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 (C_d), 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, C_d, 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.

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.

Water quality is identically important as quantity in terms of meeting basic human needs. Therefore, evaluating the surface-water quality and the associated hydrochemical characteristics is essential for managing water resources in arid and semi-arid environments. Therefore, the present research was conducted to evaluate and predict water quality for agricultural purposes across the Nile River, Egypt. For that, several irrigation water quality indices (IWQIs) were used, along with an artificial neural network (ANN), partial least square regression (PLSR) models, and geographic information system (GIS) tools. The physicochemical parameters, such as T °C, pH, EC, TDS, K⁺, Na⁺, Mg²⁺, Ca²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, CO₃²⁻, and NO₃⁻, were measured at 51 surface-water locations. As a result, the ions contents were the following: Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻ > CO₃²⁻, reflecting Ca-HCO₃ and mixed Ca-Mg-Cl-SO₄ water types. The irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), permeability index (PI), and magnesium hazard (MH) had mean values of 92.30, 1.01, 35.85, 31.75, 72.30, and 43.95, respectively. For instance, the IWQI readings revealed that approximately 98% of the samples were inside the no restriction category, while approximately 2% of the samples fell within the low restriction area for irrigation. The ANN-IWQI-6 model’s six indices, with R² values of 0.999 for calibration (Cal.) and 0.945 for validation (Val.) datasets, are crucial for predicting IWQI. The rest of the models behaved admirably in terms of predicting SAR, Na%, SSP, PI, and MR with R² values for the Cal. and validation Val. of 0.999. The findings revealed that ANN and PLSR models are effective methods for predicting irrigation water quality to assist decision plans. To summarize, integrating physicochemical features, WQIs, ANN, PLSR, models, and GIS tools to evaluate surface-water suitability for irrigation offers a complete image of water quality for sustainable development.

Standard methods are limited for monitoring and managing water quality indicators (WQIs) in real-time and on a large scale. Consequently, there is an urgent need to use reliable, practical, swift, and cost-effective monitoring tools that can be easily deployed and assist decision makers in assessing key indicators relevant to surface water quality in a comprehensive manner. Surface water samples were collected and evaluated for water quality at 16 distinct sites across the Qaroun Lake in 2018 and 2019. Different WQIs, including total dissolved solids (TDS), transparency, total suspended solids (TSS), chlorophyll-a (Chl-a), and total phosphorus (TP), were tested for aquatic utilization. An integrated approach comprising WQIs, geospatial techniques, hyperspectral reflectance indices (SRIs) (commonly used SRIs, two-band and three-band SRIs (Spectral index calculated from water spectral reflectance of two or three wavelengths)), and partial least square regression (PLSR) models were used to assess the water quality of Qaroun Lake. According to the findings, the water quality attributes are polluted to varying degrees. The majority of commonly used SRIs presented moderately relationship with four WQIs (transparency, TSS, Chl-a, and TP) (R² = 0.45 to 0.64), while the majority of newly two-band SRIs (NSRIs-2b) indicated moderate to strong relationships with WQIs (R² = 0.51 to 0.74), and the majority of newly three band SRIs (NSRIs-3b) presented strong relationships with WQIs (R² = 0.67 to 0.81). Broadly, the highest coefficients of determination were noticed with the NSRIs-3b followed by the NSRIs-2b and then the commonly used SRIs. For example, the NSRIs-3b (NDSI_648,712,696) had stronger relationships with transparency, TSS, and Chl-a with R² = 0.77, 0.66, and 0.81, respectively, than other SRIs. In addition, the NSRIs-3b (NDSI_620,610,622) showed the highest R² of 0.73 with TSS. The NSRIs-3b coupling with PLSR predicted the WQIs with satisfactory accuracy in the calibration (reach up R² = 0.85) and validation (reach up R² = 0.81) datasets. The overall findings of this research study showed that deriving an optimized NSRIs-3b from spectrum region and combining it with PLSR model could be a practical tool for managing water quality of the Qaroun Lake by accurately, timely, and non-destructively monitoring the WQIs.

Coastal environmental assessment techniques have evolved into one of the most important fields for the long-term development and management of coastal zones. So, the overall aim of the present investigation was to provide effective approaches for making informed decisions about the Gamasa coast sediment quality. Over a two-year investigation, sediment samples were meticulously collected from the Gamasa estuary and littoral shelf. The inductively coupled plasma mass spectra (ICP-MS) was used to the total concentrations of Al, Fe, Ti, Mg, Mn, Cu, P, V, Ba, Cr, Sr, Co, Ni, Zn, Pb, Zr, and Ce. Single elements environmental pollution indices including the geoaccumulation index (I_geo), contamination factor (CF), and enrichment factor (EF), as well as multi-elements pollution indices comprising the potential ecological risk index (RI), degree of contamination (Dc), and pollution load index (PLI) were used to assess the sediment and the various geo-environmental variables affecting the Mediterranean coastal system. Furthermore, the Dc, PLI, and RI were estimated using the random forest (RF) and Back-Propagation Neural Network (BPNN) depending on the selected elements. According to the Dc results, all the investigated sediment samples categories were considerably contaminated. Cr, Co, Ni, Cu, Zr, V, Zn, P, and Mn showed remarkable enrichment in sediment samples and were originated from anthropogenic sources based on the CF, EF, and I_geo data. Moreover, the RI findings revealed that all the samples tested pose a low ecologically risk. Meanwhile, based on PLI, 70% of the Gamasa estuary samples were polluted, while 93.75% of littoral shelf sediment was unpolluted. The BPNNs -PCs-CD-17 model performed the best and demonstrated a better association between exceptional qualities and CD. With R² values of 1.00 for calibration (Cal.) and 1.00 for validation (Val.). The BPNNs -PCs-PLI-17 models performed the best in terms of measuring PLI with respective R² values of 1.00 and 0.98 for the Cal. and Val. datasets. The findings showed that the RF and BPNN models may be used to precisely quantify the pollution indices (Dc, PLI, and RI) in calibration (Cal.) and validation (Val.) datasets utilizing potentially toxic elements of surface sediment.