biostudies-literatureMeng XNational Institute of Environmental Health SciencesNIEHS NIH HHSNIH Library107740https://www.ebi.ac.uk/biostudies/studies/S-EPMC9985485biostudies-literatureUnknown171Ambient fine particulate matter (PM_2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM_2.5 level has decreased mainly driven by reductions in particulate sulfate as a result of large-scale desulfurization efforts in coal-fired power plants and industrial facilities. Emerging evidence also points to the differential toxicity of particulate sulfate affecting human health. However, estimating the long-term spatiotemporal trend of sulfate is difficult because a ground monitoring network of PM_2.5 constituents has not been established in China. Spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol size and type. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR-retrieved aerosol microphysical properties, and atmospheric reanalysis data at a spatial resolution of 0.1°. Our sulfate model performed well with an out-of-bag cross-validationR² of 0.68 at the daily level and 0.93 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 enforced in 2013, ranging from 10.4 to 11.5 µg m^-3. But the sulfate level dramatically decreased to 7.7 µg m^-3 in 2018, with a change rate of -28.7 % from 2013 to 2018. Correspondingly, the annual mean total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7 % and 42.3 %, respectively. The long-term, full-coverage sulfate level estimates will support future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.Environment internationalA satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China.PMC9985485R01 ES0321401R01ES032140Fu QCao JDey SLin XHuang KHang YMeng XKan HShi XLiu YLiang FLi TWang TfalseA satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China.Ambient fine particulate matter (PM_2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM_2.5 level has decreased mainly driven by reductions in particulate sulfate as a result of large-scale desulfurization efforts in coal-fired power plants and industrial facilities. Emerging evidence also points to the differential toxicity of particulate sulfate affecting human health. However, estimating the long-term spatiotemporal trend of sulfate is difficult because a ground monitoring network of PM_2.5 constituents has not been established in China. Spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol size and type. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR-retrieved aerosol microphysical properties, and atmospheric reanalysis data at a spatial resolution of 0.1°. Our sulfate model performed well with an out-of-bag cross-validationR² of 0.68 at the daily level and 0.93 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 enforced in 2013, ranging from 10.4 to 11.5 µg m^-3. But the sulfate level dramatically decreased to 7.7 µg m^-3 in 2018, with a change rate of -28.7 % from 2013 to 2018. Correspondingly, the annual mean total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7 % and 42.3 %, respectively. The long-term, full-coverage sulfate level estimates will support future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.2023-01-01T00:00:00Z2023 Jan2025-04-03T23:49:46.396Z2025-04-03T23:49:46.396ZS-EPMC99854853663448310.1016/j.envint.2023.107740