Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3197-2022
https://doi.org/10.5194/essd-14-3197-2022
Data description paper
 | 
12 Jul 2022
Data description paper |  | 12 Jul 2022

Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China

Junting Zhong, Xiaoye Zhang, Ke Gui, Jie Liao, Ye Fei, Lipeng Jiang, Lifeng Guo, Liangke Liu, Huizheng Che, Yaqiang Wang, Deying Wang, and Zijiang Zhou

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-110', Anonymous Referee #1, 22 Apr 2022
    • AC1: 'Reply on RC1', Junting Zhong, 13 Jun 2022
  • RC2: 'Comment on essd-2022-110', Anonymous Referee #2, 28 Apr 2022
    • AC2: 'Reply on RC2', Junting Zhong, 13 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Junting Zhong on behalf of the Authors (13 Jun 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (13 Jun 2022) by Chunlüe Zhou
RR by Anonymous Referee #2 (15 Jun 2022)
RR by Anonymous Referee #1 (18 Jun 2022)
ED: Publish as is (20 Jun 2022) by Chunlüe Zhou
AR by Junting Zhong on behalf of the Authors (22 Jun 2022)
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Short summary
Historical long-term PM2.5 records with high temporal resolution are essential but lacking for research and environmental management. Here, we reconstruct site-based and gridded PM2.5 datasets at 6-hour intervals from 1960 to 2020 that combine visibility, meteorological data, and emissions based on a machine learning model with extracted spatial features. These two PM2.5 datasets will lay the foundation of research studies associated with air pollution, climate change, and aerosol reanalysis.
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