Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 9
Articles | Volume 26, issue 9
https://doi.org/10.5194/acp-26-6377-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-6377-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 9
Research article
 | 
12 May 2026
Research article |  | 12 May 2026

Deciphering the impacts of meteorology on surface ozone variability in eastern China using explainable machine learning models

Xingpei Ye, Lin Zhang, Xiaolin Wang, Ni Lu, Sebastian Hickman, Guo Luo, and Alex T. Archibald

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Short summary
This study investigates how meteorology influences long-term surface ozone trends and pollution events across three major regions in eastern China using an explainable machine learning framework. The results show physically interpretable yet model-dependent ozone-meteorology relationships, highlighting both the potential and the limitations of explainable machine learning for process understanding in atmospheric chemistry.
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