The exposed population of a vulnerable group to high ozone episodes (exceeding 60 ppb/8h) was estimated in Busan metropolitan city from 2000 to 2010. The frequency of high ozone days at monitoring sites and the number of the population aged over 65 were used to calculate the accumulated (total, seasonal, and yearly) number of the exposed older population (EOP) to high ozone episodes during the study period based on administrative areas, by interpolation and zonal mean methods in ArcGIS software. The older population in this city had increased significantly from 2000 to 2010 (representing over 10% of the total population in 2010). The vulnerable areas (e.g. the eastern area of the city) of the EOP to high ozone episodes were different from the areas with frequent high ozone episodes (e.g., the western area) due to the increase of the older population in particular areas. The difference was more significant in spring than in any other season, and in 2010 than in previous years (2000 and 2005).

The seasonal variations of ozone (O3) concentrations were investigated with regard to the relationship between O3 and wind distributions at two different sites (Jung Ang (JA): a semi-closed topography and Seo Chang (SC): a closed topography) within a valley city (Yangsan) and their comparison between these sites (JA and SC) and two non-valley sites (Dae Jeo (DJ) and Sang Nam (SN)) located downwind from coastal cities (Busan and Ulsan). This analysis was performed using the data sets of hourly O3 concentrations, meteorological factors (especially, wind speed and direction), and those on high O3 days exceeding the 8-h standard (60 ppb) during 2008-2009. In summer and fall (especially in June and October), the monthly mean values of the daily maximum O3 concentrations and the number of high O3 days at JA (and SC) were relatively higher than those at DJ (and SN). The increase in daytime O3 concentrations at JA in June was likely to be primarily impacted by the transport of O3 and its precursors from the coastal emission sources in Busan along the dominant southwesterly winds (about 5 m/s) under the penetration of sea breeze condition, compared to other months and sites. Such a phenomenon at SC in October was likely to be mainly caused by the accumulation of O3 and its precursors due to the relatively weak winds under the localized stagnant weather condition rather than the contribution of regional transport from the emission sources in Busan and Ulsan.

This paper examines the effects of the partial solar eclipse of 22 July 2009 across the Korean peninsular on surface temperature and ozone concentrations in over the Busan metropolitan region (BMR). The observed data in the BMR demonstrated that the solar eclipse phenomenon clearly affects the surface ozone concentration as well as the air temperature. The decrease in temperature ranging from 1.2 to 5.4℃ was observed at 11 meteorological sites during the eclipse as a consequence of the solar radiation decrease. A large temperature drop exceeding 4℃ was observed at most area (8 sites) of the BMR. Significant ozone drop (18∼29 ppb) was also observed during the eclipse mainly due to the decreased efficiency of the photochemical ozone formation. The ozone concentration started to decrease at approximately 1 to 2 hours after the event and reached its minimum value for a half hour to 2 hours after maximum eclipse. The rate of ozone fall ranged between 0.18 and 0.49 ppb/min. The comparison between ozone measurements and the expected values derived from the fitted curve analysis showed that the maximum drop in ozone concentrations occurred at noon or 1 PM and was pronounced at industrial areas.

The typical characteristics of seasonal winds were studied around the Pohang using two-stage (average linkage then k-means) clustering technique based on u- and v-component wind at 850 hpa from 2004 to 2006 (obtained the Pohang station) and a high-resolution (0.5 km grid for the finest domain) WRF-UCM model along with an up-to-date detailed land use data during the most predominant pattern in each season. The clustering analysis identified statistically distinct wind patterns (7, 4, 5, and 3 clusters) representing each spring, summer, fall, and winter. During the spring, the prevailed pattern (80 days) showed weak upper northwesterly flow and late sea-breeze. Especially at night, land-breeze developed along the shoreline was converged around Yeongil Bay. The representative pattern (92 days) in summer was weak upper southerly flow and intensified sea-breeze combined with sea surface wind. In addition, convergence zone between the large scale background flow and well-developed land-breeze was transported around inland (industrial and residential areas). The predominant wind distribution (94 days) in fall was similar to that of spring showing weak upper-level flow and distinct sea-land breeze circulation. On the other hand, the wind pattern (117 days) of high frequency in winter showed upper northwesterly and surface westerly flows, which was no change in daily wind direction.

The impact of urbanization on local meteorology (e.g., surface temperature, PBL height, wind speed, etc.) in the Greater Seoul Area (GSA) was quantitatively evaluated based on a numerical modeling approach during a 1-month period of 2001 (9 Sep. through 8 Oct. 2001). The analysis was carried out by two sets of simulation scenarios: (1) with the global land use and topographic data from the U.S. Geological Survey (USGS) in 1990s (i.e., LU-USGS case) and (2) with the land use data from the Environmental Geographic Information System (EGIS) along with the 3 sec elevation data from the Shuttle Radar Topography Mission (SRTM) in 2000s (i.e., LU-EGIS case). The extension of urban areas in the GSA (especially, the southern parts of Seoul) accounted for 1.8% in the LU-USGS case and 6.2% in the LU-EGIS case. For the simulations, the surface temperature and PBL height due to urbanization in the LU-EGIS case was higher (the differences of up to 0.1 ℃ and 36 m, respectively) than those in the LU-USGS case, whereas the wind speed (up to 0.3 ms-1) in the former was lower than that in the latter at 1500 LST. The increase in surface temperature due to urbanization in the GSA (especially, the southern parts of Seoul) was led to the strong convergence of air masses, causing the early sea breeze and its rapid propagation to inland locations. In addition, the vertical mixing motion in the extended urban areas for the LU-EGIS case was predicted to be stronger than that for the LU-USGS case and vice versa for the original urban areas.

The estimation of a biogenic volatile organic compound (BVOC, especially isoprene) and the influence of isoprene emissions on ozone concentrations in the Greater Busan Area (GBA) were carried out based on a numerical modeling approach during a high ozone episode. The BVOC emissions were estimated using a biogenic emission information system (BEIS v3.14) with vegetation data provided by the forest geographical information system (FGIS), land use data provided by the environmental geographical information system (EGIS), and meteorological data simulated by the MM5. Ozone simulation was performed by two sets of simulation scenarios: (1) without (CASE1) and (2) with isoprene emissions (CASE2). The isoprene emission (82 ton day -1 ) in the GBA was estimated to be the most dominant BVOC followed by methanol (56) and carbon monoxide (28). Largest impacts of isoprene emissions on the ozone concentrations (CASE2-CASE1) were predicted to be about 4 ppb in inland locations where a high isoprene was emitted and to be about 2 ppb in the downwind and/or convergence regions of wind due to both the photochemical reaction of ozone precursors (e.g., high isoprene emissions) and meteorological conditions (e.g., local transport).

In this study, we focused on the improvements in the simulation of sea surface wind over the complex coastal area. MM5 model being currently used to predict sea surface wind at Korea Meteorological Administration, was used to verify the accuracy to estimate the local wind field. A case study was performed on clear days with weak wind speed(4 m/s), chosen by the analysis of observations. The model simulations were conducted in the southeastern area of Korea during the selected periods, and observational data such as AWS, buoy and QuikSCAT were used to compare with the calculated wind components to investigate if simulated wind field could follow the tendency of the real atmospheric wind field. Results showed that current operational model, MM5, does not estimate accurately sea surface wind and the wind over the coastal area. The calculated wind speed was overestimated along the complex coastal regions but it was underestimated in islands and over the sea. The calculated diurnal changes of wind direction could not follow well the tendency of the observed wind, especially at nighttime. In order to exceed the limitations, data assimilation with high resolution data and more specificated geographical information is expected as a next best policy to estimate accurately the environment of local marine wind field.

This study presents the characteristics of nocturnal inversion layer and their effect on the concentration variations of surface air pollutants using tethersonde and automatic weather station (AWS, 2 layer tower) system in Ulsan during 2003. The method for the distinction of inversion intensity was decided based on the sum of nocturnal temperature gradient. As the results, there was a close correlation (correlation coefficient of 0.76) between the maximum inversion height obtained from tethersonde and the sum of nocturnal temperature gradient. The air pollutant concentration was also directly proportional to the inversion intensity. When the inversion intensity was strong in the nighttime, ozone (O3) concentration was lower, while nitrogen dioxide (NO2) concentration was higher. The carbon monoxide (CO) concentration was gradually higher according to the nocturnal inversion intensity, whereas sulfur dioxide (SO2) concentration was relatively constant. In addition, we found that there was no correlation between the inversion intensity and TSP concentration.