A study on the prediction of ozone formation in air pollution

Two prediction schemes-time series analysis and parameter estimation method-were investigated to predict the formation of ozone in Seoul, Korea. Moving average method and double exponential smoothing method are applied to the time-series analysis. Three typical methods, such as extended least squares (ELS), recursive maximum likelihood (RML) and generalized least squares (GLS), were used to predict ozone formation in a real time parameter estimation. Autoregressive moving average model with external input (ARMAX) is used as the model of the parameter estimation. To test the performance of the ozone formation prediction schemes proposed in the present work, the prediction results of ozone formation were compared to the real data. From the comparison it can be seen that the prediction scheme based on the parameter estimation method gives a reasonable accuracy with limited prediction horizon.