Articles | Volume 22, issue 12
Research article
29 Jun 2022
Research article |  | 29 Jun 2022

A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019

Xiang Weng, Grant L. Forster, and Peer Nowack

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Cited articles

Archibald, A. T., Turnock, S. T., Griffiths, P. T., Cox, T., Derwent, R. G., Knote, C., and Shin, M.: On the changes in surface ozone over the twenty-first century: sensitivity to changes in surface temperature and chemical mechanisms: 21st century changes in surface ozone, Philos. T. R. Soc. A, 378, 20190329,, 2020. 
Bishop, C. M.: Pattern recognition and machine learning, Springer Science+Business Media, Singapore, ISBN 978-0387-31073-2, 2006. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,, 2001. 
Ceppi, P. and Nowack, P.: Observational evidence that cloud feedback amplifies global warming, P. Natl. Acad. Sci. USA, 118, 1–7,, 2021. 
Chang, L., Xu, J., Tie, X., and Gao, W.: The impact of Climate Change on the Western Pacific Subtropical High and the related ozone pollution in Shanghai, China, Sci. Rep.-UK, 9, 1–12,, 2019. 
Short summary
We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
Final-revised paper