the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Jan 2025
Influence of biogenic NO emissions from soil on Atmospheric chemistry over Africa: a regional modelling study
Abstract. In the context of climate change and increasing anthropogenic pressures in Africa, understanding the interactions between atmospheric chemistry, regional climate, and biogeochemical cycles is critical. This study investigates the potential role of biogenic nitric oxide emissions from African soils (BioNO), particularly in arid and semi-arid ecosystems, as significant contributors to atmospheric NO2 emissions and regional atmospheric chemistry. To this end, we rely on a modelling approach based on the RegCM5 regional climate model, including an updated atmospheric chemistry module and, amongst other, a specific parametrization for BioNO emissions. Throughout the paper, the model performances are evaluated against various datasets including in-situ observations from the INDAAF network and chemical reanalyses. Sensitivity studies demonstrate that integrating BioNO emissions into the model enhances the accuracy of simulated NO2, HNO3, and O3 seasonal cycles and surface concentration magnitudes, while reducing simulated biases against ground based observations. Large differences are however still present regarding notably the simulated surface ozone concentration magnitude vs in situ measurements, while these biases are also observed for chemical reanalyses, and a state of the art chemistry transport model used for comparison. Beside outlining the impact and added value of BioNO flux representation for regional atmospheric chemistry, our findings also outline the suitability of RegCM5 coupled system for the study of regional climate, chemistry and nitrogen cycle interactions over Africa.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(22710 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(22710 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3179', Anonymous Referee #1, 18 Feb 2025
- AC1: 'Reply on RC1', Eric martial Yao, 19 Mar 2025
-
RC2: 'Comment on egusphere-2024-3179', Anonymous Referee #2, 23 Feb 2025
The manuscript addresses a very interesting topic related to the quantification of Biogenic NO (BioNO) emissions, which are often neglected in atmospheric modeling. It employs an Artificial Neural Network (ANN)-based approach to estimate BioNO emissions. These emissions are then used to explore potential improvements in the simulation of NO₂, HNO₃, and O₃ using RegCM5. While the described method provides flexibility in capturing nonlinear relationships, its validation remains limited, primarily due to the scarcity of in-situ measurements in Africa.
Below are some comments for the authors to improve the manuscript and enhance the research findings:
- The Delon et al. (2007) methodology for estimating biogenic NO emissions was developed based on measurements from sites characterized as wet agricultural soils in temperate climates and dry grassland soils in tropical climates. These measurements were mostly taken during the warm season. How do the climatic conditions and land-use types in the Delon et al. (2007) study compare to those in the manuscript? Are there inconsistencies, and if so, how might they impact the estimated BioNO emissions for the study area?
- Lines 139–145 contain repetitive sentences and should be rephrased for clarity and conciseness.
- It is unclear whether the CAMS chemical reanalysis and GEOS-Chem simulations account for BioNO emissions. Clarifying this would make the comparison with RegCM5 results more explicit.
- Lines 369–370: The manuscript states, “This is especially apparent in transitional ecosystems such as savannas and grasslands.” Where are these land-use types located within the study area? The authors should consider providing a land-use map as a supplementary file for readers unfamiliar with the geography and land-use distribution in Africa.
- Lines 371–372: Figure 9 is discussed after Figure 6. The figures should be numbered in the order of their appearance in the manuscript.
- Line 374: The reference to Figure 7 should be correctly formatted in parentheses.
- Figure 8: It would be more straightforward to compare and discuss the Base and BioNO columns of NO₂ simulated with RegCM5 against those from OMI and OMI/Aura satellite data.
- Tables 5, 6, and 7: Bias values for CAMS and GEOS-Chem models should also be included for a more comprehensive comparison.
- Line 412: No correlation values are presented in Figure 11. The authors should either include them in the figure or adjust the text accordingly.
- Expand the discussion on the CBM-Z chemical mechanism. While computationally efficient, it may oversimplify NOₓ-VOC interactions, affecting the accuracy of NO₂ oxidation and the formation of secondary pollutants such as HNO₃ and O₃.
- The overestimation of O₃ in RegCM5 results—could this be linked to overestimated chemical boundary conditions from CAMS, which, as shown in Figure 17, also overestimates O₃?
Citation: https://doi.org/10.5194/egusphere-2024-3179-RC2 - AC2: 'Reply on RC2', Eric martial Yao, 19 Mar 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3179', Anonymous Referee #1, 18 Feb 2025
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:
- The word “significant” is used numerous times in this manuscript, when the sentiment is being conveyed that something is substantial, or noteworthy. I encourage the authors to reduce the use of “significant” and “significantly” in such cases, as these words should be reserved for the context of statistical significance, as opposed to something that is notable. Consider using words such as: notable, noteworthy, considerable, or substantial.
- There are many website links and DOIs that are included within the body of the text, which need to be included in the reference section with only an appropriate citation within the body of the text. Refer to other ACP papers for an example of appropriate citations.
- Section 2.2 describing the BioNO parameterization requires attention. In equation 2, how are each of the weights determined? What do the variables S1, S2, and S3 represent? Why does each of S1, S2 and S3 contain a summation cross differing values, e.g. 1 to 7, 8 to 23, and then 17 to 23? What do the subscripts on the weights represent? This is not explained, causing the various equations feel arbitrary. Additionally, why are hyperbolic tangents used to represent this process in equation 1? This is not a common approach when modeling soil NO emissions. Why not use a more common, and newer, approach, e.g. Hudman et al., (2012), which is cited within this manuscript? There needs to be a clearer description of this parameterization and justification for decisions that were made, as the soil emissions and subsequent impacts appear to be the focal point of this manuscript.
- While section 2 describes some general details about the RegCM5 model, no details are provided about the specific model domain for this study. Is the model being run for all of Africa, or a subset? Or is the model run globally, and only a subset is being shown to focus on Africa? A clearer description is needed. This could be added to section 2.3.
Line by Line Technical Corrections:
- Line 4 (in Abstract): I would change “atmospheric NO2 emissions” to “atmospheric NO2concentrations”. Also, please define NO2, e.g. nitrogen dioxide (NO2). Please define all acronyms at their first instance before using an acronym.
- Line 6 (in Abstract): Please define INDAAF acronym.
- Line 9 (in Abstract): The sentence starting with “Large differences are however…” is a run-on sentence, and the wording is confusing. Please rephrase. This sentence also appears to contradict the claim in the previous sentence that model accuracy has been improved.
- Line 16: “… a significant source of gaseous and particulate emissions, affecting the regional….”
- Line 17: “…, there are also a significant…”. Additionally, the word “significant” is used twice in this sentence. Please reword.
- Line 22: “… (Referred to as BioNO) …”
- Line 23: “… (Ludwig et al., 2001) and above-canopy emissions estimates range from 4.7 – 26.7 Tg N yr-1…”
- Lines 27 - 32: I would reorganize these sentences, to first list the large variety of environmental factors that influence BioNO, and then following with the description of precipitation and soil moisture. While soil emissions are heavily influenced by precipitation and soil moisture, I don’t find it accurate to lead with the idea that emissions “primarily” depend on soil moisture. This is just one of numerous important factors.
- Line 33: Remove “however”
- Line 40: “… used a neural network-based…”
- Lines 41-42: Does “lower equatorial Africa’s troposphere” refer to equatorial Africa’s lower troposphere, or does this refer to Africa’s troposphere at lower latitudes? The wording is confusing.
- Line 42: “… troposphere on 6 August 2006 during the AMMA… ”. Please define the AMMA acronym.
- Line 48: “… resulting in a 10% decrease…”
- Lines 49-50: “... regional climate systems to study the impact of present and future….”
- Line 52: “…in the chemical environment…”
- Line 58: I recommend starting a new paragraph with the sentence “One goal of the present study…”
- Line 59: Please define ICTP RegCM5
- Lines 70 – 73: This sentence referring to the shared scientific goals should not be in the introduction of the manuscript. This would be more appropriate to include in an acknowledgements section. Additionally, DOIs should not be included within the body of text in a manuscript. They should be included in the references sections with a relevant citation.
- Line 74: “... Sections 2 and 3 will provide ”, to be consistent with verb tense later on in the paragraph.
- Line 80: Is the model referred to as RegCM5, or RegCM5-CHEM? I see references to both, without a clear description. I recommend choosing one for consistency.
- Line 82: Please define MOLOCH
- Line 94: Please define OI_WK.
- Line 101: I believe this is supposed to say “fine particle”, not “fine particles”.
- Lines 107-109: This sentence contains numerous grammatical errors.
- Line 122: use “degree” instead of “deg”.
- Line 142: This sentence appears to be repeated, the same as line 139.
- Line 144: It appears that a new sentence begins in the middle of a different sentence here.
- Line 165: Please define WFPS.
- Line 175: I suggest “The model has a spatial resolution of 30 km x 30 km, with 35 vertical…”
- Line 179: Add subscripts for O3, NO2 and HNO3.
- Line 196: This line says “used here as reference”. What is this referring to? Should there be a reference to a figure perhaps? Also, it is not clear what “duplicates means” is referring to.
- Line 198: The sentence states that LAERO participated bi-annually, and then later specifies twice yearly, which is redundant.
- Line 200: References to other work / studies should not be done by including a link within the body of the text. Same for DOIs.
- Line 210: ERA5 does not stand for “European Environment Agency version 5”.
- Line 241: Remove “somehow”.
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.
Citation: https://doi.org/10.5194/egusphere-2024-3179-RC1 - AC1: 'Reply on RC1', Eric martial Yao, 19 Mar 2025
-
RC2: 'Comment on egusphere-2024-3179', Anonymous Referee #2, 23 Feb 2025
The manuscript addresses a very interesting topic related to the quantification of Biogenic NO (BioNO) emissions, which are often neglected in atmospheric modeling. It employs an Artificial Neural Network (ANN)-based approach to estimate BioNO emissions. These emissions are then used to explore potential improvements in the simulation of NO₂, HNO₃, and O₃ using RegCM5. While the described method provides flexibility in capturing nonlinear relationships, its validation remains limited, primarily due to the scarcity of in-situ measurements in Africa.
Below are some comments for the authors to improve the manuscript and enhance the research findings:
- The Delon et al. (2007) methodology for estimating biogenic NO emissions was developed based on measurements from sites characterized as wet agricultural soils in temperate climates and dry grassland soils in tropical climates. These measurements were mostly taken during the warm season. How do the climatic conditions and land-use types in the Delon et al. (2007) study compare to those in the manuscript? Are there inconsistencies, and if so, how might they impact the estimated BioNO emissions for the study area?
- Lines 139–145 contain repetitive sentences and should be rephrased for clarity and conciseness.
- It is unclear whether the CAMS chemical reanalysis and GEOS-Chem simulations account for BioNO emissions. Clarifying this would make the comparison with RegCM5 results more explicit.
- Lines 369–370: The manuscript states, “This is especially apparent in transitional ecosystems such as savannas and grasslands.” Where are these land-use types located within the study area? The authors should consider providing a land-use map as a supplementary file for readers unfamiliar with the geography and land-use distribution in Africa.
- Lines 371–372: Figure 9 is discussed after Figure 6. The figures should be numbered in the order of their appearance in the manuscript.
- Line 374: The reference to Figure 7 should be correctly formatted in parentheses.
- Figure 8: It would be more straightforward to compare and discuss the Base and BioNO columns of NO₂ simulated with RegCM5 against those from OMI and OMI/Aura satellite data.
- Tables 5, 6, and 7: Bias values for CAMS and GEOS-Chem models should also be included for a more comprehensive comparison.
- Line 412: No correlation values are presented in Figure 11. The authors should either include them in the figure or adjust the text accordingly.
- Expand the discussion on the CBM-Z chemical mechanism. While computationally efficient, it may oversimplify NOₓ-VOC interactions, affecting the accuracy of NO₂ oxidation and the formation of secondary pollutants such as HNO₃ and O₃.
- The overestimation of O₃ in RegCM5 results—could this be linked to overestimated chemical boundary conditions from CAMS, which, as shown in Figure 17, also overestimates O₃?
Citation: https://doi.org/10.5194/egusphere-2024-3179-RC2 - AC2: 'Reply on RC2', Eric martial Yao, 19 Mar 2025
Peer review completion
Journal article(s) based on this preprint
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 667 | 93 | 18 | 778 | 24 | 41 |
- HTML: 667
- PDF: 93
- XML: 18
- Total: 778
- BibTeX: 24
- EndNote: 41
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Eric Martial Yao
Fabien Solmon
Marcellin Adon
Claire Delon
Corinne Galy-Lacaux
Graziano Giuliani
Bastien Sauvage
Véronique Yoboue
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(22710 KB) - Metadata XML
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.