Dr. Jie Yang from the School of Mathematical Sciences was awarded The Research Fund for International Young Scientists from National Natural Science Foundation of China (NSFC).
Project title: An investigation into predictive modelling of the impact of air pollution on China’s disease burden
Amount funded: 400,000 RMB
Duration: Jan 2018 - Dec 2019
Abstract:
Overestimating and underestimating the impact of air pollution on the disease burden in China leads to either economic growth being unnecessarily hindered or people putting a huge strain on the national or regional health and insurance system in order to treat illnesses caused by ambient air pollution. Accurate estimation is thus essential for enabling Government officials to take the most effective route in dealing with pollution. Thus, this project aims to investigate the impact of air pollution on China’s disease burden, in particular focusing on Ningbo as a case study, using the approach of predictive modelling. The existing models that are used to estimate the disease burden attributable to ambient air pollution exposure do not properly consider the influence of fluctuating weather patterns on dynamically dispersing or concentrating local sources of pollution.
This project will propose the use of a spatial-temporal mathematical model that takes into account information about levels of particulate matter (PM) less than 2.5 microns in aerodynamic diameter (PM2.5), PM less than 10 microns in aerodynamic diameter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO) and ozone (O3), but will also factor in meteorological fluctuations to generate dynamic health risk maps in China, focusing on Ningbo, caused by air pollution. The investigation will compare the cost of implementing intervention strategies to reach acceptable pollution levels determined by the local Government with current costs involved in treating the diseases caused by pollution. It is expected that the results derived from the models may then be used by health officials to make informed decisions about the optimal allocation of limited and valuable resources related to geographical areas of particular concern that require urgent action.