{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Meng X"],"funding":["National Institute of Environmental Health Sciences","NIEHS NIH HHS","NIH Library"],"pagination":["107740"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9985485"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["171"],"pubmed_abstract":["Ambient fine particulate matter (PM<sub>2.5</sub>) pollution is a major environmental and public health challenge in China. In the recent decade, the PM<sub>2.5</sub> 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<sub>2.5</sub> 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-validationR<sup>2</sup> 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<sup>-3</sup>. But the sulfate level dramatically decreased to 7.7 µg m<sup>-3</sup> 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."],"journal":["Environment international"],"pubmed_title":["A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China."],"pmcid":["PMC9985485"],"funding_grant_id":["R01 ES032140","1R01ES032140"],"pubmed_authors":["Fu Q","Cao J","Dey S","Lin X","Huang K","Hang Y","Meng X","Kan H","Shi X","Liu Y","Liang F","Li T","Wang T"],"additional_accession":[]},"is_claimable":false,"name":"A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China.","description":"Ambient fine particulate matter (PM<sub>2.5</sub>) pollution is a major environmental and public health challenge in China. In the recent decade, the PM<sub>2.5</sub> 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<sub>2.5</sub> 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-validationR<sup>2</sup> 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<sup>-3</sup>. But the sulfate level dramatically decreased to 7.7 µg m<sup>-3</sup> 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.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-03T23:49:46.396Z","creation":"2025-04-03T23:49:46.396Z"},"accession":"S-EPMC9985485","cross_references":{"pubmed":["36634483"],"doi":["10.1016/j.envint.2023.107740"]}}