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.
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.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
27 Apr 2026
Model description paper |
| 27 Apr 2026
| 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
Center for Sustainability Science and Strategy, Massachusetts Institute of Technology, Cambridge, MA, USA
Sebastian D. Eastham
Brahmal Vasudevan Institute for Sustainable Aviation, Department of Aeronautics, Faculty of Engineering, Imperial College London, South Kensington, London, UK
Erwan Monier
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
Center for Sustainability Science and Strategy, Massachusetts Institute of Technology, Cambridge, MA, USA
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
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Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022, https://doi.org/10.5194/gmd-15-8731-2022, 2022
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Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Haipeng Lin, Elizabeth W. Lundgren, Steve Goldhaber, Steven R. H. Barrett, and Daniel J. Jacob
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William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, and Noelle E. Selin
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Minghao Qiu, Corwin Zigler, and Noelle E. Selin
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Evaluating impacts of emission changes on air quality requires accounting for meteorological variability. Many studies use simple regression methods to correct for meteorology, but little is known about their performance. Using cases in the US and China, we show that widely used regression models do not perform well and can lead to biased estimates of emission-driven trends. We propose a novel machine learning method with lower bias and provide recommendations to policymakers and researchers.
Shihan Sun, Amos P. K. Tai, David H. Y. Yung, Anthony Y. H. Wong, Jason A. Ducker, and Christopher D. Holmes
Biogeosciences, 19, 1753–1776, https://doi.org/10.5194/bg-19-1753-2022, https://doi.org/10.5194/bg-19-1753-2022, 2022
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Anthony Y. H. Wong and Jeffrey A. Geddes
Atmos. Chem. Phys., 21, 16479–16497, https://doi.org/10.5194/acp-21-16479-2021, https://doi.org/10.5194/acp-21-16479-2021, 2021
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Liam Bindle, Randall V. Martin, Matthew J. Cooper, Elizabeth W. Lundgren, Sebastian D. Eastham, Benjamin M. Auer, Thomas L. Clune, Hongjian Weng, Jintai Lin, Lee T. Murray, Jun Meng, Christoph A. Keller, William M. Putman, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 14, 5977–5997, https://doi.org/10.5194/gmd-14-5977-2021, https://doi.org/10.5194/gmd-14-5977-2021, 2021
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Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.
Xuan Wang, Daniel J. Jacob, William Downs, Shuting Zhai, Lei Zhu, Viral Shah, Christopher D. Holmes, Tomás Sherwen, Becky Alexander, Mathew J. Evans, Sebastian D. Eastham, J. Andrew Neuman, Patrick R. Veres, Theodore K. Koenig, Rainer Volkamer, L. Gregory Huey, Thomas J. Bannan, Carl J. Percival, Ben H. Lee, and Joel A. Thornton
Atmos. Chem. Phys., 21, 13973–13996, https://doi.org/10.5194/acp-21-13973-2021, https://doi.org/10.5194/acp-21-13973-2021, 2021
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Halogen radicals have a broad range of implications for tropospheric chemistry, air quality, and climate. We present a new mechanistic description and comprehensive simulation of tropospheric halogens in a global 3-D model and compare the model results with surface and aircraft measurements. We find that halogen chemistry decreases the global tropospheric burden of ozone by 11 %, NOx by 6 %, and OH by 4 %.
Haipeng Lin, Daniel J. Jacob, Elizabeth W. Lundgren, Melissa P. Sulprizio, Christoph A. Keller, Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Patrick C. Campbell, Barry Baker, Rick D. Saylor, and Raffaele Montuoro
Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021, https://doi.org/10.5194/gmd-14-5487-2021, 2021
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Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to select, add, and scale emissions from different sources through a configuration file with no change to the model source code. We demonstrate the implementation of HEMCO in several models, all sharing the same HEMCO core code and database library.
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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.
We developed a fast and accurate computer tool that predicts how air pollution will change...
