In this study, the effects of an urban thermal environment on air quality were investigated using hourly surface weather observation data and air quality data over six summers from 2000 to 2005 in two cities on the Korean Peninsula. One, the city of Daegu, is representative of basin topography and the other, the city of Busan, represents a coastal area. It is known that the characteristics of an urban thermal environment are represented as an "urban heat island". Here, we focus on the nighttime urban thermal environment, which is called a "tropical night", during the summer. On tropical nights in Busan, the temperature and cloud cover levels were higher than on non-tropical nights. Wind speed did not appear to make a difference even on a tropical night. However, the frequency of southwestern winds from the sea was higher during tropical nights. The prevailing southwest winds in all areas meant an inflow of air from the sea. So at most of the air quality stations, the ozone concentration during tropical nights was lower than during non-tropical nights. In Daegu, the tropical nights had higher temperatures and cloud covers. Despite these higher temperatures, the ozone concentration during the tropical nights was lower than that on non-tropical nights at most of the air quality stations. This feature was caused by low irradiance, which in turn caused an increased cloud cover. Wind speed was stronger during the tropical nights and dispersed the air pollutants. These meteorological characteristics of the tropical nights reduced ozone concentrations in the Daegu Basin.

We investigate the amount of potential electricity energy generated by wind power in Busan metropolitan area, using the mesoscale meteorological model WRF (Weather Research & Forecasting), combined with small wind power generators. The WRF modeling has successfully simulated meteorological characteristics over the urban areas, and showed statistical significant to predict the amount of wind energy generation. The highest amount of wind power energy has been predicted at the coastal area, followed by at riverbank and upland, depending on predicted spatial distributions of wind speed. The electricity energy prediction method in this study is expected to be used for plans of wind farm constructions or the power supplies.

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