Evaluation of heavily modified water bodies in vojvodina by using multivariate statistical techniques

Abstract

This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors the aim of getting better information about the water quality and design of a monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA)/principal component analysis (PCA) and cluster analysis (CA), were applied to the evaluation of variations and the interpretation of water quality data of heavily modified water bodies, obtained during 2010 by the monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physicochemical parameters of heavily modified water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75-0.50, respectively. Four principal factors were obtained with Eigenvalues >1 summing more than 78% of the total variance in the water data sets, which is adequate to give good prior information regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounts for 28% of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounts for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounts for 17% of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounts for 13% of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA) is an objective technique to identify natural groupings in the set of data. CA divides a large number of objects into smaller number of homogenous groups on the basis of their correlation structure. CA combines the data objects together to form the natural groups involving objects with similar cluster properties and separates the objects with different cluster properties. CA showed similarities and dissimilarities among the sampling sites and explained the observed clustering in terms of affected conditions. Using FA/PCA and CA, water bodies that are under the highest pressure were identified. With regard to the factors, the identified water bodies were: for factor F1 - Plazović, Bosut, Studva, Zlatica, Stari Begej and Krivaja; for factor F2 - Krivaja and Kereš; for factor F3 - Studva, Krivaja and Kereš; for factor F4 - Studva, Zlatica, Krivaja and Kereš.