Pattern Recognition of High O3 Episodes in Forecasting Daily Maximum Ozone Levels

Abstract

In this study, a method was developed to diagnose ozone episodes exceeding environmental criteria (e.g., above 80 ppb) on the basis of a multivariate statistical method and a fuzzy expert system. This method, being capable of characterizing the occurrence patterns of high-level ozone, was employed to forecast daily maximum ozone levels. The hourly data for both air pollutants and meteorological parameters, obtained both at the surface and at high elevation (500 hPa) stations of Seoul City (1989-1996), were analyzed using this method. Through an application of the fuzzy expert system, the data sets were classified into 8 different types for common ozone episodes. In addition, the data sets were divided into patterns of 11 (Station A), 20 (Station B), 8 (Station C), and 10 (Station D) for site-specific ozone episodes. The results of the analysis were successful in demonstrating that the method was sufficiently efficient to classify each class quantitatively with its own patterns of ozone pollution.

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