Significant human health co-benefits of African emissions mitigation

Wells, Christopher David; Kasoar, Matthew; Ezzati, Majid; Voulgarakis, Apostolos

doi:https://doi.org/10.5194/egusphere-2023-2037

Preprints

https://doi.org/10.5194/egusphere-2023-2037

Preprints

19 Sep 2023

| 19 Sep 2023

Significant human health co-benefits of African emissions mitigation

Christopher David Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis

Abstract. Future African aerosol emissions, and therefore air pollution levels and health outcomes, are uncertain. Here, the range in the future impacts of African emissions in the Shared Socioeconomic Pathway (SSP) scenarios is studied, using the Earth System Model UKESM1 along with human health concentration-response functions. Using present-day demographics, annual deaths attributable to ambient particulate matter are estimated to be lower by 150,000 under stronger African aerosol mitigation by 2090, while those attributable to O₃ are lower by 15,000. The particulate matter health benefits are realised predominantly within Africa, with the O₃-driven benefits being more widespread – though still concentrated in Africa – due to the longer atmospheric lifetime of O₃. These results demonstrate the important health co-benefits from future emissions mitigation in Africa.

Received: 06 Sep 2023 – Discussion started: 19 Sep 2023

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Journal article(s) based on this preprint

24 Jan 2024

Significant human health co-benefits of mitigating African emissions

Christopher D. Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis

Atmos. Chem. Phys., 24, 1025–1039, https://doi.org/10.5194/acp-24-1025-2024,https://doi.org/10.5194/acp-24-1025-2024, 2024

Short summary

Christopher David Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2023-2037', Anonymous Referee #1, 01 Nov 2023

General comments:
Congratulations to the authors for this important study and their work to expand health and air quality research into this understudied region. I appreciate your thorough reasoning for the various decisions made to best address inherent study limitations and the clarification that these results are intended for relative comparison between themselves. Your figures are fantastic as well, and I would suggest working with the editors to make sure they are all large enough to be read clearly.
While overall design and potential impact of this study are powerful and intriguing, the text is unpolished and thus diminishes these strengths. There were a number of repeated discussion points between and within sections that should be merged to improve readability. This paper also needs some work so that the methods (throughout the paper) are presented beginning with a general outline and working into the details. As is, specific details/jargon are introduced before a general overlay is presented that is necessary to give the reader context (e.g., see comment about lines 302–304 below). Additionally, there were several cases of inconsistent formatting that should be addressed (e.g., of confidence intervals; “stippling” vs. “dashed” in figure captions).
I’m also concerned about the amount of technical jargon and lack of high-level summaries that may prevent this study from entering more interdisciplinary spaces. For instance, could certain repeated labels like “AerNonBB” and “SSP119” be replaced with something more meaningful and intuitive? I understand, however, that some of these came from previous studies, so perhaps instead a glossary of important acronyms/terms with clear definitions would be a useful quick reference for the reader. Please also provide a longer-form definition of these terms (e.g., “aerosol non-biomass burning, termed AerNonBB…”).
These changes, with greater detail offered below, should create a final paper that will reach wider audiences and have the impact it deserves.
Specific comments:
Abstract:
The abstract lacks several important aspects about the study. More background information and overall study objectives should be included at the beginning of the abstract. I suggest tying in the novelty of this study, how it progresses global research equity by focusing on the most understudied continent in terms of health and air quality research. There is also no mention of the individual scenarios run in this study (i.e., strong and weak mitigation scenarios, examined by fuel type).
Line 14: Should include the full name of the model in the abstract as well as the text: “UK Earth System Model 1 (UKESM1)”
Line 15: Please clarify that these are global annual deaths.
Lines 15–16: Confidence intervals should be provided for the results here.
Introduction:
You begin with a general overview of aerosol impacts on climate/health, but a much better way to highlight the novelty of this particular study would be to begin by covering the state of emissions in Africa. You cover this a bit in lines 65–74, but since this is really the selling point of your study it would be best to introduce these ideas earlier. I would suggest you also go into greater detail on what we do know (major or unique emissions sources, historical trends, etc.) and especially how much we don’t know due to a lack of research in this continent compared to other areas in the world. I’ve listed some potential reviews you may wish to reference below. These and/or others should be used to highlight the importance of studies like yours in improving the equity of existing global air quality and health research. At least some of these ideas should be introduced towards the start of the paper, and could also work well to strengthen the ideas in the Discussion in lines 445–449.
Abera A, Friberg J, Isaxon C, Jerrett M, Malmqvist E, Sjöström C, Taj T, Vargas AM. Air Quality in Africa: Public Health Implications. Annu Rev Public Health. 2021 Apr 1;42:193-210. doi: 10.1146/annurev-publhealth-100119-113802. Epub 2021 Dec 21. PMID: 33348996.
Coker E, Kizito S. A Narrative Review on the Human Health Effects of Ambient Air Pollution in Sub-Saharan Africa: An Urgent Need for Health Effects Studies. Int J Environ Res Public Health. 2018 Mar 1;15(3):427. doi: 10.3390/ijerph15030427. PMID: 29494501; PMCID: PMC5876972.
Katoto PDMC, Byamungu L, Brand AS, Mokaya J, Strijdom H, Goswami N, De Boever P, Nawrot TS, Nemery B. Ambient air pollution and health in Sub-Saharan Africa: Current evidence, perspectives and a call to action. Environ Res. 2019 Jun;173:174-188. doi: 10.1016/j.envres.2019.03.029. Epub 2019 Mar 16. PMID: 30913485.
Pinder RW, Klopp JM, Kleiman G, Hagler GSW, Awe Y, Terry S. Opportunities and Challenges for Filling the Air Quality Data Gap in Low- and Middle-Income Countries. Atmos Environ (1994). 2019 Oct 15;215:116794. doi: 10.1016/j.atmosenv.2019.06.032. Epub 2019 Jun 18. PMID: 33603562; PMCID: PMC7887702.
Line 31: Please check the references and clarify throughout this paragraph whether the PM_2.5 concentrations are annual/long-term or daily/short-term averages. These cannot be directly compared.
Line 45: Same comment for ozone; please confirm/specify averaging times for these levels.
Lines 75–93: This paragraph feels too technical for the introduction. I suggest generalizing this section in the Introduction and saving the details for the Methods.
Methods:
Lines 151–153: No need to separate these labels out from the paragraph unless you also include definitions alongside them; please see notes above about updating these terms or at least providing clear, easy-to-reference definitions.
Results:
Lines 302–304: This sentence does a great job explaining your methods in clear, simple terms, and should be presented this way much earlier in the manuscript.
Lines 330–344: This comparison to similar studies is more appropriate as discussion section material. It should be integrated with similar ideas repeated in lines 411–427.
Line 338: Be clear about what “smaller CRF” means here (“used an earlier CRF reflecting weaker associations between O3 and health outcomes” or similar).
Line 342: The studies that these different CRFs came from need to be specified here as well as in Table 1, with more detail so the reader can look them up for themselves in those references (i.e., many epidemiological studies report RRs by age group, location, etc.).
Lines 363–367: These ideas are very similar to those presented in the Discussion on lines 440–443, including identical wording in certain places. Please choose one place for this topic (Discussion seems best) and unify the two sections.
Discussion and Conclusions:
As noted above, a number of ideas presented in this section were also discussed in earlier sections. I suggest unifying these ideas in the discussion instead of spreading them out, since it feels a bit redundant by the time you reach the discussion.
Lines 445–449: This is a key idea that could potentially be expanded on; see notes and suggested references in Introduction section above.
Lines 450–456: This paragraph may work better if moved up, following the paragraph with a similar idea mentioned in lines 434–435.
Figures & Tables:
Figure S3: I suggest updating the global O3 plot to also include its upper boundary of the LCT, like is done for PM2.5. This would give more context/clarification that O3 remains above this boundary in all experiments.
Figure 2: Great maps, but a little difficult to see the colors beneath the stippling with how small they are. It may be worth putting this figure across multiple pages so these can be enlarged, cutting out latitudes below -60 (Antarctic region) to allow figure to be larger on the page; and/or reducing the density of the stippling so the colors come through better.
Table 1: What epidemiological studies were used for “mean”, “low RR” and “high RR”? This should be stated explicitly in the table caption and in the main text. Also, “mean” is confusing here – language like “middle RR” would be more appropriate. The table title needs to be updated to explain that this is comparing different CRFs, as is mentioned in the text.
Table 2: Please clarify in the table labels, legend, and text that the “Global” numbers exclude the “Africa” numbers – as is, it’s confusing that Africa has higher mortality counts than the global totals. Perhaps replacing “Global” with “Other Global Regions” or similar. (Please ensure this clarification is updated in other relevant places in the paper). This table also needs more details so it can stand alone (i.e., note somewhere that these numbers represent deaths, ideally noting this is 2015–2090; add a label over the first column noting these are different (weaker) mitigation scenarios.)
Technical corrections:
Please use consistent formatting for confidence intervals throughout the paper and clarify whether they are 95% CIs early on.
There are many places where paragraphs are broken up / there are very short sections standing alone that seem to belong to the preceding paragraph; these should be checked and corrected, and single-sentence paragraphs avoided as much as possible.
Line 13: Suggest changing “range in the future impacts of African” to “range of future impacts from Africa”
Line 25: Remove extra space after citation.
Line 57: Fix the degrees symbol.
Line 64: Remove comma after “less explored”.
Line 67: Remove comma after “detail”.
Line 70: Suggest “range of” replace “range in”.
Line 194: Suggest rewording from “cancel to drive little” to “cancel, resulting in little” or similar.]
Line 306: Typo; should be “centered”
Lines 365–366: Wording of this phrase is vague/confusing, please clarify: “… indicating that indirect effects of pollutant emissions on atmospheric circulation and therefore natural emissions can have a substantial influence on their overall impact.”
Lines 431–432: Typos: “SSP1” should be “SSP119”, and “SSP3” should be “SSP370”.

Citation: https://doi.org/10.5194/egusphere-2023-2037-RC1
RC2: 'Comment on egusphere-2023-2037', Anonymous Referee #2, 21 Nov 2023

The publication presents the results obtained using the UKESM1 model to quantify the impact of air pollution on mortality in Africa by comparing contrasting SSP scenarios.
The format of the publication is pleasant to read, despite a few redundancies in the text which are not problematic.
The modeling work is serious, well explained and supported by pedagogical figures.
My main reservations, which lead me not to accept the publication as it stands, are as follows:
- It is obvious that measures to reduce pollutant emissions lead to a reduction in population exposure and to excess mortality induced by this exposure
- The added value of this study is its ability to estimate the relative impact of emissions mitigated by political measures (anthropogenic emissions) and those less dependent on such measures (emissions from vegetation fires). The DACCIWA campaign, for example, showed that during the wet season, air pollution in the coastal cities of the Gulf of Guinea was dominated by advected biomass fire emissions from the southern hemisphere (Hasslet et al, Atmos. Chem. Phys., 19, 15217–15234, https://doi.org/10.5194/acp-19-15217-2019).
- Yet, as the authors quite honestly point out, the most pessimistic SSP scenarios (SSP370) lead to lower BB emissions than the more virtuous scenario (SSP119). There is no explanation for this counter-intuitive result (for exemple, Lund et al., ACP, 2019 mention that it « can be linked to assumptions about the land-use sector in the respective integrated assessment models »).

- Applying in the modelling work a trend that we know to be false disqualifies the results obtained and calls into question the value of the mortality figures obtained. It seems to me that, as a scientific community, we need to be able to explain the content of our modeling work, to avoird the risk of weakening our messages. This is of particular importance when there are no data to confirm or challenge the modeling results.
- Three of the publication's co-authors have presented a very interesting work (EGU 2021 -Improved estimates of future fire emissions under CMIP6 scenarios and implications for aerosol radiative forcing) to create an updated emissions and correct those proposed in the SSPs. Why not make use of this updated dataset, which would make the results more reliable ?
- The results on the reduction in desert dust emissions due to the disruption of the surface wind are very interesting. Is it corroborated by other similar studies ? The discussion on that point would benefit from references to past studies
- Are the indirect and semi-direct effects of aerosols considered in the study of the feedbacks on atmospheric dynamics and physics ?
Details:
- What does the number 385 in table 2 correspond to?
- To facilitate the reading of the manuscript, it would be interesting to standardise the presentation of the results : Table 1 provides the figures for Central Africa, Figure S3 illustrates the evolution of concentrations for West Africa, Figure S5 illustrates the calculation of mortality for West, North and East Africa.

Citation: https://doi.org/10.5194/egusphere-2023-2037-RC2
AC1: 'Comment on egusphere-2023-2037', Chris Wells, 29 Nov 2023

We would like to thank both reviewers for their detailed and useful reviews; please see our attached responses (in blue text).

Citation: https://doi.org/10.5194/egusphere-2023-2037-AC1

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2023-2037', Anonymous Referee #1, 01 Nov 2023

General comments:
Congratulations to the authors for this important study and their work to expand health and air quality research into this understudied region. I appreciate your thorough reasoning for the various decisions made to best address inherent study limitations and the clarification that these results are intended for relative comparison between themselves. Your figures are fantastic as well, and I would suggest working with the editors to make sure they are all large enough to be read clearly.
While overall design and potential impact of this study are powerful and intriguing, the text is unpolished and thus diminishes these strengths. There were a number of repeated discussion points between and within sections that should be merged to improve readability. This paper also needs some work so that the methods (throughout the paper) are presented beginning with a general outline and working into the details. As is, specific details/jargon are introduced before a general overlay is presented that is necessary to give the reader context (e.g., see comment about lines 302–304 below). Additionally, there were several cases of inconsistent formatting that should be addressed (e.g., of confidence intervals; “stippling” vs. “dashed” in figure captions).
I’m also concerned about the amount of technical jargon and lack of high-level summaries that may prevent this study from entering more interdisciplinary spaces. For instance, could certain repeated labels like “AerNonBB” and “SSP119” be replaced with something more meaningful and intuitive? I understand, however, that some of these came from previous studies, so perhaps instead a glossary of important acronyms/terms with clear definitions would be a useful quick reference for the reader. Please also provide a longer-form definition of these terms (e.g., “aerosol non-biomass burning, termed AerNonBB…”).
These changes, with greater detail offered below, should create a final paper that will reach wider audiences and have the impact it deserves.
Specific comments:
Abstract:
The abstract lacks several important aspects about the study. More background information and overall study objectives should be included at the beginning of the abstract. I suggest tying in the novelty of this study, how it progresses global research equity by focusing on the most understudied continent in terms of health and air quality research. There is also no mention of the individual scenarios run in this study (i.e., strong and weak mitigation scenarios, examined by fuel type).
Line 14: Should include the full name of the model in the abstract as well as the text: “UK Earth System Model 1 (UKESM1)”
Line 15: Please clarify that these are global annual deaths.
Lines 15–16: Confidence intervals should be provided for the results here.
Introduction:
You begin with a general overview of aerosol impacts on climate/health, but a much better way to highlight the novelty of this particular study would be to begin by covering the state of emissions in Africa. You cover this a bit in lines 65–74, but since this is really the selling point of your study it would be best to introduce these ideas earlier. I would suggest you also go into greater detail on what we do know (major or unique emissions sources, historical trends, etc.) and especially how much we don’t know due to a lack of research in this continent compared to other areas in the world. I’ve listed some potential reviews you may wish to reference below. These and/or others should be used to highlight the importance of studies like yours in improving the equity of existing global air quality and health research. At least some of these ideas should be introduced towards the start of the paper, and could also work well to strengthen the ideas in the Discussion in lines 445–449.
Abera A, Friberg J, Isaxon C, Jerrett M, Malmqvist E, Sjöström C, Taj T, Vargas AM. Air Quality in Africa: Public Health Implications. Annu Rev Public Health. 2021 Apr 1;42:193-210. doi: 10.1146/annurev-publhealth-100119-113802. Epub 2021 Dec 21. PMID: 33348996.
Coker E, Kizito S. A Narrative Review on the Human Health Effects of Ambient Air Pollution in Sub-Saharan Africa: An Urgent Need for Health Effects Studies. Int J Environ Res Public Health. 2018 Mar 1;15(3):427. doi: 10.3390/ijerph15030427. PMID: 29494501; PMCID: PMC5876972.
Katoto PDMC, Byamungu L, Brand AS, Mokaya J, Strijdom H, Goswami N, De Boever P, Nawrot TS, Nemery B. Ambient air pollution and health in Sub-Saharan Africa: Current evidence, perspectives and a call to action. Environ Res. 2019 Jun;173:174-188. doi: 10.1016/j.envres.2019.03.029. Epub 2019 Mar 16. PMID: 30913485.
Pinder RW, Klopp JM, Kleiman G, Hagler GSW, Awe Y, Terry S. Opportunities and Challenges for Filling the Air Quality Data Gap in Low- and Middle-Income Countries. Atmos Environ (1994). 2019 Oct 15;215:116794. doi: 10.1016/j.atmosenv.2019.06.032. Epub 2019 Jun 18. PMID: 33603562; PMCID: PMC7887702.
Line 31: Please check the references and clarify throughout this paragraph whether the PM_2.5 concentrations are annual/long-term or daily/short-term averages. These cannot be directly compared.
Line 45: Same comment for ozone; please confirm/specify averaging times for these levels.
Lines 75–93: This paragraph feels too technical for the introduction. I suggest generalizing this section in the Introduction and saving the details for the Methods.
Methods:
Lines 151–153: No need to separate these labels out from the paragraph unless you also include definitions alongside them; please see notes above about updating these terms or at least providing clear, easy-to-reference definitions.
Results:
Lines 302–304: This sentence does a great job explaining your methods in clear, simple terms, and should be presented this way much earlier in the manuscript.
Lines 330–344: This comparison to similar studies is more appropriate as discussion section material. It should be integrated with similar ideas repeated in lines 411–427.
Line 338: Be clear about what “smaller CRF” means here (“used an earlier CRF reflecting weaker associations between O3 and health outcomes” or similar).
Line 342: The studies that these different CRFs came from need to be specified here as well as in Table 1, with more detail so the reader can look them up for themselves in those references (i.e., many epidemiological studies report RRs by age group, location, etc.).
Lines 363–367: These ideas are very similar to those presented in the Discussion on lines 440–443, including identical wording in certain places. Please choose one place for this topic (Discussion seems best) and unify the two sections.
Discussion and Conclusions:
As noted above, a number of ideas presented in this section were also discussed in earlier sections. I suggest unifying these ideas in the discussion instead of spreading them out, since it feels a bit redundant by the time you reach the discussion.
Lines 445–449: This is a key idea that could potentially be expanded on; see notes and suggested references in Introduction section above.
Lines 450–456: This paragraph may work better if moved up, following the paragraph with a similar idea mentioned in lines 434–435.
Figures & Tables:
Figure S3: I suggest updating the global O3 plot to also include its upper boundary of the LCT, like is done for PM2.5. This would give more context/clarification that O3 remains above this boundary in all experiments.
Figure 2: Great maps, but a little difficult to see the colors beneath the stippling with how small they are. It may be worth putting this figure across multiple pages so these can be enlarged, cutting out latitudes below -60 (Antarctic region) to allow figure to be larger on the page; and/or reducing the density of the stippling so the colors come through better.
Table 1: What epidemiological studies were used for “mean”, “low RR” and “high RR”? This should be stated explicitly in the table caption and in the main text. Also, “mean” is confusing here – language like “middle RR” would be more appropriate. The table title needs to be updated to explain that this is comparing different CRFs, as is mentioned in the text.
Table 2: Please clarify in the table labels, legend, and text that the “Global” numbers exclude the “Africa” numbers – as is, it’s confusing that Africa has higher mortality counts than the global totals. Perhaps replacing “Global” with “Other Global Regions” or similar. (Please ensure this clarification is updated in other relevant places in the paper). This table also needs more details so it can stand alone (i.e., note somewhere that these numbers represent deaths, ideally noting this is 2015–2090; add a label over the first column noting these are different (weaker) mitigation scenarios.)
Technical corrections:
Please use consistent formatting for confidence intervals throughout the paper and clarify whether they are 95% CIs early on.
There are many places where paragraphs are broken up / there are very short sections standing alone that seem to belong to the preceding paragraph; these should be checked and corrected, and single-sentence paragraphs avoided as much as possible.
Line 13: Suggest changing “range in the future impacts of African” to “range of future impacts from Africa”
Line 25: Remove extra space after citation.
Line 57: Fix the degrees symbol.
Line 64: Remove comma after “less explored”.
Line 67: Remove comma after “detail”.
Line 70: Suggest “range of” replace “range in”.
Line 194: Suggest rewording from “cancel to drive little” to “cancel, resulting in little” or similar.]
Line 306: Typo; should be “centered”
Lines 365–366: Wording of this phrase is vague/confusing, please clarify: “… indicating that indirect effects of pollutant emissions on atmospheric circulation and therefore natural emissions can have a substantial influence on their overall impact.”
Lines 431–432: Typos: “SSP1” should be “SSP119”, and “SSP3” should be “SSP370”.

Citation: https://doi.org/10.5194/egusphere-2023-2037-RC1
RC2: 'Comment on egusphere-2023-2037', Anonymous Referee #2, 21 Nov 2023

The publication presents the results obtained using the UKESM1 model to quantify the impact of air pollution on mortality in Africa by comparing contrasting SSP scenarios.
The format of the publication is pleasant to read, despite a few redundancies in the text which are not problematic.
The modeling work is serious, well explained and supported by pedagogical figures.
My main reservations, which lead me not to accept the publication as it stands, are as follows:
- It is obvious that measures to reduce pollutant emissions lead to a reduction in population exposure and to excess mortality induced by this exposure
- The added value of this study is its ability to estimate the relative impact of emissions mitigated by political measures (anthropogenic emissions) and those less dependent on such measures (emissions from vegetation fires). The DACCIWA campaign, for example, showed that during the wet season, air pollution in the coastal cities of the Gulf of Guinea was dominated by advected biomass fire emissions from the southern hemisphere (Hasslet et al, Atmos. Chem. Phys., 19, 15217–15234, https://doi.org/10.5194/acp-19-15217-2019).
- Yet, as the authors quite honestly point out, the most pessimistic SSP scenarios (SSP370) lead to lower BB emissions than the more virtuous scenario (SSP119). There is no explanation for this counter-intuitive result (for exemple, Lund et al., ACP, 2019 mention that it « can be linked to assumptions about the land-use sector in the respective integrated assessment models »).

- Applying in the modelling work a trend that we know to be false disqualifies the results obtained and calls into question the value of the mortality figures obtained. It seems to me that, as a scientific community, we need to be able to explain the content of our modeling work, to avoird the risk of weakening our messages. This is of particular importance when there are no data to confirm or challenge the modeling results.
- Three of the publication's co-authors have presented a very interesting work (EGU 2021 -Improved estimates of future fire emissions under CMIP6 scenarios and implications for aerosol radiative forcing) to create an updated emissions and correct those proposed in the SSPs. Why not make use of this updated dataset, which would make the results more reliable ?
- The results on the reduction in desert dust emissions due to the disruption of the surface wind are very interesting. Is it corroborated by other similar studies ? The discussion on that point would benefit from references to past studies
- Are the indirect and semi-direct effects of aerosols considered in the study of the feedbacks on atmospheric dynamics and physics ?
Details:
- What does the number 385 in table 2 correspond to?
- To facilitate the reading of the manuscript, it would be interesting to standardise the presentation of the results : Table 1 provides the figures for Central Africa, Figure S3 illustrates the evolution of concentrations for West Africa, Figure S5 illustrates the calculation of mortality for West, North and East Africa.

Citation: https://doi.org/10.5194/egusphere-2023-2037-RC2
AC1: 'Comment on egusphere-2023-2037', Chris Wells, 29 Nov 2023

We would like to thank both reviewers for their detailed and useful reviews; please see our attached responses (in blue text).

Citation: https://doi.org/10.5194/egusphere-2023-2037-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Chris Wells on behalf of the Authors (29 Nov 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (30 Nov 2023) by Bryan N. Duncan

RR by Anonymous Referee #1 (04 Dec 2023)

RR by Anonymous Referee #3 (09 Dec 2023)

ED: Publish as is (09 Dec 2023) by Bryan N. Duncan

AR by Chris Wells on behalf of the Authors (11 Dec 2023)

Journal article(s) based on this preprint

24 Jan 2024

Significant human health co-benefits of mitigating African emissions

Christopher D. Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis

Atmos. Chem. Phys., 24, 1025–1039, https://doi.org/10.5194/acp-24-1025-2024,https://doi.org/10.5194/acp-24-1025-2024, 2024

Short summary

Christopher David Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis

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https://doi.org/10.5194/egusphere-2023-2037-supplement

Christopher David Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis

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Human-driven emissions of air pollutants, mostly caused by burning fossil fuels, impact both the climate and human health. Millions of deaths each year are caused by air pollution globally, and the future trends are uncertain. Here, we use a global climate model to study the effect of African pollutant emissions on surface-level air pollution, and resultant impacts on human health, in several future emissions scenarios. We find much lower health impacts under cleaner, lower-emission futures.


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