10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
Abstract View
Dynamic Health Risk Mapping and Predictive Modelling of the Impact of Metereological Fluctuations on Air Pollution in Yangtze River Delta
JIE YANG, University of Nottingham Ningbo China
Abstract Number: 785 Working Group: Aerosol Modeling
Abstract Overestimating and underestimating the impact of air pollution on the disease burden in the Yangtze River Delta (YRD) may lead to either economic growth being unnecessarily hindered or cardiorespiratory diseases induced by ambient air pollution imposing a huge strain on the national or regional health and insurance system in order to treat the related symptoms. Accurate estimation is thus essential to enable Governmental departments and health officials to implement the most effective measures for dealing with air pollution. Existing models that are employed 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 air pollution.
This study proposes the use of a novel spatial-temporal mathematical model based on a reaction-diffusion system with time delays to describe the change in relative risk of being exposed to certain particulate matter, which is dependent on both geographical location and its corresponding metereological conditions such as temperature, humidity and wind speed. The model 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), ozone (O3) and also factors in meteorological fluctuations to generate regional dynamic health risk maps in YRD caused by air pollution, focusing on Ningbo city and its surrounding areas as a case study. The simulation results of this model that employs air quality, weather and health data can be used to make predictions about the impact of air pollution on disease burden. It is expected that the results derived from the model may then be used by health officials to make informed decisions about the optimal allocation of limited and valuable resources for reducing the air pollution levels related to those geographical areas of particular concern.
Acknowledgement This study was supported by the National Natural Science Foundation of China (Grant no. 21750110446).