The stochastic variance and structures of time series data on air quality were examined by employing the techniques of autocorrelation function, variance spectrum, fourier series, ARIMA model. Among the air quality properties of atmosphere, SO$_{2}$ is one of the most siginificant and widely measured parameters. In the study, the air quality data were included hourly observations on SO$_{2}$ TSP and O$_{3}$. The data were measured by automatic recording instrument installed in Kwanghwamoon during February and March in 1991. The results of study were as follows 1. Hourly air quality series varied with the domiant 24 hour periodicity and the 12 hour periodic variation was also observed. 2. The correlation coefficients between SO$_{2}$ and O$_{3}$ is -0.4735. 3. In simulating or forecasting variation in SO$_{2}$ ARIMA models are on a useful tools. The multiplicative seasonal ARIMA (1, 1, 0) (0, 2, 1)$_{24}$ model provided satisfactory results for hourly SO$_{2}$ time series.