Meng, X., Hang, Y., Lin, X., Li, T., Wang, T., Cao, J., Fu, Q., Dey, S., Huang, K., Liang, F. and Kan, H., 2023. A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China. Environment International, p.107740.

Science Direct: Link

Ambient fine particulate matter (PM2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM2.5 level has dramatically decreased mainly contributed by reductions in particulate sulfate. This is due to the fact that desulfurization, a process to remove sulfur (precursor of sulfate) from coal-fired power plants and industrial facilities, is one of the major air quality policies in China. Therefore, it is necessary to characterize the variation of sulfate as it is a strong indicator of assessing existing efforts toward improving air quality. However, estimating the long-term spatiotemporal trend of sulfate is challenging because a ground monitoring network of PM2.5 constituents has not been established. Fortunately, spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol types. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR’s aerosol optical depth data, and other atmospheric reanalysis at a spatial resolution of 10 km. Compared with classical chemical transport models, our sulfate model performs better with a random cross-validation R2 of 0.9 at the monthly level. We found that the national mean population-weighted sulfate concentration was relatively stable before the Air Pollution Prevention and Control Action Plan was born in 2013, but dramatically decreased by 28.7% from 2013-2018. Correspondingly, the total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7% and 42.3%, respectively. Our study’s reliable sulfate estimates will advance future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.