Articles | Volume 26, issue 10
https://doi.org/10.5194/acp-26-7081-2026
https://doi.org/10.5194/acp-26-7081-2026
Research article
 | 
22 May 2026
Research article |  | 22 May 2026

Long-term ozone formation sensitivity in China: spatiotemporal evolution and machine learning attribution

Jinglan Lin, Liqing Wu, Chujun Chen, Yongkang Wu, Rui Lin, Xuemei Wang, and Weihua Chen

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Cited articles

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By integrating satellite data from 2005–2023 (warm seasons), we analyzed the spatiotemporal evolution of ozone formation sensitivity across China. Results show a shift toward NOx-limited and transitional regimes under policy measures. Explainable machine learning suggests this evolution is mainly associated with emission changes, while meteorological factors become increasingly important under cleaner conditions. These findings support more precise, region-specific ozone control strategies.
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