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

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

doi:https://doi.org/10.5194/acp-25-7485-2025

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

Supplement

https://doi.org/10.5194/acp-25-7485-2025-supplement

Data sets

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.bd0915c6

Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion (http://tapdata.org.cn/) G. Geng et al. https://doi.org/10.1021/acs.est.1c01863

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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.