MULTIVARIATE STATISTICAL ANALYSES FOR AIR QUALITY CONDITION IN A STEEL PLANT IN KAOHSIUNG CITY, TAIWAN

Abstract

Air pollution in Taiwan is mainly created domestically (by factories and power plants) or sent by other countries. Air quality is notably worse in the south compared to other areas due to the establishment of industrial areas and the northeast monsoon bringing pollutants that are not only slow to sediment, but can also result in the deterioration via air pollution. Also, it is not common to evaluate the characteristics and classification via air pollution in a region by the level of pollutants in Taiwan. Therefore, this study uses seven representative air quality variables from five air quality monitoring stations at a steel plant in Kaohsiung, Taiwan, and applies multivariate statistical analysis to discuss the actual situation of, while reflecting the differences in, air quality among the five stations. It then applies the results to the classification of air quality in Southern Taiwan. Through factor analysis under multivariate statistics, the eight air quality variables can be simplified and categorized into three main factors: photochemical polluting factors, organic polluting factors, and fuel factors. These three are the main factors that affect air quality in regions near the steel plant. Moreover, through cluster analysis, air quality in this particular area can be categorized into four clusters, with each cluster representing different characteristics and levels of pollution in the area. The results of this research can provide a reference for government agencies to propose and verify new air quality assessment models, formulate testing models of allowed increment limits of air pollutants, and determine the effectiveness of air quality improvement schemes.