Articles | Volume 15, issue 5
Atmos. Meas. Tech., 15, 1511–1520, 2022
https://doi.org/10.5194/amt-15-1511-2022
Atmos. Meas. Tech., 15, 1511–1520, 2022
https://doi.org/10.5194/amt-15-1511-2022

Research article 16 Mar 2022

Research article | 16 Mar 2022

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species

Junlei Zhan et al.

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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 amt-2021-367', Anonymous Referee #1, 13 Nov 2021
    • AC1: 'Reply on RC1', Yongchun Liu, 15 Jan 2022
  • RC2: 'Comment on amt-2021-367', Anonymous Referee #2, 18 Dec 2021
    • AC2: 'Reply on RC2', Yongchun Liu, 15 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yongchun Liu on behalf of the Authors (15 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (21 Jan 2022) by Glenn Wolfe
RR by Anonymous Referee #1 (02 Feb 2022)
ED: Publish subject to minor revisions (review by editor) (03 Feb 2022) by Glenn Wolfe
AR by Yongchun Liu on behalf of the Authors (07 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (09 Feb 2022) by Glenn Wolfe
AR by Yongchun Liu on behalf of the Authors (10 Feb 2022)  Author's response    Manuscript
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Short summary
Our study investigated the O3 formation sensitivity in Beijing using a random forest model coupled with the reactivity of volatile organic compound (VOC) species. Results found that random forest accurately predicted O3 concentration when initial VOCs were considered, and relative importance correlated well with O3 formation potential. The O3 isopleth curves calculated by the random forest model were generally comparable with those calculated by the box model.