基于地理加权随机森林的长三角PM<sub>2.5</sub>建模

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Abstract This study used Random Forest (RF) and Geographically Weighted Random Forest (GWRF) to train, verify, and test the PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5concentration distribution. In contrast, the GWRF model's interpretation of PM_2.5 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 PM_2.5. In addition, in the context of global climate warming and spatial heterogeneity of regional climate, integrating drought into PM_2.5 prediction and establishing models with localized effects can help environmental regulatory agencies and decision-makers formulate prevention and control measures.

Key words： PM_2.5 driving factors geographically weighted random forests spatial effects Yangtze River Delta region

Received: 19 January 2024

PACS:

X513

Corresponding Authors: 苏漳文,讲师,fujianszw@126.com E-mail: fujianszw@126.com

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	CHEN Yi-min
	SU Zhang-wen
	CHEN Yi-ping
	LIN Zi-peng

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CHEN Yi-min,SU Zhang-wen,CHEN Yi-ping等. Modeling of PM_2.5 in the Yangtze River delta based on geographically weighted random forest[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(8): 4240-4248.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I8/4240

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