Black carbon aerosols in China: Spatial-temporal variations and lessons from long-term atmospheric observations

Zheng, Huang; Kong, Shaofei; Ding, Deping; Savadkoohi, Marjan; Song, Congbo; Zheng, Mingming; Harrison, Roy

doi:10.5194/egusphere-2025-2113

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https://doi.org/10.5194/egusphere-2025-2113

Preprints

03 Jun 2025

| 03 Jun 2025

Black carbon aerosols in China: Spatial-temporal variations and lessons from long-term atmospheric observations

Huang Zheng, Shaofei Kong, Deping Ding, Marjan Savadkoohi, Congbo Song, Mingming Zheng, and Roy Harrison

Abstract. Using 13-year (2008–2020) continuous measurements of equivalent black carbon (eBC) in China, this study reports the spatial-temporal variations of eBC and its sources, including solid fuel (eBC_sf) and liquid fuel combustion (eBC_lf). The results show that eBC and its sources exhibit spatial heterogeneity with higher concentrations in eastern and northern China compared to western and southern China. Seasonal variations of eBC and eBC_sf generally show low values during summer and high values during winter in most stations. Long-term trends indicate that eBC and eBC_lf decreased most rapidly at urban stations while eBC_sf declined faster at rural stations. Comparisons of eBC concentrations and trends between this study and other observations reveal higher eBC levels but lower reduction rates in China. Comparison between surface eBC observations and model simulations indicates models performed well in simulating spatial distribution but poorly in simulating inter-annual variations. Weather-normalized eBC concentrations were compared to several emission inventories, revealing higher correlations and suggesting that normalized eBC concentrations can be used to adjust emission estimates. Long-term observations of eBC and its sources show decreasing trends in China, primarily driven by emission reduction. Further analysis suggests that the reduction of eBC was mainly attributed to decreased emissions from solid fuel combustion in rural and baseline stations. This study provides insights for reducing uncertainties in black carbon emission inventories and improving model performance in simulating surface concentrations.

Received: 07 May 2025 – Discussion started: 03 Jun 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

20 Nov 2025

Black carbon aerosols in China: spatial-temporal variations and lessons from long-term atmospheric observations

Huang Zheng, Shaofei Kong, Deping Ding, Marjan Savadkoohi, Congbo Song, Mingming Zheng, and Roy M. Harrison

Atmos. Chem. Phys., 25, 16363–16386, https://doi.org/10.5194/acp-25-16363-2025,https://doi.org/10.5194/acp-25-16363-2025, 2025

Short summary

Huang Zheng, Shaofei Kong, Deping Ding, Marjan Savadkoohi, Congbo Song, Mingming Zheng, and Roy Harrison

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-2113', Anonymous Referee #1, 30 Jun 2025

This study presents a comprehensive overview of Aethalometer based observations from CBNET, which includes measurements from 48 stations (including 23 urban, 18 rural sites and 7 background sites) across China during the period 2008-2020. The results are used to understand the discrepancies in chemical transport models and emission inventories.
This study attempts to take a step further and address three problems
(1) What can we learn from the comparison between BC ground observations and CTM simulations;
(2) Whether the inter-annual variations of BC can be used as an indicator of BC emissions;
(3) Which factors dominated the variations of BC in China during the past 13 years.
thereby reducing the uncertainties in BC emissions and simulations from CTMs.
The data analysis is robust and the different factors that need to be considered while handling aethalometer data were considered.
The attempt to try out season specific AAE and de-seasonalizing the data using machine learning is novel.
Minor comments
lines 113-120
The treatment of negative and following values: The manufacturer suggests retaining these values and doing a running mean before filtering the odd values. However, there is no general consensus on the methodology. Was a smoothing performed before or after removing the outliers? There would be a difference in the final values depending on the method followed. It would be good if this is also clearly stated.
lines 121-128
While a criterion of minimum of 50% data points was adopted for passing quality control for long term analysis, it is not clear whether it is 50% points for a day/month/ annually. This needs to be made clear.
Section 2.3
Station specific AAEs are determined as the 1^st and 99^th percentiles of the AAE distribution at the site. Although this avoids assuming a universal value for AAE it is still arguable that the selected values may or may not represent the actual conditions and fuel types. While the DeBC_lf (%) reduces upto 50% DeBC_sf (%) almost triples at many stations compared to default values. These variations are huge and indicate that the eBC_lf values are overestimated while the eBC_sf underestimated by conventional methods. Are these values realistic or are the values blown up due to the use of % changes? I suggest including figures comparing the values with and without the application of this method in the supplementary for easy comparison and understanding.
Figures 1&2, Tables and other places
The station codes used in this study makes it too difficult to associate with individual stations. The observatories are denoted by long numbers making the figures clumsy and difficult to identify. I am curious to know if there a specific reason behind the station codes?
Figure 2
This figure is really impressive and rich in information content. On the downside we have long observatory names and randomly distributed pie charts. It is really difficult to find the pie charts corresponding to a particular location on the map. It would be way easier for the reader if they are arranged in ascending order as in Fig 3. Also, the figure caption does not say anything about the MERRA data used in the background. It is also not clear why the inset is used. Is it intended to only show the islands to the south of the mainland which got cut off?
Figure 4
It would be good to add a legend here. As I go through this section, I am curious to know if any individual location showed a positive trend.
Section 3.3
If the eBC values for China are estimated using the SS AAE direct comparison with values from existing studies does not make much sense. The older studies use the conventional methods to estimate eBC while here the corrections pull down the eBC values.
Figure and table captions.
The figures are not self-explanatory as key information is missing in the caption. It makes it difficult for the reader to easily find out key information.
Spelling errors
Table S3 Hyperparameter tunning
Concluding comments
This study has made a great effort in comparing observations with CTMs. The authors find that models and outdated inventories fail represent BC over China leading to underestimations and increasing trends. While the station specific AAE is interesting, the method remains inherently oversimplified, as it does not fully capture complexities such as the distinction between coal and biomass sources, seasonal variability in BC lifetime, and mixing state effects that influence optical properties. While the percentile-based approach is practical, the arbitrariness of AAE selection cannot be entirely ruled out. A more robust validation such as through chemical tracer analysis (e.g., OC/EC or 14C) would be ideal, although such efforts may be beyond the scope of this study and could be considered for future work. I also feel that the random forest modeling performed in this study was a bit under utilised in the end. Pictorial representation of the changes observed after using this model would make a good impact on the reader.

Citation: https://doi.org/10.5194/egusphere-2025-2113-RC1
- AC1: 'Comment on egusphere-2025-2113', Huang Zheng, 11 Aug 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2113/egusphere-2025-2113-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-2113-AC1
RC2:
'Comment on egusphere-2025-2113', Anonymous Referee #2, 14 Jul 2025

Review of "Black carbon aerosols in China: Spatial-temporal variations and lessons from long-term atmospheric observations" by Zheng et al.
This study presents the characteristics of black carbon concentration variations in China using observational data from 2008 to 2020. The authors well describe the process of calculating black carbon concentrations through aethalometer measurements and explain the long-term variation characteristics of black carbon concentrations by comparing them with CTM and reanalysis data.
Numerous papers have already been published regarding the long-term characteristics of black carbon concentrations in China, and many of these studies are cited in this paper. All studies consistently report decreases in black carbon concentrations due to air quality reduction policies. Therefore, it would be beneficial to clearly explain how the scientific findings (conclusions) newly reported in this study are distinctive compared to previous works. Additionally, while this study reports measurement results up to 2020, several recent papers have explained black carbon concentration variation characteristics through various factors including emissions and meteorological conditions, albeit regionally within China (e.g., https://www.nature.com/articles/s44407-025-00010-z). From this perspective, I suggest extending the analysis period of this study to include recent years beyond 2020.
The study presents results of black carbon (eBC) concentrations observed through filter-based optical methods. The process of calculating eBC concentrations is clearly and well explained through citations of previous studies. However, for the methodology of dividing contributions into solid fuel combustion (eBCsf) and liquid fossil fuel combustion (eBClf), which represents a major finding of this study, detailed methodological presentation and sufficient validation of results are required. In particular, clear presentation of theoretical limitations and associated uncertainties in calculations based on AAE from multi-wavelength optical measurements is necessary.
Furthermore, many studies have explained the emission sources of black carbon or elemental carbon through carbon isotope analysis, determining contributions from solid fuel and liquid fossil fuel combustion. I believe the most critical aspect for publication of this study would be to validate the calculation results by comparing them with such previous research findings and demonstrating consistent results. For example, I suggest presenting comparisons between this study and carbon isotope analysis-based contribution assessments for specific locations and time periods in the main text.
Meanwhile, Sections 3.1 and 3.2 present concentrations, contributions, seasonal changes, and annual variation characteristics in a rather straightforward manner compared to the observational and analytical results, lacking detailed explanations of why such contributions and spatiotemporal variations occur. Such explanations would be necessary to establish some differentiation from existing studies.
In Section 3.3, the authors discuss results by simply presenting comparisons with observation points in other countries. For such international data, more detailed descriptions of the observational data used are needed, and the justification for why such comparisons are necessary in this study should be clearly presented. Given the purpose of explaining black carbon concentration changes in China, it appears that this entire section could be removed without detriment to the study.
The content of Section 4.1 has also been mentioned in many previous studies. The most important conclusion the authors wish to convey in this section is not clearly evident. For example, clear explanation is required regarding whether the contribution analysis results based on aethalometer in this study possess the accuracy required for CTM simulation validation and improvement, or whether the authors intend to discuss methods for improving CTM simulation accuracy.
The conclusions in Section 4.2 "Weather normalized concentrations: an indicator of BC emissions" also appear ambiguous. More detailed explanation is required regarding whether this explains the previous observational results or whether emission source accuracy improvement is needed from a modeling perspective.
The causes of black carbon concentration reduction ultimately mentioned in Section 4.3 are all results that have been discussed in previous studies. Specific description of what this study presents differentially compared to previous research is needed

Citation: https://doi.org/10.5194/egusphere-2025-2113-RC2
- AC1: 'Comment on egusphere-2025-2113', Huang Zheng, 11 Aug 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2113/egusphere-2025-2113-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-2113-AC1
AC1: 'Comment on egusphere-2025-2113', Huang Zheng, 11 Aug 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2113/egusphere-2025-2113-AC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-2113-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-2113', Anonymous Referee #1, 30 Jun 2025

This study presents a comprehensive overview of Aethalometer based observations from CBNET, which includes measurements from 48 stations (including 23 urban, 18 rural sites and 7 background sites) across China during the period 2008-2020. The results are used to understand the discrepancies in chemical transport models and emission inventories.
This study attempts to take a step further and address three problems
(1) What can we learn from the comparison between BC ground observations and CTM simulations;
(2) Whether the inter-annual variations of BC can be used as an indicator of BC emissions;
(3) Which factors dominated the variations of BC in China during the past 13 years.
thereby reducing the uncertainties in BC emissions and simulations from CTMs.
The data analysis is robust and the different factors that need to be considered while handling aethalometer data were considered.
The attempt to try out season specific AAE and de-seasonalizing the data using machine learning is novel.
Minor comments
lines 113-120
The treatment of negative and following values: The manufacturer suggests retaining these values and doing a running mean before filtering the odd values. However, there is no general consensus on the methodology. Was a smoothing performed before or after removing the outliers? There would be a difference in the final values depending on the method followed. It would be good if this is also clearly stated.
lines 121-128
While a criterion of minimum of 50% data points was adopted for passing quality control for long term analysis, it is not clear whether it is 50% points for a day/month/ annually. This needs to be made clear.
Section 2.3
Station specific AAEs are determined as the 1^st and 99^th percentiles of the AAE distribution at the site. Although this avoids assuming a universal value for AAE it is still arguable that the selected values may or may not represent the actual conditions and fuel types. While the DeBC_lf (%) reduces upto 50% DeBC_sf (%) almost triples at many stations compared to default values. These variations are huge and indicate that the eBC_lf values are overestimated while the eBC_sf underestimated by conventional methods. Are these values realistic or are the values blown up due to the use of % changes? I suggest including figures comparing the values with and without the application of this method in the supplementary for easy comparison and understanding.
Figures 1&2, Tables and other places
The station codes used in this study makes it too difficult to associate with individual stations. The observatories are denoted by long numbers making the figures clumsy and difficult to identify. I am curious to know if there a specific reason behind the station codes?
Figure 2
This figure is really impressive and rich in information content. On the downside we have long observatory names and randomly distributed pie charts. It is really difficult to find the pie charts corresponding to a particular location on the map. It would be way easier for the reader if they are arranged in ascending order as in Fig 3. Also, the figure caption does not say anything about the MERRA data used in the background. It is also not clear why the inset is used. Is it intended to only show the islands to the south of the mainland which got cut off?
Figure 4
It would be good to add a legend here. As I go through this section, I am curious to know if any individual location showed a positive trend.
Section 3.3
If the eBC values for China are estimated using the SS AAE direct comparison with values from existing studies does not make much sense. The older studies use the conventional methods to estimate eBC while here the corrections pull down the eBC values.
Figure and table captions.
The figures are not self-explanatory as key information is missing in the caption. It makes it difficult for the reader to easily find out key information.
Spelling errors
Table S3 Hyperparameter tunning
Concluding comments
This study has made a great effort in comparing observations with CTMs. The authors find that models and outdated inventories fail represent BC over China leading to underestimations and increasing trends. While the station specific AAE is interesting, the method remains inherently oversimplified, as it does not fully capture complexities such as the distinction between coal and biomass sources, seasonal variability in BC lifetime, and mixing state effects that influence optical properties. While the percentile-based approach is practical, the arbitrariness of AAE selection cannot be entirely ruled out. A more robust validation such as through chemical tracer analysis (e.g., OC/EC or 14C) would be ideal, although such efforts may be beyond the scope of this study and could be considered for future work. I also feel that the random forest modeling performed in this study was a bit under utilised in the end. Pictorial representation of the changes observed after using this model would make a good impact on the reader.

Citation: https://doi.org/10.5194/egusphere-2025-2113-RC1
- AC1: 'Comment on egusphere-2025-2113', Huang Zheng, 11 Aug 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2113/egusphere-2025-2113-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-2113-AC1
RC2:
'Comment on egusphere-2025-2113', Anonymous Referee #2, 14 Jul 2025

Review of "Black carbon aerosols in China: Spatial-temporal variations and lessons from long-term atmospheric observations" by Zheng et al.
This study presents the characteristics of black carbon concentration variations in China using observational data from 2008 to 2020. The authors well describe the process of calculating black carbon concentrations through aethalometer measurements and explain the long-term variation characteristics of black carbon concentrations by comparing them with CTM and reanalysis data.
Numerous papers have already been published regarding the long-term characteristics of black carbon concentrations in China, and many of these studies are cited in this paper. All studies consistently report decreases in black carbon concentrations due to air quality reduction policies. Therefore, it would be beneficial to clearly explain how the scientific findings (conclusions) newly reported in this study are distinctive compared to previous works. Additionally, while this study reports measurement results up to 2020, several recent papers have explained black carbon concentration variation characteristics through various factors including emissions and meteorological conditions, albeit regionally within China (e.g., https://www.nature.com/articles/s44407-025-00010-z). From this perspective, I suggest extending the analysis period of this study to include recent years beyond 2020.
The study presents results of black carbon (eBC) concentrations observed through filter-based optical methods. The process of calculating eBC concentrations is clearly and well explained through citations of previous studies. However, for the methodology of dividing contributions into solid fuel combustion (eBCsf) and liquid fossil fuel combustion (eBClf), which represents a major finding of this study, detailed methodological presentation and sufficient validation of results are required. In particular, clear presentation of theoretical limitations and associated uncertainties in calculations based on AAE from multi-wavelength optical measurements is necessary.
Furthermore, many studies have explained the emission sources of black carbon or elemental carbon through carbon isotope analysis, determining contributions from solid fuel and liquid fossil fuel combustion. I believe the most critical aspect for publication of this study would be to validate the calculation results by comparing them with such previous research findings and demonstrating consistent results. For example, I suggest presenting comparisons between this study and carbon isotope analysis-based contribution assessments for specific locations and time periods in the main text.
Meanwhile, Sections 3.1 and 3.2 present concentrations, contributions, seasonal changes, and annual variation characteristics in a rather straightforward manner compared to the observational and analytical results, lacking detailed explanations of why such contributions and spatiotemporal variations occur. Such explanations would be necessary to establish some differentiation from existing studies.
In Section 3.3, the authors discuss results by simply presenting comparisons with observation points in other countries. For such international data, more detailed descriptions of the observational data used are needed, and the justification for why such comparisons are necessary in this study should be clearly presented. Given the purpose of explaining black carbon concentration changes in China, it appears that this entire section could be removed without detriment to the study.
The content of Section 4.1 has also been mentioned in many previous studies. The most important conclusion the authors wish to convey in this section is not clearly evident. For example, clear explanation is required regarding whether the contribution analysis results based on aethalometer in this study possess the accuracy required for CTM simulation validation and improvement, or whether the authors intend to discuss methods for improving CTM simulation accuracy.
The conclusions in Section 4.2 "Weather normalized concentrations: an indicator of BC emissions" also appear ambiguous. More detailed explanation is required regarding whether this explains the previous observational results or whether emission source accuracy improvement is needed from a modeling perspective.
The causes of black carbon concentration reduction ultimately mentioned in Section 4.3 are all results that have been discussed in previous studies. Specific description of what this study presents differentially compared to previous research is needed

Citation: https://doi.org/10.5194/egusphere-2025-2113-RC2
- AC1: 'Comment on egusphere-2025-2113', Huang Zheng, 11 Aug 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2113/egusphere-2025-2113-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-2113-AC1
AC1: 'Comment on egusphere-2025-2113', Huang Zheng, 11 Aug 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2113/egusphere-2025-2113-AC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-2113-AC1

Peer review completion

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

AR by Huang Zheng on behalf of the Authors (11 Aug 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (02 Sep 2025) by Gunnar Myhre

RR by Anonymous Referee #2 (08 Sep 2025)

Suggestions for revision or reasons for rejection

Overall, the paper has been thoroughly revised in response to the comments; however, several comments appear to require further clarification. The main concerns regarding the revised manuscript are as follows:
• Regarding the previous comment about the relationship between studies based on long-term observational data from the Beijing site and this study dealing with data observed across many regions in China, the essence of the question pertains to the differences in major conclusions between this study and such previous research. The point was not merely to indicate that long-term observational data from a single site, like Beijing, is easily accessible. Additional clarification on this matter appears necessary.
• A clear answer has not been provided regarding the previous question about the contributions of solid fuel combustion (eBCsf) and liquid fossil fuel combustion (eBClf). Sufficient responses and their incorporation into the paper are essential.
• Additional review is needed regarding the author's response to the following previously asked question:
Furthermore, many studies have explained the emission sources of black carbon or elemental carbon through carbon isotope analysis, determining contributions from solid fuel and liquid fossil fuel combustion. I believe the most critical aspect for publication of this study would be to validate the calculation results by comparing them with such previous research findings and demonstrating consistent results. For example, I suggest presenting comparisons between this study and carbon isotope analysis-based contribution assessments for specific locations and time periods in the main text.
Examining the author's response (Fig. S4), the aethalometer-based analysis results of this study show very large differences from dual carbon isotopes analysis results depending on time and location. The differences are too substantial to be explained simply by correlation coefficients (r). Therefore, the distinction between solid fuel combustion (eBCsf) and liquid fossil fuel combustion (eBClf) based on aethalometer data in this study remains uncertain, and this reviewer finds it difficult to fully trust the results of this study. More detailed explanations are absolutely necessary regarding how the characteristics of aerosols from solid fuel and liquid fossil fuel combustion differ, and how and why wavelength-dependent differences in aethalometer observational data appear.
In addition to the above question, clear discussion of the accuracy of the methodology for distinguishing contributions of solid fuel combustion (eBCsf) and liquid fossil fuel combustion (eBClf) is crucial for deriving the main results of this study, and therefore, the related content must be sufficiently explained and validated.
• One of the original findings claimed by the authors is the factor contributing to black carbon reduction in rural and background areas of China. As the authors argue, this is clearly distinctive from previous studies that primarily reported black carbon concentration variation characteristics in urban areas. However, overall, the main results of this study largely align with results already reported in previous studies, and the authors explain their research results in various places through citations of such previous research. It is required that this study minimize discussion of similar results compared to previous studies and provide sufficient and detailed explanations of the distinctive factors of this study. For example, regarding the conclusion of the following sentence, it is necessary to provide other observational data or emissions demonstrating reduced coal and biomass use in rural and baseline areas:
The results demonstrate that the decrease in BC concentrations is mainly attributed to reduced emissions from both coal and biomass combustion in rural and baseline areas. These findings represent novel conclusions not previously reported in earlier research. (L480-482).
Additionally, it would be helpful to provide a clear definition of how "baseline area" is defined in the above sentence.

Hide

ED: Publish subject to minor revisions (review by editor) (23 Sep 2025) by Gunnar Myhre

Consider the following comments from one of the reviewers:

Overall, the paper has been thoroughly revised in response to the comments; however, several comments appear to require further clarification. The main concerns regarding the revised manuscript are as follows:
• Regarding the previous comment about the relationship between studies based on long-term observational data from the Beijing site and this study dealing with data observed across many regions in China, the essence of the question pertains to the differences in major conclusions between this study and such previous research. The point was not merely to indicate that long-term observational data from a single site, like Beijing, is easily accessible. Additional clarification on this matter appears necessary.
• A clear answer has not been provided regarding the previous question about the contributions of solid fuel combustion (eBCsf) and liquid fossil fuel combustion (eBClf). Sufficient responses and their incorporation into the paper are essential.
• Additional review is needed regarding the author's response to the following previously asked question:
Furthermore, many studies have explained the emission sources of black carbon or elemental carbon through carbon isotope analysis, determining contributions from solid fuel and liquid fossil fuel combustion. I believe the most critical aspect for publication of this study would be to validate the calculation results by comparing them with such previous research findings and demonstrating consistent results. For example, I suggest presenting comparisons between this study and carbon isotope analysis-based contribution assessments for specific locations and time periods in the main text.
Examining the author's response (Fig. S4), the aethalometer-based analysis results of this study show very large differences from dual carbon isotopes analysis results depending on time and location. The differences are too substantial to be explained simply by correlation coefficients (r). Therefore, the distinction between solid fuel combustion (eBCsf) and liquid fossil fuel combustion (eBClf) based on aethalometer data in this study remains uncertain, and this reviewer finds it difficult to fully trust the results of this study. More detailed explanations are absolutely necessary regarding how the characteristics of aerosols from solid fuel and liquid fossil fuel combustion differ, and how and why wavelength-dependent differences in aethalometer observational data appear.
In addition to the above question, clear discussion of the accuracy of the methodology for distinguishing contributions of solid fuel combustion (eBCsf) and liquid fossil fuel combustion (eBClf) is crucial for deriving the main results of this study, and therefore, the related content must be sufficiently explained and validated.
• One of the original findings claimed by the authors is the factor contributing to black carbon reduction in rural and background areas of China. As the authors argue, this is clearly distinctive from previous studies that primarily reported black carbon concentration variation characteristics in urban areas. However, overall, the main results of this study largely align with results already reported in previous studies, and the authors explain their research results in various places through citations of such previous research. It is required that this study minimize discussion of similar results compared to previous studies and provide sufficient and detailed explanations of the distinctive factors of this study. For example, regarding the conclusion of the following sentence, it is necessary to provide other observational data or emissions demonstrating reduced coal and biomass use in rural and baseline areas:
The results demonstrate that the decrease in BC concentrations is mainly attributed to reduced emissions from both coal and biomass combustion in rural and baseline areas. These findings represent novel conclusions not previously reported in earlier research. (L480-482).
Additionally, it would be helpful to provide a clear definition of how "baseline area" is defined in the above sentence.

A response to this notification from the journal is required:
Figures 1, 2, 7 and S1 may contain a territory that is disputed according to the United Nations. If and when the manuscript is accepted for final revised publication, you will be asked to choose one of the following options: (a) you could remove the disputed territory from the map and submit new figure files, or (b) we could add a statement that some figures contain disputed territories.

Hide

AR by Huang Zheng on behalf of the Authors (29 Sep 2025) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (14 Oct 2025) by Gunnar Myhre

AR by Huang Zheng on behalf of the Authors (15 Oct 2025) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (22 Oct 2025) by Gunnar Myhre

AR by Huang Zheng on behalf of the Authors (23 Oct 2025) Author's response Manuscript

Journal article(s) based on this preprint

20 Nov 2025

Black carbon aerosols in China: spatial-temporal variations and lessons from long-term atmospheric observations

Huang Zheng, Shaofei Kong, Deping Ding, Marjan Savadkoohi, Congbo Song, Mingming Zheng, and Roy M. Harrison

Atmos. Chem. Phys., 25, 16363–16386, https://doi.org/10.5194/acp-25-16363-2025,https://doi.org/10.5194/acp-25-16363-2025, 2025

Short summary

Huang Zheng, Shaofei Kong, Deping Ding, Marjan Savadkoohi, Congbo Song, Mingming Zheng, and Roy Harrison

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https://doi.org/10.5194/egusphere-2025-2113-supplement

Huang Zheng, Shaofei Kong, Deping Ding, Marjan Savadkoohi, Congbo Song, Mingming Zheng, and Roy Harrison

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This study analyzes 13 years of BC data in China, uncovering patterns in its concentration and sources. Spatial-temporal variations and trends of BC are reported. Our analysis revealed that the reduction rates of BC and its sources varied across different station types, with spatial differences in the drivers of reduction. These long-term observations provide valuable insights to enhance understanding of pollution trends and improve models for predicting air quality.


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