Machine-learning-assisted chemical characterization and optical properties of atmospheric brown carbon in Nanjing, China

Huang, Yu; Li, Xingru; Huang, Dan Dan; Lei, Ruoyuan; Zhou, Binhuang; Zhang, Yunjiang; Ge, Xinlei

doi:https://doi.org/10.5194/acp-25-7619-2025

Articles | Volume 25, issue 14

https://doi.org/10.5194/acp-25-7619-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-25-7619-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 14

Research article

|

18 Jul 2025

Research article |

| 18 Jul 2025

Machine-learning-assisted chemical characterization and optical properties of atmospheric brown carbon in Nanjing, China

Yu Huang, Xingru Li, Dan Dan Huang, Ruoyuan Lei, Binhuang Zhou, Yunjiang Zhang, and Xinlei Ge

Supplement

https://doi.org/10.5194/acp-25-7619-2025-supplement

Model code and software

ToF-AMS Software downloads D. Sueper https://cires1.colorado.edu/jimenez-group/ToFAMSResources/ToFSoftware/index.html

A lipidome atlas in MS-DIAL 4 (https://systemsomicslab.github.io/compms/msdial/main.html) H. Tsugawa et al. https://doi.org/10.1038/s41587-020-0531-2

Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data (https://slfan.shinyapps.io/ShinySERRF/) S. Fan et al. https://doi.org/10.1021/acs.analchem.8b05592

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Short summary

This work comprises a comprehensive investigation into the chemical and optical properties of brown carbon (BrC) in PM_2.5samples collected in Nanjing, China. In particular, we used a machine learning approach to identify a list of key BrC species, which can be a good reference for future studies. Our findings extend understanding of BrC properties and are valuable to the assessment of BrC's impact on air quality and radiative forcing.