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Causal-inference machine learning reveals the drivers of China''''s 2022 ozone rebound

Lin Wang Baihua Chen Jingyi Ouyang Yanshu Mu Ling Zhen Lin Yang Wei Xu Lina Tang

Environmental Science and Ecotechnology2025，Vol.24Issue(2)：P.47-58,12.

Environmental Science and Ecotechnology2025，Vol.24Issue(2)：P.47-58,12.DOI:10.1016/j.ese.2025.100524

Causal-inference machine learning reveals the drivers of China''''s 2022 ozone rebound

Lin Wang ¹Baihua Chen ¹Jingyi Ouyang ²Yanshu Mu ³Ling Zhen ²Lin Yang ¹Wei Xu ¹Lina Tang¹

作者信息

1. Key Laboratory of Urban Environment and Health,Institute of Urban Environment,Chinese Academy of Sciences,Xiamen,361021,China
2. Key Laboratory of Urban Environment and Health,Institute of Urban Environment,Chinese Academy of Sciences,Xiamen,361021,China University of Chinese Academy of Sciences,Beijing,100049,China
3. China School of Mathematics,Jilin University,Changchun,130012,China
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摘要

关键词

Ground-level ozone/Interpretable machine learning/Causal forest/Ozone rebound

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Lin Wang,Baihua Chen,Jingyi Ouyang,Yanshu Mu,Ling Zhen,Lin Yang,Wei Xu,Lina Tang..Causal-inference machine learning reveals the drivers of China''''s 2022 ozone rebound[J].Environmental Science and Ecotechnology,2025,24(2):P.47-58,12.

基金项目

supported by the National Natural Science Foundation of China(42307591) （42307591）

Fujian Provincial Natural Science Foundation project(2022J05096) （2022J05096）

National Key R&D Program of China(2022YFF1301302). （2022YFF1301302）

Environmental Science and Ecotechnology

ISSN：2666-4984

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