Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 15
Articles | Volume 25, issue 15
https://doi.org/10.5194/acp-25-8407-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-8407-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 15
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

Viewed

Total article views: 3,915 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,987 751 177 3,915 442 150 220
  • HTML: 2,987
  • PDF: 751
  • XML: 177
  • Total: 3,915
  • Supplement: 442
  • BibTeX: 150
  • EndNote: 220
Views and downloads (calculated since 20 Feb 2025)
Cumulative views and downloads (calculated since 20 Feb 2025)

Viewed (geographical distribution)

Total article views: 3,915 (including HTML, PDF, and XML) Thereof 3,802 with geography defined and 113 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 23 Aug 2026
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint