Articles | Volume 19, issue 4
https://doi.org/10.5194/gmd-19-1703-2026
https://doi.org/10.5194/gmd-19-1703-2026
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

Data sets

The dataset used by TGEOS D. Li https://doi.org/10.5281/zenodo.15717908

Model code and software

geoschem/GCClassic: GCClassic 14.2.2, The International GEOS-Chem User Community https://doi.org/10.5281/zenodo.10034814

Python source code of TGEOS v1.0 D. Li https://doi.org/10.5281/zenodo.15422797

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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.
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