Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 2
Articles | Volume 25, issue 2
https://doi.org/10.5194/acp-25-759-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-759-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 2
Research article
 | 
21 Jan 2025
Research article |  | 21 Jan 2025

Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques

Chengxin Zhang, Xinhan Niu, Hongyu Wu, Zhipeng Ding, Ka Lok Chan, Jhoon Kim, Thomas Wagner, and Cheng Liu

Viewed

Total article views: 1,140 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
942 145 53 1,140 37 10 8
  • HTML: 942
  • PDF: 145
  • XML: 53
  • Total: 1,140
  • Supplement: 37
  • BibTeX: 10
  • EndNote: 8
Views and downloads (calculated since 30 Aug 2024)
Cumulative views and downloads (calculated since 30 Aug 2024)

Viewed (geographical distribution)

Total article views: 1,140 (including HTML, PDF, and XML) Thereof 1,140 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Mar 2025
Download
Short summary
This research utilizes hourly air pollution observations from the world’s first geostationary satellite to develop a spatiotemporal neural network model for full-coverage surface NO2 pollution prediction over the next 24 hours, achieving outstanding forecasting performance and efficacy. These results highlight the profound impact of geostationary satellite observations in advancing air quality forecasting models, thereby contributing to future models for health exposure to air pollution.
Read more
Share
Altmetrics
Final-revised paper
Preprint