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Application of Multivariate Statistical Techniques in the Assessment of Water Quality in Sakarya River, Turkey


Affiliations
1 Civil Engineering Department, Bilecik University, Bilecik, Turkey
2 Department of Mining Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey
     

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In this paper, the surface water quality of the Sakarya River in Turkey is assessed by using multivariate statistical techniques. These techniques were applied to the chemical parameters obtained from the five different surface water quality observation stations. Factor and principal component analysis results reveal that the agricultural, anthropogenic and domestic pollution caused differences in terms of water quality. Cluster analysis revealed two different clusters of similarities between the stations, reflecting different chemical properties and pollution levels in the studied river. Surface water quality downstream of the river was different from the water quality upstream. Thus, this study shows the usefulness of multivariate statistical techniques for analysis and interpretation in the surface water quality problem.

Keywords

Factor Analysis, Principal Component Analysis, Cluster Analysis, Water Quality.
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  • ALKARKHI, A.F.M., AHMAD, A. and EASA, A.M. (2009) Assessment of surface water quality selected estuaries of Malaysia: multivariate statistical techniques. The Environmentalist, v.29, pp.255-262.
  • BABICH, H. and STOTSKY, G. (1982) Nickel toxicity to fungi: influence of environmental factors. Ecotoxicol Environ Saf, v.6, pp.577-589.
  • BOYACIOGLU, H. and BOYACIOGLU, H. (2008) Water pollution sources assessment by multivariate statistical methods in the Tahtali Basin, Turkey. Environ. Geol., v.54, pp.275-282.
  • COSTANZA, R., D’ARGE, R., DE GROOT, R., FARBER, S., GRASSO, M., HANNON, B., LIMBURG, K., NAEEM, S., O’NEILL, R. V., PARUELO, J., RASKIN, R. G., SUTTON, P. and VAN DEN BELT, M. (1997) The value of the world’s ecosystem services and natural capital. Nature, v.387, pp.253-260.
  • DEVLIN, D.L., WHITNEY, D.A. and MCVAY, K.A. (2000) Phosphorus and water quality in Kansas. Kansas State University, http://www.ksre.ksu.edu/library/h20ql2/MF2463.pdf.
  • DUNDAR, M.S. and ALTUNDAG, H. (2007) Investigation of heavy metal contaminations in the lower Sakarya river water and sediments. Environ Monit Assess, v.128, pp.177-181.
  • HELENA, B., PARDO, R., VEGA, M., BARRADO, E., FERNANDEZ, J.M. and FERNANDEZ, L. (2000) Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Research, v.34, pp.807-816
  • ISCEN, C.F., EMIROGLU, O., ILHAN, S., ARSLAN, N., YILMAZ, V. and AHISKA, S. (2008) Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey. Environ Monit. Assess., v.144, pp.269-276.
  • JOHNSON, R.A. and WICHERN, D.W. (2002) Applied Multivariate Statistical Analysis, Prentice Hall, US.
  • KAZI, T.C., ARAIN, M.B., JAMALI, M.K., JALBANI, N., AFRIDI, H.I., SARFRAZ, R.A., BAIG, J.A. and SHAH, A.Q. (2009) Assessment of water quality of polluted lake using multivariate statistical techniques: A case study. Ecotoxicology and Environmental Safety, v.72, pp.301-309.
  • LIU, C.W., LIN, K.H. and KUO, Y.M. (2003) Application of factor analysis in the assessment of groundwater quality in a Blackfoot disease area in Taiwan. Science of the Total Environment, v.313, pp.77-89.
  • MADRAMOOTOO, C.A., JOHNSTON, W.R. and WILLARDSON, L.S. (1997) Management of agricultural drainage water quality. FAO, http://www.fao.org/docrep/w7224e/w7224e00.
  • MAZLUM, N., OZER, A. and MAZLUM, S. (1999) Interpretation of water quality data by principal components analysis. Turkish Jour. Engg. Environ. Sci., v.23, pp.19-26.
  • REGHUNATH, R., MURTHY, T.R.S. and RAGHAVAN, B.R. (2002) The utility of multivariate statistical techniques in hydrochemical studies: an example from Karnataka, India. Water Res., v.36, pp.2437-2442.
  • SAFFRAN, K., CASH, K. and HALLARD, K. (2001) Canadian water quality guidelines for the protection of aquatic life. CCME water quality Index 1.0. User Manual, Canadian.
  • SHRESTHA, S. and KAZAMA, F. (2007) Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environmental Modelling & Software, v.22, pp.464-475.
  • SIMEONOV, V., STRATIS, J.A., SAMARA, C., ZACHARIADIS, G., VOUTSA, D., ANTHEMIDIS, A., SOFONIOU, M. and KOUIMTZIS, T. (2003) Assessment of the surface water quality in Northern Greece. Water Res., v.37, pp.4119-4124.
  • ZHOU, F., LIU, Y. and GUO, H. (2007) Application of multivariate statistical methods to water quality assessment of the watercourses in northwestern New Territories, Hong Kong. Environ. Monit. Assess., v.132, pp.1-13.

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  • Application of Multivariate Statistical Techniques in the Assessment of Water Quality in Sakarya River, Turkey

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Authors

Suheyla Yerel
Civil Engineering Department, Bilecik University, Bilecik, Turkey
Huseyin Ankara
Department of Mining Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey

Abstract


In this paper, the surface water quality of the Sakarya River in Turkey is assessed by using multivariate statistical techniques. These techniques were applied to the chemical parameters obtained from the five different surface water quality observation stations. Factor and principal component analysis results reveal that the agricultural, anthropogenic and domestic pollution caused differences in terms of water quality. Cluster analysis revealed two different clusters of similarities between the stations, reflecting different chemical properties and pollution levels in the studied river. Surface water quality downstream of the river was different from the water quality upstream. Thus, this study shows the usefulness of multivariate statistical techniques for analysis and interpretation in the surface water quality problem.

Keywords


Factor Analysis, Principal Component Analysis, Cluster Analysis, Water Quality.

References