Combining Water Quality Indices and Multivariate Modeling to Assess Surface Water Quality in the Northern Nile Delta, Egypt

Abstract

Assessing surface water quality for drinking use in developing countries is important since water quality is a fundamental aspect of surface water management. This study aims to improve surface water quality assessments and their controlling mechanisms using the drinking water quality index (DWQI) and four pollution indices (PIs), which are supported by multivariate statistical analyses, such as principal component analysis, partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR). Twenty-two physicochemical parameters were analyzed using standard analytical methods for 55 surface water sites in the northern Nile Delta, Egypt. The DWQI results indicated that 33% of the tested samples represented good water, and 67% of samples indicated poor to unsuitable water for drinking use. The PI results revealed that surface water samples were strongly affected by Pb and Mn and were slightly affected by Fe and Cr. The SMLR models of the DWQI and PIs, which were based on all major ions and heavy metals, provided the best estimations with R2 = 1 for the DWQI and PIs. In conclusion, integration between the DWQI and PIs is a valuable and applicable approach for the assessment of surface water quality, and the PLSR and SMLR models can be used through applications of chemometric techniques to evaluate the DWQI and PIs.

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

“…The correlation analysis between the two variables presents a perfect linear relationship [80]; therefore, the correlation metric with TDS and IWQ revealed that Ca 2+ was the most affected cation and alkalinity was the most affected anion. These results are in agreement with the water facies, which were presented by the Piper Diagram and reflected the effects of weathering and rock water interactions reported in the Gibbs Diagram [71]. In general, there were weak relationships between the trace elements and six WQIs of surface water.…”

“…Furthermore, alkalinity values in the samples collected registered their highest from surface water, which may have been derived from atmospheric silicate weathering and carbonate dissolution [78]. According to the previous work of Gad et al [71], the water facies in this region are belongs to Ca 2+ -Mg 2+ -HCO 3 − and Ca 2+ -Mg 2+ -Cl − -SO 4 2− , which was affected by rock water interaction processes and weathering. The major ions are powerful tools for detecting solute sources; the wide ranges of the ions in the surface water samples indicate the effect of several recharging sources, e.g., surface canals, anthropogenic practise, drains and overuse of fertilizers and pesticides [17].…”

“…To construct the SMLR models of WQIs, SPSS version 22 (SPSS Inc., Chicago, IL, USA) was used. The major ions and trace elements for IWQ and the major ions for the other five indices are indicated by the equations in Table 1 and were analysed by the SMLR model to indicate the most influential parameters that account for the greatest number of variability parameters according to Gad et al [71]. Only the major ion or trace elements, which remained significant in the models at p-values < 0.05, were maintained at each level [72].…”

“…In the same line of this study to predict Water Quality Indices with another multivariate modelling, Gad et al [71] found that the Partial Least Squares Regression (PLSR) could be used to assess the Drinking Water Quality Index (DWQI) and the three water pollution indices such as Contamination Index (CD), Trace Element Evaluation Index (HEI) and Trace Element Pollution Index (HPI) of surface water. Wang et al [98] found that hybrid wavelet-based support vector regression Structure (WA-PSO-SVR) based on wavelet analysis (WA) could be used to estimate ammonia nitrogen (NH 3 -N) and chemical oxygen demand and in the Grand Canal from Beijing to Hangzhou.…”

Assessing the Groundwater Quality in the Liwa Area, the United Arab Emirates

“…Principal Component Analysis (PCA) leads to the detection of the main water parameters that influence water quality [20]. Gad et al [21] combined a drinking water quality index and four pollution indices, principal component analysis (PCA), partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR) to evaluate the water quality for drinking purposes in the Nile Delta.…”