Exploring Natural and Anthropogenic Drivers of PM2.5 Concentrations Based on Random Forest Model: Beijing–Tianjin–Hebei Urban Agglomeration, China

PM2.5 is the key reason for frequent occurrence of smog; therefore, identifying its driving factors has far-reaching significance prevention and control air pollution. Based on long-term remote sensing inversion data, 21 in fields nature humanities were selected, random forest model was applied to study influencing concentration Beijing–Tianjin–Hebei urban agglomeration (BTH) from 2000 2016. The results indicate: (1) main affecting not only include natural such as sunshine hours (SSH), relative humidity (RHU), elevation (ELE), normalized difference vegetation index (NDVI), wind speed (WIN), average temperature (TEM), daily range (TEMR), precipitation (PRE), but also human urbanization rate (URB), total investment fixed assets (INV), number employees secondary industry (INDU); (2) changed into an inverted S-shape with increase SSH WIN, RHU, NDVI, TEM, PRS, URB INV. As ELE TEMR, it fluctuated decreased ELE, while increased then TEMR. However, change less pronounced PRE INDU; (3) influence higher than that factors, role been continuously strengthened recent years. adjustment pollution sources perspective will become effective way reduce concentrations BTH.