Ambient PM2.5 was reported to be related to numerous negative health outcomes, leading to adverse public health impacts in many countries such as China. Despite the apparent reduction in PM2.5 levels over China due to its emission control policies in recent years, the health burdens were not reduced as much as expected. This calls for a comprehensive analysis to explain the reasons behind to provide a useful reference for formulating effective emission control strategies. Taking central China as an example due to its large population and high levels of PM2.5, this study quantified the spatiotemporal dynamics of premature mortality associated with PM2.5 pollution in central China for each year during 2003-2018 and applied a decomposition analysis to dissect the contribution of various driving factors including ambient PM2.5 level, demographic distribution and baseline incidence rate of four diseases related to air pollution. Results show significant spatiotemporal variations in PM2.5-attributed health impact in central China, including Henan, Hubei, and Hunan provinces. Five Henan cities had the largest PM2.5-attributable premature mortality (∼8-12 K premature mortalities), while three Hubei cities and one Hebei city had the least chronic PM2.5-related all-cause mortality numbers (1 K mortalities). Throughout the study period, the PM2.5-caused premature mortality decreased by 54 K, in which changes in PM2.5 levels and baseline incidence rates of stroke and chronic obstructive pulmonary disease contributed to the positive effect, whereas demographic changes and baseline incidence rate change of ischemic heart disease and lung cancer brought a countervailing effect. Our findings suggest more dynamic and comprehensive policies and measures that take into account spatiotemporal variations of health burden for effective alleviation of the health impact of PM2.5 pollution in the country.
Decomposition analysis, Health burden, PM(2.5), Satellite remote sensing, Spatiotemporal variation