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 (StationA), 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.
Highlights
Elevated tropospheric ozone has been a serious air pollution problem over the past several years in Korea due to its adverse impact on human health, crops, and trees (Ghim and ChangTAO, Vol 15, No 2, June 20042000)
The production of tropospheric ozone is known to be regulated by a variety of natural and anthropogenic processes coupled with diverse meteorological conditions (Vukovich 1995; Chan et al 1998a and b; Wang et al 2003a)
This study aims to develop methodologies for classifing high-level ozone episodes within the boundary of Seoul City by cluster and disjoint principal component analysis
Summary
Elevated tropospheric ozone has been a serious air pollution problem over the past several years in Korea due to its adverse impact on human health, crops, and trees (Ghim and ChangTAO, Vol 15, No 2, June 20042000). Many attempts have been directed at the development of models dependent on statistical analysis of (current and previous) meteorological conditions and precursors of ozone (Chen et al 1998; Gardner and Dorling 2000; Yu and Chang 2000; Ballester et al 2002; Wang et al 2003b). In well-evaluated empirical modeling, it is necessary to identify the dominant patterns of past high-level ozone episodes, and to obtain their sitespecific characteristics. For this reason, this study was conducted on the basis of two statistical methods: cluster and disjoint principal components analysis, and a fuzzy expert system to characterize high-level ozone episodes
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