Articles | Volume 25, issue 3
https://doi.org/10.5194/acp-25-1749-2025
https://doi.org/10.5194/acp-25-1749-2025
Research article
 | 
07 Feb 2025
Research article |  | 07 Feb 2025

Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning

Yuqing Qiu, Xin Li, Wenxuan Chai, Yi Liu, Mengdi Song, Xudong Tian, Qiaoli Zou, Wenjun Lou, Wangyao Zhang, Juan Li, and Yuanhang Zhang

Viewed

Total article views: 617 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
477 115 25 617 33 19 19
  • HTML: 477
  • PDF: 115
  • XML: 25
  • Total: 617
  • Supplement: 33
  • BibTeX: 19
  • EndNote: 19
Views and downloads (calculated since 01 Jul 2024)
Cumulative views and downloads (calculated since 01 Jul 2024)

Viewed (geographical distribution)

Total article views: 617 (including HTML, PDF, and XML) Thereof 606 with geography defined and 11 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 Feb 2025
Download
Short summary
The chemical reactions of ozone (O3) formation are related to meteorology and local emissions. Here, a random forest approach was used to eliminate the effects of meteorological factors (dispersion or transport) on O3 and its precursors. Variations in the sensitivity of O3 formation and the apportionment of emission sources were revealed after meteorological normalization. Our results suggest that meteorological variations should be considered when diagnosing O3 formation.
Share
Altmetrics
Final-revised paper
Preprint