Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 18
Articles | Volume 18, issue 18
https://doi.org/10.5194/gmd-18-6295-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-6295-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 18
Development and technical paper
 | 
25 Sep 2025
Development and technical paper |  | 25 Sep 2025

FastCTM (v1.0): Atmospheric chemical transport modelling with a principle-informed neural network for air quality simulations

Baolei Lyu, Ran Huang, Xinlu Wang, Weiguo Wang, and Yongtao Hu

Viewed

Total article views: 1,320 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,153 127 40 1,320 55 34 67
  • HTML: 1,153
  • PDF: 127
  • XML: 40
  • Total: 1,320
  • Supplement: 55
  • BibTeX: 34
  • EndNote: 67
Views and downloads (calculated since 20 Nov 2024)
Cumulative views and downloads (calculated since 20 Nov 2024)

Viewed (geographical distribution)

Total article views: 1,320 (including HTML, PDF, and XML) Thereof 1,314 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Oct 2025
Download
Short summary
FastCTM is a neural network model to simulate key criteria air pollution levels, offering an efficient alternative to traditional chemical transport models. Its structure is informed by the physical and chemical principles of the atmosphere, allowing it to learn and replicate complex atmospheric processes. FastCTM demonstrated matching accuracy to traditional models with less computational demand. It also provides analysis of how different atmospheric processes contribute to air quality changes.
Read more
Share