ORIGINAL RESEARCH
Multivariate Statistical Analysis and Environmental Modeling of Heavy Metals Pollution by Industries
Adamu Mustapha1, 2, Ahmad Zaharin Aris1
 
More details
Hide details
1Environmental Forensics Research Centre, Faculty of Environmental Studies, Universiti Putra Malaysia,
43400 UPM Serdang, Selangor, Malaysia
2Department of Geography, Kano University of Science and Technology, Wudil, Nigeria
 
Pol. J. Environ. Stud. 2012;21(5):1359–1367
KEYWORDS
ABSTRACT
This study presents the application of some selected multivariate statistical techniques, prediction method, and confirmatory analysis to identify spatial variation and pollution sources of the Jakara-Getsi river system in Kano, Nigeria. Two-hundred and forty water samples were collected from eight different sampling sites along the river system. Fifteen physico-chemical parameters were analyzed: pH, electrical conductivity, turbidity, hardness, total dissolved solids, dissolved solids, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, mercury, lead, chromium, cadmium, iron, and nickel. Correlation analysis showed that the mean concentration of heavy metals in the river water samples were significantly positive correlated values. Principal component analysis and factor analysis (PCA/FA) investigated the origin of the water quality parameters as due to various anthropogenic activities: five principal components were obtained with 81.84% total variance. Standard, forward, and backward stepwise discriminant analysis (DA) effectively discriminate thirteen (92.5%), nine (90.1%), and six (88.5%) parameters, respectively. Multiple linear regression yielded multiple correlation coefficient R value of 0.98 and R-square value of 0.97 with significant value 0.0001 (p <0.05) showing that water qualities in Jakara-Getsi can be predicted due to high concentration of heavy metals. Structural equation modeling (SEM) confirmed the finding of multivariate and multiple linear regression analysis. This study provides a new technique of confirming exploratory data analysis using SEM in water resources management.
eISSN:2083-5906
ISSN:1230-1485