Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 13
Articles | Volume 25, issue 13
https://doi.org/10.5194/acp-25-7485-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-7485-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 13
Technical note
 | 
15 Jul 2025
Technical note |  | 15 Jul 2025

Technical note: Reconstructing missing surface aerosol elemental carbon data in long-term series with ensemble learning

Qingxiao Meng, Yunjiang Zhang, Sheng Zhong, Jie Fang, Lili Tang, Yongcai Rao, Minfeng Zhou, Jian Qiu, Xiaofeng Xu, Jean-Eudes Petit, Olivier Favez, and Xinlei Ge

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Short summary
We developed a machine-learning-based method to reconstruct missing elemental carbon (EC) data in four Chinese cities from 2013 to 2023. Using machine learning, we filled data gaps and introduced a new approach to analyze EC trends. Our findings reveal a significant decline in EC due to stricter pollution controls, though this slowed after 2020. This study provides a versatile framework for addressing data gaps and supports strategies to reduce urban air pollution and its climate impacts.
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