Spatiotemporally continuous estimates of daily 1-km PM concentrations and their long-term exposure in China from 2000 to 2020.

Monitoring long-term variations in fine particulate matter (PM) is essential for environmental management and epidemiological studies. While satellite-based statistical/machine-learning methods can be used for estimating high-resolution ground-level PM concentration data, their applications have been hindered by limited accuracy in daily estimates during years without PM measurements and massive missing values due to satellite retrieval data. To address these issues, we developed a new spatiotemporal high-resolution PM hindcast modeling framework to generate the full-coverage, daily, 1-km PM data for China for the period 2000-2020 with improved accuracy. Our modeling framework incorporated information on changes in observation variables between periods with and without monitoring data and filled gaps in PM estimates induced by satellite data using imputed high-resolution aerosol data. Compared to previous hindcast studies, our method achieved superior overall cross-validation (CV) R and root-mean-square error (RMSE) of 0.90 and 12.94 μg/m and significantly improved the model performance in years without PM measurements, raising the leave-one-year-out CV R [RMSE] to 0.83 [12.10 μg/m] at a monthly scale (0.65 [23.29 μg/m] at a daily scale). Our long-term PM estimates show a sharp decline in PM exposure in recent years, but the national exposure level in 2020 still exceeded the first annual interim target of the 2021 World Health Organization air quality guidelines. The proposed hindcast framework represents a new strategy to improve air quality hindcast modeling and can be applied to other regions with limited air quality monitoring periods. These high-quality estimates can support both long- and short-term scientific research and environmental management of PM in China.