Articles | Volume 25, issue 15
https://doi.org/10.5194/acp-25-8407-2025
https://doi.org/10.5194/acp-25-8407-2025
Research article
 | 
01 Aug 2025
Research article |  | 01 Aug 2025

Explainable ensemble machine learning revealing spatiotemporal heterogeneity in driving factors of particulate nitro-aromatic compounds in eastern China

Min Li, Xinfeng Wang, Tianshuai Li, Yujia Wang, Yueru Jiang, Yujiao Zhu, Wei Nie, Rui Li, Jian Gao, Likun Xue, Qingzhu Zhang, and Wenxing Wang

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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-2025-165', Anonymous Referee #1, 13 Mar 2025
  • RC2: 'Comment on egusphere-2025-165', Anonymous Referee #2, 25 Mar 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Xinfeng Wang on behalf of the Authors (06 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (12 May 2025) by Qi Chen
AR by Xinfeng Wang on behalf of the Authors (14 May 2025)  Manuscript 
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Short summary
By integrating field measurements with an interpretable ensemble machine learning framework, we comprehensively identified key driving factors of nitro-aromatic compounds (NACs), demonstrated complex interrelationships, and quantified their contributions across different locations. This work provides a reliable modeling approach for recognizing causes of NAC pollution, enhances our understanding of variations of atmospheric NACs, and highlights the necessity of strengthening emission controls.
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