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

A critical analysis of parameter choices in water quality assessment

The determination of water quality heavily depends on the selection of parameters recorded from water samples for the water quality index (WQI). Data-driven methods, including machine learning models and statistical approaches, are frequently used to refine the parameter set for four main reasons: reducing cost and uncertainty, addressing the eclipsing problem, and enhancing the performance of models predicting the WQI. Despite their widespread use, there is a noticeable gap in comprehensive reviews that systematically examine previous studies in this area. Such reviews are essential to assess the validity of these objectives and to demonstrate the effectiveness of data-driven methods in achieving these goals. This paper sets out with two primary aims: first, to provide a review of the existing literature on methods for selecting parameters. Second, it seeks to delineate and evaluate the four principal motivations for parameter selection identified in the literature. This manuscript categorizes existing studies into two methodological groups for refining parameters: one focuses on preserving information within the dataset, and another ensures consistent prediction using the full set of parameters. It characterizes each group and evaluates how effectively each approach meets the four predefined objectives. The study presents that the minimal WQI approach, common to both categories, is the only approach that has successfully reduced recording costs. Nonetheless, it notes that simply reducing the number of parameters does not guarantee cost savings. Furthermore, the group of studies classified as preserving information within the dataset has demonstrated potential to decrease the eclipsing problem, whereas studies in the consistent prediction group have not been able to mitigate this issue. Additionally, since data-driven approaches still rely on the initial parameters chosen by experts, they do not eliminate the need for expert judgment. The study further points out that the WQI formula is a straightforward and expedient tool for assessing water quality. Consequently, the paper argues that employing machine learning solely to reduce the number of parameters to enhance WQI prediction is not a standalone solution. Rather, this objective should be integrated with a more comprehensive set of research goals. The critical analysis of research objectives and the characterization of previous studies lay the groundwork for future research. This groundwork will enable subsequent studies to evaluate how their proposed methods can effectively achieve these objectives.

Exploring seasonal variability in water quality of Nyabarongo River in Rwanda via water quality indices and DPSIR modelling

Understanding seasonal variations in water quality is crucial for effective management of freshwater rivers amidst changing environmental conditions. This study employed water quality index (WQI), metal index (MI), and pollution indices (PI) to comprehensively assess water quality and pollution levels in Nyabarongo River of Rwanda. A dynamic driver-pressure-state-impact-response model was used to identify factors influencing quality management. Over 4 years (2018-2021), 69 samples were collected on a monthly basis from each of the six monitoring stations across the Nyabarongo River throughout the four different seasons. Maximum WQI values were observed during dry long (52.90), dry short (21.478), long rain (93.66), and short rain (37.4) seasons, classified according to CCME 2001 guidelines. Ion concentrations exceeded WHO standards, with dominant ions being HCO 3 - andMg 2 + . Variations in water quality were influenced by factors such as calcium bicarbonate dominance in dry seasons and sodium sulfate dominance in rainy seasons. Evaporation and precipitation processes primarily influenced ionic composition. Metal indices (MI) exhibited wide ranges: long dry (0.2-433.0), short dry (0.1-174.3), long rain (0.1-223.7), and short rain (0.3-252.5). The hazard index values for Cu2+, Mn4+, Zn2+, and Cr3+ were below 1, ranging from 8.89E - 08 to 7.68E - 07 for adults and 2.30E - 07 to 5.02E - 06 for children through oral ingestion, and from 6.68E - 10 to 5.07E - 07 for adults and 6.61E - 09 to 2.54E - 06 for children through dermal contact. With a total carcinogenic risk of less than 1 for both ingestion and dermal contact, indicating no significant health risks yet send strong signals to Governmental management of the Nyabarongo River. Overall water quality was classified as marginal in long dry, poor in short dry, good in long rain, and poor again in short rain seasons.

Spatio-temporal evaluation of metals and metalloids in the water of high Andean livestock micro-watersheds, Amazonas, Peru

Cattle ranching is a fundamental economic activity in northern Peru, where proper management of water resources is crucial. This study, a pioneer in the region, evaluated water quality and its suitability for human consumption, vegetable irrigation, and livestock production. It is also the first study to document the presence of metals and metalloids in vulnerable areas because they are located at the headwaters of river watersheds. The spatiotemporal evaluation of physicochemical parameters, metals, and metalloids was performed in five micro-watersheds (Cabildo, Timbambo, Pomacochas, Atuen, and Ventilla) from water samples collected in the dry season (October 2017) and wet season (March 2018). The parameters were analyzed using microwave plasma atomic emission spectrometry. The results were contrasted with international and Peruvian quality standards related to dairy cow production. The highest values of pH, total dissolved solids, and electrical conductivity were reported during the dry season, and the highest turbidity during the wet season. Of the metals evaluated, arsenic (As) was omnipresent in all the micro-watersheds, followed by lead (Pb). In contrast to World Health Organization regulations, concentrations of As, cadmium (Cd), Pb, and iron represent a risk; according to Peruvian regulations, As and Pb exceed the concentrations established for use in animal drinking water and vegetable irrigation, and according to water guidelines for dairy cattle, concentrations of As, Pb, Cd, and Al exceed the permitted limits. The high concentrations of these metals in the study area are attributable to a synergy between natural factors, such as Andean geology and livestock activity. The data reported will allow for proper water resource management, pollution prevention, and the design and adoption of mitigation measures.

Water quality assessment of Elgo river in Ethiopia using CCME, WQI and IWQI for domestic and agricultural usage

The increasing demand for water due to the escalation in population and aggressive agricultural activities for drinking and irrigation purposes in the rural areas of Ethiopia has put tremendous stress on water requirements. The Elgo River in southern Ethiopia is deteriorating due to sedimentation, soil erosion, stormwater runoff, and anthropogenic activities. Elgo village faces water shortages and a lack of safe drinking water. The purpose of this research was to identify the extent of pollution in Elgo River water using the Canadian Council of Ministers of the Environment (CCME), Water Quality Index (WQI), and Irrigation Water Quality Index (IWQI). A total of 12 water samples were collected from 3 river sampling sites for the dry and wet seasons to test the physicochemical and biological parameters. Results obtained were: turbidity (46.5-156) NTU, colour (103.65-606.5) TCU, EC (182-268) μS/cm, TDS (192.5-275.5) mg/l, TSS (680-2774) mg/l, Ca2+ (22-45) mg/l, Mg2+ (19.5-23.5) mg/l, Cl- (10.5-16.65) mg/l, and SO42- (17.18-47) mg/l for both the dry and wet seasons, respectively. The CCME WQI revealed that the overall results were 38.38 for the dry season and 36.6 for the wet season for drinking water parameters. The CCME WQI categorization indicates that the Elgo River water is classified as poor, with results ranging from 0 to 44. For irrigation purposes 10, parameters such as SAR, PS, PI, MAR, KI, RSC, EC, SSP, TH, and %Na were examined to compute indices using the IWQI model. The overall result of water quality indicated that IWQIs of 81.4 and 62.14 are good for the dry season and poor for the wet season, respectively. This research provides a thorough analysis through modelling to determine the suitability of water for different purposes for the tribal and backward communities of the area.

Detrimental influence of industrial effluents, especially heavy metals, on limnological parameters of water and nutritional profile in addition to enzymatic activities of fish, Sperata seenghala (Sykes, 1839) from diverse Ramsar sites, India (Punjab)

The present research was designed to determine the nutritive value of the liver and intestine of fish, Sperata seenghala, the impact of effluence load on limnological parameters of water and proximate composition of fish organs, especially on fatty acids, liver enzymatic activities, seasonal variations in the nutritional profile of fish, and to check and compare the pollution status of Ramsar sites in Punjab by calculating the water quality index, heavy metal pollution index, and metal index from June 2018 to August 2020. WQI of Harike wetland was found to be 53.95, which depicts that water quality in this region is "poor". At Nangal wetland, water quality index was reported to be "excellent" quality water and fit for the whole ecological unit. Overall heavy metal pollution index for Harike wetland was reported 174.569, whereas for Nangal wetland it was 5.994, depicting massive contaminant loads in a polluted region. MI value was also recorded as being higher (6.9336) in polluted habitat than in control habitat (0.8175). In fish liver, significant (p < 0.05) higher mean total lipids (6.73%), total proteins (3.98%), moisture (77.69%), ash (3.56%), and carbohydrates (3.79%) were observed in the samples from Nangal wetland than Harike wetland. A similar trend was reported in all biochemical contents of the fish intestine. Enzyme activities such as aspartate-aminotransferase and alanine-aminotransferase were significantly elevated (p < 0.05) in the specimens collected from the polluted region. The mean total n-3 (except in spring), n-6 polyunsaturated fatty acids (except in winter), and average monounsaturated and saturated fatty acids diminished significantly (p < 0.05) in the liver of fish from contaminated habitat than control site. In the intestine of fish collected from the polluted region, significant reductions in the mean total n-3 (except in autumn as well as summer), total n-6 PUFAs (in autumn and winter), and total SFAs were reported than control site.

Bioaccumulation, Biosedimentation, and Health Hazards of Elements in Crayfish, Procambarus clarkii from El-Rahawi Drain and El-Qanatir in the River Nile, Egypt

Elements accumulation in crayfish is proportional to the increase in bioavailability (direct contact) with the surrounding water, sediment, and feeding. Five heavy metals (Cu, Cr, Mn, Ni, and Ag) and lithium (Li) were analyzed in the sediment, water, and crayfish tissues. Elements (heavy metals and lithium) concentrations in sediment, water, and crayfish tissues showed significant differences between the two sampling stations (El-Qanatir and El-Rahawi drain). However, the levels of elements in crayfish tissues were arranged in declining order as hepatopancreas > gills > exoskeleton > muscles for Cu and Cr; hepatopancreas > exoskeleton > gills > muscles for Ni and Ag; and exoskeleton > gills > hepatopancreas > muscles for Li and Mn. The human health hazard evaluation of heavy metals and lithium exposure via edible tissue consumption was assessed for both children and adult consumers. The target hazard quotient THQ values of crayfish edible tissues (less than 1) will not impose any health implications for consumers who ingest edible tissues in sufficient quantities. Furthermore, the hazard index (HI) values reported for children and adult consumers were lower than one, indicating non-carcinogenic and carcinogenic hazards, suggesting that crayfish edible tissues are safe for human ingestion. This evidence also found that Procambarus clarkii could be a good bio-indicator organism for monitoring potentially metals in aquatic systems.

CTX-M-Producing Bacteria Isolated from a Highly Polluted River System in Portugal

Enterobacteriaceae resistant to third-generation cephalosporins are a great concern for public health, as these are first-line drugs to treat infections. The production of carbapenemases and extended spectrum beta-lactamases (ESBLs) and/or the overexpression of AmpC β-lactamases are the main mechanisms of resistance to these antibiotics. Among the ESBLs, CTX-M β-lactamases are the most prevalent worldwide. Our aims were to determine the prevalence of cefotaxime-resistant Enterobacteriaceae along a heavily polluted river and characterize bla_CTX-M carriers. River water was collected in 11 sites along the main course and tributaries, in two sampling moments. Water quality was evaluated and a collection of cefotaxime-resistant isolates was obtained. bla_CTX-M carriers were characterized regarding phylogenetic affiliation, clonality, antibiotic susceptibility, gene diversity, and context. Water presented very low quality in all sites. From 147 cefotaxime-resistant isolates, 46% carried bla_CTX-M and were affiliated with Escherichia, Klebsiella, Enterobacter, and Citrobacter. Molecular typing revealed clonal isolates in different sites and over the two years, suggesting survival of the strains in the river or continuous pollution inputs from the same sources. Eight variants of bla_CTX-M were found, with bla_CTX-M-15 being the most prevalent (52.5%). Sites with a lower water quality showed the highest resistance rates and prevalence of bla_CTX-M, suggesting that river water may embody human health risks.

Evaluation of the Hydrochemical and Water Quality Characteristics of an Aquifer Located in an Urbanized Area

Groundwater is an important source of fresh water in the world. However, the excessive extraction and increasing pollution represent a major challenge for water sustainability in Mexico. Nowadays, since water quality changes in aquifers are not noticeable, aquifer monitoring and assessment are imperious. In this study, the water quality of the Cuernavaca aquifer was evaluated using a database of 23 parameters in 4 sampling points from 2012 to 2019. The spatial behavior of water quality variables was described by using interpolation. The temporal evaluation of groundwater quality was carried out through time series. Water quality indices (WQI) were obtained in this aquifer and the WQI values suggest that the groundwater could be considered as good quality for potable use and of medium-high quality for irrigation. The chemical characteristics of the groundwater were also evaluated using Gibb, Piper, and Schoeller diagrams. Finally, with a total of 34 samples of each parameter in each sampling site, a multivariate statistical analysis was performed using a Pearson correlation and hierarchical cluster analysis. This analysis showed a correlation between hydrochemical features and groundwater quality parameters, where nitrates presented the highest number of significant correlations with other parameters. These results may be useful for the authorities to adopt planning methods to improve the sustainable development of the aquifer.

Combinations of multivariate statistical analysis and analytical hierarchical process for indexing surface water quality under arid conditions

Novel methods for water quality indexing increase insight into the fitness of water bodies for different uses. We hypothesized that integrating multivariate statistical analysis (MSA) with the analytical hierarchical process (AHP) may provide a reliable estimation of water quality status. Hence, twenty water samples from canals and drains in the northern Nile Delta, Egypt were collected during summer, autumn, winter, and spring and analyzed. Data were subjected to MSA, including correlation analysis, principal component analysis (PCA), and hierarchal cluster analysis (HCA). The AHP was applied to derive weights of parameters implied in developing water quality indices for irrigation (IWQI) and fish farming (FFWQI). Human health risks due to exposure to potentially toxic elements (PTEs) via dermal contact were also considered. The average concentrations of water constituents were acceptable for irrigation, except sodium adsorption ratio (SAR) and Cl^-. The dissolved oxygen, total dissolved solids, Cl^-, NO₂-N, NO₃-N, NH₃, and PTEs (except Zn) did not meet standard limits for fish production. The MSA revealed that water contamination resulted from human activities (agriculture, industry, and domestic wastes) and hydrochemical processes. The PCA indicated that SAR, Cu, and pH could adequately represent water quality for irrigation, while temperature, NO₂-N, Cr, and Zn could reflect fish farming requirements. The AHP provided consistent weights for the original and shortlisted parameters. The water quality varied from good to poor for irrigation and from excellent to low for fish farming. The minimum IWQI could adequately represent the IWQI (R² = 0.83) and thus reduce the time, effort, and cost for monitoring water quality. However, the minimum FFWQI showed moderate consistency (R² = 0.51) with FFWQI, implying that increasing the sampling size is essential for better performance. The hazard quotient of all PTEs was below 1.0 for both adults and children, indicating a safe limit. The potential cancer risk was acceptable (1.36E-06) for adults and safe (8.03E-07) for children. Results of this work would be a start point for efficient quality control programs in arid regions.

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

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.

Utilization of Pollution Indices, Hyperspectral Reflectance Indices, and Data-Driven Multivariate Modelling to Assess the Bottom Sediment Quality of Lake Qaroun, Egypt

Assessing the environmental hazard of potentially toxic elements in bottom sediments has always been based entirely on ground samples and laboratory tests. This approach is remarkably accurate, but it is slow, expensive, damaging, and spatially constrained, making it unsuitable for monitoring these parameters effectively. The main goal of the present study was to assess the quality of sediment samples collected from Lake Qaroun by using different groups of spectral reflectance indices (SRIs), integrating data-driven (Artificial Neural Networks; ANN) and multivariate analysis such as multiple linear regression (MLR) and partial least square regression (PLSR). Jetty cruises were carried out to collect sediment samples at 22 distinct sites over the entire Lake Qaroun, and subsequently 21 metals were analysed. Potential ecological risk index (RI), organic matter (OM), and pollution load index (PLI) of lake’s bottom sediments were subjected to evaluation. The results demonstrated that PLI showed that roughly 59% of lake sediments are polluted (PLI > 1), especially samples of eastern and southern sides of the lake’s central section, while 41% were unpolluted (PLI < 1), which composed samples of the western and western northern regions. The RI’s findings were that all the examined sediments pose a very high ecological risk (RI > 600). It is obvious that the three band spectral indices are more efficient in quantifying different investigated parameters. The results showed the efficiency of the three tested models to predict OM, PLI, and RI, revealing that the ANN is the best model to predict these parameters. For instance, the determination coefficient values of the ANN model of calibration datasets for predicting OM, PLI, and RI were 0.999, 0.999, and 0.999, while they were 0.960, 0.897, and 0.853, respectively, for the validation dataset. The validation dataset of the PLSR produced R2 values higher than with MLR for predicting PLI and RI. Finally, the study’s main conclusion is that combining ANN, PLSR, and MLR with proximal remote sensing could be a very effective tool for the detection of OM and pollution indices. Based on our findings, we suggest the created models are easy tools for forecasting these measured parameters.

Groundwater Suitability for Drinking and Irrigation Using Water Quality Indices and Multivariate Modeling in Makkah Al-Mukarramah Province, Saudi Arabia

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.

Environmental Pollution Indices and Multivariate Modeling Approaches for Assessing the Potentially Harmful Elements in Bottom Sediments of Qaroun Lake, Egypt

This research intends to offer a scientific foundation for environmental monitoring and early warning which will aid in the environmental protection management of Qaroun Lake. Qaroun Lake is increasingly influenced by untreated wastewater discharge from many anthropogenic activities, making it vulnerable to pollution. For that, six environmental pollution indices, namely contamination factor (Cf), enrichment factor (EF), geo-accumulation index (Igeo), degree of contamination (Dc), pollution load index (PLI), and potential ecological risk index (RI), were utilized to assess the bottom sediment and to determine the different geo-environmental variables affecting the lake system. Cluster analysis (CA), and principal component analysis (PCA) were used to explore the potential pollution sources of heavy metal. Moreover, the efficiency of partial least-square regression (PLSR) and multiple linear regression (MLR) were tested to assess the Dc, PLI, and RI depending on the selected elements. The sediment samples were carefully collected from 16 locations of Qaroun Lake in two investigated years in 2018 and 2019. Total concentrations of Al, As, Ba, Cd, Co, Cr, Cu, Fe, Ga, Hf, Li, Mg, Mn, Mo, Ni, P, Pb, Sb, Se, Zn, and Zr were quantified using inductively coupled plasma mass spectra (ICP-MS). According to the Cf, EF, and Igeo results, As, Cd, Ga, Hf, P, Sb, Se, and Zr demonstrated significant enrichment in sediment and were derived from anthropogenic sources. According to Dc results, all collected samples were categorized under a very high degree of contamination. Further, the results of RI showed that the lake is at very high ecological risk. Meanwhile, the PLI data indicated 59% of lake was polluted and 41% had PLI < 1. The PLSR and MLR models based on studied elements presented the highest efficiency as alternative approaches to assess the Dc, PLI, and RI of sediments. For examples, the validation (Val.) models presented the best performance of these indices, with R2val = 0.948–0.989 and with model accuracy ACCv = 0.984–0.999 for PLSR, and with R2val = 0.760–0.979 and with ACCv = 0.867–0.984 for MLR. Both models for Dc, PLI, and RI showed that there was no clear overfitting or underfitting between measuring, calibrating, and validating datasets. Finally, the combinations of Cf, EF, Igeo, PLI, Dc, RI, CA, PCA, PLSR, and MLR approaches represent valuable and applicable methods for assessing the risk of potentially harmful elemental contamination in the sediment of Qaroun Lake.

Water quality modeling and management for Rosetta Branch, the Nile River, Egypt

Rosetta Branch (RB) is the Nile western branch, which is considered the main freshwater resource for the western governorates of the Nile Delta, in addition to El-Beheira governorate, Egypt. This work aims to investigate the effectiveness of applying six feasible water quality management scenarios for the RB at Kafr Elzayat (KZ) City, where two major pollution sources (industrial companies and the Tala Drain) are discharging their effluents to the branch. A hydro-ecological model for the branch was developed based on MIKE 11 modeling system (3 modules). The developed model was calibrated using field hydrodynamic and water quality records for the branch during the period from November 2014 to August 2015. The calibrated model results were considered a base case for the simulated management scenarios. According to the study results, diverting the Tala Drain effluents to the nearest main drain will significantly enhance the branch water quality, while diverting of the industrial companies' effluents to the city sewer system will significantly enhance the water quality conditions downstream of the study. The expected scenario of decreasing the RB discharge, due to future climatic changes and/or construction of the new Ethiopian dams, will significantly affect the RB water quality status in a negative way. Urgent management plans, based on the presented scenarios results (or a combination of them), should be applied to enhance the branch water quality. The Tala Drain should be, at least, provided by a water quality treatment plant, and the branch freshwater discharge should be increased.

Integration of Water Quality Indices and Multivariate Modeling for Assessing Surface Water Quality in Qaroun Lake, Egypt

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.

Uptake prediction of nine heavy metals by Eichhornia crassipes grown in irrigation canals: A biomonitoring approach

The principal objective of this study is to generate mathematical regression equations that facilitate the estimation of the extent to which Eichhornia crassipes (C. Mart.) Solms, water hyacinth, absorbs heavy metals (HMs) into four plant organs (laminae, petioles, roots, and stolons). This study considers the absorption of nine HMs (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn), and the E. crassipes evaluated in this study were located in three irrigation canals in the North Nile Delta in Egypt, with sampling being conducted in both monospecific and homogenous E. crassipes. Samples of both E. crassipes and water were collected on a monthly basis during one growing season. Analysis of the water samples showed that the HM concentrations ranged from 1.1 μg/l for Cd to 2079.8 μg/l for Fe. All HMs were more concentrated in the E. crassipes roots than in any other organ. Typically, there was a significant correlation between the HM levels in the water and the HM levels in the E. crassipes organs. E. crassipes was documented by a bioconcentration factor > 1.0 for all HMs. The translocation factor in this study was <1.0 for all HMs. The t-values that referred to the discrepancies between the measured and predicted values of the HMs in the four E. crassipes organs were not significant. This finding can be considered to be an indication of the goodness of fit with respect to the ability of the equations to forecast HM uptake. Therefore, the developed equations will benefit the prediction of HM uptake by E. crassipes grown in irrigation canals in the Nile Delta. The efficacy of E. crassipes as a metric for gauging the aggregate impact of environmental pollution in water sources and its potential application in biomonitoring are confirmed in this study.

Assessment of Soil Pollution Levels in North Nile Delta, by Integrating Contamination Indices, GIS, and Multivariate Modeling

The proper assessment of trace element concentrations in the north Nile Delta of Egypt is needed in order to reduce the high levels of toxic elements in contaminated soils. The objectives of this study were to assess the risks of contamination for four trace elements (nickel (Ni), cobalt (Co), chromium (Cr), and boron (B)) in three different layers of the soil using the geoaccumulation index (I-geo) and pollution load index (PLI) supported by GIS, as well as to evaluate the performance of partial least-square regression (PLSR) and multiple linear regression (MLR) in estimating the PLI based on data for the four trace elements in the three different soil layers. The results show a widespread contamination of I-geo Ni, Co, Cr, and B in the three different layers of the soil. The I-geo values varied from 0 to 4.74 for Ni, 0 to 6.56 for Co, 0 to 4.11 for Cr, and 0 to 4.57 for B. According to I-geo classification, the status of Ni, Cr, and B ranged from uncontaminated/moderately contaminated to strongly/extremely contaminated. Co ranged from uncontaminated/moderately contaminated to extremely contaminated. There were no significant differences in the values of I-geo for Ni, Co, Cr, and B in the three different layers of the soil. According to the PLI classification, the majority of the samples were very highly polluted. For example, 4.76% and 95.24% of the samples were unpolluted and very highly polluted, respectively, in the surface layer of the soil profiles. Additionally, 14.29% and 85.71% of the samples were unpolluted and very highly polluted, respectively, in the subsurface layer of the soil profiles. Both calibration (Cal.) and validation (Val.) models of the PLSR and MLR showed the highest performance in predicting the PLI based on data for the four studied trace elements, as an alternative method. The validation (Val.) models performed the best in predicting the PLI, with R2 = 0.89–0.93 in the surface layer, 0.91–0.96 in the subsurface layer, 0.89–0.94 in the lowest layers, and 0.92–0.94 across the three different layers. In conclusion, the integration of the I-geo, PLI, GIS technique, and multivariate models is a valuable and applicable approach for the assessment of the risk of contamination for trace elements, and the PLSR and MLR models could be used through applying chemometric techniques to evaluate the PLI in different layers of the soil.

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

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

Last period groundwater quality raises big concerns all over the world since it is a limited source of drinkable water and for agricultural and industrial use. While the suitability of the groundwater of Liwa aquifer (Abu Dhabi Emirate) for agricultural use has been previously partially studied, not all the water parameters have been taken into account. Therefore, in this paper, we propose the study of 42 concentrations series of 19 groundwater parameters. We test the hypothesis that the water parameters series recorded at different locations are similar and group the samples in clusters. The main parameters that determine the differences between the clusters are determined by Principal Component Analysis (PCA). Finally, we use a quality index for assessing the water suitability for drinking. The conclusions emphasize the necessity of using more than one technique to evaluate water quality for different purposes and to cross-validate the results.