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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 8
Atmos. Chem. Phys., 18, 5343–5358, 2018
https://doi.org/10.5194/acp-18-5343-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 18, 5343–5358, 2018
https://doi.org/10.5194/acp-18-5343-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 Apr 2018

Research article | 19 Apr 2018

Understanding meteorological influences on PM2.5 concentrations across China: a temporal and spatial perspective

Ziyue Chen 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 Ziyue Chen on behalf of the Authors (30 Oct 2017)  Author's response
ED: Referee Nomination & Report Request started (21 Nov 2017) by Sally E. Pusede
RR by Anonymous Referee #3 (18 Dec 2017)
ED: Reconsider after major revisions (19 Dec 2017) by Sally E. Pusede
AR by Ziyue Chen on behalf of the Authors (08 Jan 2018)  Author's response    Manuscript
ED: Reconsider after major revisions (26 Jan 2018) by Sally E. Pusede
AR by Ziyue Chen on behalf of the Authors (21 Feb 2018)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (11 Mar 2018) by Sally E. Pusede
AR by Ziyue Chen on behalf of the Authors (14 Mar 2018)  Author's response    Manuscript
ED: Publish as is (01 Apr 2018) by Sally E. Pusede
Publications Copernicus
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Short summary
With an advanced causality analysis method CCM, we quantified the influence of individual meteorological factors on PM2.5 concentrations and revealed notable spatial and temporal variations of meteorological influences on PM2.5 concentrations across China. Temperature, humidity and wind exert the strongest influences on PM2.5 concentrations in most cities across China. Given the notable spatial variations, meteorological means for reducing PM2.5 concentrations should be designed accordingly.
With an advanced causality analysis method CCM, we quantified the influence of individual...
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