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.
Funding Notes
CENTA studentships are for 3.5 years and are funded by the Natural Environment Research Council (NERC). In addition to the full payment of their tuition fees, successful candidates will receive the following financial support.
• Annual stipend, set at £15,009 for 2019/20
• Research training support grant (RTSG) of £8,000
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.
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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