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Evaluating the impact of clean air actions on air quality, health and economy using big data and machine learning techniques


NERC Doctoral Training Centre Studentships with CENTA

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Dr Z Shi , Prof Robert Elliott No more applications being accepted Competition Funded PhD Project (Students Worldwide)
Birmingham United Kingdom Geographical Information Systems Gis Pollution

About the Project

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., 2017, 2018).

Machine learning algorithms can also be used to decouple the effects of meteorology from observed air pollutant concentrations (Figure 1, Vu et al., 2019), which reflect the real trend in air quality. This information can then be used to evaluate the effectiveness of the air pollution interventions (such as clean air zone). 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 and economy. This will assist local authorities and the government to design future air pollution control strategies.

How to apply

Applications need to be submitted via the University of Birmingham postgraduate portal by midnight on 11.01.2021. Please first check whether the primary supervisor is within Geography, Earth and Environmental Sciences, or in Biosciences, and click on the corresponding PhD program on the application page.

This application should include

• a brief cover letter, CV, and the contact details for at least two referees

• a CENTA application form

• the supervisor and title of the project you are applying for under the Research Information section of the application form.

Referee’s will be invited to submit their references once you submit your application, but we strongly encourage applicants to ensure referees are aware of your submission and expecting a reference request from us. Students are also encouraged to visit and explore the additional information available on the CENTA website.


References

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.
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.
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. Under review in Science Advances (minor revision since 22 Sep 2020).
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
Zhan, Y., Luo, Y., Deng, X., Grieneisen, M.L., Zhang, M., Di, B. (2017). Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ. Pollu., 233, 464-473, doi: 10.1016/j.envpol.2017.10.029.


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