Modeling the Effects of Drivers on PM2.5 in the Yangtze River Delta with Geographically Weighted Random Forest

Abstract

Establishing an efficient PM2.5 prediction model and in-depth knowledge of the relationship between the predictors and PM2.5 in the model are of great significance for preventing and controlling PM2.5 pollution and policy formulation in the Yangtze River Delta (YRD) where there is serious air pollution. In this study, the spatial pattern of PM2.5 concentration in the YRD during 2003–2019 was analyzed by Hot Spot Analysis. We employed five algorithms to train, verify, and test 17 years of data in the YRD, and we explored the drivers of PM2.5 exposure. Our key results demonstrated: (1) High PM2.5 pollution in the YRD was concentrated in the western and northwestern regions and remained stable for 17 years. Compared to 2003, PM2.5 increased by 10–20% in the southeast, southwest, and western regions in 2019. The hot spot for percentage change of PM2.5 was mostly located in the southwest and southeast regions in 2019, while the interannual change showed a changeable spatial distribution pattern. (2) Geographically Weighted Random Forest (GWRF) has great advantages in predicting the presence of PM2.5 in comparison with other models. GWRF not only improves the performance of RF, but also spatializes the interpretation of variables. (3) Climate and human activities are the most important drivers of PM2.5 concentration. Drought, temperature, and temperature difference are the most critical and potentially threatening climatic factors for the increase and expansion of PM2.5 in the YRD. With the warming and drying trend worldwide, this finding can help policymakers better consider these factors for PM2.5 prediction. Moreover, the effect of interference from humans on ecosystems will increase again after COVID-19, leading to a rise in PM2.5 concentration. The strong explanatory power of comprehensive ecological indicators for the distribution of PM2.5 will be a crucial indicator worthy of consideration by decision-making departments.