My research project
Air pollutants transport modelling of nanoparticles in the urban environment
Arvind Tiwari is a postgraduate researcher (PhD Student) that holds a postgraduate degree in environmental and management from Indian institute of Technology Delhi (IIT-D) and an undergraduate degree in civil engineering from Madhav institute of technology & Science Gwalior, India.
He has six years work experience in modelling and simulation of environmental flow problems and currently is a PhD student in the Department of Civil and Environmental Engineering at University of Surrey. During his stay in Guildford, he will be under the guidance of Professor Prashant Kumar (Principal Supervisor) and Dr Devendra Saroj (Co- Supervisor) on the topic: Air pollutants transport modelling of nanoparticles in the urban environment.
Courses I teach on
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.
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 (
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 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.
Fibre steering is involved in the development of non-conventional variable stiffness laminates (VSL) with curvilinear paths as well as in the lay-up of conventional laminates with complex shapes. Manufacturability is generally overlooked in design and, as a result, industrial applications do not take advantage of the potential of composite materials. This work develops a design for manufacturing (DFM) tool for the introduction in design of the manufacturing requirements and limitations derived from the fibre placement technology. This tool enables the automatic generation of continuous fibre paths for manufacturing. Results from its application to a plate with a central hole and an aircraft structure – a windshield front fairing – are presented, showing good correlation of resulting manufacturable paths to initial fibre trajectories. The effect of manufacturing constraints is assessed to elucidate the extent to which the structurally optimal design can be reached while conforming to existing manufacturing specifications.
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.
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.
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.