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Much of the population is exposed to PM2.5 (particulate matter) pollution in China, and establishing a high-precision PM2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R2), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m−3 and 8.25 (8.78) μg m−3, respectively. The monthly, seasonal, and annual mean PM2.5 were also successfully captured (CV-R2 = 0.91–0.92, RMSE = 4.35–6.72 μg m−3). Then, the spatial characteristics of PM2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m−3). In addition, 19 provinces or administrative regions have annual PM2.5 concentrations >35 μg m−3, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m−3), Tianjin (~49.65 μg m−3), and Henan Province (~48.60 μg m−3). Our estimated surface PM2.5 concentrations are accurate, which could benefit further research on air pollution in China.

Inferring Near-Surface PM2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model