Professor Prashant Kumar

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.

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.

Underground railway systems are recognised spaces of increased personal pollution exposure. We studied the number-size distribution and physico-chemical characteristics of ultrafine (PM0.1), fine (PM0.1–2.5) and coarse (PM2.5–10) particles collected on a London underground platform. Particle number concentrations gradually increased throughout the day, with a maximum concentration between 18: 00 h and 21:00 h (local time). There was a maximum decrease in mass for the PM2.5, PM2.5–10 and black carbon of 3.9, 4.5 and ~ 21-times, respectively, between operable (OpHrs) and non-operable (N-OpHrs) hours. Average PM10 (52 μg m−3) and PM2.5 (34 μg m−3) concentrations over the full data showed levels above the World Health Organization Air Quality Guidelines. Respiratory deposition doses of particle number and mass concentrations were calculated and found to be two- and four-times higher during OpHrs compared with N-OpHrs, reflecting events such as train arrival/departure during OpHrs. Organic compounds were composed of aromatic hydrocarbons and polycyclic aromatic hydrocarbons (PAHs) which are known to be harmful to health. Specific ratios of PAHs were identified for underground transport that may reflect an interaction between PAHs and fine particles. Scanning transmission electron microscopy (STEM) chemical maps of fine and ultrafine fractions show they are composed of Fe and O in the form of magnetite and nanosized mixtures of metals including Cr, Al, Ni and Mn. These findings, and the low air change rate (0.17 to 0.46 h−1), highlight the need to improve the ventilation conditions.

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.

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.

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.

Accurate calibration of low-cost gas sensors is, at present, a time consuming and difficult process. Laboratory calibration and field calibration methods are currently used, but laboratory calibration is generally discounted due to poor transferability, and field methods requiring several weeks are standard. The Enhanced Ambient Sensing Environment (EASE) method described in this article, is a hybrid of the two, combining the advantages of a laboratory calibration with the increased accuracy of a field calibration. It involves calibrating sensors inside a duct, drawing in ambient air with similar properties to the site where the sensors will operate, but with the added feature of being able to artificially increases or decrease pollutant levels, thus condensing the calibration period required. Calibration of both metal-oxide (MOx) and electrochemical (EC) gas sensors for the measurement of NO2 and O3 (0–120 ppb) were conducted in EASE, laboratory and field environments, and validated in field environments. The EC sensors performed marginally better than MOx sensors for NO2 measurement and sensor performance was similar for O3 measurement, but the EC sensor nodes had less node inter-node variability and were more robust. For both gasses and sensor types the EASE calibration outperformed the laboratory calibration, and performed similarly to or better than the field calibration, whilst requiring a fraction of the time.

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

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.

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 

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.

In UK urban areas, due to ease of accessibility and convenience, many schools are located close to main busy roads. This often results in pollution hotspots around schools due to on-road vehicular pollutant emissions, especially during drop-off and pickup times of students to and from schools. As well as being exposed to air pollutants in the school grounds, students are also exposed indoors. Thus, the need for scientific investigations focusing on mitigation of air pollution exposure indoors and outdoors for schools has significantly increased. The main objectives of the present study were therefore to: 1) obtain a clearer understanding of the extent of the air pollution problem inside and around schools and the factors that affect it; and 2) determine how it may be mitigated effectively using a range of interventions. These were achieved by carrying out monitoring of air pollution and associated parameters in and around three primary schools in London, UK. The study investigated the exposure reduction potential of various interventions, such as green screens, air purifiers, and school streets. A good understanding was obtained of the improvement in air quality that was achieved by the interventions both inside and outside the classrooms in the three schools. A green screen along the fences of the school reduced PM concentration by up to 44% in the playground. Installing air purifiers in a classroom resulted in lowering in PM concentration of about 57%. The school street initiative decreased PM concentration by about 36% in front of the school during pick up time. From the overall findings, practical recommendations have been included, as far as has been possible, that will enable formal guidance to be produced to help improve air quality in and around UK schools.

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.

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.

Several research studies have ranked indoor pollution among the top environmental risks to public health in recent years. Good indoor air quality is an essential component of a healthy indoor environment and significantly affects human health and well-being. Poor air quality in such environments may cause respiratory disease for millions of pupils around the globe and, in the current pandemic-dominated era, require ever more urgent actions to tackle the burden of its impacts. The poor indoor quality in such environments could result from poor management, operation, maintenance, and cleaning. Pupils are a different segment of the population from adults in many ways, and they are more exposed to the poor indoor environment: They breathe in more air per unit weight and are more sensitive to heat/cold and moisture. Thus, their vulnerability is higher than adults, and poor conditions may affect proper development. However, a healthy learning environment can reduce the absence rate, improves test scores, and enhances pupil/teacher learning/teaching productivity. In this article, we analyzed recent literature on indoor air quality and health in schools, with the primary focus on ventilation, thermal comfort, productivity, and exposure risk. This study conducts a comprehensive review to summarizes the existing knowledge to highlight the latest research and solutions and proposes a roadmap for the future school environment. In conclusion, we summarize the critical limitations of the existing studies, reveal insights for future research directions, and propose a roadmap for further improvements in school air quality. More parameters and specific data should be obtained from in-site measurements to get a more in-depth understanding at contaminant characteristics. Meanwhile, site-specific strategies for different school locations, such as proximity to transportation routes and industrial areas, should be developed to suit the characteristics of schools in different regions. The socio-economic consequences of health and performance effects on children in classrooms should be considered. There is a great need for more comprehensive studies with larger sample sizes to study on environmental health exposure, student performance, and indoor satisfaction. More complex mitigation measures should be evaluated by considering energy efficiency, IAQ and health effects

The objective of this work is to evaluate the impact of vehicular emissions on the formation of fine particles (PM2.5;  ≤  2.5 µm in diameter) in the Sao Paulo Metropolitan Area (SPMA) in Brazil, where ethanol is used intensively as a fuel in road vehicles. The Weather Research and Forecasting with Chemistry (WRF-Chem) model, which simulates feedbacks between meteorological variables and chemical species, is used as a photochemical modelling tool to describe the physico-chemical processes leading to the evolution of number and mass size distribution of particles through gas-to-particle conversion. A vehicular emission model based on statistical information of vehicular activity is applied to simulate vehicular emissions over the studied area. The simulation has been performed for a 1-month period (7 August–6 September 2012) to cover the availability of experimental data from the NUANCE-SPS (Narrowing the Uncertainties on Aerosol and Climate Changes in Sao Paulo State) project that aims to characterize emissions of atmospheric aerosols in the SPMA. The availability of experimental measurements of atmospheric aerosols and the application of the WRF-Chem model made it possible to represent some of the most important properties of fine particles in the SPMA such as the mass size distribution and chemical composition, besides allowing us to evaluate its formation potential through the gas-to-particle conversion processes. Results show that the emission of primary gases, mostly from vehicles, led to a production of secondary particles between 20 and 30 % in relation to the total mass concentration of PM2.5 in the downtown SPMA. Each of PM2.5 and primary natural aerosol (dust and sea salt) contributed with 40–50 % of the total PM10 (i.e. those  ≤  10 µm in diameter) concentration. Over 40 % of the formation of fine particles, by mass, was due to the emission of hydrocarbons, mainly aromatics. Furthermore, an increase in the number of small particles impaired the ultraviolet radiation and induced a decrease in ozone formation. The ground-level O3 concentration decreased by about 2 % when the aerosol-radiation feedback is taken into account.

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.

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.

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

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.

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.

https://eac2021.co.uk/

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.

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.

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 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.

The arid and semi-arid regions are facing a huge brunt of fugitive Particulate Matter (fPM) pollution, usually ascribed to the natural dust generated at the regional level (>100 km). In this study, the contribution of locally generated fPM to air pollution and it's environmental risk were assessed at a typical dry-arid area in the Middle East (i.e., State of Qatar, 200 × 200 km2 domain) with the use of different emission and dispersion models. Four modelling scenarios were constructed to reflect standard practices (e.g., regional emission models and the World Health Organization's (WHO) Environmental Burden of Disease (EBD) method) and higher resolution calculations with emission models that were developed in past field campaigns. Emphasis was given to the effect on the WHO methodology beyond the typical emission estimates and ambient concentration levels. Eventually, the use of higher spatial resolution population and concentration data revealed fPM hot spots yielding up to 11.0 times higher short-term excess mortalities (an average increase of 1.8 times) compared to the baseline WHO methodology, where the whole population was exposed to a single average concentration. A difference that could be attributed to the improvement of the emission estimations for barren lands and traffic. For example, the estimated PM10 emission fluxes from barren lands, within the main metropolitan area, using the improved emissions model ranged from 0.05 to 42.0 μg m−2 s−1, which is considerably higher than the emissions predicted using just the literature models (0.03 to 2.0 μg m−2 s−1). Overall, the barren lands emissions accounted for more than 90% of the fPM emissions during the study period. Consequently, this study is one of the first to quantify the significance of locally induced fPM and highlight the need for dedicated field studies and improved emissions estimation tools.

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.

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.

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.

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.

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 UK. 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 PM1, PM2.5 and PM10 (0.70 < r < 0.98) in all phases and an expected inverse correlation between Nitrogen Dioxide NO2 and ground-level Ozone O3 (-0.68 < r < -0.78) as NO2 is a precursor of O3. Noise levels exhibit a weak correlation with the measured air pollutants and moderate correlation with meteorological factors, including wind direction, temperature, and relative humidity. There was a consistent and significant increase in noise levels (p < 0.01) of up to 3 dB with initial easing and this was maintained through the remaining phases.

A newly developed instrument, the [`]fast response differential mobility spectrometer (DMS500)', was deployed to measure the particles in the 5-1000nm range in a Cambridge (UK) street canyon. Measurements were taken for 7weekdays (from 09:00 to 19:00h) between 8 and 21 June 2006 at three heights close to the road level (i.e. 0.20m, 1.0m and 2.60m). The main aims of the measurements were to investigate the dependence of particle number distributions (PNDs) and concentrations (PNCs) and their vertical variations on wind speed, wind direction, traffic volume, and to estimate the particle number flux (PNF) and the particle number emission factors (PNEF) for typical urban streets and driving conditions. Traffic was the main source of particles at the measurement site. Measured PNCs were inversely proportional to the reference wind speed and directly proportional to the traffic volume. During the periods of cross-canyon flow the PNCs were larger on the leeward side than the windward side of the street canyon showing a possible effect of the vortex circulation. The largest PNCs were unsurprisingly near to road level and the pollution sources. The PNCs measured at 0.20m and 1.0m were the same to within 0.5-12.5% indicating a well-mixed region and this was presumably due to the enhanced mixing from traffic produced turbulence. The PNCs at 2.60m were lower by 10-40% than those at 0.20m and 1.0m, suggesting a possible concentration gradient in the upper part of the canyon. The PNFs were estimated using an idealised and an operational approach; they were directly proportional to the traffic volume confirming the traffic to be the main source of particles. The PNEF were estimated using an inverse modelling technique; the reported values were within a factor of 3 of those published in similar studies.

Built-up environments limit air pollution dispersion in street canyons and lead to complex trade-offs between green infrastructure (GI) usage and its potential to reduce near-road exposure. This study evaluated the effects of an evergreen hedge on the distribution of particulate matter (PM1, PM2.5, PM10), black carbon (BC) and particle number concentrations (PNCs) in a street canyon in West London. Instrumentation was deployed around the hedge at 13 fixed locations to assess the impact of the hedge on vertical and horizontal concentration distributions. Changes in concentrations behind the hedge were measured with reference to the corresponding sampling point in front of the hedge for all sets of measurements. Results showed a significant reduction in vertical concentrations between 1 and 1.7 m height, with maximum reductions of –16% (PM1 and PM10) and –17% (PM2.5) at ∼1 m height. Horizontal concentrations revealed two zones between the building façade and the hedge, with opposite trends: (i) close to hedge (within 0.2 m), where a reduction of PM1 and PM2.5 was observed, possibly due to dilution, deposition and the barrier effect; and (ii) 0.2–3 m from the hedge, showing an increase of 13–37% (PM1) and 7–21% (PM2.5), possibly due to the blockage effect of the building, restricting dispersion. BC showed a significant reduction at breathing height (1.5 m) of between –7 and –50%, followed by –15% for PNCs in the 0.02–1 µm size range. The ELPI + analyser showed a peak of ∼30 nm. The presence of the hedge led to a ∼39 ± 32% decrease in total PNCs (0.006–10 µm), suggesting a greater removal in different modes, such as a 83 ± 12% reduction in nucleation mode (0.006–0.030 µm), 74 ± 15% in ultrafine (≤0.1 µm), and 34 ± 30% in accumulation mode (0.03–0.3 µm). These findings indicate graded filtering of particles by GI in a near-road street canyon environment. This insight will guide the improved design of GI barriers and the validation of microscale dispersion models.

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.

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

Significant spatial and temporal variation in ultrafine particle (UFP;

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]

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.

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 (

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.

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.

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.

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.

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.

Demolition of buildings produce large quantities of particulate matter (PM) that could be inhaled by on-site workers and people living in the neighbourhood, but studies assessing ambient exposure at the real-world demolition sites are limited. We measured concentrations of PM10 (≤10 μm), PM2.5 (≤2.5 μm) and PM1 (≤1 μm) along with local meteorology for 54 working hours over the demolition period. The measurements were carried out at (i) a fixed-site in the downwind of demolished building, (ii) around the site during demolition operation through mobile monitoring, (iii) different distances away from the demolition site through sequential monitoring, and (iv) inside an excavator vehicle cabin and on-site temporary office for engineers. Position of the PM instrument was continuously recorded using a Global Positioning System on a second basis during mobile measurements. Fraction of coarse particles (PM2.5–10) contributed 89 (with mean particle mass concentration, PMC ≈ 133 ± 17 μg m−3), 83 (100 ± 29 μg m−3), and 70% (59 ± 12 μg m−3) of total PMC during the fixed-site, mobile monitoring and sequential measurements, respectively, compared with only 50% (mean 12 ± 6 μg m−3) during the background measurements. The corresponding values for fine particles (PM2.5) were 11, 17 and 30% compared with 50% during background, showing a much greater release of coarse particles during demolition. The openair package in R and map source software (ArcGIS) was used to assess spatial variation of PMCs in downwind and upwind of the demolition site. A modified box model was developed to determine the emission factors, which were 210, 73 and 24 μg m−2 s−1 for PM10, PM2.5 and PM1, respectively. The average respiratory deposited doses to coarse (and fine) particles inside the excavator cabin and on-site temporary office increased by 57- (and 5-) and 13- (and 2-) times compared with the local background level, respectively. The monitoring stations in downwind direction illustrated a logarithmic decrease of PM with distance. Energy-dispersive X-ray spectroscopy and scanning electron microscopy were used to assess physicochemical features of particles. The minerals such as silica were found as a marker of demolition dust and elements such as sulphur coming from construction machinery emissions. Findings of this study highlight a need to limit occupational exposure of individuals to coarse and fine particles by enforcing effective engineering controls.

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.

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.

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.

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.

Equatorial warming conditions in urban areas can influence the particle number concentrations (PNCs), but studies assessing such factors are limited. The aim of this study was to evaluate the level of size-resolved PNCs, their potential deposition rate in the human respiratory system, and probable local and transboundary inputs of PNCs in Kuala Lumpur. Particle size distributions of a 0.34 to 9.02 μm optical-equivalent size range were monitored at a frequency of 60 s between December 2016 and January 2017 using an optical-based compact scanning mobility particle sizer (SMPS). Diurnal and correlation analysis showed that traffic emissions and meteorological confounding factors were potential driving factors for changes in the PNCs (Dp ≤1 μm) at the modeling site. Trajectory modeling showed that a PNC ˂100/cm3 was influenced mainly by Indo-China region air masses. On the other hand, a PNC >100/cm3 was influenced by air masses originating from the Indian Ocean and Indochina regions. Receptor models extracted five potential sources of PNCs: industrial emissions, transportation, aged traffic emissions, miscellaneous sources, and a source of secondary origin coupled with meteorological factors. A respiratory deposition model for male and female receptors predicted that the deposition flux of PM1 (particle mass ≤1 μm) into the alveolar (AL) region was higher (0.30 and 0.25 μg/h, respectively) than the upper airway (UA) (0.29 and 0.24 μg/h, respectively) and tracheobronchial (TB) regions (0.02 μg/h for each). However, the PM2.5 deposition flux was higher in the UA (2.02 and 1.68 μg/h, respectively) than in the TB (0.18 and 0.15 μg/h, respectively) and the AL regions (1.09 and 0.91 μg/h, respectively); a similar pattern was also observed for PM10.

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.

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.

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.

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.

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.

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.

Emission inventories are one of the most critical inputs for the successful modeling of air quality. The performance of the modeling results is directly affected by the quality of atmospheric emission inventories. Consequently, the development of representative inventories is always required. Due to the lack of regional inventories in Brazil, this study aimed to investigate theuseoftheparticulatematter(PM)emissionestimationfromtheBraziliantop-downvehicleemissioninventory(VEI)of2012 for air quality modeling. Here, we focus on road vehicles since they are usually responsible for significant emissions of PM in urban areas. The total Brazilian emission of PM (63,000 t year−1) from vehicular sources was distributed into the urban areas of 5557municipalities,with1-km2 gridspacing,consideringtwoapproaches:(i)populationand(ii)fleetofeachcity.Acomparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling(August2015)wasperformedwithWRF-ChemforthefourmetropolitanareasofBrazilianSoutheast:BeloHorizonte (MABH), Great Vitória (MAGV), Rio de Janeiro (MARJ), and São Paulo (MASP). In addition, modeling with the Emission Database for Global Atmospheric Research (EDGAR) inventory was carried out to compare the results. Overall, EDGAR inventory obtained higher PM emissions than the VEI segregated by population and fleet, which is expected owing to considerations of additional sources of emission (e.g., industrial and residential). This higher emission of EDGAR resulted in higher PM10 and PM2.5 concentrations, overestimating the observations in MASP, while the proposed inventory well represented the ambient concentrations, obtaining better statistics indices. For the other three metropolitan areas, both EDGAR and the VEI inventories obtained consistent results. Therefore, the present work endorses the fact that vehicles are responsible for the more substantial contribution to PM emissions in the studied urban areas. Furthermore, the use of VEI can be representative for modeling air quality in the future.

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.

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.

Green infrastructure (GI) includes trees, hedges, individual shrubs, green walls, and green roofs. GI offers many different benefits or services, including flood risk mitigation, microclimate regulation, carbon sequestration, improved health and wellbeing and – the focus of this document – air pollution abatement. Air pollution comprises variable quantities of many different types of pollutants, including gaseous pollutants, such as nitrous oxides (NOx) and particulate matter (PM), which is composed of particles such as black carbon (BC). Road traffic is a dominant source of air pollution in urban areas globally. In near-road environments, vegetation can act as a barrier between traffic emissions and pedestrians (figure below), by collecting pollutants and/or redirecting the flow of polluted air. This document summarises best practice regarding GI implementation for improved urban air quality and reduced pedestrian exposure to air pollution. Generic (i.e. not site-specific) recommendations are offered for typical urban environments. These recommendations are based upon contemporary scientific evidence and knowledge, and may therefore be subject to modification as the evidence base develops. This guidance document consolidates major findings from relevant publications, including a detailed report on the relationship between vegetation and urban air quality, review articles and other guidance documents.

This review assesses the current state of air pollution in the Middle East and North Africa (MENA) region. Emission types and sources in the region are identified and quantified to understand the monitoring, legislative and reduction need through a systematic review of available literature. It is found that both health (e.g., particulate matter, PM, and heavy metals) and climate change (e.g., carbon dioxide and methane) emissions are increasing with the time. Regarding health emissions, over 99% of the MENA population is exposed to PM levels that exceed the standards set by the World Health Organization (WHO). The dominant source of climate change emissions is the energy sector contributing ~38% of CO2 emissions, followed by the transport sector at ~25%. Numerous studies have been carried out on air pollution in the region, however, there is a lack of comprehensive regional studies that would provide a holistic assessment. Most countries have air quality monitoring systems in place, however, the data is not effectively evaluated to devise pollution reduction strategies. Moreover, comprehensive emission inventories for the individual countries in the region are also lacking. The legislative and regulatory systems in MENA region follow the standards set by international environmental entities such as the WHO and the U.S. Environmental Protection Agency but their effective reinforcement remains a concern. It is concluded that the opportunities for emission reduction and control could be best implemented in the road transportation sector using innovative technologies. One of the potential ways forward is to channel finance flows from fossil fuel subsidies to upgrade road transport with public transportation systems such as buses and trains, as suggested by a ‘high shift’ scenario for MENA region. Furthermore, emission control programs and technologies are more effective when sponsored and implemented by the private sector; the success of Saudi Aramco in supporting national emission monitoring is onesuch example. Finally, an energy-pollution-water nexus is assessed for the region as an integrated approach to address urban issues. The assessment of topic areas covered clearly suggest a need to control the main sources of air pollution to limit its relatively high impact on the human health in the MENA region.

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.

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.

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.

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

Cyclists are exposed to direct traffic emissions due to their proximity to on-road vehicles. Several studies associate black carbon (BC) exposure with both mortality and morbidity caused by cardiovascular and respiratory diseases. We did a comparative assessment of cyclists' exposure to BC in three cities: London, Rotterdam and São Paulo. We measured personal exposure to BC during the peak and off-peak hours in all three cities using the same instrument. Three origin-destination (O-D) pairs, each with two routes, for a total of six routes, were chosen in each city. The first route of each O-D pair was along busy major roads and the other perceived to be clean passing close to green/blue/quiet areas. This work brings together results from three different Latin American and European cities, with an aim to understand the BC exposure variabilities while cycling during peak and off-peak hours, identify main pollution hotspots resulting in enhanced exposure and associate the measured concentrations with proximity to green areas and waterways. BC concentrations were higher during the morning-peak hours compared with evening-peak hours in Rotterdam and São Paulo. London showed an opposite trend, with higher concentrations during evening hours. In most cases, the cyclists using the alternative route were found to be less exposed to BC in London and São Paulo. In Rotterdam, the differences in absolute concentrations between main and alternate routes were modest. Each city is different but the common features among all were that the exposure is related to route choice, a period of the day and proximity with the mobile sources. These findings have implications in terms of considering the pollutants exposure when establishing new cycle routes.

Recent Euro 5 and Euro 6 vehicle emission standards are the first ever initiative to control particles on a number basis at the source. Related standards are also desirable for ambient nanoparticles (taken in this article to be those below 300 nm) to protect against possible adverse effects on public health and the environment. However, there are a number of technical challenges that need to be tackled before developing a regulatory framework for atmospheric nanoparticles. Some of the challenges derive from a lack of standardisation of the key measurement parameters, including sampling, necessary for robust evaluation of particle number concentrations, especially in the context of insufficient knowledge of the physicochemical characteristics of emerging sources (i.e. bio-fuel derived and manufactured nanoparticles). Ideally, ambient concentrations of primary particles could be linked to primary particle emissions by use of nanoparticle dispersion models, and secondary nanoparticles using photochemical modeling tools. The limitations in these areas are discussed. Although there is inadequate information on the exact biological mechanism through which these particles cause harm, it is argued that this should not in itself delay the introduction of regulation. This article reviews the missing links between the existing knowledge of nanoparticle number concentrations and the advances required to tackle the technical challenges implied in developing regulations

Understanding of the emissions of coarse (PM10 ≤10 μm), fine (PM2.5 ≤2.5 μm) and ultrafine particles (UFP 90 % of the total particle number concentrations and

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.

Pinpointing the contribution of sources in complex urban areas, affected by large point sources such as oil refineries, is important for developing emission control strategies. Receptor models based on chemical composition of particulate matter PM), such as Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) are useful means for source apportionment but their results are usually affected by the lack of appropriate inclusion of meteorological parameters that significantly affect the distribution of pollutants in the atmosphere and deserve considerations. This work applies and evaluates different source apportionment techniques to identify the sources of fine particulate matter (PM2.5) and to the less represented – hydrogen sulphide (H2S), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2) – in an urban area influenced by a large point source (an oil refinery) in Brod of Bosnia and Herzegovina. Although domestic heating and refinery contributed equally to PM2.5 primary emissions, source apportionment receptor model method based on conditional bivariate probability function (CPBF) revealed that probability ~70% for the PM2.5 concentrations higher than 80th percentile (>37 µg/m3) is assigned to the refinery while ~30% is attributed to the urban sources. The composition of PM2.5 is seen to be dominated by carbonaceous combustion particles, mainly organic carbon (OC), with maximum values appearing during winter. Summer PM2.5 levels were dominated by the sulphate, which can be related to the oil refinery, and ammonium pointing towards the agriculture activities. Urban and highway traffic was the main source (probability ~20%) of NO2 concentrations >80th percentile. Results of multi-pollutant analyses using various source apportionment techniques (i.e. emissions, temporal pollutant variations, chemical PM speciation and CPBF) are ummarized in the form of blame matrix that relates observed concentrations to the sources. An oil refinery was identified as the major source of PM2.5, SO2, H2S and O3 in the area while the city (domestic heating, biomass burning and traffic) is a second contributing source to PM2.5 and SO2 and traffic is the major source of NO2. This work brings an evaluation of source apportionment methods in the assessment of PM and less represented gaseous pollutants NO2, SO2, H2S and O3 that can be used for future scientific applications and assures more efficient air quality management in the analyzed area of Southeastern Europe with prominent air pollution problems.

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.

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.

Regular use of incense and earthen lamps in temples leads to the release of particulate matter (PM), airborne flecks, and gaseous pollutants. Similarly, the cremation of dead bodies using timber and other accessories such as incense, organic chemicals containing carbon, and clothes generates air pollutants. It is currently unclear how much emissions and exposure these activities may lead. This work attempts to fill this gap in our understanding by assessing the associated emissions of PM2.5 and the corresponding exposure. Ten temples and two cremation grounds were considered for the sampling of PM2.5. The average PM2.5 concentration at the ten temples and the two crematoriums was found to be 658.30 ± 112.63 µg/m3 and 1043.50 ± 191.63 µg/m3, respectively. The range of real-time PM2.5 data obtained from the nearest twelve stations located in the vicinity was 113–191 µg/m3. The exposure assessment in terms of deposition dose was carried out using the ICRP model. The maximum and minimum total respiratory deposition dose rate for PM2.5 for temples was 175.75 µg/min and 101.15 µg/min, respectively. For crematoriums, the maximum and minimum value of same was 252.3 µg/min and 194.31 µg/min, respectively, for an exposure period of 10 min.

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%.

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.

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.

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.

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.

This study presents a comparison between measured and modelled particle number concentrations (PNCs) in the 10-300 nm size range at different heights in a canyon. The PNCs were modelled using a simple modelling approach (modified Box model, including vertical variation), an Operational Street Pollution Model (OSPM) and Computational Fluid Dynamics (CFD) code FLUENT. All models disregarded any particle dynamics. CFD simulations have been carried out in a simplified geometry of the selected street canyon. Four different sizes of emission sources have been used in the CFD simulations to assess the effect of source size on mean PNC distributions in the street canyon. The measured PNCs were between a factor of two and three of those from the three models, suggesting that if the model inputs are chosen carefully, even a simplified approach can predict the PNCs as well as more complex models. CFD simulations showed that selection of the source size was critical to determine PNC distributions. A source size scaling the vehicle dimensions was found to better represent the measured PNC profiles in the lowest part of the canyon. The OSPM and Box model produced similar shapes of PNC profile across the entire height of the canyon, showing a well-mixed region up to first [approximate]2m and then decreasing PNCs with increased height. The CFD profiles do correctly reproduce the increase from road level to a height of [approximate]2m; however, they do not predict the measured PNC decrease higher in the canyon. The PNC differences were largest between idealised (CFD and Box) and operational (OSPM) models at upper sampling heights; these were attributed to weaker exchange of air between street and roof-above in the upper part of the canyon in the CFD calculations. Possible reasons for these discrepancies are given.

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.

The elderly population is relatively vulnerable to air pollution and thermal stress due to their low mobility and high prevalence of chronic disorders. Appropriate green infrastructure (GI) deployment can improve both the indoor and outdoor air quality and thermal environments of elderly care centres (ECCs), yet a systematic review on this topic area is lacking. This review aims to fill this gap by investigating the impacts of GI on ECC building environment and presents the approaches for integrating GI into the building environment design. We discussed the significance of linking air quality with the thermal environment to ECCs and the effects of GI on the elderly's physical health. We investigated the key design considerations for GI in ECC buildings (e.g., spatial layout, species, aesthetics and fire prevention). Also, the diversity of monitoring and modelling approaches for evaluating the benefits of GI in indoor and outdoor environments was assessed. Finally, we evaluated the associated challenges and provided design recommendations for improving the environments in and around the ECC buildings (e.g., bedrooms, indoor gardens, green roofs and courtyards). The quantitative evidence for linking GI with indoor and outdoor air pollution and extreme heat around the ECC buildings are limited. However, this evidence-base is important for providing generic advice to the building designers and the elderly. Further studies such as the evaluation criteria and monitoring standard are required to develop holistic design recommendations for ECC buildings. The empirical research about the social and economic impacts is also necessary to facilitate the sustainable development of the ageing societies.

The importance of selecting appropriate air pollution monitoring sites in a city is vital for accurately reporting air quality, enhancing the quality of high-resolution modelling and informing policy to implement measures to deliver cleaner air in the urban environment. COVID-19 restrictions impacted air quality in urban centres worldwide as reduced mobility led to changes in traffic-related air pollution (TRAP). As such, it offered a unique dataset to examine the spatial and temporal variations in air quality between monitoring stations in Dublin, Ireland. Firstly, an analysis of mobility data showed reductions across almost all sectors after COVID-19 restrictions came into place, which was expected to lower TRAP. In addition, similar changes in air quality were evident to other cities around the world: reductions in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and an increase in ozone (O3) concentrations. Average daily and diurnal concentrations for these three pollutants presented more statistically significant spatial and temporal changes during COVID-19 restrictions at monitoring sites with urban or traffic classifications than suburban background sites. Furthermore, substantial reductions in the range of average hourly pollutant concentrations were observed, 79% for PM2.5 and 75% for NO2, with a modest 24% reduction for O3. Correlation analysis of air pollution between monitoring sites and years demonstrated an improvement in the R2 for NO2 concentrations only, suggesting that spatiotemporal homogeneity was most notable for this TRAP due to mobility restrictions during COVID-19. The spatiotemporal representativeness of monitoring stations across the city will change with greener transport, and air quality during COVID-19 can provide a benchmark to support the introduction of new policies for cleaner air. [Display omitted] •Dublin presented similar changes in air quality during COVID-19 restrictions for PM2.5, NO2 and O3.•More significant spatial and temporal changes in air quality were observed at urban or traffic classified monitoring sites.•A reduction in the range of concentrations observed at monitoring stations was evident during COVID restrictions.•A correlation analysis between sites noted increased NO2 homogeneity for this pollutant.•Greening transport will reduce the spatiotemporal representativeness of monitoring stations in Dublin in the future.

© 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.

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.

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 (

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.

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.

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

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.

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.

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.

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.

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.

Most major cities around the world experience periods of elevated air pollution levels, which exceed international health-based air quality standards (Kumar et al., 2013). Although it is a global problem, some of the highest air pollution levels are found in rapidly expanding cities in India and China. The sources, emissions, transformations and broad effects of meteorology on air pollution are reasonably well accounted in air quality control strategies in many developed cities; however these key factors remain poorly constrained in the growing cities of countries with emerging economies. We focus here on Delhi, one of the largest global population centres, which faces particular air pollution challenges, now and in the future.

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.

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.

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.

Mitigating the impact on human health worldwide is important to mitigate the morbidity and mortality arising from its exposure. 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 megacity Delhi (India) through the review of published literature. The study aims at describing the extent of air pollution in Delhi, the magnitude of health problems due to air pollution and 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 was described by various researchers from about 1986 onwards. We synthesised the findings and discussed 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 discussed 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 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 non-uniform. Even the parameters studied are varying, as some studies focused on PM10 and PM2.5, some of the suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM). Similarly, the locations of data collection have been widely varied. Some of the sites were at busy traffic intersections, some on the terrace of offices and residential houses and others in university campus or airports. As a result, the key question of the extent of pollution and its distribution across various parts of the city cannot be inferred. None of the studies or a combination of it could present a complete picture of the burden of diseases like COPD, bronchial asthma and other allergic condition attributable to pollution in Delhi. Neither it could be established that what fraction of the burden of above diseases is attributable to ambient air pollution, given that other factors like tobacco smoke and indoor air pollution are also contributors to the causation of such diseases. In our discussion, we highlighted the knowledge gaps and in the conclusion, we suggested what research can be undertaken to fill the existing knowledge gaps.

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.

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.

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.

This paper explores 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). In addition, 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 most. Furthermore, although specific results varied between regions, the overall findings show an association between ACT and satisfaction. Likewise, commuting mode is 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.

The ambient fine particulate matter is a considerable hazard to human health and the surrounding environment of the majority of Chinese cities. This article reviews the status of air pollution, especially PM2.5, in 21 cities of China, on the basis of their status, chemical characteristics, and regulations data collected from published literature. The observed results show Zhengzhou, Yulin, Jinan, Qingdao and Changchun as significantly polluted cities where the annual mean concentration of PM2.5 was noted to be greater than 120 µg m-3. However, some cities such as Xiamen, Hong Kong, Shenzhen, and Jinchang reported average annual PM2.5 concentrations less than 40 µg m-3. In general, the results of spatial distribution reported that the cities of the east, north and northeast China are highly polluted. According to the average mass of PM2.5 in maximum cities of China, the sum of sulfate, nitrate and ammonium (SNA) and organic matter (OM) contributed over 40% and 35%, respectively. The higher amount of SNA and OM in PM2.5 result from heavy traffic or vehicle emission and burning solid fuel utilized in most part of China. A proposed systemic approach to address the PM2.5 in China can improve the quality of ambient atmosphere.

The estimates of airborne fine particles (PM2.5) concentrations are possible through rigorous empirical correlations based on the monitored PM10 data. However, such correlations change depending on the nature of sources in diverse ambient environments and, therefore, have to be environment specific. Studies presenting such correlations are limited but needed, especially for those areas, where PM2.5 is not routinely monitored. Moreover, there are a number of studies focusing on urban environments but very limited for coal mines and coastal areas. The aim of this study is to comprehensively analyze the concentrations of both PM10 and PM2.5 and develop empirical correlations between them. Data from 26 different sites spread over three distinct environments, which are a relatively clean coastal area, two coal mining areas, and a highly urbanized area in Delhi were used for the study. Distributions of PM in the 0.43-10 μm size range were measured using eight stage cascade impactors. Regression analysis was used to estimate the percentage of PM2.5 in PM10 across distinct environments for source identification. Relatively low percentage of PM2.5 concentrations (21, 28 and 32 %) in PM10 were found in clean coastal and two mining areas respectively. Percentage of PM2.5 concentrations in PM10 in the highly urbanized area of Delhi was 51%, indicating a presence of a much higher percentage of fine particles due to vehicular combustion in Delhi. The findings of this work is important in estimating concentrations of much harmful fine particles from coarse particles across distinct environments. The results are also useful in source identification of particulates as differences in the percentage of PM2.5 concentrations in PM10 can be attributed to characteristics of sources in the diverse ambient environments.

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.

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.

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.

Many dispersion models are available to simulate the mass concentrations of particulate matter in an urban environment. Still, fewer are capable of simulating the effect of green infrastructure (GI) on the airborne nanoparticles represented by total particle number concentration (ToNC). We developed an integrated approach capable of simulating the dispersion of airborne nanoparticles under the various scenarios of green infrastructure (GI). We demonstrated the usefulness of this approach by simulating a high-resolution spatial (250 × 250 m) concentration of traffic-emitted airborne nanoparticles at an urban scale under eight GI urban planning scenarios: the base year 2015 (2015-Rl-GI); business-as-usual for 2039 (2039-BAU-GI); three hypothetical future scenarios with maximum possible coniferous (2039-HMax-Con), deciduous (2039-HMax-Dec) trees, and grassland (2039-HMax-Grl) over the available land; and three alternative future scenarios by considering coniferous (2039-HNR-Con), deciduous (2039-HNR-Dec) trees, and grassland (2039-HNR-Grl) around traffic lanes. We assessed both the parametric and structural uncertainties due to particle transformation processes (nucleation, coagulation and deposition) and uncertainty in particle number emission factors (PNEFs) on ToNC, respectively. We also simulated the combined impact of deposition and aerodynamic dispersion of GI on ToNC reduction. The annual average ToN emission (ToNE) reduced from 5.36 × 1022 (2015) to 2.84 × 1021 (2039) particles due to the UK's air quality plan in future. Parametric uncertainty due to variable PNEFs might cause variation in annual ToNC from −57% to +60%. However, structural uncertainties in ToNC, due to particle transformation processes were up to −12%, −11% and +0.14% for deposition, coagulation, and nucleation, respectively. The annual ToN deposition (ToND) and concentration were 28–4800 × 1019 particles and 3.94–19.10 × 103 # cm−3, respectively, depending on the percentage share of GI type and annual traffic emissions. Planting maximum coniferous trees (2039-HMax-Con) simulated maximum reduction in annual ToNC. Coniferous trees near traffic lanes (2039-HNR-Con) also found to be more effective to reduce annual ToNC.

Hydro-meteorological risk (HMR) management involves a range of methods, such as monitoring of uncertain climate, planning and prevention by technical countermeasures, risk assessment, preparedness for risk by early-warnings, spreading knowledge and awareness, response and recovery. To execute HMR management by risk assessment, many models and tools, ranging from conceptual to sophisticated/numerical methods are currently in use. However, there is still a gap in systematically classifying and documenting them in the field of disaster risk management. This paper discusses various methods used for HMR assessment and its management via potential nature-based solutions (NBS), which are actually lessons learnt from nature. We focused on three hydro-meteorological hazards (HMHs), floods, droughts and heatwaves, and their management by relevant NBS. Different methodologies related to the chosen HMHs are considered with respect to exposure, vulnerability and adaptation interaction of the elements at risk. Two widely used methods for flood risk assessment are fuzzy logic (e.g. fuzzy analytic hierarchy process) and probabilistic methodology (e.g. univariate and multivariate probability distributions). Different kinds of indices have been described in the literature to define drought risk, depending upon the type of drought and the purpose of evaluation. For heatwave risk estimation, mapping of the vulnerable property and population-based on geographical information system is a widely used methodology in addition to a number of computational, mathematical and statistical methods, such as principal component analysis, extreme value theorem, functional data analysis, the Ornstein–Uhlenbeck process and meta-analysis. NBS (blue, green and hybrid infrastructures) are promoted for HMR management. For example, marshes and wetlands in place of dams for flood and drought risk reduction, and green infrastructure for urban cooling and combating heatwaves, are potential NBS. More research is needed into risk assessment and management through NBS, to enhance its wider significance for sustainable living, building adaptations and resilience.

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.

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.

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 * m.stettler@imperial.ac.uk 1 of models of airborne particles exhaled by humans and transported in the environment during breathing and speaking.

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.

Monitoring of ambient PM10 concentrations was carried out using two co–located samplers at 10 different locations over the three seasons (summer, winter and post–monsoon) in Delhi, India. The samplers used for the study were the high volume sampler fitted with a cyclone (commonly known in India as respirable particulate matter sampler or RPM sampler), and a 4–channel speciation sampler (4-SS). The RPM sampler separates the PM10 fraction using centrifugal inertia while the 4-SS separates them using the principle of mass inertial impaction. Comparison of the measured data are made using different graphical techniques and statistical analysis, comprising classical two tailed paired t–test and the criteria recommended by the European Commission working group on particulate matter. The PM10 data monitored by both the samplers showed good overall correlations for the entire data set, with a regression co-efficient value of 0.61. Results indicated that inertial impaction based 4-SS consistently measures higher PM10 concentration compared with the cyclone fitted RPM sampler. Such results were valid for 81% of the total data set and this difference in measured concentrations was ∼66% in the regulatory limit value ranges. Both the samplers have their merits and limitations and hence a conscious choice and appropriate data correction is needed when deploying them for scientific and regulatory monitoring purposes.

Nature-based Solutions function (NBS) as an umbrella concept for ecosystem-based approaches that are an alternative to traditional engineering solutions for Disaster Risk Reduction. Their rising popularity is explained partly by their entailing additional benefits (so-called co-benefits) for the environment, society, and economy. The few existing frameworks for assessing co-benefits are lacking guidance on co-benefit pre-assessment that is required for the NBS selection and permission process. Going beyond these, this paper develops a comprehensive guidance on quantitative pre-assessment of potential co-benefits and disbenefits of NBS tackling Disaster Risk Reduction. It builds on methods and frameworks from existing NBS literature and related disciplines. Furthermore, this paper discusses the evaluation of the quantified results of the pre-assessment. In particular, the evaluation focuses on the significance of change of the estimated co-benefits and disbenefits as well as the sustainability of the NBS. This paper will support decision-making in planning processes on suitability and sustainability of Nature-based Solutions and assist in the preparation of Environmental Impact Assessments of projects.

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.

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.

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.

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.

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 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 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.

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

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.

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.

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 COVID-19 lockdown resulted in improved air quality in many cities across the world. With the objective of what could be the new learning from the COVID-19 pandemic and subsequent lockdowns for better air quality and human health, a critical synthesis of the available evidence concerning air pollution reduction, the population at risk and natural versus anthropogenic emissions was conducted. Can the new societal norms adopted during pandemics, such as the use of face cover, awareness regarding respiratory hand hygiene, and physical distancing, help in reducing disease burden in the future? The use of masks will be more socially acceptable during the high air pollution episodes in lower and middle-income countries, which could help to reduce air pollution exposure. Although post-pandemic, some air pollution reduction strategies may be affected, such as car-pooling and the use of mass transit systems for commuting to avoid exposure to airborne infections like coronavirus. However, promoting non-motorized modes of transportation such as cycling and walking within cities as currently being enabled in Europe and other countries could overshadow such losses. This demand focus on increasing walkability in a town for all ages and populations, including for a differently-abled community. The study highlighted that for better health and sustainability there. is also a need to promote other measures such as work-from-home, technological infrastructure, the extension of smart cities, and the use of information technology.

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the Generalized Linear Models (GLM). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM model considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25 °C. The best performance for modelled results against the measured data was achieved for model with values of air temperature above 25 °C compared with model considering all range of air temperatures and with model considering only temperature below 25 °C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when this data is not available by measurements from air quality monitoring stations or other acquisition means.

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.

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.

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]

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.

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.

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.

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.

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.

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.

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.

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 (

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.

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).

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.

Passive air pollution control devices known as aspiration efficiency reducers (AER) have been developed using aspiration efficiency (AE) concepts. Their purpose is to reduce the concentration of particulate matter (PM) drawn into a building air handling unit (AHU) through alterations in the inlet design improving energy consumption. In this paper an examination is conducted into the effect of installing a deflector system around an AER-AHU inlet for both a forward and rear-facing orientations relative to the wind. The results of the study found that these deflectors are an effective passive control method for reducing AE at various ambient wind speeds over a range of microparticles of varying diameter. The deflector system was found to induce a large wake zone at low ambient wind speeds for a rear-facing AER-AHU, resulting in significantly lower AE in comparison to without. As the wind speed increased, both contained a wake zone but have much lower concentration gradients with the deflectors. For the forward-facing models, the deflector system at low ambient wind speed was preferred at higher Stokes numbers but there was negligible difference as the Stokes number decreased. Similarly, there was no significant difference at higher wind speeds across the Stokes number range tested. The above results demonstrate that a deflector system is a viable passive control method for the reduction of ventilation energy consumption.

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.

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.

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.

The terms green infrastructure and natural capital are interrelated. Natural capital as a concept is focused upon environmental assets which can provide services, either directly or indirectly to humans; it emphasizes the benefits humans obtain from the natural environment. Green infrastructure is a concept with a wide range of definitions. The term is sometimes applied to networks of green open spaces found in or around urban areas. In other contexts green infrastructure can describe alternative engineering approaches for storm water management, with co-benefits of temperature control, air quality management, wildlife habitats and/or recreation and amenity space. No environments are completely free of human influence and therefore no environments are entirely natural. Rather, there is a spectrum of degrees of ‘naturalness’ ranging from environments with minimal human influence through to built environments. A trio of case studies presented herein illustrates how green infrastructure projects are a practical application of the natural capital concept in that they seek to preserve and enhance natural capital via a management approach which emphasizes the importance of environmental systems and networks for the direct provision of ecosystem services to human populations. Natural capital forms critical components of all green infrastructure projects.

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.

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.

13th International conference on harmonisation within atmospheric dispersion modelling for regulatory purposes, Paris, France

To bring to fruition the capability of nature-based solutions (NBS) in mitigating hydro-meteorological risks (HMRs) and facilitate their widespread uptake require a consolidated knowledge-base related to their monitoring methods, efficiency, functioning and the ecosystem services they provide. We attempt to fill this knowledge gap by reviewing and compiling the existing scientific literature on methods, including ground-based measurements (e.g. gauging stations, wireless sensor network) and remote sensing observations (e.g. from topographic LiDAR, multispectral and radar sensors) that have been used and/or can be relevant to monitor the performance of NBS against five HMRs: floods, droughts, heatwaves, landslides, and storm surges and coastal erosion. These can allow the mapping of the risks and impacts of the specific hydro-meteorological events. We found that the selection and application of monitoring methods mostly rely on the particular NBS being monitored, resource availability (e.g. time, budget, space) and type of HMRs. No standalone method currently exists that can allow monitoring the performance of NBS in its broadest view. However, equipments, tools and technologies developed for other purposes, such as for ground-based measurements and atmospheric observations, can be applied to accurately monitor the performance of NBS to mitigate HMRs. We also focused on the capabilities of passive and active remote sensing, pointing out their associated opportunities and difficulties for NBS monitoring application. We conclude that the advancement in airborne and satellite-based remote sensing technology has signified a leap in the systematic monitoring of NBS performance, as well as provided a robust way for the spatial and temporal comparison of NBS intervention versus its absence. This improved performance measurement can support the evaluation of existing uncertainty and scepticism in selecting NBS over the artificially built concrete structures or grey approaches by addressing the questions of performance precariousness. Remote sensing technical developments, however, take time to shift toward a state of operational readiness for monitoring the progress of NBS in place (e.g. green NBS growth rate, their changes and effectiveness through time). More research is required to develop a holistic approach, which could routinely and continually monitor the performance of NBS over a large scale of intervention. This performance evaluation could increase the ecological and socio-economic benefits of NBS, and also create high levels of their acceptance and confidence by overcoming potential scepticism of NBS implementations.

Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM10 and PM2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM2.5, were 32,000±5,300 due to all-cause mortality, between 16,000±2,100 and 51,000±3,000 due non-accidental causes, between 7,300±1,300 and 16,700±1,500 due to cardiovascular disease, between 4,750±900 and 10,950±870 due ischemic heart diseases and 1,220±330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM2.5 among ‘children’ age group than for PM10 (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840±3,950 avoidable hospitalizations in the elderly related to a decrease in PM2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management.

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.

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.

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.

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.

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.

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.

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.

We present a comprehensive review of published results from the last 30 years regarding the sources and atmospheric characteristics of particles and ozone in the Metropolitan Area of São Paulo (MASP). During the last 30 years, many efforts have been made to describe the emissions sources and to analyse the primary and secondary formation of pollutants under a process of increasing urbanisation in the metropolitan area. From the occurrence of frequent violations of air quality standards in the 1970s and 1980s (due to the uncontrolled air pollution sources) to a substantial decrease in the concentrations of the primary pollutants, many regulations have been imposed and enforced, although those concentrations do not yet conform to the World Health Organization guidelines. The greatest challenge currently faced by the São Paulo State Environmental Protection Agency and the local community is controlling secondary pollutants such as ozone and fine particles. Understanding the formation of these secondary pollutants, by experimental or modelling approaches, requires the description of the atmospheric chemical processes driven by biofuel, ethanol and biodiesel emissions. Exposure to air pollution is the cause of many injuries to human health, according to many studies performed not only in the region but also worldwide, and affects susceptible populations such as children and the elderly. The MASP is the biggest megacity in the Southern Hemisphere, and its specifics are important for other urban areas that are facing the challenge of intensive growth that puts pressure on natural resources and worsens the living conditions in urban areas. This text discusses how imposing regulations on air quality and emission sources, mainly related to the transportation sector, has affected the evolution of pollutant concentrations in the MASP.

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.

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.

The emergence of low-cost sensors (LCSs) has rapidly changed the landscape of air pollution monitoring. Unlike regulatory standards with comprehensive processes for performance evaluation and certification for reference equipment, no accreditations or regulatory standards exist for LCSs. Hence, calibration and performance assessment of the LCSs are carried out via co-location experiments with reference instruments under limited ranges of environmental conditions and pollutant concentrations. We designed and tested an environmental-pollution (referred to as ‘Envilution™’) chamber to generate controlled environment for temperature and relative humidity (RH) along with different concentrations of particles so that varied real-world environmental conditions and pollution concentrations can be generated for the performance evaluation of LCSs. The custom-made 125L Envilution™ chamber consists of a humidifier/dehumidifier system, heat pump, particulate matter (PM) generator, a connection for gaseous air pollutants and reference measuring instruments. In the experiments under controlled conditions, the chamber was able to maintain diverse ambient and indoor environmental conditions (temperature range from 5 to 40 °C and RH from 10 to 90%) and stable pollutant concentrations, thereby enabling the use of chamber as a reference environment for LCSs' testing. For demonstration, the assessment was conducted based on temperature/RH (HDC1000 digital) and PM2.5 (HPMA115S0 Honeywell) sensors. A Vaisala HMT120 temperature/RH sensor and optical particle counter (Grimm EDM 107) were employed as reference instruments. The evaluation of LCSs, which were placed inside small enclosure kits, showed excellent correlation for temperature (R2 > 0.96), RH (R2 = 0.99), and PM2.5 (R2 = 0.97) with the reference instruments. The LCSs also demonstrated high linearity agreement (R2 > 0.98) among themselves at temperature (5–35 °C), RH (20–80%), PM2.5 (65–200 μg/m3) measurement ranges. The unique features of the chamber, including affordable cost, small size and lightweight, low maintenance/operational costs and ease of operation, has the potential to make it an on-demand package for LCSs' testing.

The ambient air of hospitals contains a wide range of biological and chemical pollutants. Exposure to these indoor pollutants can be hazardous to the health of hospital staff. This study aims to evaluate the factors affecting indoor air quality and their effect on the respiratory health of staff members in a busy Iranian hospital. We surveyed 226 hospital staff as a case group and 222 office staff as a control group. All the subjects were asked to fill in a standard respiratory questionnaire. Pulmonary function parameters were simultaneously measured via a spirometry test. Environmental measurements of bio-aerosols, particulate matter, and volatile organic compounds in the hospital and offices were conducted. T-tests, chi-square tests, and multivariable logistic regressions were used to analyze the data. The concentration of selected air pollutants measured in the hospital wards was more than those in the administrative wards. Parameters of pulmonary functions were not statistically significant (p > 0.05) between the two groups. However, respiratory symptoms such as coughs, phlegm, phlegmatic coughs, and wheezing were more prevalent among the hospital staff. Laboratory staff members were more at risk of respiratory symptoms compared to other occupational groups in the hospital. The prevalence of sputum among nurses was significant, and the odds ratio for the presence of phlegm among nurses was 4.61 times greater than office staff (p = 0.002). The accumulation of indoor pollutants in the hospital environment revealed the failure of hospital ventilation systems. Hence, the design and implementation of an improved ventilation system in the studied hospital is recommended.

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 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 

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 × 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 30–250 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, Normalized Mean Bias (NMB) was in the range 6%–190% compared to PNC>10 and 90%–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-h time resolution. The data seem to provide a good indication of the relative differences in PNC at Danish addresses.

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.

Atmospheric quasi-ultrafine particles (qUFP; PM

Springer Science+Business Media Dordrecht Particulate matter (PM) is a major pollutant in and around opencast mine areas. The problem of degradation of air quality due to opencast mine is more severe than those in underground mine. Prediction of dust concentration must be known to implement control strategies and techniques to control air quality degradation in the workplace environment. Limited studies have reported the dispersion profile and travel time of PM between the benches inside the mine. In this paper, PM concentration has been measured and modeled in Malanjkhand Copper Project (MCP), which is one of the deepest opencast copper mines in India. Meteorological parameters (wind speed, temperature, relative humidity) and PM concentration in seven size ranges (i.e., PM0.23–0.3, PM0.3–0.4, PM0.4–0.5, PM0.5–0.65, PM0.65–0.8, PM0.8–1, and PM1–1.6) have been measured for 8 days. The results of the field study provide an understanding of the dispersion of the PM generated due to mining activities. This research work presents an approach to assess the exposure of enhanced level of PM concentration on mine workers and its variation with depth. The correlations study shows that concentration of PM during its travel from source to surface is associated with depth. Empirical equations are developed to represent relationships between concentrations of PM and depth. Artificial neural network (ANN) model showing the relationship between PM concentration and meteorological parameters has been developed. The performance of the ANN model is evaluated in terms of the correlation coefficient between the real and the forecasted data. The results show strong agreement between the experimental data and the modeled output. The findings of this work are important in understanding fine PM variation inside the mine at the workplace and the associated exposure of mine workers.

An integrated experimental methodology has been applied to measure number and size distributions of particles in the 5-560nm size range in the wake of a diesel car running at different speeds. Measurements were made at both ground-fixed (0.10 and 0.25m above the ground level) and on-board (in 12 different sampling locations behind the moving car) measurement configurations using a fast response differential mobility spectrometer (Cambustion DMS50) with a sampling frequency up to 10Hz. Results from both the experimental campaigns were analysed to understand the dynamics, dispersion and transport of nanoparticle emissions in the wake of a moving vehicle. Temporal changes in results were divided into three main stages (pre-evolution, evolution and post-evolution) after the release of exhaust emissions from the tailpipe. Evolution stage is of most interest where all the changes to particle number and size distribution occurred. Up to four evolution sub-stages were observed, each showing distinct evolution patterns of particle size distributions, depending on the particular experimental run. In agreement with previous studies, dilution was found to be the dominant process throughout all the evolution stages. The first evolution sub-stage was common to all the measurements, and consisted of an initial particle number concentrations and distributions change due to rapid (less than 1s) nucleation followed by a rapid increase of accumulation mode particle number concentrations. After this first sub-stage the presence of vehicle wake with recirculating particles and the possible influence of other transformation processes lead to complex interactions. Results from the two experimental datasets clearly confirm the presence of two separate groups of particles: (i) new particles, which are freshly emitted and come directly from the tailpipe and (ii) relatively aged particles, which are entrained within the recirculation vortices of the vehicle wake and reside there for a longer time. The two groups have different characteristics and interact with each other. This interaction has often been overlooked in past studies about local scale dispersion of nanoparticle from moving vehicles.

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.

Seven different types of gasification-based coal conversion processes for producing mainly electricity and in some cases hydrogen (H2), with and without carbon dioxide (CO2) capture, were compared on a consistent basis through simulation studies. The flowsheet for each process was developed in a chemical process simulation tool “Aspen Plus”. The pressure swing adsorption (PSA), physical absorption (Selexol), and chemical looping combustion (CLC) technologies were separately analysed for processes with CO2 capture. The performances of the above three capture technologies were compared with respect to energetic and exergetic efficiencies, and the level of CO2 emission. The effect of air separation unit (ASU) and gas turbine (GT) integration on the power output of all the CO2 capture cases is assessed. Sensitivity analysis was carried out for the CLC process (electricity-only case) to examine the effect of temperature and water-cooling of the air reactor on the overall efficiency of the process. The results show that, when only electricity production in considered, the case using CLC technology has an electrical efficiency 1.3% and 2.3% higher than the PSA and Selexol based cases, respectively. The CLC based process achieves an overall CO2 capture efficiency of 99.9% in contrast to 89.9% for PSA and 93.5% for Selexol based processes. The overall efficiency of the CLC case for combined electricity and H2 production is marginally higher (by 0.3%) than Selexol and lower (by 0.6%) than PSA cases. The integration between the ASU and GT units benefits all three technologies in terms of electrical efficiency. Furthermore, our results suggest that it is favourable to operate the air reactor of the CLC process at higher temperatures with excess air supply in order to achieve higher power efficiency.

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 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.

There have been many studies concerning dispersion of gaseous pollutants from vehicles within street canyons; fewer address the dispersion of particulate matter, particularly particle number concentrations separated into the nucleation (10-30nm or N10-30) or accumulation (30-300nm or N30-300) modes either separately or together (N10-300). This study aimed to determine the effect of wind direction and speed on particle dispersion in the above size ranges. Particle number distributions (PNDs) and concentrations (PNCs) were measured in the 5-2738nm range continuously (and in real-time) for 17days between 7th and 23rd March 2007 in a regular (aspect ratio~unity) street canyon in Cambridge (UK), using a newly developed fast-response differential mobility spectrometer (sampling frequency 0.5Hz), at 1.60m above the road level. The PNCs in each size range, during all wind directions, were better described by a proposed two regime model (traffic-dependent and wind-dependent mixing) than by simply assuming that the PNC was inversely proportional to the wind speed or by fitting the data with a best-fit single power law. The critical cut-off wind speed (Ur,crit) for each size range of particles, distinguishing the boundary between these mixing regimes was also investigated. In the traffic-dependent PNC region (UrUrUr,critUr,crit), concentrations were inversely proportional to Ur irrespective of any particle size range and wind directions. The wind speed demarcating the two regimes (Ur,critUr,crit) was 1.230.55m s-1 for N10-300, (1.470.72m s-1) for N10-30 but smaller (0.780.29m s-1) for N30-300.

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.

This study analyses the impact of integrated mass rapid transit system (IMRTS) and other policy measures on air emissions from vehicular sources in Delhi region. The impacts have been studied for the passenger and goods vehicles separately. For this purpose three alternative scenarios for the passenger vehicles and two alternative scenarios for the goods vehicles have been analysed for the year 2021. The interventions include stringent source emission norms, modal shift resulting from introduction of effective public transport alternatives, speed regulation measures and hiking of parking fee of private vehicles. These scenarios have been compared to the base year 2007. An important finding that emerged from the study is that stringent fuel emission norms and introduction of alternative public transport systems alone may not result in the modal shift and hence reduction in exhaust emissions. It is actually a combination of these measures and management measures such as increased parking fee and regulated uniform speed of public transport that results in desired benefits. Further, the inclusion of goods vehicle demand during transport policy formulation can help in controlling air pollution in new urban centers in India and in major developing regions of the world.

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.

In the present study, the daily dose in terms of particle surface area received by citizens living in five cities in Western countries, characterized by different lifestyle, culture, climate and built-up environment, was evaluated and compared. For this purpose, the exposure to sub-micron particle concentration levels of the population living in Barcelona (Spain), Cassino (Italy), Guilford (United Kingdom), Lund (Sweden), and Brisbane (Australia) was measured through a direct exposure assessment approach. In particular, measurements of the exposure at a personal scale were performed by volunteers (15 per each population) that used a personal particle counter for different days in order to obtain exposure data in microenvironments/activities they resided/performed. Non-smoking volunteers performing non-industrial jobs were considered in the study. Particle concentration data allowed obtaining the exposure of the population living in each city. Such data were combined in a Monte Carlo method with the time activity pattern data characteristics of each population and inhalation rate to obtain the most probable daily dose in term of particle surface area as a function of the population gender, age, and nationality. The highest daily dose was estimated for citizens living in Cassino and Guilford (>1000 mm2), whereas the lowest value was recognized for Lund citizens (around 100 mm2). Indoor air quality, and in particular cooking and eating activities, was recognized as the main influencing factor in terms of exposure (and thus dose) of the population: then confirming that lifestyle (e.g. time spent in cooking activities) strongly affect the daily dose of the population. On the contrary, a minor or negligible contribution of the outdoor microenvironments was documented.

This article investigates the effectiveness of two distinct formative assessment methods for promoting deep learning and hence improving the performance amongst engineering students. The first method, applied for undergraduate students, employs a lecturer-led approach whereas the second method uses a student-led approach and e-learning for postgraduate teaching. Both studies demonstrate that the formative assessment and feedback has a positive effect on the performance of engineering students, especially those lying on the middle and lower grade tail. The mean exam marks increased by 15 to 20% as a result of introducing formative assessment to the case study modules. The main catalysts for performance improvement were found to be the feedback provided by the lecturer to the students, and by the students to their peer partners. Comparison of the two practices leads to the conclusion that whilst both methods are equally effective, peer assessment requires less time commitment from the lecturer.

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.

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.

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.

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 (

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.

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.

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 (

Rapid urbanisation in developing megacities like Delhi has resulted in an increased number of road vehicles and hence total particle number (ToN) emissions. For the first time, this study presents preliminary estimates of ToN emissions from road vehicles, roadside and ambient ToN concentrations, and exposure related excess deaths in Delhi in current and two future scenarios; business as usual (BAU) and best estimate scenario (BES). Annual ToN emissions are estimated as 1.37 × 1025 for 2010 which are expected to increase by ∼4 times in 2030-BAU, but to decrease by ∼18 times in 2030-BES. Such reduction is anticipated due to a larger number of compressed natural gas driven vehicles and assumed retrofitting of diesel particulate filters to all diesel vehicles by 2020. Heavy duty vehicles emit the majority (∼65%) of ToN for only ∼4% of total vehicle kilometres traveled in 2010. Their contribution remains dominant under both scenarios in 2030, clearly requiring major mitigation efforts. Roadside and ambient ToN concentrations were up to a factor of 30 and 3 higher to those found in respective European environments. Exposure to ambient ToN concentrations resulted in ∼508, 1888, and 31 deaths per million people in 2010, 2030-BAU and 2030-BES, respectively.

This study presents a comparison between measured and modelled particle number concentrations (PNCs) in the 10-300nm size range at different heights in a canyon. The PNCs were modelled using a simple modelling approach (modified Box model, including vertical variation), an Operational Street Pollution Model (OSPM) and Computational Fluid Dynamics (CFD) code FLUENT. All models disregarded any particle dynamics. CFD simulations have been carried out in a simplified geometry of the selected street canyon. Four different sizes of emission sources have been used in the CFD simulations to assess the effect of source size on mean PNC distributions in the street canyon. The measured PNCs were between a factor of two and three of those from the three models, suggesting that if the model inputs are chosen carefully, even a simplified approach can predict the PNCs as well as more complex models. CFD simulations showed that selection of the source size was critical to determine PNC distributions. A source size scaling the vehicle dimensions was found to better represent the measured PNC profiles in the lowest part of the canyon. The OSPM and Box model produced similar shapes of PNC profile across the entire height of the canyon, showing a well-mixed region up to first [approximate]2m and then decreasing PNCs with increased height. The CFD profiles do correctly reproduce the increase from road level to a height of [approximate]2m; however, they do not predict the measured PNC decrease higher in the canyon. The PNC differences were largest between idealised (CFD and Box) and operational (OSPM) models at upper sampling heights; these were attributed to weaker exchange of air between street and roof-above in the upper part of the canyon in the CFD calculations. Possible reasons for these discrepancies are given.

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

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.

The design of low to medium-rise buildings is based on quasi-static analysis of wind loading. Such procedures do not fully address issues such as interference from other structures, wind directionality, across-wind response and dynamic effects including acceleration, structural stiffness and damping which influence comfort criteria of the occupants. This paper studies wind loads on a prototype, rectangular cross-section building, 80 m high. Computational Wind Tunnel (CWT) tests were performed using Autodesk Flow Design with the buildings located in London and New York City. The analysis included tests with and without the surrounding structures and manual computation of wind loads provided data for comparison. Comfort criteria (human response to building motion) were assessed from wind-induced horizontal peak accelerations on the top floor. As expected, analytical methods proved conservative, with wind pressures significantly larger than those from the CWT tests. Surrounding structures reduced the mean component of the wind action. As for comfort criteria, across-wind direction governed the horizontal accelerations with wind targeted on the building’s narrow face. CWT tests provide a cheaper alternative to experimental wind tunnel tests and can be used as preliminary design tools to aid civil engineers, architects and designers with high-rise developments in urban environments.

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.

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.

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.

Many people live, work and spend time during their commute in near-road environments (

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.

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 green infrastructure (GI) is identified as a passive exposure control measure of air pollution. This work examines particulate matter (PM) reduction by a roadside hedge and its deposition on leaves. The objectives of this study are to (i) quantify the relative difference in PM concentration in the presence of GI and at an adjacent clear area; (ii) estimate the total mass and number density of PM deposited on leaves of a hedge; (iii) ascertain variations in PM deposition at adult (1.5m) and child (0.6 m) breathing levels on either side of a hedge; (iv) illustrate the relationship between PM deposition to leaves and ambient PM concentration reductions; and (v) quantify the elemental composition of collected particles of the leaves on different heights and sides of hedge. PM reduction of 2–9% was observed behind hedge compared to a clear area and followed a trend of ΔPM1 ˃ΔPM10 ˃ΔPM2.5. Counting of particles was found to be an effective method to quantify deposition than weighting methods. Sub-micron particles (PM1) dominated particle deposition on leaves at all sampling points on both sides of the hedge. PM mass deposition and number concentration to the leaves on traffic-facing side was up to 36% and 58% higher at 0.6m compared with 1.5m height, respectively. Such a difference was absent on the backside of the hedge. The SEM-EDS analysis showed up to 12% higher traffic-originated particles deposited to leaves on the traffic-facing side compared to the backside. The naturally occurring particles dominated in identified particles on leaf samples from all collection points on the hedge. These new evidence expand our understanding of PM reduction of GI in the near-road environment and its variations in particle deposition, depending on height and sides of GI, which could allow a better parameterisation of dispersion-deposition models for GI assessment at micro-scale.

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.

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.

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.

© 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.

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.

Aerosol particles scatter and absorb solar radiation and affect the 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 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 Jaisalmer (arid environment) and Delhi (urban environment) of India and subjected to Focused Ion-Beam (FIB) coupled with Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscope (EDS). Based on analysis of #2 particles from Jaisalmer, particles were observed to be composed of Fe, Ca, C, Al, Cu and Mg rich shell with Si and O rich core as opposed to those of Delhi particles (no #3) which were observed to be with Cu and S rich core and Hg, Ag, C, S and N rich shell. Based on the homogeneous sphere/spheroid assumption, conventional SEM-EDS and FIB-SEM-EDS results, different particle model shapes [single species homogeneous sphere (SP1) and spheroid (SPH1); multiple species homogeneously mixed sphere (SP2) and spheroid (SPH2); and core-shell (CS)] were considered for simulating their respective optical properties; SSA (Single Scattering Albedo) and g (Asymmetry 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 have high light absorbing property which causes positive radiative forcing.

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.

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.

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

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.

Understanding the distribution levels and sources of volatile organic compounds (VOC), mainly benzene, toluene, ethylbenzene and xylenes (BTEX), in the ambient atmosphere is important for efficiently managing and implementing the associated control strategies. We measured BTEX compounds at an industrial location in the west Tehran city (Iran), which is highly influenced by industrial activities and traffic during the winter and spring seasons during 2014-2015. A multivariate receptor model, UNMIX, was applied on the measured data for the identification of the sources and their contributions to BTEX compounds in a highly industrialised and trafficked atmospheric environment of Tehran city. Three main groups of sources were identified. These included solvent and painting sources (e.g. vehicle manufacturing), motorised road vehicles and mixed origin sources. While the solvent and painting sources and vehicle exhaust emissions contributed to about 5 and 29% of total BTEX mass, respectively, the mixed origin source contributed to about two-third (~66%) of the remaining mass. These mixed origin sources included rubber and plastic manufacturing (39%), leather industries (28%) and the unknown sources (33%). The mean concentrations of benzene, toluene, ethylbenzene and average xylene(o, p,m) compounds were measured as 28.96±9.12 µg m –3 , 29.55±9.73 µg m –3 , 28.61±12.2 µg/m-3 and 25.68±10.58 µg m–3 , respectively. A high correlation coefficient (R 2 >0.94) was also found between predicted (modelled) and measured concentrations for each sample. Further analyses from UNMIX receptor model showed that the average weekday contributions of BTEX compounds were significantly higher during winter compared with those during spring. This higher concentration during winter may be attributed to calm wind conditions and high stability of the atmosphere, along with the after effect of government policies on the use of cleaner fuel in refineries that became operational in winter 2014

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.

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.

Three naturally and six mechanically ventilated microenvironments (MEs) of a mix-use commercial building in Delhi are used to study indoor-outdoor (I/O) relationships of particulate matter ≤10 μm (PM), ≤2.5 μm (PM), and ≤1 μm (PM). Effect of environmental and occupancy parameters on the concentrations of PM during working and non-working hours (i.e., activity and non-activity periods, respectively) are also investigated. Average outdoor concentration of PM and PM were found to exceed the 24-h averaged national standard values, showing a polluted environment surrounding the studied building. During working hours, indoor PM concentration was found 6-10 times, both PM and PM were 1.5-2 times, higher than the non-working hours in the selected MEs. The variations of indoor concentrations were highest (17.1-601.2 μg/m) for PM compared with PM (16.9-102.6 μg/m) and PM (10.6-63.6 μg/m). The I/O for PM, PM, and PM varied from 0.37-3.1, 0.2-3.2, and 0.17-2.9, respectively. The results suggest highest I/O for PM, PM, and PM as 3.1, 2.15, and 1.76, respectively, in all the three natural-ventilated MEs (canteen, kitchen, reception). Irrespective of PM types, the average I/O was 1 for naturally ventilated MEs. As opposed to PM, better correlation (r > 0.6) was noted between indoor PM, PM, and CO concentrations in most of the airtight MEs. © 2013 Springer Science+Business Media Dordrecht.

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 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.

Vehicles are commonly the main source of outdoor air pollution in urban areas and vehicular emission inventory is a tool to identify the emissions contribution from mobile sources. In this study, we developed an emission inventory to Belo Horizonte, a densely populated urban city in Brazil, with approximately 2.0 million vehicles. The vehicular emission inventory was developed applying the National Vehicle Emission Inventory model (VEIN) using emission factor from São Paulo State Environmental Protection Agency, different traffic behavior profile (constant and different diurnal cycle per vehicle type) established from local radar data and kriging interpolation method considering four different scenarios with reductions in fleet composition. The scenarios were described as according the combination between traffic behavior profiles, vehicle flow, vehicle type and a fuel consumption. The comparison between scenarios showed reductions of emissions around 8.5 % (CO), 8.8 % (CO2), when it was considered 10 % of reduction in fleet composition of passenger cars and light commercial vehicles. Considering 20 % reduction in diesel fleet composition (trucks and buses), a decrease of 8.4 % (NOx) and 8.6 % (PM) was observed. Furthermore, this work presented that the kriging method to define a spatial/temporal distributing using radar traffic data is an alternative low- cost method to investigate the effect of real traffic data on the vehicular emissions modeling. This study is pioneer in Brazil and reinforced the importance of detailing traffic activities using real data to estimate vehicular emissions in an urban area. Transportation management strategies to reduce air pollution and to assist users to reduce air pollution exposure are mandatory to create a collaborative network and build sustainable cities in the future. It is necessary more investigations methods to generate an accuracy spatial distribution aggregated coupled with different traffic behavior profile to develop actions to reduce vehicular emissions in urban areas, investigate air pollution exposure, perform project-level emissions and hot-spot analysis.

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,

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.

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.

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).

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.

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 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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

Aerodyne Research, Inc., Billerica, Massachusetts, USA

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.

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.

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.

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.

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.

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.