Search Results (42)

This study evaluates the quality and irrigation suitability of drainage water in the Al-Jouf Region, Saudi Arabia, where water scarcity necessitates the reuse of nonconventional resources. Eighteen drainage water samples were analyzed for physicochemical parameters and irrigation indices, including electrical conductivity (EC), sodium percentage (Na⁺%), sodium adsorption ratio (SAR), magnesium hazard (MH), Kelly’s ratio (KR), permeability index (PS), and irrigation water quality index (IWQI). Multivariate statistical tools were applied to identify dominant hydrogeochemical processes. Inverse Distance Weighting (IDW) interpolation in ArcGIS Desktop 10.8 was employed to map significant physicochemical data and irrigation indicators. Results revealed that while EC values indicated low to moderate salinity (0.74–25.2 μS/cm), most samples showed high Na⁺%, SAR, and KR, classifying them as doubtful to unsuitable for irrigation. The IWQI ranged from 84.47 to 1617.87, indicating poor to inferior quality due to evaporation, fertilizer leaching, and sodium accumulation. Furthermore, the results highlight the importance of precise geographic modeling in determining whether drainage water is suitable for long-term agricultural use in arid regions such as Al-Jouf. Sustainable reuse of such drainage water requires freshwater blending, gypsum application, and the cultivation of salt-tolerant crops, aligning with Saudi Vision 2030 objectives for sustainable water management in arid regions. Full article

Groundwater quality in the semi-arid region of Oum El Bouaghi, Northeastern Algeria, was assessed for irrigation suitability using hydrogeochemical analyses, water quality indices, and GIS techniques. The study analyzed 23 groundwater samples during dry and wet seasons in 2022–2023, several physicochemical parameters were measured. Results revealed neutral to slightly alkaline pH levels, except for one acidic sample, with salinity (EC: 527–5001 µS·cm⁻¹) exceeding WHO guidelines, particularly during the dry season due to evaporation and anthropogenic activities. Hydrogeochemical facies showed dominance of Na⁺-HCO₃⁻ and Ca²⁺-Cl⁻/SO₄²⁻ water types, indicating rock–water interactions and evaporation control, as confirmed by Gibbs plots. The IWQI classified water into five categories, with severe restrictions (IWQI < 40) in 13% of samples during the dry season, improving slightly in the wet season. Indices such as SAR, Na%, and RSC indicated low to moderate sodium hazard, while KR and PS highlighted salinity risks in specific areas. Spatial analysis revealed localized pollution hotspots, with the (GPI) identifying minimal to high contamination levels, linked to agricultural and geogenic sources. These findings underscore needs for sustainable groundwater management, including monitoring, optimized irrigation practices, and mitigation of anthropogenic impacts, to ensure long-term agricultural viability in this water-scarce region. Full article

Groundwater is the main source of water for both domestic and agricultural use in arid regions. This study assessed the hydrogeochemical characteristics and suitability of groundwater for drinking and irrigation in Kenya’s Ewaso Ng’iro–Lagh Dera Basin. A total of 129 borehole groundwater samples were collected and analyzed for pH, electrical conductivity (EC), total hardness, and major ions. The groundwater was found to be mostly neutral to slightly alkaline and ranged from marginal to brackish in salinity. The dominant water type is Na-HCO₃, with the ionic order Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻. Mineral saturation indices indicate that the water is undersaturated with gypsum and anhydrite but is saturated with calcite, dolomite, and aragonite. Groundwater chemistry is primarily influenced by ion exchange, the mixing of fresh and paleo-saline water, and rock weathering processes. The water quality index (WQI) reveals that 80.5% of groundwater is suitable for drinking. The rest have high levels of sodium, EC, and bicarbonate. Thus, they are not suitable. The irrigation water quality index (IWQI) places most samples in the moderate-to-severe restriction category due to high salinity and sodicity. These findings highlight the importance of properly treating groundwater before use. Full article

This study focuses on assessing the hydrogeochemical characteristics and irrigation suitability of groundwater in the Ras El Aioun and Merouana districts, using an integrated approach that combines physicochemical analysis, machine learning (ML), and Geographic Information Systems (GISs). Thirty groundwater samples were collected in June 2023 and subjected to extensive analyses, including major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃⁻, Cl⁻, SO₄²⁻), pH, TDS, alkalinity, and hardness. Hydrochemical facies analysis revealed that the Ca-HCO₃ type was dominant (93.33%), with some samples exceeding FAO limits, particularly for Na⁺, K⁺, SO₄²⁻, Cl⁻, Mg²⁺, and HCO₃⁻. Assessment of groundwater irrigation suitability revealed generally favorable conditions based on three key parameters: all samples (100%) were classified as excellent based on the Sodium Adsorption Ratio (SAR < 10), 70% showed good-to-permissible status by Sodium Percentage (Na% < 60), and 83.3% were within safe limits for Residual Sodium Carbonate (RSC < 1.25 meq/L). However, the Permeability Index (PI > 75%) categorized 96.7% of samples as unsuitable for long-term irrigation due to potential soil permeability reduction. Additionally, Total Hardness (TH < 75 mg/L) indicated predominantly soft water characteristics (90% of samples), particularly in the central study area, suggesting possible limitations for certain agricultural applications that require mineral-rich water. GIS-based spatial analysis showed that irrigation suitability was higher in the eastern and western regions than in the central zone. Advanced machine learning algorithms provide superior predictive capability for water quality parameters by effectively modeling complex, non-linear feature interactions that conventional statistical approaches frequently fail to capture. Three ML models—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were used to predict the Irrigation Water Quality Index (IWQI). XGBoost outperformed the others (RMSE = 2.83, R² = 0.957), followed by RF (RMSE = 3.12, R² = 0.93) and SVR (RMSE = 3.45, R² = 0.92). Integrating ML and GIS improved groundwater quality assessment and provided a robust framework for sustainable irrigation management. These findings provide critical insights for optimizing agricultural water use in water-scarce regions. Full article

Water and its management have played a pivotal role in the evolution of organisms and civilizations, fulfilling essential roles in personal use, industry, irrigation, and drinking from ancient times to the present. This study seeks to evaluate groundwater quality for irrigation and drinking in the Northern Peloponnese region, specifically the wells of Loutraki and Schinos areas and the springs of the Gerania Mountains (Mts.), using geo-environmental indices and ionic ratios. For the first time, geo-environmental indices have been applied to a region where groundwater serves multiple purposes, addressing the challenge of understanding their dynamics to optimize their application in environmental science and groundwater pollution research. To achieve this, 68 groundwater samples from the study area were utilized, and a total of 25 geo-environmental indices were calculated to assess water quality. These indices examined: (i) drinking suitability (NPI, RI, PIG, WQI, and WPI), (ii) irrigation suitability (SAR, KR, %Na, PS, MAR, RSC, SSP, TH, PI, IWQI, and TDS), (iii) potentially toxic element (PTE) loadings (Cd, HEI, and HPI), and (iv) major hydrogeochemical processes, expressed as ionic ratios (Ca/Mg, Ca/SO₄, Ca/Na, Cl/NO₃, Cl/HCO₃, and Si/NO₃). Data processing involved descriptive statistics, hydrogeochemical bivariate plots, Spearman correlation coefficients, and multivariate statistical analyses, including factor analysis (FA) and R-mode hierarchical cluster analysis (HCA). Results revealed that all groundwater samples (100%) from the Loutraki area and the Gerania Mts. were of good quality for both drinking and irrigation purposes. In contrast, groundwater from the Schinos area exhibited lower quality, with most samples (93.9%) considered suitable only for irrigation. The deterioration in the coastal aquifer of the Schinos area is attributed to elevated concentrations of Cl⁻, Na⁺, NO_3⁻, As, and Cr resulting from salinization and relatively limited anthropogenic influences. The study highlights that relying on individual geo-environmental indices can yield misleading results due to their dependence on factors such as researcher expertise, methodological choices, and the indices’ inherent limitations. Consequently, this research emphasizes the necessity of combining indices to enhance the reliability, accuracy, and robustness of groundwater quality assessments and hydrogeochemical evaluations. Last but not least, the findings demonstrate that calculating all available geo-environmental indices is unnecessary. Instead, selecting a subset of indices that either reflect the impact of specific elemental concentrations or can be effectively integrated with others is sufficient. This streamlined approach addresses challenges in optimizing geo-environmental index applications and contributes to improved groundwater resource management. Full article

Irrigation water quality is crucial for sustainable agriculture and environmental health, influencing crop productivity and ecosystem balance globally. This study evaluates the performance of multiple deep learning models in classifying the Irrigation Water Quality Index (IWQI), addressing the challenge of accurate water quality prediction by examining the impact of increasing input complexity, particularly through chemical ions and derived quality indices. The models tested include convolutional neural networks (CNN), CNN-Long Short-Term Memory networks (CNN-LSTM), CNN-bidirectional Long Short-Term Memory networks (CNN-BiLSTM), and CNN-bidirectional Gated Recurrent Unit networks (CNN-BiGRUs). Feature selection via SHapley Additive exPlanations (SHAP) provided insights into individual feature contributions to the model predictions. The objectives were to compare the performance of 16 models and identify the most effective approach for accurate IWQI classification. This study utilized data from 166 wells in Algeria’s Naama region, with 70% of the data for training and 30% for testing. Results indicate that the CNN-BiLSTM model outperformed others, achieving an accuracy of 0.94 and an area under the curve (AUC) of 0.994. While CNN models effectively capture spatial features, they struggle with temporal dependencies—a limitation addressed by LSTM and BiGRU layers, which were further enhanced through bidirectional processing in the CNN-BiLSTM model. Feature importance analysis revealed that the quality index (qi) qi-Na was the most significant predictor in both Model 15 (0.68) and Model 16 (0.67). The quality index qi-EC showed a slight decrease in importance, from 0.19 to 0.18 between the models, while qi-SAR and qi-Cl maintained similar importance levels. Notably, Model 16 included qi-HCO₃ with a minor importance score of 0.02. Overall, these findings underscore the critical role of sodium levels in water quality predictions and suggest areas for enhancing model performance. Despite the computational demands of the CNN-BiLSTM model, the results contribute to the development of robust models for effective water quality management, thereby promoting agricultural sustainability. Full article

Assessing the water quality of surface water bodies is one of the primary duties of environmental authorities in charge of water management. Irrigation water quality (IWQ) of the irrigation canals in the middle Nile delta, Egypt, was assessed by GIS-based research of water suitability indicators (risks connected with salinity, permeability, ion toxicity, and other factors on delicate crops), utilizing a thorough examination of 27 samples gathered from the irrigation canals surrounding the Kitchener drain Egypt, based on thirteen chemical characteristics in 2023. The maps in this work were created with ArcGIS version 10.7. A procedure known as Inverse Distance Weight (IDW) was used to show the variations in the concentrations of the different heavy metals and to offer a geographic representation of the water quality. We utilized principal component analysis (PCA) to pinpoint potential sources of heavy metals. To assess soil contamination levels in the study area, various methods were used such as contamination factors (CFs), heavy metal pollution index (HPI), ecological risks index (ERI), pollution load index (PLI), and the modified degree of contamination (mCd) for seven targeted metals: As, Cd, Co, Cu, Ni, Pb, and Zn. The findings showed that every sample had a medium irrigation appropriateness rating as the IWQI values range from 25.43 to 34.50. According to the different contamination indices, the study area is suffering high contamination as the mean values of HPI, ERI, PLI, and MC_d are 3570.26 ± 621.40, 804.62 ± 164.88, 6.62 ± 6.06, and 5.10 ± 0.89, respectively. PCA results revealed significant metal contamination in multiple enterprises showing that they are present simultaneously and may have a common source. This source could be an industrial discharge, agricultural runoff or other process that affects the metals’ concentrations in surface water. These results give decision-makers important information for managing surface water resources and encouraging sustainable water management in the research region. By educating the local community about artificial groundwater recharge, rainwater collection, and surface water canal management, government authorities can gradually lessen the potential effects of poor water quality in these areas. It is also recommended to develop a risk management module that can assess water threats for agricultural and public health applications. The ultimate goal is to incorporate this descriptive and sensitive research into a risk management system that can generate quick reports for policymakers and decision-makers. Full article

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications. Full article

The hydrogeochemical properties and evolution of groundwater in the Essaouira syncline basin in northwestern Morocco were investigated in this study, with a total of 447 samples during different campaigns (April 2017, May 2018, March 2019, and July 2020). These samples were analyzed for major ions and stable and radioactive water isotopes (δ²H, δ¹⁸O, and ³H). With decreasing rainfall from climate change in Morocco, it is crucial to assess the sustainability of groundwater reserves. This shortage leads to the degradation of water and soil quality. To ensure sustainable water management and preserve the environment in the study area, it is necessary to assess groundwater quality for drinking and irrigation, take precautions, and establish management plans. This study assessed groundwater quality using two water quality index methods (WQI and IWQI). Several natural processes control groundwater mineralization, including the dissolution of evaporite and carbonate minerals, cation exchange phenomena, evaporation, and seawater intrusion. According to the results obtained using the WQI method, all groundwater samples in the study area are generally of poor quality and must be treated before being used for domestic purposes. Based on the results obtained by the IWQI method, the samples are suitable for use as irrigation water, especially for plants resistant to high salinity concentrations. Stable isotope measurements (δ²H and δ¹⁸O) indicate that Atlantic precipitation continuously recharges the recharge areas of the Essaouira Basin. Thus, the low values of tritium (³H) in groundwater mean that the freshwater in the Essaouira Basin is ancient. Full article

Traditional Water Quality Indices (WQIs) often fail to capture the significant impact of flow velocity on water quality, especially under varying hydrological conditions. In this study, an Integrated Water Quality Index (IWQI) was developed by combining water quality parameters and flow rate, providing a more comprehensive assessment under various flow conditions. Compared to traditional indices, the IWQI showed slightly lower correlations in individual parameter performance, but it performed well in evaluating water quality changes associated with flow variations. Parameters such as Total Phosphorus (TP), Total Coliforms (TC), and Fecal Coliforms (FC), which are prevalent pollutants in the Cheongmi River, significantly influenced IWQI scores. River water quality was evaluated using input data simulated under a climate change scenario. When precipitation was abundant, the IWQI score remained relatively stable even with reduced flow rates. However, during periods of insufficient rainfall, water quality deteriorated sharply. While general water quality parameters exhibited approximately a 10% change as flow decreased, TC and FC showed rapid deterioration, with change rates ranging from 20% to 60%. These findings underscore the importance of managing TC and FC, particularly when insufficient rainfall is predicted, as they are major sources of pollution. Full article

This study focused on assessing surface water quality in the northwest part of the Egyptian Nile Delta (El-Menoufia Governorate) and evaluated water suitability for irrigation purposes using the Irrigation Water Quality Index (IWQI), Permeability Index (PI), Wilcox, United State Salinity (USSL) diagram, and Piper trilinear diagrams categories, taking into consideration various water quality parameters. The results showed that, based on the IWQI, most of the water samples (61.8%) in the investigated area fell under the no restriction water (NR) category. Furthermore, the Wilcox diagram demonstrated that most of the investigated water samples (93.6%) are categorized as doubtful water; this shows that those samples have a higher sodium content material. According to the USSL diagram, most of the water samples (70.9%) fell into the high salinity (C) and moderate sodium (S) content (C3S2) class. According to the PI index, 8.2% of the tested water samples fell into class II (suitable for irrigation) and 91.8% fell into class III (unsuitable for irrigation). Based on the Piper trilinear, the water type is Na-Cl-HCO₃. According to these results, most of the water samples require more water regulations, are categorized as doubtful water that causes plants’ augmentation sensitivity if used for irrigation, and fell into the high salinity (EC) and sodium absorption ratio (SAR) magnitude, which might have negative outcomes on soil and plant health if used for irrigation, have extensive obstacles, and are improper for irrigation. Therefore, proper management practices and treatments may be vital to mitigate the adverse effects of salinity and SAR on soil and plant health in this study area. Therefore, addressing water deficiency and quality in Egypt’s northwest Nile delta is crucial for suitable irrigation purposes. Full article

Groundwater, an invaluable resource crucial for irrigation and drinking purposes, significantly impacts human health and societal advancement. This study aims to evaluate the groundwater quality in the Mnasra region of the Gharb Plain, employing a comprehensive analysis of thirty samples collected from various locations, based on thirty-three physicochemical parameters. Utilizing tools like the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), as well as Multivariate Statistical Approaches (MSA), and the Geographic Information System (GIS), this research identifies the sources of groundwater pollution. The results revealed Ca²⁺ dominance among cations and Cl⁻ as the primary anion. The Piper and Gibbs diagrams illustrated the prevalent Ca²⁺-Cl⁻ water type and the significance of water–rock interactions, respectively. The PIG values indicated that 86.66% of samples exhibited “Insignificant pollution”. NPI showed notable nitrate pollution (1.48 to 7.06), with 83.33% of samples rated “Good” for drinking based on the WQI. The IWQI revealed that 80% of samples were classified as “Excellent” and 16.66% as “Good”. Spatial analysis identified the eastern and southern sections as highly contaminated due to agricultural activities. These findings provide valuable insights for decision-makers to manage groundwater resources and promote sustainable water management in the Gharb region. Full article

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices ($\mathrm{S}\mathrm{A}\mathrm{R},\mathrm{R}\mathrm{S}\mathrm{C},\mathrm{N}\mathrm{a}\mathrm{\%},\mathrm{M}\mathrm{H},\mathrm{a}\mathrm{n}\mathrm{d} \mathrm{P}\mathrm{I}),$ as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca²⁺, Mg²⁺, Na⁺, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region. Full article

In the Zeroud basin, a diverse array of methodologies were employed to assess, simulate, and predict the quality of groundwater intended for irrigation. These methodologies included the irrigation water quality indices (IWQIs); intricate statistical analysis involving multiple variables, supported with GIS techniques; an artificial neural network (ANN) model; and an XGBoost regression model. Extensive physicochemical examinations were performed on groundwater samples to elucidate their compositional attributes. The results showed that the abundance order of ions was Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and SO₄²⁻ > HCO₃⁻ > Cl⁻. The groundwater facies reflected Ca-Mg-SO₄, Na-Cl, and mixed Ca-Mg-Cl/SO₄ water types. A cluster analysis (CA) and principal component analysis (PCA), along with ionic ratios, detected three different water characteristics. The mechanisms controlling water chemistry revealed water–rock interaction, dolomite dissolution, evaporation, and ion exchange. The assessment of groundwater quality for agriculture with respect IWQIs, such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), and residual sodium carbonate (RSC), revealed that the domination of the water samples was valuable for agriculture. However, the IWQI and PS fell between high-to-severe restrictions and injurious-to-unsatisfactory. The ANN and XGBoost regression models showed robust results for predicting IWQIs. For example, ANN-HyC-9 emerged as the most precise forecasting framework according to its outcomes, as it showcased the most robust link between prime attributes and IWQI. The nine attributes of this model hold immense significance in IWQI prediction. The R² values for its training and testing data stood at 0.999 (RMSE = 0.375) and 0.823 (RMSE = 3.168), respectively. These findings indicate that XGB-HyC-3 emerged as the most accurate forecasting model, displaying a stronger connection between IWQI and its exceptional characteristics. When predicting IWQI, approximately three of the model’s attributes played a pivotal role. Notably, the model yielded R² values of 0.999 (RMSE = 0.001) and 0.913 (RMSE = 2.217) for the training and testing datasets, respectively. Overall, these results offer significant details for decision-makers in managing water quality and can support the long-term use of water resources. Full article

Irrigation water quality is important to sustain agricultural productivity. The primary irrigation water sources in Al-Ahsa Oasis, KSA, are groundwater wells, mixed with treated wastewater and agricultural drainage. This study sought to evaluate irrigation water quality in Al-Ahsa Oasis with the aid of using irrigation water quality indices (IWQIs). One hundred and forty-eight different water samples were collected from various irrigation water resources throughout Al-Ahsa Oasis. The investigated physiochemical characteristics include pH, temperature, TDS, EC, turbidity, free chlorine, total hardness, cations (Na, K, Ca, Mg), anions (Cl, CO₃, HCO₃, SO₄), organic matter indices (DO, BOD, COD), and nutrients (NH₄, NO₃, PO₄). The IWQIs used in this study include salinity hazard, sodium adsorption ratio (SAR), Kelly’s ratio (KR), soluble sodium percentage (SSP), Permeability index (PI), residual sodium carbonate (RSC), and magnesium hazard (MH). The results indicated that treated wastewater mixed with groundwater is acceptable for irrigation. Spatial variations in irrigation water quality throughout Al-Ahsa are associated with water resources. For instance, groundwater mixed with agricultural drainage has the highest values of TDS, cations, and anions, whereas the lowest values were reported for treated wastewater, reflecting the good efficiency of wastewater treatment plants. The IWQI results revealed that 4.1% and 62.1% of the investigated irrigation water samples were considered good (class III) and satisfactory (class IV) for irrigation, respectively, whereas 33.8% of the collected water samples fall within the severe irrigation restrictions. Moreover, 79.7% of the investigated water samples were classified to have high to very high salinity hazards (C3, C4) and medium to high sodium hazards (S2, S3). Regular monitoring and assessment of treated water quality and wastewater treatment plant efficiency are important factors in achieving the sustainability of treated wastewater reuse in irrigation and consequently food security. Full article