From the journal Environmental Science: Atmospheres Peer review history

09-Apr-2021

Dear Dr Jathar:

Manuscript ID: EA-ART-02-2021-000014
TITLE: A Computationally Efficient Model to Represent the Chemistry, Thermodynamics, and Microphysics of Secondary Organic Aerosol: Model Development and Application to α-pinene SOA

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Reviewer 1

• Suitability of the article for the journal’s scope
The article deals with a method for parameterizing chemistry of multiphase atmospheric systems for inclusion into 3-D models, which is entirely relevant to the journal’s scope, and will likely be of great interest to the journal’s readership.
• Impact and novelty of the work
The work demonstrates a significant evolution to modeling capabilities for gas-phase chemistry and processes relevant to atmospheric aerosol formation. An existing model by this group (SOM) has previously been successfully implemented in a regional 3–D model, albeit at large computational cost. The current work simplifies the parameterization, reducing by a significant factor the number of bins required to simulate the evolution of each precursor system, while only marginally increasing the number of adjustable parameters (to be obtained by fitting to chamber data). This simplification, essentially, assumes that the carbon number either remains practically constant, or is reduced sufficiently by fragmentation that the products no longer contribute to SOA, thus allowing the model to track products in terms of volatility alone instead of carbon and oxygen counts, as was previously done. Simultaneously the model improves upon previous volatility-based representations by explicitly considering multi-generational chemical processes, i.e. those most likely to be responsible for a significant fraction of SOA mass growth and properties. The conceptual framework is clearly explained, noting the various and reasonable assumptions made. The model is then fit to chamber data and compared to historical data for a demonstration precursor (a-pinene) in a series of sensitivity tests of the adjustable input parameters. It is clear that there is much future work to be done in terms of developing suitable parameterizations for each of the relevant precursor classes, however the model seems to have the potential to explicitly include multigenerational aging and particle-phase processes in regional 3-D models at a modest computational cost, which will greatly improve the representation of SOA in regional air quality modeling.
• The length of the article – does it reflect the level of scientific content and fit within any relevant page limits?
The length of the article is sufficient to allow clear and comprehensive explanation of the model and its testing.
• Whether the article type is appropriate
The article is eminently appropriate and also timely. The reference list is extensive and relevant, and the authors state where their code and chamber data are or will be available to the public.
• The title – does it reflect the content and contain relevant search terms for discoverability?
The title is comprehensive and clear. I suggest including the model name in the title to help with discoverability, if this is compatible with the journal style.
• The abstract – is it self-contained without reference to the main text?
The abstract is very clear and an appropriate length. I would like to see a mention in the abstract of whether the SOA yield was found to be at all sensitive to the modeled heterogeneous chemistry (which is mentioned as an input). Otherwise it is comprehensive and self-contained.
• Which revisions are major concerns preventing publication, and which are minor concerns the authors can easily resolve, and indicate this in your report
I have no major concerns preventing publication. I have a couple of small requests for expansion of the discussion clarification, and several points at the level of consistency of nomenclature, which will be easily resolved.

Specific Comments:
- Equations: Please consider using square brackets [] for consistency wherever concentrations are denoted? E.g. Equations 8a, 8b: d[VOC] and d[Ci¬g], which already have [VOC] and [Ci¬g]. Similarly for Eqns 9, 10, 14, 15, 16. Use of ‘[]’ notation would reduce potential throughout for confusion with other uses of ‘c’ (c*, cOH, Cwall).
- Equations 5, 6a, 6k: are missing the ‘*’ from ‘c*’
- Equation 10: S seems to be missing its subscript j
- Line 308: can we have a reference for Dig?
- Line 309: please mention whether Db is species-specific?
- Isn’t reference 65 usually cited as Seinfeld & Pandis (2006), not the other way around?
- Equation 15: Do you mean dOi,i not dOi,i ?
- Equation 16: Please explain notation Mi,j
- Line 488: Delta-LVP here seems to be the same as Delta-logC*; similarly pf1-pf4 is perhaps the same as PO1-PO4? (as in Fig 1 and line 226 etc.) Please be consistent.
- Line 520: term “floss” here seems to be the same as Ploss (Fig 1, line 228 etc.). Please be consistent.
- Line 503-505: Can you briefly explain the different amounts of O-addition under low- and high-NOx conditions (2&3 vs 1&4) in terms of possible product functionality or typical reaction classes?
- Line 522: Can you briefly explain the lower volatility of the high-NOx-derived fragmentation products in terms of likely chemical identity / functionality?
- Line 514: Do you mean “In the high-NOx no-aging simulations…” (rather than in ALL the high-NOx simulations, as this reviewer first understood it)?
- Reference 115 (line 535) appears out of order. If this is important, please re-order the references accordingly.

Supplementary Material Specific Comments
- Supplementary Material Tables appear in a different order from their call-outs in the text. If this is important, please re-order and re-label the tables.
- Table S3: Please clarify here whether these are all a-pinene experiments? Please add umlaut to “Järvinen”.
- Figure S2 is mis-labeled as “Figure 1”. Please revise.
- Figure S4 is mis-labeled as “Figure S3”. Please revise.
- Please consider standardizing the y-axis on Figures 3b, S4b, and S5b to facilitate comparison. Also please consider including the end-of-experiment measurements on Figures S4b,d, and on S5b,d.

Reviewer 2

Review of “A Computationally Efficient Model to Represent the Chemistry, Thermodynamics, and Microphysics of Secondary Organic Aerosol: Model Development and Application to -pinene SOA” by Jathar et al., 2021.

Summary:
This manuscript describes the development and application of simpleSOM, an offshoot/advancement of SOM: the Statistical Oxidation Model. The authors note that SOA formation in models is often highly simplified and rarely includes condensed-phase chemistry, often due to the amount of computational power required to implement what is already known from measurements. The goal of this work is to develop a model that can implement the knowledge of the current state of the field and takes a reasonable (or, in their case, really short!) amount of time to run. The development of the model is clearly laid out in nice subsections in the first section, the measurements that are used to evaluate the model and the different model runs are described in the next section, followed by a section on the model application to the canonical SOA system, and wraps up with a nice discussion of how simpleSOM might be included in a 3D atmospheric model.

This paper is really well written, the figures are clear and informative, and the organization is superb. I found each argument to be well thought out and supported. This was an easy to read, enjoyable paper that makes a significant contribution to the field. I recommend publication with minor suggestions below.

Specific comments:
Line 68: which chemistry scheme in CMAQ is referred to here? Or do they all (carbon bond, SAPRC, and RACM) have roughly the same number of SOA species (~35)? Or is one a “default” for CMAQ that you’re referring to?

Line 106 (& line 682), “typically performed under dry conditions”: this isn’t strictly true, especially with more recent experiments over the last ~decade. For example:
• Up to 70% RH: https://acp.copernicus.org/articles/19/12749/2019/.
• Up to 82% RH: https://pubs.acs.org/doi/10.1021/acs.est.9b05514
• 50% RH: https://pubs.acs.org/doi/10.1021/acs.est.9b01019
Maybe change “typically” to “often” or “historically”?

Line 118: are wall interactions strictly via volatility or also via heterogeneous chem? I.e., at higher RH’s, can you have some Henry’s Law-based partitioning as well?

Line 181-183: How robust is this assumption that + 1 oxygen lowers the c* by 1-2 orders of magnitude? I’m not positive but I don’t think the reference from 2008 correctly represents the current state of the field, why not use a reference for a SAR-based vapor pressure calculation like SIMPOL or EVAPORATION?

Line 276: I assume the more bins you use, the longer the computational time required? Do you recommend ~14 c* bins for all/most oxidation systems?

Line 285: why don’t you track the carbon number here?

Section 2.3: do you make any assumptions for these often hard or impossible to measure parameters, for example the mass transfer coefficients or SOA density? Do you recommend others use certain values?

Section 2.6: can this be applied to non-teflon chambers like glass (JPAC chamber at Julich, Germany) and stainless steel (like the CERN chamber used for CLOUD experiments)? Can simpleSOM handle different chamber operations, i.e. batch vs. continuous flow?

Line 395: what is a “forward” chamber simulation?

Line 490: why is “(3.4%)” in parenthesis?

Line 493: The “high NOx” concentrations are pretty high (447 ppb of NO and 400 ppb of NO2 from line 436) relative to ambient, and especially in comparison to the “low NOx” experiment (assumed to be 0 (?), below the instrumental level of detection of 2 ppb). Did you do any additional tests at NOx levels within this range, or how would you expect the model to perform?

Line 508: could you provide a value for “a significant fraction”?

Line 522: what does this mean that the floss = 0 for high NOx? That no formic acid or other very small species are formed and the fragmentation reactions lead to generally larger (less than 10 carbons but presumably still >~5 carbons?), low volatility species? Do you think this is accurate?

Paragraph starting line 523: the vapor wall loss for the low NOx experiments agrees well, but not the high NOx (off by a factor of ~2.5). Can you discuss that?

Line 529: why do you think heterogeneous chem played such a small role on SOA formation? It seems like a gamma of 1 is very high and would drive significant chemistry? What do other people/models assume for an uptake coefficient for OH, if that information exists?

Line 559: could this sensitivity to initial VOC concentration also be due to different chemistry occuring at the two concentrations? I.e. autoxidation will play a larger role at lower precursor concentrations relative to higher.

Fig 2—how were historical observations of c* measured? Are they assumed to be accurate within an order of magnitude?

Line 825: does including the size distribution increase model predictive ability?

Line ~847-852 (aqueous aerosol discussion): What if the aerosol was phase separated into organic and aqueous? Can simpleSOM handle that? Is this something worth considering?

We thank both reviewers for their comments. Below, we have provided responses to all reviewer comments and included revisions made to the manuscript (in double quotes).

Reviewer #1

1. I suggest including the model name in the title to help with discoverability, if this is compatible with the journal style.

Thank you for the excellent suggestion. We have included the model name in the title: “A Computationally Efficient Model to Represent the Chemistry, Thermodynamics, and Microphysics of Secondary Organic Aerosol (simpleSOM): Model Development and Application to -pinene SOA”.


2. I would like to see a mention in the abstract of whether the SOA yield was found to be at all sensitive to the modeled heterogeneous chemistry (which is mentioned as an input).

This finding is now mentioned in the abstract: “Heterogeneous chemistry did not seem to affect SOA formation over the short timescales for oxidation experienced in the chamber experiments.”.


3. Equations: Please consider using square brackets [] for consistency wherever concentrations are denoted? E.g. Equations 8a, 8b: d[VOC] and d[Ci¬g], which already have [VOC] and [Ci¬g]. Similarly for Eqns 9, 10, 14, 15, 16. Use of ‘[]’ notation would reduce potential throughout for confusion with other uses of ‘c’ (c*, cOH, Cwall).

Great suggestion. This has now been addressed.


4. Equations 5, 6a, 6k: are missing the ‘*’ from ‘c*’

Thank you for catching that. The * has been added.


5. Equation 10: S seems to be missing its subscript j

This has been added.


6. Line 308: can we have a reference for Dig?

Following Eluri et al. 1, the gas-phase diffusion coefficient was calculated using the equation:
Dgi=DCO2MWCO2MWi
Where DCO2 is the gas-phase diffusion coefficient of CO2 (1.38×10-5 m2 s-1), MWCO2(g mole-1) is the molecular weight of CO2 , and MWi (g mole-1 ) is the molecular weight of species i. The Eluri et al.1 reference is now added to the manuscript.


7. Line 309: please mention whether Db is species-specific?

Db is specific to the model species but is assumed to be the same for all model species. This has been clarified as follows: “...Db is the particle-phase diffusion coefficient of the model species in m2 s-1...” and “The same Db was used for all model species.”.


8. Isn’t reference 65 usually cited as Seinfeld & Pandis (2006), not the other way around?

The reviewer is absolutely right. This was due to a bibliographic error. This has been corrected.


9. Equation 15: Do you mean dOi,i not dOi,i ?

Yes, it should have been Oi,j and not Oi,i. This has been corrected.


10. Equation 16: Please explain notation Mi,j

Mi,j should have actually been Ci,jp. This has been corrected.


11. Line 488: Delta-LVP here seems to be the same as Delta-logC*; similarly pf1-pf4 is perhaps the same as PO1-PO4? (as in Fig 1 and line 226 etc.) Please be consistent. Line 520: term “floss” here seems to be the same as Ploss (Fig 1, line 228 etc.). Please be consistent.

Thank you for catching this. We have reviewed and revised the manuscript for similar inconsistencies.


12. Line 503-505: Can you briefly explain the different amounts of O-addition under low- and high-NOx conditions (2&3 vs 1&4) in terms of possible product functionality or typical reaction classes?

The simpleSOM model employs a statistical approach to model the oxidation chemistry and corresponding changes in the volatility of the oxidation products. As simpleSOM does not track the molecular structure or addition/removal of functional groups, the parameters that specify the oxygen addition to the carbon backbone are not directly interpretable. Hence, we avoid drawing any conclusions from the differences in the oxygen atoms being added per reaction across the two different sets of simpleSOM parameters. We have clarified this as follows: “Being a statistical model, the differences in the oxygen additions to the precursor are not directly interpretable.”. In addition, we have added the following sentence in Section 2.2 to describe the nature of the oxidation products being formed: “It is assumed that a given reaction can add 1 to 4 oxygen atoms that characterize the addition of various functional groups to the precursor’s carbon backbone (e.g., alcohol, carbonyl, acid, ester, ether).”.


13. Line 522: Can you briefly explain the lower volatility of the high-NOx-derived fragmentation products in terms of likely chemical identity / functionality?

We think the reviewer is only looking at differences in the Ploss terms between the parameter sets derived for the low and high NOX experiments. Ploss works in addition to the mfrag term and the evolving product distribution to influence the eventual volatility distribution of the SOA. As shown in Figures 2(c) and 2(d), the SOA volatility distribution ends up being much less volatile in the low NOX experiments compared to the high NOX experiments. A major reason for the modeled differences in the volatility distribution with NOX is that we allow for HOM (extremely low-volatility products) production in the low NOX experiments but not in the high NOX experiments. As described in the response to comment #12, as simpleSOM is a statistical model, the parameters are not directly interpretable to comment on the molecular structure or addition/removal of functional groups.


14. Line 514: Do you mean “In the high-NOx no-aging simulations…” (rather than in ALL the high-NOx simulations, as this reviewer first understood it)?

Yes, that is correct. The sentence has been updated to: “In the high-NOX simulations without multigenerational aging, as the first-generation oxidation products in the gas-phase were not allowed to react, their loss to the walls drove evaporation of the SOA that had condensed up to that point.”.


15. Reference 115 (line 535) appears out of order. If this is important, please re-order the references accordingly.

Thank you for pointing that out. The bibliography has been rebuilt and this issue will be addressed during the copy editing process.


16. Supplementary Material Tables appear in a different order from their call-outs in the text. If this is important, please re-order and re-label the tables.

We apologize for the oversight. This is now fixed.


17. Table S3: Please clarify here whether these are all a-pinene experiments?

Yes, these are all alpha-pinene experiments. The caption was changed to: “SOA mass concentration and O:C observations from several environmental chamber studies performed on -pinene.”.


18. Please add umlaut to “Järvinen”.

Added.


19. Figure S2 is mis-labeled as “Figure 1”. Please revise. Figure S4 is mis-labeled as “Figure S3”. Please revise.

This has been corrected.


20. Please consider standardizing the y-axis on Figures 3b, S4b, and S5b to facilitate comparison. Also please consider including the end-of-experiment measurements on Figures S4b,d, and on S5b,d.

Figures 3b, S4b, and S5b have now been homogenized for the y-axis and the end-of-experiment measured data have been added to the supplementary material figures.


Reviewer #2

1. Line 68: which chemistry scheme in CMAQ is referred to here? Or do they all (carbon bond, SAPRC, and RACM) have roughly the same number of SOA species (~35)? Or is one a “default” for CMAQ that you’re referring to?

The number of model species used to track SOA is somewhat independent of the gas-phase chemical mechanism used in CMAQ. In fact, for the Murphy et al. (2017) citation provided for CMAQ, the study used two different gas-phase chemical mechanisms (i.e., SAPRC, CB05) to model OA formation and evolution over the continental US and used between 35 and 50 model species to track OA. The sentence has been modified as follows: “A typical example of this is the treatment of SOA in the Community Multiscale Air Quality Model (CMAQ), a regional chemical transport model that dedicates between 35 and 50 model species to represent SOA, somewhat independent of the gas-phase chemical mechanism used.”.


2. Line 106 (& line 682), “typically performed under dry conditions”: this isn’t strictly true, especially with more recent experiments over the last ~decade. For example: Up to 70% RH (Takeguchi and Ng, 2019), Up to 82% RH (Zaveri et al., 2020), 50% RH (Riva et al., 2019). Maybe change “typically” to “often” or “historically”?

This is a fair point. We have changed the sentence to: “The aerosol Db might be especially important to consider in chamber experiments since they have often been performed under dry conditions, conducive to producing semisolid SOA.”.


3. Line 118: are wall interactions strictly via volatility or also via heterogeneous chem? I.e., at higher RH’s, can you have some Henry’s Law-based partitioning as well?

In the current version of the model, one which is also consistent with observations of vapor wall losses,2,3 we have assumed that the vapors are reversibly lost to the walls of the chamber via absorptive partitioning to the Teflon matrix. The vapor wall loss rates used in our study for the Caltech chamber are based on previous measurement4 and modeling5 studies. While we agree that a certain fraction of the vapors could also be lost via heterogeneous reactions on the wall surface or be taken up into any adsorbed water on the chamber wall, there is no evidence so far that either of these processes are important in our experiments. We have added the following sentence: “Here, as in our previous work, we only considered absorptive reversible losses of vapors to the chamber wall and assumed that other potential modes of vapor loss (e.g., heterogeneous chemistry on the wall surface, uptake to water adsorbed on the wall) were unimportant.”.


4. Line 181-183: How robust is this assumption that + 1 oxygen lowers the c* by 1-2 orders of magnitude? I’m not positive but I don’t think the reference from 2008 correctly represents the current state of the field, why not use a reference for a SAR-based vapor pressure calculation like SIMPOL or EVAPORATION?

Although we only cite the Kroll and Seinfeld6 paper, the assumption that a single oxygen atom addition leads to a 1 to 2 orders of magnitude change in volatility is also generally consistent with the work of Pankow and Asher7 (i.e., SIMPOL.1) and Compernolle et al.8 (i.e., EVAPORATION). In fact, the original SOM (Cappa and Wilson, 2011), on which simpleSOM is based, was developed based on consideration of SIMPOL.1 and tied to the predicted behavior for multi-component species as determined by the GECKO-A model, which uses SAR-based vapor pressures. For instance, SIMPOL.1 estimates a -2.2, -1.3, -0.93, -1.2, and -0.71 change in logc* for the addition of an oxygen atom linked to an alcohol, aldehyde, ketone, ester, and ether functional group, respectively. The Pankow and Asher7 (i.e., SIMPOL.1) and Compernolle et al.8 (i.e., EVAPORATION) citations are now added to this sentence.


5. Line 276: I assume the more bins you use, the longer the computational time required? Do you recommend ~14 c* bins for all/most oxidation systems?

Yes, more bins should translate to an increased computational expense for running the model. This increased computational expense should be trivial to handle for box model applications given that our base model was able to perform a multi-hour chamber simulation in under 5 s. The upper logc* bin is set to the logc* of the precursor, rounded up or down to the nearest integer. The lower logc* for the case study in this work was set to -6 to accommodate the production of highly oxygenated organic molecules (HOMs). Recommendations on the number of bins to be used with simpleSOM, with and without particle size resolution and in box and 3D models, are provided in Section 5 (Summary and Discussion). That section is reproduced here for reference: “A modern-day 3D model typically simulates SOA formation from several classes of VOC precursors that include, but are not limited to, isoprene, terpenes, alkanes, alkenes, aromatics, and semi-volatile and intermediate volatility organic compounds (SVOCs and IVOCs).9 While some of the SOA precursors are modeled separately (e.g., isoprene, benzene), those with a similar potential to form SOA are frequently lumped together for computational efficiency (e.g., C8+ single-ring aromatics). Regardless, there is a fair amount of diversity in 3D models as to the precise number of SOA precursors included and this precursor number can vary between 2 and 10.10 To calculate, as an example, the computational burden simpleSOM would impose on a 3D model, let us assume that a typical 3D model simulates the SOA formation from 5 unique precursors. An advantage of a simpleSOM set is that oxidation products from multiple VOCs can be tracked in the same set, assuming that the parameters can be extended to simulate the SOA formation from those VOCs. Hence, SOA from VOCs with similar characteristics could be ‘lumped’ into the same simpleSOM set, akin to how VOCs are lumped together in gas-phase chemical mechanisms for computational efficiency. For each precursor, let us assume that the simpleSOM set spans across 12 logc* bins (e.g., -3 to 8). The lowest logc* bin could be set to -3 (instead of -6 as used in this work) since species with logc* of ‑3 are functionally non-volatile for most conditions relevant to the atmosphere. This simpleSOM set could be reduced to track a subset of the model species. For instance, we could choose to ignore the gas-phase species for the low logc* bins (e.g., logc* from -3 to 0) and ignore the particle-phase species for the high logc* bins (e.g., logc* from 4 to 8) because these model species are expected to exclusively reside in the gas and particle phases, respectively, under most atmospherically-relevant conditions. The exact reduction would need to be configured depending on the atmospheric conditions being simulated. Up to 12 additional species will be needed to track the oxygen content in each c* bin although the O:C could be parameterized to the c* bin for a given simpleSOM set to further optimize the implementation. In total, a minimum of ~27 model species will need to be tracked per simpleSOM set. For 5 unique SOA precursors, a simpleSOM representation in a 3D model with no particle size resolution would require a minimum of 40 gas-phase, 35 particle-phase, and 60 oxygen model species. If the 3D model included a particle size-resolved model with, e.g., 10 size sections, the number of particle-phase model species would be 350. Altogether, a basic simpleSOM representation would require a total of 135 (no size resolution) or 425 (with size resolution) species in a 3D model. Several recent studies with regional and global models have included a similar range of gas- and particle-phase species to model SOA and the estimates provided here are now well within reach of modern-day 3D models.11–13”.


6. Line 285: why don’t you track the carbon number here?

To track the carbon number of the model species, we would need to make additional assumptions regarding the scission of the carbon backbone when the model species participates in fragmentation reactions. As noted in the manuscript, this remains an important limitation of this version of the model (as opposed to SOM, which explicitly tracks the carbon number explicitly), which will be addressed in future work. We also note that to track carbon number explicitly would necessarily expand the number of species that we need to track and increase the complexity of the model, potentially to a point where implementation in large-scale 3D models would be infeasible. Additionally, while carbon number is not explicitly tracked, there is an implicit assumption regarding the influence of fragmentation when reactions lead to product species for which the vapor pressure has increased. The detailed behavior is subsumed into the simplification of the simpleSOM framework that allows its application in a 3D model.


7. Section 2.3: do you make any assumptions for these often hard or impossible to measure parameters, for example the mass transfer coefficients or SOA density? Do you recommend others use certain values?

Assumptions were made for both the SOA density and the mass accommodation coefficient. The following sentence was added to specify the values used with the appropriate citations: “In this study, a of 1.18 g cm-3 was used based on the estimates produced by Bahreini et al.14 for -pinene SOA and a mass accommodation coefficient of unity was used when calculating FSj based on recent work by Krechmer et al.15 and Liu et al.16.”.


8. Section 2.6: can this be applied to non-teflon chambers like glass (JPAC chamber at Julich, Germany) and stainless steel (like the CERN chamber used for CLOUD experiments)? Can simpleSOM handle different chamber operations, i.e. batch vs. continuous flow?

We are confident that simpleSOM can be applied to non-Teflon chambers as well as to simulate the aerosol evolution under different modes of chamber operation. These applications will require a few modifications to the code. The following sentence was added to Section 5: “In theory, simpleSOM should also be able to simulate SOA formation in oxidation flow reactors (OFRs) and non-Teflon chambers17,18 noting that certain processes (e.g., vapor wall losses, batch versus steady-state mode) will need to be modeled differently.”.


9. Line 395: what is a “forward” chamber simulation?

We used the adjective ‘forward’ to indicate that all simulations move forward in time but are now realizing that this word is redundant. The word ‘forward’ was used to distinguish the simulations where the simpleSOM ran once based on a given set of SOA parameters and ran many times (>100) to iterate on an SOA parameter set. The section - without the word ‘forward’ - now reads as follows: “On an ordinary desktop computer (circa 2017), chamber simulations were computed in <5 s and atmospheric simulations were computed in <30 s for a single VOC precursor (i.e., -pinene). This translates to a compute time of <0.4 s per wall hour of simulation. Fitting to chamber data (explained later) took slightly longer, e.g., ~15 minutes, as this required on the order of 100 iterative, chamber simulations. ”.


10. Line 490: why is “(3.4%)” in parenthesis?

The ELVOC yield was described using a decimal and as a percentage. We have retained the information as a percentage.


11. Line 493: The “high NOx” concentrations are pretty high (447 ppb of NO and 400 ppb of NO2 from line 436) relative to ambient, and especially in comparison to the “low NOx” experiment (assumed to be 0 (?), below the instrumental level of detection of 2 ppb). Did you do any additional tests at NOx levels within this range, or how would you expect the model to perform?

In this work, we developed separate parameter sets for the SOA formed under low (<2 ppbv) and high (850 ppbv) NOX conditions. Historically, SOA experiments have been performed under these bounding conditions for NOX and there are a limited number of datasets that have performed SOA experiments under a range of NOX conditions. Such experiments were not available for the Caltech chamber. We agree with the reviewer that the model could be tested against such experimental datasets in the future and have added a sentence when we discuss future applications with the simpleSOM model: “Finally, we will also aim to study the ability of the simpleSOM parameters to simulate the SOA formation from -pinene (and other SOA precursors) under different and continuously varying NOX conditions.”.


12. Line 508: could you provide a value for “a significant fraction”?

Yes, we can. The sentence was modified to: “However, a significant fraction of the total SOA was composed of much lower volatility material (c*<1 µg m-3) (61 and 83% for the low and high NOX simulations, respectively) than that suggested by the first-generation oxidation products; c*-resolved contributions to SOA are shown in Figure S.1.”.


13. Line 522: what does this mean that the floss = 0 for high NOx? That no formic acid or other very small species are formed and the fragmentation reactions lead to generally larger (less than 10 carbons but presumably still >~5 carbons?), low volatility species? Do you think this is accurate?

The simpleSOM model is fundamentally a parameterized model that, in its formulation, attempts to capture the effects of the chemical processes that transform organic aerosol. However, it is limited in the sense that it is, ultimately, parameterized. Further, the simpleSOM model fits are not unique (although we do try to check for robustness) and we are certain that we can produce other fit sets where Ploss is greater than 0 that have a reasonably similar fit fidelity to those shown here, for example by simply constraining the Ploss to be greater than some value above zero (e.g., 0.1). Large differences in the obtained Ploss values may be interpretable in terms of differences in the chemical processes involved, but we would certainly caution against over-interpreting the meaning of the parameters.


14. Paragraph starting line 523: the vapor wall loss for the low NOx experiments agrees well, but not the high NOx (off by a factor of ~2.5). Can you discuss that?

We would not characterize the differences between this work and the work of Zhang et al.4 from accounting for the effects of vapor wall losses in the high NOX experiments as being off by a factor of 2.5. This is because we have expressed the increase in SOA formation in the absence of vapor wall losses as a percentage increase over the SOA formation in the presence of vapor wall losses. For the low NOX experiments, our study predicted a slightly smaller increase in SOA formation compared to Zhang et al.4; 6% smaller. For the high NOX experiments, our study predicted a larger increase in SOA formation compared to Zhang et al.4; 35% larger.


15. Line 529: why do you think heterogeneous chem played such a small role on SOA formation?

In chamber experiments where OH concentrations are usually much smaller than 107 molecules cm-3, the timescales for reaction of an organic species with OH are much shorter in the gas phase than they are for that same species on the surface of a particle. The lifetime for a gas-phase organic compound with a reaction rate constant with OH (kOH) of 5×10-11 cm3 molecules-1 s-1 and an OH concentration of 3×106 molecules-1 cm-3 would be slightly less than 2 hours. The lifetime for the same organic compound with an aerosol size distribution with a surface area concentration of ~300 µm2 cm-3 would be ~300 hours, assuming an uptake coefficient of 1. These timescale differences suggest that heterogeneous chemistry should not be very important in chamber experiments that simulate only up to a day of photochemical aging. Heterogeneous chemistry, however, is very likely to play an important role in oxidation flow reactors where OH concentrations regularly exceed 109 molecules-1 cm-3 as well as in the atmosphere where these reactions can continue to take place over weeks.


16. It seems like a gamma of 1 is very high and would drive significant chemistry? What do other people/models assume for an uptake coefficient for OH, if that information exists?

An uptake coefficient of 1 is high but well within the 0.1 to 6 range previously measured in OA model systems19–21. We have added the following sentence and citations to justify our use of an uptake coefficient of unity: “This value is within the range of uptake coefficients determined in previous experimental work performed on OA model systems (0.1 to 6).19–21”.


17. Line 559: could this sensitivity to initial VOC concentration also be due to different chemistry occuring at the two concentrations? I.e. autoxidation will play a larger role at lower precursor concentrations relative to higher.

The reviewer is right to point out that in reality the autoxidation chemistry is expected to be relatively more important at lower initial VOC concentrations.22 However, within the simpleSOM model, the yields for the autoxidation products are not assumed to vary with the initial VOC concentration or to at least remain constant within the range of initial VOC concentrations considered (i.e., 40 to 160 ppbv). To respond to the reviewer’s comment, the sensitivity in model predictions to the initial VOC concentration are hence not due to differences in the autoxidation reactions.


18. Fig 2—how were historical observations of c* measured? Are they assumed to be accurate within an order of magnitude?

Historical estimates of the volatility distribution of SOA were based on observations of the chemical composition of SOA and based on the response of SOA to heating. Only a few of these studies report uncertainties alongside their mean estimates but, as the reviewer points out, these are expected to be fairly uncertain. We have modified the sentence as follows: “We should note that these volatility distribution data, which were recently summarized in Morino et al.23, were mostly gathered from experiments performed with OH and O3 as oxidants under low NOX conditions. These volatility distribution data were averaged because there was significant uncertainty (~1 order of magnitude) in the individual estimates and significant variability across studies.”.


19. Line 825: does including the size distribution increase model predictive ability?

An explicit representation of the aerosol size distribution should allow for improved estimates of particle-size-dependent properties (e.g., heterogeneous chemistry, deposition velocities, cloud activation, wet removal) and, in theory, improve the ability of the model to predict mass, composition, properties, and impacts of atmospheric aerosols. We are not aware of any studies that have systematically examined the influence of accounting for particle sizes on model performance for OA. Not all 3D models have particle size resolution and hence the discussion here was framed around no particle size resolution on one hand and an explicit particle size resolution on the other.


20. Line ~847-852 (aqueous aerosol discussion): What if the aerosol was phase separated into organic and aqueous? Can simpleSOM handle that? Is this something worth considering?

There is sufficient experimental and modeling evidence that ambient aerosols, modulated by the aerosol composition and environmental conditions (e.g., relative humidity), can be part of one single phase or can separate into two or more phases.24,25 In modeling the dry chamber experiments presented in this work we assumed that the organic and inorganic (or aqueous) phases were separate. For simplicity, we assumed no water uptake and no inorganic aerosol in the pseudo atmospheric simulations. In ongoing work where simpleSOM is being integrated with MOSAIC, we will also assume that the organic and inorganic species are phase separated with no interactions between the two. So, yes, the current implementation of simpleSOM allows for a phase-separated treatment of the organic and inorganic constituents. In future work, following the approach described in Pye et al.,25 we may consider updating the simpleSOM-MOSAIC code to dynamically treat the phase state of ambient aerosols that is tied to the aerosol composition. We have added the following sentence when describing aqueous chemistry: “In the case of aqueous chemistry, we may have to further develop the code to account for interactions and separation of the organic and aqueous phases, as modulated by the aerosol composition and environmental conditions (e.g., relative humidity).24,25”.

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26-May-2021

Dear Dr Jathar:

Manuscript ID: EA-ART-02-2021-000014.R1
TITLE: A Computationally Efficient Model to Represent the Chemistry, Thermodynamics, and Microphysics of Secondary Organic Aerosol (simpleSOM): Model Development and Application to α-pinene SOA

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Reviewer 2

The responses to the reviewers look great. The authors thoroughly address each point and update the manuscript where appropriate.