Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 16
Articles | Volume 24, issue 16
https://doi.org/10.5194/acp-24-9645-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-24-9645-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 16
Research article
 | 
30 Aug 2024
Research article |  | 30 Aug 2024

Estimation of ground-level NO2 and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model

Naveed Ahmad, Changqing Lin, Alexis K. H. Lau, Jhoon Kim, Tianshu Zhang, Fangqun Yu, Chengcai Li, Ying Li, Jimmy C. H. Fung, and Xiang Qian Lao

Viewed

Total article views: 1,373 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,085 245 43 1,373 73 21 28
  • HTML: 1,085
  • PDF: 245
  • XML: 43
  • Total: 1,373
  • Supplement: 73
  • BibTeX: 21
  • EndNote: 28
Views and downloads (calculated since 13 Mar 2024)
Cumulative views and downloads (calculated since 13 Mar 2024)

Viewed (geographical distribution)

Total article views: 1,373 (including HTML, PDF, and XML) Thereof 1,392 with geography defined and -19 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Nov 2024
Download
Short summary
This study developed a nested machine learning model to convert the GEMS NO2 column measurements into ground-level concentrations across China. The model directly incorporates the NO2 mixing height (NMH) into the methodological framework. The study underscores the importance of considering NMH when estimating ground-level NO2 from satellite column measurements and highlights the significant advantages of new-generation geostationary satellites in air quality monitoring.
Read more
Special issue
GEMS: first year in operation (AMT/ACP inter-journal SI)
Altmetrics
Final-revised paper
Preprint