Multivariate statistical analysis of surface water quality based on correlations and variations in the data set

In the research, determination of principal and non-principal monitoring stations was carried out using principal component analysis (PCA) technique for the Karoon River, Iran. Also canonical correlation analysis (CCA) was used to determine relationship between physical and chemical water quality pa...

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Bibliographic Details
Published in:Desalination 2010-09, Vol.260 (1), p.129-136
Main Authors: Noori, R., Sabahi, M.S., Karbassi, A.R., Baghvand, A., Taati Zadeh, H.
Format: Article
Language:English
Subjects:
Applied sciences
Canonical correlation analysis
Continental surface waters
Correlation analysis
Crack opening displacement
Exact sciences and technology
Freshwater
Karoon River
Monitoring
Natural water pollution
Pollution
Principal component analysis
Rivers
Samples
Stations
Statistical methods
Surface water
Water quality
Water treatment and pollution
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Summary:In the research, determination of principal and non-principal monitoring stations was carried out using principal component analysis (PCA) technique for the Karoon River, Iran. Also canonical correlation analysis (CCA) was used to determine relationship between physical and chemical water quality parameters. Water quality parameters including BOD 5, COD, EC,NO 3 −, SO 4 − 2, temperature, Cl −, DO, hardness, TDS, pH, and turbidity were measured in samples collected from 17 stations along Karoon River from 1999 to 2002. Four of our monitoring stations proved less telling in explaining the annual variation of the river water quality, and were removed. Further investigations indicated that all water quality parameters were important. In CCA, the first four canonical correlations were 0.993, 0.822, 0.785, and 0.660, respectively, suggesting that EC and TDS were two dominant physical parameters in the all canonical variates whilst ions and hardness were highly scored from chemical parameters. Verifying the ability of PCA and CCA methods was carried out by simple regression and correlation methods, respectively.
ISSN:0011-9164
1873-4464
DOI:10.1016/j.desal.2010.04.053