Vol 5 No 8 (2019): EPH - International Journal of Agriculture and Environmental Research (ISSN: 2208-2158)

Multivariate Statistical Analyses for Air Quality Condition in a Steel Plant in Kaohsiung City, Taiwan

Shu-Lung Kuo
Open University of Kaohsiung
Edward Ming-Yang Wu
National Kaohsiung University of Science and Technology
Published August 24, 2019
  • multivariate statistical analysis,
  • air quality,
  • factor analysis,
  • variable,
  • steel plant
How to Cite
Kuo, S.-L., & Edward Ming-Yang Wu. (2019). Multivariate Statistical Analyses for Air Quality Condition in a Steel Plant in Kaohsiung City, Taiwan. EPH - International Journal of Agriculture and Environmental Research (ISSN: 2208-2158), 5(8), 01-15. Retrieved from https://ephjournal.com/index.php/aer/article/view/1532


  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.


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1. Cairncross, E.K., John, J., and Zunckel, M. (2007). A novel air pollution index based on the relative risk of daily mortality associated with short-term exposure to common air pollutants. Atmos. Environ. 41, 8442-8454.
2. Cheng, F.Y., Chin, S.C., Liu, T.H. (2012). The role of boundary layer schemes in meteorological and air quality simulations of the Taiwan area. Atmos. Environ. 54: 714–727.
3. Chou, T.G. (2010). The research of air quality management in fine particulate matters. Central Office of Administration, Academia Sinica Research weekly 1276, Central Office of Administration, Academia Sinica, Taiwan. (in Chinese)
4. Chuang, M.T., Chou, C.C.K., Lin, N.H., Takami, A., Hsiao, T.C., Lin, T.H., Fu, J.S., Pani, S.K., Lu, Y.R., Yang, T.Y. (2017). A simulation study on PM2.5 sources and meteorological characteristics at the northern tip of Taiwan in the early stage of the Asian haze period. Aerosol Air Qual. Res. 17: 3166-3178.
5. EPA (Environmental Protection Agency) (1994), Measuring air quality: the pollutant standards index, EPA 451/K-94-001, U.S.A..
6. Henry, R.C., Lewis, C.W., Hopke, P.K., and Williamson, H.J. (1984). Review of receptor model fundamentals. Atmos. Environ., 18, 1507-1515.
7. Hsu, C.H. and Cheng, F.Y. (2016). Classification of weather patterns to study the influence of meteorological characteristics on PM2.5 concentrations in Yunlin County, Taiwan. Atmos. Environ. 144: 397-408.
8. Kuo, S.L., Ho, C.L. (2018). The assessment of time Series for an entire air quality control district in southern Taiwan using GARCH model. International Journal of Engineering & Technology 7, 119-124.
9. Kuo, S.L., Wu, E.M.Y. (2018). Highway tunnel air quality assessment using multivariate statistical classification on factor, cluster, and discriminant analysis. International Journal of Engineering & Technology 7, 287-291.
10. Hsu, C.H., Cheng, F.Y. (2019). Synoptic weather patterns and associated air pollution in Taiwan. Aerosol and Air Quality Research 19, 1139-1151.
11. Lai, L.W. (2014). Relationship between fine particulate matter events with respect to synoptic weather patterns and the implications for circulatory and respiratory disease in Taipei, Taiwan. Int. J. Environ. Health Res. 24: 528-545.
12. Martinez, M.A., Caballero, P., Carrillo, O., Mendoza, A., Mejia, G.M. (2012). Chemical characterization and factor analysis of PM2.5 in two sites of Monterrey, Mexico. Journal of the Air & Waste Management Association 62, 817-827.
13. McKenna, Jr. J.E. (2003). An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis. Environmental Modeling & Softwar 18, 205-220.
14. Murena, F. (2004). Measuring air quality over large urban areas: development and application of an air pollution index at the urban area of Naples. Atmos. Environ. 38, 6195-6202.
15. Tobiszewski, M.; Tsakovski, S.; Simeonov, V.; Namieśnik, J. (2010). Surface water quality assessment by the use of combination of multivariate statistical classification and expert information. Chemosphere 80, 740-746.
16. Vega, M.; Pardo, R.; Barrado, E.; Deban, L. (1998). Assessment of seasonal and polluting effects on the qualityof river water by exploratory data analysis. Water Res. 32, 3581-592.