Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 22, issue 12
Articles | Volume 22, issue 12
https://doi.org/10.5194/acp-22-8385-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-8385-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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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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.
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