Tracking surface ozone responses to clean air interventions under a warming climate in China

Fang, Jie; Zhang, Yunjiang; Hauglustaine, Didier; Zheng, Bo; Wang, Ming; Li, Jingyi; Sun, Yong; Li, Haiwei; Wang, Junfeng; Wu, Yun; Chen, Mindong; Ge, Xinlei

doi:10.5194/egusphere-2025-4014

Preprints

https://doi.org/10.5194/egusphere-2025-4014

Preprints

11 Sep 2025

| 11 Sep 2025

Tracking surface ozone responses to clean air interventions under a warming climate in China

Jie Fang, Yunjiang Zhang, Didier Hauglustaine, Bo Zheng, Ming Wang, Jingyi Li, Yong Sun, Haiwei Li, Junfeng Wang, Yun Wu, Mindong Chen, and Xinlei Ge

Abstract. Surface ozone, a major air pollutant with profound implications for human health, ecosystems, and climate, shows long-term trends shaped by both anthropogenic and climatic drivers. Here, we develop a machine learning-based approach – the Fixed Emission Approximation (FEA) – to disentangle the effects of meteorological variability and anthropogenic emissions on summertime ozone trends in China. We identify three distinct phases of ozone trends corresponding to clean air actions. Anthropogenic emissions drove a +23.2 ± 1.1 μg m⁻³ increase in summer maximum daily 8-hour average ozone during 2013–2017, followed by a −4.6 ± 1.5 μg m⁻³ decrease during 2018–2020. However, during 2021–2023, extreme meteorological anomalies – including heatwaves and extended monsoon rainfall – emerged as key drivers of ozone variability. Satellite-derived formaldehyde-to-nitrogen dioxide ratios reveal widespread urban volatile organic compounds-limited regimes, with a shift toward nitrogen oxides-limited sensitivity under influence of heatwaves. Finally, we assess ozone trends under sustained climate warming from 1970 to 2023 based on the FEA framework. The results indicate a significant climate-driven increase in ozone levels across China's urban agglomerations, underscoring the amplifying role of climate change in ozone pollution. Together, these findings highlight the dual influence of anthropogenic and climatic factors on ozone pollution and emphasize the need for integrated strategies that couple emission mitigation with climate adaptation to effectively manage ozone risks in a warming world.

Received: 18 Aug 2025 – Discussion started: 11 Sep 2025

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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Journal article(s) based on this preprint

19 Jan 2026

Tracking surface ozone responses to clean air actions under a warming climate in China using machine learning

Jie Fang, Yunjiang Zhang, Didier Hauglustaine, Bo Zheng, Ming Wang, Jingyi Li, Yong Sun, Haiwei Li, Junfeng Wang, Yun Wu, Bin Yuan, Mindong Chen, and Xinlei Ge

Atmos. Chem. Phys., 26, 851–867, https://doi.org/10.5194/acp-26-851-2026,https://doi.org/10.5194/acp-26-851-2026, 2026

Short summary

Jie Fang, Yunjiang Zhang, Didier Hauglustaine, Bo Zheng, Ming Wang, Jingyi Li, Yong Sun, Haiwei Li, Junfeng Wang, Yun Wu, Mindong Chen, and Xinlei Ge

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-4014', Anonymous Referee #2, 30 Sep 2025

Tropospheric ozone is a globally important air pollutant and a short-lived climate forcer, with substantial impacts on human health, climate change, and terrestrial ecosystems. Understanding the relationship between ozone concentration changes and their driving factors is essential for developing effective control strategies. This study utilizes ground-based observational data from the Chinese monitoring network during 2013-2023 and develops a machine-learning-based method to quantitatively disentangle the contributions of meteorological conditions and anthropogenic emissions. The analysis is further extended to evaluate the sensitivity of ozone to climate change. In addition, the authors employ satellite retrievals to explore the changes in precursor ratios and to diagnose the shifts in chemical regimes. The proposed analytical framework provides valuable insights and will be highly informative for future studies. Overall, the manuscript is clearly structured, well-designed, and well-written, and it fits well within the scope of ACP. I would recommend publication after the following issues are addressed:

Title suggestion: Consider revising the title to “Tracking surface ozone responses to clean air actions under a warming climate in China” for clarity and stronger alignment with the scope.

Lines 52-54: This sentence requires additional references. In particular, the 2021 IPCC report should be cited and carefully verified.

Line 81: Provide the full name of XGBoost when first introduced.

Line 89: Please remove the word “monsoon.”

Line 109: The study develops an innovative machine-learning framework for attribution analysis, including an extension to climate change. This is a key contribution, but I suggest adding more technical details, such as a conceptual diagram of the methodology, to improve clarity and accessibility for readers.

Line 114: The role of time variables requires clarification. Were the diurnal and seasonal/monthly variables included to remove short-term and seasonal variability, leaving the long-term trend for quantitative attribution? Please explain explicitly.

Line 125: Why was the modeling performed separately for each city, rather than by grouping cities into regions? Please explain the rationale.

Lines 152-161: The uncertainty analysis, particularly for the Fixed Emission Approximation (FEA) method, is highly valuable. I strongly recommend moving these results (currently Figure S3) into the main text.

Lines 202-205: The manuscript highlights several regions in China. Please explain why these regions were emphasized and include a map showing their geographic distribution for better context.

Line 227: The references here primarily address ecological impacts, yet the text mentions “both human and ecological health.” Please provide more references specific to human health. Also, revise “ecological health” to “ecosystem health.”

Line 229: The phrase “reflecting initial policy effectiveness” is unclear. Please rephrase for precision.

Lines 230-232: The conclusion drawn here seems overstated, as the evidence provided is insufficient. This section mainly discusses temporal and spatial ozone concentration trends. A more cautious interpretation is recommended: instead of attributing trends directly to policy effectiveness, the authors could note that observed trends occurred under varying emission control backgrounds, while meteorology also played an important role.

Line 243: Please define the parameters τ and p shown in Figure 1.

Line 276: Correct “Emission-driven” to “emission-driven.”

Lines 279-281: The logic here is confusing. The discussion first emphasizes the role of anthropogenic emissions, but then suggests that changes in emissions highlight the role of meteorology. Please clarify or restructure this argument.

Lines 299-300: Please rephrase the sentence. The term “near-baseline” is ambiguous and requires clarification.

Line 342: There is an editorial error that needs correction.

Line 390: I recommend revising the y-axis labels for greater accuracy. For instance, in panel (a), the label currently suggests “extreme weather,” but it actually represents only “extreme heatwave”. In contrast, panel (b) provides a more specific description. The labeling should be made consistent and precise to avoid potential misinterpretation.

Line 397: The title of this section should be revised, since the authors are not reconstructing the ozone trend per se. A more accurate option could be “Reshaping distributions of ozone controlled by a warming climate.” This section is indeed interesting and methodologically innovative. However, the manuscript should elaborate more clearly on which specific factors are included in the climate-change-driven trend, especially considering the constraints posed by the limited length and coverage of historical observational records.

Line 331 (Figure 3): Ensure the map format is consistent with that in the Supplementary Figures.

Lines 374-376: Since the SHAP interpreter is a key tool used to analyze predictor contributions, it should be briefly described in the Methods section.

Line 437: In Figure 5, the authors present both climate-change-driven and emission-driven trends. I am curious about how the results from the proposed FEA method compare with those from other widely used machine-learning approaches for trend analysis, such as de-weather. A comparison between different methods would not only be interesting but also serve as a useful validation of the robustness of the proposed framework.

Line 445 (Conclusion): The conclusion is overly lengthy. Please condense and refine this section for clarity and impact.

Citation: https://doi.org/10.5194/egusphere-2025-4014-RC1
- AC1: 'Reply on RC1', Yunjiang Zhang, 21 Nov 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4014/egusphere-2025-4014-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-4014-AC1
RC2:
'Comment on egusphere-2025-4014', Anonymous Referee #1, 05 Oct 2025
General Comments:
The manuscript provides valuable insights into surface ozone trends in China, driven by emissions and meteorological factors, using the Fixed Emission Approximation (FEA) method. While the study is important, several key issues require attention before it can be accepted.
The explanation of emission surrogates and the impact of coarse-resolution meteorological data on the regression process is insufficient. Additionally, regional ozone trends, especially in the PRD, need clearer explanations, and the manuscript should include maps or time-series for key ozone precursors (CO, NOx, VOCs).

The discussion on the COVID-19 lockdown requires more detailed analysis of TROPOMI data to explain regional ozone changes, and maps for regions like the North China Plain would help clarify spatial patterns. Figures, particularly Figure 3, lack clear quantitative results, and Section 3.3 requires deeper analysis.

Finally, the roles of temperature, humidity, and radiation in ozone formation need clearer interpretation.

In conclusion, a major revision is needed to address these issues, strengthen the analysis, and improve the clarity and presentation of the results.
Specific Comments:
Line 193: What is TAP?
Line 114: How does the author account for the uncertainty in coarse-resolution meteorological variables and site data during the RF regression process? Is the specified elevation referring to the site elevation or the average elevation of the coarse-resolution meteorological variable grid?
Line 115: What is an emission surrogate? Where did the author obtain it? It should be mentioned in Section 2.1.
Line 117: “The aforementioned variables”: It’s not clear.
Line 127-129: Rewrite this sentence.
Line 243: “Anthropogenic drivers”  “Anthropogenic emission drivers”.
Line 243: You should show the anthropogenic emission map or time-series line for major ozone precursor such as CO, NOx, and VOC.
Line 249-251: The author needs to explain why the PRD is rising and why it is falling in other regions.
Line 260: Authors should show a map to tell reader where is northern China?
Line 260-262: Prior to 2017, PRD consistently recorded the lowest ozone concentrations among these major regions. Intuitively, the PRD appears to have the least potential for ozone reduction. Yet why did both the YRD and SCB regions exhibit smaller reduction margins than the PRD?
Line 266-268: The author should inform readers: How much have anthropogenic emissions decreased in major regions due to the nationwide lockdown?
Line 273-275: The author suggests that the rise in ozone concentrations is due to suppressed NO titration. Therefore, the author could fully utilize TROPOMI data to analyze changes in sensitive zones during the COVID-19 period. This may help explain why ozone concentrations in the remaining 20% of cities decreased during the COVID-19 pandemic.
Line 275-276: The author should explain how anthropogenic emissions have changed during this period.
Line 295: Where is North China Plain?
Line 297: “The Yangtze River Delta”  “YRD”.
Line 298-299: You can show a ∆temperature map from ERA5 data.
Line 306-307: The author might use this to explain why the PRD's changes differ from those of other cities in Section 3.2.
Figure 3: It is recommended that the author label the locations of major regions on this map.
Section 3.3: Lack of quantitative results. Figure 3 fails to clearly show the interannual changes in sensitive areas. The authors should adopt a more explicit presentation method to highlight the differences in area among sensitive zones in each region.
Line 337-350: Authors cannot merely provide quantitative descriptions; ACP journals require authors to conduct deeper analysis of these quantitative results.
Line 356-357: That’s a boring sentence. Although the author provides some explanation in lines 358-359, this explanation is overly broad. For example, why are RH and temperature the dominant factors in other regions, while shortwave radiation and RH are the dominant factors in the YRD region? What is the relationship between temperature and radiation? Which variable is the fundamental cause of ozone concentration changes, and what role does RH play in this process?
Line 369-371: Figure S13 shows that the correlation between T and SR in inland southern China is very low during both HW and NHW periods. Why is this? Additionally, the correlation for RH between HW and NHW appears significantly different. I recommend that the authors adjust the colorbar in Figure S13E-F to a red-blue scale with zero centered. This may reveal positive correlations for RH in certain regions.
Line 379: “amplifying”  “increasing”.
Line 383-384: I do not agree that. Cloud cover directly influences temperature and shortwave radiation variations and should not be considered a secondary effect.
Line 387: What is the RH effect?
Citation: https://doi.org/10.5194/egusphere-2025-4014-RC2
- AC2: 'Reply on RC2', Yunjiang Zhang, 21 Nov 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4014/egusphere-2025-4014-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-4014-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-4014', Anonymous Referee #2, 30 Sep 2025

Tropospheric ozone is a globally important air pollutant and a short-lived climate forcer, with substantial impacts on human health, climate change, and terrestrial ecosystems. Understanding the relationship between ozone concentration changes and their driving factors is essential for developing effective control strategies. This study utilizes ground-based observational data from the Chinese monitoring network during 2013-2023 and develops a machine-learning-based method to quantitatively disentangle the contributions of meteorological conditions and anthropogenic emissions. The analysis is further extended to evaluate the sensitivity of ozone to climate change. In addition, the authors employ satellite retrievals to explore the changes in precursor ratios and to diagnose the shifts in chemical regimes. The proposed analytical framework provides valuable insights and will be highly informative for future studies. Overall, the manuscript is clearly structured, well-designed, and well-written, and it fits well within the scope of ACP. I would recommend publication after the following issues are addressed:

Title suggestion: Consider revising the title to “Tracking surface ozone responses to clean air actions under a warming climate in China” for clarity and stronger alignment with the scope.

Lines 52-54: This sentence requires additional references. In particular, the 2021 IPCC report should be cited and carefully verified.

Line 81: Provide the full name of XGBoost when first introduced.

Line 89: Please remove the word “monsoon.”

Line 109: The study develops an innovative machine-learning framework for attribution analysis, including an extension to climate change. This is a key contribution, but I suggest adding more technical details, such as a conceptual diagram of the methodology, to improve clarity and accessibility for readers.

Line 114: The role of time variables requires clarification. Were the diurnal and seasonal/monthly variables included to remove short-term and seasonal variability, leaving the long-term trend for quantitative attribution? Please explain explicitly.

Line 125: Why was the modeling performed separately for each city, rather than by grouping cities into regions? Please explain the rationale.

Lines 152-161: The uncertainty analysis, particularly for the Fixed Emission Approximation (FEA) method, is highly valuable. I strongly recommend moving these results (currently Figure S3) into the main text.

Lines 202-205: The manuscript highlights several regions in China. Please explain why these regions were emphasized and include a map showing their geographic distribution for better context.

Line 227: The references here primarily address ecological impacts, yet the text mentions “both human and ecological health.” Please provide more references specific to human health. Also, revise “ecological health” to “ecosystem health.”

Line 229: The phrase “reflecting initial policy effectiveness” is unclear. Please rephrase for precision.

Lines 230-232: The conclusion drawn here seems overstated, as the evidence provided is insufficient. This section mainly discusses temporal and spatial ozone concentration trends. A more cautious interpretation is recommended: instead of attributing trends directly to policy effectiveness, the authors could note that observed trends occurred under varying emission control backgrounds, while meteorology also played an important role.

Line 243: Please define the parameters τ and p shown in Figure 1.

Line 276: Correct “Emission-driven” to “emission-driven.”

Lines 279-281: The logic here is confusing. The discussion first emphasizes the role of anthropogenic emissions, but then suggests that changes in emissions highlight the role of meteorology. Please clarify or restructure this argument.

Lines 299-300: Please rephrase the sentence. The term “near-baseline” is ambiguous and requires clarification.

Line 342: There is an editorial error that needs correction.

Line 390: I recommend revising the y-axis labels for greater accuracy. For instance, in panel (a), the label currently suggests “extreme weather,” but it actually represents only “extreme heatwave”. In contrast, panel (b) provides a more specific description. The labeling should be made consistent and precise to avoid potential misinterpretation.

Line 397: The title of this section should be revised, since the authors are not reconstructing the ozone trend per se. A more accurate option could be “Reshaping distributions of ozone controlled by a warming climate.” This section is indeed interesting and methodologically innovative. However, the manuscript should elaborate more clearly on which specific factors are included in the climate-change-driven trend, especially considering the constraints posed by the limited length and coverage of historical observational records.

Line 331 (Figure 3): Ensure the map format is consistent with that in the Supplementary Figures.

Lines 374-376: Since the SHAP interpreter is a key tool used to analyze predictor contributions, it should be briefly described in the Methods section.

Line 437: In Figure 5, the authors present both climate-change-driven and emission-driven trends. I am curious about how the results from the proposed FEA method compare with those from other widely used machine-learning approaches for trend analysis, such as de-weather. A comparison between different methods would not only be interesting but also serve as a useful validation of the robustness of the proposed framework.

Line 445 (Conclusion): The conclusion is overly lengthy. Please condense and refine this section for clarity and impact.

Citation: https://doi.org/10.5194/egusphere-2025-4014-RC1
- AC1: 'Reply on RC1', Yunjiang Zhang, 21 Nov 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4014/egusphere-2025-4014-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-4014-AC1
RC2:
'Comment on egusphere-2025-4014', Anonymous Referee #1, 05 Oct 2025
General Comments:
The manuscript provides valuable insights into surface ozone trends in China, driven by emissions and meteorological factors, using the Fixed Emission Approximation (FEA) method. While the study is important, several key issues require attention before it can be accepted.
The explanation of emission surrogates and the impact of coarse-resolution meteorological data on the regression process is insufficient. Additionally, regional ozone trends, especially in the PRD, need clearer explanations, and the manuscript should include maps or time-series for key ozone precursors (CO, NOx, VOCs).

The discussion on the COVID-19 lockdown requires more detailed analysis of TROPOMI data to explain regional ozone changes, and maps for regions like the North China Plain would help clarify spatial patterns. Figures, particularly Figure 3, lack clear quantitative results, and Section 3.3 requires deeper analysis.

Finally, the roles of temperature, humidity, and radiation in ozone formation need clearer interpretation.

In conclusion, a major revision is needed to address these issues, strengthen the analysis, and improve the clarity and presentation of the results.
Specific Comments:
Line 193: What is TAP?
Line 114: How does the author account for the uncertainty in coarse-resolution meteorological variables and site data during the RF regression process? Is the specified elevation referring to the site elevation or the average elevation of the coarse-resolution meteorological variable grid?
Line 115: What is an emission surrogate? Where did the author obtain it? It should be mentioned in Section 2.1.
Line 117: “The aforementioned variables”: It’s not clear.
Line 127-129: Rewrite this sentence.
Line 243: “Anthropogenic drivers”  “Anthropogenic emission drivers”.
Line 243: You should show the anthropogenic emission map or time-series line for major ozone precursor such as CO, NOx, and VOC.
Line 249-251: The author needs to explain why the PRD is rising and why it is falling in other regions.
Line 260: Authors should show a map to tell reader where is northern China?
Line 260-262: Prior to 2017, PRD consistently recorded the lowest ozone concentrations among these major regions. Intuitively, the PRD appears to have the least potential for ozone reduction. Yet why did both the YRD and SCB regions exhibit smaller reduction margins than the PRD?
Line 266-268: The author should inform readers: How much have anthropogenic emissions decreased in major regions due to the nationwide lockdown?
Line 273-275: The author suggests that the rise in ozone concentrations is due to suppressed NO titration. Therefore, the author could fully utilize TROPOMI data to analyze changes in sensitive zones during the COVID-19 period. This may help explain why ozone concentrations in the remaining 20% of cities decreased during the COVID-19 pandemic.
Line 275-276: The author should explain how anthropogenic emissions have changed during this period.
Line 295: Where is North China Plain?
Line 297: “The Yangtze River Delta”  “YRD”.
Line 298-299: You can show a ∆temperature map from ERA5 data.
Line 306-307: The author might use this to explain why the PRD's changes differ from those of other cities in Section 3.2.
Figure 3: It is recommended that the author label the locations of major regions on this map.
Section 3.3: Lack of quantitative results. Figure 3 fails to clearly show the interannual changes in sensitive areas. The authors should adopt a more explicit presentation method to highlight the differences in area among sensitive zones in each region.
Line 337-350: Authors cannot merely provide quantitative descriptions; ACP journals require authors to conduct deeper analysis of these quantitative results.
Line 356-357: That’s a boring sentence. Although the author provides some explanation in lines 358-359, this explanation is overly broad. For example, why are RH and temperature the dominant factors in other regions, while shortwave radiation and RH are the dominant factors in the YRD region? What is the relationship between temperature and radiation? Which variable is the fundamental cause of ozone concentration changes, and what role does RH play in this process?
Line 369-371: Figure S13 shows that the correlation between T and SR in inland southern China is very low during both HW and NHW periods. Why is this? Additionally, the correlation for RH between HW and NHW appears significantly different. I recommend that the authors adjust the colorbar in Figure S13E-F to a red-blue scale with zero centered. This may reveal positive correlations for RH in certain regions.
Line 379: “amplifying”  “increasing”.
Line 383-384: I do not agree that. Cloud cover directly influences temperature and shortwave radiation variations and should not be considered a secondary effect.
Line 387: What is the RH effect?
Citation: https://doi.org/10.5194/egusphere-2025-4014-RC2
- AC2: 'Reply on RC2', Yunjiang Zhang, 21 Nov 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4014/egusphere-2025-4014-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-4014-AC2

Peer review completion

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

AR by Yunjiang Zhang on behalf of the Authors (21 Nov 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (22 Nov 2025) by Zhonghua Zheng

RR by Anonymous Referee #1 (23 Nov 2025)

RR by Anonymous Referee #2 (12 Dec 2025)

Suggestions for revision or reasons for rejection

The authors have satisfactorily addressed all of my previous comments and concerns. I also note that the responses to the other reviewers’ comments are thorough and that appropriate revisions have been made accordingly. Overall, the manuscript is substantially improved, and I believe it is close to being suitable for publication after the authors address the following new minor issues:
Line 88: Please revise “machine learning-based framework” to “machine learning-based model framework.”
Line 89: Replace “respective roles” with “relative contribution.”
Line 145: Replace “errors” with “uncertainty.”
Line 196: Please carefully evaluate the use of “significant” or “significantly” throughout the manuscript, especially where no significance test is provided. In addition, could the authors offer a numerical range for the uncertainty reduction here?
Line 200: The description should be improved. The authors may directly state that the x-axis represents the years used for model training, and the y-axis represents the years predicted by the trained model.
Line 270: The abbreviations “BTH” and “YRD” should be defined upon first use. Also, Lines 278–279 contain redundant definitions, please remove duplicates.
Lines 289–291: I suggest removing this sentence, as a similar concluding remark has already been presented earlier.
Line 301: Please replace “first phase” with “Phase I,” and ensure consistent formatting for subsequent phases.
Line 361: I recommend removing the phrase “suggesting that ozone responses to further emission reductions may have reached a saturation point.”
Line 399: Please clarify which region “f” refers to in this context.
Line 418: It would be more accurate to state that the analysis “provides an indication of meteorological conditions” rather than drawing a direct conclusion.
Lines 436–438: This sentence can be further streamlined for clarity.
Line 463: Please revise the section title to “Reshaping distributions of ozone by climate change and emission controls.”
Line 477: A brief clarification of the scenarios referenced here would be helpful, as the current wording is vague even though earlier sections describe them in detail.
Line 525: Please check whether “anthropogenic precursor emissions” refers specifically to HCHO here and revise accordingly for accuracy.
Line 538: I suggest removing the word “growing.”
Lines 550–551: The conclusion should be reframed to indicate that a warming climate modulates the long-term evolution of ozone trends. The subsequent sentence may then be adjusted for a smoother logical transition.

Hide

ED: Publish subject to minor revisions (review by editor) (20 Dec 2025) by Zhonghua Zheng

Please revise the manuscript according to the reviewers’ comments below.

The authors have satisfactorily addressed all of my previous comments and concerns. I also note that the responses to the other reviewers’ comments are thorough and that appropriate revisions have been made accordingly. Overall, the manuscript is substantially improved, and I believe it is close to being suitable for publication after the authors address the following new minor issues:
Line 88: Please revise “machine learning-based framework” to “machine learning-based model framework.”
Line 89: Replace “respective roles” with “relative contribution.”
Line 145: Replace “errors” with “uncertainty.”
Line 196: Please carefully evaluate the use of “significant” or “significantly” throughout the manuscript, especially where no significance test is provided. In addition, could the authors offer a numerical range for the uncertainty reduction here?
Line 200: The description should be improved. The authors may directly state that the x-axis represents the years used for model training, and the y-axis represents the years predicted by the trained model.
Line 270: The abbreviations “BTH” and “YRD” should be defined upon first use. Also, Lines 278–279 contain redundant definitions, please remove duplicates.
Lines 289–291: I suggest removing this sentence, as a similar concluding remark has already been presented earlier.
Line 301: Please replace “first phase” with “Phase I,” and ensure consistent formatting for subsequent phases.
Line 361: I recommend removing the phrase “suggesting that ozone responses to further emission reductions may have reached a saturation point.”
Line 399: Please clarify which region “f” refers to in this context.
Line 418: It would be more accurate to state that the analysis “provides an indication of meteorological conditions” rather than drawing a direct conclusion.
Lines 436–438: This sentence can be further streamlined for clarity.
Line 463: Please revise the section title to “Reshaping distributions of ozone by climate change and emission controls.”
Line 477: A brief clarification of the scenarios referenced here would be helpful, as the current wording is vague even though earlier sections describe them in detail.
Line 525: Please check whether “anthropogenic precursor emissions” refers specifically to HCHO here and revise accordingly for accuracy.
Line 538: I suggest removing the word “growing.”
Lines 550–551: The conclusion should be reframed to indicate that a warming climate modulates the long-term evolution of ozone trends. The subsequent sentence may then be adjusted for a smoother logical transition.

Hide

AR by Yunjiang Zhang on behalf of the Authors (02 Jan 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (03 Jan 2026) by Zhonghua Zheng

AR by Yunjiang Zhang on behalf of the Authors (11 Jan 2026) Manuscript

Journal article(s) based on this preprint

19 Jan 2026

Tracking surface ozone responses to clean air actions under a warming climate in China using machine learning

Jie Fang, Yunjiang Zhang, Didier Hauglustaine, Bo Zheng, Ming Wang, Jingyi Li, Yong Sun, Haiwei Li, Junfeng Wang, Yun Wu, Bin Yuan, Mindong Chen, and Xinlei Ge

Atmos. Chem. Phys., 26, 851–867, https://doi.org/10.5194/acp-26-851-2026,https://doi.org/10.5194/acp-26-851-2026, 2026

Short summary

Jie Fang, Yunjiang Zhang, Didier Hauglustaine, Bo Zheng, Ming Wang, Jingyi Li, Yong Sun, Haiwei Li, Junfeng Wang, Yun Wu, Mindong Chen, and Xinlei Ge

Supplement

https://doi.org/10.5194/egusphere-2025-4014-supplement

Jie Fang, Yunjiang Zhang, Didier Hauglustaine, Bo Zheng, Ming Wang, Jingyi Li, Yong Sun, Haiwei Li, Junfeng Wang, Yun Wu, Mindong Chen, and Xinlei Ge

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Short summary

Surface ozone pollution is a pressing global challenge driven by human activities and a warming climate. Using nationwide observations (2013–2023) across China together with satellite data, we developed a new machine learning approach to separate the impacts of emission controls and weather changes. Our results show that while emission reductions improved ozone in some regions, climate change is increasingly offsetting these gains, underscoring the need for joint air quality and climate actions.


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