Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Abstract. Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes of atmospheric and environmental data relevant to ozone. We highlight the rapid progress made in the field since Phase 1 of the Tropospheric Ozone Assessment Report (TOAR), focussing particularly on the most active areas of research, namely short-term ozone forecasting, emulation of atmospheric chemistry and the use of remote sensing for ozone estimation. This review provides a comprehensive synthesis of recent advancements, highlights critical challenges, and proposes actionable pathways to develop ML in ozone research. Further advances hinge on addressing domain-specific issues such as the dependence of ozone concentrations on several poorly observed precursor species, as well as making progress on generic ML challenges such as the definition of suitable benchmarks and developing robust, explainable models. Reaping the full potential of ML for ozone research and operational applications will require close collaborations across atmospheric chemistry, ML and computational science and vigilant pursuit of the rapid developments in adjacent fields.

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Publication Details

Journal
Geoscientific model development
Published
2025-11-20
DOI
https://doi.org/10.5194/gmd-18-8777-2025
Citations
1
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.80

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article

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Fernando Iglesias‐Suarez, Zhenze Liu, J. Jason West, Makoto Kelp et al.
1 citations
Geoscientific model development
Air Quality Monitoring and Forecasting
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article

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Fernando Iglesias‐Suarez, Zhenze Liu, J. Jason West, Makoto Kelp, Alexander T. Archibald, Paul T. Griffiths, Sebastian Hickman, Martin G. Schultz, Owen R. Cooper, David Carlson, Kazuyuki Miyazaki, Roberto Sommariva, Kelsey Doerksen, Elyse A. Pennington, Kai‐Lan Chang, Gerbrand Koren, Hantao Wang
article en
1 citations

Abstract

Abstract. Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes of atmospheric and environmental data relevant to ozone. We highlight the rapid progress made in the field since Phase 1 of the Tropospheric Ozone Assessment Report (TOAR), focussing particularly on the most active areas of research, namely short-term ozone forecasting, emulation of atmospheric chemistry and the use of remote sensing for ozone estimation. This review provides a comprehensive synthesis of recent advancements, highlights critical challenges, and proposes actionable pathways to develop ML in ozone research. Further advances hinge on addressing domain-specific issues such as the dependence of ozone concentrations on several poorly observed precursor species, as well as making progress on generic ML challenges such as the definition of suitable benchmarks and developing robust, explainable models. Reaping the full potential of ML for ozone research and operational applications will require close collaborations across atmospheric chemistry, ML and computational science and vigilant pursuit of the rapid developments in adjacent fields.

Geoscientific model developmentVol. 18(22)
University of North Carolina at Chapel Hill (US), Jet Propulsion Laboratory (US), University of Leicester (GB), Cooperative Institute for Research in Environmental Sciences (US), Duke University (US), Forschungszentrum Jülich (DE), University of Cologne (DE), University of Colorado Boulder (US), Utrecht University (NL), Nanjing University of Information Science and Technology (CN), University of Cambridge (GB), At Bristol (GB), University of Bristol (GB), University of Oxford (GB), National Centre for Atmospheric Science (GB), NOAA Chemical Sciences Laboratory (US), University of Birmingham (GB), Stanford University (US)
National Aeronautics and Space Administration, California Institute of Technology, Underwriters Laboratories, European Commission, National Natural Science Foundation of China, Nuclear Safety and Security Commission, National Oceanic and Atmospheric Administration, Jet Propulsion Laboratory, Engineering and Physical Sciences Research Council, Natural Environment Research Council
Openalex Percentile: Top 23%
Air Quality Monitoring and Forecasting
0.80
DOIOpen Access PDFOpenAlex
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