the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Nov 2024
Sources and trends of Black Carbon Aerosol in a Megacity of Nanjing, East China After the China Clean Action Plan and Three-Year Action Plan
Abstract. Black carbon (BC) is an essential component of particulate matter (PM) with a significant impact on climate change. Few studies have investigated the long-term changes in BC and the sources, particularly considering primary emissions of BC, which is crucial for developing effective mitigation strategies. Here, based on three-year observations (2019–2021), random forest (RF) algorithms were employed to reconstruct BC concentrations in Nanjing from 2014 to 2021. Source apportionment was conducted on the reconstructed data to investigate long-term trends of BC and its sources. The results showed that the three-year average BC concentration was 2.5±1.6 μg m-3, peaking in winter, with approximately 80 % attributed to liquid fuel combustion. Notably, the reconstructed time series revealed a significant decrease (p < 0.05) in BC levels over the eight-year period, primarily due to reduced emissions from liquid fuels. The comparison between two control polices periods (P1:2014–2017 and P2:2018–2021) indicate that BC concentrations decline more steeply during S2 since significant (p < 0.05) reduction in biomass burning. The seasonal analysis showed significant reductions (p < 0.05) in BC, BCliquid (black carbon from liquid fuel combustion) and BCsolid (black carbon from solid fuel combustion) during winter, with BCliquid accounting for 77 % of the reduction. Overall, emission reduction was the dominant factor in reducing BC levels, contributing between 62 % and 86 %, as revealed by Kolmogorov-Zurbenko (KZ) filter. However, during P2, meteorological conditions played a more significant role, especially in reducing BC and BCliquid, with an increase in their impact on BCsolid compared to P1. Our results demonstrated that target control measures for liquid fuel combustion are necessary, as liquid fuel combustion is a major driver of decreasing BC, especially in summer, while the influence of meteorological factors on BC variations cannot be overlooked.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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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.
- Preprint
(1200 KB) - Metadata XML
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Supplement
(652 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2503', Anonymous Referee #1, 05 Dec 2024
Comments：
Few researches have focused on the long-term changes in BC. Three-year observation data of BC in Nanjing were used to predicte the changes of BC in 2014-2021 by the machine learning method. Moreover, the Aethalometer model was used to identify the source contributions of BC, including liquid fuels and solid fuels. Results revealed that the contributions of liquid fuels combustion to BC were estimated to be 80 %, which was responsible for 77% reduction of BC. The study develop a novel methodology of predicting long-term changes in BC. However, some issues need to be clarified. I would thus recommend a minor revision to improve this manuscript.
Minor comments:
- Lines 19-35, the logical coherence of the abstract needs adjustment. For instance, the conclusions of the three-year observational data of BC should be summarized first, followed by the results of the long-term prediction of BC.
- Lines 167-170, here, why are 470nm and 950nm chosen to identify source contributions of BC? Please add the related description of this in the manuscript.
- Lines 279-280, it shows that the proportion of BC to PM2.5 can be as high as 99%. Is there a possibility of an expression error here?
Citation: https://doi.org/10.5194/egusphere-2024-2503-RC1 - AC1: 'Reply on RC1', Aduburexiati Abulimiti, 17 Feb 2025
- AC2: 'Reply on RC1', Aduburexiati Abulimiti, 17 Feb 2025
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RC2: 'Comment on egusphere-2024-2503', Anonymous Referee #2, 08 Dec 2024
Review of egusphere-2024-2503
The study focuses on the long-term trends and sources of black carbon (BC) aerosol in Nanjing, China, using three years of observational data (2019–2021) combined with historical reconstruction (2014–2021) via a random forest model. Based on K-Z filter approach, it investigates the contributions of liquid and solid fuel combustion, the effectiveness of emission reduction measures, and the interplay between meteorology and emissions. The results highlight significant decreases in BC levels driven predominantly by reductions in liquid fuel combustion, with varying seasonal and meteorological influences. The findings are of significance, and I have some comments for the authors to consider.
- In Figure 5, are the data presented from the training dataset or the test dataset of the random forest model? High R-square values for the training dataset could indicate overfitting if the model fails to replicate results for the test dataset, potentially compromising its generalizability. To evaluate the robustness and reliability of the random forest model, it is essential to include validation results specifically for the test dataset.
- Line 505: The AAE values of 1 and 2 used for the Aethalometer model require justification within your study. Source apportionment outputs can vary significantly depending on the AAE values assigned for fossil fuel and biomass burning. Please provide evidence supporting the chosen values.
- Precipitation is an essential parameter for BC scavenging, yet it appears to be absent from the input variables in your random forest model and should be included.
- Lines 212–215. Please present figures showing the results of the 10-fold CV used for the random forest model. These figures would allow reviewers to assess the model-building process and ensure robustness. Such details could be included in the supplementary materials.
- A figure illustrating the K-Z filter results, showing the long-term trends for emissions and meteorology, can enhance clarity. Please include. Additionally, when discussing the trends of pollutants, consider applying the Mann-Kendall test to assess the statistical significance of these trends.
- Provide an explanation for the differing impacts of meteorology on the extent of reductions in BC liquid and BC solid by the K-Z method.
- Extend the comparison of your findings to other regions in China and globally. In China, BC emissions exhibit notable geographic heterogeneity. Beyond the commonly studied regions like the North China Plain and East China, comparisons to other areas would be beneficial. For example, studies conducted in Nanning, Guangxi province (DOI: 10.1016/j.scitotenv.2023.166747) and in Liaoning province (DOI: 10.1016/j.envpol.2024.124470) can offer valuable context. Similarly, discussing the BC/CO and BC/PM2.5 ratios in your study relative to that in these works can enhance the scientific depth of your analysis.
- Some typos and grammar need to be corrected, such as
line 71 “severe”
line 120-121. “was then incorporated”
Line 318, “Similarly”
Thoroughly check the manuscript for similar errors to ensure clarity and precision.
Citation: https://doi.org/10.5194/egusphere-2024-2503-RC2 - AC3: 'Reply on RC2', Aduburexiati Abulimiti, 17 Feb 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2503', Anonymous Referee #1, 05 Dec 2024
Comments：
Few researches have focused on the long-term changes in BC. Three-year observation data of BC in Nanjing were used to predicte the changes of BC in 2014-2021 by the machine learning method. Moreover, the Aethalometer model was used to identify the source contributions of BC, including liquid fuels and solid fuels. Results revealed that the contributions of liquid fuels combustion to BC were estimated to be 80 %, which was responsible for 77% reduction of BC. The study develop a novel methodology of predicting long-term changes in BC. However, some issues need to be clarified. I would thus recommend a minor revision to improve this manuscript.
Minor comments:
- Lines 19-35, the logical coherence of the abstract needs adjustment. For instance, the conclusions of the three-year observational data of BC should be summarized first, followed by the results of the long-term prediction of BC.
- Lines 167-170, here, why are 470nm and 950nm chosen to identify source contributions of BC? Please add the related description of this in the manuscript.
- Lines 279-280, it shows that the proportion of BC to PM2.5 can be as high as 99%. Is there a possibility of an expression error here?
Citation: https://doi.org/10.5194/egusphere-2024-2503-RC1 - AC1: 'Reply on RC1', Aduburexiati Abulimiti, 17 Feb 2025
- AC2: 'Reply on RC1', Aduburexiati Abulimiti, 17 Feb 2025
-
RC2: 'Comment on egusphere-2024-2503', Anonymous Referee #2, 08 Dec 2024
Review of egusphere-2024-2503
The study focuses on the long-term trends and sources of black carbon (BC) aerosol in Nanjing, China, using three years of observational data (2019–2021) combined with historical reconstruction (2014–2021) via a random forest model. Based on K-Z filter approach, it investigates the contributions of liquid and solid fuel combustion, the effectiveness of emission reduction measures, and the interplay between meteorology and emissions. The results highlight significant decreases in BC levels driven predominantly by reductions in liquid fuel combustion, with varying seasonal and meteorological influences. The findings are of significance, and I have some comments for the authors to consider.
- In Figure 5, are the data presented from the training dataset or the test dataset of the random forest model? High R-square values for the training dataset could indicate overfitting if the model fails to replicate results for the test dataset, potentially compromising its generalizability. To evaluate the robustness and reliability of the random forest model, it is essential to include validation results specifically for the test dataset.
- Line 505: The AAE values of 1 and 2 used for the Aethalometer model require justification within your study. Source apportionment outputs can vary significantly depending on the AAE values assigned for fossil fuel and biomass burning. Please provide evidence supporting the chosen values.
- Precipitation is an essential parameter for BC scavenging, yet it appears to be absent from the input variables in your random forest model and should be included.
- Lines 212–215. Please present figures showing the results of the 10-fold CV used for the random forest model. These figures would allow reviewers to assess the model-building process and ensure robustness. Such details could be included in the supplementary materials.
- A figure illustrating the K-Z filter results, showing the long-term trends for emissions and meteorology, can enhance clarity. Please include. Additionally, when discussing the trends of pollutants, consider applying the Mann-Kendall test to assess the statistical significance of these trends.
- Provide an explanation for the differing impacts of meteorology on the extent of reductions in BC liquid and BC solid by the K-Z method.
- Extend the comparison of your findings to other regions in China and globally. In China, BC emissions exhibit notable geographic heterogeneity. Beyond the commonly studied regions like the North China Plain and East China, comparisons to other areas would be beneficial. For example, studies conducted in Nanning, Guangxi province (DOI: 10.1016/j.scitotenv.2023.166747) and in Liaoning province (DOI: 10.1016/j.envpol.2024.124470) can offer valuable context. Similarly, discussing the BC/CO and BC/PM2.5 ratios in your study relative to that in these works can enhance the scientific depth of your analysis.
- Some typos and grammar need to be corrected, such as
line 71 “severe”
line 120-121. “was then incorporated”
Line 318, “Similarly”
Thoroughly check the manuscript for similar errors to ensure clarity and precision.
Citation: https://doi.org/10.5194/egusphere-2024-2503-RC2 - AC3: 'Reply on RC2', Aduburexiati Abulimiti, 17 Feb 2025
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Abudurexiati Abulimiti
Mingyuan Yu
Yihang Hong
Yu-Chi Lin
Chaman Gul
Fang Cao
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1200 KB) - Metadata XML
-
Supplement
(652 KB) - BibTeX
- EndNote
- Final revised paper