Articles | Volume 15, issue 5
https://doi.org/10.5194/amt-15-1511-2022
https://doi.org/10.5194/amt-15-1511-2022
Research article
 | 
16 Mar 2022
Research article |  | 16 Mar 2022

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species

Junlei Zhan, Yongchun Liu, Wei Ma, Xin Zhang, Xuezhong Wang, Fang Bi, Yujie Zhang, Zhenhai Wu, and Hong Li

Viewed

Total article views: 3,216 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,253 888 75 3,216 299 59 80
  • HTML: 2,253
  • PDF: 888
  • XML: 75
  • Total: 3,216
  • Supplement: 299
  • BibTeX: 59
  • EndNote: 80
Views and downloads (calculated since 05 Nov 2021)
Cumulative views and downloads (calculated since 05 Nov 2021)

Viewed (geographical distribution)

Total article views: 3,216 (including HTML, PDF, and XML) Thereof 3,141 with geography defined and 75 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 04 Nov 2024
Download
Short summary
Our study investigated the O3 formation sensitivity in Beijing using a random forest model coupled with the reactivity of volatile organic compound (VOC) species. Results found that random forest accurately predicted O3 concentration when initial VOCs were considered, and relative importance correlated well with O3 formation potential. The O3 isopleth curves calculated by the random forest model were generally comparable with those calculated by the box model.