Predicting the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis

The monthly water quality data sets from ten stations on the Tigris River within Baghdad for the year 2016 were studied. The water quality index (WQJ) was calculated using 11 important parameters according to the assigned weight, and its values were used as the dependent variable in stepwise multiple linear regression (MLR) analysis to develop a water quality model (WQM) for the river. Twenty-three physicochemical water quality variables (2760 values) were included in developing the WQM , they are: Aluminum (A1(+3)), Fluoride (F-1), Nitrite (NO2-1), Nitrate (NO3-1), Ammonia (NH3), Temperature (T), Total Alkalinity (TA.), Turbidity (Tur.), Total Hardness (TH), Calcium (Ca+2), Chloride (Cl-1), Magnesium (Mg+2), Potential of Hydrogen (pH), Electrical Conductivity (EC), Sulfate (SO4(-2)), Total Dissolved Solids (TDS), Iron(Fe+2), Silica (SiO2), Phosphate (PO4-3), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), and Sodium (Na+1). The annual WQI mean value during the study was 266; more than the safe value of 100; consequently, the water quality was considered as unsuitable for drinking. Significant differences in WQI values were detected over the months and across stations with the highest WQI values (poor quality) in winter and spring, while the lowest values (better quality) were in summer and autumn. The WQM, which was developed based on the stepwise MLR analysis, consisted of five parameters: Tur, EC, COD, TH, and pH with significant value (r 0.987, R-2 0.974, p <0.01) and the model formula is: WQI = (-1.597)(Tur)(0.478)(EC)(0.409)(COD)(0.089)(TH)(0.291)(pH)(0.095) The study results show that the use of WQI as the dependent variable input improved the prediction of MLR model as a tool to understand, simplify and model the water quality variation. The model developed here can help in rapid low-cost water quality evaluation for best management of the Tigris River. (C) 2018 Elsevier B.V. All rights reserved.