From the journal Environmental Science: Atmospheres Peer review history

15-Aug-2023

Dear Dr Jathar:

Manuscript ID: EA-ART-07-2023-000111
TITLE: Multi-Day Photochemical Evolution of Organic Aerosol from Biomass Burning Emissions

Thank you for your submission to Environmental Science: Atmospheres, published by the Royal Society of Chemistry. I sent your manuscript to reviewers and I have now received their reports which are copied below.

I have carefully evaluated your manuscript and the reviewers’ reports, and the reports indicate that major revisions are necessary.

Please submit a revised manuscript which addresses all of the reviewers’ comments. Further peer review of your revised manuscript may be needed. When you submit your revised manuscript please include a point by point response to the reviewers’ comments and highlight the changes you have made. Full details of the files you need to submit are listed at the end of this email.

Please submit your revised manuscript as soon as possible using this link:

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You should submit your revised manuscript as soon as possible; please note you will receive a series of automatic reminders. If your revisions will take a significant length of time, please contact me. If I do not hear from you, I may withdraw your manuscript from consideration and you will have to resubmit. Any resubmission will receive a new submission date.

The Royal Society of Chemistry requires all submitting authors to provide their ORCID iD when they submit a revised manuscript. This is quick and easy to do as part of the revised manuscript submission process. We will publish this information with the article, and you may choose to have your ORCID record updated automatically with details of the publication.

Please also encourage your co-authors to sign up for their own ORCID account and associate it with their account on our manuscript submission system. For further information see: https://www.rsc.org/journals-books-databases/journal-authors-reviewers/processes-policies/#attribution-id

Environmental Science: Atmospheres strongly encourages authors of research articles to include an ‘Author contributions’ section in their manuscript, for publication in the final article. This should appear immediately above the ‘Conflict of interest’ and ‘Acknowledgement’ sections. I strongly recommend you use CRediT (the Contributor Roles Taxonomy, https://credit.niso.org/) for standardised contribution descriptions. All authors should have agreed to their individual contributions ahead of submission and these should accurately reflect contributions to the work. Please refer to our general author guidelines https://www.rsc.org/journals-books-databases/author-and-reviewer-hub/authors-information/responsibilities/ for more information.

I look forward to receiving your revised manuscript.

Yours sincerely,
Dr Tzung-May Fu
Associate Editor
Environmental Science: Atmospheres
Royal Society of Chemistry

************

Reviewer 1

The authors used a process-level model to simulate the evolution of biomass burning organic aerosols from 18 chamber experiments from various fuels. The manuscript reveled discrepancies between the model simulations and observations in OA mass and O/C for all experiments and examined how different inputs based on observations and model adjustment affect the simulation OA profiles and properties. This work provides insight into the chemical evolution of biomass burning aerosols. I recommend the manuscript for publication after addressing the following comments:

1. The originality and significance of this modeling work is not fully emphasized. I suggest the authors condense the information on observational and laboratory findings and add discussions on the current status of modeling work on understanding BB SOA in the Introduction, and also highlight the novelty of this work compared to the existing modeling studies.

2. Section 2.2.2 model inputs in Methods is a bit hard to follow. I would consider shortening the text to only keep the key summary information and moving the details to the SI. I would also make a table here listing the different “sensitivity simulations” in Figure 1. The manuscript presented two types of simulation tests that are both referred to as “sensitivity” simulations: 1) the “sensitivity tests” related to model inputs, Figure 1, and Section 3.1 and 2) the “Sensitivity analysis” described in Section 2.2.3 & Section 3.3. This is quite confusing. I suggest the authors rename these two types of simulation predictions differently.

3. Line 106-418 the authors attributed the inconsistent decays between the model and observations to the inferences of the PTR-MS. Can the authors comment on the species included for the measurements vs. the model that you compared? Are they the exact same list of compounds? How about the possibility of misrepresented chemical and physical processes in the model? How other factors would affect the model-observation discrepancies?

4. There are several places in the text that doesn’t seem to be consistent with what is shown in the figures. I cannot fully agree with what the authors state for those results:

- Line 396, I would say the modeled OA has started to decrease shortly after 0.5 equivalent day, before 1 equivalent day.
- Line 420 – 421, figure 1c shows that the aromatic hydrocarbons actually had a larger contribution to SOA within
- Line 422-424, this is a strong statement. If SOA production was balanced by POA and SOA evaporation, total OA mass would be constant. However, Figure 1 shows that after one equivalent day of photochemical aging, OA decreased…The statement is inconsistent with the modeled results.
- Line 444-449. I would disagree with the statement that PWL produced a small change. How was the number 1% calculated? Figure 2a and 2c show that the differences between the measured and the modeled OA mass concentration at both the model peak and the end of the equivalent days are quite different from the differences in the base case in Figure 2b…
- Line 652 – 653, it is hard to differentiate rOH=1 and the average SOA precursor profile in the figure. The color is too close, and the solid vs dashed lines are not reflected in the legend. I would hesitate to agree with the authors on this statement because it seems that the het. Chem. rOH=1 yields higher OA than using the average SOA precursor profile.

5. Some statements in the manuscript would benefit greatly from additional evidence or proper reference to figures or tables. For example,
- Line 405 – 406, is this referring to figure 1d?
- Line 411-412, referring to Figure S7? Is the statement “SVOCs … depleted within 1 equivalent day” based on model results or observations? Figure 1c shows that within 1 equivalent days, SVOC had a minor contribution to SOA though…
- Line 418 – 419, burst in SOA production refers to Figure 1a?
- Line 458-460, what figures are the authors referring to? I don’t find the basis of this statement.
- Line 646, which trace in the figure does this sentence refer to?
- Line 650 – 651, do have the figure showing the VOCs and SOA precursors?

6. Line 512-514, is the statement on “this was linked to the near depletion of dominant SOA precursors” based on the model or the observations?

7. Line 530 – 532, did the authors model it, what is the basis of this statement? Any evidence or this is an educated guess?

8. Line 550-552, do the authors mean that the model underestimated OA mass for all types of fuels?

Other comments:
1. line 35, I would be more specific to say it is “on average of all experiments” to avoid confusion
2. line 395-396, the comparison was “mixed”. What do you mean by mixed? I would reword it.
3. Line 413, change “summed by precursor class, with…” to “grouped by precursor class, as a function of”
4. For all figures, please make the legend consistent with what is shown in the figures, for example, figure 1a and 1b shows solid line and dashed lines, but the legend shows solid bars.
5. Figure 5, please explain the N±50%, NZMB, and NME in the caption.
6. Figure 7, missing trace in the legend for Monte-Carlo simulations.

Reviewer 2

Reviewer report on
Multi-Day Photochemical Evolution of Organic Aerosol from Biomass Burning Emissions
by Dearden et al.

This study by Dearden et al. reported the modeling results on the photochemical evolution of organic aerosol (OA) derived from biomass burning emissions using the previously reported chamber experiments in Lim et al. (2019), which was part of FIREX. The process-level kinetic model (SOM-TOMAS) including key physical and chemical processes was used to simulate the time-dependent evolutions of the OA mass concentration and the oxygen-to-carbon ratio (O:C) over up to ~10 equivalent days of photochemical aging. The model was able to predict well the OA mass concentration and O:C at short photochemical ages (0.5–1 equivalent day), though the model and measurements started diverging at longer photochemical ages (>1 equivalent day). Sensitivity analysis was performed to explore the potential reasons of the model-measurement discrepancy at longer photochemical ages either by removing each specific process to see its impact on the model results or by adjusting model inputs or chemical schemes. The conclusion was that the model likely missed important SOA precursors and/or SOA formation pathways not included in the current SOM-TOMAS to explain the observed evolutions of OA mass concentration and O:C at longer photochemical ages. This work presents an important step towards having a better understanding of aging effects on biomass burning OA and is indeed of great interest to the readers of Environmental Science: Atmospheres. However, I believe that a number of issues need to be clarified and addressed in a revised version of the manuscript.
Major comments
• Overall, there are two different aspects to be noted in the model-measurement comparison. One is, as the authors extensively discussed, the magnitude of the OA mass concentration and O:C at shorter and longer photochemical ages. At line 420, the authors discussed based on the model results that slower reacting precursors and multigenerational chemistry contributed little to SOA formation. Figure 7 shows the sensitivity analysis regarding additional slow reacting species but does not include one for multigenerational chemistry. I wonder if any change in the setup of multigenerational chemistry could affect the modeled OA mass concentration to better match with the measurement. Also, it is interesting that although for many of the experiments the modeled OA is lower than the measurement, the model over-predicted OA in several experiments. With the PTR-ToF-MS data available, I wonder if you can observe any similarities/differences in the VOCs profile as a function of modeled/measured OA and potentially deduce likely missing precursors.
• The other, not much mentioned or discussed in the current manuscript, is the differing shapes of the time-dependent OA mass concentration and O:C between model and measurement even at short photochemical ages. From the example results presented in 3.1 or in Figure 1, the model does not appear to capture well the dynamics of the OA evolution despite a similar magnitude. In addition to discussing the model-measurement divergence at longer photochemical ages, it would be beneficial to discuss the discrepancy in the shape of the OA mass concentration and O:C between the model (relatively constant) and measurement (steadily increasing) at short photochemical ages as well.
• Particle wall loss (PWL) rate can be greatly dependent on particle size (order of magnitude) (Mcmurry and Grosjean, 1985; Wang et al., 2018; Pierce et al., 2008), and it becomes very difficult to predict how much impact the PWL correction could have on OA mass concentration without knowing how similar/different the particle size distributions are among the experiments. With the SEMS available (line 177), is it not possible to derive size-dependent PWL rate constants?

Specific comments
• Line 234: pf,1-4, are they mass or molar yields? Their sums in Table S3 and S4 are all 1, so I expect they’re actually molar/stoichiometric yield? If so, each product needs representative MW to compute mass? For small compounds, not including the mass of oxygen could greatly reduce the total mass estimated.
• Line 240: What is the range of equilibrium timescale estimated in the model? Because of relatively short timescale of the chamber experiment (based on the line 465, one equivalent day of photochemical aging is close to 6 min in chamber time), slight differences in assumed Db can greatly impact the shape of the time-dependent evolution of OA. I wonder if the discrepancy in the shape of the time-dependent OA evolution is because of this.
• Line 253: The latest understanding of vapor wall loss is based on the two-layer sorption model (Huang et al., 2018), rather than first-order loss as cited in the current manuscript. I understand that because of the relatively short chamber experiment timescale in this study may not be much affected by the inner layer diffusion process, but it would be good to be clear such that readers receive the up-to-date understanding.
• Line 329: I do not find this explicitly mentioned and am thus confused as to whether the PWL process was included in the model (to compare with PWL uncorrected measurement data) or the PWL correction was applied for the measurement data (to compare with the model results with no PWL process included in the model). This makes me difficult to understand Figure 2. In Figure 2, why do both measured and modeled OA mass concentrations get affected by different PWL assumption? If the measurement is corrected by PWL (by different PWL assumption), the modeled OA mass concentration should not change regardless of the assumption because no PWL process should be included in the model.
• Line 347: The reported dissociation rate for α-pinene SOA based on the measurement (D'ambro et al., 2018) is an order of magnitude slower than used in the current manuscript. Dimer formation rates can also greatly differ depending on reactions (Barsanti et al., 2017). Have the authors considered trying different combinations of oligomerization rates? Fast oligomerization followed by slow uptake of volatile oxidation products with slow reserve dissociation reaction could partly explain the observed steadily increasing OA mass concentration.
• Line 359: Potential typo? Uptake coefficient may not be greater than 1.
• Line 428: What does it mean by non-reactive POA?
• Line 503: This is somewhat different from Palm et al. (2020) showing that oxidation of SVOCs from POA evaporation is the dominant source of SOA formation, ~90%. What could contribute to such differences?
• Figure 6: What do + and X in some markers represent?
• Figure 7: I cannot see the trace for “Mod. Het. Chem. γOH=6. Also there is no symbol next to “Monte-Carlo Simulations”.
• Table S3 and S4:
o How are these values derived? A brief description in addition to line 280 would be good for readers wanting to know more technical details.
o I found it surprising that mfrag is much lower under low NOx than under high NOx conditions. Understanding the equation correctly (Eq. 4 in Cappa and Wilson (2012)), this means higher fragmentation probability under low NOx than under high NOx conditions, which is counter-intuitive because high NOx conditions favor the formation of alkoxy radicals leading to fragmentation.


References

Barsanti, K. C., Kroll, J. H., and Thornton, J. A.: Formation of Low-Volatility Organic Compounds in the Atmosphere: Recent Advancements and Insights, J Phys Chem Lett, 8, 1503-1511, 10.1021/acs.jpclett.6b02969, 2017.
Cappa, C. D. and Wilson, K. R.: Multi-generation gas-phase oxidation, equilibrium partitioning, and the formation and evolution of secondary organic aerosol, Atmos Chem Phys, 12, 9505-9528, 10.5194/acp-12-9505-2012, 2012.
D'Ambro, E. L., Schobesberger, S., Zaveri, R. A., Shilling, J. E., Lee, B. H., Lopez-Hilfiker, F. D., Mohr, C., and Thornton, J. A.: Isothermal Evaporation of alpha-Pinene Ozonolysis SOA: Volatility, Phase State, and Oligomeric Composition, Acs Earth Space Chem, 2, 1058-1067, 10.1021/acsearthspacechem.8b00084, 2018.
Huang, Y. L., Zhao, R., Charan, S. M., Kenseth, C. M., Zhang, X., and Seinfeld, J. H.: Unified Theory of Vapor-Wall Mass Transport in Teflon-Walled Environmental Chambers, Environ Sci Technol, 52, 2134-2142, 10.1021/acs.est.7b05575, 2018.
Lim, C. Y., Hagan, D. H., Coggon, M. M., Koss, A. R., Sekimoto, K., de Gouw, J., Warneke, C., Cappa, C. D., and Kroll, J. H.: Secondary organic aerosol formation from the laboratory oxidation of biomass burning emissions, Atmos Chem Phys, 19, 12797-12809, 10.5194/acp-19-12797-2019, 2019.
Mcmurry, P. H. and Grosjean, D.: Gas and Aerosol Wall Losses in Teflon Film Smog Chambers, Environ Sci Technol, 19, 1176-1182, DOI 10.1021/es00142a006, 1985.
Palm, B. B., Peng, Q. Y., Fredrickson, C. D., Lee, B., Garofalo, L. A., Pothier, M. A., Kreidenweis, S. M., Farmer, D. K., Pokhrel, R. P., Shen, Y. J., Murphy, S. M., Permar, W., Hu, L., Campos, T. L., Hall, S. R., Ullmann, K., Zhang, X., Flocke, F., Fischer, E. V., and Thornton, J. A.: Quantification of organic aerosol and brown carbon evolution in fresh wildfire plumes, P Natl Acad Sci USA, 117, 29469-29477, 10.1073/pnas.2012218117, 2020.
Pierce, J. R., Engelhart, G. J., Hildebrandt, L., Weitkamp, E. A., Pathak, R. K., Donahue, N. M., Robinson, A. L., Adams, P. J., and Pandis, S. N.: Constraining particle evolution from wall losses, coagulation, and condensation-evaporation in smog-chamber experiments: Optimal estimation based on size distribution measurements, Aerosol Sci Tech, 42, 1001-1015, 10.1080/02786820802389251, 2008.
Wang, N. X., Jorga, S. D., Pierce, J. R., Donahue, N. M., and Pandis, S. N.: Particle wall-loss correction methods in smog chamber experiments, Atmos Meas Tech, 11, 6577-6588, 10.5194/amt-11-6577-2018, 2018.

Thanks for reviewing our manuscript and forwarding the reviewer's comments. We thank both reviewers for their comments. In the attached documents, we have provided responses (black regular) to all reviewer comments (in black italic) and included the additions made to the manuscript (blue regular; original text from manuscript in green regular).

19-Feb-2024

Dear Dr Jathar:

Manuscript ID: EA-ART-07-2023-000111.R1
TITLE: Multi-Day Photochemical Evolution of Organic Aerosol from Biomass Burning Emissions

Thank you for your submission to Environmental Science: Atmospheres, published by the Royal Society of Chemistry. I sent your manuscript to reviewers and I have now received their reports which are copied below.

I have carefully evaluated your manuscript and the reviewers’ reports, and the reports indicate that major revisions are necessary. As you can see for the two reviewers' comments, overall they were both positive about the changes you made in the revision. However, there are some further details about the methodolgy and analyses that need to be clarified.

Please submit a revised manuscript which addresses all of the reviewers’ comments. Further peer review of your revised manuscript may be needed. When you submit your revised manuscript please include a point by point response to the reviewers’ comments and highlight the changes you have made. Full details of the files you need to submit are listed at the end of this email.

Please submit your revised manuscript as soon as possible using this link:

*** PLEASE NOTE: This is a two-step process. After clicking on the link, you will be directed to a webpage to confirm. ***

https://mc.manuscriptcentral.com/esatmos?link_removed

(This link goes straight to your account, without the need to log on to the system. For your account security you should not share this link with others.)

Alternatively, you can login to your account (https://mc.manuscriptcentral.com/esatmos) where you will need your case-sensitive USER ID and password.

You should submit your revised manuscript as soon as possible; please note you will receive a series of automatic reminders. If your revisions will take a significant length of time, please contact me. If I do not hear from you, I may withdraw your manuscript from consideration and you will have to resubmit. Any resubmission will receive a new submission date.

The Royal Society of Chemistry requires all submitting authors to provide their ORCID iD when they submit a revised manuscript. This is quick and easy to do as part of the revised manuscript submission process. We will publish this information with the article, and you may choose to have your ORCID record updated automatically with details of the publication.

Please also encourage your co-authors to sign up for their own ORCID account and associate it with their account on our manuscript submission system. For further information see: https://www.rsc.org/journals-books-databases/journal-authors-reviewers/processes-policies/#attribution-id

Environmental Science: Atmospheres strongly encourages authors of research articles to include an ‘Author contributions’ section in their manuscript, for publication in the final article. This should appear immediately above the ‘Conflict of interest’ and ‘Acknowledgement’ sections. I strongly recommend you use CRediT (the Contributor Roles Taxonomy, https://credit.niso.org/) for standardised contribution descriptions. All authors should have agreed to their individual contributions ahead of submission and these should accurately reflect contributions to the work. Please refer to our general author guidelines https://www.rsc.org/journals-books-databases/author-and-reviewer-hub/authors-information/responsibilities/ for more information.

I look forward to receiving your revised manuscript.

Yours sincerely,
Dr Tzung-May Fu
Associate Editor
Environmental Science: Atmospheres
Royal Society of Chemistry

************

Reviewer 2

Thank you for point-by-point responses to the comments by the reviewers. This revised manuscript by Dearden et al. improved by appropriately addressing the reviewers’ concerns. The authors have addressed my major comments in an adequate manner so I believe that this revised manuscript can be considered for publication in Environmental Science: Atmospheres.

Reviewer 3

This study applied the SOM-TOMAS model to simulate the organic aerosol (OA) evolution in FIREX-2016 laboratory experiments. The main finding is that the model exhibited poor skill in predicting the OA mass concentration, not even the OA evolution trend in many cases. This is likely because of missing precursors and chemical pathways in the model. Despite of being a process-level model, comprehensive sensitivity tests, parameter tuning, the model still cannot simulate the measured OA evolution trend. This highlights the knowledge gaps in wildfire OA chemistry. However, besides this highlight, the significance of this manuscript is not clear. Overall, the manuscript is well-written, and I recommend publication after major revisions.
Comments
1. A recent study by He et al. argued that the long-time aging of wildfire OA can be accurately simulated1. The corresponding author of this manuscript is a co-author in He et al. The difference between He et al. and this study must be discussed.
2. In the reviewer’s opinion, the fact that changing vapor wall loss parameters has the largest impact on simulated OA concentration indicates that missing SOA precursors is the key reason for inaccurate simulation of OA evolution. It has been established that the VOCs measured by PTR are not sufficient to explain the observed SOA formation on different platforms. Along with this hypothesis, any sensitivity changes that increase the SOA precursors would likely bring the model closer to measurements.
3. Line 199 – 200. How is “SOA precursors” defined? What species are considered as SOA precursors?
4. Throughout the discussions, please be explicit if the conclusion is drawn from measurements or modeling. For example, are the numbers in Line 726 from model or measurements?
5. Line 421-422. This conclusion is a bit misleading and raised doubts regarding the applicability of the findings in this manuscript to ambient observations. In the atmosphere, after 6 days of aging, one would expect the POA is largely gone because of dilution-driven evaporation and SOA would dominate. This expectation contradicts the finding in these sentences.
6. Would the adjustment of parameters relevant to autoxidation reactions improve the simulation of O:C trend?
7. Given the wide array of sensitivity tests conducted in the manuscript, it would be helpful to move Table S5 to the main text.

Reference
1. He, Y.; Zhao, B.; Wang, S.; Valorso, R.; Chang, X.; Yin, D.; Feng, B.; Camredon, M.; Aumont, B.; Dearden, A.; Jathar, S. H.; Shrivastava, M.; Jiang, Z.; Cappa, C. D.; Yee, L. D.; Seinfeld, J. H.; Hao, J.; Donahue, N. M., Formation of secondary organic aerosol from wildfire emissions enhanced by long-time ageing. Nature Geoscience 2024.

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

We thank both reviewers for their comments. Below, we have provided responses (black regular) to all
reviewer comments (in black italic) and included the additions made to the manuscript (blue regular;
original text from manuscript in green regular).

Reviewer 1
Thank you for point-by-point responses to the comments by the reviewers. This revised manuscript by
Dearden et al. improved by appropriately addressing the reviewers’ concerns. The authors have addressed
my major comments in an adequate manner so I believe that this revised manuscript can be considered for
publication in Environmental Science: Atmospheres.

Thank you for the positive feedback.

Reviewer 3
1. This study applied the SOM-TOMAS model to simulate the organic aerosol (OA) evolution in
FIREX-2016 laboratory experiments. The main finding is that the model exhibited poor skill in predicting
the OA mass concentration, not even the OA evolution trend in many cases. This is likely because of
missing precursors and chemical pathways in the model. Despite of being a process-level model,
comprehensive sensitivity tests, parameter tuning, the model still cannot simulate the measured OA
evolution trend. This highlights the knowledge gaps in wildfire OA chemistry. However, besides this
highlight, the significance of this manuscript is not clear. Overall, the manuscript is well-written, and I
recommend publication after major revisions.

Thank you for the positive feedback.

2. A recent study by He et al. argued that the long-time aging of wildfire OA can be accurately simulated.
The corresponding author of this manuscript is a co-author in He et al. The difference between He et al.
and this study must be discussed.

We thank the reviewer for this comment. Since both manuscripts were under review at roughly the same
time and He et al. (2024) had not been accepted for publication when the response-to-reviewer-comments
was due on the prior iteration of this manuscript, we did not allude to the other manuscript in either of the
submissions. We (lead authors on both manuscripts) had decided that whichever manuscript would be
published later (in this case, ours) would have to undertake the task of explaining the differences between
the studies.

To alert the reader of the 2 concurrent papers earlier in the manuscript, we have added the following text
in Section 1: “In a very recently published paper, He et al. (2024) used a kinetic model to also simulate
the experimental data from Lim et al.40 As He et al. (2024) and this work use the same primary dataset
and because He et al. (2024) was published while this work was in review, we take special care to contrast
the model results and interpretation from both efforts in the final section of this paper (Summary and
Discussion).”.

We have added the following text in Section 4: “Although our work and that of He et al. (2024) both use
kinetic models and rely on the same primary laboratory data, they differ in some of their analysis and
conclusions. Hence, it becomes important to directly compare conclusions from our work against those
from He et al. (2024). Broadly, both studies seemed to agree that the two different models can modestly
reproduce the OA mass concentration evolution and that SVOCs, oxygenated aromatics, and
heterocyclics are important SOA precursors; we should note that the model performance for OA mass
concentrations does seem to be slightly better for He et al. (2014) relative to our work. The big difference,
however, is in the ability of the two studies to reproduce the change in OA O:C. He et al. (2024) closely
reproduce the change in OA O:C for all photochemical ages and experiments while this work significantly
underestimates that change. This is very likely because He et al. (2024), within the two-dimensional
volatility basis set (2D-VBS) framework, use an explicit chemical mechanism (i.e., GECKO-A) to inform
the distribution of first-generation oxidation products and use an aggressive fragmentation kernel, features
that produce higher O:C species relative to the functionalization and fragmentation schemes used in the
SOM-TOMAS model. Another aspect that distinguishes our work from that of He et al. (2024) is that we
investigated the model-measurement comparison for individual experiments, examined the sensitivity to
physical and chemical processes, and assessed model performance across photochemical age. We will
admit that differences between the two studies likely point to the superiority of the He et al. (2024)
approach in simulating the multigenerational aging of SOA.”.

3. In the reviewer’s opinion, the fact that changing vapor wall loss parameters has the largest impact on
simulated OA concentration indicates that missing SOA precursors is the key reason for inaccurate
simulation of OA evolution.

The reviewer is correct to point out that vapor wall losses are an important loss process for gas-phase
oxidation products in small chambers, which severely affects SOA production and the OA evolution.
There is uncertainty in knowing the precise vapor wall loss rate since it was estimated from earlier work
and not directly measured for this chamber. If vapor wall loss rates were lower, end-of-experiment OA
mass concentrations would be, on average, up to 30% higher (upper bound estimate for no vapor wall
losses) and end-of-experiment OA O:C would be, on average, up to 8% higher (upper bound estimate for
no vapor wall losses) (see Figure S10). Discounting the fact that a lower vapor wall loss rate would
produce an inconsistent OA mass concentration profile with photochemical age relative to the
measurements (see Figure 1a), it is unlikely that uncertainty in the vapor wall loss rate is responsible for
the poor model performance.

4. It has been established that the VOCs measured by PTR are not sufficient to explain the observed SOA
formation on different platforms. Along with this hypothesis, any sensitivity changes that increase the
SOA precursors would likely bring the model closer to measurements.

In previous work, we (Akherati et al., 2020) and others (Ahern et al., 2019) have shown that VOCs
measured by a PTR-ToF-MS were sufficient in explaining the SOA formation and OA evolution in
chamber experiments performed on biomass burning emissions. That does not, however, mean that SOA
precursors are missing in this work. To respond to the reviewer’s question, adding SOA precursors
(performed by using an average SOA profile) did increase the OA mass concentrations and improved
model performance. These results are shown in Figures S11a and S11c and are discussed in Section 3.3.
We do acknowledge the role of missing precursors in that same paragraph.

4. Line 199 – 200. How is “SOA precursors” defined? What species are considered as SOA precursors?

The VOC classes included within ‘SOA precursors’ are mentioned in the subsequent sentences, on lines
202-204. A more detailed description of SOA precursors is also included in the third and fourth
paragraphs in Section 2.2.2.

5. Throughout the discussions, please be explicit if the conclusion is drawn from measurements or
modeling. For example, are the numbers in Line 726 from model or measurements?

We have gone through Section 4 (Summary and Discussion) and made light edits to make it clear whether
the conclusions have been drawn from model results or measurements.

6. Line 421-422. This conclusion is a bit misleading and raised doubts regarding the applicability of the
findings in this manuscript to ambient observations. In the atmosphere, after 6 days of aging, one would
expect the POA is largely gone because of dilution-driven evaporation and SOA would dominate. This
expectation contradicts the finding in these sentences.

Lines 421-422 in the revised manuscript describe the finding that nearly all of the SOA precursors would
have reacted with the OH radical in the chamber experiments on account of their high OH reactivity.
These lines do not discuss POA evaporation or the POA-SOA split with photochemical age.
Assuming the reviewer is instead alluding to the text in Section 4 (lines 721-722 in the submitted
version), it is not clear why the reviewer thinks that the POA would completely evaporate after 6 days of
photochemical aging since dilution will affect both POA and SOA. What will eventually determine the
amount of POA and SOA left and the POA-SOA split after a week of atmospheric evolution, will depend
on the initial POA volatility, the plume and background conditions (e.g., OH concentrations, OA mass
loading, temperature), and the changes in the POA and SOA volatility from photochemistry. Lines
786-801 (in the updated version) comment on the importance of accounting for SOA formation regardless
of the fate of POA.

7. Would the adjustment of parameters relevant to autoxidation reactions improve the simulation of O:C
trend?

Autoxidation reactions were modeled in this work only under low NOX conditions and these reactions
were assumed to produce a highly oxygenated organic molecule (HOM) with an O:C of 1 (e.g., a C10
SOA precursor autoxidizes in our scheme to produce a C10O10 HOM). A similar approach was used in
previous work with the SOM-TOMAS model (He et al., 2022). HOM yields were informed from previous
literature (Bianchi et al., 2019) and these yields were specific to each SOA precursor (see Table S3 for
more details). As the reviewer correctly pointed out, HOM production did lead to a more oxygenated
SOA. However, the HOM yields were not large enough to significantly influence OA mass concentrations
or OA O:C (see small differences in the predicted OA and O:C between the low and high NOX
simulations in Figures 3 and 4). The following sentence in Section 3.2 summarizes this well: “The Base
(i.e., low NOX) simulations predicted a slightly higher OA O:C compared to the high NOX simulations as
autoxidation reactions, which result in production of highly oxygenated products (O:C~1), were only
considered in the Base simulations.”.

8. Given the wide array of sensitivity tests conducted in the manuscript, it would be helpful to move Table
S5 to the main text.

We have now moved Table S5 to the main text.

References
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12-Jun-2024

Dear Dr Jathar:

Manuscript ID: EA-ART-07-2023-000111.R2
TITLE: Multi-Day Photochemical Evolution of Organic Aerosol from Biomass Burning Emissions

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Reviewer 3

Thank the authors for responding to my comments.