Quantifying the driving factors of particulate  matter variabilities in the Beijing-Tianjin-Hebei  and Yangtze River Delta regions from 2015  to 2022 by machine learning approach

Pan, Zhongfeng; Yin, Hao; Sun, Zhenda; Li, Chongyang; Sun, Youwen; Liu, Cheng

doi:10.5194/acp-26-2545-2026

Articles | Volume 26, issue 4

https://doi.org/10.5194/acp-26-2545-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-26-2545-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 4

Research article

|

18 Feb 2026

Research article |

| 18 Feb 2026

Quantifying the driving factors of particulate matter variabilities in the Beijing-Tianjin-Hebei and Yangtze River Delta regions from 2015 to 2022 by machine learning approach

Zhongfeng Pan, Hao Yin, Zhenda Sun, Chongyang Li, Youwen Sun, and Cheng Liu

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https://doi.org/10.5194/acp-26-2545-2026-supplement

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Quantifying the driving factors of particulate matter variabilities in the Beijing-Tianjin-Hebei and Yangtze River Delta regions from 2015 to 2022 by machine learning approach Pan, Zhongfeng, Sun, Youwen, Yin, Hao, Liu, Cheng https://doi.org/10.5281/zenodo.17779780

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This study examines air pollution in Beijing-Tianjin-Hebei and the Yangtze River Delta from 2015 to 2022. PM_2.5 (particulate matter) decreased by 9.1-31.4 μg/m³ and PM₁₀ by 9.8–42.9 μg/m³. Weather factors like humidity, air pressure, and rainfall influenced pollution, with tailored solutions needed for different regions.