Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 20
Articles | Volume 25, issue 20
https://doi.org/10.5194/acp-25-13863-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-13863-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 20
Research article
 | 
27 Oct 2025
Research article |  | 27 Oct 2025

Meteorological influence on surface ozone trends in China: assessing uncertainties caused by multi-dataset and multi-method

Xueqing Wang, Jia Zhu, Guanjie Jiao, Xi Chen, Zhenjiang Yang, Lei Chen, Xipeng Jin, and Hong Liao

Viewed

Total article views: 1,387 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,196 145 46 1,387 87 36 41
  • HTML: 1,196
  • PDF: 145
  • XML: 46
  • Total: 1,387
  • Supplement: 87
  • BibTeX: 36
  • EndNote: 41
Views and downloads (calculated since 19 May 2025)
Cumulative views and downloads (calculated since 19 May 2025)

Viewed (geographical distribution)

Total article views: 1,387 (including HTML, PDF, and XML) Thereof 1,387 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
Short summary
Impacts of meteorology on ozone vary with diverse meteorological datasets and analytical methods. Uncertainties of meteorology-driven ozone trends in China were examined. Multi-dataset analysis shows the largest meteorology-driven ozone trend with the best consistency occurs in spring. Multi-method analysis shows the best (worst) consistency occurs in winter (summer). Overall, meteorology boosts ozone growth in all seasons, with uncertainty from multi-method larger than that from multi-dataset.
Read more
Share
Altmetrics
Final-revised paper
Preprint