Evaluation of significantly modified water bodies in Vojvodina by using multivariate statistical techniques
This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Association of Chemical Engineers of Serbia
2013-01-01
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| Series: | Hemijska Industrija |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/0367-598X/2013/0367-598X1300007V.pdf |
| Summary: | This paper illustrates the utility of multivariate statistical techniques for
analysis and interpretation of water quality data sets and identification of
pollution sources/factors with a view to get better information about the
water quality and design of 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 for the evaluation of variations and for the interpretation of a
water quality data set of the natural water bodies obtained during 2010 year
of 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
physico-chemical parameters of natural 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
accounting for 28 % of the total variance and represents the hydrochemical
dimension of water quality. The second factor F2 accounting for 18% of the
total variance and may be taken factor of water eutrophication. The third
factor F3 accounting 17 % of the total variance and represents the influence
of point sources of pollution on water quality. The fourth factor F4
accounting 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 explain the
observed clustering in terms of affected conditions. Using FA/PCA and CA have
been identified water bodies that are under the highest pressure. With regard
to the factors identified water bodies are: for factor F1 (Plazović, Bosut,
Studva, Zlatica, Stari Begej, Krivaja), for factor F2 (Krivaja, Kereš), for
factor F3 (Studva, Zlatica, Tamiš, Krivaja i Kereš) and for factor F4
(Studva, Zlatica, Krivaja, Kereš). |
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| ISSN: | 0367-598X 2217-7426 |