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Published May 17, 2022 | Supplemental Material
Journal Article Open

Process-Level Modeling Can Simultaneously Explain Secondary Organic Aerosol Evolution in Chambers and Flow Reactors


Secondary organic aerosol (SOA) data gathered in environmental chambers (ECs) have been used extensively to develop parameters to represent SOA formation and evolution. The EC-based parameters are usually constrained to less than one day of photochemical aging but extrapolated to predict SOA aging over much longer timescales in atmospheric models. Recently, SOA has been increasingly studied in oxidation flow reactors (OFRs) over aging timescales of one to multiple days. However, these OFR data have been rarely used to validate or update the EC-based parameters. The simultaneous use of EC and OFR data is challenging because the processes relevant to SOA formation and evolution proceed over very different timescales, and both reactor types exhibit distinct experimental artifacts. In this work, we show that a kinetic SOA chemistry and microphysics model that accounts for various processes, including wall losses, aerosol phase state, heterogeneous oxidation, oligomerization, and new particle formation, can simultaneously explain SOA evolution in EC and OFR experiments, using a single consistent set of SOA parameters. With α-pinene as an example, we first developed parameters by fitting the model output to the measured SOA mass concentration and oxygen-to-carbon (O:C) ratio from an EC experiment (<1 day of aging). We then used these parameters to simulate SOA formation in OFR experiments and found that the model overestimated SOA formation (by a factor of 3–16) over photochemical ages ranging from 0.4 to 13 days, when excluding the abovementioned processes. By comprehensively accounting for these processes, the model was able to explain the observed evolution in SOA mass, composition (i.e., O:C), and size distribution in the OFR experiments. This work suggests that EC and OFR SOA data can be modeled consistently, and a synergistic use of EC and OFR data can aid in developing more refined SOA parameters for use in atmospheric models.

Additional Information

© 2022 American Chemical Society. Received 14 December 2021. Accepted 23 March 2022. Revised 22 March 2022. Published online 3 May 2022. Published in issue 17 May 2022. This work was supported by the U.S. Department of Energy (DOE), Office of Science (DE-SC0017975, DE-SC0019000). This publication was partly developed under Assistance Agreement No. R840008 awarded by the U.S. Environmental Protection Agency to Shantanu Jathar. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. We would like to thank Delphine Farmer and Beth Friedman for useful discussions related to OFR use and data. Data Availability: EC data in this work are from Dr. John Seinfeld's group at the California Institute of Technology and OFR data in this work are from Dr. Andrew Lambe at Aerodyne Research Inc., both of which can be obtained upon request. The latest version of the Fortran model for SOM-TOMAS is archived on Github (https://github.com/yicongh/SOM-TOMAS-v2), and the model outputs are available at this link: http://dx.doi.org/10.25675/10217/234640. Author Contributions. Y.H. and S.H.J. designed the modeling study. Y.H. developed the model. Y.H. performed the simulations and analyzed the data along with S.H.J. and J.R.P. A.L. and J.H.S. provided the experimental data. C.D.C. supported the model development and analysis. Y.H. and S.H.J. wrote the paper with contributions from all co-authors. The authors declare no competing financial interest.

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