the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Secondary Organic Aerosol Formation via Multiphase Reaction of Hydrocarbons in Urban Atmospheres Using the CAMx Model Integrated with the UNIPAR model
- 1Department of Environmental Engineering Sciences, Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL, USA
- 2Department of Environmental and Safety Engineering, Ajou University, Suwon, South Korea
- 3Air Quality Research Division, National Institute of Environmental Research, Environmental Research Complex, Incheon, South Korea
- 1Department of Environmental Engineering Sciences, Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL, USA
- 2Department of Environmental and Safety Engineering, Ajou University, Suwon, South Korea
- 3Air Quality Research Division, National Institute of Environmental Research, Environmental Research Complex, Incheon, South Korea
Abstract. The prediction of Secondary Organic Aerosol (SOA) in regional scales is traditionally performed by using gas-particle partitioning models. In the presence of inorganic salted wet aerosols, aqueous reactions of semivolatile organic compounds can also significantly contribute to SOA formation. The UNIfied Partitioning-Aerosol phase Reaction (UNIPAR) model utilizes explicit gas chemistry to better predict SOA mass from multiphase reactions. In this work, the UNIPAR model was incorporated with the Comprehensive Air Quality Model with Extensions (CAMx) to predict the ambient concentration of organic matter (OM) in urban atmospheres during the Korean-United States Air Quality (2016 KORUS-AQ) campaign. The SOA mass predicted with the CAMx-UNIPAR model changed with varying levels of humidity and emissions and in turn, has the potential to improve the accuracy of OM simulations. The CAMx-UNIPAR model significantly improved the simulation of SOA formation under the wet condition, which often occurred during the KORUS-AQ campaign, through the consideration of aqueous reactions of reactive organic species and gas-aqueous partitioning. The contribution of aromatic SOA to total OM was significant during the low-level transport/haze period (24–31 May 2016) because aromatic oxygenated products are hydrophilic and reactive in aqueous aerosols. The OM mass predicted with the CAMx-UNIPAR model was compared with that predicted with the CAMx model integrated with the conventional two product model (SOAP). Based on estimated statistical parameters to predict OM mass, the performance of CAMx-UNIPAR was noticeably better than the conventional CAMx model although both SOA models underestimated OM compared to observed values, possibly due to missing precursor hydrocarbons such as sesquiterpenes, alkanes, and intermediate VOCs. The CAMx-UNIPAR model simulation suggested that in the urban areas of South Korea, terpene and anthropogenic emissions significantly contribute to SOA formation while isoprene SOA minimally impacts SOA formation.
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Zechen Yu et al.
Status: closed
-
RC1: 'Comment on acp-2021-1002', Anonymous Referee #1, 28 Feb 2022
In this manuscript, the authors incorporated their sophisticated SOA model (UNIPAR) with an air quality model (CAMx) and simulated SOA concentrations from different formation pathways and different precursors. Observed concentration of organic matter (OM) is better reproduced by the UNIPAR mode than by a conventional two product model (SOAP). By applying the UNIPAR model, the SOA formation from gas-particle partitioning, in-particle oligomerization, and aqueous-phase reactions are separately calculated, and their contributions have been quantified.
This manuscript is well written and includes useful information about the numerical modeling of SOA formation processes in the ambient air. However, I have several concerns as below. I recommend this manuscript for publication after the following concerns are adequately addressed.
1: Methodology
I am afraid that methodology (model and emissions) is not comprehensively described or adequate references are cited.
- You wrote in L109 that "The mathematical equations used to construct the stoichiometric coefficient array are reported in Section S1" and four parameters (A, B, C, and D) for different precursors and conditions (NOx level and aging status) are given in Table 3, . However, I could not find the information how did you consider dependence on NOx (high/low) and aging degree (fresh/aged) for the calculation of stoichiometric coefficients in the ambient conditions.
- You set six categories for oxidation products: non-reactive (P), slow (S), medium (M), fast (F), very fast (VF), and multifunctional alcohols (MA). Products with these categories are always produced or did you consider any condition dependence?
- Thermodynamic parameters of oxidation products (vapor pressure and vaporization enthalpy) are not explicitly shown.
- Information of emission amounts is not shown. As you estimated the contributions of SOA precursors, total emissions or their distributions are important information. I have two more concerns about emissions:
- You wrote in L255 that “During the wet period, HC emissions increased”. It appears from Figures S5 and S6 that daytime temperature is higher during the dry periods than wet, and thus, I speculate that BVOC emissions are higher during the dry period. Quantitative information and reasons for the increase of HC emissions should be given.
- L308: “isoprene SOA is negligible at all sites due to low isoprene emissions”. Information of isoprene emissions (preferably with terpene and aromatics) is required.
2: Precursors’ contributions:
You wrote in L308 as “Isoprene SOA is negligible at all sites”, and concentrations of isoprene SOA was small over the domain as shown in Figure 8 (g) and (h). However, previous observational and simulation studies have indicated that isoprene SOA has important contributions in East Asia in May-June (e.g., Hu et al., Zhu et al., and Ding et al.). I recommend the authors to discuss the differences of your estimate with previous studies.
Hu et al. (2017) doi:10.5194/acp-17-77-2017
Zhu et al. (2018) doi: 10.1016/j.apr.2017.09.001
Ding et al. (2016) doi: 10.1038/srep20411
3: OMH and OMP
You wrote in L324-326 that "Under the dry period (Fig. 3), the predicted SOA mass by the UNIPAR model is dominated by gas-particle partitioning onto organic phase and oligomerization in organic aerosol. During the wet period, SOA production forms mainly through gas-aqueous partitioning and aqueous reactions."
I could not get how did you separate contributions of oligomer SOA and SOA from aqueous-phase reactions (I guess both are categorized OMH). Quantitative information of the contributions of the three pathways is helpful to readers.
4: OM and OC
It is not clear whether you showed organic mass (OM) or organic carbon (OC) in Figures 3-5. I guess OM concentration is calculated by your simulation model, whereas OC concentration is measured by carbon analyzers. Conversion factor from OC to OM (or vice versa) should be explicitly noted.
Specific comments:
L51: References for the following sentence is necessary: “In particular, the current model applied to regional scales suffers from a substantial negative bias under high humidity conditions.”
L104: eight aromatics?
L214: VCPs sourced from “residential, commercial, and industrial sectors”?
L300: “OMH attributes to 50% of aromatic SOA”: it appears OMH contribution is smaller than 50% in Fig. 7 (during the wet period).
L342: 53% of total anthropogenic VOC emissions in LA?
-
AC1: 'Reply on RC1', Myoseon Jang, 18 Apr 2022
We thank the reviewer for the valuable comments on this manuscript. To response to the comments from the reviewer, the explanation and discussions are added in the revised manuscript. A line-by-line response for each comment are listed in the attached pdf.
-
RC2: 'Comment on acp-2021-1002', Anonymous Referee #2, 23 Mar 2022
Review of Yu et al., "Secondary Organic Aerosol Formation via Multiphase Reaction of Hydrocarbons in Urban Atmospheres Using the CAMx Model Integrated with the UNIPAR model"
Yu et al., describe the impact of implementing a state-of-science module for the formation of secondary organic aerosol from traditional as well as "novel" pathways including multi-phase processes involving particles. They evaluate their model against ground observations taken during a recent field campaign over South Korea for the duration of 1 month.
The manuscript is well written and presents the main findings in a concise and understandable fashion. Conclusions are sound presented in a balanced manner, mostly considering the state of the science in the field at this time. My main points are (1) the need to also focus on the remainder of the lifecycle of organic matter in the atmosphere, (2) to make better use of the wealth of data generated during KORUS-AQ to evaluate the model, and (3) a broader evaluation of the model performance. I would recommend major revisions.
Main points:
(1) Organic aerosol lifecycle
Concentrations of OA in the atmosphere are determined by its sources (emission, secondary production) as well as its sinks. The authors claim to do better firstly because their model represents more of the physics and chemistry that probably takes place in the atmosphere, and secondly because it evaluates better against observations. I concur with the former, but find the latter needs to be discussed (further) in the manuscript. A lot of work has shown that OA can photolyse, age, and deposit in ways most models do not consider, thereby changing its properties and lifetime. Why is being closer to observations now "better" with UNIPAR, maybe you are just compensating model deficiencies in other areas?
(2) KORUS-AQ campaign data
KORUS-AQ was also a large aircraft campaign, a treasure trove of observations is readily available (including OA data!) from several aircraft platforms. It would be almost negligent to not use this data to evaluate a 3D m model simulation and instead focus only on three ground stations. There is so much more to learn about OA model performance when looking "up in the sky"!
(3) Model performance evaluation
The authors have provided quite some data to look at overall model performance, but I suggest to complete this in the following areas: how well is NOx represented, what is the performance for temperature and humidity, and how well does the model represent the main SOA precursor levels (aromatics, terpenes
and isoprene)? Again, see point 2, there is a wealth of data available!Specific comments:
15ff "explicit" gas-phase chemistry?
37 why italic for "via"?
37 HC abbrevation, first mention, explain!
48: The fact that SOA precursors can undergo multi-phase chemistry involving a liquid-phase implies they are hygroscopic, which leads to important questions regarding their fate in the atmosphere. E.g., is deposition accounted for correctly (see, e.g., Knote et al., 2015)? Also, given that at least during daytime, we are in a photochemically active environment, what about photolysis losses of OVOCs (e.g., Hodzic et al, 2015)?
49: citations are for box models, better suited in relation to this study are examples for the regional and global scale, e.g. Budisulistiorini et al., 2017 (IEPOX), Knote et al., 2015 (GLYOXAL) and Stadler et al., 2018 (IEPOX), Myriokefalitakis et al., 2008 (GLYOXAL), respectively
51: citation to prove this claim?
52: which "conventional model", not true in this broad claim form!
56: all these citations are the reference for UNIPAR, or is there a single one that serves as reference? It needs to be made clear where UNIPAR is scientifically published.
59: what is "arrayed" supposed to mean?
62: CAMx needs to be introduced (regional scale model...) and cited!
75: SOAP is quite outdated - there should be more recent developments for CAMx that would better show the effect of UNIPAR over the _current_ state of science. See e.g. Jiang et al., 2021, for references.
75: Also, how do comparable model systems fare during KORUS-AQ? There is a good overview by Park et a., 2021, on multi-model results that should provide insights into how the model used here fares compared to others.
141 ff: are organic acids considered when calculating aerosol acidity? How good is your aerosol water content, as it is crucial for acidity calculations...
142: typo "ISORRIPIA"
155: "MOZART", all caps
194: I would expect at least a short model evaluation for the main drivers of OA formation: meteorology (temperature, humidity, radiation), oxidants (O3, NOx) and precursors (aromatics, terpenes, isoprene). See also main concerns.
210ff: how well does your model capture the precursors you actually included? Measurements of aromatics, terpenes and isoprene should be available!
356ff: This statement is too broad to be supported by the analysis shown here - why are you better equipped represent future scenarios better? Because you seem to compare better to 3 ground stations in one geographical corner of the world for 1 month in one year? Because you represent processes better? Address!
Figure S5: do model and measurements coincide (i.e., the model is perfect), or might there be a difference in modelled vs. measured temperature, leading to differences in the thermodynamic environment that should be discussed?
Figure S6: same question as for S5!References:
Sri Hapsari Budisulistiorini, Athanasios Nenes, Annmarie G. Carlton, Jason D. Surratt, V. Faye McNeill, and Havala O. T. Pye Environmental Science & Technology 2017 51 (9), 5026-5034 DOI: 10.1021/acs.est.6b05750
Hodzic, A., Kasibhatla, P. S., Jo, D. S., Cappa, C. D., Jimenez, J. L., Madronich, S., and Park, R. J.: Rethinking the global secondary organic aerosol (SOA) budget: stronger production, faster removal, shorter lifetime, Atmos. Chem. Phys., 16, 7917–7941, https://doi.org/10.5194/acp-16-7917-2016, 2016.
Jiang, J., El Haddad, I., Aksoyoglu, S., Stefenelli, G., Bertrand, A., Marchand, N., Canonaco, F., Petit, J.-E., Favez, O., Gilardoni, S., Baltensperger, U., and Prévôt, A. S. H.: Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50, Geosci. Model Dev., 14, 1681–1697, https://doi.org/10.5194/gmd-14-1681-2021, 2021.
Knote, C., Hodzic, A., Jimenez, J. L., Volkamer, R., Orlando, J. J., Baidar, S., Brioude, J., Fast, J., Gentner, D. R., Goldstein, A. H., Hayes, P. L., Knighton, W. B., Oetjen, H., Setyan, A., Stark, H., Thalman, R., Tyndall, G., Washenfelder, R., Waxman, E., and Zhang, Q.: Simulation of semi-explicit mechanisms of SOA formation from glyoxal in aerosol in a 3-D model, Atmos. Chem. Phys., 14, 6213–6239, https://doi.org/10.5194/acp-14-6213-2014, 2014.
Knote, C., Hodzic, A., and Jimenez, J. L.: The effect of dry and wet deposition of condensable vapors on secondary organic aerosols concentrations over the continental US, Atmos. Chem. Phys., 15, 1–18, https://doi.org/10.5194/acp-15-1-2015, 2015.
Myriokefalitakis, S., Vrekoussis, M., Tsigaridis, K., Wittrock, F., Richter, A., Brühl, C., Volkamer, R., Burrows, J. P., and Kanakidou, M.: The influence of natural and anthropogenic secondary sources on the glyoxal global distribution, Atmos. Chem. Phys., 8, 4965–4981, https://doi.org/10.5194/acp-8-4965-2008, 2008.
Rokjin J. Park, Yujin J. Oak, Louisa K. Emmons, Cheol-Hee Kim, Gabriele G. Pfister, Gregory R. Carmichael, Pablo E. Saide, Seog-Yeon Cho, Soontae Kim, Jung-Hun Woo, James H. Crawford, Benjamin Gaubert, Hyo-Jung Lee, Shin-Young Park, Yu-Jin Jo, Meng Gao, Beiming Tang, Charles O. Stanier, Sung Soo Shin, Hyeon Yeong Park, Changhan Bae, Eunhye Kim; Multi-model intercomparisons of air quality simulations for the KORUS-AQ campaign. Elementa: Science of the Anthropocene 21 January 2021; 9 (1): 00139. doi: https://doi.org/10.1525/elementa.2021.00139
Stadtler, S., Kühn, T., Schröder, S., Taraborrelli, D., Schultz, M. G., and Kokkola, H.: Isoprene-derived secondary organic aerosol in the global aerosol–chemistry–climate model ECHAM6.3.0–HAM2.3–MOZ1.0, Geosci. Model Dev., 11, 3235–3260, https://doi.org/10.5194/gmd-11-3235-2018, 2018.
-
AC2: 'Reply on RC2', Myoseon Jang, 18 Apr 2022
We appreciate the reviewer for the time and effort on this study. Additional discussion about the aerosol lifecycle and the model evaluation using the field data are added in the revised manuscript. A line-by-line response to the reviewer’s comment is listed in the attached pdf.
-
AC2: 'Reply on RC2', Myoseon Jang, 18 Apr 2022
Status: closed
-
RC1: 'Comment on acp-2021-1002', Anonymous Referee #1, 28 Feb 2022
In this manuscript, the authors incorporated their sophisticated SOA model (UNIPAR) with an air quality model (CAMx) and simulated SOA concentrations from different formation pathways and different precursors. Observed concentration of organic matter (OM) is better reproduced by the UNIPAR mode than by a conventional two product model (SOAP). By applying the UNIPAR model, the SOA formation from gas-particle partitioning, in-particle oligomerization, and aqueous-phase reactions are separately calculated, and their contributions have been quantified.
This manuscript is well written and includes useful information about the numerical modeling of SOA formation processes in the ambient air. However, I have several concerns as below. I recommend this manuscript for publication after the following concerns are adequately addressed.
1: Methodology
I am afraid that methodology (model and emissions) is not comprehensively described or adequate references are cited.
- You wrote in L109 that "The mathematical equations used to construct the stoichiometric coefficient array are reported in Section S1" and four parameters (A, B, C, and D) for different precursors and conditions (NOx level and aging status) are given in Table 3, . However, I could not find the information how did you consider dependence on NOx (high/low) and aging degree (fresh/aged) for the calculation of stoichiometric coefficients in the ambient conditions.
- You set six categories for oxidation products: non-reactive (P), slow (S), medium (M), fast (F), very fast (VF), and multifunctional alcohols (MA). Products with these categories are always produced or did you consider any condition dependence?
- Thermodynamic parameters of oxidation products (vapor pressure and vaporization enthalpy) are not explicitly shown.
- Information of emission amounts is not shown. As you estimated the contributions of SOA precursors, total emissions or their distributions are important information. I have two more concerns about emissions:
- You wrote in L255 that “During the wet period, HC emissions increased”. It appears from Figures S5 and S6 that daytime temperature is higher during the dry periods than wet, and thus, I speculate that BVOC emissions are higher during the dry period. Quantitative information and reasons for the increase of HC emissions should be given.
- L308: “isoprene SOA is negligible at all sites due to low isoprene emissions”. Information of isoprene emissions (preferably with terpene and aromatics) is required.
2: Precursors’ contributions:
You wrote in L308 as “Isoprene SOA is negligible at all sites”, and concentrations of isoprene SOA was small over the domain as shown in Figure 8 (g) and (h). However, previous observational and simulation studies have indicated that isoprene SOA has important contributions in East Asia in May-June (e.g., Hu et al., Zhu et al., and Ding et al.). I recommend the authors to discuss the differences of your estimate with previous studies.
Hu et al. (2017) doi:10.5194/acp-17-77-2017
Zhu et al. (2018) doi: 10.1016/j.apr.2017.09.001
Ding et al. (2016) doi: 10.1038/srep20411
3: OMH and OMP
You wrote in L324-326 that "Under the dry period (Fig. 3), the predicted SOA mass by the UNIPAR model is dominated by gas-particle partitioning onto organic phase and oligomerization in organic aerosol. During the wet period, SOA production forms mainly through gas-aqueous partitioning and aqueous reactions."
I could not get how did you separate contributions of oligomer SOA and SOA from aqueous-phase reactions (I guess both are categorized OMH). Quantitative information of the contributions of the three pathways is helpful to readers.
4: OM and OC
It is not clear whether you showed organic mass (OM) or organic carbon (OC) in Figures 3-5. I guess OM concentration is calculated by your simulation model, whereas OC concentration is measured by carbon analyzers. Conversion factor from OC to OM (or vice versa) should be explicitly noted.
Specific comments:
L51: References for the following sentence is necessary: “In particular, the current model applied to regional scales suffers from a substantial negative bias under high humidity conditions.”
L104: eight aromatics?
L214: VCPs sourced from “residential, commercial, and industrial sectors”?
L300: “OMH attributes to 50% of aromatic SOA”: it appears OMH contribution is smaller than 50% in Fig. 7 (during the wet period).
L342: 53% of total anthropogenic VOC emissions in LA?
-
AC1: 'Reply on RC1', Myoseon Jang, 18 Apr 2022
We thank the reviewer for the valuable comments on this manuscript. To response to the comments from the reviewer, the explanation and discussions are added in the revised manuscript. A line-by-line response for each comment are listed in the attached pdf.
-
RC2: 'Comment on acp-2021-1002', Anonymous Referee #2, 23 Mar 2022
Review of Yu et al., "Secondary Organic Aerosol Formation via Multiphase Reaction of Hydrocarbons in Urban Atmospheres Using the CAMx Model Integrated with the UNIPAR model"
Yu et al., describe the impact of implementing a state-of-science module for the formation of secondary organic aerosol from traditional as well as "novel" pathways including multi-phase processes involving particles. They evaluate their model against ground observations taken during a recent field campaign over South Korea for the duration of 1 month.
The manuscript is well written and presents the main findings in a concise and understandable fashion. Conclusions are sound presented in a balanced manner, mostly considering the state of the science in the field at this time. My main points are (1) the need to also focus on the remainder of the lifecycle of organic matter in the atmosphere, (2) to make better use of the wealth of data generated during KORUS-AQ to evaluate the model, and (3) a broader evaluation of the model performance. I would recommend major revisions.
Main points:
(1) Organic aerosol lifecycle
Concentrations of OA in the atmosphere are determined by its sources (emission, secondary production) as well as its sinks. The authors claim to do better firstly because their model represents more of the physics and chemistry that probably takes place in the atmosphere, and secondly because it evaluates better against observations. I concur with the former, but find the latter needs to be discussed (further) in the manuscript. A lot of work has shown that OA can photolyse, age, and deposit in ways most models do not consider, thereby changing its properties and lifetime. Why is being closer to observations now "better" with UNIPAR, maybe you are just compensating model deficiencies in other areas?
(2) KORUS-AQ campaign data
KORUS-AQ was also a large aircraft campaign, a treasure trove of observations is readily available (including OA data!) from several aircraft platforms. It would be almost negligent to not use this data to evaluate a 3D m model simulation and instead focus only on three ground stations. There is so much more to learn about OA model performance when looking "up in the sky"!
(3) Model performance evaluation
The authors have provided quite some data to look at overall model performance, but I suggest to complete this in the following areas: how well is NOx represented, what is the performance for temperature and humidity, and how well does the model represent the main SOA precursor levels (aromatics, terpenes
and isoprene)? Again, see point 2, there is a wealth of data available!Specific comments:
15ff "explicit" gas-phase chemistry?
37 why italic for "via"?
37 HC abbrevation, first mention, explain!
48: The fact that SOA precursors can undergo multi-phase chemistry involving a liquid-phase implies they are hygroscopic, which leads to important questions regarding their fate in the atmosphere. E.g., is deposition accounted for correctly (see, e.g., Knote et al., 2015)? Also, given that at least during daytime, we are in a photochemically active environment, what about photolysis losses of OVOCs (e.g., Hodzic et al, 2015)?
49: citations are for box models, better suited in relation to this study are examples for the regional and global scale, e.g. Budisulistiorini et al., 2017 (IEPOX), Knote et al., 2015 (GLYOXAL) and Stadler et al., 2018 (IEPOX), Myriokefalitakis et al., 2008 (GLYOXAL), respectively
51: citation to prove this claim?
52: which "conventional model", not true in this broad claim form!
56: all these citations are the reference for UNIPAR, or is there a single one that serves as reference? It needs to be made clear where UNIPAR is scientifically published.
59: what is "arrayed" supposed to mean?
62: CAMx needs to be introduced (regional scale model...) and cited!
75: SOAP is quite outdated - there should be more recent developments for CAMx that would better show the effect of UNIPAR over the _current_ state of science. See e.g. Jiang et al., 2021, for references.
75: Also, how do comparable model systems fare during KORUS-AQ? There is a good overview by Park et a., 2021, on multi-model results that should provide insights into how the model used here fares compared to others.
141 ff: are organic acids considered when calculating aerosol acidity? How good is your aerosol water content, as it is crucial for acidity calculations...
142: typo "ISORRIPIA"
155: "MOZART", all caps
194: I would expect at least a short model evaluation for the main drivers of OA formation: meteorology (temperature, humidity, radiation), oxidants (O3, NOx) and precursors (aromatics, terpenes, isoprene). See also main concerns.
210ff: how well does your model capture the precursors you actually included? Measurements of aromatics, terpenes and isoprene should be available!
356ff: This statement is too broad to be supported by the analysis shown here - why are you better equipped represent future scenarios better? Because you seem to compare better to 3 ground stations in one geographical corner of the world for 1 month in one year? Because you represent processes better? Address!
Figure S5: do model and measurements coincide (i.e., the model is perfect), or might there be a difference in modelled vs. measured temperature, leading to differences in the thermodynamic environment that should be discussed?
Figure S6: same question as for S5!References:
Sri Hapsari Budisulistiorini, Athanasios Nenes, Annmarie G. Carlton, Jason D. Surratt, V. Faye McNeill, and Havala O. T. Pye Environmental Science & Technology 2017 51 (9), 5026-5034 DOI: 10.1021/acs.est.6b05750
Hodzic, A., Kasibhatla, P. S., Jo, D. S., Cappa, C. D., Jimenez, J. L., Madronich, S., and Park, R. J.: Rethinking the global secondary organic aerosol (SOA) budget: stronger production, faster removal, shorter lifetime, Atmos. Chem. Phys., 16, 7917–7941, https://doi.org/10.5194/acp-16-7917-2016, 2016.
Jiang, J., El Haddad, I., Aksoyoglu, S., Stefenelli, G., Bertrand, A., Marchand, N., Canonaco, F., Petit, J.-E., Favez, O., Gilardoni, S., Baltensperger, U., and Prévôt, A. S. H.: Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50, Geosci. Model Dev., 14, 1681–1697, https://doi.org/10.5194/gmd-14-1681-2021, 2021.
Knote, C., Hodzic, A., Jimenez, J. L., Volkamer, R., Orlando, J. J., Baidar, S., Brioude, J., Fast, J., Gentner, D. R., Goldstein, A. H., Hayes, P. L., Knighton, W. B., Oetjen, H., Setyan, A., Stark, H., Thalman, R., Tyndall, G., Washenfelder, R., Waxman, E., and Zhang, Q.: Simulation of semi-explicit mechanisms of SOA formation from glyoxal in aerosol in a 3-D model, Atmos. Chem. Phys., 14, 6213–6239, https://doi.org/10.5194/acp-14-6213-2014, 2014.
Knote, C., Hodzic, A., and Jimenez, J. L.: The effect of dry and wet deposition of condensable vapors on secondary organic aerosols concentrations over the continental US, Atmos. Chem. Phys., 15, 1–18, https://doi.org/10.5194/acp-15-1-2015, 2015.
Myriokefalitakis, S., Vrekoussis, M., Tsigaridis, K., Wittrock, F., Richter, A., Brühl, C., Volkamer, R., Burrows, J. P., and Kanakidou, M.: The influence of natural and anthropogenic secondary sources on the glyoxal global distribution, Atmos. Chem. Phys., 8, 4965–4981, https://doi.org/10.5194/acp-8-4965-2008, 2008.
Rokjin J. Park, Yujin J. Oak, Louisa K. Emmons, Cheol-Hee Kim, Gabriele G. Pfister, Gregory R. Carmichael, Pablo E. Saide, Seog-Yeon Cho, Soontae Kim, Jung-Hun Woo, James H. Crawford, Benjamin Gaubert, Hyo-Jung Lee, Shin-Young Park, Yu-Jin Jo, Meng Gao, Beiming Tang, Charles O. Stanier, Sung Soo Shin, Hyeon Yeong Park, Changhan Bae, Eunhye Kim; Multi-model intercomparisons of air quality simulations for the KORUS-AQ campaign. Elementa: Science of the Anthropocene 21 January 2021; 9 (1): 00139. doi: https://doi.org/10.1525/elementa.2021.00139
Stadtler, S., Kühn, T., Schröder, S., Taraborrelli, D., Schultz, M. G., and Kokkola, H.: Isoprene-derived secondary organic aerosol in the global aerosol–chemistry–climate model ECHAM6.3.0–HAM2.3–MOZ1.0, Geosci. Model Dev., 11, 3235–3260, https://doi.org/10.5194/gmd-11-3235-2018, 2018.
-
AC2: 'Reply on RC2', Myoseon Jang, 18 Apr 2022
We appreciate the reviewer for the time and effort on this study. Additional discussion about the aerosol lifecycle and the model evaluation using the field data are added in the revised manuscript. A line-by-line response to the reviewer’s comment is listed in the attached pdf.
-
AC2: 'Reply on RC2', Myoseon Jang, 18 Apr 2022
Zechen Yu et al.
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