Estimation of the chronic health effects of PM2.5 exposure has been hindered by the lack of long-term PM2.5 data in China. To support this, high-performance machine-learning models were developed to estimate PM2.5 concentrations at 1-km resolution in China from 2000 to 2016, based on satellite data, meteorological conditions, land cover information, road networks, and air pollution emission indicators. By adopting imputation techniques, relatively unbiased spatiotemporally continuous exposure estimates were generated. Annual mortality burdens attributable to long-term PM2.5 exposure were estimated at the provincial scale, and the national total adult premature deaths were estimated at 30.8 million over the 17-y period in China.
This study was published in PNAS (link).