Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-8777-2025
https://doi.org/10.5194/gmd-18-8777-2025
Review and perspective paper
 | Highlight paper
 | 
20 Nov 2025
Review and perspective paper | Highlight paper |  | 20 Nov 2025

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research

Sebastian H. M. Hickman, Makoto M. Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu

Related authors

A nitrate photolysis source of tropospheric HONO is incompatible with current understanding of atmospheric chemistry
Matthew J. Rowlinson, Lucy J. Carpenter, Mat J. Evans, James D. Lee, Simone T. Andersen, Tomas Sherwen, Anna B. Callaghan, Roberto Sommariva, William Bloss, Siqi Hou, Leigh R. Crilley, Klaus Pfeilsticker, Benjamin Weyland, Thomas B. Ryerson, Patrick R. Veres, Pedro Campuzano-Jost, Hongyu Guo, Benjamin A. Nault, Jose L. Jimenez, and Khanneh Wadinga Fomba
Atmos. Chem. Phys., 25, 16945–16968, https://doi.org/10.5194/acp-25-16945-2025,https://doi.org/10.5194/acp-25-16945-2025, 2025
Short summary
Applying deep learning to a chemistry-climate model for improved ozone prediction
Zhenze Liu, Ke Li, Oliver Wild, Ruth M. Doherty, Fiona M. O’Connor, and Steven T. Turnock
Atmos. Chem. Phys., 25, 16969–16981, https://doi.org/10.5194/acp-25-16969-2025,https://doi.org/10.5194/acp-25-16969-2025, 2025
Short summary
AerChemMIP2 – Unraveling the role of reactive gases, aerosol particles, and land use for air quality and climate change in CMIP7
Stephanie Fiedler, Fiona M. O'Connor, Duncan Watson-Parris, Robert J. Allen, William J. Collins, Paul T. Griffiths, Matthew Kasoar, Jarmo Kikstra, Jasper F. Kok, Lee T. Murray, Fabien Paulot, Maria Sand, Steven Turnock, James Weber, Laura J. Wilcox, and Vaishali Naik
EGUsphere, https://doi.org/10.5194/egusphere-2025-5669,https://doi.org/10.5194/egusphere-2025-5669, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Intercomparison of global ground-level ozone datasets for health-relevant metrics
Hantao Wang, Kazuyuki Miyazaki, Haitong Zhe Sun, Zhen Qu, Xiang Liu, Antje Inness, Martin Schultz, Sabine Schröder, Marc Serre, and J. Jason West
Atmos. Chem. Phys., 25, 15969–15990, https://doi.org/10.5194/acp-25-15969-2025,https://doi.org/10.5194/acp-25-15969-2025, 2025
Short summary
Global CO emissions and drivers of atmospheric CO trends constrained by MOPITT satellite observations
Zhaojun Tang, Panpan Yang, Kazuyuki Miyazaki, John Worden, Helen Worden, Daven K. Henze, Dylan B. A. Jones, and Zhe Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2025-5432,https://doi.org/10.5194/egusphere-2025-5432, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary

Cited articles

Agapiou, A.: Remote sensing heritage in a petabyte-scale: satellite data and heritage Earth Engine© applications, International Journal of Digital Earth, 10, 85–102, https://doi.org/10.1080/17538947.2016.1250829, 2017. a
Alari, A., Schwarz, L., Zabrocki, L., Le Nir, G., Chaix, B., and Benmarhnia, T.: The effects of an air quality alert program on premature mortality: A difference-in-differences evaluation in the region of Paris, Environment International, 156, 106583, https://doi.org/10.1016/j.envint.2021.106583, 2021. a
Anderson, D. C., Follette-Cook, M. B., Strode, S. A., Nicely, J. M., Liu, J., Ivatt, P. D., and Duncan, B. N.: A machine learning methodology for the generation of a parameterization of the hydroxyl radical, Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022, 2022. a
Archibald, A. T., Neu, J. L., Elshorbany, Y. F., Cooper, O. R., Young, P. J., Akiyoshi, H., Cox, R. A., Coyle, M., Derwent, R. G., Deushi, M., Finco, A., Frost, G. J., Galbally, I. E., Gerosa, G., Granier, C., Griffiths, P. T., Hossaini, R., Hu, L., Jöckel, P., Josse, B., Lin, M. Y., Mertens, M., Morgenstern, O., Naja, M., Naik, V., Oltmans, S., Plummer, D. A., Revell, L. E., Saiz-Lopez, A., Saxena, P., Shin, Y. M., Shahid, I., Shallcross, D., Tilmes, S., Trickl, T., Wallington, T. J., Wang, T., Worden, H. M., and Zeng, G.: Tropospheric Ozone Assessment Report: Critical review of changes in the tropospheric ozone burden and budget from 1960–2100, Elementa: Science of the Anthropocene, 8, 034, https://doi.org/10.1525/elementa.2020.034, 2020. a
Arroyo, A., Herrero, A., Tricio, V., Corchado, E., and Wozniak, M.: Neural models for imputation of missing ozone data in air-quality datasets, Complexity, 1, 7238015, https://doi.org/10.1155/2018/7238015, 2018. a
Executive editor
I agree on publishing this manuscript as a Review and perspective paper.
Short summary
Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
Share