Dong-Ju Kim

This study investigates how urban morphology and pollutant emissions influence near-surface meteorology and the spatial distributions of CO, NO2, O3, and PM2.5 in Songdo, Incheon, Republic of Korea. We use a fine-scale air quality modeling framework that couples computational fluid dynamics with atmospheric chemistry, driven by mesoscale meteorological and chemical fields and high-resolution emissions developed using a top-down approach. Model performance is evaluated against meteorological observations and multi-site air quality measurements within the study area. To examine the determinants of spatial variability, the domain is divided into subzones and statistical analyses are applied to relate simulated surface concentrations to emissions and building morphology parameters, including building surface fraction and occlusivity. Surface concentrations show the strongest associations with emissions for CO, NO2, and O3, whereas occlusivity exhibits the strongest association with PM2.5. Notably, systematic concentration differences are observed even under comparable emission levels, highlighting the influence of morphology on ventilation and near-surface pollutant accumulation. The findings suggest that preserving wind corridors and allocating open spaces, particularly in high-rise districts, can enhance ventilation and reduce pollutant buildup. This work supports urban planning and air quality management and provides a basis for future exposure and environmental health analyses.

This study analyzes the spatiotemporal characteristics of PM₁₀ across 53 Asian dust events that affected the Korean Peninsula between January 2019 and June 2024. Self-Organizing Map (SOM) analysis was applied to sea level pressure and 850 hPa wind fields from the NCEP/DOE Reanalysis II dataset, classifying synoptic patterns into four distinct clusters. Cluster 1, associated with a deep low over Manchuria and strong westerly inflow, produced the highest PM₁₀ concentrations and the longest durations across most regions, with sharp afternoon peaks and the highest skewness values, and was mainly sourced from the Gobi Desert. Cluster 2 featured a high–low pressure dipole, generating localized impacts in northwestern regions and shorter durations, with moderate afternoon increases, originating primarily from the Gobi Desert and Inner Mongolia. Cluster 3, linked to a low east of Japan, resulted in elevated PM₁₀ mainly in central and southeastern regions, with peaks often occurring earlier in the day, and was associated with Manchurian dust sources. Cluster 4 exhibited a straight northwesterly flow with the high shifted eastward, producing moderate but spatially widespread concentrations and relatively consistent afternoon peaks, also linked to Manchurian sources. These results suggest that integrating synoptic pattern classification into dust forecasting can improve accuracy, enable early recognition of high-concentration events, and support the development of timely and region-specific warning strategies.

The vertical eddy diffusion process plays a crucial role in PM_2.5 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_2.5 prediction tendencies vary according to the floor value of this parameter (Kz_min). This study aims to examine prediction characteristics according to Kz_min values, targeting days exceeding the Korean air quality standards, and to derive appropriate Kz_min values for predicting PM_2.5 concentrations in the DJFM Seoul Metropolitan Area (SMA). Kz_min 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_min 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_min 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_min values on winter season PM_2.5 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.

Objectives : This study aims to identify the changes and statistical characteristics of the observed concentration data of fine particles (PM-2.5), one of the air environment indicators, and to analyze the variability of observation data in South Korea based on the statistical significance of the observation data.Methods : We analyzed the changes and characteristics of daily average PM-2.5 observations based on descriptive statistics (the basic statistical and frequency analysis), and inference statistics (analysis of variance and post-hoc analysis) from December to March for the period 2015 to 2021.Results and Discussion : Through ANOVA analysis and post-hoc analysis, it was confirmed that there was a statistically significant difference in the observed PM-2.5 as of the winter of 2019. Before 2019, the frequency of observed data greater 30 µg/m;3 was relatively more than after 2019, and on the contrary, the frequency of observed data less than 20 µg/m;3 was smaller than after 2019. After 2019, the frequency of high-concentration observations greater than 50 µg/m;3 was insignificant, and on the contrary, the frequency of observations less than 20 µg/m;3 was more than before 2019. As a result, It’s an average difference of 7.5 µg/m;3 between before and after 2019, and it was reduced by about 24% compared to the average before 2019. Meanwhile, as a result of examining the weather influence (wind direction and wind speed) to analyze the causes of the observed data differences before and after 2019, no significant differences were identified between the two periods due to the influence of meteorological conditions.Conclusion : The observed concentration of PM-2.5 data set in South Korea showed variability as of the winter season of 2019 at the significance level of 5%. The variability could be more attributed to anthropogenic activities and socio-environmental changes such as COVID-19 rather than natural environmental factors such as meteorological factors. In order to more effectively manage air quality, it is necessary to identify social changes and their flows, and at the same time conduct continuous and systematic research such as data expansion and analysis technique development.

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories (urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains) based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s⁻¹) and underestimated temperature (average TE = −0.63 °C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s⁻¹, average TE = 0.12 °C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.