From the journal Environmental Science: Atmospheres Peer review history

05-Feb-2023

Dear Dr Berkemeier:

Manuscript ID: EA-ART-12-2022-000179
TITLE: Influence of ambient and endogenous H₂O₂ on reactive oxygen species concentrations and OH radical production in the lung

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Reviewer 1

Overall, I think this is a very interesting paper which is well written, easy to follow and well within the scope of the journal. I agree with the authors that it is likely to generate interesting scientific discussion and it has the potential to change how acellular assays are used for studying the health effects of ambient air pollution. While there are several uncertainties in the model (such as e.g the contribution of SOA to OH production), assumptions are well explained and the sensitivity of the model has been well explored. In principle, I would therefore really like to see these results published

However, I have one major point of criticism: as far as I can see, the authors have used ambient concentrations of pollutants to simulate the gas phase in the lung. It is well established that water-soluble gases are easily taken up in the upper airways, which is why they are generally not considered when looking into effects on the lungs. Since H2O2 is highly water soluble, I would expect the concentrations in the lung to be far below ambient levels. As far as I can see, this reduction in pollutant concentration upon transport into the lung has not been taken into account in the model.

Unless I have overlooked this information somehow, I really think that the simulations would need to be redone in a way that accounts for this issue, since this could substantially change the results regarding the exogenous contributions to H2O2 in the lung lining fluid.

Specific comments:

Page 5, line 30-32: While this statement is true for the widely used DTT assay, I would argue that this is a bit of an overstatement since other acellular assays such as ascorbic acid can be quite sensitive to iron.

Page S1, line 24-25: I believe you mean KM-SUB-ELF here (the older model)?

Reviewer 2

Dovrou et al. developed a KM-SUB-LUNG kinetic model to study the sources of H2O2 in lung lining fluid. The main finding are H2O2 concentrations in lung lining fluid is determined by gas phase diffusion and endogenous H2O2 not the chemical production by inhaled PM2.5. They then called cautions to use the so-called oxidative potential to assess adverse health effects. This study provides new insights on understanding the H2O2 sources in lung. However, there are a few concerns need to be addressed before considering this manuscript for publication.
1. One major criticism is that the paper draws the conclusion that PM2.5 may play a minor role compared to other ROS sources and adverse health effects may not be primarily related to oxidative potential of PM2.5. “Our findings suggest that the adverse health effects may not be primarily related to direct chemical production of H2O2 and the so-called oxidative potential of PM2.5,” I believe this statement is too strong. Since the findings are limited to H2O2, while oxidative potential includes the production of other ROS such as superoxide, singlet oxygen etc, it cannot be deduced that oxidative potential may not be related to adverse health effects. I suggest the author revise their statement by specifying the adverse health effects related to H2O2 alone.

2. The authors have highlighted a recent work by Fang et al. 2022 who find that cellular superoxide release can dominate chemical production when exposed to PM in the lung. The authors have acknowledged that this can “tip the scale fully in favor of endogenous sources of H2O2”. Fang et al. reported values for O2- production rates. I wonder if it is possible to implement the reported numbers in this model and estimate the amount of H2O2 from this source. This will directly address if the scale can be tipped and be very helpful. This can completely change the main conclusions in this work.

3. I find it hard to figure what are the major new findings with regards to OH? Please clarify. The title states the “influence of H2O2 on OH production” but the abstract did not mention any model results on OH. Please summarize the major finding for the influence of H2O2 on OH production or revise the title.

4. Epithelial cells are also contributors to ROS, which is not considered in this model.

5. “endogenous H2O2 is produced at a constant rate of 7.7×10^11 cm-3 s-1”, can the authors clarify how the number was converted from literature values? Values from the cited references are in ranges while here a certain number was used. What are the uncertainties associated with endogenous H2O2?

6. It would be helpful to add discussion on the sources of gas-phase ambient H2O2 as this work finds it so major.
7. Discussions on uncertainties are largely lacking. For example in Figure 2A, one would wonder what are the confidence ranges for the solid lines? Since this is a modeling work, uncertainties analyses should be not omitted.

Response to reviewer 1

In the following, we will address the reviewer comments (blue italic font, enumerated) in point-by-point responses (black). Citations from the revised manuscript will be presented in indented paragraphs with changes marked in red font.

Overall, I think this is a very interesting paper which is well written, easy to follow and well within the scope of the journal. I agree with the authors that it is likely to generate interesting scientific discussion and it has the potential to change how acellular assays are used for studying the health effects of ambient air pollution. While there are several uncertainties in the model (such as e.g the contribution of SOA to OH production), assumptions are well explained and the sensitivity of the model has been well explored. In principle, I would therefore really like to see these results published

We would like to thank the reviewer for the positive evaluation and valuable comments that help improve our manuscript.

Comment 1. However, I have one major point of criticism: as far as I can see, the authors have used ambient concentrations of pollutants to simulate the gas phase in the lung. It is well established that water-soluble gases are easily taken up in the upper airways, which is why they are generally not considered when looking into effects on the lungs. Since H2O2 is highly water soluble, I would expect the concentrations in the lung to be far below ambient levels. As far as I can see, this reduction in pollutant concentration upon transport into the lung has not been taken into account in the model.

Unless I have overlooked this information somehow, I really think that the simulations would need to be redone in a way that accounts for this issue, since this could substantially change the results regarding the exogenous contributions to H2O2 in the lung lining fluid.

We thank the reviewer and agree that the concentration of a water-soluble gas like H2O2 in the lung is much lower than in the ambient atmosphere and this is explicitly considered and reflected in the model. We also agree that this was not clear from the manuscript text. The model uses an ambient atmospheric concentration to calculate the influx of trace gases like H2O2 into the lung gas phase as a separate compartment (Fig. 1), in which they are subject to reactive uptake. The availability of these gases is thus limited by the inhalation rate. The steady-state gas phase concentration of H2O2 in the lung gas phase in a typical pollution scenario (1 ppb H2O2, 30 µg/m3 PM2.5, 30 µg/m3 NO2, 30 ppb O3) is approximately 4 orders of magnitude lower at ~0.1 ppt. Previously, this was only mentioned implicitly:

l. 130 – “The cellular consumption causes the human respiratory tract to act as a net sink of ambient H2O2, i.e., inhaled concentrations are generally larger than exhaled concentrations (Fig. S2).”

and shown for H2O2 in Fig. S2c. We changed the following statement in the methods section to be clearer:

l. 75 – “The model simulates a 2 h exposure window in which pollutants are inhaled into the lung gas phase of the respiratory tract with a breathing rate of 1.5 m3 h-1.”

We now show the lung gas phase concentrations of all inhaled gases (H2O2, NO2, O3) in comparison with their atmospheric/inhaled concentrations in a new Fig. S3 and added the following sentence to the main text:

l. 132 – “This leads to very low gas-phase concentrations of H2O2 in the respiratory tract in the sub-ppt range (Fig. S3).”

[IMAGE OF NEW FIG. S3]

Fig. S3. Comparison of ambient and respiratory tract gas-phase concentrations. The ambient (dotted lines) and respiratory tract (solid lines) gas-phase concentrations of H2O2 (blue), O3 (purple), and NO2 (black line) as a function of various model parameters: (a) effective membrane permeability coefficient of H2O2, (b) H2O2-scavenging enzyme concentration in cells, (c) ambient PM2.5 concentration, and (d) cellular H2O2 production rate in a standard pollution scenario (PM2.5=30 µg m-3, NO2=30 µg m-3, O3=30 ppb, H2O2=1 ppb) unless otherwise indicated. All respiratory tract / exhaled concentrations are significantly below their ambient / inhaled concentrations due to reactive uptake to the epithelial lining fluid. The parameters used in the standard scenario in this study are marked with a vertical dashed line.

Further, we modified Fig. 1 and added the ambient gas phase into the schematic so the distinction to the gas phase in the respiratory tract becomes clearer.
We also agree with the reviewer that soluble trace gases such as H2O2 will exhibit a gradient from high to low concentration and hence the upper respiratory tract will experience a stronger influx of ambient H2O2 than the alveolar sacs. Thus, our model results have to be interpreted as averages over the entire respiratory tract.

l. 81 – “For simplicity, the model does not resolve concentration gradients of gases or particulates between the upper and lower parts of the respiratory tract.”

We want to make this limitation of the model very clear in this work. When discussing the role of ambient gas-phase H2O2 on the ROS budget, we now emphasize that this is likely more relevant for the upper respiratory tract:

l. 155 – “We note that these numbers must be interpreted as averages over the entire respiratory tract and concentrations of the water-soluble gases H2O2 and O3 will be higher in the upper parts of the respiratory tract (Hlastala et al., 2013), which likely decreases their importance for the deep lung as detailed in the ESI (Sect. S7).”

l. 184 – “Because of the high water-solubility and efficient enzymatic removal of H2O2, we expect this effect to be most relevant in the upper respiratory tract.”
If the reviewer knows more or better references for the concentration gradients of soluble trace gases in the respiratory tract, we would be very thankful.

We stress, however, that the main result of this work, the influence of direct chemical production of PM2.5 being insignificant compared to other sources, is not affected by this assumption of the model. We tried implementing a differentiation between respiratory tract compartments into the model for this revision and preliminary results showed that the order of the relative importance of H2O2 sources (endogenous > ambient gas phase > ambient particulate phase) is very robust. The model we tested showed that between 10 and 90 % of ambient gas-phase H2O2 will be consumed in the extrathoracic space (nasal cavity to trachea). While this estimation decreases the importance of ambient H2O2 for the deep lung, direct chemical production from PM2.5 should remain a negligible source of H2O2 in the model. Due to the much more complicated model setup, which is still in development, and the added uncertainty of including many further input parameters, we think that such a model upgrade is outside the scope of this work, and will be addressed in future publications. We added the following statement to the manuscript:

l. 193 – “This follow-up study will also address the gradients of water-soluble trace gases between the upper and lower respiratory tract. Both aspects may reconcile the agreement with measurement data and would decrease the importance of ambient H2O2 for the deep lung. First preliminary estimations are outlined and discussed in ESI Sect. S7.”

In the Supplementary Information, we added the following statement to Sect. S7:

“The model structure of the respiratory tract could be further subdivided, for example into extrathoracic, bronchial, and alveolar space (Fig. S9), which likely experience different concentrations of deposited PM2.5 and inhaled water-soluble trace gases such as H2O2, NO2 and O3. Preliminary calculations separating upper and lower respiratory tract show that between 10-90 % of ambient gas-phase H2O2 will be consumed in the extrathoracic space (nasal cavity to trachea) alone, depending on factors such as geometry, ELF volume, H2O2-scavenging enzyme concentrations, and membrane permeability in the upper respiratory tract. The exhaled H2O2 concentrations in this early test simulation were strongly increased, due to a higher saturation of the extrathoracic ELF with H2O2, coming much closer to values reported for exhaled breath condensate. We expect that the upgraded model structure and inclusion of macrophages as additional endogenous ROS sources will reconcile the agreement with these measurements, and decrease the importance of ambient H2O2 for the deep lung. However, this model upgrade is still in development, outside the scope of this work, and will be addressed in future publications. Nevertheless, this work provides important insights in the evaluation of toxicity of the main air pollutants and the effect of endogenous processes to ROS levels in the respiratory tract.”

[IMAGE OF NEW FIG. S9]

Fig. S9. Proposed model structure for follow-up study. To improve the model, we suggest a sub-division of the respiratory tract. Shown here is a schematic representation of the respiratory tract in three parts: extrathoracic, bronchial and alveolar space. Geometry of those three spaces will be different with the extrathoracic space having the largest layer thicknesses but least overall surface area, and the alveolar space the smallest layer thicknesses but largest surface area, respectively (schematic not to scale). The gas-phase compartments will be connected through a fast flux according to the breathing rate.

Furthermore, we replace most instances of “lung” in the manuscript to “respiratory tract”, including the title, and rename the model from KM-SUB-LUNG to “kinetic multi-layer model of surface and bulk chemistry in the lung epithelial lining fluid of the respiratory tract (KM-SUB-ELF 2.0)”, so the reader does not have the impression that this model is only of the lung (and not the entire respiratory tract). Likewise, we change all mention of “lung lining fluid” and “LLF” to “epithelial lining fluid” and “ELF”, respectively.

Title – “Influence of ambient and endogenous H2O2 on reactive oxygen species concentrations and OH radical production in the lung respiratory tract”

l. 50 – “In this work, we develop and apply a detailed kinetic multi-layer model of surface and bulk chemistry in the lung epithelial lining fluid of the respiratory tract (KM-SUB-LUNG KM-SUB-ELF 2.0) to quantify the interplay of chemical ROS production from PM2.5 with the diffusion and exchange of H2O2 between the air, LLF ELF, cellular tissues and blood vessels in the respiratory tract.”

l. 68 – “The model is an extended version of the model KM-SUB-ELF (Lakey et al., 2016; Lelieveld et al., 2021), which it expands through the inclusion of the cellular layer, the blood layer, and H2O2 as gas-phase pollutant.”

Comment 2. Specific comments:
Page 5, line 30-32: While this statement is true for the widely used DTT assay, I would argue that this is a bit of an overstatement since other acellular assays such as ascorbic acid can be quite sensitive to iron.

We agree with the reviewer that this sentence may not reflect all types of OP assays. We toned down the message and changed this sentence to:

l. 229 – “Acellular oxidative potential assays, which are commonly used to assess potential PM2.5 toxicity, tend to be most sensitive to superoxide and H2O2 producers such as copper ions and quinones and less sensitive to •OH producers like iron ions.”

Comment 3. Page S1, line 24-25: I believe you mean KM-SUB-ELF here (the older model)?

We thank the referee for pointing this out and corrected the oversight.
 

Response to reviewer 2

In the following, we will address the reviewer comments (blue italic font, enumerated) in point-by-point responses (black). Citations from the revised manuscript will be presented in indented paragraphs with changes marked in red font.

Dovrou et al. developed a KM-SUB-LUNG kinetic model to study the sources of H2O2 in lung lining fluid. The main finding are H2O2 concentrations in lung lining fluid is determined by gas phase diffusion and endogenous H2O2 not the chemical production by inhaled PM2.5. They then called cautions to use the so-called oxidative potential to assess adverse health effects. This study provides new insights on understanding the H2O2 sources in lung. However, there are a few concerns need to be addressed before considering this manuscript for publication.

We would like to thank the reviewer for the positive evaluation and valuable comments that help improve our manuscript.

Comment 1. One major criticism is that the paper draws the conclusion that PM2.5 may play a minor role compared to other ROS sources and adverse health effects may not be primarily related to oxidative potential of PM2.5. “Our findings suggest that the adverse health effects may not be primarily related to direct chemical production of H2O2 and the so-called oxidative potential of PM2.5,” I believe this statement is too strong. Since the findings are limited to H2O2, while oxidative potential includes the production of other ROS such as superoxide, singlet oxygen etc, it cannot be deduced that oxidative potential may not be related to adverse health effects. I suggest the author revise their statement by specifying the adverse health effects related to H2O2 alone.

We thank the reviewer for the comment and agree that we cannot deduce that oxidative potential is not related to adverse health effects. Our results show that the role of PM2.5 in air pollution health effects is likely not direct chemical production of H2O2 (which is the most prevalent ROS), but rather the conversion of peroxides into more reactive species that cannot be scavenged by antioxidants and enzymes. Many oxidative potential assays are sensitive to such direct chemical production of H2O2 though, which was the point we were trying to make. We agree, however, that oxidative potential is not only due to H2O2 production and rephrased the statement:

l. 19 - “Hence, our findings suggest that the adverse health effects of PM2.5 may not be primarily related to direct chemical production of H2O2 and the so-called oxidative potential of PM2.5, but rather to the conversion of peroxides into more reactive species such as the hydroxyl •OH radical, or the stimulation of biological ROS production.”

Accordingly, we refined our statement later in the manuscript text:

l. 229 – “Acellular oxidative potential assays, which are commonly used to assess potential PM2.5 toxicity, tend to be most sensitive to superoxide and H2O2 producers such as copper ions and quinones and less sensitive to •OH producers like iron ions (Charrier and Anastasio, 2012).”

Further, we added the word “some” when referring to acellular oxidative potential assays,

l. 238 – “We therefore propose that chemical production of superoxide and H2O2 in a cell-free assay may not be a suitable metric for assessing the differential toxicity of individual PM2.5 components and some acellular oxidative potential assays may not capture the actual deleterious effects of PM2.5.”

and added citations referring to acellular assays that selectively measure hydroxyl radical production:

l. 241 – “Alternatives may be cellular cytotoxicity assays using air–liquid interface cell culture (Lacroix et al., 2018) or acellular assays determining the production of •OH radicals (Gonzalez et al., 2021), preferably in the presence of physiological concentrations of H2O2.”

Comment 2. The authors have highlighted a recent work by Fang et al. 2022 who find that cellular superoxide release can dominate chemical production when exposed to PM in the lung. The authors have acknowledged that this can “tip the scale fully in favor of endogenous sources of H2O2”. Fang et al. reported values for O2- production rates. I wonder if it is possible to implement the reported numbers in this model and estimate the amount of H2O2 from this source. This will directly address if the scale can be tipped and be very helpful. This can completely change the main conclusions in this work.

We agree with the reviewer that inclusion of endogenous superoxide production rates will be an important next step in the development of these models. The implementation of superoxide production rates, however, requires knowledge of many additional and uncertain model parameters. Fang et al. demonstrate this effect by adding SOA and quinones, i.e. only two of components of PM2.5, and use concentrations of these pollutants that are larger than achieved in typical pollution scenarios. Furthermore, we do not know how exactly the macrophage concentration in the assay relates to the ELF of the respiratory tract. Thus, despite the impressive steps undertaken by Fang et al. to explain ROS formation from endogenous sources by PM2.5 constituents, the numbers are not directly transferable to a model such as KM-SUB-ELF 2.0. We note that also the work by Fang et al. did not include this endogenous production rate into their model, KM-SUB-ELF, but compared the experimentally determined numbers to a simulation without endogenous influence. Thus, we conclude that this implementation is out of scope for the current publication and something we will follow up on it in future studies. The main conclusions of our work are formulated in a way that will not change when including another superoxide source:

l. 15 – “Here, we show that H2O2 concentrations in the ELF may be primarily determined the release of endogenous H2O2 and the inhalation of ambient gas-phase H2O2, while the chemical production of H2O2 through inhaled PM2.5 is less important.”

l. 19 – “Hence, our findings suggest that the adverse health effects of PM2.5 may not be primarily related to direct chemical production of H2O2, but rather to the conversion of peroxides into more reactive species such as the •OH radical, or the stimulation of biological ROS production.”

Comment 3. I find it hard to figure what are the major new findings with regards to OH? Please clarify. The title states the “influence of H2O2 on OH production” but the abstract did not mention any model results on OH. Please summarize the major finding for the influence of H2O2 on OH production or revise the title.

We thank the reviewer for pointing out that the results regarding OH production (i.e., Fig. 3B, D) were only indirectly mentioned in the abstract. In the model, OH production is mainly due to the transition metals contained in PM2.5 that enable the conversion of peroxides to OH through Fenton chemistry (Figs. S7, S8; Sect. S5). We added the following sentence:

l. 17 - “The production of hydroxyl radicals (•OH), however, was strongly correlated with Fenton chemistry of PM2.5 in the model calculations.”

Comment 4. Epithelial cells are also contributors to ROS, which is not considered in this model. “endogenous H2O2 is produced at a constant rate of 7.7×10^11 cm-3 s-1”, can the authors clarify how the number was converted from literature values? Values from the cited references are in ranges while here a certain number was used. What are the uncertainties associated with endogenous H2O2?

We thank the reviewer for this comment, which prompted us to re-evaluate the values we found in our literature search. The cellular production rate of H2O2 spans several orders of magnitude in the literature and we agree that the previously used value of 7.7×1011 cm-3 s-1 may have given a false sense of certainty. Furthermore, it may not represent a good estimate for lung epithelial cells. We now derive an estimate from two studies that investigated type II alveolar cells as detailed in Sect. S4 of the Supplementary Information.

“Section S4: Production of H2O2 in the cellular layer
Respiratory cells are known ROS producers, among them type II alveolar cells and endothelial cells. Type II alveolar cells constitute about 4 % of the alveolar surface area and 10-15 % of all lung cells (Castranova et al., 1988). Kinnula et al. (1991) find a production rate of 0.7 nmol H2O2 min-1 mg protein-1 for type II alveolar cells and 0.06 nmol H2O2 min-1 mg protein-1 for endothelial cells. Piotrowski et al. (2000) find a baseline production of type II alveolar cells of 0.15 nmol H2O2 min-1 mg protein-1. We estimate from literature that the protein mass density is in the range of 150 – 300 mg cm-3 (Albe et al., 1990). Using these numbers, we can derive a best estimate for the cellular H2O2 production in the range of 2×1013 – 5×1014 cm-3 s-1 as detailed below. Note, however, that these measurements stem from in vitro experiments using rat alveolar cells and can only be regarded as order of magnitude estimates for the human lung. We thus choose a central value of 1×1014 cm-3 s-1 from this range for the calculations in this study.

Upper Estimate: 15 % of cells are type II alveolar cells (0.7 nmol mg protein-1 min-1) and the rest behave like endothelial cells (0.06 nmol mg protein-1 min-1) at a protein mass density of 300 mg cm-3

(0.15⋅0.7 nmol mg^(-1) min^(-1) + 0.85 ⋅ 0.06 nmol mg^(-1) min^(-1) ) ⋅ 300 mg cm^(-3) ⋅ 6.022⋅10^23 mol^(-1) ⋅ 1/60 min s^(-1) ≈ 5⋅10^14 cm^(-3) s^(-1)

Lower Estimate: 10 % of cells are type II cells (0.15 nmol mg protein-1 min-1) at a protein mass density of 150 mg cm-3 and the remaining cells do not produce significant amounts of H2O2

(0.1⋅0.15 nmol mg^(-1) min^(-1) ) ⋅ 150 mg cm^(-3) ⋅ 6.022⋅10^23 mol^(-1) ⋅ 1/60 min⁡ s^(-1) ≈ 2⋅10^13 cm^(-3) s^(-1) “

We updated all calculations and figures in the manuscript according to this input parameter change. The cellular production rate of H2O2 becomes a sensitive model parameter and now contributes significantly to the endogenous H2O2 sources in ELF. For example, the contribution of endogenous sources to the H2O2 concentrations in the ELF increased from 51 % to 59 % in the clean urban scenario and from 34 % to 40 % in the polluted urban scenario (Fig. 2b). The concentration of H2O2 in ELF under the standard pollution scenario (and using default model input parameters) increases slightly from 4.7 nM to 5.7 nM (Fig. 2a). The major conclusions of this work, however, remain unchanged.

l. 146 - “The endogenous sources are dominated by include transport from the blood stream, with only minor contributions (<0.1%) from and production of H2O2 in the cellular layer.”

We note that while the contribution of this H2O2 source is not dominant, the high model sensitivity towards this parameter leaves the possibility that endogenous ROS production is the clearly dominating H2O2 source in the ELF. This was previously only shown in SI figure S4d. We now point this out in the main text and mention the general uncertainty associated with this model parameter:

l. 158 - “A very sensitive and rather uncertain model parameter in these calculations is the cellular production rate of H2O2, for which we can give only an order of magnitude estimate as detailed in the ESI (Sect. S4), and which may dominate H2O2 sources at higher values (Fig. S4d).”

l. 190 - “The deviation from measurements may be due to an underestimation of the H2O2 production rate of epithelial cells (ESI Sect. S4) or a missing endogenous source of ROS, possibly superoxide production by alveolar macrophages, which will be further investigated in a follow-up study.”

Comment 5. It would be helpful to add discussion on the sources of gas-phase ambient H2O2 as this work finds it so major.

Following the reviewer’s suggestion, we added the following sentence to the manuscript:

l. 45 – “Atmospheric H2O2 is mainly produced via self-reaction of the hydroperoxyl radical (HO2•), which is an abundant atmospheric radical and formed in many (photo-)chemical processes (ESI Sect. S1) (Vione et al., 2003; Seinfeld and Pandis, 2016).”

We added a more in-depth discussion to Sect. S1 of the Supplementary Information.

“Gas-phase H2O2 is mainly produced via dismutation of the hydroperoxyl radical (HO2•), which is formed by atmospheric photochemical processes. HO2•is generated by the photolysis of formaldehyde and by reactions between hydroxyl radicals (•OH) and hydrocarbons. The formation of •OH is triggered by photolysis of ozone, producing molecular and atomic oxygen, with the latter reacting with water vapor yielding •OH. Subsequently, •OH reacts with hydrocarbons acting as a final source of HO2•and H2O2. Anthropogenic sources, such as biomass burning, fire plumes, combustion facilities and vehicle exhausts yield both HO2•and formaldehyde, contributing to H2O2 formation. An important source of high concentrations of H2O2 is thunderstorms, which produce H2O2 via electrical discharges during high electric field conditions. Thus, gas-phase H2O2 is continuously produced in the atmosphere via both natural and anthropogenic sources and constitutes the most abundant peroxide (Vione et al., 2003; Seinfeld and Pandis, 2016).”

Comment 6. Discussions on uncertainties are largely lacking. For example in Figure 2A, one would wonder what are the confidence ranges for the solid lines? Since this is a modeling work, uncertainties analyses should be not omitted.

We would like to thank the reviewer for the comment and have added discussion on model uncertainty as outlined in previous responses to both reviewers. We think that including confidence bands would make Fig. 2 very overloaded and confusing. We prefer to address model uncertainty quantitatively with sensitivity studies to specific parameters in the Supplementary Information (Figs. S1-3, 6-7). Alongside these figures, we now included the following discussion in the Supplementary Information as new Sect. S7:

“Section S7: Discussion of model sensitivity and limitations

The model presented in this work, KM-SUB-ELF 2.0, describes chemical mechanisms and transport of ROS within the respiratory tract, aiming to investigate the influence of ambient and endogenous H2O2 as well as main air pollutants in ROS production. Due to the complexity of the respiratory tract, KM-SUB-ELF 2.0 outlines the essential processes to provide a first estimate on evaluating the parameters influencing ROS production. A key model parameter is the effective membrane permeability of H2O2 (μeff. Figures 2a and S1a show the concentrations of H2O2 in all model compartments as a function of μeff. The concentrations in atmosphere and blood in the model are not affected, but the ELF and cellular concentrations are strongly influenced by μeff. The reported range of cellular H2O2 (1-10 nM) constrains this important model parameter. The value of μeff also affects the •OH source (Fig. S7a): at the best guess value for μeff, •OH production is mainly due to Fenton chemistry of peroxides contained in SOA, while at very high or very low μeff, Fenton chemistry of H2O2 is the dominant •OH source.
It is important to note that the •OH yield from SOA is a challenging topic, requiring further investigation and thus contributing to the uncertainty of the calculations. This topic is discussed in detail in Sect. S5, but a more in-depth investigation is out of the scope of this study.
Another important parameter influencing the ROS levels in the respiratory tract is the cellular concentration of H2O2-scavenging enzymes. Figure S1b shows a linear dependence of H2O2 concentration in the cells and a similarly strong dependence of H2O2 concentration in the LLF, which levels off towards very high enzyme concentrations.
The concentration of PM2.5 determines the production of •OH in the ELF, as shown in Figure 3B and D. The concentrations of H2O2 in ELF and respiratory tract gas phase, however, are not affected by PM2.5 concentrations (Figs. S1c, S3c). PM2.5 becomes a significant source of ELF only at the highest concentrations investigated in this study (1000 µg/m3; Fig. S4c), which are exceedingly high even for the most polluted cities on Earth.
We finally note that the biological mechanisms contributing to ROS production and consumption are strongly simplified in the model. Future work is needed to include biological sources of superoxide and their stimulation with PM2.5 (Fang et al., 2022). The model structure of the respiratory tract could be further subdivided into bronchi, bronchioles and alveoli, as further discussed in Sect. S7, which likely experience different concentrations of deposited PM2.5 and inhaled water-soluble trace gases such as H2O2, NO2 and O3. However, in accordance with the current literature, this work provides important insights in the evaluation of toxicity of the main air pollutants and the effect of endogenous processes to ROS levels in the respiratory tract.”


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28-Apr-2023

Dear Dr Berkemeier:

Manuscript ID: EA-ART-12-2022-000179.R1
TITLE: Influence of ambient and endogenous H₂O₂ on reactive oxygen species concentrations and OH radical production in the respiratory tract

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Reviewer 2

All my previous concerns and comments are well addressed and I have no further suggestions.

Reviewer 1

All my comments have been sufficiently addressed and I am happy to recommend this manuscript for publication.

I have one very minor comment regarding the revision: in the new section S7, it is stated that "PM2.5 becomes a significant source of ELF only at the highest concentrations investigated in this study". I would assume that the authors mean "PM2.5 becomes a significant source of H2O2 in ELF only at the highest concentrations investigated in this study", is that correct?