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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 Sep 2025
Development and technical paper |
| 25 Sep 2025
| 25 Sep 2025
FastCTM (v1.0): Atmospheric chemical transport modelling with a principle-informed neural network for air quality simulations
Huayun Sounding Meteorological Technology Co. Ltd., Beijing 102299, China
Key Laboratory of Intelligent Meteorological Observation Technology, Beijing 100081, China
China Meteorological Administration Xiong'an Atmospheric Boundary Layer Key Laboratory, Xiong'an, 071000, China
Ran Huang
CORRESPONDING AUTHOR
Hangzhou AiMa Technologies, Hangzhou, Zhejiang 311121, P.R. China
Nanjing AiMa Environmental, Nanjing, Jiangsu 210000, P.R. China
Xinlu Wang
Hangzhou AiMa Technologies, Hangzhou, Zhejiang 311121, P.R. China
Weiguo Wang
SAIC, at Environment Modelling Center, NOAA/National Centers for Environmental Prediction, College Park, Maryland 20740, United States
Yongtao Hu
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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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.
FastCTM is a neural network model to simulate key criteria air pollution levels, offering an...
