratio (HR) of the ECC, with 10 m wide of trees between it and the road, was respectively 38% and 80% higher than that with 40 m and 105 m wide space of dense trees between roads and ECCs. A trade-off exists between air quality in ECCs, transportation convenience and urban design. Consequently, to minimise the negative effects of traffic emissions, ECC should preferably be located away from heavily trafficked roads and near green infrastructure.

Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and people's exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality is also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network.

This study estimates exposure and inhaled dose to air pollutants of children residing in a tropical coastal-urban area in Southeast Brazil. For that, twenty-one children filled their time-activities diaries and wore the passive samplers to monitor NO2. The personal exposure was also estimated using data provided by the combination of WRF-Urban/GEOS-Chem/CMAQ models, and the nearby monitoring station. Indoor/outdoor ratios were used to consider the amount of time spent indoors by children in homes and schools. The model's performance was assessed by comparing the modelled data with concentrations measured by urban monitoring stations. A sensitivity analyses was also performed to evaluate the impact of the model's height on the air pollutant concentrations. The results showed that the mean children's personal exposure to NO2 predicted by the model (22.3 μg/m3) was nearly twice to those measured by the passive samplers (12.3 μg/m3). In contrast, the nearest urban monitoring station did not represent the personal exposure to NO2 (9.3 μg/m3), suggesting a bias in the quantification of previous epidemiological studies. The building effect parameterisation (BEP) together with the lowering of the model height enhanced the air pollutant concentrations and the exposure of children to air pollutants. With the use of the CMAQ model, exposure to O3, PM10, PM2.5, and PM1 was also estimated and revealed that the daily children's personal exposure was 13.4, 38.9, 32.9, and 9.6 μg/m3, respectively. Meanwhile, the potential inhalation daily dose was 570-667 μg for PM2.5, 684-789 μg for PM10, and 163-194 μg for PM1, showing to be favourable to cause adverse health effects. The exposure of children to air pollutants estimated by the numerical model in this work was comparable to other studies found in the literature, showing one of the advantages of using the modelling approach since some air pollutants are poorly spatially represented and/or are not routinely monitored by environmental agencies in many regions.

This study investigates the determining factors behind the adverse health effects of traffic policemen in National Capital Territory (NCT) of Delhi. A comparative analysis between 532 traffic policemen (subject population) and 150 office workers (control group) was undertaken to study the prevalence of disease. A primary survey was conducted over a period of six months between July 2015 and February 2016 using a questionnaire survey as a primary tool. A significantly higher (p = 0.005) prevalence rate of respiratory and cardiovascular diseases was observed among traffic policemen compared with the control group. Symptoms such as thick sputum, pain in joints, and shortness of breath were prevalent in approximately 59%, 56%, and 45% of subjects, as compared to about 15%, 11% and 6% of the control population. The relative risk of developing respiratory and cardiovascular diseases was found to be significantly higher (RR>1) for the traffic policemen in comparison to the office workers (control group). This is a first cross-sectional study to highlight the plight of traffic policemen in the NCT region of Delhi. The influence of factors such as Body Mass Index (BMI), age, habits (smoking and alcohol consumption) and service duration on disease prevalence was assessed among traffic policemen using statistical tests. The service duration was found to be the most important determinant compared with other influencing factors such as BMI, age, which is significantly (p = 0.02) affecting the health of traffic policemen in the current study. A number of potential measures for improving the health conditions of traffic policemen are also discussed.

Abstract Carbon dioxide (CO2) emitted from conventional coal-based power plants is a growing concern for the environment. Chemical looping combustion (CLC), pre-combustion and oxy-fuel combustion are promising CO2 capture technologies which allow clean electricity generation from coal in an integrated gasification combined cycle (IGCC) power plant. This work compares the characteristics of the above three capture technologies to those of a conventional IGCC plant without CO2 capture. CLC technology is also investigated for two different process configurations—(i) an integrated gasification combined cycle coupled with chemical looping combustion (IGCC–CLC), and (ii) coal direct chemical looping combustion (CDCLC)—using exergy analysis to exploit the complete potential of CLC. Power output, net electrical efficiency and CO2 capture efficiency are the key parameters investigated for the assessment. Flowsheet models of five different types of IGCC power plants, (four with and one without CO2 capture), were developed in the Aspen plus simulation package. The results indicate that with respect to conventional IGCC power plant, IGCC–CLC exhibited an energy penalty of 4.5%, compared with 7.1% and 9.1% for pre-combustion and oxy-fuel combustion technologies, respectively. IGCC–CLC and oxy-fuel combustion technologies achieved an overall CO2 capture rate of ∼100% whereas pre-combustion technology could capture ∼94.8%. Modification of IGCC–CLC into CDCLC tends to increase the net electrical efficiency by 4.7% while maintaining 100% CO2 capture rate. A detailed exergy analysis performed on the two CLC process configurations (IGCC–CLC and CDCLC) and conventional IGCC process demonstrates that CLC technology can be thermodynamically as efficient as a conventional IGCC process.

Human health has been identified to be affected more significantly by indoor air quality. Among numerous pollutants present in indoor air, formaldehyde (FA) is of great concern because of its highly hazardous nature. The concentrations of FA were determined from 20 newly decorated homes in the city of Gonabad, Iran during 2015. It was found that the indoor air levels of FA in all the sampled houses were exceptionally high in the range of 21 to 360 µg/m3 (mean of 149.3 µg/m3). If the 24-h average concentrations of FA measured from those sites were concerned, nearly 40% of them were seen to exceed the WHO guideline values (i.e., 100 µg/m3). One of the important reasons for the high concentrations could be low air exchange rates in those houses (e.g., from 0.18 to 0.37 h−1), high levels of humidity in the newly decorating houses and stronger sources in the indoor environment. Furthermore, its pollution in homes with natural ventilation was seen to be much higher than those of mechanical ventilation. Due to high levels of indoor FA, more effective control procedures should be developed and employed to reduce the risk associated with formaldehyde exposure.

Anthropogenic airborne particulates are among the major contributors to urban air pollution and pose a significant health risk. Particulate matter has emerged as a serious pollution threat in India, specifically to the capital—New Delhi. The objective of this study is to map PM2.5 profile using two widely used spatial interpolation techniques (Kriging and IDW) by predicting their concentrations at distinct unmonitored locations. The implemented methodology has a wide-scoped utility in the field of air pollution; especially in Low-Middle Income Countries where setting up new monitoring stations include financial/logistical/location problems. The generated maps can help in policy formulation and decision making by providing aid in PM2.5 visualisation of spatial and temporal variability. First phase of study involves prediction of concentrations at two sites (reinforcing the need for sustainable development of the city) using concentrations for 2015-2017.In the second phase, pollutant mixing ratios were obtained for four winter months between Nov-2017 to Feb-2018 at 17 monitoring stations. In this phase, predictions were made for 11 supersites (zones of important land-use). The average error of Kriging and IDW (taking both phases) was ~22% and 24%, respectively. The magnitude of change in the daily concentration was relatively negligible and annual trend can be identified.

Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers’ quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory, e.g. (Castell et al. 2017, Han et al. 2017, Sousan et al. 2017). The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

DRASTIC is a very simple and common model used for the assessment of groundwater to contamination. This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assessment. The Ohio Water Well Association (OWWA) developed DRASTIC model in 1987. Over the years, several modifications have been made in this model as per the need of the regional assessment of groundwater to contamination. This model has fixed weights for its parameters and fixed ratings for the sub-parameters under the main parameters. The weights and ratings of DRASTIC parameters were fixed on the basis of Delphi network technique, which is the best technique for the consensus-building of experts, but it lacks scientific explanations. Over the years, several optimization techniques have been used to optimize these weights and ratings. This work intends to present a critical analysis of decision optimization techniques used to get the optimum values of weights and ratings. The inherent pros and cons and the optimization challenges associated with these techniques have also been discussed. The finding of this study is that the application of MCDA optimization techniques used to optimize the weights and ratings of DRASTIC model to assess the vulnerability of groundwater depend on the availability of hydrogeological data, the pilot study area and the level of required accuracy for earmarking the vulnerable regions. It is recommended that one must choose the appropriate MCDA technique for the particular region because unnecessary complex structure for optimization process takes more time, efforts, resources, and implementation costs.

This book presents select proceedings of the 2nd Asian Conference on Indoor Environmental Quality (ACIEQ-2023) and explores the current research in the field of indoor environmental quality which includes indoor air quality, adaptive thermal comfort, productivity and health, indoor lighting, and acoustics. These themes include exposure assessment in various microenvironments, i.e., commercial, residential, and institutional and its effect on human health and performance for better well-being. The book also discusses the strategies to improve thermal and visual comfort along with filtration technologies for improving indoor air quality in urban built environment. It also emphasizes on profiling of indoor air pollutants such as bioaerosols, volatile organic compounds, particulate matter in schools, offices, dyeing/printing industry, and modes of commute. The book is a valuable reference for researchers and professionals in engineering, architecture, lighting, and acoustic areas interested in the relevant aspects of indoor environmental quality.

Tropospheric ozone threatens human health and crop yields, exacerbates global warming, and fundamentally changes atmospheric chemistry. Evidence has pointed toward widespread ozone increases in the troposphere, and particularly surface ozone is chemically complex and difficult to abate. Despite past successes in some regions, a solution to new challenges of ozone pollution in a warming climate remains unexplored. In this perspective, by compiling surface measurements at ∼4,300 sites worldwide between 2014 and 2019, we show the emerging global challenge of ozone pollution, featuring the unintentional rise in ozone due to the uncoordinated emissions reduction and increasing climate penalty. On the basis of shared emission sources, interactive chemical mechanisms, and synergistic health effects between ozone pollution and climate warming, we propose a synergistic ozone-climate control strategy incorporating joint control of ozone and fine particulate matter. This new solution presents an opportunity to alleviate tropospheric ozone pollution in the forthcoming low-carbon transition.

The citizen science approach engages the public to co-design effective solutions for air pollution challenges. Guildford Living Lab (GLL) and Zero Carbon Guildford (ZCG) initiated a collaborative air quality study to synergistically employ low-cost sensors within a public building. The aims were to develop a real-time Live Air Pollution Data (LAPD) tool for the public and raise awareness and citizen engagement through interactive quiz system. Whilst doing so, we monitored indoor and outdoor (I/O) concentrations of particulate matter (PM) and carbon dioxide (CO2) and assessed horizontal and vertical variation of CO2. We found that short indoor public events can raise CO2 up to 1000 ppm; people's movements during these events can elevate PM10 concentration. The PM10 and CO2 concentrations increased with the number of occupants and their distribution inside the ZCG building. Dust resuspension due to occupant activities was the main driver for high PM10 concentrations, while the smaller particles (PM2.5 and PM1) were linked to the ingress from outside. In addition, the change in the number of occupants showed no effect on PM2.5 and PM1 concentrations. We found significant stratification in CO2 in the vertical direction, accumulating CO2 close to the ceiling inside the building. Concurrently, CO2 in the horizontal direction was uniform without any significant variation. The analysis of data from the Interactive Quiz System revealed that 16% of the participants had the highest air pollution exposure in their day-to-day activities. The collaborative development of LAPD and the live presentation of air pollution data to the public effectively disseminated air quality information, leading to improved awareness among individuals. This work demonstrated the citizen science approach's effectiveness in understanding and mitigating air pollution issues through a collaborative, inclusive, knowledge-sharing environment. This study inspires further citizen science initiatives between scientists, the public, research funding institutes, authorities and agencies.

Accurate prediction of nanoparticles is essential to provide adequate mitigation strategies for air quality management. On the contrary to PM10, SO2, O3, NOx and CO, nanoparticles are not routinely–monitored by environmental agencies as they are not regulated yet. Therefore, a prognostic supervised machine learning technique, namely feed–forward artificial neural network (ANN), has been used with a back–propagation algorithm, to stochastically predict PNCs in three size ranges (N5–30, N30–100 and N100–300 nm). Seven models, covering a total of 525 simulations, were considered using different combinations of the routinely–measured meteorological and five pollutants variables as covariates. Each model included different numbers of hidden layers and neurons per layer in each simulation. Results of simulations were evaluated to achieve the optimum correspondence between the measured and predicted PNCs in each model (namely Models, M1–M7). The best prediction ability was provided by M1 when all the covariate variables were used. The model, M2, provided the lowest prediction performance since all the meteorological variables were omitted in this model. Models, M3–M7, that omitted one pollutant covariate, showed prediction ability similar to M1. The results were within a factor of 2 from the measured values, and provided adequate solutions to PNCs’ prognostic demands. These models are useful, particularly for the studied site where no nanoparticles measurement equipment exist, for determining the levels of particles in various size ranges. The model could be further used for other locations in Kuwait and elsewhere after adequate long–term measurements and training based on the routinely–monitored environmental data.

© 2015 Elsevier Ltd. Quantification of disproportionate contribution made by signalised traffic intersections (TIs) to overall daily commuting exposure is important but barely known. We carried out mobile measurements in a car for size-resolved particle number concentrations (PNCs) in the 5-560 nm range under five different ventilation settings on a 6 km long busy round route with 10 TIs. These ventilation settings were windows fully open and both outdoor air intake from fan and heating off (Set1), windows closed, fan 25% on and heating 50% on (Set2), windows closed, fan 100% on and heating off (Set3), windows closed, fan off and heating 100% on (Set4), and windows closed, fan and heating off (Set5). Measurements were taken sequentially inside and outside the car cabin at 10 Hz sampling rate using a solenoid switching system in conjunction with a fast response differential mobility spectrometer (DMS50). The objectives were to: (i) identify traffic conditions under which TIs becomes hot-spots of PNCs, (ii) assess the effect of ventilation settings in free-flow and delay conditions (waiting time at a TI when traffic signal is red) on in-cabin PNCs with respect to on-road PNCs at TIs, (iii) deriving the relationship between the PNCs and change in driving speed during delay time at the TIs, and (iv) quantify the contribution of exposure at TIs with respect to overall commuting exposure. Congested TIs were found to become hot-spots when vehicle accelerate from idling conditions. In-cabin peak PNCs followed similar temporal trend as for on-road peak PNCs. Reduction in in-cabin PNC with respect to outside PNC was highest (70%) during free-flow traffic conditions when both fan drawing outdoor air into the cabin and heating was switched off. Such a reduction in in-cabin PNCs at TIs was highest (88%) with respect to outside PNC during delay conditions when fan was drawing outside air at 25% on and heating was 50% on settings. PNCs and change in driving speed showed an exponential-fit relationship during the delay events at TIs. Short-term exposure for ~2% of total commuting time in car corresponded to ~25% of total respiratory doses. This study highlights a need for more studies covering diverse traffic and geographical conditions in urban environments so that the disparate contribution of exposure at TIs can be quantified.

Airborne nanoparticles have been studied worldwide, but little is known about their sources in the Middle East region, where hot, arid and dusty climatic conditions generally prevail. For the first time in Kuwait, we carried out size-resolved measurements of particle number distributions (PNDs) and concentrations (PNCs) in the 5-1000 nm size range. Measurements were made continuously for 31 days during the summer months of May and June 2013 using a fast-response differential mobility spectrometer (Cambustion DMS500) at a sampling rate of 10 Hz. Sources and their contributions were identified using the positive matrix factorization (PMF) approach that was applied to the PND data. Simultaneous measurements of gaseous pollutants (i.e., O3, NO, NOx, SO2 and CO), PM10, wind speed and direction were also carried out to aid the interpretation of the PMF results through the conditional probability function plots and Pearson product-moment correlations. Six major sources of PNCs were identified, contributing ~46% (fresh traffic emissions), 27% (aged traffic emissions), 9% (industrial emissions), 9% (regional background), 6% (miscellaneous sources) and 3% (Arabian dust transport) of total PNCs. The sources of nanoparticles and their particle number distribution profiles identified could serve as a reference data to design more detailed field studies in future and treat these sources in dispersion modelling and health impact assessment studies.

Urban Heat Island (UHI) is posing a significant challenge due to growing urbanisations across the world. Green infrastructure (GI) is popularly used for mitigating the impact of UHI, but knowledge on their optimal use is yet evolving. The UHI effect for large cities have received substantial attention previously. However, the corresponding effect is mostly unknown for towns, where appreciable parts of the population live, in Europe and elsewhere. Therefore, we analysed the possible impact of three vegetation types on UHI under numerous scenarios: baseline/current GI cover (BGI); hypothetical scenario without GI cover (HGI-No); three alternative hypothetical scenarios considering maximum green roofs (HGR-Max), grasslands (HG-Max) and trees (HT-Max) using a dispersion model ADMS-Temperature and Humidity model (ADMS-TH), taking a UK town (Guildford) as a case study area. Differences in an ambient temperature between three different landforms (central urban area, an urban park, and suburban residential area) were also explored. Under all scenarios, the night-time (0200 h; local time) showed a higher temperature increase, up to 1.315 °C due to the lowest atmospheric temperature. The highest average temperature perturbation (change in ambient temperature) was 0.563 °C under HGI-No scenario, followed by HG-Max (0.400 °C), BGI (0.343 °C), HGR-Max (0.326 °C) and HT-Max (0.277 °C). Furthermore, the central urban area experienced a 0.371 °C and 0.401 °C higher ambient temperature compared with its nearby suburban residential area and urban park, respectively. The results allow to conclude that temperature perturbations in urban environments are highly dependent on the type of GI, anthropogenic heat sources (buildings and vehicles) and the percentage of land covered by GI. Among all other forms of GI, trees were the best-suited GI which can play a viable role in reducing the UHI. Green roofs can act as an additional mitigation measure for the reduction of UHI at city scale if large areas are covered.

The majority of people spend most of their time indoors, where they are exposed to indoor air pollutants. Indoor air pollution is ranked among the top ten largest global burden of a disease risk factor as well as the top five environmental public health risks, which could result in mortality and morbidity worldwide. The spent time in indoor environments has been recently elevated due to coronavirus disease 2019 (COVID-19) outbreak when the public are advised to stay in their place for longer hours per day to protect lives. This opens an opportunity to low-cost air pollution sensors in the real-time Spatio-temporal mapping of IAQ and monitors their concentration/exposure levels indoors. However, the optimum selection of low-cost sensors (LCSs) for certain indoor application is challenging due to diversity in the air pollution sensing device technologies. Making affordable sensing units composed of individual sensors capable of measuring indoor environmental parameters and pollutant concentration for indoor applications requires a diverse scientific and engineering knowledge, which is not yet established. The study aims to gather all these methodologies and technologies in one place, where it allows transforming typical homes into smart homes by specifically focusing on IAQ. This approach addresses the following questions: 1) which and what sensors are suitable for indoor networked application by considering their specifications and limitation, 2) where to deploy sensors to better capture Spatio-temporal mapping of indoor air pollutants, while the operation is optimum, 3) how to treat the collected data from the sensor network and make them ready for the subsequent analysis and 4) how to feed data to prediction models, and which models are best suited for indoors.