The 17-y spatiotemporal trend of PM2.5and its mortality burden in China

Investigations on the chronic health effects of fine particulate matter (PM) exposure in China are limited due to the lack of long-term exposure data. Using satellite-driven models to generate spatiotemporally resolved PMlevels, we aimed to estimate high-resolution, long-term PMand associated mortality burden in China. The multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) at 1-km resolution was employed as a primary predictor to estimate PMconcentrations. Imputation techniques were adopted to fill in the missing AOD retrievals and provide accurate long-term AOD aggregations. Monthly PMconcentrations in China from 2000 to 2016 were estimated using machine-learning approaches and used to analyze spatiotemporal trends of adult mortality attributable to PMexposure. Mean coverage of AOD increased from 56 to 100% over the 17-y period, with the accuracy of long-termaverages enhanced after gap filling.Machine-learning models performed well with a random cross-validation R2 of 0.93 at the monthly level. For the time period outside the model training window, prediction R2 values were estimated to be 0.67 and 0.80 at the monthly and annual levels. Across the adult population in China, long-term PMexposures accounted for a total number of 30.8 (95% confidence interval [CI]: 28.6, 33.2) million premature deaths over the 17-y period, with an annual burden ranging from 1.5 (95% CI: 1.3, 1.6) to 2.2 (95% CI: 2.1, 2.4) million. Our satellite-based techniques provide reliable long-term PMestimates at a high spatial resolution, enhancing the assessment of adverse health effects and disease burden in China.