Articles | Volume 21, issue 5
https://doi.org/10.5194/acp-21-3919-2021
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, Jan Cermak, Simone Kotthaus, Martial Haeffelin, Hendrik Andersen, Julia Fuchs, Miae Kim, Jean-Eudes Petit, and Olivier Favez

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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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