Clean Air Research

Project Details

The project demonstrated that the ENHANCE system can offer a robust, continuous intervention for simultaneously reducing both air pollution and pathogen loading, with PM_2.5 reduction potential of over 97% and TVOC of up to 95%. Based on the efficacy evaluations, the system is deemed suitable for its deployment in a transport environment to reduce exposure in a constrained space, such as waiting lounge and lifts. We reckon full-scale deployment of the ENHANCE system in such locations would serve two-fold purpose – first, provide a control measure in any future pandemic preparation; second, offer an active health intervention at public transport facilities, specifically alleviating the health risk posed from air pollution and pathogen exposure to vulnerable population, who tend to use these facilities more frequently.

Project Details

“Using travel behaviour during the pandemic to provide insights into the air quality impacts of changes in working patterns and shopping behaviour”

The Covid-19 pandemic has resulted in an unprecedented ‘shock’ to regular traffic levels, in terms of the scale of the drop, the geographic coverage and the longevity of the impacts. Improvements in air quality were observed in many cities during the initial lockdown starting in March 2020, at least in terms of nitrogen oxides (NOx), although traffic emissions rose steadily through the summer of 2020 in most areas. This project seeks to examine travel behaviour during the pandemic to inform policies related to air quality and transport decarbonisation.

During lockdown many people worked exclusively from home and others shifted to often working from home. Although this has resulted in fewer trips on the roads, it is unclear what types of vehicles have stopped making trips. People on higher incomes are more likely to be able to work from home, so is the increase in working from home removing trips by newer, ‘cleaner’ vehicles? The pandemic has also resulted in more online shopping which may reduce personal travel, although it is likely to result in more van trips. By examining behaviour during different stages of the pandemic we will gain valuable information on truly ‘essential’ trips.

Firstly, aggregated data on traffic flows and air quality sensor data in Bristol will be analysed to explore spatial differences in the impact of the pandemic on travel and pollution. The project will then consider how individuals’ behaviour has changed, using Automatic Number Plate Recognition (ANPR) data and the associated vehicle type and emissions data for a sample of vehicles.

The responses of different types of road users will be examined, for example occasional visitors, regular commuters or delivery vans. The project therefore looks beyond the change in traffic volumes to see the changes in individual behaviour and the types of vehicles affected.

Project Details

“Exposures to ultrafine particles and volatile organic compounds inside and outside of multiple transportation modes”

The harm caused by emissions from vehicles to air quality and the health of humans outside is increasingly well understood. It is generally accepted that it is a policy priority to remove high- emitting vehicles from the road and to swap these for low-emission vehicles or public transport. What is less well understood is the exposure of the occupants in various transportation modes. Aggregate time spent in vehicles is significant, and can be measured in hours per day for certain commuters and professional drivers. Our hypothesis is that greater policy attention should be paid to the interior air quality and ventilation of vehicles.

Existing research by Emissions Analytics shows that the worst-performing cars can have particle number concentrations – more than three times that in the ambient air. In broad terms, public awareness of exposure to health dangers has increased due to COVID-19. The focus of this project will be on particulates and volatile organic compounds (VOCs). Particles measured will include ultrafine particles, and VOCs will be analysed so as to identify their component species, using highly sensitive equipment. Therefore, a much wider range of pollutants will be tested than in standard air quality monitoring. With Net Zero, particles are likely to be the dominant traffic pollutant.

The modes of transport that will be studied include diesel and electric trains, the London Underground, diesel and electric buses, and old and new cars, including a battery electric vehicle. As a baseline and reference, the exposures of pedestrians and cyclist to pollutants will also be measured. The main output will be average “factors” describing typical exposures by transport mode. 

Project Details

“Real Time Particulate Matter Source Apportionment”

Tiny particles in the air, known as particulate matter (PM) air pollution, are harmful to human health with some particles worse than others. PM comes in different size patterns that are typical of different sources, i.e. diesel smoke is smaller than construction/brake dust. Low cost ways to measure and understand the sources of PM pollution form a new field pioneered by the University of Birmingham (UoB) that has great commercial potential. This project joins the business and consulting expertise of DustScanAQ with the academic expertise of the UoB. The measurement and source apportionment of atmospheric pollutants is crucial for the assessment of air quality and the implementation of policies for its improvement.

Up to now for particle sizing, measurements have used bulky, expensive regulatory grade instruments costing approximately £100k, which makes it difficult to put many instruments around sources. Low cost sensors (£sub-5k) provide an affordable alternative, as evidenced by recent work by Bousiotis et al. 2021 (https://doi.org/10.5194/amt-2021-11), but their capability and reliability have yet to be tested with real world problems. In this project, low cost PM sensors will be used to differentiate particle sources from two policy relevant areas that relate to low carbon transport transitions: 1. Nuisance dust from railways infrastructure construction; and 2. Non-exhaust vehicle emissions.

Furthermore, the project will develop the technology so it can perform real-time source identification in the cloud, utilizing machine learning techniques to allow for source identification in real-time. This gives the potential for real-time identification of emissions from sources, helping drive the transition to a cleaner world. The project is in collaboration with HS2, which is building the next generation infrastructure for the UK’s high speed rail service.

Project Details

“3-Dimensional Modelling of Pollutant Dispersion and Exposure”

Vehicles on UK roads today are a significant source of nitrogen oxides (NOx) and Particulate Matter (PM); traffic levels are expected to rise significantly (17% to 51% by 2050). The health concerns associated with exposure to emissions are serious and wide ranging; even short-term exposure has been linked to a measurable impairment in cognitive performance. PM will continue to pose a significant threat in the context of transport decarbonisation, due to the impact of non-exhaust emissions (from brakes and tyres), which are potentially much larger than current tailpipe limits.

A newly emerging air quality and public health challenge comes from exposure to high, momentary peaks of air pollution which arise from vehicles stop-start manoeuvres and accelerations, typical of congested urban areas. Roadside air quality instrumentation does not routinely measure these events, and the health implications – especially for vulnerable groups (e.g. children, the elderly) who use streets and public transport more frequently – remain unknown. While literature is starting to discuss the weaknesses of the “point-fixed/uniform exposure” approach, there is a clear necessity of building up data to support specific air quality and medical research.

Leveraging years of experience on emissions, and Computational Fluid Dynamics (CFD) modelling, Oxford Brookes University have developed a new ultra-high definition 3-Dimensional CFD urban model, capable of: predicting the complex dynamics of pollutants dispersion from moving traffic; and quantifying actual exposure for the public occupying the space. The model is computationally demanding, but offers a vast accuracy advantage compared to other approaches (e.g. Gaussian Plume Models) for application in dense urban environments.

This project aims to: increase the technical capabilities of the Oxford Brookes University model; perform its validation using purposely-collected field data; and carry out a case study on bus stop shelters, to assess the effective protection they may offer from short-term peak concentrations of air pollution.