Estimating PM_2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset...

Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM_2.5) poses adverse impacts on public health and the environment. It is still a great challenge to estimate high-resolution PM_2.5 concentrations at moderate scales. The current study calibrated PM_2.5 concentrations at a 1 km resolution scale using ground-level monitoring data, Aerosol Optical Depth (AOD), meteorological data, and auxiliary data via Random Forest (RF) model across China in 2017. The three ten-folded cross-validations (CV) methods including sample-based, time-based, and spatial-based validation combined with Coefficient Square (R²), Root-Mean-Square Error (RMSE), and Mean Predictive Error (MPE) have been used for validation at different temporal scales in terms of daily, monthly, heating seasonal, and non-heating seasonal. Finally, the distribution map of PM_2.5 concentrations was illustrated based on the RF model. Some findings were achieved. The RF model performed well, with a relatively high sample-based cross-validation R² of 0.74, a low RMSE of 16.29 μg × m^-3, and a small MPE of -0.282 μg × m^-3. Meanwhile, the performance of the RF model in inferring the PM_2.5 concentrations was well at urban scales except for Chengyu (CY). North China, the CY urban agglomeration, and the northwest of China exhibited relatively high PM_2.5 pollution features, especially in the heating season. The robustness of the RF model in the present study outperformed most statistical regression models for calibrating PM_2.5 concentrations. The outcomes can supply an up-to-date scientific dataset for epidemiological and air pollutants exposure risk studies across China.