Main Article Content
Abstract
Water is an essential resource for all the organisms, plants and animals including human beings. It is the backbone for the agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of freshwater for various competing end users.
The quality evaluation of water is represented in terms of physical, chemical and biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing a large number of measured variables. The data sets contain rich information about the behaviour of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modelling and interpretation of monitored data are the most important steps in the assessment of water quality.
The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system.
In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations. Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable.
Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study, three factors explained 99.827% of variances. F1 marked 51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695, 0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively.
It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with pH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively.
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References
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References
EU Directive (1998/83/EC) Official journal of the European communities. No L 31/1.
EU Directive (2000/60/EC) Official journal of the European communities. No. L 297/1.
EU Directive (2006/118/EU) Official journal of the European communities No. L 372/1.
WHO (2004) Guidelines for drinking-water quality, vol 1. World Health Organization, Geneva.
Mustapha A, Nabegu AB, Surface water pollution source identification using principal component analysis and factor analysis in Getsi River, Kano, Nigeria. Austr J Basic ApplSciVol (5), (2011), 1507-1512.
Cukrov N, Tepic N, OmanovićD, Logen S, Bura-NakićS, Vojvodic E etal, Qualitative interpretation of physico-chemical and isotopic parameters in the Krka River (Croatia) assessed by multivariate statistical analysis. Int J Environ Anal ChemVol (92), No (10), (2012), 1187-1199. DOI: https://doi.org/10.1080/03067319.2010.550003
https://doi.org/10.1080/03067319.2010.550003 DOI: https://doi.org/10.1080/03067319.2010.550003
Tobiszewski M, Tsakovski S, Simeonov V, Namiesnik J, Surface water quality assessment by the use of combination of multivariate statistical classification and expert information. Chemosphere Vol (80), No (7), (2010), 740-746. DOI: https://doi.org/10.1016/j.chemosphere.2010.05.024
https://doi.org/10.1016/j.chemosphere.2010.05.024 DOI: https://doi.org/10.1016/j.chemosphere.2010.05.024
PMid:20554310
Ntengwe, F. W. (2006). Pollutant loads and water quality in streams of heavily populated and industrialised towns. Physics and Chemistry of the Earth, 31, 832-839. DOI: https://doi.org/10.1016/j.pce.2006.08.025
https://doi.org/10.1016/j.pce.2006.08.025 DOI: https://doi.org/10.1016/j.pce.2006.08.025
Boyacioglu, H. (2006).Surface water quality assessment usingfactor analysis. Water S.A., 32(3), 389-393. DOI: https://doi.org/10.4314/wsa.v32i3.5264
https://doi.org/10.4314/wsa.v32i3.5264 DOI: https://doi.org/10.4314/wsa.v32i3.5264
Simeonov, V., Einax, J. W., Stanimirova, I., & Kraft, J.(2002). Environmetric modeling and interpretation of river water monitoring data. Analytical and Bioanalytical Chemistry, 374, 898-905. DOI: https://doi.org/10.1007/s00216-002-1559-5
https://doi.org/10.1007/s00216-002-1559-5 DOI: https://doi.org/10.1007/s00216-002-1559-5
PMid:12434248
Kotti, M. E., Vlessidis, A. G., Thanasoulias, N. C., &Evmiridis, N. P. (2005). Assessment of river water quality in Northwestern Greece. Water Resources Management, 19, 77-94. DOI: https://doi.org/10.1007/s11269-005-0294-z
https://doi.org/10.1007/s11269-005-0294-z DOI: https://doi.org/10.1007/s11269-005-0294-z
Lopez, F. J. S., Garcia, M. D. G., Vidal, J. L. M., Aguilera, P. A., &Frenich, A. G. (2004). Assessment of metal contamination in Doñana National Park (Spain) using Crayfish (Procamburusclarkii). Environmental Monitoring and Assesment, 93, 17-29. DOI: https://doi.org/10.1023/B:EMAS.0000016789.13603.e5
https://doi.org/10.1023/B:EMAS.0000016789.13603.e5 DOI: https://doi.org/10.1023/B:EMAS.0000016789.13603.e5
PMid:15074607
Ouyang, Y., Nkedi-Kizza, P., Wu, Q. T., Shinde, D., & Huang, C.H. (2006). Assessment of seasonal variations in surface water quality. Water Research, 40, 3800-3810. DOI: https://doi.org/10.1016/j.watres.2006.08.030
https://doi.org/10.1016/j.watres.2006.08.030 DOI: https://doi.org/10.1016/j.watres.2006.08.030
PMid:17069873
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.
https://doi.org/10.1016/j.envsoft.2006.02.001
Yu, S., Shang, J., Zhao, J., & Guo, H. (2003). Factor analysis and dynamics of water quality of the Songhua River Northeast China. Water, Air, and Soil Pollution, 144, 159-169. DOI: https://doi.org/10.1023/A:1022960300693
https://doi.org/10.1023/A:1022960300693 DOI: https://doi.org/10.1023/A:1022960300693
http://www.cgwb.gov.in/District_Profile/UP/Allahabad.pdf.
APHA.Standard methods for the examination of water and waste water. American Public Health Association Washington D.C. (2005).
. Bureau of Indian Standards, Indian Standards (IS: 10500) Drinking Water Specification: New Delhi (2004).
World Health Organization; Guidelines for drinking Water Quality, Recommendation 2ndEdition; Geneva, WHO Vol.1, (2008).
Tabachnick, B.G., & Fidell, L.S. (1996). Using multivariate statistics (3rd ed.). New York: Harper Collins CollegePublishers.
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. DOI: https://doi.org/10.1016/j.envsoft.2006.02.001
https://doi.org/10.1016/j.envsoft.2006.02.001 DOI: https://doi.org/10.1016/j.envsoft.2006.02.001
Simeonov, V., Stratis, J. A., Samara, C., Zachariadis, G.,Voutsa, D., &Anthemidis, A., et al. (2003).Assessment of the surface water quality in Northern Greece. Water Research, 37, 4119-4124. DOI: https://doi.org/10.1016/S0043-1354(03)00398-1
https://doi.org/10.1016/S0043-1354(03)00398-1 DOI: https://doi.org/10.1016/S0043-1354(03)00398-1
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis with readings (4th ed.).London: Prentice-Hall.
Sharma, S. (1996).Applied multivariate techniques. New York: Wiley.
Vega, M., Pardo, R., Barrato, E., &Deban, L. (1998).Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Research, 32, 3581-3592. DOI: https://doi.org/10.1016/S0043-1354(98)00138-9
https://doi.org/10.1016/S0043-1354(98)00138-9 DOI: https://doi.org/10.1016/S0043-1354(98)00138-9
Wunderlin, D. A., Diaz, M. P., Ame,M. V., Pesce, S. F., Hued, A. C., &Bistoni, M. A. (2001). Pattern recognitiontechniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquira river basin (Cordoba-Argentina). Water Research, 35, 1894-2881.
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. DOI: https://doi.org/10.1016/S0048-9697(02)00683-6
https://doi.org/10.1016/S0048-9697(02)00683-6 DOI: https://doi.org/10.1016/S0048-9697(02)00683-6
Lindeman, M. A. (2004). Exploring the effects of urban and agricultural land use on surface water quality. 2004 Denver annual meeting. Paper no. 72-9. Geological Society of America abstracts with programs. 36, 184.