Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 4
Articles | Volume 19, issue 4
https://doi.org/10.5194/gmd-19-1703-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1703-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 4
Model description paper
 | 
27 Feb 2026
Model description paper |  | 27 Feb 2026

A Transformer-based agent model of GEOS-Chem v14.2.2 for informative prediction of PM2.5 and O3 levels to future emission scenarios: TGEOS v1.0

Dehao Li, Jianbing Jin, Guoqiang Wang, Mijie Pang, Weihong Zhang, and Hong Liao

Viewed

Total article views: 9,218 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
7,397 1,612 209 9,218 663 212 233
  • HTML: 7,397
  • PDF: 1,612
  • XML: 209
  • Total: 9,218
  • Supplement: 663
  • BibTeX: 212
  • EndNote: 233
Views and downloads (calculated since 28 May 2025)
Cumulative views and downloads (calculated since 28 May 2025)

Viewed (geographical distribution)

Total article views: 9,218 (including HTML, PDF, and XML) Thereof 9,127 with geography defined and 91 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 20 Sep 2026
Download
Short summary
To support air quality decision-making in future emission scenarios, this study presents an agent model for a classic chemical transport model based on a transformer deep-learning framework. Addressing the long runtimes and input/output limitations of previous approaches, our agent model accurately reproduces simulations of fine particulate matter and ozone, enabling rapid air quality assessment.
Read more
Share