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Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China

  • Qi Zhang
  • Zhongwu Li
  • Guangming Zeng
  • Jianbing Li
  • Yong Fang
  • Qingshui Yuan
  • Yamei Wang
  • Fangyi Ye
Article

Abstract

In the study, multivariate statistical methods including factor, principal component and cluster analysis were applied to analyze surface water quality data sets obtained from Xiangjiang watershed, and generated during 7 years (1994–2000) monitoring of 12 parameters at 34 different profiles. Hierarchical cluster analysis grouped 34 sampling sites into three clusters, including relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, and based on the similarity of water quality characteristics, the watershed was divided into three zones. Factor analysis/principal component analysis, applied to analyze the data sets of the three different groups obtained from cluster analysis, resulted in four latent factors accounting for 71.62%, 71.77% and 72.01% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The PCs obtained from factor analysis indicate that the parameters for water quality variations are mainly related to dissolve heavy metals. Thus, these methods are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.

Keywords

Water quality management Principle component analysis Cluster analysis Xiangjiang watershed Red soil hilly region 

References

  1. Boyacioglu, H., & Boyacioglu, H. (2007). Water pollution sources assessment by multivariate statistical methods in the Tahtali Basin, Turkey. Environmental Geology, 54, 275–282.CrossRefGoogle Scholar
  2. Cao, X. Z., & Zhang, G. S. (1995). Formation and countermeasures of the vulnerable eco-environment of red soil hilly region. Rural Eco-environment, 11(4), 45–48 (In Chinese).Google Scholar
  3. Carpenter, S. R., Caraco, N. E., Correll, D. L., Howarth, R. W., & Smith, V. H. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological Applications, 8(3), 559–568.CrossRefGoogle Scholar
  4. Chapman, D. (1992). Water Quality Assessment. In: Chapman D. On behalf of UNESCO, WHO and UNEP. London: Chapman & Hall, 585.Google Scholar
  5. Chen, G. H. (2005). Problems and solutions of the protection and utilization of water resources in Hunan. Journal of Hunan Economic Management College, 16(6), 3–5 (in Chinese).Google Scholar
  6. Chen, Y. S., Wu, F. C., Lu, H. Z., & Yao, C. S. (2004). Analysis on the water quality changes in the Xiangjiang River from 1981 to 2000. Resources and Environment in the Yangtze Basin, 13(5), 508–512 (in Chinese).Google Scholar
  7. Hussain, M., Ahmed, S. M., & Abderrahman, W. (2008). Cluster analysis and quality assessment of logged water at an irrigation project, eastern Saudi Arabia. Journal of Environmental Management, 86(1), 297–307.CrossRefGoogle Scholar
  8. Jarvie, H. P., Whitton, B. A., & Neal, C. (1998). Nitrogen and phosphorus in east coast British rivers: speciation, sources and biological significance. Science of the Total Environment, 210/211, 79–109.CrossRefGoogle Scholar
  9. Kim, J. O., & Mueller, C. W. (1978). Introduction to factor analysis: what it is and how to do it. Quantitative applications in the social sciences series. Newbury Park, CA: Sage.Google Scholar
  10. Liu, C. W., Lin, K. H., & 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, 313, 77–89.CrossRefGoogle Scholar
  11. Lu, R. K., & Shi, Z. Y. (2000). Features and recover of degraded red soil. Soil, 4, 198–209 (In Chinese).Google Scholar
  12. McGarial, K., Cushman, S., & Stafford, S. (2000). Multivariate statistics for wildlife and ecology research. New York: Springer.Google Scholar
  13. Mendiguchía, C., Moreno, C., Galindo-Riaño, M. D., & García-Vargas, M. (2004). Using chemometric tools to assess anthropogenic effects in river water: A case study: Guadalquivir River (Spain). Analytica Chimica Acta, 515(1,5), 143–149.CrossRefGoogle Scholar
  14. Praus, P. (2005). Water quality assessment using SVD-based principal component analysis of hydrological data. Water SA, 31(4), 417–422.Google Scholar
  15. Shrestha, S., & Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environmental Modelling & Software, 22(4), 464–475.CrossRefGoogle Scholar
  16. Simeonov, V., Stratis, J. A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., Sofoniou, M., & Kouimtzis, Th. (2003). Assessment of the surface water quality in northern Greece. Water Research, 37(17), 4119–4124.CrossRefGoogle Scholar
  17. Sun, S. Q., Hu, G. H., Wang, Y. Z., & Li, C. (2006). Water environmental health risk assessment of Xiangjiang River. Journal of Safety and Environment, 6(2), 12–15 (in Chinese).Google Scholar
  18. Wang, Q. H., Wang, S. Y., & Liu, M. Y. (2004). Safety evaluation on pollution of Xiang River Valley in Hunan Province. China Water and Wastewater, 20(8), 104–106 (in Chinese).Google Scholar
  19. Wu, M. L., & Wang, Y. S. (2007). Using chemometrics to evaluate anthropogenic effects in Daya Bay, China. Estuarine, Coastal and Shelf Science, 72(4), 732–742.CrossRefGoogle Scholar
  20. Wunderlin, D. A., Días, M. P., AméMaría, V., Pesce, S. F., Hued, A. C., & Bistoni, M. Á. (2001). Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquia river basin (Cordoba–Argentina). Water Research, 35(12), 2881–2894.CrossRefGoogle Scholar
  21. Yu, S. X., Shang, J. C., Zhao, J. S., & Guo, H. C. (2003). Factor analysis and dynamics of water quality of the Songhua River Northeast China. Water, Air and Soil Pollution, 144(1–4), 159–169.CrossRefGoogle Scholar
  22. Zhou, F., Guo, H. C., Liu, Y., & Jiang, Y. M. (2007). Chemometrics data analysis of marine water quality and source identification in Southern Hong Kong. Marine Pollution Bulletin, 54(6), 745–756.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Qi Zhang
    • 1
  • Zhongwu Li
    • 1
  • Guangming Zeng
    • 1
  • Jianbing Li
    • 1
    • 2
  • Yong Fang
    • 1
  • Qingshui Yuan
    • 1
  • Yamei Wang
    • 1
  • Fangyi Ye
    • 1
  1. 1.College of Environment Science and EngineeringHunan UniversityChangshaPeople’s Republic of China
  2. 2.Environmental Engineering ProgramUniversity of Northern British ColumbiaPrince GeorgeCanada

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