the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Sep 2023
Variation in chemical composition and volatility of oxygenated organic aerosol in different rural, urban, and remote environments
Abstract. The apparent volatility of atmospheric organic aerosol (OA) particles is determined by their chemical composition and environmental conditions (e.g., ambient temperature). A quantitative, experimental assessment of volatility and the respective importance of these two factors remains challenging, especially in ambient measurements. We present molecular composition and volatility of oxygenated OA (OOA) particles in different rural, urban, and remote environments across the globe (including Chacaltaya, Bolivia; Alabama, U.S.; Hyytiälä, Finland; Stuttgart and Karlsruhe, Germany; and Delhi, India) based on deployments of a filter inlet for gases and aerosols coupled to a high-resolution time-of-flight chemical ionization mass spectrometer (FIGAERO-CIMS). We find on average larger carbon numbers (nC) and lower oxygen-to-carbon (O:C) ratios at the urban sites (nC: 9.8±0.7; O:C: 0.76±0.03; average ± 1 standard deviation), compared to the rural (nC: 8.8±0.6; O:C: 0.80±0.05) and remote mountain stations (nC: 8.1±0.8; O:C: 0.91±0.07), indicative of different emission sources and chemistry. Compounds containing only carbon, hydrogen, and oxygen atoms (CHO) contribute highest to the total OOA mass at the rural sites (79.9±5.2 %), in accordance with their proximity to forested areas (66.2±5.5 % at the mountain sites and 72.6±4.3 % at the urban sites). The largest contribution of nitrogen-containing compounds (CHON) are found at the urban stations (27.1±4.3 %), consistent with their higher NOx levels. Besides, we parametrize OOA volatility (saturation mass concentrations, Csat) using molecular composition information and compare it with the bulk apparent volatility derived from thermal desorption of the OOA particles within the FIGAERO. We find differences in Csat values of up to ~3 orders of magnitude, and variation in thermal desorption profiles (thermograms) across different locations and systems. From our study, we draw the general conclusion that environmental conditions (e.g., ambient temperature) do not directly affect OOA apparent volatility, but rather indirectly by influencing the sources and chemistry of the environment and thus the chemical composition. The comprehensive global dataset provides results that show the complex thermodynamics and chemistry of OOA and their changes during its lifetime in the atmosphere, and that generally the chemical description of OOA suffices to predict its apparent volatility, at least qualitatively. Our study thus provides new insights that will help guide choices of e.g. descriptions of OOA volatility in different model frameworks which has been previously simplified due to challenges to measure and represent it in models.
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Interactive discussion
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RC1: 'Comment on egusphere-2023-1821', Anonymous Referee #1, 02 Oct 2023
Organic aerosols are a major contributor to total aerosol mass concentrations and have implications for both human health and climate change. However, the formation of these aerosols is a complex supersaturation-driven process, involving highly dynamic vapor-particle interactions. Therefore, constraining the volatility of condensable vapors and the associated particles is critical for understanding the underlying oxidative chemistry and for better representation of organic aerosols in air quality models.
This paper presents data from ambient measurements of the chemical composition and thermogram of organic aerosols in various environments using the online and offline FIGAERO-CIMS methods. In addition, the authors estimated the particle volatility using a volatility parameterization and compared it with the thermal desorption profile in the lumped thermogram. The research topic of this paper is novel, the dataset is comprehensive, and the measurement techniques are state-of-the-art. Overall, this is a relevant study that fits within the scope of the ACP. However, the way the results are interpreted and discussed needs major revision to improve scientific rigor and to make it clearer to non-specialist readers. Here are my major comments:
- While the dataset is comprehensive and covers various environments, I’m not convinced that it does not have a global representation. I would suggest that the authors remove text such as “across the globe” and “global dataset”.
- Volatility calculation: It is not clear whether the authors used one parameterization for all data, or different parameterizations for data in different environments (or rather, for different types of compounds). If it’s the former, I would suggest that the authors redo some of the calculations, because molecular formulas with the same number of carbon and oxygen atoms can have very different volatilities due to different functionalities (e.g., -OH and -OOH reduce volatility by the same amount). This could be a source of discrepancy between the calculated volatility and the thermal desorption profile. The authors can refer to this paper for further information (https://doi.org/10.1038/s41561-022-00922-5).
- Carbon number analysis: The author did not justify that nC alone can tell us much about the emission source, especially when comparing data from different environments. For example, nC for the urban sites is greater than that for the rural sites, but then nC for the MCC-d (anthropogenic influence) is less than that for the MCC-t (biogenic influence). Actually, I don’t really see a pattern in the nC analysis. I would suggest that the authors condense the discussion and focus more on the oxidation state.
- Mass contribution of organonitrates: Lacking explanations. Why do remote mountain sites have such a high CHON fraction? I don’t think “in accordance with their proximity to forested areas” explains it. And why “possibly due to the less efficient production rates of CHON”?
- Tmax: “given the large spread in Tmax for …or over a whole campaign.” I understand that the authors couldn’t quantify the discrepancies between different instruments and operating conditions, but I think the authors should at least discuss how much uncertainty is associated with these discrepancies since the authors are doing the intercomparison after all.
- Thermal decomposition: “the decomposition fraction is estimated to be 5.8–35.9%”, and “The discrepancy could be due to thermal fragmentation of larger oligomeric molecules, which bias the Csat results towards higher volatilities and the sum thermogram shape to a lesser extent due to the dominance of monomer species”. Then how does this affect the volatility calculation using the fragmented formulas? To what extent should we trust the calculated volatility? Need more discussion here.
- In my opinion, some of the conclusions are too strong and lack supporting information. For example:
“we achieve a comprehensive picture of the relationship between volatility and chemical composition of OOA particles”, what is the exact relationship?
“however, the effects on the bulk molecular composition and sum thermograms of all detected OOA compounds are small as these thermally-unstable oligomers do not dominate the OOA mass.” I would suggest the authors reword it because 35.9% is not small.
“and that environmental conditions (e.g., ambient temperature) play a lesser, secondary role through their influence on sources and chemistry of a particular environment,” I don’t think any strong conclusions can be drawn about source and chemistry, because there are no analysis of source apportionment and oxidative chemistry.
“Our study thus provides new insights that will help guide choices of e.g. descriptions of OOA volatility in different model frameworks” The authors would need to explain more about this.
Citation: https://doi.org/10.5194/egusphere-2023-1821-RC1 -
RC2: 'Comment on egusphere-2023-1821', Anonymous Referee #2, 04 Oct 2023
General comment
The authors present evaluations of a combination of aerosol field data taken in 5 different regions of the world (India, Germany, Bolivia, USA, Finland). The central instrumentation is FIGAERO-CIMS, a method often applied in field and laboratory studies. Some seasonal aspects are addressed for the Bolivian and German data sets.
The focus is on comparison of campaign averages for vapor pressures / volatility in relation to particle composition and some other atmospheric parameters. The data set the paper is based on represents a lot of work and effort and is quite impressive.
The manuscript is well written and well organized. The presented material is well chosen and suited to support the discussions and results presented in the manuscript. The manuscript is interesting to read in that presents some critical aspects of vapor pressure and volatility determinations.
The difficulty of the manuscript lies in selection of observations (sites). I believe that they are too singular in time and space to conclude something from the comparison with respect to particle properties in the atmosphere. (I understand that such observations are limited.) This prevents conclusions but very general ones. That is probably the reason why the authors focus more on the methodological aspects. However, whenever they found something interesting, which may be related to atmospheric processes, they step back and question the relations by referring to the experimental difficulties and operational aspects of FIGAERO measurements. The best indication is the statement on page 12 beginning in line 387 and ending in line 398.
And I am not sure if the results support the conclusion that just more efforts (“alternative approaches”) are needed “for more quantitative estimations of volatility from FIGAERO-CIMS measurements” (line 534f). Overall, I would say the conclusions are bit weak regarding the atmospheric aspects.
I would still suggest to publishing the paper in ACP as it addresses important aspects and limits of FIGAERO approaches, which should be realized by a broader community. I suggest that authors should address the minor aspects below.
Minor comments
I would say that “volatility” of/in a mixture depends on the chemical composition, i.e. on the vapor pressure of the components, and the physical conditions, mainly the temperature. Translated to atmospheric situations this means that chemical composition depends on emissions and the atmospheric chemistry on the way to the observation point and the physical conditions depend on the let’s say the (local) meteorology. Since you are looking at campaign averages (“bulk apparent volatility of OOA particles”) you are looking at a kind of a systemic property of aerosol particles, but what you are searching for is still the physical aspects of vapor/pressure of an ensemble of compounds. However, in re-constructing the systemic volatility from the individual components, one is a priori limited by the mass spectrometric approach, which can give (here) chemical formulas at best, and the limits of vapor pressure information for the individual compounds or detected formulas. (On top the operational aspects of FIGAERO measurements.)
Could it be that what you tried in this manuscript is inherently an impossible task? A way out could be to drive everything empirically and relate the observations to classes of conditions. However, for that the presented data set is too particulate. Could you explain or justify explicitly your approach using campaign averages?
Independently, I am asking myself when the use of campaign averages make sense. Naively, I would say if you had a bimodal distribution of conditions for example, then the campaign average cannot be observed by measurement. This would be different for a simple monomodal distribution of conditions where is a certain chance to indeed observe the campaign average. Can you comment on that?
line 385: Please can you shortly explain in the experimental sections what sum thermograms are, for non- FIGAERO users. What is exactly is summed in a sum thermogram?
line 399- 431: Shouldn’t sumTmax tell us something about the persistency of the particles, when they are moving out of the source region?
line 432-448: If I understand correctly, this questions the approach using Li et al. vapor pressure parametrization. If so, that should be mentioned.
line 456f: Here you show something interesting, but you discuss it away. If you don’t trust the finding, why mentioning it?
Figure 2: Please, take out the legend from the figure. It is hiding information. Could you tabulate the values of log10Csat in Table S1?
Figures S4 and S8: It could be helpful to correlate log(Csat) and sumTmax also with the campaign averages of the OA mass concentrations. For ideal mixtures, those determine the critical threshold which "vapor pressures" are sufficient for a compound to remain in the condensed phase. And that should be related to the bulk apparent volatility of OOA particles. The data look like a correlation and if so, that should be mentioned in the main manuscript.
Citation: https://doi.org/10.5194/egusphere-2023-1821-RC2 - AC1: 'Response to all referee comments on egusphere-2023-1821', Wei Huang, 21 Dec 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1821', Anonymous Referee #1, 02 Oct 2023
Organic aerosols are a major contributor to total aerosol mass concentrations and have implications for both human health and climate change. However, the formation of these aerosols is a complex supersaturation-driven process, involving highly dynamic vapor-particle interactions. Therefore, constraining the volatility of condensable vapors and the associated particles is critical for understanding the underlying oxidative chemistry and for better representation of organic aerosols in air quality models.
This paper presents data from ambient measurements of the chemical composition and thermogram of organic aerosols in various environments using the online and offline FIGAERO-CIMS methods. In addition, the authors estimated the particle volatility using a volatility parameterization and compared it with the thermal desorption profile in the lumped thermogram. The research topic of this paper is novel, the dataset is comprehensive, and the measurement techniques are state-of-the-art. Overall, this is a relevant study that fits within the scope of the ACP. However, the way the results are interpreted and discussed needs major revision to improve scientific rigor and to make it clearer to non-specialist readers. Here are my major comments:
- While the dataset is comprehensive and covers various environments, I’m not convinced that it does not have a global representation. I would suggest that the authors remove text such as “across the globe” and “global dataset”.
- Volatility calculation: It is not clear whether the authors used one parameterization for all data, or different parameterizations for data in different environments (or rather, for different types of compounds). If it’s the former, I would suggest that the authors redo some of the calculations, because molecular formulas with the same number of carbon and oxygen atoms can have very different volatilities due to different functionalities (e.g., -OH and -OOH reduce volatility by the same amount). This could be a source of discrepancy between the calculated volatility and the thermal desorption profile. The authors can refer to this paper for further information (https://doi.org/10.1038/s41561-022-00922-5).
- Carbon number analysis: The author did not justify that nC alone can tell us much about the emission source, especially when comparing data from different environments. For example, nC for the urban sites is greater than that for the rural sites, but then nC for the MCC-d (anthropogenic influence) is less than that for the MCC-t (biogenic influence). Actually, I don’t really see a pattern in the nC analysis. I would suggest that the authors condense the discussion and focus more on the oxidation state.
- Mass contribution of organonitrates: Lacking explanations. Why do remote mountain sites have such a high CHON fraction? I don’t think “in accordance with their proximity to forested areas” explains it. And why “possibly due to the less efficient production rates of CHON”?
- Tmax: “given the large spread in Tmax for …or over a whole campaign.” I understand that the authors couldn’t quantify the discrepancies between different instruments and operating conditions, but I think the authors should at least discuss how much uncertainty is associated with these discrepancies since the authors are doing the intercomparison after all.
- Thermal decomposition: “the decomposition fraction is estimated to be 5.8–35.9%”, and “The discrepancy could be due to thermal fragmentation of larger oligomeric molecules, which bias the Csat results towards higher volatilities and the sum thermogram shape to a lesser extent due to the dominance of monomer species”. Then how does this affect the volatility calculation using the fragmented formulas? To what extent should we trust the calculated volatility? Need more discussion here.
- In my opinion, some of the conclusions are too strong and lack supporting information. For example:
“we achieve a comprehensive picture of the relationship between volatility and chemical composition of OOA particles”, what is the exact relationship?
“however, the effects on the bulk molecular composition and sum thermograms of all detected OOA compounds are small as these thermally-unstable oligomers do not dominate the OOA mass.” I would suggest the authors reword it because 35.9% is not small.
“and that environmental conditions (e.g., ambient temperature) play a lesser, secondary role through their influence on sources and chemistry of a particular environment,” I don’t think any strong conclusions can be drawn about source and chemistry, because there are no analysis of source apportionment and oxidative chemistry.
“Our study thus provides new insights that will help guide choices of e.g. descriptions of OOA volatility in different model frameworks” The authors would need to explain more about this.
Citation: https://doi.org/10.5194/egusphere-2023-1821-RC1 -
RC2: 'Comment on egusphere-2023-1821', Anonymous Referee #2, 04 Oct 2023
General comment
The authors present evaluations of a combination of aerosol field data taken in 5 different regions of the world (India, Germany, Bolivia, USA, Finland). The central instrumentation is FIGAERO-CIMS, a method often applied in field and laboratory studies. Some seasonal aspects are addressed for the Bolivian and German data sets.
The focus is on comparison of campaign averages for vapor pressures / volatility in relation to particle composition and some other atmospheric parameters. The data set the paper is based on represents a lot of work and effort and is quite impressive.
The manuscript is well written and well organized. The presented material is well chosen and suited to support the discussions and results presented in the manuscript. The manuscript is interesting to read in that presents some critical aspects of vapor pressure and volatility determinations.
The difficulty of the manuscript lies in selection of observations (sites). I believe that they are too singular in time and space to conclude something from the comparison with respect to particle properties in the atmosphere. (I understand that such observations are limited.) This prevents conclusions but very general ones. That is probably the reason why the authors focus more on the methodological aspects. However, whenever they found something interesting, which may be related to atmospheric processes, they step back and question the relations by referring to the experimental difficulties and operational aspects of FIGAERO measurements. The best indication is the statement on page 12 beginning in line 387 and ending in line 398.
And I am not sure if the results support the conclusion that just more efforts (“alternative approaches”) are needed “for more quantitative estimations of volatility from FIGAERO-CIMS measurements” (line 534f). Overall, I would say the conclusions are bit weak regarding the atmospheric aspects.
I would still suggest to publishing the paper in ACP as it addresses important aspects and limits of FIGAERO approaches, which should be realized by a broader community. I suggest that authors should address the minor aspects below.
Minor comments
I would say that “volatility” of/in a mixture depends on the chemical composition, i.e. on the vapor pressure of the components, and the physical conditions, mainly the temperature. Translated to atmospheric situations this means that chemical composition depends on emissions and the atmospheric chemistry on the way to the observation point and the physical conditions depend on the let’s say the (local) meteorology. Since you are looking at campaign averages (“bulk apparent volatility of OOA particles”) you are looking at a kind of a systemic property of aerosol particles, but what you are searching for is still the physical aspects of vapor/pressure of an ensemble of compounds. However, in re-constructing the systemic volatility from the individual components, one is a priori limited by the mass spectrometric approach, which can give (here) chemical formulas at best, and the limits of vapor pressure information for the individual compounds or detected formulas. (On top the operational aspects of FIGAERO measurements.)
Could it be that what you tried in this manuscript is inherently an impossible task? A way out could be to drive everything empirically and relate the observations to classes of conditions. However, for that the presented data set is too particulate. Could you explain or justify explicitly your approach using campaign averages?
Independently, I am asking myself when the use of campaign averages make sense. Naively, I would say if you had a bimodal distribution of conditions for example, then the campaign average cannot be observed by measurement. This would be different for a simple monomodal distribution of conditions where is a certain chance to indeed observe the campaign average. Can you comment on that?
line 385: Please can you shortly explain in the experimental sections what sum thermograms are, for non- FIGAERO users. What is exactly is summed in a sum thermogram?
line 399- 431: Shouldn’t sumTmax tell us something about the persistency of the particles, when they are moving out of the source region?
line 432-448: If I understand correctly, this questions the approach using Li et al. vapor pressure parametrization. If so, that should be mentioned.
line 456f: Here you show something interesting, but you discuss it away. If you don’t trust the finding, why mentioning it?
Figure 2: Please, take out the legend from the figure. It is hiding information. Could you tabulate the values of log10Csat in Table S1?
Figures S4 and S8: It could be helpful to correlate log(Csat) and sumTmax also with the campaign averages of the OA mass concentrations. For ideal mixtures, those determine the critical threshold which "vapor pressures" are sufficient for a compound to remain in the condensed phase. And that should be related to the bulk apparent volatility of OOA particles. The data look like a correlation and if so, that should be mentioned in the main manuscript.
Citation: https://doi.org/10.5194/egusphere-2023-1821-RC2 - AC1: 'Response to all referee comments on egusphere-2023-1821', Wei Huang, 21 Dec 2023
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Cited
2 citations as recorded by crossref.
- Nighttime NO emissions strongly suppress chlorine and nitrate radical formation during the winter in Delhi S. Haslett et al. 10.5194/acp-23-9023-2023
- Variation in chemical composition and volatility of oxygenated organic aerosol in different rural, urban, and mountain environments W. Huang et al. 10.5194/acp-24-2607-2024
Wei Huang
Cheng Wu
Linyu Gao
Yvette Gramlich
Sophie L. Haslett
Joel Thornton
Felipe D. Lopez-Hilfiker
Ben H. Lee
Junwei Song
Harald Saathoff
Xiaoli Shen
Ramakrishna Ramisetty
Sachchida N. Tripathi
Dilip Ganguly
Feng Jiang
Magdalena Vallon
Siegfried Schobesberger
Taina Yli-Juuti
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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