the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Jul 2023
Exploring the amplied role of HCHO during the wintertime ozone and PM2.5 pollution in a coastal city of southeast China
Abstract. To develop the effective strategies for controlling both PM2.5 and O3 levels, it is crucial to understand their synergistic mechanisms, key precursors, and atmospheric physiochemical processes involved. In this study, a wintertime co-occurring O3 and PM2.5 pollution event in a coastal city in southeast China was investigated based on high-time resolution measurements of criteria air pollutants, chemical compositions of PM2.5, and O3 precursors, such as NOx, HCHO, and VOCs. The results of this study revealed a positive correlation between PM2.5 and MDA8 O3 concentrations during the whole periods, suggesting an increase in atmospheric oxidation capacity (AOC) during the cold seasons. Strong correlations (R2 = 0.415–0.477) were observed between HCHO, Fe, Mn, and sulfate concentrations, suggesting the influence of catalyzed oxidation processes in the coastal city. Through an observation-based model (OBM) analysis coupled with the regional atmospheric chemistry mechanism version 2 (RACM2) and the chemical aqueous-phase radical mechanism version 3.0 (CAPRAM 3.0), we found that high concentrations of precursors (SO2 and HCHO), high relative humidity, and moderately acidic pH conditions enhanced the heterogeneous formation of hydroxymethanesulfonate (HMS) in PM2.5. Furthermore, by employing the Master Chemical Mechanism (OBM-MCM), we verified that disabling the HCHO mechanism could decrease daytime net O3 production rates by reducing the production rates of HO2+NO. These results were consistent with the daily values of AOC, OH, HO2, and RO2 concentrations. This study contributes to a better understanding of the significance of HCHO in photochemical reactions and the formation of secondary aerosols in a coastal city.
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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-2023-1242', Anonymous Referee #1, 14 Jul 2023
The manuscript by Hong et al. investigated the synergistic mechanisms between fine particulate matter (PM2.5) and surface ozone (O3) in a coastal city of southeast China. Especially, the authors explore the influence mechanism of HCHO on co-occurring O3 and PM2.5 pollution based on the observation-based model (OBM). They employed well established analytical techniques for identification and quantification of HCHO effects on hydroxymethanesulfonate (HMS) and O3 formations. The obtained results are interesting and would be helpful for understanding the role of HCHO in photochemical pollution and secondary aerosol formation. I can recommend its publication in Atmospheric Chemistry and Physics (ACP) after addressing the comments below:
- Line 37-38：the authors mentioned, “suggesting an increase in atmospheric oxidation capacity (AOC) during the cold seasons”. Could you elaborate why or provide more evidence to support it. This is not clear.
- Lines 79 to 90: Additional references about the effects of HCHO on HMS in the introduction are needed.
- Lines 198 to 209: The methodology for HMS modeling needs to be presented in detail.
- Line 348-355: The manuscript focused on the HCHO mechanisms, so it is suggested to discuss more about the effects of HCHO on HMS in PM5.
- Line 313-314：The authors state " As shown in Fig. 3(b) and (c), a good correlation was found between SO42- and Fe and Mn.”. The use of correlations is indeed a helpful tool to explore some specific trends; however, such data processing techniques are not sufficient to reach a definite conclusion on that the TMI-catalyzed oxidation contributed to the formation of SO42-.
Citation: https://doi.org/10.5194/egusphere-2023-1242-RC1 - AC1: 'Reply on RC1', Youwei Hong, 18 Aug 2023
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RC2: 'Comment on egusphere-2023-1242', Anonymous Referee #2, 25 Jul 2023
The authors examined the role of HCHO in the formation of PM2.5 and ozone using integrated measurements in a coastal city of China. They conducted box model runs to illustrate the chemical role of HCHO in PM2.5 formation, ozone, as well as atmospheric oxidation capacity. Overall, I believe the topic of this study fits well with the scope of ACP. The manuscript is also easy-reading. I am happy to see its publication in due course. However, in current version, I think more discussion on HCHO should be added and its role in PM2.5 needs to be further justified. Please find my comments below:
The authors highlight the important role of HCHO in PM2.5, but HMS concentrations are very limited and I am also not convinced that HCHO plays very important role in inorganic sulfate formation. Please justify your argument.
The authors ran box model with and without the HCHO mechanism. But how was this conducted? E.g., disable the HCHO photolysis? The method should be well explained.
I am very interested in where does HCHO come from? Secondary vs primary origin?
How is the HCHO level compared with other studies?
L219-224: is this backward trajectory analysis necessary?
L244: I suggest to replace “might be” with “are”.
In Fig.1, it looks halogenated VOC contributed greatly to TVOCs. What are they coming from? Do you include HCHO concentrations in your TVOCs calculation?
L300-301: “under different periods” refers to EP1 and EP2?
L334: change “HCOH” to “HCHO”
Citation: https://doi.org/10.5194/egusphere-2023-1242-RC2 - AC2: 'Reply on RC2', Youwei Hong, 18 Aug 2023
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RC3: 'Comment on egusphere-2023-1242', Anonymous Referee #3, 03 Aug 2023
Hong et al examine two pollution episodes in Xiamen, China characterized by elevated PM and ozone. They look at the impact of formaldehyde on both, using a box model to demonstrate the role of HCHO on hydroxymethanesulfonate formation in PM2.5 and the importance of HCHO on ozone production. While the authors do a fine job of presenting their results, the main weakness of this paper is the strong dependence on a box model without any attempts at validation or discussion of the potential limitations/uncertainties. Before publication, concerns related to this issue, outlined in more detail below, and other minor points need to be addressed.
Line 85: I am unfamiliar with HMS, as I imagine, will be many of the readers of this paper. More background should be given as to the importance and relevance of this species in the introduction.
Line 106: You introduce the term “observation-based model” analysis here like it is a standard term. You need to explain here that this is the name of the modeling framework that you are using for this study.
Line 117: Since your study is focused on Feb – Mar, isn’t the average RH and T for that time period more relevant than the annual average.
Line 132: Information about the uncertainties of your observations is needed, particularly for formaldehyde.
Line 155: 23:00 local time?
Line 167: What’s the resolution of the ERA reanalysis and how dependent are your results on the accuracy of the PBL height? I would imagine deposition in your model could be quite sensitive to the accuracy of this term.
Line 195: I’m confused as to why you are using boundary layer heights from autumn when your study is based on Feb. – Mar.
Section 2.4: I agree with the other reviewer that more details about this model are needed. I assume this is a box model, although you never say that explicitly. How do you handle NO/NO2 constraints in this model? Are they both constrained to observations and held constant? Is NOx constrained as a family like in other box models (e.g. F0AM, DSMACC)? The handling of NOx will have considerable impact on your discussion of ozone production so more details are required here.
Line 212: To what time-scale are you interpolating?
Line 221: What meteorology are you using and at what resolution?
Line 223: You need to include a citation for cluster analysis.
Line 302: I might have missed it, but I don’t think you ever defined what SOR and NOR mean.
Line 324: More information about the HCHO + H2O2 reaction is needed here. How easily does this reaction happen in ambient air, ie, is there actually significant production of hydroxymethyl hydroperoxide from this reaction? Is this a gas phase or aqueous phase reaction?
Section 3.4: Here is where more investigation is needed. While I realize that you don’t have aqueous phase measurements of HCHO, you need to present some form of evaluation of your model or at the very least an uncertainty analysis to put the accuracy of your results in context. Are there previous studies you can cite that use this model that can reproduce observations of any of the species you are modeling here? How do measurement uncertainties affect your results? If you perturb your input HCHO by its uncertainty, how does that change your aqueous phase HCHO, for example? How is gamma determined in your model? Is it just a set value or is it parameterized in the model somehow? How does uncertainty in gamma affect your results? What’s the uncertainty in the Henry’s law constant for HCHO? Etc.
Figure 4: You need to indicate which of these panels are from observations and which are output from your model.
Line 350: “through” is mis-spelled.
Line 354 and 359: HCHO (aq) and HMS were not directly observed, correct? You only calculate it with your model. Please don’t use the word “observed” when you are talking about model output.
Section 3.5: My comment about model validation extends to this section as well, although it is less concerning given that your results are broadly consistent with other studies.
Line 377: I’m unclear as to what you mean by input HCHO and non-input HCHO. As well as when, in the abstract for example, you say you “disabled the HCHO mechanism”. Are you just not constraining your model to observed HCHO? Are you removing all reactions that produce or remove HCHO from the chemical mechanism? Please explain this more clearly.
Citation: https://doi.org/10.5194/egusphere-2023-1242-RC3 - AC3: 'Reply on RC3', Youwei Hong, 24 Aug 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1242', Anonymous Referee #1, 14 Jul 2023
The manuscript by Hong et al. investigated the synergistic mechanisms between fine particulate matter (PM2.5) and surface ozone (O3) in a coastal city of southeast China. Especially, the authors explore the influence mechanism of HCHO on co-occurring O3 and PM2.5 pollution based on the observation-based model (OBM). They employed well established analytical techniques for identification and quantification of HCHO effects on hydroxymethanesulfonate (HMS) and O3 formations. The obtained results are interesting and would be helpful for understanding the role of HCHO in photochemical pollution and secondary aerosol formation. I can recommend its publication in Atmospheric Chemistry and Physics (ACP) after addressing the comments below:
- Line 37-38：the authors mentioned, “suggesting an increase in atmospheric oxidation capacity (AOC) during the cold seasons”. Could you elaborate why or provide more evidence to support it. This is not clear.
- Lines 79 to 90: Additional references about the effects of HCHO on HMS in the introduction are needed.
- Lines 198 to 209: The methodology for HMS modeling needs to be presented in detail.
- Line 348-355: The manuscript focused on the HCHO mechanisms, so it is suggested to discuss more about the effects of HCHO on HMS in PM5.
- Line 313-314：The authors state " As shown in Fig. 3(b) and (c), a good correlation was found between SO42- and Fe and Mn.”. The use of correlations is indeed a helpful tool to explore some specific trends; however, such data processing techniques are not sufficient to reach a definite conclusion on that the TMI-catalyzed oxidation contributed to the formation of SO42-.
Citation: https://doi.org/10.5194/egusphere-2023-1242-RC1 - AC1: 'Reply on RC1', Youwei Hong, 18 Aug 2023
-
RC2: 'Comment on egusphere-2023-1242', Anonymous Referee #2, 25 Jul 2023
The authors examined the role of HCHO in the formation of PM2.5 and ozone using integrated measurements in a coastal city of China. They conducted box model runs to illustrate the chemical role of HCHO in PM2.5 formation, ozone, as well as atmospheric oxidation capacity. Overall, I believe the topic of this study fits well with the scope of ACP. The manuscript is also easy-reading. I am happy to see its publication in due course. However, in current version, I think more discussion on HCHO should be added and its role in PM2.5 needs to be further justified. Please find my comments below:
The authors highlight the important role of HCHO in PM2.5, but HMS concentrations are very limited and I am also not convinced that HCHO plays very important role in inorganic sulfate formation. Please justify your argument.
The authors ran box model with and without the HCHO mechanism. But how was this conducted? E.g., disable the HCHO photolysis? The method should be well explained.
I am very interested in where does HCHO come from? Secondary vs primary origin?
How is the HCHO level compared with other studies?
L219-224: is this backward trajectory analysis necessary?
L244: I suggest to replace “might be” with “are”.
In Fig.1, it looks halogenated VOC contributed greatly to TVOCs. What are they coming from? Do you include HCHO concentrations in your TVOCs calculation?
L300-301: “under different periods” refers to EP1 and EP2?
L334: change “HCOH” to “HCHO”
Citation: https://doi.org/10.5194/egusphere-2023-1242-RC2 - AC2: 'Reply on RC2', Youwei Hong, 18 Aug 2023
-
RC3: 'Comment on egusphere-2023-1242', Anonymous Referee #3, 03 Aug 2023
Hong et al examine two pollution episodes in Xiamen, China characterized by elevated PM and ozone. They look at the impact of formaldehyde on both, using a box model to demonstrate the role of HCHO on hydroxymethanesulfonate formation in PM2.5 and the importance of HCHO on ozone production. While the authors do a fine job of presenting their results, the main weakness of this paper is the strong dependence on a box model without any attempts at validation or discussion of the potential limitations/uncertainties. Before publication, concerns related to this issue, outlined in more detail below, and other minor points need to be addressed.
Line 85: I am unfamiliar with HMS, as I imagine, will be many of the readers of this paper. More background should be given as to the importance and relevance of this species in the introduction.
Line 106: You introduce the term “observation-based model” analysis here like it is a standard term. You need to explain here that this is the name of the modeling framework that you are using for this study.
Line 117: Since your study is focused on Feb – Mar, isn’t the average RH and T for that time period more relevant than the annual average.
Line 132: Information about the uncertainties of your observations is needed, particularly for formaldehyde.
Line 155: 23:00 local time?
Line 167: What’s the resolution of the ERA reanalysis and how dependent are your results on the accuracy of the PBL height? I would imagine deposition in your model could be quite sensitive to the accuracy of this term.
Line 195: I’m confused as to why you are using boundary layer heights from autumn when your study is based on Feb. – Mar.
Section 2.4: I agree with the other reviewer that more details about this model are needed. I assume this is a box model, although you never say that explicitly. How do you handle NO/NO2 constraints in this model? Are they both constrained to observations and held constant? Is NOx constrained as a family like in other box models (e.g. F0AM, DSMACC)? The handling of NOx will have considerable impact on your discussion of ozone production so more details are required here.
Line 212: To what time-scale are you interpolating?
Line 221: What meteorology are you using and at what resolution?
Line 223: You need to include a citation for cluster analysis.
Line 302: I might have missed it, but I don’t think you ever defined what SOR and NOR mean.
Line 324: More information about the HCHO + H2O2 reaction is needed here. How easily does this reaction happen in ambient air, ie, is there actually significant production of hydroxymethyl hydroperoxide from this reaction? Is this a gas phase or aqueous phase reaction?
Section 3.4: Here is where more investigation is needed. While I realize that you don’t have aqueous phase measurements of HCHO, you need to present some form of evaluation of your model or at the very least an uncertainty analysis to put the accuracy of your results in context. Are there previous studies you can cite that use this model that can reproduce observations of any of the species you are modeling here? How do measurement uncertainties affect your results? If you perturb your input HCHO by its uncertainty, how does that change your aqueous phase HCHO, for example? How is gamma determined in your model? Is it just a set value or is it parameterized in the model somehow? How does uncertainty in gamma affect your results? What’s the uncertainty in the Henry’s law constant for HCHO? Etc.
Figure 4: You need to indicate which of these panels are from observations and which are output from your model.
Line 350: “through” is mis-spelled.
Line 354 and 359: HCHO (aq) and HMS were not directly observed, correct? You only calculate it with your model. Please don’t use the word “observed” when you are talking about model output.
Section 3.5: My comment about model validation extends to this section as well, although it is less concerning given that your results are broadly consistent with other studies.
Line 377: I’m unclear as to what you mean by input HCHO and non-input HCHO. As well as when, in the abstract for example, you say you “disabled the HCHO mechanism”. Are you just not constraining your model to observed HCHO? Are you removing all reactions that produce or remove HCHO from the chemical mechanism? Please explain this more clearly.
Citation: https://doi.org/10.5194/egusphere-2023-1242-RC3 - AC3: 'Reply on RC3', Youwei Hong, 24 Aug 2023
Peer review completion
Journal article(s) based on this preprint
Data sets
Dataset for ACP by Hong et al., 2023 Youwei Hong https://doi.org/10.5281/zenodo.7799302
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- 1
- 165
Youwei Hong
Keran Zhang
Dan Liao
Gaojie Chen
Min Zhao
Yiling Lin
Xiaoting Ji
Ke Xu
Yu Wu
Ruilian Yu
Gongren Hu
Sung-Deuk Choi
Likun Xue
Jinsheng Chen
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2210 KB) - Metadata XML
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Supplement
(1754 KB) - BibTeX
- EndNote
- Final revised paper