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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 May 2026
Research article |
| 12 May 2026
| 12 May 2026
Deciphering the impacts of meteorology on surface ozone variability in eastern China using explainable machine learning models
Xingpei Ye
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
Lin Zhang
CORRESPONDING AUTHOR
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Institute of Carbon Neutrality, Peking University, Beijing, China
Center for Environment and Health, Peking University, Beijing, China
Xiaolin Wang
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Ni Lu
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Sebastian Hickman
Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
Guo Luo
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Alex T. Archibald
Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
National Centre for Atmospheric Science, University of Cambridge, Cambridge, United Kingdom
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Atmos. Chem. Phys., 21, 9009–9029, https://doi.org/10.5194/acp-21-9009-2021, https://doi.org/10.5194/acp-21-9009-2021, 2021
Cited articles
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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.
This study investigates how meteorology influences long-term surface ozone trends and pollution...
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