Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model

Fine particulate matter such as PM2.5 has been the focus of increasing public concerns because of its adverse effect on environment and health risks. However, existing efforts of mapping PM2.5 concentrations are always limited by coarse spatial resolutions and temporal frequencies. Addressing this shortcoming, here we explicitly estimated hourly PM2.5 concentrations at 1-km spatial resolution in China from March 2018 to February 2019 using a two-stage random forest model. In the first stage, we used a gap-filling method to generate full-coverage Aerosol Optical Depth (AOD) by fusing AOD data from satellite (Himawari-8 and MODIS) and weather forecast model (CAMS), and additional meteorological and geographical variables. Gap-filled AOD was subsequently used to estimate hourly PM2.5 in the Stage II. Results showed that our model achieved accurate and robust estimations of PM2.5 concentrations, with an overall cross-validated R-2 of 0.85, root mean squared error of 11.02 mu g/m(3), and mean absolute error of 6.73 mu g/m(3). CAMS-simulated PM2.5, elevation, and gap-filled AOD were identified to be important variables contributing to the model performance of PM2.5 estimation. The model performance varied over the daily temporal scale. Specifically, daily estimation model performed better in spring and winter but worse in summer and autumn. We provide an alternative to generate spatially and temporally explicit mapping of PM2.5 concentrations with fine resolutions, making it possible to achieve real-time monitoring of air pollutions. The detailed spatial heterogeneity and diurnal variability of PM2.5 concentrations will also be valuable for environmental exposure assessments.