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Published September 1, 2021 | Supplemental Material + Published
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A computationally efficient model to represent the chemistry, thermodynamics, and microphysics of secondary organic aerosols (simpleSOM): model development and application to α-pinene SOA


Secondary organic aerosols (SOA) constitute an important fraction of fine-mode atmospheric aerosol mass. Frameworks used to develop SOA parameters from laboratory experiments and subsequently used to simulate SOA formation in atmospheric models make many simplifying assumptions about the processes that lead to SOA formation in the interest of computational efficiency. These assumptions can limit the ability of the model to predict the mass, composition, and properties of SOA accurately. In this work, we developed a computationally efficient, process-level model named simpleSOM to represent the chemistry, thermodynamic properties, and microphysics of SOA. simpleSOM simulates multigenerational gas-phase chemistry, phase-state-influenced kinetic gas/particle partitioning, heterogeneous chemistry, oligomerization reactions, and vapor losses to the walls of Teflon chambers. As a case study, we used simpleSOM to simulate SOA formation from the photooxidation of α-pinene. This was done to demonstrate the ability of the model to develop parameters that can reproduce environmental chamber data, to highlight the chemical and microphysical processes within simpleSOM, and discuss implications for SOA formation in chambers and in the real atmosphere. SOA parameters developed from experiments performed in the chamber at the California Institute of Technology (Caltech) reproduced observations of SOA mass yield, O : C, and volatility distribution gathered from other experiments. Sensitivity simulations suggested that multigenerational gas-phase aging contributed to nearly half of all SOA and that in the absence of vapor wall losses, SOA production in the Caltech chamber could be nearly 50% higher. Heterogeneous chemistry did not seem to affect SOA formation over the short timescales for oxidation experienced in the chamber experiments. Simulations performed under atmospherically relevant conditions indicated that the SOA mass yields were sensitive to whether and how oligomerization reactions and the particle phase state were represented in the chamber experiment from which the parameters were developed. simpleSOM provides a comprehensive, process-based framework to consistently model the SOA formation and evolution in box and 3D models.

Additional Information

© 2021 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Submitted 26 Feb 2021; Accepted 26 May 2021; First published 07 Jun 2021. This work was supported by the U.S. Department of Energy (DOE), Office of Science (DE-SC0017975). RAZ acknowledges support from the U.S. DOE Office of Science as part of the Atmospheric System Research Program at the Pacific Northwest National Laboratory (PNNL). MS was supported by the U.S. DOE, Office of Science, and Office of Biological and Environmental Research through the Early Career Research Program. PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL0 1830. We thank Dr Benjamin Murphy at the U.S. Environmental Protection Agency for helpful conversations concerning CMAQ. Data availability: Experimental data used in this work are from Dr John Seinfeld's group at the California Institute of Technology and are now available as part of the Index of Chamber Atmospheric Research in the United States (ICARUS; https://icarus.ucdavis.edu). The latest versions of the IGOR and Fortran models for simpleSOM/simpleSOM-MOSAIC along with the simulation data are archived with Colorado State University Libraries (http://dx.doi.org/10.25675/10217/232634) and Github (https://github.com/ARM-Synergy/simpleSOM_boxmodel). The data from all the figures in the paper are also available with Colorado State University Libraries (http://dx.doi.org/10.25675/10217/232634). Author contributions: SHJ and CDC designed the model framework and overall study. CDC developed the IGOR model and YH and WC updated the model. YH developed the Fortran-Python model and YH and KRB benchmarked this model against the IGOR model. YH performed the simulations and YH and SHJ analyzed the data. JHS provided the experimental data. RAZ, MS, and JRP supported the model development and analysis. KRB helped with archiving the code and model output. SHJ and YH wrote the paper with contributions from all co-authors. There are no conflicts to declare.

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August 20, 2023
October 20, 2023