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

Weng, Xiang; Forster, Grant L.; Nowack, Peer

doi:https://doi.org/10.5194/acp-22-8385-2022

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

Supplement

https://doi.org/10.5194/acp-22-8385-2022-supplement

Data sets

Historical air quality data in China Wang, 2021 https://quotsoft.net/air/

ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) Hersbach et al., 2018a https://doi.org/10.24381/cds.bd0915c6

ERA5 hourly data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) Hersbach et al., 2018b https://doi.org/10.24381/cds.adbb2d47

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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.