Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 8
Articles | Volume 19, issue 8
https://doi.org/10.5194/gmd-19-3335-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-3335-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 8
Model description paper
 | 
27 Apr 2026
Model description paper |  | 27 Apr 2026

A Geographically Weighted Gaussian Process Regression (GW-GPR) emulator of anthropogenic PM2.5 from the GEOS-Chem High Performance (GCHP) 13.0.0 global chemical transport model

Anthony Y. H. Wong, Sebastian D. Eastham, Erwan Monier, and Noelle E. Selin

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Short summary
We developed a fast and accurate computer tool that predicts how air pollution will change around the world under different climate and policy choices. Using machine learning and real model data, our tool can estimate changes in harmful fine particulate pollution in seconds instead of thousands of hours. This makes it easier for researchers and policymakers to explore future air quality and health impacts under a wide range of scenarios.
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