Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

Feng, Jin; Li, Yanjie; Qiu, Yulu; Zhu, Fuxin

doi:https://doi.org/10.5194/acp-23-375-2023

Articles | Volume 23, issue 1

https://doi.org/10.5194/acp-23-375-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-375-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 1

Research article

|

10 Jan 2023

Research article |

| 10 Jan 2023

Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

Jin Feng, Yanjie Li, Yulu Qiu, and Fuxin Zhu

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Final revised paper (published on 10 Jan 2023)
Supplement to the final revised paper
Preprint (discussion started on 19 Aug 2022)
Supplement to the preprint

Interactive discussion

Status: closed

RC1:
'Comment on acp-2022-563', Anonymous Referee #1, 08 Oct 2022

Please see the attached file.

Citation: https://doi.org/10.5194/acp-2022-563-RC1
- AC1: 'Response to reviewers for the manuscript #acp-2022-563', Jin Feng, 06 Dec 2022
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-563/acp-2022-563-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2022-563-AC1
RC2:
'Comment on acp-2022-563', Anonymous Referee #2, 04 Nov 2022

The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-563/acp-2022-563-RC2-supplement.pdf

Citation: https://doi.org/10.5194/acp-2022-563-RC2
- AC1: 'Response to reviewers for the manuscript #acp-2022-563', Jin Feng, 06 Dec 2022
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-563/acp-2022-563-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2022-563-AC1
AC1: 'Response to reviewers for the manuscript #acp-2022-563', Jin Feng, 06 Dec 2022

The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-563/acp-2022-563-AC1-supplement.pdf

Citation: https://doi.org/10.5194/acp-2022-563-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Jin Feng on behalf of the Authors (06 Dec 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (08 Dec 2022) by Duncan Watson-Parris

RR by Anonymous Referee #1 (11 Dec 2022)

RR by Anonymous Referee #2 (18 Dec 2022)

ED: Publish as is (19 Dec 2022) by Duncan Watson-Parris

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Short summary

It is important to use weather data to estimate aerosol concentrations. Here, a weather index for aerosol concentration based on deep learning was developed, linking weather and short-term variations in aerosol concentrations over China. The index provides better performance than chemical transport model simulation and other data-based estimation approaches. It can be used as a robust tool for estimating daily variations in aerosol concentrations.