资源环境科技发展态势分析平台

Fine particulate matter (PM_2.5) has been the focus of increasing public concerns because of its adverse effect on environment and health risks. However, existing efforts of mapping PM_2.5 concentrations are always limited by coarse spatial resolutions and temporal frequencies. Addressing this shortcoming, here we explicitly estimated hourly PM_2.5 concentrations at 1-km spatial resolution across China from March 2018 to February 2019 using a two-stage random forest model. In the first stage, we conducted 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 generated in Stage I was subsequently used to estimate hourly PM_2.5 in Stage II. Results showed that our model achieved accurate and robust estimations of PM_2.5 concentrations, with an overall cross-validated R² of 0.85, root mean squared error of 11.02 μg/m³, and mean absolute error of 6.73 μg/m³. CAMS-simulated PM_2.5, elevation, and gap-filled AOD were identified to be relatively important variables contributing to the model performance of PM_2.5 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. In this study, we provided an alternative to generate spatially and temporally explicit mapping of PM_2.5 concentrations with fine resolutions, making it possible to achieve real-time monitoring of air pollutions. The detailed spatial heterogeneity and diurnal variability of PM_2.5 concentrations will also be valuable and supportive for environmental exposure assessment and related policy-driven regulations.