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Predictive Regression Models of Water Quality Parameters for river Amba in Nasarawa State, Nigeria

Journal: International Journal of Innovation Engineering and Science Research (Vol.2, No. 5)

Publication Date:

Authors : ;

Page : 10-33

Keywords : physio-chemical parameters; Regression models; Statistical study; standards; water quality;

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Abstract

The challenges of river water quality management are so enormous, due to the unpredictive modes of contamination. Monitoring different sources of pollutant load contribution to the river basin is also quite tasking, resulting to laborious and expensive process which sometimes lead to analytical errors. This study deals with the assessment of the physico– chemicaland bacteriological parameters of water samples from River Amba during the period of August 2017 to January 2018 and developing regression models. Water quality Parameters such as Temperature, Turbidity (NTU), Suspended solids (mg/l), Colour, Total solids, Total dissolved solids, Electrical conductivity (µs/cm), pH, Hardness, Chemical Oxygen Demand, Dissolved Oxygen (DO), and Total Coliform were obtained and compared with water quality standards. The results of the water quality analysis of the study in comparison with drinking water quality standard issued byWorld Health Organization(WHO) and National Agency for Food and Drug Administration Control (NAFDAC) revealed that most of the water quality parameters were not adequate to pronounce the water potable. Hence adequate water treatment processes should be employed to make the water fit for consumption and other domestic uses. Statistical analysis was done, in which the systematic correlation and regressionanalysis showed a significant linear relationship between different pairs of water quality parameters. The highest correlation coefficient between different pairs of parameters obtained is (r = 0.999), resulting from the correlation between TS and SS. Multiple regression analysis was also carried out and regression equations were developed. It was observed that the parameters studied had a positive correlation with each other.

Last modified: 2018-12-25 21:38:55