Articles | Volume 24, issue 10
https://doi.org/10.5194/acp-24-5803-2024
https://doi.org/10.5194/acp-24-5803-2024
Research article
 | 
22 May 2024
Research article |  | 22 May 2024

Bayesian inference-based estimation of hourly primary and secondary organic carbon in suburban Hong Kong: multi-temporal-scale variations and evolution characteristics during PM2.5 episodes

Shan Wang, Kezheng Liao, Zijing Zhang, Yuk Ying Cheng, Qiongqiong Wang, Hanzhe Chen, and Jian Zhen Yu

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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 egusphere-2023-2286', Anonymous Referee #1, 17 Nov 2023
    • AC1: 'Reply on RC1', Jian Zhen Yu, 03 Mar 2024
  • RC2: 'Comment on egusphere-2023-2286', Anonymous Referee #2, 21 Jan 2024
    • AC2: 'Reply on RC2', Jian Zhen Yu, 03 Mar 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Jian Zhen Yu on behalf of the Authors (07 Mar 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (22 Mar 2024) by Qi Chen
AR by Jian Zhen Yu on behalf of the Authors (28 Mar 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (31 Mar 2024) by Qi Chen
AR by Jian Zhen Yu on behalf of the Authors (08 Apr 2024)  Manuscript 
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Short summary
In this work, hourly primary and secondary organic carbon were estimated by a novel Bayesian inference approach in suburban Hong Kong. Their multi-temporal-scale variations and evolution characteristics during PM2.5 episodes were examined. The methodology could serve as a guide for other locations with similar monitoring capabilities. The observation-based results are helpful for understanding the evolving nature of secondary organic aerosols and refining the accuracy of model simulations.
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