Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ > Na+ > Mg2+ > K+ and alkalinity > SO42− > Cl− > NO3− > F−. The trace elements concentrations were reported in the following order: Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd. The surface water belongs to the Ca2+-Mg2+-HCO3− and Ca2+-Mg2+-Cl−-SO42− water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

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.

Water shortage and quality are major issues in many places, particularly arid and semi-arid regions such as Makkah Al-Mukarramah province, Saudi Arabia. The current work was conducted to examine the geochemical mechanisms influencing the chemistry of groundwater and assess groundwater resources through several water quality indices (WQIs), GIS methods, and the partial least squares regression model (PLSR). For that, 59 groundwater wells were tested for different physical and chemical parameters using conventional analytical procedures. The results showed that the average content of ions was as follows: Na+ > Ca2+ > Mg 2+ > K+ and Cl− > SO42− > HCO32− > NO3− > CO3−. Under the stress of evaporation and saltwater intrusion associated with the reverse ion exchange process, the predominant hydrochemical facies were Ca-HCO3, Na-Cl, mixed Ca-Mg-Cl-SO4, and Na-Ca-HCO3. The drinking water quality index (DWQI) has indicated that only 5% of the wells were categorized under good to excellent for drinking while the majority (95%) were poor to unsuitable for drinking, and required appropriate treatment. Furthermore, the irrigation water quality index (IWQI) has indicated that 45.5% of the wells were classified under high to severe restriction for agriculture, and can be utilized only for high salt tolerant plants. The majority (54.5%) were deemed moderate to no restriction for irrigation, with no toxicity concern for most plants. Agriculture indicators such as total dissolved solids (TDS), potential salinity (PS), sodium absorption ratio (SAR), and residual sodium carbonate (RSC) had mean values of 2572.30, 33.32, 4.84, and −21.14, respectively. However, the quality of the groundwater in the study area improves with increased rainfall and thus recharging the Quaternary aquifer. The PLSR models, which are based on physicochemical characteristics, have been shown to be the most efficient as alternative techniques for determining the six WQIs. For instance, the PLSR models of all IWQs had determination coefficients values of R2 ranging between 0.848 and 0.999 in the Cal., and between 0.848 and 0.999 in the Val. datasets, and had model accuracy varying from 0.824 to 0.999 in the Cal., and from 0.817 to 0.989 in the Val. datasets. In conclusion, the combination of physicochemical parameters, WQIs, and multivariate modeling with statistical analysis and GIS tools is a successful and adaptable methodology that provides a comprehensive picture of groundwater quality and governing mechanisms.

Water quality has deteriorated in recent years as a result of rising population and unplanned development, impacting ecosystem health. The water quality parameters of Qaroun Lake are contaminated to varying degrees, particularly for aquatic life consumption. For that, the objective of this work is to improve the assessments of surface water quality and to determine the different geo-environmental parameters affecting the lake environmental system in Qaroun Lake utilizing the weighted arithmetic water quality index (WAWQI) and four pollution indices (heavy metal pollution index (HPI), metal index (MI), contamination index (Cd), and pollution index (PI), that are enhanced by multivariate analyses as cluster analysis (CA), principal component analysis (PCA), and support vector machine regression (SVMR). Surface water samples were collected at 16 different locations from the lake during years 2018 and 2019. Thirteen physiochemical parameters were measured and used to calculate water quality indices (WQIs). The WQIs of Qaroun Lake such WAWQI, HPI, MI, Cd, PI revealed a different degree of contamination, with respect to aquatic life utilization. The WQIs result revealed that surface water in the lake is unsuitable, high polluted, and seriously affected by pollution for an aquatic environment. The PI findings revealed that surface water samples of Qaroun Lake were significantly impacted by Al, moderately affected by Cd and Cu, and while slightly affected by Zn due to uncontrolled releases of domestic and industrial wastewater. Furthermore, increasing salinity accelerates the deterioration of the lake aquatic environment. Therefore, sewage and drainage wastewater should be treated before discharging into the lake. The SVMR models based on physiochemical parameters presented the highest performance as an alternative method to predict the WQIs. For example, the calibration (Val.) and the validation (Val.) models performed best in assessing the WQIs with R2 (0.99) and with R2 (0.97–0.99), respectively. Finally, a combination of WQIs, CA, PCA, and SVMR approaches could be employed to assess surface water quality in Qaroun Lake.

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Hyperspectral reflectance sensing provides a rapid and cost-effective technique for assessing the suitability of groundwater for irrigation by monitoring real-time changes in its quality at a large scale. In this study, we assessed the quality of 15 groundwater samples from El Fayoum depression in the Western Desert (WD) and 25 groundwater samples from the Central Nile Delta (CND) in Egypt using a traditional approach of the physiochemical parameters, irrigation water quality indicators (IWQIs), and hydrochemical facies. The spectral reflectance data of the water surface was used to build new simple reflectance indices (SRIs), and the performance of these indices for assessing IWQIs was compared with those by partial least square regression (PLSR) that was based on all SRIs or the full-spectrum ranges. Generally, the groundwater of the CND was fresher and more suitable for irrigation purposes than those of the WD. Based on the six IWQIs, ~6.7–60.0% and 85.0–100.0% of the groundwater samples of the WD and CND, respectively, were categorized as highly suitable for irrigation purposes. Based on hydrochemical facies, Na-Cl and Ca-HCO3 were dominant in the WD and CND, respectively, as well as the alkali earth metals (Na+ + K+), which significantly exceeded the alkaline earth metals (Ca2+ + Mg2+) in the WD, with the reverse for the CND. Most developed SRIs had a moderate, weak, and moderate to strong relationship with physiochemical parameters and IWQIs in the WD, CND, and across both regions, respectively. The PLSR models based on all SRIs provided a more accurate estimation of IWQIs in calibration and validation datasets than those based on full-spectrum ranges, and both PLSR models provided better estimation than the individual SRIs. These findings support the feasibility of using ground reflectance measurements as a fast and low-cost tool for the assessment and management of groundwater for irrigation in arid and semiarid regions.

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