the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Oct 2024
Machine Learning Assisted Chemical Characterization and Optical Properties of Atmospheric Brown Carbon in Nanjing, China
Abstract. The light-absorbing organics, namely brown carbon (BrC), can significantly affect atmospheric visibility and radiative forcing, yet their chemical and optical properties remain poorly understood. Here, a comprehensive analysis was conducted on the particulate matter (PM2.5) samples collected in Nanjing, China during 2022 ~ 2023 with a particular interest on the identification of key BrC molecules. First, the water-soluble organic aerosol (WSOA) was more oxygenated during cold season (CS) due to a highly oxidized secondary OA (SOA) factor that was strongly associated with aqueous/heterogeneous reactions especially during nighttime, while the WSOA during summer season (SS) was less oxygenated and the SOA was mainly from photochemical reactions. Fossil fuel combustion hydrocarbon-like OA was the largest and dominant contributor to the light absorption during CS (55.6 ~ 63.7 %). Secondly, our observations reveals that aqueous oxidation can lead to notable photo-enhancement during CS, while photochemical oxidation on the contrary caused photo-bleaching during SS; Both water-soluble and methanol-soluble organics had four key fluorophores, including three factors relevant with humic-like substances (HULIS) and one protein-like component. Thirdly, molecular characterization show that CHON compounds were overall the most abundant species, followed by CHO and CHN compounds, and significant presence of organosulfates in CS samples reaffirmed the importance of aqueous-phase formation. Finally, building upon the molecular characterization and light absorption measurement results, the machine learning approach was applied to identify the key BrC molecules, and 31 compounds including polycyclic aromatic hydrocarbons (PAHs), oxyheterocyclic PAHs, quinones, and nitrogen-containing species, etc., which can be a good reference for future studies.
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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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Supplement
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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
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Supplement
(2744 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-2024-2757', Anonymous Referee #1, 08 Nov 2024
The manuscript by Huang et al. explores the chemical and optical properties of ambient PM2.5 in an urban area of China. The authors employ a detailed chemical characterization and use a machine learning approach to link optical properties with molecular characterization, providing a valuable reference for future studies. Overall, the manuscript is well-written, and the method is reasonable. I have a few points that could be addressed to strengthen the manuscript:
- The title, “Machine Learning Assisted Chemical Characterization and Optical Properties of Atmospheric Brown Carbon in Nanjing, China,” suggests a focus on machine learning. Consider expanding the discussion of this aspect within the manuscript.
- Since the authors also examine the fluorescent properties of organic aerosols (OA), it would be beneficial to include a brief introduction to this topic in addition to the discussion on light absorption.
- Add more comparisons with similar offline PMF-OA analyses to strengthen and justify the robustness of your PMF results on WSOA.
- Clarify the reasoning for using MAE365 initially and MAE405 in Figure 4b. This discrepancy should be explained.
- The turning point in Figure 6 is unclear. Please provide additional explanations to enhance understanding.
- Discuss any structural similarities among the key BrC species identified using the machine learning RF approach, as this would add value to the findings.
- Increase the font sizes in the figures to improve readability.
Citation: https://doi.org/10.5194/egusphere-2024-2757-RC1 -
RC2: 'Comment on egusphere-2024-2757', Anonymous Referee #2, 15 Nov 2024
General comments:
This study provides a detailed investigation into the chemical and optical properties of brown carbon (BrC) in ambient PM2.5 samples collected in Nanjing, China. It sheds new light on the chemical, molecular, and optical characteristics of BrC. The paper is well-written, logically organized, and includes appropriate figures and tables. However, there are major errors or gaps that need to be addressed. With significant revisions, additional data, and/or other changes, it has the potential to make a meaningful contribution.Major:
1/ I am unclear about the innovation and contributions of this study after reading the abstract and introduction. I suggest that the authors clearly articulate the limitations of existing research and the potential contributions of this study in the abstract and background sections. For example, in the third paragraph (Lines 91-92), the authors state, 'the aforementioned identified species only account for a limited fraction of BrC total light absorption.' Can we then infer that this study has identified additional BrC species contributing to light absorption?2/ My second major concern is with the results. I noticed that the mass concentration and absorption of MSOC are higher than those of WSOC. Why didn't the authors analyze the sources and characteristics of MSOC in Section 3.1.2?
3/ In Line 484, I am unsure how the authors reconstructed Abs365, WSOC. I suggest that the authors explain the reconstruction of Abs365, WSOC in the Methods section or in the supplementary materials.
4/ In Section 3.2.4, the authors mention that the fluorescent properties of WSOC and MSOC are governed by HULIS during CS, while they are governed by a protein-like component during SS. What does this mean? What is the connection between HULIS and the protein-like component in relation to the sources? I suggest discussing this in the Results section.
5/ I would suggest adding a discussion on the limitations of this study and the uncertainties associated with the methods or equipment in the conclusion section.
Minor:
1/ The text and figures require a thorough review for general errors. e.g., the last pannel in Fig 2a, 0.0.59 -> 0.59?
2/ I would suggest adding a sentence to transition after 'Here, a comprehensive analysis was conducted on the particulate matter (PM2.5) samples collected in Nanjing, China, during 2022–2023, with a particular focus on the identification of key BrC molecules' (Lines 21-23). For example, 'Several important clues related to BrC were found.'
3/ I would suggest the authors cite the newest studies to illustrate the importance of BrC in the first pargraph of introduction.
Here i recommend some,
Chakrabarty, R. K., N. J. Shetty, A. S. Thind, et al., 2023: Shortwave absorption by wildfire smoke dominated by dark brown carbon. Nature Geoscience, 2023, 16(8): 683-688.
Brown, H., H. Wang, M. Flanner, et al., 2022: Brown carbon fuel and emission source attributions to global snow darkening effect. Journal of Advances in Modeling Earth Systems, 14, e2021MS002768.
DeLessio, M. A., K. Tsigaridis, S. E. Bauer, et al., 2023: Modeling atmospheric brown carbon in the GISS ModelE Earth system model. EGUsphere, 1-50.
Xu, L., Lin, G., Liu, X., Wu, C., Wu, Y., & Lou, S. (2024). Constraining light absorption of brown carbon in China and implications for aerosol direct radiative effect. Geophysical Research Letters, 51, e2024GL109861. https://doi.org/10.1029/2024GL109861Citation: https://doi.org/10.5194/egusphere-2024-2757-RC2 - AC1: 'Reply to Comments on egusphere-2024-2757', Xinlei Ge, 14 Feb 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2757', Anonymous Referee #1, 08 Nov 2024
The manuscript by Huang et al. explores the chemical and optical properties of ambient PM2.5 in an urban area of China. The authors employ a detailed chemical characterization and use a machine learning approach to link optical properties with molecular characterization, providing a valuable reference for future studies. Overall, the manuscript is well-written, and the method is reasonable. I have a few points that could be addressed to strengthen the manuscript:
- The title, “Machine Learning Assisted Chemical Characterization and Optical Properties of Atmospheric Brown Carbon in Nanjing, China,” suggests a focus on machine learning. Consider expanding the discussion of this aspect within the manuscript.
- Since the authors also examine the fluorescent properties of organic aerosols (OA), it would be beneficial to include a brief introduction to this topic in addition to the discussion on light absorption.
- Add more comparisons with similar offline PMF-OA analyses to strengthen and justify the robustness of your PMF results on WSOA.
- Clarify the reasoning for using MAE365 initially and MAE405 in Figure 4b. This discrepancy should be explained.
- The turning point in Figure 6 is unclear. Please provide additional explanations to enhance understanding.
- Discuss any structural similarities among the key BrC species identified using the machine learning RF approach, as this would add value to the findings.
- Increase the font sizes in the figures to improve readability.
Citation: https://doi.org/10.5194/egusphere-2024-2757-RC1 -
RC2: 'Comment on egusphere-2024-2757', Anonymous Referee #2, 15 Nov 2024
General comments:
This study provides a detailed investigation into the chemical and optical properties of brown carbon (BrC) in ambient PM2.5 samples collected in Nanjing, China. It sheds new light on the chemical, molecular, and optical characteristics of BrC. The paper is well-written, logically organized, and includes appropriate figures and tables. However, there are major errors or gaps that need to be addressed. With significant revisions, additional data, and/or other changes, it has the potential to make a meaningful contribution.Major:
1/ I am unclear about the innovation and contributions of this study after reading the abstract and introduction. I suggest that the authors clearly articulate the limitations of existing research and the potential contributions of this study in the abstract and background sections. For example, in the third paragraph (Lines 91-92), the authors state, 'the aforementioned identified species only account for a limited fraction of BrC total light absorption.' Can we then infer that this study has identified additional BrC species contributing to light absorption?2/ My second major concern is with the results. I noticed that the mass concentration and absorption of MSOC are higher than those of WSOC. Why didn't the authors analyze the sources and characteristics of MSOC in Section 3.1.2?
3/ In Line 484, I am unsure how the authors reconstructed Abs365, WSOC. I suggest that the authors explain the reconstruction of Abs365, WSOC in the Methods section or in the supplementary materials.
4/ In Section 3.2.4, the authors mention that the fluorescent properties of WSOC and MSOC are governed by HULIS during CS, while they are governed by a protein-like component during SS. What does this mean? What is the connection between HULIS and the protein-like component in relation to the sources? I suggest discussing this in the Results section.
5/ I would suggest adding a discussion on the limitations of this study and the uncertainties associated with the methods or equipment in the conclusion section.
Minor:
1/ The text and figures require a thorough review for general errors. e.g., the last pannel in Fig 2a, 0.0.59 -> 0.59?
2/ I would suggest adding a sentence to transition after 'Here, a comprehensive analysis was conducted on the particulate matter (PM2.5) samples collected in Nanjing, China, during 2022–2023, with a particular focus on the identification of key BrC molecules' (Lines 21-23). For example, 'Several important clues related to BrC were found.'
3/ I would suggest the authors cite the newest studies to illustrate the importance of BrC in the first pargraph of introduction.
Here i recommend some,
Chakrabarty, R. K., N. J. Shetty, A. S. Thind, et al., 2023: Shortwave absorption by wildfire smoke dominated by dark brown carbon. Nature Geoscience, 2023, 16(8): 683-688.
Brown, H., H. Wang, M. Flanner, et al., 2022: Brown carbon fuel and emission source attributions to global snow darkening effect. Journal of Advances in Modeling Earth Systems, 14, e2021MS002768.
DeLessio, M. A., K. Tsigaridis, S. E. Bauer, et al., 2023: Modeling atmospheric brown carbon in the GISS ModelE Earth system model. EGUsphere, 1-50.
Xu, L., Lin, G., Liu, X., Wu, C., Wu, Y., & Lou, S. (2024). Constraining light absorption of brown carbon in China and implications for aerosol direct radiative effect. Geophysical Research Letters, 51, e2024GL109861. https://doi.org/10.1029/2024GL109861Citation: https://doi.org/10.5194/egusphere-2024-2757-RC2 - AC1: 'Reply to Comments on egusphere-2024-2757', Xinlei Ge, 14 Feb 2025
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Yu Huang
Xingru Li
Dan Dan Huang
Ruoyuan Lei
Binhuang Zhou
Yunjiang Zhang
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2549 KB) - Metadata XML
-
Supplement
(2744 KB) - BibTeX
- EndNote
- Final revised paper