Modeling of PM2.5 in the Yangtze River delta based on geographically weighted random forest
This study used Random Forest(RF)and Geographically Weighted Random Forest(GWRF)to train,verify,and test the PM2.5 concentration and data of its driving factors in the Yangtze River delta region from 2003 to 2019 and explore their relationships.The results show that:(1)Compared with the RF model,the GWRF model performs better in training and predicting PM2.5 concentration.The evaluation indicators of the GWRF model are better,and the residual spatial autocorrelation is lower than the RF model.(2)The GWRF model predicts a better PM2.5 concentration distribution in 2019 than the RF model,which is basically consistent with the actual observed concentration distribution.However,overestimation in the north,underestimation in the south,and the area of overestimation are more significant than the underestimation for the two models.(3)The RF model is global in studying the most significant driving factors of PM2.5concentration distribution.In contrast,the GWRF model's interpretation of PM2.5 influence by drought,temperature,temperature difference,wind,and human interference shows a localized effect.On a large scale,this localized effect has practical guidance for preventing and controlling PM2.5.In addition,in the context of global climate warming and spatial heterogeneity of regional climate,integrating drought into PM2.5 prediction and establishing models with localized effects can help environmental regulatory agencies and decision-makers formulate prevention and control measures.
PM2.5 driving factorsgeographically weighted random forestsspatial effectsYangtze River Delta region
万方数据
