In order to improve the prediction of the regional air quality modeling in the Seoul metropolitan area, a sensitivity analysis using two PBL and microphysics (MP) options of the WRF model was performed during four seasons. The results from four sets of the simulation experiments (EXPs) showed that meteorological variables (especially wind field) were highly sensitive to the choice of PBL options (YSU or MYJ) and no significant differences were found depending on MP options (WDM6 or Morrison) regardless of specific time periods, i.e. day and night, during four seasons. Consequently, the EXPs being composed of YSU PBL option were identified to produce better results for meteorological elements (especially wind field) regardless of seasons. On the other hand, the accuracy of all simulations for summer and winter was somewhat lower than those for spring and autumn and the effect according to physics options was highly volatile by geographical characteristics of the observation site.

The seasonal variations of sea surface winds and significant wave heights were investigated using the data observed from the marine meteorological buoys (nine stations) and Automatic Weather Stations (AWSs) in lighthouse (nine stations) around the Korean Peninsula during 2010~2012. In summer, the prevailing sea surface winds over the East/West Sea and the South Sea were northerly/southerly and easterly/westerly winds due to both of southeast monsoon and the shape of Korean Peninsula. On the other hand, the strong northerly winds has been observed at most stations near Korean marginal seas under northwest monsoon in winter. However, the sea surface winds at some stations (e.g. Galmaeyeo, Haesuseo in the West Sea) have different characteristics due to topographic effects such as island or coastal line. The significant wave heights are the highest in winter and the lowest in summer at most stations. In case of some lighthouse AWSs surrounded by islands (e.g. Haesuseo, Seosudo) or close to coast (e.g. Gangan, Jigwido), very low significant wave heights (below 0.5 m) with low correlations between sea surface wind speeds and significant wave heights were observed.

In this study, the regional climate (WRF) and air quality (CMAQ) models were used to simulate the effects of future urban growth on surface ozone concentrations in the Seoul metropolitan region (SMR). These analyses were performed based on changes in ozone concentrations during ozone seasons (May–June) for the year 2050 (future) relative to 2012 (present) by urban growth. The results were compared with the impacts of RCP scenarios on ozone concentrations in the SMR. The fractions of urban in the SMR (25.8 %) for the 2050 were much higher than those (13.9 %) for the 2012 and the future emissions (e.g., CO, NO, NO2, SO2, VOC) were increased from 121 % (NO) to 161.3 % (NO2) depending on emission material. The mean and daily maximum 1-h ozone in the SMR increased about 3 - 7 ppb by the effect the RCP scenarios. However, the effect of urban growth reduced the mean ozone by 3 ppb in the SMR and increased the daily maximum 1-h ozone by 2 - 5 ppb over the northeastern SMR and around the coastline. In particular, the ozone pollution days exceeding the 1-h regulatory standard (100 ppb) were far more affected by urban growth than mean values. As a result, the average number of days exceeding the 1-h regulatory standard increased up to 10 times.

The spatial and temporal changes of the annual mean urban heat island(UHI) intensity were investigated using near surface temperature data measured at 16 automatic weather systems(AWS) in Busan metropolitan area(BMA) during the 11-yr period, from 2000 to 2010. For nighttime, the annual mean UHI intensity at Dongnae(U1) in 2000 was weaker than it in 2010. However the change of the annual mean UHI intensity at Daeyeon(U2) during 11 years was different from it at U1. The annual frequency of the UHI intensity over 5℃ considerably increased at U2 and decreased at U1 during 11 years. The center of the UHI also spatially shifted southward with Daeyeon and Haeundae in BMA. It would be caused by the increase of urban area, population-density and transportation near U2 and by the decrease of them near U1. We found that the spatial and temporal differences of the UHI intensity have coincided with changes of land-use, population density and transportation in BMA.