[PM_2.5 Concentration Influencing Factors in China Based on the Random Forest Model]

In this paper, aerosol optical depth (AOD), elevation (DEM), annual precipitation (PRE), annual average temperature (TEM), annual average wind speed (WS), population density (POP), gross domestic product density (GDP), and normalized difference vegetation index (NDVI) were selected as factors influencing PM_2.5 concentration. The random forest model, order of feature importance, and partial dependency plots were applied to investigate these factors and their regional differences in PM_2.5 spatial pattern. The results showed that:① The random forest model was more accurate than multiple regression, generalized additive, and back propagation neural network models in estimating PM_2.5 concentration, which can be applied to quantifying PM_2.5 influencing factors. ② PM_2.5 concentration initially increased and then remained stable with increases in AOD, POP, and GDP, and initially decreased and then stabilized with increases in PRE, WS, and NDVI. The responses of DEM and TEM to PM_2.5 concentration changed from decline to ascend and then changed to decline again. ③ AOD had the largest influence on PM_2.5 annual concentrations with a spatial influencing magnitude of 37.96%, whereas PRE had the least influence with a merely individual spatial influencing magnitude of 5.75%. ④ The relationships between PM_2.5 pollution and influencing variables vary with geography and thus exhibit significant spatial heterogeneity. The same factor had different spatial influencing magnitudes on PM_2.5 annual concentrations in seven geographical subareas. AOD had the greatest influence on PM_2.5 concentration in the south of China, with the least influence in the northeast.