Articles | Volume 22, issue 12
https://doi.org/10.5194/acp-22-8385-2022
https://doi.org/10.5194/acp-22-8385-2022
Research article
 | 
29 Jun 2022
Research article |  | 29 Jun 2022

A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019

Xiang Weng, Grant L. Forster, and Peer Nowack

Viewed

Total article views: 4,117 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,011 1,046 60 4,117 271 35 67
  • HTML: 3,011
  • PDF: 1,046
  • XML: 60
  • Total: 4,117
  • Supplement: 271
  • BibTeX: 35
  • EndNote: 67
Views and downloads (calculated since 25 Jan 2022)
Cumulative views and downloads (calculated since 25 Jan 2022)

Viewed (geographical distribution)

Total article views: 4,117 (including HTML, PDF, and XML) Thereof 4,228 with geography defined and -111 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Apr 2024
Download
Short summary
We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
Altmetrics
Final-revised paper
Preprint