The long-term trend of PM_2.5-related mortality in China: The effects of source data selection

Quantification of PM_2.5 exposure and associated mortality is critical to inform policy making. Previous studies estimated varying PM_2.5-related mortality in China due to the usage of different source data, but rarely justify the data selection. To quantify the sensitivity of mortality assessment to source data, we first constructed state-of-the-art PM_2.5 predictions during 2000-2018 at a 1-km resolution with an ensemble machine learning model that filled missing data explicitly. We also calibrated and fused various gridded population data with a geostatistical method. Then we assessed the PM_2.5-related mortality with various PM_2.5 predictions, population distributions, exposure-response functions, and baseline mortalities. We found that in addition to the well documented uncertainties in the exposure-response functions, missingness in PM_2.5 prediction, PM_2.5 prediction error, and prediction error in population distribution resulted to a 40.5%, 25.2% and 15.9% lower mortality assessment compared to the mortality assessed with the best-performed source data, respectively. With the best-performed source data, we estimated a total of approximately 25 million PM_2.5-related mortality during 2001-2017 in China. From 2001 to 2017, The PM_2.5 variations, growth and aging of population, decrease in baseline mortality led to a 7.8% increase, a 42.0% increase and a 24.6% decrease in PM_2.5-related mortality, separately. We showed that with the strict clean air policies implemented in 2013, the population-weighted PM_2.5 concentration decreased remarkably at an annual rate of 4.5 μg/m³, leading to a decrease of 179 thousand PM_2.5-related deaths nationwide during 2013-2017. The mortality decrease due to PM_2.5 reduction was offset by the population growth and aging population.