Application of Correlation and Regression Models in Predicting the Physico-chemical Quality of Groundwater from Insitu Measured Parameters

Abstract

Groundwater is the major source of municipal and private potable water supply for meeting the drinking, domestic, agricultural and industrial requirements on man around the world. The cost of analyzing water quality in the laboratory to ascertain its potability is usually high and sometimes not available. Groundwater samples were collected from fifty (50) spatially referenced bore well locations in Warri and its environs in the dry and wet seasons (November 2019 to January 2020) in the study area. The water samples were analyzed for twenty-six (26) physical, chemical and bacteriological parameters both in the field and laboratory in line with APHA standard procedures for testing water and waste water inorder to evaluate the status of potability of groundwater across Warri, Delta State Nigeria. The data analysis tool in Microsoft Excel was used to explore and study the interrelationship between some conservative parameters measured in the field (pH, EC, TDS, and DO) as independent variables and some cations, anions and heavy metals (Na, Mg, Ca, HCO3, SO4 Cl, Fe, Cd, Cr, Cu and Pb) analysed in the laboratory as dependent variables. The results obtained from the parameters analysed insitu in the field which are cheap to perform and easily affordable were used to check and evaluate and the inter-relationships with some cations, anions and heavy metals. Highly correlated water quality parameters were determined by correlation coefficient (R) values obtained from correlation matrix and related by Regression equations (models). The regression models can be adopted to predict the concentration of these cations, anions and heavy metals before the rigorous laboratory analysis, to serve as a quick check for concentration of most disease-causing pollutants and to save time, money and resources, especially the near absence of AAS for analysing heavy metals in a good number of laboratories. The regression models developed in the study can be used for monitoring the water quality parameters by knowing the concentration of independent parameters obtained in the field alone. There is a relationship between variables which show that one variable actually causes changes in another variable. It was observed that multiple regression models can predict most parameters at 5% level of significance. Significantly positive correlation at 1 and 5% was found between many parameters. This technique studied and calculated the correlation coefficients between various physico-chemical parameters of drinking water and provided an excellent device for the calculation of parameter values within realistic degree of accuracy. The results proved to be easiest, useful, and rapid means for monitoring of water quality with the help of systematic calculations of correlation coefficient. It is recommended to treat groundwater prior to domestic use.