This study analyzes the surface ozone, NO and NO2 concentration data from 1997 to 1999 in Daegu. It investigates effect on precursor during high-ozone episode days. The high-ozone episode is defined when a daily maximum ozone concentration is higher than 100 ppb(ambient air quality standard of Korea) in at least one station among six air quality monitoring stations. The frequency of episodes is 13 days(33 hours). The frequency is the highest in May and September, and the area with the highest frequency is Nowondong and Manchondong. The average value of daily maximum ozone concentration with high ozone episode is 81.6 ppb, and that of 8-hour average ozone concentration is 58.6 ppb. It means that ozone pollution is continuous and wide-ranging in Daegu. The daily variation of NO, NO2 and O3 in high-ozone episodes are inversely proportional one another. Nowondong an industrial area, is affected by pollutants that are emitted from the primary sources, while Manchondong a residential area, is affected by the advection of O3 or by the primary pollutants like VOCs.

We will calculate concentration of air pollutants using ISCST3, FDM and AERMOD of models recommended in U. S. EPA which are able to predict concentration of short term for point source, complex like industrial complex, power plant and burn-up institution. Before executing model, as analyzing computational result of many cases according to selecting of input data, we will increasing predictable ability of model in limit range of model. Especially, we analyzed three cases - case of considering various emission rate according to time scale and not, case considering effect of atmospheric pollution materials removed by physical process. In our study, after comparing and analyzing results of three model, we choose the atmospheric dispersion model reflected well the characteristic of the area. And we will investigate how large the complex pollutant sources such as industrial complex contribute to atmospheric environment and air quality of the surrounding the area as predicting and estimating chosen model.