Abstract
Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned above, which is a very good startup to establish a rule of thumb in the laboratories to compare between observations. The importance of linear regression equations in predicting surface water quality parameters is a method that can be applied to any other location.
Highlights
It is shown that there is strong evidence that electrical conductivity is the most appropriate variable predicting or explaining more than values of the dependent variables (Joarder et al, 2008)
To perform the linear Regression analysis for the five parameters (TDS, total hardness (TH), Ca, Cl, and SO4) vs. electrical conductivity (EC), the data set containing more than 600 observations is separated into two data sets
All significance value (p-value) were below 0.001, which indicate that the predictor parameter (EC) were significant to predict the criterion parameters (TDS, TH, etc.)
Summary
It is shown that there is strong evidence that electrical conductivity is the most appropriate variable predicting or explaining more than values of the dependent variables (Joarder et al, 2008). Sustainable development is closely linked to obtaining adequate quantitative and qualitative water to maintain environmental and health systems. Continuous monitoring of a large number of quality parameters is crucial for the efficient maintenance of water quality It is a complex and challenging responsibility for routine monitoring of all the parameters, even if satisfactory personnel and laboratory resources are available. Another approach based on correlation and Regression has recently been deployed to improve empirical relationships for comparison between physicochemical parameters (Bhandari and Nayal, 2008)
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