edu.ucar.opensky::articles:25227 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T22:52:49.803886 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7n301k3 https://doi.org/10.5067/Suborbital/KORUSAQ/DATA01 https Related Dataset #1 Korea United States Air Quality Study Evaluation of Secondary Organic Aerosol (SOA) simulations for Seoul, Korea 2022-02-01T00:00:00Z publication https://n2t.org/ark:/85065/d7n301k3 https://doi.org/10.1029/2021MS002760 Oak, Y. J. author Park, R. J. author Jo, Duseong author Hodzic, Alma author Jimenez, J. L. author Campuzano-Jost, P. author Nault, B. A. author Kim, H. author Kim, H. author Ha, E. S. author Song, C. author Yi, S. author Diskin, G. S. author Weinheimer, Andrew author Blake, D. R. author Wisthaler, A. author Shim, M. author Shin, Y. author UCAR/NCAR - Library publisher Organic aerosols (OA) represent a significant fraction of total submicron particulate matter (PM1) concentrations globally, including densely populated megacities such as Seoul. However, scientific understanding of the atmospheric formation and removal processes of OA, especially for secondary organic aerosols (SOA), is still highly uncertain. In this study, we examine the characteristics of SOA formation in Seoul during spring-summer 2016 and fall-winter 2017/2018, using airborne and ground observations along with a 3-D global chemical transport model, GEOS-Chem. We use four different SOA schemes in the model, including simplified and complex volatility-based frameworks, and evaluate them by comparing the simulations with the observations to examine how our scientific understanding embedded in each SOA scheme affects the observed biases. Our analysis of the model performance of each scheme also provides the most suitable approach in simulating SOA in a typical urban environment. Comparisons of the simulated versus observed OA concentrations show that model biases range from -72% to +118%, with considerable variability among different schemes and seasons. We find that the inclusion of semi/intermediate volatile precursors, in addition to the traditional precursors, and chemical aging (functionalization) are important factors to simulate surface SOA concentrations in Seoul. However, a comparison of observed and simulated SOA/ increment CO enhancement ratios suggests that most schemes underpredict SOA aging in upper levels in the boundary layer. We also find that the simplified SOA scheme can reproduce observed OA but often shows overestimation in surface air, indicating that uncertainties exist in bottom-up emissions and precursor parameterization in Seoul. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation