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

Data sets

Models and Data for Reproducing the Result in "A Geographically Weighted Gaussian Process Regression Emulator of the GCHP 13.0.0 Global Air Quality Model Anthony Y. H. Wong https://doi.org/10.5281/ZENODO.15484655

Code and data used in "A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies'' (1.0) [Data set] William Atkinson et al. https://doi.org/10.5281/zenodo.7158380

Model code and software

geoschem/gchp: GCHP 13.0.0 (13.0.0) The International GEOS-Chem User Community https://doi.org/10.5281/zenodo.4618205

ayhwong/GW-GPR: Initial version (V1.0) Anthony Wong https://doi.org/10.5281/zenodo.19391330

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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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