Process analysis of Contribution to High Concentration PM2.5

To prevent the occurrence of high concentrations of PM2.5, Korea has implemented policies to reduce domestic emissions, such as seasonal fine dust control systems and restrictions on the operation of old diesel vehicles, and has established intensive measurement stations by region to analyze and monitor the ionic composition of major substances such as PM2.5 and PM1, which are used for policy formulation and research. The secondary reaction precursors of PM2.5 are nitrate, sulfate, and ammonium, and the contribution of these precursors varies depending on geographical characteristics and emission source characteristics. According to previous studies, the contribution of OA in Seoul was 27%, nitrate 42%, ammonium 21%, sulfate 8%, and other 2%, while the contribution of OA in Seosan was 40%, nitrate 29%, ammonium 15%, sulfate 12%, and other 4%.(Kim et al, 2022) PM2.5 is a representative secondary phase pollutant along with ozone, which is highly influenced by meteorological factors such as humidity, wind speed, and wind direction, and its concentration changes due to the combination of particulate matter and gaseous matter, so it is important to predict it. In general, chemical transport models such as CMAQ (Community Multi-scale Air Quality) and CAMx (Comprehensive Air quality Model with extensions) are mainly used to prediction PM2.5, and in recent years, LSTM models using machine learning have also been utilized. In this study, we analyze the hourly concentration of PM2.5 in Korea in 2019, analyze the physicochemical characteristics of PM2.5 concentrations to identify the causes of high concentration episodes, and identify the effects of topography characteristics on PM2.5. To analyze the causes of high PM2.5 concentrations in high concentration episodes, we first analyze the physicochemical contribution through the Process Analysis (PA) tool of CMAQ. In addition, to identify the causes of high concentrations by region, we analyze the effects of topography characteristics on PM2.5 using the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is a reverse trajectory model, to evaluate the causes of high concentrations. Reference: N.K. Kim, Y.P. Kim, Y.S. Ghim, M.J. Song, C.H. Kim, K.S. Jang, K.Y. Lee, H.J. Shin, J.S. Jung, Z. Wu, A. Matsuki, N. Tang, Y. Sadanaga, S. Kato, A. Natsagdorj, S. Tseren-Ochir, B. Baldorj, C.K. Song, J.Y. Lee, Spatial distribution of PM2.5 chemical components during winter at five sites in Northeast Asia: High temporal resolution measurement study, Atmospheric Environment, Volume 290, 1 December 2022, 119359https://doi.org/10.1016/j.atmosenv.2022.119359 Acknowledgments This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry Technology Institute(KEITI) funded by the Ministry of Environment(MOE)