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Home  >  Volume 46 (May, 2018)

26. STUDIES ON THE SPATIAL DEPENDENCE AND TEMPORAL VARIABILITY OF GROUNDWATER QUALITY PARAMETERS IN SOME PARTS OF BENIN CITY METROPOLIS USING KRIGING INTERPOLATION AND MULTIVARIATE ANALYSIS OF VARIANCE by Ilaboya I.R, Ibhadode C.A.E. and Okpoko J.S. Volum
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STUDIES ON THE SPATIAL DEPENDENCE AND TEMPORAL VARIABILITY OF GROUNDWATER QUALITY PARAMETERS IN SOME PARTS OF BENIN CITY METROPOLIS USING KRIGING INTERPOLATION AND MULTIVARIATE ANALYSIS                OF VARIANCE

Ilaboya I.R, Ibhadode C.A.E. and Okpoko J.S. 

Department of Civil Engineering, Faculty of Engineering, University of Benin, P.M.B 1154, 

Benin City, Edo State, Nigeria.

Abstract

In this study, geospatial modelling technique using kriging interpolation and multivariate analysis of variance were employed to evaluate and analyze the spatial dependence and temporal variability of groundwater quality in parts of Benin City Metropolis.

Various ground water quality parameters such as pH, turbidity, total suspended solids (TSS), Electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), phosphate, nitrate, sulphate, total solids (TS), sodium, potassium, calcium, magnesium, iron, lead, cadmium, zinc and chloride were investigated for fifteen (15) boreholes around Idunmwowina, Isiohor and Oluku communities. For the spatial dependence and multivariate analysis, six physico-chemical parameters, namely; pH, electrical conductivity (EC), alkalinity, dissolved oxygen (DO), concentration of zinc and iron were employed.

Results obtained revealed that the parameters tested possess very strong spatial dependence. The computed degree of spatial dependency was observed to be less than 25% for all the water quality test parameters. For the multivariate analysis of variance, the assumption of multivariate outliers was not violated which justify the use of multivariate analysis of variance. In addition, it was observed that more than 95% of the computed significant values (p-value) for all the dependent variables were less than 0.05; (p <0.05), hence the null hypothesis of covariance matrix was rejected and it was concluded that the covariance matrix assumption was not satisfied. This means that the covariance matrices of the dependent variables are not equal across group an indication that temporal variability exists. On the percent variability that is accounted for due to the different sampling locations, the partial Eta squared value of the Pillai’s trace was employed. From the calculated partial Eta squared of the Pillai’s trace, 86.70% temporal variability of the dependent variables was observed.

Keywords: MANOVA, Covariance Matrix, Mahalanobis Constant, Null Hypothesis and Normal Distribution

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