Appraisal of Surface Water Quality of Nile River Using Water Quality Indices, Spectral Signature and Multivariate Modeling

Abstract

Surface water quality management is an important facet of the effort to meet increasing demand for water. For that purpose, water quality must be monitored and assessed via the use of innovative techniques, such as water quality indices (WQIs), spectral reflectance indices (SRIs), and multivariate modeling. Throughout the Rosetta and Damietta branches of the Nile River, water samples were collected, and WQIs were assessed at 51 different distinct locations. The drinking water quality index (DWQI), metal index (MI), pollution index (PI), turbidity (Turb.) and total suspended solids (TSS) were assessed to estimate water quality status. Twenty-three physicochemical parameters were examined using standard analytical procedures. The average values of ions and metals exhibited the following sequences: Ca2+ > Na2+ > Mg2+ > K+, HCO32− > Cl− > SO42− > NO3− > CO3− and Al > Fe > Mn > Ba > Ni > Zn > Mo > Cr > Cr, respectively. Furthermore, under the stress of evaporation and the reverse ion exchange process, the main hydrochemical facies were Ca-HCO3 and mixed Ca-Mg-Cl-SO4. The DWQI values of the two Nile branches revealed that 53% of samples varied from excellent to good water, 43% of samples varied from poor to very poor water, and 4% of samples were unsuitable for drinking. In addition, the results showed that the new SRIs extracted from VIS and NIR region exhibited strong relationships with DWQI and MI and moderate to strong relationships with Turb. and TSS for each branch of the Nile River and their combination. The values of the R2 relationships between the new SRIs and WQIs varied from 0.65 to 0.82, 0.64 to 0.83, 0.41 to 0.60 and 0.35 to 0.79 for DWQI, MI, Turb. and TSS, respectively. The PLSR model produced a more accurate assessment of DWQI and MI based on values of R2 and slope than other indices. Furthermore, the partial least squares regression model (PLSR) generated accurate predictions for DWQI and MI of the Rosetta branch in the Val. datasets with an R2 of 0.82 and 0.79, respectively, and for DWQI and MI of the Damietta branch with an R2 of 0.93 and 0.78, respectively. Therefore, the combination of WQIs, SRIs, PLSR and GIS approaches are effective and give us a clear picture for assessing the suitability of surface water for drinking and its controlling factors.