Articles | Volume 25, issue 13
https://doi.org/10.5194/acp-25-7485-2025
https://doi.org/10.5194/acp-25-7485-2025
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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Cited articles

Arhami, M., Kuhn, T., Fine, P. M., Delfino, R. J., and Sioutas, C.: Effects of sampling artifacts and operating parameters on the performance of a semicontinuous particulate elemental carbon/organic carbon monitor, Environ. Sci. Technol., 40, 945–954, https://doi.org/10.1021/es0510313, 2006. 
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Bao, F., Cheng, T., Li, Y., Gu, X., Guo, H., Wu, Y., Wang, Y., and Gao, J.: Retrieval of black carbon aerosol surface concentration using satellite remote sensing observations, Remote Sens. Environ., 226, 93–108, https://doi.org/10.1016/j.rse.2019.03.036, 2019. 
Birch, M. and Cary, R.: Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust, Aerosol Sci. Tech., 25, 221–241, https://doi.org/10.1080/02786829608965393, 1996. 
Bond, T. C. and Bergstrom, R. W.: Light absorption by carbonaceous particles: An investigative review, Aerosol Sci. Tech., 40, 27–67, https://doi.org/10.1080/02786820500421521, 2006. 
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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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