Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 16
Articles | Volume 26, issue 16
https://doi.org/10.5194/acp-26-11683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-11683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 16
Research article
 | 
19 Aug 2026
Research article |  | 19 Aug 2026

Quantitative insights into regime-dependent aerosol pH variability in ammonia-rich urban Beijing from explainable machine learning

Jing Duan, Ting Wang, Ru-Jin Huang, Jingye Ren, Haobin Zhong, Wei Xu, Chunshui Lin, Yanan Zhan, Huabin Huang, and Yongjie Li

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Short summary
This study combines explainable machine learning with thermodynamic modeling to quantitatively assess how meteorological conditions and chemical composition jointly contribute to aerosol pH variation in an ammonia-rich urban atmosphere. The analysis highlights regime-dependent interactions, threshold behaviors, and sample-specific variability, providing a data-driven framework for interpreting aerosol acidity under diverse environmental conditions.
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