Articles | Volume 25, issue 14
https://doi.org/10.5194/acp-25-7619-2025
https://doi.org/10.5194/acp-25-7619-2025
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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2757', Anonymous Referee #1, 08 Nov 2024
  • RC2: 'Comment on egusphere-2024-2757', Anonymous Referee #2, 15 Nov 2024
  • AC1: 'Reply to Comments on egusphere-2024-2757', Xinlei Ge, 14 Feb 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Xinlei Ge on behalf of the Authors (14 Feb 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (23 Feb 2025) by Minghuai Wang
RR by Anonymous Referee #1 (26 Feb 2025)
RR by Anonymous Referee #2 (17 Mar 2025)
ED: Publish subject to minor revisions (review by editor) (07 Apr 2025) by Minghuai Wang
AR by Xinlei Ge on behalf of the Authors (08 Apr 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (09 Apr 2025) by Minghuai Wang
AR by Xinlei Ge on behalf of the Authors (09 Apr 2025)  Manuscript 
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Short summary
This work comprises a comprehensive investigation into the chemical and optical properties of brown carbon (BrC) in PM2.5 samples 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.
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