Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 1
Articles | Volume 24, issue 1
https://doi.org/10.5194/acp-24-345-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-345-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 1
Research article
 | 
11 Jan 2024
Research article |  | 11 Jan 2024

Multidecadal ozone trends in China and implications for human health and crop yields: a hybrid approach combining a chemical transport model and machine learning

Jia Mao, Amos P. K. Tai, David H. Y. Yung, Tiangang Yuan, Kong T. Chau, and Zhaozhong Feng

Viewed

Total article views: 1,726 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,355 316 55 1,726 96 42 60
  • HTML: 1,355
  • PDF: 316
  • XML: 55
  • Total: 1,726
  • Supplement: 96
  • BibTeX: 42
  • EndNote: 60
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads (calculated since 08 Jun 2023)

Viewed (geographical distribution)

Total article views: 1,726 (including HTML, PDF, and XML) Thereof 1,744 with geography defined and -18 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Jul 2024
Download
Short summary
Surface ozone (O3) is well-known for posing great threats to both human health and agriculture worldwide. However, a multidecadal assessment of the impacts of O3 on public health and agriculture in China is lacking without sufficient O3 observations. We used a hybrid approach combining a chemical transport model and machine learning to provide a robust dataset of O3 concentrations over the past 4 decades in China, thereby filling the gap in the long-term O3 trend and impact assessment in China.
Read more
Altmetrics
Final-revised paper
Preprint