| Summary: | The vertical eddy diffusion process plays a crucial role in PM<sub>2.5</sub> prediction, yet accurately predicting it remains challenging. In the three-dimensional atmospheric chemistry transport model (3-D AQM) CMAQ, a parameter, Kz, is utilized, and it is known that PM<sub>2.5</sub> prediction tendencies vary according to the floor value of this parameter (Kz<sub>min</sub>). This study aims to examine prediction characteristics according to Kz<sub>min</sub> values, targeting days exceeding the Korean air quality standards, and to derive appropriate Kz<sub>min</sub> values for predicting PM<sub>2.5</sub> concentrations in the DJFM Seoul Metropolitan Area (SMA). Kz<sub>min</sub> values of 0.01, 0.5, 1.0, and 2.0, based on the model version and land cover, were applied as single values. Initially focusing on December 4th to 12th, 2020, the prediction characteristics were examined during periods of local and inflow influence. Results showed that in both periods, as Kz<sub>min</sub> increased, surface concentrations over land decreased while those in the upper atmosphere increased, whereas over the sea, concentrations increased in both layers due to the influence of advection and diffusion without emissions. During the inflow period, the increase in vertically diffused pollutants led to increased inflow concentrations and affected contribution assessments. Long-term evaluations from December 2020 to March 2021 indicated that the prediction performance was superior when Kz<sub>min</sub> was set to 0.01, but it was not significant for the upwind region (China). To improve trans-boundary effects, optimal values were applied differentially by region (0.01 for Korea, 1.0 for China, and 0.01 for other regions), resulting in significantly improved prediction performance with an R of 0.78, IOA of 0.88, and NMB of 0.7%. These findings highlight the significant influence of Kz<sub>min</sub> values on winter season PM<sub>2.5</sub> prediction tendencies in the SMA and underscore the need for considering differential application of optimal values by region when interpreting research and making policy decisions.
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