Environmental Science and Pollution Research

, Volume 21, Issue 17, pp 10045–10066 | Cite as

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

  • Musthafa Odayooth Mavukkandy
  • Subhankar Karmakar
  • P. S. Harikumar
Research Article

Abstract

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources.

Highlights

• The effectiveness of existing river water quality monitoring network is assessed

• Significance of seasonal redesign of the monitoring network is demonstrated

• Rationalization of water quality parameters is performed in a statistical framework

Keywords

Cluster analysis Factor analysis Kabbini River Multivariate statistics Principal component analysis Rationalization Water quality monitoring network 

Notes

Acknowledgment

The authors sincerely thank the editor and the anonymous reviewers for offering insightful comments, which has significantly improved work quality and readability of the manuscript. The research work presented in this paper was partially supported by the Seed Grant (Project code: 07IR053) of the second author, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.

References

  1. Abdul-Wahab SA, Bakheit CS, Al-Alawi SM (2005) Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environmental Modelling and Software 20:1263–71CrossRefGoogle Scholar
  2. Anderson TW, Sclove SL (1986) The statistical analysis of data. Palo Alto, CA: Scientific PressGoogle Scholar
  3. Ainslie B, Reuten C, Steyn DG, Le ND, Zidek JV (2009) Application of an entropy-based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants. Journal of Environmental Management 90:2715–29CrossRefGoogle Scholar
  4. Alameddine I, Karmakar S, Qian SS, Paerl HW, Reckhow KH (2013) Optimizing an estuarine water quality monitoring program through an entropy-based hierarchical spatiotemporal Bayesian framework. Water Resources Research 49:6933–6945CrossRefGoogle Scholar
  5. Alberto WD, Del Pilar DM, Valeria AM, Fabiana PS, Cecilia HA, De Los Angeles BM (2001) Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquıa River Basin (Córdoba–Argentina). Water Research 35:2881–94Google Scholar
  6. Bartram J, Ballance R, Programme UNE, and Organization WH (1996) In water quality monitoring: a practical guide to the design and implementation of freshwater quality studies and monitoring programmes. vol pp E & FN SponGoogle Scholar
  7. Battegazzore M, Renoldi M (1995) Integrated chemical and biological evaluation of the quality of the River Lambro (Italy). Water, Air, and Soil Pollution 83:375–90CrossRefGoogle Scholar
  8. Bricker OP and Jones BF (1995) In Main factors affecting the composition of natural waters. vol pp CRC Press, Boca Raton, FL, 1-5Google Scholar
  9. Briggs JC, Ficke JF (1978) Quality of rivers of United States, 1975 water year—based on the National Stream Quality Accounting Network (NASQUAN). In (ed), vol pp 78 - 200, Reston, VA: USGSGoogle Scholar
  10. Cetinkaya CP, Harmancioglu NB (2012) Assessment of water quality sampling sites by a dynamic programming approach. Journal of Hydrologic Engineering 17:305–17CrossRefGoogle Scholar
  11. Chapman DV, Unesco, Organization WH, Programme UNE (1996) In water quality assessments: a guide to the use of biota, sediments, and water in environmental monitoring. vol pp E & FN SponGoogle Scholar
  12. CPCB (2007) Guidelines for water quality monitoring. In (ed), vol pp Central Pollution Control Board, IndiaGoogle Scholar
  13. DeCoster J (1998 (accessed on January 30, 2013)) Overview of factor analysis. In (ed), vol 2013, pp http://www.stat-help.com/notes.html
  14. Dixon W, Chiswell B (1996) Review of aquatic monitoring program design. Water Research 30:1935–48CrossRefGoogle Scholar
  15. Harmancioglu N, Alpaslan N (1992) Water quality monitoring network design: a problem of multi-objective decision making. Water Resources Bulletin 28:179–92CrossRefGoogle Scholar
  16. Harmancioglu NB, Yevjevich V (1985) Transfer of hydrologic information along rivers partially fed by karstified limestones. In Int. Symp. on Karst Water Resources. (ed), vol pp IAHS Publ., AnkaraGoogle Scholar
  17. Harmancioglu NB, Ozer A, Alpaslan N (1987) Pro-curement of water quality information (in Turkish). In IX. Technical Congress of Civil Engineering. (ed), vol II, pp 113-29, Proceedings of the Turkish Society of Civil EngineersGoogle Scholar
  18. Harmancioglu NB, Fistikoglu O, Ozkul SD, Singh VP, Alpaslan MN (1999) Water quality monitoring network design. Kluwer Academic Publishers, DordrechtCrossRefGoogle Scholar
  19. Jolliffe IT (2002) In principal component analysis. vol pp SpringerGoogle Scholar
  20. Karamouz M, Kerachian R, Akhbari M, Hafez B (2009) Design of river water quality monitoring networks: a case study. Environmental Modeling and Assessment 14:705–14CrossRefGoogle Scholar
  21. Kazi TG, Arain MB, Jamali MK, Jalbani N, Afridi HI, Sarfraz RA et al (2009) Assessment of water quality of polluted lake using multivariate statistical techniques: a case study. Ecotoxicology and Environmental Safety 72:301–9CrossRefGoogle Scholar
  22. Khalil B, Ouarda TBMJ, St-Hilaire A, Chebana F (2010) A statistical approach for the rationalization of water quality indicators in surface water quality monitoring networks. Journal of Hydrology 386:173–85CrossRefGoogle Scholar
  23. Kowalkowski T, Zbytniewski R, Szpejna J, Buszewski B (2006) Application of chemometrics in river water classification. Water Research 40:744–52CrossRefGoogle Scholar
  24. KSCSTE (2009) Environmental monitoring programme on water quality. p. Kerala State Council for Science Technology and Environment, NationGoogle Scholar
  25. Liu CW, Lin KH, Kuo YM (2003) Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Science of the Total Environment 313:77–89CrossRefGoogle Scholar
  26. Love BC, Medin DL, Gureckis TM (2004) SUSTAIN: a network model of category learning. Psychological Review 111:309–32CrossRefGoogle Scholar
  27. Manly BFJ (2005) In multivariate statistical methods: a primer. vol pp Chapman & Hall/CRC PressGoogle Scholar
  28. Massart DL, Vandeginste BGM, Deming SN, Michotte Y, Kaufman L (1988) In chemometrics: a textbook. vol pp Elsevier, AmsterdamGoogle Scholar
  29. Mendiguchia C, Moreno C, Garcia-Vargas M (2007) Evaluation of natural and anthropogenic influences on the Guadalquivir River (Spain) by dissolved heavy metals and nutrients. Chemosphere 69:1509–17CrossRefGoogle Scholar
  30. MoEF (2005) Notification. In (ed), vol pp New Delhi, IndiaGoogle Scholar
  31. Munro BH (2000) In statistical methods for health care research. vol pp Lippincott Williams & WilkinsGoogle Scholar
  32. Noori R, Sabahi MS, Karbassi AR, Baghvand A, Zadeh HT (2010) Multivariate statistical analysis of surface water quality based on correlations and variations in the data set. Desalination 260:129–36CrossRefGoogle Scholar
  33. Ouyang Y (2005) Evaluation of river water quality monitoring stations by principal component analysis. Water Research 39:2621–35CrossRefGoogle Scholar
  34. Ozkul S, Harmancioglu NB, Singh VP (2000) Entropy-based assessment of water quality monitoring networks. Journal of Hydrologic Engineering 5:90–100CrossRefGoogle Scholar
  35. Perkins RG, Underwood GJC (2000) Gradients of chlorophyll a and water chemistry along an eutrophic reservoir with determination of the limiting nutrient by in situ nutrient addition. Water Research 34:713–24CrossRefGoogle Scholar
  36. Sanders TG, Ward RC, Loftis JC, Steele TD, Adrian DD, Yevjevich V (1983) In design of networks for monitoring water quality. vol pp Water Resources Publications LLC, Highlands Ranch, COGoogle Scholar
  37. Sharp WE (1971) A topologically optimum water sampling plan for rivers and streams. Water Resources Research 7:1641–6CrossRefGoogle Scholar
  38. Shine JP, Ika RV, Ford TE (1995) Multivariate statistical examination of spatial and temporal patterns of heavy metal contamination in New Bedford Harbor marine sediments. Environmental Science and Technology 29:1781–8CrossRefGoogle Scholar
  39. 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 and Software 22:464–75CrossRefGoogle Scholar
  40. Simeonov V, Stratis JA, Samara C, Zachariadis G, Voutsa D, Anthemidis A et al (2003) Assessment of the surface water quality in Northern Greece. Water Research 37:4119–24CrossRefGoogle Scholar
  41. Singh KP, Malik A, Mohan D, Sinha 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 38:3980–92CrossRefGoogle Scholar
  42. Singh KP, 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 538:355–74CrossRefGoogle Scholar
  43. Smith ER, DeCoster J (1998) Knowledge acquisition, accessibility, and use in person perception and stereotyping: simulation with a recurrent connectionist network. Journal of Personality and Social Psychology 74:21–35CrossRefGoogle Scholar
  44. Strobl RO, Robillard PD (2008) Network design for water quality monitoring of surface freshwaters: a review. Journal of Environmental Management 87:639–48CrossRefGoogle Scholar
  45. Strobl RO, Robillard PD, Shannon RD, Day RL, McDonnell AJ (2006) A water quality monitoring network design methodology for the selection of critical sampling points: part I. Environmental Monitoring and Assessment 112:137–58CrossRefGoogle Scholar
  46. Tauler R, Barcelo D, Thurman EM (2000) Multivariate correlation between concentrations of selected herbicides and derivatives in outflows from selected U.S. Midwestern reservoirs. Environmental Science and Technology 34:3307–14CrossRefGoogle Scholar
  47. Telci IT, Nam K, Guan J, Aral MM (2009) Optimal water quality monitoring network design for river systems. Journal of Environmental Management 90:2987–98CrossRefGoogle Scholar
  48. Tirsch FS, Male JW (1984) River basin water quality monitoring network design: options for reaching water quality goals. In proceedings of the Twentieth Annual Conference of American Water Resources Associations. In: Schad TM (ed) (ed), vol, pp AWRA PublicationsGoogle Scholar
  49. Varekar V, Karmakar S, Jha R (2012) Seasonal evaluation and redesign of surface water quality monitoring network: An application to Kali river basin, India. In American Geophysical Union (AGU) joint assembly. (ed), vol pp Asia Oceania Geosciences Society (AOGS), Resorts World Convention Centre, SingaporeGoogle Scholar
  50. Vega M, Pardo R, Barrado E, Debán L (1998) Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Research 32:3581–92CrossRefGoogle Scholar
  51. Voutsa D, Zachariadis G, Samara C, Kouimtzis T (1995) Evaluation of chemical parameters in Aliakmon river/Northern Greece. Part II: dissolved and particulate heavy metals. J Environ Sci Health A Tox Hazard Subst Environ Eng 30:1–13Google Scholar
  52. Wang YS, Lou ZP, Sun CC, Wu ML, Han SH (2006) Multivariate statistical analysis of water quality and phytoplankton characteristics in Daya Bay, China, from 1999 to 2002. Oceanologia 48:193–211Google Scholar
  53. Ward RC (1996) Water quality monitoring: where’s the beef? Water Resources Bulletin 32:673–80CrossRefGoogle Scholar
  54. Ward RC, Loftis JC, McBride GB (1990) In design of water quality monitoring systems. vol pp WileyGoogle Scholar
  55. Wenning RJ, Erickson GA (1994) Interpretation and analysis of complex environmental data using chemometric methods. TrAC - Trends in Analytical Chemistry 13:446–57CrossRefGoogle Scholar
  56. Winter TC, Mallory SE, Allen TR, Rosenberry DO (2000) The use of principal component analysis for interpreting ground water hydrographs. Ground Water 38:234–46CrossRefGoogle Scholar
  57. Wunderlin DA, María Del Pilar D, María Valeria A, Fabiana PS, Cecilia HA, María De Los Ángeles B (2001) Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía River basin (Córdoba-Argentina). Water Research 35:2881–94CrossRefGoogle Scholar
  58. 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 173:17–27CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Musthafa Odayooth Mavukkandy
    • 1
  • Subhankar Karmakar
    • 2
    • 3
  • P. S. Harikumar
    • 4
  1. 1.Centre for Environmental Science and Engineering (CESE)Indian Institute of Technology BombayMumbaiIndia
  2. 2.Centre for Environmental Science and Engineering (CESE)Indian Institute of Technology BombayMumbaiIndia
  3. 3.Interdisciplinary Program in Climate StudiesIndian Institute of Technology BombayMumbaiIndia
  4. 4.Central Water Analysis LaboratoryCentre for Water Resources Development and Management (CWRDM)CalicutIndia

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