Preprints
https://doi.org/10.5194/egusphere-2025-2253
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2025-2253
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
05 Aug 2025
| 05 Aug 2025
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Simulated reductions in Heterogeneous Isoprene Epoxydiol Reactive Uptake from aerosol morphology in the contiguous United States using the Community Multiscale Air Quality Model (CMAQv5.3.2)
Abstract. Aerosol particles contain complex mixtures of polar and non-polar species that can undergo organic-inorganic phase separations. In phase-separated aerosol particles, the phase state of the outer organic coating can modulate heterogeneous chemistry. Heterogeneous chemistry leading to isoprene epoxydiol (IEPOX)-derived secondary organic aerosol (IEPOX-SOA) is encoded in the Community Multiscale Air Quality (CMAQ) model and has been the focus of previous aerosol phase separation and phase state work. In a previous study, a constant ratio of water in the organic coating (ws) was assumed in modeling phase separation and state. Recent studies, however, have highlighted ws as an important modulator of phase state. This work uses CMAQ version (version 5.3) with capabilities to model dynamic water uptake to the organic coating to better predict ws and its impact on the organic coating phase state. In addition, new parameterizations for estimating organic aerosol phase state were implemented in CMAQ, and the subsequent model predictions were used to compare their impacts on phase state and IEPOX-SOA predictions. These evaluations were completed simulating a summertime episode over the continental United States. Simulated diurnal profiles of aerosol phase state agreed within one standard deviation of observationally-derived field measurements. The implementation of phase separation and phase state parameterizations, on average, decreased IEPOX reactive uptake by up to 99.99 % compared to Base CMAQ, resulting in mixed model performance. While 2-methyltetrol performance improved with phase separation and phase state updates, methyltetrol sulfates and total IEPOX-SOA concentrations further underpredicted field observations in comparison to Base CMAQ.
How to cite. Farrell, S. L., Rasool, Q. Z., Pye, H. O. T., Zhang, Y., Li, Y., Chen, Y., Wang, C.-T., Zhang, H., Schmedding, R., Shiraiwa, M., Greene, J., Budisulistiorini, S. H., Jimenez, J. L., Hu, W., Surratt, J. D., and Vizuete, W.: Simulated reductions in Heterogeneous Isoprene Epoxydiol Reactive Uptake from aerosol morphology in the contiguous United States using the Community Multiscale Air Quality Model (CMAQv5.3.2), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-2253, 2025.
Competing interests: MS is a member of the editorial board of the journal ACP.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.Download & links
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Status: open (until 29 Sep 2025)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on egusphere-2025-2253', Anonymous Referee #1, 02 Sep 2025
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Farrell et al. present an interesting investigation into the impact of various organic aerosol phase state parametrizations on the prediction of IEPOX-SOA phase states using CMAQ. This represents a significant development for the CMAQ modelling community. However, before I can recommend publication in an EGU journal, major modifications with respect to context and presentation need to be made. Overall, the manuscript reads more like a combination of model description and evaluation. In this context, it might be better suited to publication in Geoscientific Model Development (GMD) instead of ACP.General comments:Version 5.3.2 of CMAQ was published in 2020. Since then, there have been two major updates to CMAQ, which is now available as version 5.5. Please explain why you are relying on an outdated version of CMAQ. As far as I can tell, your code adjustments are not included in the official CMAQ repository. Can your code modifications be transferred to the current and future versions of CMAQ?The representation style is generally good. However, the figures require significant quality improvements and harmonisation. Figure 3 is completely unreadable. No information can be obtained from panels (e) and (f). Figures 1 and 4 also have quality issues, with some text being illegible. Please harmonise the layout of Figures 2, 3 and 6. Figure 3 has latitudes and longitudes, but the other two do not.Specific comments:Line 39: Does the 99.99% indicate that the reactive uptake is completely suppressed?Line 39: Please specify what you are referring to with 'mixed model performance'. Do you mean numerical performance or when evaluating predictions with observations?Line 41: I assume that 'Base CMAQ' refers to the same CMAQ version without your modifications with otherwise the same model setup?Line 93: Please elaborate why 10% was chosen for previous studies.Line 142: I assume that AIETET and other name given in parenthesis are the names of the corresponding species in the CMAQ mechanism. Indicating this could be beneficial for readers that are not familiar with CAMQ.Line 157: How was the Vogel-Tamman-Fulcher equation modified?Line 169: Is the 'numerical precision' given as a general comment on CMAQ's performance or are you referring to calculated precision (single vs double precision in Fortran)?Line 214: What influence do you expect from assuming an activity coefficient of 1?Line 242 and 244: Please provide approximate pressure altitudes for layer 18, 28, and 35.Line 259-261: How about representing these conditions in a table?Line 266: Do you expect this transition to be valid for other seasons as well?Figure 4: I recommend listing the simulation name in each sub-figures heading in addition to (a), (b), and (c).Table 2: The text in column 'Species Description' is hardly readable.Line 330-332: Did you perform sensitivity simulations for this assumption? How would this affect your predictions?Line 376: Please specify which reduction corresponds to which phase state simulation.Line 381: How was this run-time improvement measured? Is it a simple comparison of run-time provided by the model (ignoring variability introduced by the cluster used)? Or did you use a specific tool to analyze run-time behavior?Figure 7: Between 2013-06-08 and 2013-06-22 it looks as if all models fail to reproduce specific features in the observation. Can you comment on this?Line 411: Where the WRF simulations used for the meteorology in this study nudged? How do the temperature and RH simulated by CAMQ compare to SOAS observations?Code/Data Availability: I recommend to archive the code adjustments currently only available in GitHub to a permanent storage (e.g., Zenodo) to insure future reproducibility.Units: Throughout the manuscript, various styles for units are used. I strongly recommend harmonizing this in a revised version according to ACP guidelines.Citation: https://doi.org/
10.5194/egusphere-2025-2253-RC1
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Sara L. Farrell
CORRESPONDING AUTHOR
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
Quazi Z. Rasool
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
Department of Chemistry and Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado, Boulder, CO, USA
Havala O. T. Pye
Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, 27709
Yue Zhang
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
now at: Department of Atmospheric Sciences, Texas A&M University, College Station, TX, 77843
Department of Chemistry, University of California, Irvine, 92697, CA, USA
now at: State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yuzhi Chen
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
Chi-Tsan Wang
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
now at: Center for Spatial Information Science and Systems (CSISS), George Mason University, VA 22030
Haofei Zhang
Department of Chemistry, University of California, Riverside, CA 92521
Ryan Schmedding
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Canada, H3A 2K6
Manabu Shiraiwa
Department of Chemistry, University of California, Irvine, 92697, CA, USA
Jaime Greene
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
Sri H. Budisulistiorini
Wolfson Atmospheric Chemistry Laboratories, University of York, York, UK
Jose L. Jimenez
Department of Chemistry and Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado, Boulder, CO, USA
Weiwei Hu
Department of Chemistry and Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado, Boulder, CO, USA
now at: State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
Jason D. Surratt
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
Department of Chemistry, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
William Vizuete
CORRESPONDING AUTHOR
Department of Environmental Science and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
Short summary
Fine particulate matter (PM2.5) has become increasingly important to regulate and model. In this study, we parameterize non-ideal aerosol mixing and phase state into the Community Multiscale Air Quality (CMAQ) model and analyze its impact on the formation of a globally important source of PM2.5, isoprene epoxydiol (IEPOX)-derived PM2.5. Incorporating these features furthers model bias in IEPOX-derived PM2.5, however, this work provides potential phase state bounds for future PM2.5 modeling work.
Fine particulate matter (PM2.5) has become increasingly important to regulate and model. In this...