- You will join a large cross-university multidisciplinary research network working on air quality observations and modelling using novel instrumentations, mobile vans and low cost sensors, and air quality, health impact assessment and economic models, as part of a NERC funded major research projects – West Midlands Air Quality Improvement Programme (£5m) and UK Air Quality Supersite Triplet (UK-AQST, £1.3m)
- Air quality big data from the monitoring network and low cost sensors will be analysed by using machine learning techniques to model and visualize the pollution distribution, human exposure and to assess the effectiveness (and health benefit and equity) of air quality control measures
- Hand-on training will be provided to the candidate to develop skills in novel machine learning techniques and economic modelling, which will benefit future career of candidate whether in academia, industry or governmental departments
Air pollution is the single largest risk to human health, contributing to more deaths (7 million) than all other environmental risks combined (Landrigan et al., 2017). In the UK, air pollution causes up to 36,000 deaths a year premature deaths and costs the economy £20 b per year.
Understanding the spatial/temporal distribution of air pollutants is essential to estimate the human exposure and health effects. The number of air quality monitoring stations (AURN) is very limited, e.g., only 5 stations in Birmingham. Using the AURN data to estimate human exposure bears large uncertainty. Satellite can provide an estimate of ground level concentrations but again with large uncertainty. Recently machine learning algorithms are used to combine low cost sensor network and satellite data with ground-based monitoring data to provide a more accurate modelling of spatial and temporal distribution of air pollutants (Zhan et al., 2018).
Machine learning algorithms can also be used to decouple the effects of meteorology from observed air pollutant concentrations (Figure 1, Shi et al., 2021), which reflect the real trend in air quality. This information can then be used to evaluate the air pollution interventions (such as clean air zone in Birmingham or London). We recently showed that a machine-learning based random forest algorithms has a superior performance than traditional statistical and air quality modelling (Vu et al., 2019) and offers an independent method.
The aim of this project is to evaluate the impact of clean air actions on air quality, health equity and economy. This will assist local authorities and the government to design future air pollution control strategies.
HostUniversity of Birmingham
- Climate and Environmental Sustainability
- Data source: long-term and real-time data from DEFRA and other open sources such as openaq; low cost sensor data from NERC funded WM-Air (£5m); meteorological data from ERA5 and open sources (NOAA); aerosol optical depth from satellite instruments
- Random forest algorithms (Vu et al., 2019) will be used for quantifying the real trend in concentrations of air pollutants in selected cities (before and after the implementation of clean air zone) using. This will allow the real (weather normalized) trend to be obtained so that the real impact of emission change quantified.
- A spatially explicit machine learning technique will be used to provide a spatial distribution of key air pollutants such as NO2 and PM5 in Birmingham incorporating automatic monitoring data, low cost sensor network, and aerosol optical depth.
- Health burdens and economic cost due to air pollution before and after the implementation of the clean air zone in Birmingham will be used to evaluate its impact on health and economy
Training and skills
Student will be trained to use R programme, the commonly used data analysis software package such as OpenAir, machine learning techniques, and economic (augmented synthetic control) models based on regression discontinuity design (Fu and Gu, 2017; Abadie A, Cattaneo, MD, 2018). Hand-on training by research staff and students will also be provided to the candidate to improve or develop codes based on machine learning techniques to analyse the air quality data across networks and from low cost sensors.
Specific training on literature reading and review and scientific writing will be provided on a regular basis during weekly supervisory meetings.
Partners and collaboration
This is jointly supervised by a senior air quality officer the Birmingham City Council. We are well engaged in research within the WM-Air project including deploying the low cost PM2.5 sensors and to evaluate the effectiveness of clean air zone. This project will apply a novel method that has been successfully used in global cities (Vu et al., 2019; Shi et al., 2021) to Birmingham to support policy processes.
Professor Zongbo SHI
School of Geography Earth and Environmental Sciences
University of Birmingham
Email: [email protected]
If you wish to apply to the project please visit: https://sits.bham.ac.uk/lpages/LES068.htm
Learn to use R and machine learning techniques (such as random forest algorithms), download air quality, meteorology, and satellite aerosol optical depth data from DEFRA, obtain low cost sensor network data from WM-Air; Apply the machine learning algorithms to visualize the spatial and temporal distribution of PM2.5 and NO2 in Birmingham; write up for publication
Overlay the spatial and temporal distribution of PM2.5 and NO2 with population density to estimate the human exposure to PM2.5 and NO2 in Birmingham and associated health effects; write up for publication
Apply a random forest algorithm to decouple of meteorological effect on air quality to derive the real trend in air quality; evaluate the effectiveness of clean air zone; Apply an economic model to estimate the monetary benefit and cost of the Birmingham clean air zone; write up for publication and for thesis.
Year 4: write up for publication and for thesis
Fu, S., Gu, Y. (2017). Highway toll and air pollution: Evidence from Chinese cities. J. Environ, Econom. Mangt., 83, 32-48, doi: 10.1016/j.jeem.2016.11.007.
Landrigan, P.J. et al. (2017). The Lancet Commission on pollution and health. The Lancet, 391, 462-512, doi: 10.1016/S0140-6736(17)32345-0.
Shi, Z., Song, C., Liu, B., Lu, G., Xu, J., Vu, T., Elliot, R.J.R., Li, W., Bloss, W.J., Harrison, R.M., 2020. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances, 7, add6696, doi: 10.1126/sciadv.abd6696.
Vu, V., Shi, Z., Cheng, J., Zhang, Q., He, K., Wang, S., Harrison, R.M. (2019). Assessing the impact of Clean Air Action Plan on Air Quality Trends in Beijing Megacity using a machine learning technique. Atmos. Chem. Phys., 19, 11303-11314, doi: 10.5194/acp-19-11303-2019.
Zhan, Y., Luo, Y., Deng, X., Zhang, K., Zhang, M., Grieneisen, M.L., Di, B. (2018). Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model. Environ. Sci. Technol., 2018, 52, 4180−4189, doi: 10.1021/acs.est.7b05669
WHO air quality guidelines: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health
Public Health England on air pollution health effect: https://www.gov.uk/government/news/public-health-england-publishes-air-pollution-evidence-review
UK government clean air strategy: https://www.gov.uk/government/publications/clean-air-strategy-2019
World Bank, report on The Cost of Air Pollution: Strengthening the Economic Case for Action – https://openknowledge.worldbank.org/handle/10986/25013