Advertisement

Assessment of surface water quality using multivariate statistical techniques: case study of the Nampong River and Songkhram River, Thailand

  • Somphinith Muangthong
  • Sangam Shrestha
Article

Abstract

Multivariate statistical techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), and discriminant analysis (DA) were applied for the assessment of spatial and temporal variations of a large complex water quality data set of the Nampong River and Songkhram River, generated for more than 10 years (1996–2012) by monitoring of 16 parameters at different sites. According to the water quality characteristics, hierarchical CA grouped 13 sampling sites of the Nampong River into two clusters, i.e., upper stream (US) and lower stream (LS) sites, and five sampling sites of the Songkhram River into three clusters, i.e., upper stream (US), middle stream (MS) and lower stream (LS) sites. PCA/FA applied to the data sets thus obtained five latent factors explaining 69.80 and 69.32 % of the total variance in water quality data sets of LS and US areas, respectively, in the Nampong River and six latent factors explaining 80.80, 73.95, and 73.78 % of the total variance in water quality data sets of LS, MS, and US areas, respectively, in the Songkhram River. This study highlights the usefulness of multivariate statistical assessment of complex databases in the identification of pollution sources to better comprehend the spatial and temporal variations for effective river water quality management.

Keywords

Surface water quality Factor analysis Principal component analysis Discriminant analysis Nampong River Songkhram River Thailand 

Notes

Acknowledgments

The authors wish to thank Faculty of Engineering and Architecture, Rajamangala University of Technology Isan for providing financial assistance. The authors also sincerely thank Pollution Control Department (PCD), Thailand, for providing river water quality monitoring data.

References

  1. Abdul-Wahab, S. A., Bakheit, C. S., & Al-Alawi, S. M. (2005). Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environmental Modelling & Software, 20(10), 1263–1271.CrossRefGoogle Scholar
  2. Alexakis, D. (2011). Assessment of water quality in the Messolonghi–Etoliko and Neochorio region (West Greece) using hydrochemical and statistical analysis methods. Environmental Monitoring and Assessment, 182(1-4), 397–413.Google Scholar
  3. Blake, D., & Pitakthepsombut, R. (2006a). Situation analysis: lower Songkhram river basin, Thailand: a publication of the Mekong Wetlands biodiversity conservation and sustainable use programme.Google Scholar
  4. Batayneh, A., & Zumlot, T. (2012). Multivariate statistical approach to geochemical methods in water quality factor identification; application to the shallow aquifer system of the Yarmouk basin of north Jordan. Research Journal of Environmental and Earth Sciences, 4(7), 756–768.Google Scholar
  5. Blake, D. & Pitakthepsombut, R. (2006b). Situation analysis: lower Songkhram river basin, Thailand: a publication of the Mekong wetlands biodiversity conservation and sustainable use programme.Google Scholar
  6. 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
  7. Coletti, C., Testezlaf, R., Ribeiro, T. A. P., Souza, R. T. G., & Pereira, D. A. (2010). Water quality index using multivariate factorial analysis. Revista Brasileira de Engenharia Agrícola e Ambiental, 14, 517–522.CrossRefGoogle Scholar
  8. Hair, J. E., William, C. B., Barry, J. B., Rolph, E. A., & Tatham, R. L. (2006). Multivariate data analysis (6 ed., ). New Jersy:Pearson Printice Hall.Google Scholar
  9. Helena, B., Pardo, R., Vega M., Barrado, E., Fernandez, J.M., & Fernandez, L. (2000). Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga river, Spain) by principal component analysis. Water Research, 34, 807-816.CrossRefGoogle Scholar
  10. Guangjia, J., Dianwei, L., Kaishan, S., Zongming, W., Bai, Z., & Yuandong, W. (2010). Application of multivariate model based on three simulated sensors for water quality variables estimation in Shitoukoumen reservoir, Jilin province, China. Chinese Geographical Science, 20(4), 337–344.CrossRefGoogle Scholar
  11. Kim, J.-O., & Mueller, C. W. (1987). Introduction to factor analysis: what it is and how to do it. Quantitative applications in the social sciences series. Newbury Park:Sage University Press.Google Scholar
  12. 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 in the Total Environment, 313, 77–89.CrossRefGoogle Scholar
  13. Love, D., Hallbauer, D., Amos, A., & Hranova, R. (2004). Factor analysis as a tool in groundwater quality management: two southern African case studies. Physics and Chemistry of the Earth, 29, 1135–1143.CrossRefGoogle Scholar
  14. McKenna Jr., J. E. (2003). An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis. Environmental Modelling & Software, 18(3), 205–220.CrossRefGoogle Scholar
  15. Muangthong, S. (2015). Assessment of surface water quality using multivariate statistical techniques: a case study of the Nampong river basin, Thailand. The Journal of Industrial Technology, 11(1), 25–37.Google Scholar
  16. Reghunath, R., Murthy, T. R. S., & Raghavan, B. R. (2002). The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Research, 36, 2437–2442.CrossRefGoogle Scholar
  17. Sarbu, C., & Pop, H. F. (2005). Principal component analysis versus fuzzy principal component analysis. A case study: the quality of Danube water (1985–1996). Talanta, 65, 1215–1220.CrossRefGoogle Scholar
  18. Shihab, A. S., & Abdul Baqi, Y. T. (2010a). Multivariate analysis of ground water quality of Makhmor plain/north Iraq. Damascus University Journal, 26(1), 19–26.Google Scholar
  19. Simeonov, V., Stratis, J. A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., & Sofoniou, M. (2003). Assessment of the surface water quality in northern Greece. Water Research, 37, 4119–4124.CrossRefGoogle Scholar
  20. Singh, K. P., Amrita, M., Dinesh, M., & Sarita, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti river (India) a case study. Water Research, 38, 3980–3992.CrossRefGoogle Scholar
  21. Singh, K. P., Malik, A., & Sinha, S. (2005). Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques: a case study. Analytica Chimica Acta, 538, 355–374.CrossRefGoogle Scholar
  22. 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, 464–475.CrossRefGoogle Scholar
  23. Shrestha, S., Kazama, F., & Nakamura, T. (2008). Use of principal component analysis, factor analysis and discriminant analysis to evaluate spatial and temporal variations in water quality of the Mekong river. Journal of Hydroinformatics, 10, 43–54.CrossRefGoogle Scholar
  24. Shrestha, S., & Muangthong, S. (2014). Assessment of surface water quality of Songkhram river (Thailand) using environmettric techniques. International Journal of River Basin Management, 12(4), 341–356.CrossRefGoogle Scholar
  25. Sombutputorn N. (1998). An application of remotely sensed data and geographic information system for wetland ecosystem mapping. Master thesis of Korn Kaen University.Google Scholar
  26. Zhang, X., Wang, Q., Liu, Y., Wu, J., & Yu, M. (2011). Application of multivariate statistical techniques in the assessment of water quality in the southwest new territories and Kowloon, Hong Kong. Environmental Monitoring and Assessment, 173(1–4), 17–27.CrossRefGoogle Scholar
  27. Zhou, F., Liu, Y., & Guo, H. C. (2007). Application of multivariate statistical methods to the water quality assessment of the watercourses in the northwestern new territories, Hong Kong. Environmental Monitoring and Assessment, 132(1–3), 1–13.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Engineering and ArchitectureRajamangala University of Technology IsanMoungThailand
  2. 2.Water Engineering and Management, School of Engineering and TechnologyAsian Institute of TechnologyKhlong LuangThailand

Personalised recommendations