the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 May 2024
Enhanced daytime secondary aerosol formation driven by gas-particle partitioning in downwind urban plumes
Abstract. Anthropogenic emissions from city clusters can significantly enhance secondary organic aerosol (SOA) formation in the downwind regions, while the mechanism is poorly understood. To investigate the effect of pollutants within urban plumes on organic aerosol (OA) evolution, a field campaign was conducted at a downwind site of the Pearl River Delta region of China in the fall of 2019. A time-of-flight chemical ionization mass spectrometer coupled with a Filter Inlet for Gases and Aerosol (FIGAERO-CIMS) was used to probe the gas- and particle-phase molecular composition and thermograms of organic compounds. For air masses influenced by urban pollution, strong daytime SOA formation through gas-particle partitioning was observed, resulting in higher OA volatility. The obvious SOA enhancement was mainly attributed to the equilibrium partitioning of non-condensable (C * ≥ 100.5 μg m-3) organic vapors. We speculated that the elevated NOx concentration could suppress the formation of highly oxidized products, resulting in a smooth increase of condensable (C * < 100.5 μg m-3) organic vapors. Evidence showed that urban pollutants (NOx and VOCs) could enhance the oxidizing capacity, while the elevated VOCs was mainly responsible for promoting daytime SOA formation by increasing the RO2 production rate. Our results highlight the important role of urban anthropogenic pollutants in SOA control in the suburban region.
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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.
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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.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-887', Anonymous Referee #1, 21 May 2024
This study conducted by Cai et al. demonstrates the significant role of volatile organic compounds (VOCs) from urban plumes in the formation of daytime secondary organic aerosols (SOA) in suburban areas by gas-particle partition through observation using a time-of-flight chemical ionization mass spectrometer coupled with a Filter Inlet for Gases and Aerosol (FIGAERO-CIMS) and other instruments at a suburban site. This manuscript is well-written and fits well to the scope of ACP. I recommend it for publication after the following comments have been addressed.
Major Comments:
- It is noted that this manuscript utilizes Positive Matrix Factorization (PMF) to distinguish different types of organic compounds. It is necessary to supplement the PMF spectra and diagnosed plot in the supporting information.
- Figure 6 illustrates the difference in carbon oxidation state () between different periods. However, the description of Figure 6 in the text is insufficient. The authors should provide further explanation on Figure 6, detailing the observed differences in organic aerosol states in different carbon numbers during various periods.
- Figure 7 present the different production of OH and RO2 in different VOC and NOX Adding the boundary of VOCs and NOx limited region could help reader better understanding the change of production of OH and RO2 in different periods.
- Line 243: The author mentions that the correlation between LOOA concentration and particle surface area suggests a relationship between gas-particle partitioning and LOOA formation. However, particle mass concentration is also positively correlated with the particle surface area.
The authors should provide more evidence about the contribution of gas-particle partitioning on LOOA formation.
- Line 367: In urban plumes, the production rate of OH increases with the increase of NOx and VOC, transitioning to the VOC-limited regime. Why is it stated that NOx suppressed the production rate of OH at this period?
Specific comments:
- Line 259: It should be “long-range transport”.
- Figure S8: please provide R value.
- Line 389: It should be “dramatic”.
Citation: https://doi.org/10.5194/egusphere-2024-887-RC1 -
AC1: 'Reply on RC1', Bin Yuan, 05 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-887/egusphere-2024-887-AC1-supplement.pdf
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RC2: 'Comment on egusphere-2024-887', Anonymous Referee #2, 31 May 2024
Cai et al discuss the enhancement of secondary organic aerosol downwind of urban centers due to increased partitioning of semi-volatile vapors. FIGAERO-CIMS is employed to assess the volatility evolution of particulate species over time. PMF is applied to SP-AMS data in order to understand the sources of OA. Overall the work is thorough and the limitations are clearly stated. I think the content of the work is appropriate for ACP and therefore I would recommend publication after the following comments are addressed.
- I greatly appreciate the transparency of the PMF factors being provided in the SI, however, more description of these factors and how they were determined is required in the main text. Each factor should be described individually and it should be discussed why it was attributed to the specific source it was. Particularly, the Night-OA, BBOA, and aBBOA factors seem visually quite similar and a discussion of the specific difference would be very helpful. Additionally the average composition values for HOA and aBBOA are identical which seems surprising.
- This is likely beyond the scope of this study, but I wonder if any consideration was given to using PMF to identify different sources or formation pathways from the FIGAERO-CIMS data as in Buchholz et al as well as other studies. Perhaps just an idea for future work.
- Line 168: A MW of 200 g mol-1 is assumed, but can you not retrieve an average MW from the CIMS data? Would it be biased too high due to low detection efficiency of less oxidized, low MW species?
- The determination of the volatility of gas-phase species is based on the formation pathway of the species. This is important to consider due to the different functional groups likely to be dominant in products formed via autoxidation and I applaud the authors consideration of this. Given the uncertainties associated with the volatility estimation, the method employed in this study is likely good enough, however, as explicit determination of the pathway of formation is impossible, some discussion of this limitation should be added. I also think a sentence describing how H:C and O:C (Fig S6) were used to determine the pathway of formation as well as relevant references in the main text would be helpful.
- I am confused about the assignment of species with C*>100.5 as “non-condensable.” This boundary is within the SVOC VBS range and particularly under the high mass loadings one could see even downwind of urban plumes, it seems species with higher volatilities may contribute substantially to the particle phase. Is this determination specific to the conditions of this study in some way or based on other literature?
- While stated correctly in the text, I think the boundary of the SVOC class is incorrect in Fig 4b. SVOCs should extend to 10-0.5 not 100.5 ug m-3, assuming these are C*(300 K).
References
Buchholz, A., Ylisirniö, A., Huang, W., Mohr, C., Canagaratna, M., Worsnop, D. R., Schobesberger, S., and Virtanen, A.: Deconvolution of FIGAERO–CIMS thermal desorption profiles using positive matrix factorisation to identify chemical and physical processes during particle evaporation, Atmos. Chem. Phys., 20, 7693–7716, https://doi.org/10.5194/acp-20-7693-2020, 2020.
Citation: https://doi.org/10.5194/egusphere-2024-887-RC2 -
AC2: 'Reply on RC2', Bin Yuan, 05 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-887/egusphere-2024-887-AC2-supplement.pdf
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RC3: 'Comment on egusphere-2024-887', Anonymous Referee #3, 03 Jun 2024
The work by Cai et al. investigates the SOA formation in downwind regions of urban areas, focusing on the PRD region of China in the fall of 2019. The FIGAERO-CIMS was employed to analyze the molecular composition and volatility of organic compounds in both gas and particle phases. The findings highlight significant daytime SOA formation driven by gas-particle partitioning, influenced by urban pollutants such as NOx and volatile organic compounds (VOCs). The paper is well-structured, clearly written, and a valuable contribution to the field of atmospheric sciences, particularly in understanding the dynamics of SOA formation in urban-influenced suburban areas. With the following comments addressed, it would be suitable for publication in ACP.
- The aBBOA factor appears to have a lower O:C and a higher H:C compared to the BBOA factor (Figure S3). This is contrary to what it is expected for aging. This makes me wonder how these PMF factors were exactly assigned. Some explanation will be helpful.
- Line 179-183: There does not seem to be a clear trend between mass loading and Tmax and the calibration mass loading range does not cover the campaign mass loading center (Figure S5). Can the authors explain the rationale of picking the fitting parameters of the experiment with Dp 200 nm and mass loading = 407 ng rather than for example the parameters from fitting all experiments? What is the direction of bias introduced by this choice?
- Line 184-186: it would be helpful to describe how the black line in Figure S6 that differentiates the oxidation pathways was determined in light of existing literature in a sentence or two.
- Line 217-220: These observation data used to constrain F0AM simulations were not mentioned in the instrumentation section of the paper. Are these collocated and published data? Adding a brief description would provide necessary context.
- Line 302: The term “non-condensable” (𝐶∗ >10^0.5 μg m-3) is a bit confusing. These vapors are apparently condensable SVOCs that would partition between gas and particle phases. Is this definition based on specific literature? Clarifying this term would enhance understanding.
- Line 308-313: I wonder if the authors can quantitatively estimate the contribution of the “non-condensable” organic vapors to the total organic aerosol mass to strengthen this point. The saturation vapor concentration for the gas-phase organic vapors have already been estimated. The organic aerosol mass loadings from SP-AMS are available. Then the particle-phase concentrations of these compounds can be calculated based on equilibrium partitioning and compared with the mass that FIGAERO is missing out (mass balance).
Technical corrections:
Line 180: “estimation” should be “estimating”.
Line 390: “ddramatic” should be “dramatic”.
SI Line 63: Figure S7 was mislabeled as Figure S8.
Citation: https://doi.org/10.5194/egusphere-2024-887-RC3 -
AC3: 'Reply on RC3', Bin Yuan, 05 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-887/egusphere-2024-887-AC3-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-887', Anonymous Referee #1, 21 May 2024
This study conducted by Cai et al. demonstrates the significant role of volatile organic compounds (VOCs) from urban plumes in the formation of daytime secondary organic aerosols (SOA) in suburban areas by gas-particle partition through observation using a time-of-flight chemical ionization mass spectrometer coupled with a Filter Inlet for Gases and Aerosol (FIGAERO-CIMS) and other instruments at a suburban site. This manuscript is well-written and fits well to the scope of ACP. I recommend it for publication after the following comments have been addressed.
Major Comments:
- It is noted that this manuscript utilizes Positive Matrix Factorization (PMF) to distinguish different types of organic compounds. It is necessary to supplement the PMF spectra and diagnosed plot in the supporting information.
- Figure 6 illustrates the difference in carbon oxidation state () between different periods. However, the description of Figure 6 in the text is insufficient. The authors should provide further explanation on Figure 6, detailing the observed differences in organic aerosol states in different carbon numbers during various periods.
- Figure 7 present the different production of OH and RO2 in different VOC and NOX Adding the boundary of VOCs and NOx limited region could help reader better understanding the change of production of OH and RO2 in different periods.
- Line 243: The author mentions that the correlation between LOOA concentration and particle surface area suggests a relationship between gas-particle partitioning and LOOA formation. However, particle mass concentration is also positively correlated with the particle surface area.
The authors should provide more evidence about the contribution of gas-particle partitioning on LOOA formation.
- Line 367: In urban plumes, the production rate of OH increases with the increase of NOx and VOC, transitioning to the VOC-limited regime. Why is it stated that NOx suppressed the production rate of OH at this period?
Specific comments:
- Line 259: It should be “long-range transport”.
- Figure S8: please provide R value.
- Line 389: It should be “dramatic”.
Citation: https://doi.org/10.5194/egusphere-2024-887-RC1 -
AC1: 'Reply on RC1', Bin Yuan, 05 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-887/egusphere-2024-887-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2024-887', Anonymous Referee #2, 31 May 2024
Cai et al discuss the enhancement of secondary organic aerosol downwind of urban centers due to increased partitioning of semi-volatile vapors. FIGAERO-CIMS is employed to assess the volatility evolution of particulate species over time. PMF is applied to SP-AMS data in order to understand the sources of OA. Overall the work is thorough and the limitations are clearly stated. I think the content of the work is appropriate for ACP and therefore I would recommend publication after the following comments are addressed.
- I greatly appreciate the transparency of the PMF factors being provided in the SI, however, more description of these factors and how they were determined is required in the main text. Each factor should be described individually and it should be discussed why it was attributed to the specific source it was. Particularly, the Night-OA, BBOA, and aBBOA factors seem visually quite similar and a discussion of the specific difference would be very helpful. Additionally the average composition values for HOA and aBBOA are identical which seems surprising.
- This is likely beyond the scope of this study, but I wonder if any consideration was given to using PMF to identify different sources or formation pathways from the FIGAERO-CIMS data as in Buchholz et al as well as other studies. Perhaps just an idea for future work.
- Line 168: A MW of 200 g mol-1 is assumed, but can you not retrieve an average MW from the CIMS data? Would it be biased too high due to low detection efficiency of less oxidized, low MW species?
- The determination of the volatility of gas-phase species is based on the formation pathway of the species. This is important to consider due to the different functional groups likely to be dominant in products formed via autoxidation and I applaud the authors consideration of this. Given the uncertainties associated with the volatility estimation, the method employed in this study is likely good enough, however, as explicit determination of the pathway of formation is impossible, some discussion of this limitation should be added. I also think a sentence describing how H:C and O:C (Fig S6) were used to determine the pathway of formation as well as relevant references in the main text would be helpful.
- I am confused about the assignment of species with C*>100.5 as “non-condensable.” This boundary is within the SVOC VBS range and particularly under the high mass loadings one could see even downwind of urban plumes, it seems species with higher volatilities may contribute substantially to the particle phase. Is this determination specific to the conditions of this study in some way or based on other literature?
- While stated correctly in the text, I think the boundary of the SVOC class is incorrect in Fig 4b. SVOCs should extend to 10-0.5 not 100.5 ug m-3, assuming these are C*(300 K).
References
Buchholz, A., Ylisirniö, A., Huang, W., Mohr, C., Canagaratna, M., Worsnop, D. R., Schobesberger, S., and Virtanen, A.: Deconvolution of FIGAERO–CIMS thermal desorption profiles using positive matrix factorisation to identify chemical and physical processes during particle evaporation, Atmos. Chem. Phys., 20, 7693–7716, https://doi.org/10.5194/acp-20-7693-2020, 2020.
Citation: https://doi.org/10.5194/egusphere-2024-887-RC2 -
AC2: 'Reply on RC2', Bin Yuan, 05 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-887/egusphere-2024-887-AC2-supplement.pdf
-
RC3: 'Comment on egusphere-2024-887', Anonymous Referee #3, 03 Jun 2024
The work by Cai et al. investigates the SOA formation in downwind regions of urban areas, focusing on the PRD region of China in the fall of 2019. The FIGAERO-CIMS was employed to analyze the molecular composition and volatility of organic compounds in both gas and particle phases. The findings highlight significant daytime SOA formation driven by gas-particle partitioning, influenced by urban pollutants such as NOx and volatile organic compounds (VOCs). The paper is well-structured, clearly written, and a valuable contribution to the field of atmospheric sciences, particularly in understanding the dynamics of SOA formation in urban-influenced suburban areas. With the following comments addressed, it would be suitable for publication in ACP.
- The aBBOA factor appears to have a lower O:C and a higher H:C compared to the BBOA factor (Figure S3). This is contrary to what it is expected for aging. This makes me wonder how these PMF factors were exactly assigned. Some explanation will be helpful.
- Line 179-183: There does not seem to be a clear trend between mass loading and Tmax and the calibration mass loading range does not cover the campaign mass loading center (Figure S5). Can the authors explain the rationale of picking the fitting parameters of the experiment with Dp 200 nm and mass loading = 407 ng rather than for example the parameters from fitting all experiments? What is the direction of bias introduced by this choice?
- Line 184-186: it would be helpful to describe how the black line in Figure S6 that differentiates the oxidation pathways was determined in light of existing literature in a sentence or two.
- Line 217-220: These observation data used to constrain F0AM simulations were not mentioned in the instrumentation section of the paper. Are these collocated and published data? Adding a brief description would provide necessary context.
- Line 302: The term “non-condensable” (𝐶∗ >10^0.5 μg m-3) is a bit confusing. These vapors are apparently condensable SVOCs that would partition between gas and particle phases. Is this definition based on specific literature? Clarifying this term would enhance understanding.
- Line 308-313: I wonder if the authors can quantitatively estimate the contribution of the “non-condensable” organic vapors to the total organic aerosol mass to strengthen this point. The saturation vapor concentration for the gas-phase organic vapors have already been estimated. The organic aerosol mass loadings from SP-AMS are available. Then the particle-phase concentrations of these compounds can be calculated based on equilibrium partitioning and compared with the mass that FIGAERO is missing out (mass balance).
Technical corrections:
Line 180: “estimation” should be “estimating”.
Line 390: “ddramatic” should be “dramatic”.
SI Line 63: Figure S7 was mislabeled as Figure S8.
Citation: https://doi.org/10.5194/egusphere-2024-887-RC3 -
AC3: 'Reply on RC3', Bin Yuan, 05 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-887/egusphere-2024-887-AC3-supplement.pdf
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- 1
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Mingfu Cai
Chenshuo Ye
Shan Huang
E Zheng
Suxia Yang
Zelong Wang
Yi Lin
Tiange Li
Weiwei Hu
Wei Chen
Qicong Song
Wei Li
Yuwen Peng
Baolin Liang
Qibin Sun
Duohong Chen
Jiaren Sun
Zhiyong Yang
Min Shao
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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(2051 KB) - Metadata XML
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Supplement
(1599 KB) - BibTeX
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- Final revised paper