Articles | Volume 25, issue 19
https://doi.org/10.5194/acp-25-12101-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.Influence of biogenic NO emissions from soil on atmospheric chemistry over Africa: a regional modelling study
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- Final revised paper (published on 07 Oct 2025)
- Supplement to the final revised paper
- Preprint (discussion started on 20 Jan 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on egusphere-2024-3179', Anonymous Referee #1, 18 Feb 2025
- AC1: 'Reply on RC1', Eric martial Yao, 19 Mar 2025
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RC2: 'Comment on egusphere-2024-3179', Anonymous Referee #2, 23 Feb 2025
- AC2: 'Reply on RC2', Eric martial Yao, 19 Mar 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Eric martial Yao on behalf of the Authors (20 Mar 2025)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (26 Mar 2025) by Maria Kanakidou
RR by Anonymous Referee #2 (23 Apr 2025)
RR by Anonymous Referee #3 (23 Jul 2025)

ED: Reconsider after major revisions (01 Jul 2025) by Maria Kanakidou

ED: Publish subject to minor revisions (review by editor) (28 Jul 2025) by Maria Kanakidou

AR by Eric martial Yao on behalf of the Authors (30 Jul 2025)
Author's response
Author's tracked changes
Manuscript
ED: Publish subject to technical corrections (19 Aug 2025) by Maria Kanakidou

AR by Eric martial Yao on behalf of the Authors (19 Aug 2025)
Author's response
Manuscript
Review of Influence of biogenic NO emissions from soil on Atmospheric chemistry over Africa: a regional modelling study
General Comments:
This work evaluates the impact of soil nitric oxide emissions (BioNO) on regional air quality over Africa using the RegCM5 regional climate model. The authors applied a revised chemistry scheme within the model, and also used updated BioNO emissions developed from an artificial neural network (ANN). The authors find that by incorporating BioNO, evaluation of model chemical output against observations is improved across various time scales and for multiple chemical species.
This manuscript contains numerous grammatical errors and typos, particularly related to poor sentence structure, incorrect verb tense, and missing articles (e.g. “a” or “the”), and there are an abundance of acronyms which are not defined throughout the manuscript. Additionally, there are many instances of phrasing and terminology that deviate from the conventional language typically used in atmospheric science / ACP publications. While not necessarily incorrect, using unconventional language can make the text feel less polished and can be distracting. These grammatical errors and text issues unfortunately detract from the manuscript quality, making it difficult to assess the overall quality of this work objectively. I have suggested some edits in the line by line comments, however I encourage the authors to more thoroughly go through the entire manuscript to correct remaining grammatical errors and to carefully revise the manuscript to improve clarity and readability for an ACP audience before resubmitting.
I do find the model evaluation of surface NO2, O3 and HNO3 concentrations against observations to be worthwhile.
Specific Comments:
Line by Line Technical Corrections:
Figure Comments:
Figure 1: I recommend removing the underlying emissions data from this figure, as those results do not get discussed at any point. I recommend modifying this figure to be a simpler introduction to the model domain, with a rectangle denoting the extent of the model domain, as well as the INDAAF points, to show where measurements were taken. Otherwise there is no introduction to the model domain.