From the journal Environmental Science: Atmospheres Peer review history

20-Dec-2022

Dear Dr Deguillaume:

Manuscript ID: EA-ART-11-2022-000153
TITLE: Assessing the efficiency of water-soluble organic compounds biodegradation in clouds under various environmental conditions

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Reviewer 1

This study examines the role of microbial degradation of four water-soluble compounds, acetic acid, formic acid, formaldehyde, and hydrogen peroxide, in cloud chemistry under various environmental conditions. The authors conducted biodegradation experiments with three bacterial strains representing the commonly found genera in clouds to determine the biodegradation rates of target compounds of interest in artificial cloud water. Further, the authors performed cloud model simulations with the implementation of measured biodegradation rates in the model. Overall, I think this manuscript is well-written, and the conclusions are supported by the results. I have a few minor questions and comments about this manuscript, which I hope will be addressed before it is published.

Comments
Line 115-120: It looks like cloud acidity and pH are treated as different environmental parameters. What is the main difference between them in this context?
Line 135-138, S2:
1) For biodegradation experiments, the artificial cloud solutions were adjusted to pH 6, while cloud model simulations were performed at pH 5.5 and pH 4.5. Do changes in cloud pH affect microbial metabolic activity and biodegradation rates? Would the biodegradation rates determined at pH 6 then be applicable to these simulations?
2) Cloud pH may also have an impact on the distribution of acids and conjugated bases (i.e., protonated and deprotonated forms of acids). Would the biodegradation rates be different for different forms of chemicals?
Line 151-159: I could have missed this. Were the data for substrate stability presented somewhere?
Line 179-180: I am a bit confused about the statement saying that “H+ was produced explicitly and consumed in acid-base reaction.” Could the authors please clarify this?

Reviewer 2

Reviewer comments regarding manuscript "Assessing the efficiency of water-soluble organic compounds biodegradation in clouds under various environmental conditions " in the journal Environmental Science: Atmospheres with manuscript ID: EA-ART-11-2022-000153.
The present study is undoubtedly an interesting investigation, and the authors have used state-of-the-art methods to collect a valuable data set that shows a successful combination of laboratory results and model calculations.
I have some comments: Can the authors explain why the biodegradation of acetic acid is not concentration dependent as it is for the other compounds? Can the authors describe how they determine the biodegradation rate error, e.g., in ESI. How could the measurement be improved to reduce the biodegradation rate error? As far as I can see, there is something wrong with the colors of the legend in Figure 3 for the winter case, e.g. formic acid and acetic acid compared to formaldehyde.
On page 22, on line 27, please remove the % of 81%.
In the supporting information:
On page 2 in table SM1, center "pH 6.0".
On page 3 in table SM2, please check the significant digits for the given numbers.
On page 14 in Table SM5, the "y =" is missing in the H2O2 column.
On page 13 in Figure SM4 d, the y-axis label is cut out.

Reviewer 3

General
This is a study combining experimental an modeling work of the degradation of four model compounds by microorganisms which may take place in aqueous atmospheric systems.

The paper is interesting as the biodegradation rates are carrie in a substrate-dependent manner and not as constant rates. It would be good to clarify this even more.

All in all , this is a nice modelling study in the scope of the journal but requires revisions.

I would like to advise the authors to remove all self-judgements of their study from the text such as line 33 'originality' or similar.

Details

Abstract
I do not understand the second sentence of the abstract, i.a. 'To achieve this objective, we measured the rates of biodegradation for four chemical compounds of interest in atmospheric chemistry (formic and acetic acids, formaldehyde and hydrogen peroxide) in an explicit model of cloud chemistry by simulating the exchange processes between air, droplets and chemical reactivity in both phases.' - What does that mean ?? There were measurments, yes, but surely not 'in a model' - so please clean up this sentence.

Other: The abstract could mak use of more numbers, it sjpuld really reflect the most important quantitative finding of this study.

Line 62: This discussion goes on for long.

LIne 80: I am surprised to see the statement that Fankhauser et al, ref 22 (2019) should have been 'the first study' where biotic degradations were implmented in parallel to abiotic degradation steps. What about previous work fm Clermont-Ferrant, especially work led by Pr. Chaumerliac who is an author on this present submission ? Maybe you mean 'a recent study ' ?

Line 139ff: Why exactly is there such a big difference of a factor of 100 in the number of <i>Pseudomonas</i> per volume for formic & acteic acis vs. H2O2 ?

Line 145ff: And, are you now following what study #18 suggests ?

Line 160ff: What about previous Clermont-Ferrant models - is CLEPS the latest mechanism ?

LIne 200ff: These findings: 'At the end of the spinup, we noticed that, as expected, the concentrations of oxidants were higher in the summer than in the winter. The HO• and H2O2 concentrations were much lower for nighttime (for example, in summer,
H2O2 was 21-fold lower and 100-fold lower for HO• ).' are what is expected. It would be more interesting to compare the HOx levels obtained with the results of other studies / models in order to place them in scientific context.

Figure 1: This Figure does not contain much information, it could be moved to the SI.

Line 231 ff: Section 2.3: What is really new here ? Isn't this the treatment you would expect to be used for the biodegradation reactions ? Maybe you can detail a bit more ?

LIne 321: Compare the OH and HO2 levels with no biodegradation implemented, this is missing somehow.

Note, continous line numbering ended in my file with the end of page 20.

Page 29, line 9: It is written 'The biodegradation of the organic species investigated herein competed with biotic degradation' but I think that should be '...abiotic degradation.' instead at the end of sentence. Please check. Also check all the manuscript for issues like this.

Reviewer 4

Pailler et al. address the importance of the biodegradation of water-soluble organic compounds in cloud droplets. Their methodology is sound and their findings are presented in a good manner. The manuscript provides a significant contribution to the field and improved the understanding of biotic processes in clouds. I suggest accepting this manuscript after the authors address the following comments.

Major comments:

Within warm clouds, formaldehyde undergoes fast hydration, leading to methanediol. Recently, Franco et al. 2021 identified that methanediol undergoes fast outgassing but slow dehydration. Additionally, its gas phase oxidation becomes a major source of formic acid. I noticed that the authors do not consider the phase transfer of methanediol in their mechanism. Within this study, the authors are investigating both formaldehyde and formic acid. Neglecting this pathway will surely influence the presented budget analysis and will influence their conclusions on the relative importance of biodegradation within clouds (e.g., Figure 5). I strongly recommend that the authors implement the before mentioned pathway and redo their analysis. This will also allow the authors to identify how sensitive their findings are to uncertainties in the chemical mechanism.

The authors discuss the sensitivity of the liquid water content (lwc) to their findings. Here, they vary the lwc in a range observed at PUY. In addition to the lwc, the phase transfer is inherently dependent on the radius of the cloud droplet (Schwartz, 1986). By lowering the lwc, I would also expect that the cloud droplet radius decreases. By assuming that the droplet radius is the same for both lwc, the authors findings might be biased with respect to the changes induced by phase transfer. I suggest that the authors redo their analysis by also adapting the droplet radius in order to represent realistic cloud droplets.

The conclusion section of the manuscript feels more like a summary of the work performed. The authors state that they observe a global increase in concentrations at the end of their simulation (Page 27, Line 30 to 31). The manuscript would greatly benefit from a discussion putting their findings into the broader context. For example: Which regions do the authors expect to be most impact by this process? Do the authors expect that the discussed process and the related changes in acid concentrations in cloud droplets, influence the overall cloud acidity? When implemented into a regional or global atmospheric chemistry model, could this resolve some of the underprediction observed in total formic acid columns over boreal forests, when compared to satellite observations?

Overall, the manuscript is well written. However, the authors tend to refer more to figures and tables included in the supplemental material. I suggest that the authors perform an editorial review before resubmitting the manuscript in order to improve its readability in this regard.

Minor comments:

1. Page 8, Line 214: Earlier you mentioned that within the model, the pH may evolve dynamically. Now you use a fixed pH values. Just to clarify. This also means that H+ is fixed and does not dynamically evolve?
2. Page 9, Line 236: How sensitive do you expect your results to be, considering assuming a 50/50 ratio? Please elaborate.
3. Page 12, Line 311 to 315: Would this mean that you expect your results to be biased high? If so, to which degree?
4. Figure 3: In this figure, different scales are used for night and daytime within the same subplot. The reader might get the impression that night and daytime concentrations might be comparable. At least mention this in the caption, in order to avoid confusion. Why not use a), b), c) labels as done in figure 6?
5. Page 15, Line 382 to 384: This statement should be moved to the methodology section.
6. Figure 4: Same comment as for figure 3.
7. Figure 6 and text: Within cloud droplets, I would expect that formaldehyde is mainly present in its hydrated form (methandiol). When talking about the total concentrations, do you also consider the hydrated form of the respective compound for this sum?

References:

Franco, B., Blumenstock, T., Cho, C. et al. Ubiquitous atmospheric production of organic acids mediated by cloud droplets. Nature 593, 233–237 (2021). https://doi.org/10.1038/s41586-021-03462-x

Schwartz, S. E.: Mass-Transport Considerations Pertinent to Aqueous Phase Reactions of Gases in Liquid-Water Clouds, in: Chemistry of Multiphase Atmospheric Systems, edited by: Jaeschke, W., Springer Berlin Heidelberg, Berlin, Heidelberg, 415–471, 1986.

This text has been copied from the PDF response to reviewers and does not include any figures, images or special characters.

Referee: 1

Comments to the Author

This study examines the role of microbial degradation of four water-soluble compounds, acetic acid, formic acid, formaldehyde, and hydrogen peroxide, in cloud chemistry under various environmental conditions. The authors conducted biodegradation experiments with three bacterial strains representing the commonly found genera in clouds to determine the biodegradation rates of target compounds of interest in artificial cloud water. Further, the authors performed cloud model simulations with the implementation of measured biodegradation rates in the model. Overall, I think this manuscript is well-written, and the conclusions are supported by the results.

I have a few minor questions and comments about this manuscript, which I hope will be addressed before it is published.
We would like to thank the reviewer for their time spent to review the paper. We considered all the proposed comments/corrections; our answers are indicated in blue in the following.

Comments

Line 115-120: It looks like cloud acidity and pH are treated as different environmental parameters. What is the main difference between them in this context?
Line 135, we mention “cloud acidity” as a key parameter in the transformation processes of chemical compounds in the cloud. The aqueous phase acidity is calculated by the model at each time step of the simulations by evaluating the concentration of the H+ ion which is controlled by the acid-base equilibria. We decided to keep the pH in our simulations at constant values (5.5 and 4.5) to perform sensitivity tests.
Line 137: we refer in a broad sense to the fact that the biological activity and viability of microorganisms in the water droplets considered in the model is not modulated by the cloud aqueous phase acidity (i.e., pH).
Thus, “pH” and “cloud acidity” represent the same environmental parameter. To avoid confusion, we have replaced line 135 "Cloud acidity" with "pH".

Line 135-138, S2:
1) For biodegradation experiments, the artificial cloud solutions were adjusted to pH 6, while cloud model simulations were performed at pH 5.5 and pH 4.5. Do changes in cloud pH affect microbial metabolic activity and biodegradation rates? Would the biodegradation rates determined at pH 6 then be applicable to these simulations?
In clouds, water acidity is a stressor that bacteria are subjected to. pH of the atmospheric aqueous phase can vary between 3 and 6 (Pye et al., 2020) with global mean around 5.2 (Shah et al., 2020). In this study, we decided to change the pH from 5.5 to 4.5 that are values commonly measured in clouds sampled at the puy de Dôme station, under remote condition (Deguillaume et al., 2014).
Bacteria are able to regulate their intracellular pH; this is achieved through control over cation (and anion) permeability. This involves the transfer of for example K+, Na+ and H+ through the membrane, thus facilitating the survival and metabolic activity of the cell after exposure to toxic electrophiles (Guan and Liu, 2020). For example, strains can export protons from their cytoplasm to cope with pH stress (Krulwich et al., 2011).
Even if we know that bacteria metabolic activity can be impacted by the acidity, there are limited studies on this topic, especially for microorganisms in clouds. Some studies have shown that the biological composition of the cloud microflora was influenced by the cloud water pH (Peng et al., 2019); the study from Vaïtilingom et al. (2013) on real cloud waters presenting different pH (“marine” cloud with pH around 6 and “polluted” cloud with pH around 4) indicated that biodegradation rates were slightly less efficient for more acidic condition.
The work from Vaïtilingom et al. (2011) measured biodegradation rates of acetate and formate in two different incubation media presenting different pH conditions (6-6.5 and 4.7-5.2). They selected strains were isolated from cloud water; those are the same strains used in the present study. Their results showed that 55 % of the measured biodegradation rates per cell were higher at less acidic condition. However, no significant difference of the average biodegradation rate per cell of a given compound between these two pH conditions was observed.
Recently, the work from Liu et al. (2023) is the most detailed study investigating the role of acidity on microorganisms’ survival and activity. They analyzed the survival of microorganisms and their ability to biodegrade organic compounds in microcosms, at 25°C, containing artificial cloud water mimicking (like our study) the pH and chemical composition of atmospheric waters. Two Enterobacter bacterial strains isolated from an aerosol sample in Hong Kong were used as model bacteria. These bacteria strains are pathogenic and originate from anthropogenic activities. They showed that, for pH>5, minimal effect was observed in the energetic metabolism and survival of the 2 strains. At pH between 4 and 5, the energetic metabolism and survival can be negatively impacted only when the microcosms are irradiated with light (combined effect). At pH below 4 (representative of polluted fog for example), low survival of the strains was measured.
This study is interesting but presents some limitations. First, this work is performed with strains isolated from an aerosol sample and these lab experiments should be extended to bacteria species that are commonly reported in cloud waters (Shingomonas, Pseudomonas strains, for instance). All the experiments should also be performed at temperature more representative of warm clouds. Additionally, the combination of stressors (T, actinic flux, pH, concentration of oxidants) has to be studied to analyze their synergetic effects on bacteria survival and on their ability to biodegrade organic matter.

At this moment, based on the data of this study, it seems to our opinion difficult to modulate with the pH the biodegradation rates we quantified in our study. The incubation experiments we performed in the lab at different temperature and at different substrate concentration should also be done in artificial medium at different pH. This allows to correctly modulate the biodegradation rates with the pH.
For the simulations performed at pH 4.5, we can argue that biodegradation rates measured at less acidic pH can be lower than predicted; this is true. So, the effect of biodegradation for our sensitivity tests performed at pH 4.5 may be overestimated. So, page 27, line 2, we suppressed the sentence “this assumption is realistic since cloud microorganisms can withstand a high range of pH.” and we added in the text the following paragraph to discuss this point:

“This approximation is questionable since bacterial metabolic activity could be modified by the acidity of the cloud aqueous phase. Recently, the work from Liu et al. (2023) investigated the role of acidity on microorganisms’ survival and activity. They showed that, for pH>5, minimal effect was observed in the energetic metabolism and survival of the 2 studied strains (Enterobacter). However, at pH between 4 and 5, the energetic metabolism and survival can be negatively impacted especially under light exposure. This study is of particular interest since few studies investigated how biodegradation rates are impacted by the pH. However, the incubation experiments were performed with strains isolated from an aerosol sample and these investigations should be extended to bacteria species that are commonly reported in cloud waters. At this moment, it seems critical, based on the results from Liu et al. (2023), to modulate with the pH our biodegradation rates. Nevertheless, we are aware that, by decreasing the pH from 5.5 to 4.5, biodegradation rates can be lower than predicted. So, the effect of biodegradation for our sensitivity tests performed at pH 4.5 is surely overestimated and results must be analyzed with caution.”.

References:
H. O. Pye, A. Nenes, B. Alexander, A. P. Ault, M. C. Barth, S. L. Clegg, J. L. Collett Jr, K. M. Fahey, C. J. Hennigan, and H. Herrmann, The acidity of atmospheric particles and clouds, Atmospheric Chemistry and Physics, 2020, 20, 4809-4888.
V. Shah, D. J. Jacob, J. M. Moch, X. Wang and S. Zhai, Global modeling of cloud water acidity, precipitation acidity, and acid inputs to ecosystems, Atmospheric Chemistry and Physics, 2020, 20, 12223-12245.
L. Deguillaume, T. Charbouillot, M. Joly, M. Vaïtilingom, M. Parazols, A. Marinoni, P. Amato, A. M. Delort, V. Vinatier, A. Flossmann, N. Chaumerliac, J. M. Pichon, S. Houdier, P. Laj, K. Sellegri, A. Colomb, M. Brigante and G. Mailhot, Classification of clouds sampled at the puy de Dôme (France) based on 10 yr of monitoring of their physicochemical properties, Atmospheric Chemistry and Physics, 2014, 14, 1485-1506.
N. Guan and L. Liu, Microbial response to acid stress: mechanisms and applications, Applied Microbiology and Biotechnology, 2020, 104, 51-65.
T. A. Krulwich, G. Sachs, and E. Padan, Molecular aspects of bacterial pH sensing and homeostasis, Nature Reviews Microbiology, 2011, 9, 330-343.
J. Peng, S. Zhou, K. Xiao, J. Zeng, C. Yao, S. Lu, W. Zhang, Y. Fu, Y. Yang, and X. Bi, Diversity of bacteria in cloud water collected at a National Atmospheric Monitoring Station in Southern China, Atmospheric Research, 2019, 218, 176-182.
M. Vaïtilingom, L. Deguillaume, V. Vinatier, M. Sancelme, P. Amato, N. Chaumerliac and A.-M. Delort, Potential impact of microbial activity on the oxidant capacity and organic carbon budget in clouds, Proceedings of the National Academy of Sciences, 2013, 110, 559-564.
M. Vaïtilingom, T. Charbouillot, L. Deguillaume, R. Maisonobe, M. Parazols, P. Amato, M. Sancelme and A. M. Delort, Atmospheric chemistry of carboxylic acids: microbial implication versus photochemistry, Atmospheric Chemistry and Physics, 2011, 11, 8721-8733.
Y. Liu, C. K. Lim, Z. Shen, P. K. H. Lee and T. Nah, Effects of pH and light exposure on the survival of bacteria and their ability to biodegrade organic compounds in clouds: Implications for microbial activity in acidic cloud water, Atmospheric Chemistry and Physics, 2023, 23, 1731-1747.

2) Cloud pH may also have an impact on the distribution of acids and conjugate bases (i.e., protonated and deprotonated forms of acids). Would the biodegradation rates be different for different forms of chemicals?
At pH 5.5, the conjugated bases of acetic and formic acids are the dominant forms with 99% of formate (HCOO-) and 87% of acetate (CH2COO-). These proportion are reduced for the sensitivity test (pH=4.5) to respectively 86% for formate and 37% for acetate. For formic acid, formate is still the dominant form but obviously this is not for acetic acid that is mainly present under its acid form. In the present study, similarly to other modeling works studying the effect of biodegradation on cloud chemistry, we assumed that the acids and their conjugated bases are degraded by microorganisms at the same rates. It may seem questionable for acetic acid but, similarly to the discussion above on the effect of pH, additional experiments should be performed to investigate this point.

Line 151-159: I could have missed this. Were the data for substrate stability presented somewhere?
Yes, experiments have been performed in the lab to evaluate the substrate stability over time. One solution without microorganisms to see if the substrate concentration remains constant and one with microorganisms but no substrate to see if there is no biological production of the different targeted compounds. The following table gives the concentration variation of the control samples used for the different biodegradation experiments.

Chemical
Compounds H2O2 Acetic acid Formic acid Formaldehyde
Temperature
(°C) 5 17 5 17 5 17 5 17
Concentration
variation (%) 4.6 4.4 0.8 1.3 1.7 0.5 2.2 1.9

These results were obtained in the artificial cloud solution (see SI (S2)). The control samples were incubated with the ones used for the biodegradation experiments and the substrate concentration was controlled over the time (between 3 and 5 times per incubation). Each control sample was performed three times for repeatability. The results presented are the average values obtained for the triplicates at each temperature. Substrate concentration variations are lower than 5% for all species. No substrate production was observed in the control samples with microorganisms only (no substrate). A short comment was added in the SI (S5).

Line 179-180: I am a bit confused about the statement saying that “H+ was produced explicitly and consumed in acid-base reaction.” Could the authors please clarify this?
In general, in cloud chemistry model (see Barth et al., 2021), the pH (i.e., aqueous concentration of H+) can be calculated by different methods. Some models diagnose the H+ concentration based on the electroneutrality equation for cations and anions; other models such as ours predict H+ concentration explicitly as part of the solution of the set of chemistry ODEs (Ordinary differential equations). This means that H+ is a chemical compound numerically treated by the model as other chemical compounds and considering reactions producing or degrading H+ such as acid-base reactions.
We agree that this sentence was unclear; this has been rephrased as following (line 215-217):
“The model explicitly predicts H+ concentration as part of the solution of the system of ordinary differential equations (ODEs). Therefore, pH may evolve dynamically during the simulation time; it can also be fixed at a constant value for sensitivity tests”.

Reference:
M. C. Barth, B. Ervens, H. Herrmann, A. Tilgner, V. F. McNeill, W. G. Tsui, L. Deguillaume, N. Chaumerliac, A. Carlton and S. M. Lance, Box model intercomparison of cloud chemistry, Journal of Geophysical Research: Atmospheres, 2021, 126, e2021JD035486.
 
Referee: 2

Comments to the Author

Reviewer comments regarding manuscript "Assessing the efficiency of water-soluble organic compounds biodegradation in clouds under various environmental conditions" in the journal Environmental Science: Atmospheres with manuscript ID: EA-ART-11-2022-000153.
The present study is undoubtedly an interesting investigation, and the authors have used state-of-the-art methods to collect a valuable data set that shows a successful combination of laboratory results and model calculations.
We would like to thank the reviewer for their time spent to review the paper. We considered all the proposed comments/corrections; our answers are indicated in blue in the following.

I have some comments:

Can the authors explain why the biodegradation of acetic acid is not concentration dependent as it is for the other compounds?
As you can see in the SI (S8), for acetic acid, the uncertainties on the biodegradation rate are more important than for the other compounds. This makes it difficult to evaluate a tendency in how biodegradation rates evolve with substrate concentration. We can also argue that for the selected strain and the range of substrate concentration, we were reaching a saturation where the enzymatic activity was at its maximum. The enzyme produced by the bacteria degrades the chemical compound following a model of enzyme kinetics (Michaelis-Menten kinetics). Maximal reaction rate can be achieved by the system at a specific saturating substrate concentration.
Based on this uncertainty and the possibility of being in a saturated regime, we decided to maintain the biodegradation rates of acetic acid constant; the constant biodegradation rates are the averages of the experimental data.

Can the authors describe how they determine the biodegradation rate error, e.g., in ESI.
Incubation experiments have been conducted in the lab at 5 and 17°C in triplicate (independent replicates) for the 4 studied compounds and for different initial substrate concentrations. The biodegradation rates have been evaluated for each replicate following the calculation detailed in SI S(6). The error indicating in Table SM2 for each experiment represents the standard deviation calculated with the 3 biodegradation rates estimated from the 3 independent replicates.
A short paragraph has been added in the SI at the end of section S(6): “Biodegradation rates for all experiments are summarized in Table SM2. The biodegradation rates errors were estimated by calculating standard deviations of the 3 biodegradation rates estimated from the 3 independent replicates.”.

How could the measurement be improved to reduce the biodegradation rate error?
The errors associated with the biodegradation rates can appear to be large. These uncertainties are calculated based on incubation experiments performed three times. However, these uncertainties are expected to be high since those experiments rely on the use of biological material.
To perform the incubations, strains are grown on nutritive medium; then, cells in exponential growth are collected by centrifugation and bacteria are added to the incubation medium. Bacteria in the 3 parallel incubation experiments can present different viability/activity; this explains that triplicates for a specific incubation experiment can present differences in terms of degradation rates. Those incubation experiments are time-consuming and performing triplicates allows to robustly evaluate the efficiency of biodegradation (Vaïtilingom et al., 2011; Liu et al., 2023).

References:
M. Vaïtilingom, T. Charbouillot, L. Deguillaume, R. Maisonobe, M. Parazols, P. Amato, M. Sancelme and A. M. Delort, Atmospheric chemistry of carboxylic acids: microbial implication versus photochemistry, Atmospheric Chemistry and Physics, 2011, 11, 8721-8733.
Y. Liu, C. K. Lim, Z. Shen, P. K. H. Lee and T. Nah, Effects of pH and light exposure on the survival of bacteria and their ability to biodegrade organic compounds in clouds: Implications for microbial activity in acidic cloud water, Atmospheric Chemistry and Physics, 2023, 23, 1731-1747.

As far as I can see, there is something wrong with the colors of the legend in Figure 3 for the winter case, e.g. formic acid and acetic acid compared to formaldehyde.
We agree with your comment, the Figure has been corrected to distinguish the color between daytime and nighttime simulations. There was also an error in the legend that has been corrected.

On page 22, on line 27, please remove the % of 81%.
Thanks, this is now corrected.

In the supporting information:

On page 2 in table SM1, center "pH 6.0".
This has been corrected.

On page 3 in table SM2, please check the significant digits for the given numbers.
We agree with your comment. We have accordingly modified the table.

On page 14 in Table SM5, the "y =" is missing in the H2O2 column.
This has been changed in the new version of the SI.

On page 13 in Figure SM4 d, the y-axis label is cut out.
This has been corrected.
 
Referee: 3

Comments to the Author

General

This is a study combining experimental a modeling work of the degradation of four model compounds by microorganisms which may take place in aqueous atmospheric systems.
The paper is interesting as the biodegradation rates are carried in a substrate-dependent manner and not as constant rates. It would be good to clarify this even more.
This important point is still pointed out in a paragraph (lines 108 to 114) but surely not enough. We added the following sentences in the text:
Line 125-126: “Biodegradation rates were evaluated for different initial concentrations of substrate and for two temperature (5 and 17°C).”.
Line 131-132: “Contrary to previous modeling studies, biodegradation rates were modulated by the substrate concentrations.”.

All in all, this is a nice modelling study in the scope of the journal but requires revisions.
We would like to thank the reviewer for their time spent to review the paper. We considered all the proposed comments/corrections; our responses are indicated in blue in the following.

I would like to advise the authors to remove all self-judgements of their study from the text such as line 33 'originality' or similar.
We followed the reviewer’s comment and modified the sentence line 33 as following:
“The biodegradation rates were not kept constant; rather, they depended on the concentration of the four targeted species”.
The sentence line 141 has been modified following your comment:
“This research aims at providing a new concept for the role of microorganisms in the atmospheric transformation of chemical compounds”.

Details

Abstract
I do not understand the second sentence of the abstract, i.a. 'To achieve this objective, we measured the rates of biodegradation for four chemical compounds of interest in atmospheric chemistry (formic and acetic acids, formaldehyde and hydrogen peroxide) in an explicit model of cloud chemistry by simulating the exchange processes between air, droplets and chemical reactivity in both phases.'
What does that mean? There were measurements, yes, but surely not 'in a model' - so please clean up this sentence.
We apologize, the sentence made no sense. We rephrased it as following (line 29):
“To achieve this objective, we measured in the laboratory the rates of biodegradation for four chemical compounds of interest in atmospheric chemistry (formic and acetic acids, formaldehyde and hydrogen peroxide). We implemented them in an explicit model of cloud chemistry simulating the exchange processes between air, droplets and chemical reactivity in both phases”.

Other: The abstract could make use of more numbers, it should really reflect the most important quantitative finding of this study.
Yes, we totally agree. We modified the abstract following your comment.
Line 62: This discussion goes on for long.
We modified the text following your comment.

Line 80: I am surprised to see the statement that Fankhauser et al, ref 22 (2019) should have been 'the first study' where biotic degradations were implemented in parallel to abiotic degradation steps. What about previous work from Clermont-Ferrand, especially work led by Pr. Chaumerliac who is an author on this present submission? Maybe you mean 'a recent study ' ?
In Clermont-Ferrand, our research team has been working on the chemical and biological characterization of clouds for many years. As you mentioned, we also studied in Clermont-Ferrand the potential role of microorganisms in the transformation of chemical compounds but under laboratory conditions. The studies from Vaïtilingom et al. (2011, 2013) and Wirgot et al. (2017) have shown that biodegradation rates of pure strains and of the real cloud microflora can compete with degradations induced by photochemistry. But these laboratory investigations did not consider the contribution of the mass transfer in the chemical budget of the studied chemical species degraded by the light or by microorganisms. The only way to evaluate the role of biodegradation in clouds (i.e., polydisperse medium with mass exchanges between phases) is to implement biodegradation rates in a model reproducing the multiphase chemistry and the mass transfer of chemicals.
To our knowledge, the study from Fankhauser et al. (2019) is the first work that considered biodegradation rates in a numerical cloud chemistry model simulating multiphase reactivity and mass transfer of chemical compounds. This model (GAMMA) has been developed by V.F. McNeill’s research team from Colombia University (USA).
Regarding the contribution of N. Chaumerliac, she has been strongly involved in the recent development of the cloud chemistry model used in the present study. A detailed multiphase mechanism has then been implemented in a model based on the Dynamically Simple Model for Atmospheric Chemical Complexity (DSMACC) (Emmerson and Evans, 2009) using the Kinetic PreProcessor (KPP) (Damian et al., 2002). A new aqueous phase mechanism named CLEPS (Cloud Explicit Physico-chemical Scheme) has been specifically developed for describing abiotic transformations occurring in cloud water (Mouchel-Vallon et al., 2017; Rose et al., 2018). N. Chaumerliac was involved in the development of the cloud chemistry model to consider biodegradation rates; she also participated in interpreting the simulations that have been performed in the present work.
We hope this is clearer now for the reviewer. But we do not think that additional information should be added to the manuscript regarding this specific point. Section 2.2 presents in a synthetic manner the cloud chemistry model that is used in this study and contains enough information to our opinion.

References:
M. Vaïtilingom, T. Charbouillot, L. Deguillaume, R. Maisonobe, M. Parazols, P. Amato, M. Sancelme and A. M. Delort, Atmospheric chemistry of carboxylic acids: microbial implication versus photochemistry, Atmospheric Chemistry and Physics, 2011, 11, 8721-8733.
M. Vaïtilingom, L. Deguillaume, V. Vinatier, M. Sancelme, P. Amato, N. Chaumerliac and A.-M. Delort, Potential impact of microbial activity on the oxidant capacity and organic carbon budget in clouds, Proceedings of the National Academy of Sciences, 2013, 110, 559-564.
N. Wirgot, V. Vinatier, L. Deguillaume, M. Sancelme and A. M. Delort, H2O2 modulates the energetic metabolism of the cloud microbiome, Atmospheric Chemistry and Physics, 2017, 17, 14841-14851.
A. M. Fankhauser, D. D. Antonio, A. Krell, S. J. Alston, S. Banta and V. F. McNeill, Constraining the impact of bacteria on the aqueous atmospheric chemistry of small organic compounds, ACS Earth and Space Chemistry, 2019, 3, 1485-1491.
K. M. Emmerson and M. J. Evans, Comparison of tropospheric gas-phase chemistry schemes for use within global models, Atmospheric Chemistry and Physics, 2009, 9, 1831-1845.
V. Damian, A. Sandu, M. Damian, F. Potra and G. R. Carmichael, The kinetic preprocessor KPP-a software environment for solving chemical kinetics, Computers & Chemical Engineering, 2002, 26, 1567-1579.
C. Mouchel-Vallon, L. Deguillaume, A. Monod, H. Perroux, C. Rose, G. Ghigo, Y. Long, M. Leriche, B. Aumont, L. Patryl, P. Armand and N. Chaumerliac, CLEPS 1.0: A new protocol for cloud aqueous phase oxidation of VOC mechanisms, Geoscientific Model Development, 2017, 10, 1339-1362.
C. Rose, N. Chaumerliac, L. Deguillaume, H. Perroux, C. Mouchel-Vallon, M. Leriche, L. Patryl and P. Armand, Modeling the partitioning of organic chemical species in cloud phases with CLEPS (1.1), Atmospheric Chemistry and Physics, 2018, 18, 2225-2242.

Line 139: Why exactly is there such a big difference of a factor of 100 in the number of Pseudomonas per volume for formic & acetic acids vs. H2O2?
Line 145: And, are you now following what study #18 suggests?
The incubation experiment consists in adding to an artificial cloud solution containing bacteria a certain concentration of substrate (formic acid, acetic acid, formaldehyde, H2O2) and in following the concentration of these substrates during the incubation time. This allows calculating biodegradation rates. All the information regarding the lab experiments is given in SI (S(1) to S(6)).
Due to methodological constrains (detection limits and quantification of the biodegradation rates), for acetic acid and formaldehyde we were constrained to work with more elevated concentrations than those encountered in cloud water. The concentrations of these two substrates were multiplied by a factor 100 and the cell concentration was consequently multiplied by the same factor. This way, we maintain a constant cell/chemical concentration ratio and this ratio is similar to the ratio found in real clouds. The calculated biodegradation rates can thus be extrapolated to real cloud condition. This is confirmed by the cited study N°18 that has been done by our team. This publication (Vaïtilingom et al., 2010) evaluated biodegradation rates in the lab regarding 4 organic compounds at different concentrations of substrate and cells but always maintaining a constant cell/chemical concentration ratio. They indicated that “the results attested that there was no effect of the absolute cell and acid concentrations for a given ratio”.
This information is given in the manuscript and some sentences have been rephrased for more clarity (section 2.1, line 165-171):
“The artificial cloud solution in which bacteria were incubated was concentrated by a factor of 100 specifically for acetic acid and formaldehyde, to keep the same cell/chemical concentration ratio during the determination of biodegradation rates. Previous studies in a laboratory environment have shown that maintaining a constant ratio of cell concentration vs. degraded chemical compound concentration (in the range of the investigated concentrations) allows the evaluation of biodegradation rates independently of the absolute cell and chemical concentrations (Vaïtilingom et al., 2010).”

Reference:
M. Vaïtilingom, P. Amato, M. Sancelme, P. Laj, M. Leriche and A.-M. Delort, Contribution of microbial activity to carbon chemistry in clouds, Applied and Environmental Microbiology, 2010, 76, 23-29.

Line 160: What about previous Clermont-Ferrand models - is CLEPS the latest mechanism?
Historically, our team in Clermont-Ferrand had developed an explicit cloud chemistry model initially presented in Leriche et al. (2000, 2001). This model named subsequently “M2C2” (Model of Multiphase Cloud Chemistry) has been developed and used by our team in several papers (Leriche et al., 2003, 2007; Deguillaume et al., 2004, 2005; Long et al., 2010, 2013; Bianco et al., 2015).
As explained above, a new cloud chemistry model has been recently developed by our team (see for more details the paper from Mouchel-Vallon et al., 2017). This model considers a newly developed aqueous phase mechanism named “CLEPS” (Cloud Explicit Physico-chemical Scheme). CLEPS 1.0 considers the detailed chemical reactions of HxOy, chlorine, carbonates, NOy, sulfur, and the chemistry of the transition metals for iron, manganese, and copper in the aqueous phase. It also describes the oxidation of organic species for C1 to C4 carbon atoms based on the protocol described in Mouchel-Vallon et al. (2017). CLEPS 1.0 was extended to CLEPS 1.1 to include the chemistry of the newly added dicarboxylic acids dissolved from the particulate phase (Rose et al., 2018).
We believe that the description of the model in the manuscript is sufficient; we do not think that giving additional information of the historical development of the model is necessary.

References:
M. Leriche, D. Voisin, N. Chaumerliac, A. Monod and B. Aumont, A model for tropospheric multiphase chemistry: application to one cloudy event during the CIME experiment, Atmospheric Environment, 2000, 34, 5015-5036.
M. Leriche, N. Chaumerliac and A. Monod, Coupling quasi-spectral microphysics with multiphase chemistry: a case study of a polluted air mass at the top of the Puy de Dôme mountain (France), Atmospheric Environment, 2001, 35-32, 5411-5423.
M. Leriche, L. Deguillaume and N. Chaumerliac, Modeling study of strong acids formation and partitioning in a polluted cloud during wintertime, Journal of Geophysical Research, 2003, 108-D14, 4433.
M. Leriche, L. Curier, L. Deguillaume, D. Caro, K. Sellegri, and N. Chaumerliac, Numerical quantification of sources and phase partitioning of chemical species in cloud: application to wintertime anthropogenic air masses at the Puy de Dôme station, Journal of Atmospheric Chemistry, 2007, 57-3, 281-297.
L. Deguillaume, M. Leriche, A. Monod and N. Chaumerliac, The role of transition metal ions on HOx radicals in clouds: a numerical evaluation of its impact on multiphase chemistry, Atmospheric Chemistry and Physics, 2004, 4, 95-110.
L. Deguillaume, M. Leriche and N. Chaumerliac, Impact of radical versus non-radical pathway in the Fenton chemistry on the iron redox cycle in clouds, Chemosphere, 2005, 60-5, 718-724.
Y. Long, N. Chaumerliac, L. Deguillaume, M. Leriche and F. Champeau, Effect of mixed-phase cloud on the chemical budget of trace gases: A modeling approach, Atmospheric Research, 97-4, 540-554.
Y. Long, T. Charbouillot, M. Brigante, G. Mailhot, A.-M. Delort, N. Chaumerliac and L. Deguillaume, Evaluation of modeled cloud chemistry mechanism against laboratory irradiation experiments: The HxOy/iron/carboxylic acid chemical system, Atmospheric Environment, 2013, 77, 686-695.
A. Bianco, M. Passananti, H. Perroux, G. Voyard, C. Mouchel-Vallon, N. Chaumerliac, G. Mailhot, Deguillaume, L. and M. Brigante, A better understanding of hydroxyl radical photochemical sources in cloud waters collected at the puy de Dôme station – experimental versus modelled formation rates, Atmospheric Chemistry and Physics, 2015, 5, 9191–9202.

Line 200: These findings: 'At the end of the spinup, we noticed that, as expected, the concentrations of oxidants were higher in the summer than in the winter. The HO• and H2O2 concentrations were much lower for nighttime (for example, in summer, H2O2 was 21-fold lower and 100-fold lower for HO•).' are what is expected. It would be more interesting to compare the HOx levels obtained with the results of other studies / models in order to place them in scientific context.
You are right, the levels of HOx (HO• and HO2•) were not discussed and compared to existing in situ measurements or model evaluation.
The spinup simulation has been performed to create a realistic chemical environment before the cloud formation. It is based on chemical scenarios derived from previous modelling works (McNeill et al., 2012; Mouchel-Vallon et al., 2017). The chemical gas phase mechanism is a reduced version of the explicit MCM mechanism (v3.3.1, Jenkin et al., 2015) coupled with a radiative transfer model to calculate gaseous phase photolysis rates. HOx concentrations for the different simulations are briefly recalled below.

Mixing ratio
(ppb / molec cm-3) Summer Winter
HO• 3.8 10-6 / 8.2 104 1.8 10-4 / 3.9 106 1.8 10-6 / 3.8 104 6.9 10-5 / 1.5 106
HO2• 2.7 10-3 / 5.7 107 3.4 10-2 / 7.2 108 1.4 10-3 / 3.1 107 7.6 10-3 / 1.7 108

For the summer day simulation, the HO• and HO2• levels were realistic and comparable to measurements observed in low-NOx environments influenced by biogenic emissions. The review from Stone et al. (2012) presented a summary of measurements and model comparisons for HO• and HO2• levels (see Table 3 in Stone et al., 2012) under these environmental conditions. For example, in Julich (Germany), during summer, the HO• and HO2• levels were measured equal to 5–15 × 106 molec cm-3 and 2–10 × 108 molec cm-3, respectively.
Even if there is much less information on HOx levels during nighttime condition, some studies investigated the HOx diurnal variability as reported in the review from Stone et al. (2012). At night, the simulated HO• concentrations were two orders of magnitude lower than daytime values; this is consistent with previous studies (both modelling and experimental works) (Lu and Khalil, 1992; Sillman et al., 2002; Stones et al., 2012). For HO2•, the modelled nighttime concentrations presented a decrease by one order of magnitude compared to daytime concentrations. This is consistent with previous studies measurements performed during the BERLIOZ campaign in Germany (Platt et al., 2002) or during the PROPHET campaign in Michigan (US, Sillman et al., 2002).
For winter simulations, much less data on HOx levels were available. During the day, we simulated around 3 times lower HO• level and 4 times lower HO2• levels in comparison to summer simulations. The actinic flux simulated in winter decreases by a factor 1.7 contributing to this lower HOx levels in winter vs summer. This range of decrease was consistent with some studies over semi-polluted or polluted areas (Ren et al., 2006; Kanaya et al., 2004; Heard et al., 2004).

References:
D. Stone, L.K. Whalley and D.E. Heard, Tropospheric OH and HO2 radicals: field measurements and model comparisons, Chemical Society Reviews, 2012, 41, 6348-6404.
Y. Lu and K. Khalil, Model calculations of night-time atmospheric OH, Tellus, 1992, 44B, 106-113.
S. Sillman, et al., Loss of isoprene and sources of nighttime OH radicals at a rural site in the United States: Results from photochemical models, Journal of Geophysical Research, 2002, 107(D5).
U. Platt, B. Alicke, R. Dubois, et al., Free radicals and fast photochemistry during BERLIOZ. Journal of Atmospheric Chemistry, 2002, 42, 359-394.
X. Ren, et al., Behavior of OH and HO2 in the winter atmosphere in New York City, Atmospheric Environment, 2006, 40, 252-263.
Y. Kanaya et al., Urban photochemistry in central Tokyo: 1. Observed and modeled OH and HO2 radical concentrations during the winter and summer of 2004, Journal of Geophysical Research, 2004, 112, D21312.
D.E. Heard et al., High levels of the hydroxyl radical in the winter urban troposphere, Geophysical Research Letter, 2004, 31, L18112.
M. E. Jenkin, J. C. Young and A. R. Rickard, The MCM v3.3.1 degradation scheme for isoprene, Atmospheric Chemistry and Physics, 2015, 15, 11433-11459.

The objective of the present study is to analyze under realistic conditions the effect of biodegradation on the cloud chemical budget. Therefore, we think that additional information could be added in the SI, not in the manuscript. We added a paragraph in the SI (S(7) c)) and it is now referred to in the manuscript.

Figure 1: This Figure does not contain much information, it could be moved to the SI.
We understand your point of view. However, we prefer to keep this figure in the manuscript for two reasons: (1) it gives the names of the different simulations; (2) and it is a short summary of the different environmental conditions of the simulations.

Line 231: Section 2.3: What is really new here? Isn't this the treatment you would expect to be used for the biodegradation reactions? Maybe you can detail a bit more?
The correct treatment to consider biodegradation in a cloud chemistry model is the one we developed for this submitted paper. It is obvious that biodegradation rates depend on the concentration of the substrate consumed in the cloud aqueous phase. However, previous modelling studies have treated these biodegradation rates as constants in their models (Fankhauser et al., 2019) or linear with only one experimental value (Khaled et al., 2021). This is because biodegradation rates available in the literature were measured at a specific substrate concentration.
The biodegradation rates treatment in the model is slightly different to what is usual for traditional chemical reactions kinetics, because the loss rate is computed from piece-wise linear functions with non-zero y-axis intercept (see S8, Tab. SM5), requiring model adaptations such as (i) interpolating degradation rates in a separate step and (ii) removing the substrate concentration from the loss rate term (Vcell*Ccell) which is usually automatically accounted for by chemistry ODE solvers. Furthermore, this approach is flexible as it will allow considering more complex, non-linear, enzymatic kinetics in future studies.

References:
A. Khaled, M. Zhang, P. Amato, A. M. Delort and B. Ervens, Biodegradation by bacteria in clouds: an underestimated sink for some organics in the atmospheric multiphase system, Atmospheric Chemistry and Physics, 2021, 21, 3123-3141.
A. M. Fankhauser, D. D. Antonio, A. Krell, S. J. Alston, S. Banta and V. F. McNeill, Constraining the impact of bacteria on the aqueous atmospheric chemistry of small organic compounds, ACS Earth and Space Chemistry, 2019, 3, 1485-1491.
M. Vaïtilingom, T. Charbouillot, L. Deguillaume, R. Maisonobe, M. Parazols, P. Amato, M. Sancelme and A. M. Delort, Atmospheric chemistry of carboxylic acids: microbial implication versus photochemistry, Atmospheric Chemistry and Physics, 2011, 11, 8721-8733.

Line 321: Compare the OH and HO2 levels with no biodegradation implemented, this is missing somehow.
First, we added in the SI the time evolutions of HO2/O2•- aqueous concentrations that were missing (now Figure SM5b in the SI). We also refer to this new figure in the manuscript (section 3.2, line 369).
When adding the biodegradation rates in the model, no significant modification of HO• and HO2•/O2•- concentrations is observed in the aqueous phase for daytime simulations (summer and winter). For the summer nighttime simulation, we observed a weak increase of the HO• concentration with the consideration of biodegradation; this is because HO• loss is weaker with lower concentrations of the 3 organic compounds that are biodegraded. The opposite trend is observed for HO2•/O2•- concentration that is slightly lower with biodegradation. Indeed, when considering biodegradation, the oxidation of formate by HO• producing O2•- is reduced. However, these differences on the HOx budget observed at night should be analyzed with caution since the simulated HOx concentrations are very low.
We added a paragraph in section 3.3 (lines 469-473) to discuss this point.

Note, continuous line numbering ended in my file with the end of page 20.
Yes, we noticed this problem, we apologize for this.

Page 29, line 9: It is written 'The biodegradation of the organic species investigated herein competed with biotic degradation' but I think that should be '...abiotic degradation.' instead at the end of sentence.
Yes, we agree! We modified this sentence accordingly.

Please check. Also check all the manuscript for issues like this.
We checked all the manuscript, and we did not find similar errors.
 
Referee: 4

Comments to the Author

Pailler et al. address the importance of the biodegradation of water-soluble organic compounds in cloud droplets. Their methodology is sound and their findings are presented in a good manner. The manuscript provides a significant contribution to the field and improved the understanding of biotic processes in clouds.
I suggest accepting this manuscript after the authors address the following comments.
We would like to thank the reviewer for their time spent to review the paper. We considered all the proposed comments/corrections; our responses are indicated in blue in the following.

Major comments:

Within warm clouds, formaldehyde undergoes fast hydration, leading to methanediol. Recently, Franco et al. 2021 identified that methanediol undergoes fast outgassing but slow dehydration. Additionally, its gas phase oxidation becomes a major source of formic acid. I noticed that the authors do not consider the phase transfer of methanediol in their mechanism. Within this study, the authors are investigating both formaldehyde and formic acid. Neglecting this pathway will surely influence the presented budget analysis and will influence their conclusions on the relative importance of biodegradation within clouds (e.g., Figure 5). I strongly recommend that the authors implement the before mentioned pathway and redo their analysis. This will also allow the authors to identify how sensitive their findings are to uncertainties in the chemical mechanism.
The work from Franco et. al. (2021) was not considered in our work. This study demonstrated the role of clouds as a significant indirect source in the gas phase of formic acid. Indeed, inside droplets, formaldehyde (HCHO) is converted into methanediol (HOCH2OH) that can outgas from cloud droplets and reacts in the gas phase with the hydroxyl radicals HO• to form formic acid. As redoing the whole analysis was not feasible in a reasonable time, we decided to carry out sensitivity tests to assess the potential importance of this pathway for formaldehyde and formic acid cloud chemistry. The chamber experiments also showed that methanediol might dehydrate in the gas phase.

Based on Franco et al. (2021), we added the following reactions to the chemical mechanism:

Gas phase reactivity:
Reactions Kinetic constant
HOCH2OH(g) + HO•(g)  HCOOH(g) + HO2•(g) k = 7.5 10-12 cm3 s-1
HOCH2OH(g)  HCHO(g) + H2O(g) k = 8.5 10-5 s-1

The rate constant used for the reaction of HOCH2OH with HO• was extrapolated from results obtained in chamber experiments thanks to a box model calculation, as presented in Franco et al. (2021). The rate constant has no temperature dependency, and it certainly was evaluated at higher temperature than those commonly found in clouds; this could lead to a more uncertainty of the production of formic acid in the gas phase. Moreover, the rate constant used here is higher than values from the theoretical prediction described in Franco et al. (2021) (9.18 10-13 cm3 s-1 at 290K). This model therefore gives an upper bound to the gaseous formic acid production in the gas phase.

The mass transfer of HOCH2OH(g) has been also implemented based on the Franco et al. study:

HOCH2OH(g) ↔ HOCH2OH(aq) Henry’s law constant: H = 104 - 106 M atm-1

Henry’s law constant was estimated to vary between 104 and 106 M atm-1; we considered no temperature dependency, and the accommodation coefficient (dimensionless) was set as 5 10-2. The mass transfer is parameterized in the model following Schwartz et al. (1986).
No measurements were done in Franco et al. study to evaluate the HOCH2OH Henry’s law constant. They determined two extreme values were determined: 104 M atm-1 that is estimated by the HENRYWIN software at 298K and 106 M atm-1 that considered the uncertainties of the estimation and that is closer to Henry’s law values of compounds comparable to methanediol such as hydroxymethyl hydroperoxide HMHP (~106 at 298K and up to 107 in clouds with lower temperature) (Sander, 2015). Once again, the estimated Henry’s law value of HOCH2OH might correspond to more elevated temperatures than the one used in our simulations which could lead to an underestimation of the Henry’s law constant for our work.
Sensitivity tests were carried out to evaluate the impact of these new reactions implemented in CLEPS. Tests were done in summer as it is the season that shows significant impact of microorganisms on formic acid concentration. Tests were performed using the two different Henry’s law constant used in Franco et al. (2021) (106 (“Franco_6”) and 104 (“Franco_4”) M atm-1). To evaluate the impact of this new mechanism on the biodegradation of formic acid each new simulation was additionally carried out considering biological activity. The different simulations are summarized in the table below and the results are depicted on the next figure:

Ref Reference simulation presented in this work
Ref_bio Reference simulation considering the biological activity
Franco_6 Adding the Franco’s mechanism with H = 106 M atm-1
Franco_6_bio Adding the Franco’s mechanism with H = 106 M atm-1 + Biodegradation
Franco_4 Adding the Franco’s mechanism with H = 104 M atm-1
Franco_4_bio Adding the Franco’s mechanism with H = 104 M atm-1 + Biodegradation

For the highest Henry’s law constant of HOCH2OH, formic acid and formaldehyde concentrations remain nearly the same in comparison to the Ref simulation. In the gas phase, the new source of formic acid due to methanediol accounts for around 20% of its gaseous production. This additional gaseous formic acid production is directly transferred to the aqueous phase. Most of the formic acid production occurs in cloud droplets in total (over 90%). Thus, this new gaseous source does not significantly impact the total (gaseous +aqueous) formic acid production and concentrations in our simulation. Formaldehyde concentrations are similarly not impacted by the consideration of this new mechanism.
In our simulation, formic acid production in the aqueous phase results mainly from the aqueous oxidation of compounds issued from isoprene oxidation that are transferred in the aqueous phase. It seems like this aqueous source is not described in the aqueous mechanism used in the Franco et al. (2021) study; that might explain why the impact in our simulations is weak. The effect of biodegradation on the formic acid and formaldehyde remains unchanged.
For the lowest Henry’s law constant of HOCH2OH, the impact on the gaseous and aqueous concentrations of formic acid is significant (80% of increase of the total (gaseous +aqueous) concentration). As methanediol is more volatile, the gaseous production of formic acid becomes the main source of formic acid (almost 50% of the total production of formic acid is due to the methanediol oxidation). Formaldehyde (HCHO and methanediol) concentration in the gas phase increases and represents 94% of total formaldehyde (hydrated and non-hydrated formaldehyde in the aqueous and gaseous phases). The aqueous phase concentration decreases but this does not significantly impact the total concentration increase. As the aqueous concentration of formic acid is significantly higher in the droplets (80% of increase), biodegradation is more important than in the reference simulation; this leads to a decrease of the total concentration of formic acid of almost 35% (25% for the reference simulation). The impact of microorganisms on formaldehyde concentration is still negligible, as in the Ref simulation.


This figure presents the gaseous, aqueous and total concentrations of formic acid and formaldehyde. Total concentration corresponds to the sum of the aqueous and the gaseous concentrations. Aqueous concentration of formic acid considers the acid form (formic acid) and the conjugate base (formate). Aqueous and gaseous concentrations of formaldehyde consider the formaldehyde and its hydrated form (methanediol).

The CLEPS mechanism is sensitive to the implementation of this new mechanism and thus, it is essential to consider it as it might highly increase the formic acid concentration. Nevertheless, it highly depends on the Henry’s law constant that is not yet well-documented. If the methanediol is more volatile (typically for tropical environment, for example), the gaseous production of formic acid becomes predominant and then the impact of the new pathway is significant. It may be interesting to evaluate the intensity of this new formic acid gaseous production in contrasted environments (tropical/ boreal) to understand how the formic acid production is modulated.
For our simulations, the highest Henry’s law constant seems more representative of the cloud environment we simulated, characterized by lower temperature (5 and 17°C) thus increasing the solubility. As formic acid is mainly produced in the aqueous phase, if methanediol is highly soluble (106 M atm-1), the new gaseous pathways have no significant impact on the simulations.
Even for a Henry’s law constant lower than 106 M atm-1, the biodegradation impact on our targeted organic compounds, that is the scope of this study, may vary a little. As our objective is to assess the efficiency of biodegradation in clouds on water soluble organic compounds, to our opinion, analyzing the effect of this new pathways is beyond the scope of the present study. A special study should be dedicated to this interesting scientific question.

References:
B. Franco, T. Blumenstock, C. Cho, L. Clarisse, C. Clerbaux, P. F. Coheur, M. De Mazière, I. De Smedt, H. P. Dorn, T. Emmerichs, H. Fuchs, G. Gkatzelis, D. W. T. Griffith, S. Gromov, J. W. Hannigan, F. Hase, T. Hohaus, N. Jones, A. Kerkweg, A. Kiendler-Scharr, E. Lutsch, E. Mahieu, A. Novelli, I. Ortega, C. Paton-Walsh, M. Pommier, A. Pozzer, D. Reimer, S. Rosanka, R. Sander, M. Schneider, K. Strong, R. Tillmann, M. Van Roozendael, L. Vereecken, C. Vigouroux, A. Wahner and D. Taraborrelli, Ubiquitous atmospheric production of organic acids mediated by cloud droplets, Nature, 2021, 593, 233-237.
R. Sander, Compilation of Henry's law constants (version 4.0) for water as solvent, Atmospheric Chemistry and Physics, 2015, 15, 4399-4981.
Schwartz, S. E., Mass-transport considerations pertinent to aqueous phase reactions of gases in liquid-water clouds, in: Chemistry of Multiphase Atmospheric Systems, edited by: Jaeschke, W., Springer Berlin Heidelberg, Berlin, Heidelberg, 1986, 415–471.

The authors discuss the sensitivity of the liquid water content (lwc) to their findings. Here, they vary the lwc in a range observed at PUY. In addition to the lwc, the phase transfer is inherently dependent on the radius of the cloud droplet (Schwartz, 1986). By lowering the lwc, I would also expect that the cloud droplet radius decreases. By assuming that the droplet radius is the same for both lwc, the authors findings might be biased with respect to the changes induced by phase transfer. I suggest that the authors redo their analysis by also adapting the droplet radius in order to represent realistic cloud droplets.
We understand your point of view. In the present study, we performed different simulations varying the environmental conditions (day vs night, summer vs winter) with fixed microphysical cloud properties (cloud droplet radius R and liquid water content LWC). When performing the sensitivity tests, our objective was to modify one selected parameter independently from the others to investigate its effect on multiphase cloud transformations. This is commonly performed in the frame of box modelling studies. The idea beyond the sensitivity test is to reproduce independent clouds with different liquid water content but maintaining the droplet radius identical to the reference simulations (10 µm). The simulations can be considered as realistic since the clouds with this radius and the two extreme LWC values are observed in situ. To illustrate this, the figure below plots the evolution of the water content vs. the average effective radius of clouds measured at the puy de Dôme for the year 2019.

By modulating, at the same time, the LWC and the radius, it will be extremely difficult to explain the individual role of these two parameters when analyzing the difference in the multiphase chemical budget of the sensitivity tests vs the reference simulation (as presented in section 3.5).
Therefore, we would like to leave the LWC sensitivity tests as performed in the submitted manuscript.

The conclusion section of the manuscript feels more like a summary of the work performed. The authors state that they observe a global increase in concentrations at the end of their simulation (Page 27, Line 30 to 31). The manuscript would greatly benefit from a discussion putting their findings into the broader context.
For example: Which regions do the authors expect to be most impact by this process?
Do the authors expect that the discussed process and the related changes in acid concentrations in cloud droplets, influence the overall cloud acidity?
When implemented into a regional or global atmospheric chemistry model, could this resolve some of the underprediction observed in total formic acid columns over boreal forests, when compared to satellite observations?
We agree with your comment. We consequently add at the end of the conclusion the following paragraph that also gives you the response to your above questions. However, we focused on the role of biodegradation on the budget of formic acid that is the most impacted compound.

“Nevertheless, the knowledge on ecology of bacteria in the atmosphere is limited; this implies that, at this moment, modeling studies on the role of microorganisms on atmospheric chemistry still consider rough assumptions. For example, the modulation of biodegradation rates by environmental factors such as light intensity and acidity should be investigated (Liu et al., 2023). Additionally, little is known on the preferred degradation of one substrate over another by microorganisms in a complex organic medium. Bacteria are also known to produce substances such as biosurfactants and siderophores, whose impact on the physics and chemistry (notably heterogeneous) of the cloud is certainly neglected (Gonzáles et al., 2022; Ahern et al., 2007; Renard et al., 2016).
Even if this work presents some limitations, it allows discussing the possible role of biodegradation in a broader context. Formic acid could be efficiently consumed in warm clouds, at relatively high temperatures (10-20°C), conditions typically encountered in tropical environments (Dominutti et al., 2022). The recent study by Franco et al. (2021) highlighted the important role of clouds on formic acid production via multiphase reactivity. Their modeling investigations indicated overestimated concentrations of formic acid in the tropics; various reasons were given in this work to explain this statement (especially the elevated isoprene emissions) but biological activity (especially at night) could strongly reduce formic acid levels in these regions. Oppositely, over boreal forests biological activity would be less effective and overall, the presence of clouds could lead to an increase in formic acid concentrations (for clouds with long enough lifetimes). This could lead to an increase in formic acid levels over these regions and explain the satellite observation of a large source of formic acid in boreal forest, which models are unable to reproduce (Stavrakou et al., 2012). More broadly, the approach we developed in this work could become the basis for implementing biological sources and sinks of organic compounds, dependent on environmental conditions, in regional to global chemistry models with the aim of reducing the modeling uncertainties related to the fate of atmospheric organic compounds.”.

References (some of them were added in the manuscript):
Y. Liu, C. K. Lim, Z. Shen, P. K. H. Lee and T. Nah, Effects of pH and light exposure on the survival of bacteria and their ability to biodegrade organic compounds in clouds: Implications for microbial activity in acidic cloud water, Atmospheric Chemistry Physics, 2023, 23, 1731-1747.
A. G. González, A. Bianco, J. Boutorh, M. Cheize, G. Mailhot, A.-M. Delort, H. Planquette, N. Chaumerliac, L. Deguillaume and G. Sarthou, Influence of strong iron-binding ligands on cloud water oxidant capacity, Science of The Total Environment, 2022, 829, 154642.
H. E. Ahern, K. A. Walsh, T. C. J. Hill and B. F. Moffett, Fluorescent pseudomonas isolated from Hebridean cloud and rain water produce biosurfactants but do not cause ice nucleation, Biogeosciences, 2007, 4, 115-124.
P. Renard, I. Canet, M. Sancelme, N. Wirgot, L. Deguillaume and A. M. Delort, Screening of cloud microorganisms isolated at the Puy de Dôme (France) station for the production of biosurfactants, Atmospheric Chemistry and Physics, 2016, 16, 12347-12358.
P. A. Dominutti, P. Renard, M. Vaïtilingom, A. Bianco, J. L. Baray, A. Borbon, T. Bourianne, F. Burnet, A. Colomb, A. M. Delort, V. Duflot, S. Houdier, J. L. Jaffrezo, M. Joly, M. Leremboure, J. M. Metzger, J. M. Pichon, M. Ribeiro, M. Rocco, P. Tulet, A. Vella, M. Leriche and L. Deguillaume, Insights into tropical cloud chemistry in Réunion (Indian Ocean): results from the BIO-MAÏDO campaign, Atmospheric Chemistry and Physics, 2022, 22, 505-533.
B. Franco, T. Blumenstock, C. Cho, L. Clarisse, C. Clerbaux, P. F. Coheur, M. De Mazière, I. De Smedt, H. P. Dorn, T. Emmerichs, H. Fuchs, G. Gkatzelis, D. W. T. Griffith, S. Gromov, J. W. Hannigan, F. Hase, T. Hohaus, N. Jones, A. Kerkweg, A. Kiendler-Scharr, E. Lutsch, E. Mahieu, A. Novelli, I. Ortega, C. Paton-Walsh, M. Pommier, A. Pozzer, D. Reimer, S. Rosanka, R. Sander, M. Schneider, K. Strong, R. Tillmann, M. Van Roozendael, L. Vereecken, C. Vigouroux, A. Wahner and D. Taraborrelli, Ubiquitous atmospheric production of organic acids mediated by cloud droplets, Nature, 2021, 593, 233-237.
T. Stavrakou, J.F. Müller, J. Peeters, et al., Satellite evidence for a large source of formic acid from boreal and tropical forests, Nature Geosciences, 2012, 5, 26-30.

Overall, the manuscript is well written. However, the authors tend to refer more to figures and tables included in the supplemental material. I suggest that the authors perform an editorial review before resubmitting the manuscript in order to improve its readability in this regard.
We agree that the manuscript often refers to the supplementary material. However, we prefer to limit the number of figures in the paper to avoid overloading it with many similar looking figures. If the reviewer strongly recommends adding figures from the SI in the manuscript, we will consider this request.

Minor comments:

1. Page 8, Line 214: Earlier you mentioned that within the model, the pH may evolve dynamically. Now you use a fixed pH values. Just to clarify. This also means that H+ is fixed and does not dynamically evolve?
In cloud chemistry models (see Barth et al., 2021), the pH (i.e., aqueous concentration of H+) can be calculated by different methods. Some models diagnose the H+ concentration based on the electroneutrality equation for cations and anions; other models such as ours predict H+ concentration explicitly as part of the solution of the set of chemistry ODEs (Ordinary differential equations). This means that H+ is a chemical compound numerically treated by the model just like other chemical compounds and H+ is explicitly produced and lost in acid-base reactions.
In the present work, we decided to keep the pH (H+ concentration) in our simulations at a constant value (5.5) like the microphysical cloud properties (LWC and radius are maintained constant). The idea behind this was to perform sensitivity tests independently modulating the pH (5.5 vs 4.5) and the LWC values.

2. Page 9, Line 236: How sensitive do you expect your results to be, considering assuming a 50/50 ratio? Please elaborate.

The study from Husárová et al. (2011) analyzed the biotransformation of 13C-formaldehyde at 5 °C and 17 °C was studied by 1H NMR or 13C NMR spectroscopy. They showed that a selection of bacterial strains was able to oxidize formaldehyde into formic acid.


This Figure from Husárová et al. (2011) illustrates the known (non-exhaustive) degradation pathways of C1 compounds by radical chemistry and by metabolism. Known enzymes are described for each reaction in which they can be involved, as well as cofactors (NAD: Nicotinamide adenine dinucleotide; H4F: Tetrahydrofolate; H4MPT: Dephospho-tetrahydromethanopterin; GSH: Glutathione; MySH: Mycothiol). This shows the complexity of the biotransformation of formaldehyde that can be both oxidized or reduced depending on the bacteria metabolism regulation. Some studies have also shown that formaldehyde can also enter serine metabolism potentially leading to larger molecules.
In this study, regarding the biodegradation of formaldehyde, we considered that bacteria lead to the formation of methanol and formic acid, with a 50/50 ratio. To evaluate how this ratio can modified our main conclusion, we performed sensitivity tests considering 25/75 to 75/25 ratios. These tests have been done for the summer daytime simulation where the contribution of biodegradation of formaldehyde leads to a significant production of formic acid.



Figures above respectively display the variations in formic acid concentration for these three ratios, in cloud water only (left), and the gaseous + aqueous phases (right). As expected, we observed that formic acid can be modified with modifying the methanol/formic acid ratio The final and maximum variations, after a 6 h cloud simulation, are ± 6% in comparison to our reference case (50/50).
Aqueous methanol variation is more significant (± 22%). But most of the methanol (99.3%) transfers to the gas phase, where it reacts with the HO• radical producing formaldehyde. However, this does not modify the gaseous formaldehyde budget in our simulation since this source is not dominant. No other impact on the concentration of the other compounds (acetic acid, formaldehyde, and other organic compounds) is observed. We also performed the same tests for other environmental conditions and the conclusions are similar.
Sensitivity tests that we decided to present in this paper have been selected based on robust field data (cell concentrations, LWC, pH…). Since the uncertainty associated with the determination of the methanol/formic acid ratio is rather important, we think that, as a first step, the chosen 50/50 ratio is reasonable. We added in the manuscript, line 282-283, page 9:
“A ratio 50/50 was chosen since no quantitative data exist on the modulation of the methanol/formic acid ratio by bacteria metabolism.”

3. Page 12, Line 311 to 315: Would this mean that you expect your results to be biased high? If so, to which degree?
Considering the effect of microorganisms in a model is challenging. Our approach is to determine biodegradation rates in the lab by performing incubation experiments on microbial strains collected in situ and cultivated in the lab. Of course, we are a little (!) far from what happens in a real cloud with a much more diverse microflora. The survival and microbial activity are certainly modulated by several environmental factors that are not well-characterized at this time (pH, oxidant concentration, light exposure…) (Amato et al., 2005; Joly et al., 2015; Anglada et al., 2015 ; Wirgot et al., 2017 ; Peng et al., 2019; Liu et al., 2023). This point is now discussed in the conclusion (Line 8-15, Page 30).
We decided in this study to evaluate the role of microorganisms in an ideal framework for their activities by considering different environmental parameters. We are aware of the limitations of our study/approach and that our conclusions could be modulated in the future if more accurate information on biological activities under atmospheric conditions becomes available. To our point of view, we can still have some confidence in the model because the biodegradation rates implemented in the model present same orders of magnitude as the ones determined in real clouds with endogenous microorganisms.
But for now, constraining the model requires more field and laboratory investigations. So, it is not yet possible to argue to which degree our results could be biased high.

References:
P. Amato, M. Ménager, M. Sancelme, P. Laj, G. Mailhot and A.-M. Delort, Microbial population in cloud water at the Puy de Dôme: Implications for the chemistry of clouds, Atmospheric Environment, 2015, 39-22, 4143-4153.
M. Joly, P. Amato, M. Sancelme, V. Vinatier, M. Abrantes, L. Deguillaume and A.-M. Delort, Survival of microbial isolates from clouds toward simulated atmospheric stress factors, Atmospheric Environment, 2015, 117, 92-98.
J. M. Anglada, M. Martins-Costa, J. S. Francisco and M. F. Ruiz-López, Interconnection of reactive oxygen species chemistry across the interfaces of atmospheric, environmental, and biological processes, Accounts of Chemical Research, 2015, 48 (3), 575-583.
N. Wirgot, V. Vinatier, L. Deguillaume, M. Sancelme and A. M. Delort, H2O2 modulates the energetic metabolism of the cloud microbiome, Atmospheric Chemistry and Physics, 2017, 17, 14841-14851.
J. Peng, S. Zhou, K. Xiao, J. Zeng, C. Yao, S. Lu, W. Zhang, Y. Fu, Y. Yang, and X. Bi, Diversity of bacteria in cloud water collected at a National Atmospheric Monitoring Station in Southern China, Atmospheric Research, 2019, 218, 176-182.
Y. Liu, C.K. Lim, Z. Shen, P. Lee and T. Nah, Effects of pH and light exposure on the survival of bacteria and their ability to biodegrade organic compounds in clouds: Implications for microbial activity in acidic cloud water, Atmospheric Chemistry and Physics, 23, 1731-1747, 2023.

4. Figure 3: In this figure, different scales are used for night and daytime within the same subplot. The reader might get the impression that night and daytime concentrations might be comparable. At least mention this in the caption, in order to avoid confusion.
We agree with your comment. We added the following sentence in the legend of Figure 3: “Note that a secondary scale for formaldehyde and hydrogen peroxide concentrations is used to represent their lower concentrations at night.”
Why not use a), b), c) labels as done in figure 6?
We added labels on Figure 3 and consequently modified the text where this figure is cited.

5. Page 15, Line 382 to 384: This statement should be moved to the methodology section.
Yes, we agree. We followed your comment and modified the manuscript and SI.

6. Figure 4: Same comment as for figure 3.
We agree with your comment. We added the following sentence in the legend of Figure 4: “Note that the scale for the rates is different for simulations at day and night.”.
We also added labels on Figure 4 and consequently modify the text where this figure is cited.

7. Figure 6 and text: Within cloud droplets, I would expect that formaldehyde is mainly present in its hydrated form (methandiol). When talking about the total concentrations, do you also consider the hydrated form of the respective compound for this sum?
Yes, formaldehyde is mainly present in the aqueous phase as in its hydrated form. When performing the chemical budget (both the aqueous concentration and the total concentration), we consider all the forms of formaldehyde.

19-Feb-2023

Dear Dr Deguillaume:

Manuscript ID: EA-ART-11-2022-000153.R1
TITLE: Assessing the efficiency of water-soluble organic compounds biodegradation in clouds under various environmental conditions

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Reviewer 4

Pailler et al. address the importance of the biodegradation of water-soluble organic compounds in cloud droplets using a sound methodology. Following the first review iteration, the authors provide sufficient elaborations on all my comments and the comments of the other reviewers. In addition, they provide extensive sensitivity simulations to back their statements. The resulting changes to the manuscript and supplementary material are reasonable, such that I can recommend publication.