Articles | Volume 21, issue 5
Atmos. Chem. Phys., 21, 3919–3948, 2021
https://doi.org/10.5194/acp-21-3919-2021
Atmos. Chem. Phys., 21, 3919–3948, 2021
https://doi.org/10.5194/acp-21-3919-2021

Research article 17 Mar 2021

Research article | 17 Mar 2021

Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning

Roland Stirnberg et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Roland Stirnberg on behalf of the Authors (07 Dec 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (08 Dec 2020) by Leiming Zhang
RR by Anonymous Referee #4 (17 Dec 2020)
ED: Reconsider after major revisions (17 Dec 2020) by Leiming Zhang
AR by Roland Stirnberg on behalf of the Authors (04 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (05 Feb 2021) by Leiming Zhang
RR by Yves Rybarczyk (08 Feb 2021)
ED: Publish as is (09 Feb 2021) by Leiming Zhang
AR by Roland Stirnberg on behalf of the Authors (14 Feb 2021)  Author's response    Manuscript
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Short summary
Air pollution endangers human health and poses a problem particularly in densely populated areas. Here, an explainable machine learning approach is used to analyse periods of high particle concentrations for a suburban site southwest of Paris to better understand its atmospheric drivers. Air pollution is particularly excaberated by low temperatures and low mixed layer heights, but processes vary substantially between and within seasons.
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