Irrigation Water Quality Index Research Articles

Applying the water quality indices and geographic information system approach to assessing the suitability of groundwater quality for drinking and irrigation purposes in the semi-arid region of Tebessa-Ain Chabro, Northeastern Algeria

Assessing the groundwater quality is important for the efficient exploitation of water resources in semi-arid areas. The study area in northeast Algeria mostly depends on groundwater as its main source of water, and the quality of groundwater is becoming important due to the increasing need for freshwater. The hydrochemical characteristics and water quality of groundwater in the Tebessa-Ain Chabro were assessed using water quality indices, geochemical modeling, and multivariate statistical approaches. The study discovered that the groundwater samples could be classified into four distinct water groups using hierarchical cluster analysis in Q mode (HCA) based on their electrical conductivity. We identified three forms of water: mixed (Ca2+–Mg2+–Cl−), Na+–K+–HCO3−, and Ca2+–Cl−. According to the water quality assessment, only 38% of the samples were deemed suitable for human consumption, while 34% were categorized as poor water, 10% as extremely poor, and 17% as unsafe for drinking. The irrigation water quality index identified four classifications: low, moderate, high, and severe restriction, with corresponding percentages of 31%, 31%, 7%, and 31%. The nitrate pollution index (NPI) showed that 48% of samples fell into the moderate pollution class. Human activity, such as sewage infiltration and waste disposal in open areas, was the cause of this nitrate contamination. The saturation index values showed that groundwater was less saturated in halite and sylvite and more saturated in aragonite, calcite, dolomite, anhydrite, gypsum, and hydroxyapatite. The Tebessa region’s groundwater’s hydrochemical properties and water quality have been assessed using multivariate statistical techniques, geochemical modeling, and water quality indexes. The study’s conclusions can provide a foundation for upcoming research evaluating the region’s groundwater quality.

Hydrogeochemical evaluation of groundwater evolution and quality in some Voltaian aquifers of Kintampo South District, Bono East Region, Ghana: Implications from chemometric analysis, geochemical modeling and geospatial mapping techniques

Kintampo South District in the Bono East Region of Ghana is dominated by metavolcanic and metasedimentary rocks of the Birimian and the Voltaian Supergroups, which are reported to show aquifer complexities and quality issues. Therefore, assessment of groundwater evolution and quality is important in this area because of the growing need for groundwater for both agricultural and residential use. The people heavily rely on agriculture as their main source of livelihood, and the agricultural practices could potentially impact the groundwater quality. Consequently, this research aimed to evaluate groundwater appropriateness in the Voltaian aquifers of the Kintampo South District for irrigation and consumption. Irrigation water quality indices, geochemical modeling, hydrochemical graphing, and chemometric analysis were all used in the assessment. The investigation reported potassium ion (K+) as the principal cation in the groundwater, showing concentrations within the range of 0.200 to 61.0 mg/L and a mean of 6.32 mg/L. Bicarbonate (HCO3−) is also the major anion in the samples with values that vary from 2.00 to 238 mg/L and a mean of 27.8 mg/L. Geospatial mapping indicates that these predominant ions show higher concentrations in the central part of the study area. Groundwater evolved from K-SO4-HCO3 to Na-HCO3-Cl water types and other mixed forms, which are influenced by atmospheric precipitation and rock weathering. Geochemical modeling reveals that undersaturation of the groundwater occurs in relation to all the silicate and carbonate minerals in the aquifer. However, most samples show positive correlation with these minerals, suggesting precipitation under pre-existing conditions. Chemometric analysis indicated that the chemical composition of groundwater is controlled by human-induced and geogenic activities. With respect to all hydrochemical parameters, the groundwater is largely deemed safe for residential use based on the computed Water Quality Index values. The groundwater in the district is also appropriate for irrigation, according to irrigation indices of water quality such as the Wilcox diagram, sodium adsorption ratio (SAR), Electrical conductivity (EC), magnesium ratio (MR), and sodium percent (Na%).

Cultivating sustainability: a multi-assessment of groundwater quality and irrigation suitability in the arid agricultural district of Dzira (Ksour Mountains, Algeria).

Groundwater serves a range of essential functions such as supplying drinking water, facilitating agricultural practices, and supporting industrial processes. This study examines with multiple methods the quality of groundwater in the agricultural region of Dzira, Algeria. By collecting 38 groundwater samples of different wells and boreholes, valuable awareness of the aptness of groundwater for irrigation in this arid landscape was gained. Most wells met Food and Agriculture Organization (FAO) criteria for the total dissolved solids (TDS) and the potential of hydrogen pH, but some areas had higher mineral content and electrical conductivity. Results show significant TDS variations, with 10.81% of wells exceeding limits and acceptable pH levels. Elevated EC values in 67.57% of wells show high salinity, affecting soil and plant growth. Major ions such as Mg2+ and SO4- exceeded FAO standards in 43.24% and 64.86% of wells, respectively, highlighting substantial mineral content in the groundwater. Suitability indices reveal that most wells pose low sodium hazards and are generally suitable for irrigation, though some areas face moderate to high restrictions. The irrigation water quality index (IWQI) ranged from 45.36 to 96.30, averaging 80.77, with 54.04% classified as "low restriction," suitable for sandy soils with good permeability but requiring caution on salt-sensitive soils. Hydrogeochemical analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) identifies rapid evaporite dissolution from Triassic saline formations, with a correlation matrix showing associations between TDS and Ca2⁺, Mg2⁺, Na⁺, Cl⁻, and SO₄2⁻. This mineralization is likely from gypsum and halite. Zoning maps based on IWQI and other parameters depicted spatial variations in groundwater quality, guiding effective irrigation management strategies. Overall, the study underscores the importance of comprehensive water quality assessment for sustainable agriculture and emphasizes the need for targeted interventions to mitigate potential challenges associated with soil salinity and sodicity. Therefore, these findings can be useful to decision-makers and stakeholders in order to optimize water use and protect this vital resource.

Assessing RO and NF Desalination Technologies for Irrigation-Grade Water

In this work, the performance of a Reverse Osmosis (RO) process using different types of reverse osmosis (RO) and nanofiltration (NF) membranes is evaluated for brackish water desalination for producing irrigation-grade water. The proposed desalination system is a single-stage system, where three types of RO and two NF membranes were examined. The different desalination systems were simulated using ROSA72 software. In order to validate the theoretical model, the results obtained from the simulation were compared to those obtained from the experiment conducted in this work. The El-Moghra aquifer of Egypt is considered the test bed due to a considerable amount of data being available for this aquifer. The El-Moghra aquifer has 79 wells, and the available water data, when checked against several quality parameters, show that none of the investigated wells are suitable for direct irrigation without treatment due to problems of salinity, the sodium adsorption ratio, and low water quality according to the irrigation water quality index values. The obtained results show that nanofiltration membranes exhibited superior energy efficiency compared to reverse osmosis membranes. However, what sets the nanofiltration membranes apart is their ability to elevate water quality in 89.9% of the total investigated wells to an acceptable level for agricultural purposes. This underscores the nanofiltration membranes as a highly effective alternative to reverse osmosis membranes, demonstrating the capability to produce water suitable for irrigation while concurrently reducing operational costs due to the lower energy consumption in nanofiltration-based systems.

A generalized machine learning approach for cost-effective monitoring of irrigation suitability: A demonstration case in El Fahs aquifer (Tunisia)

This study develops and evaluates the performance of machine learning (ML) regression models, namely the Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbors (kNN) and eXtreme Gradient Boosting (XgBoost), for estimating the Irrigation Water Quality Index (IWQI) based on groundwater samples collected from El Fahs aquifer in Tunisia. The groundwater data are used as predictors for training and testing the machine learning models. Results indicate that sodium concentration has the most influence on the IWQI estimations for all ML models. The best-performing algorithms are found to be the SVR and XgBoost. Different datasets are also collected from existing studies, and merged to generate a single dataset that is used to develop generalized machine learning models. Despite regional variations, the generalized models performed adequately when tested against the unseen data, particularly the data collected from El Fahs case site. This work highlights the potential of using ML models, together with established metrics, as screening tools for predicting and classifying the irrigation suitability of groundwater based on available literature, even when metrics are not universally applicable. The findings of this study can be used by the water authorities in data scarce environments as cost-effective monitoring tools to assess water suitability for irrigation purposes.

Machine learning models for predicting water quality index: optimization and performance analysis for El Moghra, Egypt

ABSTRACT Assessing groundwater quality is vital for irrigation, but financial constraints in developing countries often result in infrequent sampling. This study comprehensively analyzes the groundwater quality of the El Moghra aquifer in Egypt's arid Western Desert, for its suitability for irrigation uses. Detailed hydrochemical analysis and advanced machine learning (ML) techniques, including geographic information systems, were employed to enhance spatial analysis and predictive accuracy. Various ML models, such as random forest, adaptive boosting, and extreme gradient boosting (XGBoost), were optimized using Bayesian optimization to predict the irrigation water quality index (IWQI) accurately. The evaluation incorporated visual and quantitative methods, alongside ranking analysis, to validate model effectiveness. Shapley Additive exPlanations feature importance analysis and a graphical user interface (GUI) model were developed based on the best predictive model. The results indicated that the groundwater quality is generally suitable for irrigation, with XGBoost showing the best performance, achieving a root mean square error of 5.602 and a determination coefficient (R²) of 0.872. Sodium concentration was identified as the most significant factor affecting the IWQI. The GUI facilitates easy prediction of IWQI, aiding agricultural water management and resource allocation within the region.

Hydrochemical characterization and evaluation of irrigation water quality using indexing approaches, multivariate analysis, and GIS techniques in K'sob Valley, Algeria

Irrigation plays a vital role in addressing increasing need for food production and promoting economic advancement. To meet the demands for food supply and economic progress, it is essential to underscore the significance of assessing water quality in dry regions. The current study was carried out to evaluate and predict the suitability of water quality for agricultural use in the K'sob valley in the M'sila region (Northeast Algeria). A combination of irrigation water quality indices (IWQIs), Geographic Information System (GIS) analysis and multivariate statistical methods were used for this purpose. Several physicochemical parameters, such as temperature (T°), hydrogen ion concentration (pH), electrical conductivity (EC), total dissolved solids (TDS), turbidity (Turb), chemical oxygen demand (COD), Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, NO3−, NO2−, NH4+, PO4− and SiO22+ were all measured from 40 samples collected at ten surface water locations during four seasons. The concentrations of the main cation and anion were shown as follows: Na+>Ca2+> K+ > Mg2+, and SO42− > HCO3− > Cl− > NO3− indicating mixed Na-Cl-K or Na-SO4 water facies. Significant seasonal variation for each parameter (T, pH, Turbidity, Salinity, COD, NH4+, Cl−, SO4−, and NO2−) was reported (p < 0.05). Additionally, a significant spatial variation (p < 0.05) was observed among different stations for the parameters: TDS, EC, Ca2+, Na+, HCO3−, SO4−, NO3−, and PO43− (p < 0.05). The irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), Kelly index (KI), and permeability index (PI) had values varying between 28.1 and 56.8, 5.65 and 12.45, 75 and 87, 2.61 and 6.54 and 83, and 97, respectively, and a significant seasonal effect was recorded. According to the Wilcox diagram, 70% of samples were unsuitable for irrigation, while 30% of samples were questionable. The IWQI map revealed that 50% of the samples fell within the very poor category for irrigation, while 20% and 30% of the samples were inside the poor and unsuitable categories, respectively. The principal component analysis (PCA) and hierarchical cluster analysis (HCA) of K'SobValley water revealed three different categories of water based on elemental composition and seasonal variations. The results obtained in this study can be valuable for surface water management. Furthermore, the developed methodology can serve as a useful tool for identifying critical hydrogeochemical components in arid and semi-arid environments related to surface water.

Assessing surface water quality in Hungary’s Danube basin using geochemical modeling, multivariate analysis, irrigation indices, and Monte Carlo simulation

Evaluation of water quality is crucial for managing surface water effectively, ensuring its suitability for human use, and sustaining the environment. In the lower Danube River basin, various methods were employed to assess surface water quality for irrigation, drinking, human health risk purposes and the main mechanism control the surface water chemistry. These methods included water quality indicators (WQIs), complex statistical analyses, geographic information systems (GIS), Monte Carlo simulation, and geochemical modeling. Physicochemical analyses of surface water samples revealed primarily Ca–Mg–HCO3− is the dominant water types. Principal component analysis (PCA), ionic ratios and piper, chloro alkaline index, Chadha, and Gibbs diagrams identified three distinct water characteristics influenced by water-rocks interaction, evaporation, ions exchange, and human activities. The geochemical modeling showed Danube River water’s strong ability to dissolve gypsum, halite, and anhydrite (SI < 0) and precipitate aragonite, dolomite, and calcite with saturation index (SI) value greater than 0 along its flow path. The irrigation water quality index (IWQI = 99.6–107.6), sodium adsorption ratio (SAR = 0.37–0.68), sodium percentage (Na% = 13.7–18.7), soluble sodium percentage (SSP = 12.5–17.5), Potential Salinity (PS = 0.73–1.6), and Residual Sodium Carbonate (RSC = − 1.27–0.58) values were used, mainly indicating acceptable quality with some limitations. Danube River water was unsuitable for drinking based on WQI value (WQI = 81–104). Oral exposure of children to specific components showed a higher hazard index (HI > 1) compared to adults, indicating a 2.1 times higher overall non-carcinogenic risk hazard index. However, Monte Carlo simulation demonstrated negligible iron, manganese, and nitrate health hazards for both age groups. These findings are valuable for water quality management decisions, contributing to long-term resource sustainability.

Assessment of groundwater quality and suitability for irrigation purpose using irrigation indices, remote sensing and GIS approach

Assessing both the quantitative and qualitative aspects of groundwater is essential for evaluating its suitability for irrigation and drinking purpose. Consequently, this current study showcases its importance by determining groundwater quality in Sundargarh district of, India, where groundwater serves as vital water resources. 360 groundwater samples were collected from various dug wells during pre-monsoon season of 2014–21 and analyzed for major ions, some important irrigation water quality indices along with pH, EC and TDS. Statistical evaluation of groundwater quality parameters was performed by physiochemical analysis with association of ArcGIS 10.4 for developing the spatial mapping. Furthermore, statistical and hydrochemical analysis was executed by exhibiting box plot generation, correlation study and hierarchical cluster analysis followed by categorization of water facies in , 1920; CGWB, 2013; Gad and Hattab, 2019’ has/have not been found in the reference list. Please add the corresponding reference(s) to the reference list.>Xlstat 2023, SPSS 23 and Diagrammes 6.1. Groundwater is mostly characterized as Na+-SO42- and deep meteoric percolation (DMP) type based on Base Exchange Indices with the dominance of Ca2+ and HCO3− as the major cation and anion respectively. Besides that, groundwater from 92%, 96%, 84%, 100%, 99%, 98%, 57%, 64%, 100%, 77%, 98% and 89% dug wells are found to be highly suitable for cultivation of major crops of this district depending on spatial variation of EC, TDS. Cl−, SAR, RSC, KR, PI, %Na, ESP, MAR, SSP and PS respectively. Groundwater facies are mostly categorized as Ca–HCO3 (60.8%) and C2–S1 (65.5%) type based on Piper and Wilcox diagram respectively. The dominant geochemical controlling factors are evaporation, reverse ion exchange and silicate weathering. Therefore, the objectives of this present research may provide necessary information to the regulatory boards and policy makers for efficient monitoring of groundwater quality and contaminated aquifers in Sundargarh district to make fit this crucial water resources for agricultural purpose.

Irrigation Water Quality Assessment in Egyptian Arid Lands, Utilizing Irrigation Water Quality Index and Geo-Spatial Techniques

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.

Seasonal variation effect on water quality and sediments criteria and its influence on soil pollution: Fayoum Governorate, Egypt

BackgroundIrrigation with low-quality water without considering the acceptable levels negatively impacts not only humans, but also extends to the whole surrounding ecosystem. The current research is a comprehensive-integrated appraisal of the irrigation water quality gathered from El-Batts drain in Fayoum Governorate, from September 2021 to June 2022, together with the drain sediments and the soils adjacent to it. The characteristics (physical, chemical and biological), and the risk fate of some heavy metals (As, B, Cr, Cu, Zn, Cd, Pb, Ni, Fe and Mn) were measured. Moreover, the risks of the studied pollutants were assessed using many indices: irrigation water quality index (IWQI), contamination factor (Cf), degree of contamination (Cd), Salinity indices and Zn equivalent (ZE). Additionally, kinetic studies of the inorganic pollutants were evaluated to determine their availability and impact on the surrounding environment.ResultsData from IQWI showed that water in all five sites was assumed to be very bad and unsuitable for usage with a general average (14.62 and 25.35) in all four seasons. ZE exceeds the critical level of 250, which means there is a risk of soil contamination.ConclusionsThe elevated levels of heavy metals, microbial enzymes, pathogenic fecal coliform and Salmonella reflect bad and direct risk of dealing with such water in irrigation and its effect on the soil ecosystem and the growing crops. Most of the tested parameters exceeded the acceptable levels of the Egyptian Governmental Law Guidelines, WHO and FAO, which may threaten aquatic life. Best managements practices and remediation techniques should be applied to minimize the hazards in such waters.

Predicting irrigation water quality indices in a typical mining dominated area in the Upper West region of Ghana using multiple machine learning techniques

The quality of groundwater resources in artisanal mining districts in Ghana is under threat due to pollution; rendering the resource unsafe for drinking and irrigation purposes. This makes the assessment of the quality of groundwater resources a relevant aspect of groundwater studies as it informs decision making and monitoring. This study adopts 3 Machine Learning (ML) models, Support Vector Regression (SVR), Gradient Boost Regression (GBR), and Artificial Neural Network (ANN), to evaluate a variety of irrigation water quality metrics such as Sodium Percentage (Na%), Soluble Sodium Percentage (SSP), Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Pollution Index of Groundwater (PIG), Kelly’s Ratio (KR), and Magnesium Hazard (MH). 105 samples were collected from a mining area in Northern Ghana and analysed through traditional methods. The Irrigation Water Quality Indices (IWQIs) demonstrate that all water samples are suitable for use as irrigable water with the exception of MH, Na%, PI, and PIG which revealed that 69.52%, 8.57%, 29.52%, and 3.81% are inappropriate for irrigation. SVR, GBR and ANN were used to establish important factors that may influence IWQIs in the area. The measured data was used as independent variables, and the derived IWQIs, the dependent variables. The results revealed that ANN, GBR, and SVR are all viable options for the prediction of IWQIs, but GBR exhibited variable performance in some indices making it lack consistency and thus falls a bit short compared to ANN and SVR. SVR models overall performed best with SVR-RSC having the highest accuracy.

Spatial distribution of physicochemical parameters and drinking and irrigation water quality indices in the Jhelum River, Pakistan.

Sustainable management of river systems is a serious concern, requiring vigilant monitoring of water contamination levels that could potentially threaten the ecological community. This study focused on the evaluation of water quality in the Jhelum River (JR), Azad Jammu and Kashmir, and northern Punjab, Pakistan. To achieve this, 60 water samples were collected from various points within the JR Basin (JRB) and subjected to a comprehensive analysis of their physicochemical parameters. The study findings indicated that the concentrations of physicochemical parameters in the JRB water remained within safety thresholds for both drinking and irrigation water, as established by the World Health Organization and Pakistan Environmental Protection Agency. These physicochemical parameters refer to various chemical and physical characteristics of the water that can have implications for both human health (drinking water) and agricultural practices (irrigation water). The spatial variations throughout the river course distinguished between the upstream, midstream, and downstream sections. Specifically, the downstream section exhibited significantly higher values for physicochemical parameters and a broader range, highlighting a substantial decline in its quality. Significant disparities in mean values and ranges were evident, particularly in the case of nitrates and total dissolved solids, when the downstream section was compared with its upstream and midstream counterparts. These variations indicated a deteriorating downstream water quality profile, which is likely attributable to a combination of geological and anthropogenic influences. Despite the observed deterioration in the downstream water quality, this study underscores that the JRB within the upper Indus Basin remains safe and suitable for domestic and agricultural purposes. The JRB was evaluated for various irrigation water quality indices. The principal component analysis conducted in this study revealed distinct covariance patterns among water quality variables, with the first five components explaining approximately 79% of the total variance. Recommending the continued utilization of the JRB for irrigation, we advocate for the preservation and enhancement of water quality in the downstream regions.

Investigation of the impact of municipal solid waste disposal site leachate on surface water resources in Hosanna Town, Ethiopia.

Surface water resources are the most precious, yet they are also the most vulnerable to pollution. Consequently, maintaining the sustainability of water supplies is critical for livestock support to achieve SDG goals. Landfill leachate poses a significant threat to water resources in developing countries. This study aims to determine how the Hosanna town landfill site affects the Jewie River by analysing the quality of the Alela and Ajew streams and the landfill leachate in both wet and dry seasons. Furthermore, assess the suitability of the water quality for agricultural purposes. The leachate pollution index (LPI), Canadian Council Member of the Environment Water Quality Index (CCMEWQI), and irrigation water quality indices (IWQI) were computed for both seasons using two composite leachate samples and five flow-pace composite river samples. In the wet season, the leachate pollution indices for L1 and L2 ranged from 20.87 to 22.47, respectively. During the dry season, the leachate pollutant index of L1 and L2 was found to be 24.42 and 27.98. Only the Ajew River stream was affected during the dry season because the landfill site is only 46m away. Both the Ajew and Alela River streams are infested during the rainy season. Irrigation index concentrations revealed that the river water is suitable for cultivation. The early stages of landfill waste maturation are evident from the LPI results. Relocating the dump site is necessary to safeguard water resources because leachate has entered the river streams.

Appraising water resources for irrigation and spatial analysis based on fuzzy logic model in the tribal-prone areas of Bangladesh.

The lack of quality water resources for irrigation is one of the main threats for sustainable farming. This pioneering study focused on finding the best area for farming by looking at irrigation water quality and analyzing its location using a fuzzy logic model on a Geographic Information System platform. In the tribal-prone areas of Khagrachhari Sadar Upazila, Bangladesh, 28 surface water and 39 groundwater samples were taken from shallow tube wells, rivers, canals, ponds, lakes, and waterfalls. The samples were then analyzed for irrigation water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), residual sodium bicarbonate (RSBC), magnesium hazard ratio (MHR), Kelley's ratio (KR), and permeability index (PI). Fuzzy Irrigation Water Quality Index (FIWQI) was employed to determine the irrigation suitability of water resources. Spatial maps for parameters like EC, KR, MH, Na%, PI, SAR, and RSBC were developed using fuzzy membership values for groundwater and surface water. The FIWQI results indicate that 100% of the groundwater and 75% of the surface water samples range in the categories of excellent to good for irrigation uses. A new irrigation suitability map constructed by overlaying all parameters showed that surface water (75%) and some groundwater (100%) in the northern and southwestern portions are fit for agriculture. The western and central parts are unfit for irrigation due to higher bicarbonate and magnesium contents. The Piper and Gibbs diagram also indicated that the water in the study area is magnesium-bicarbonate type and the primary mechanism of water chemistry is controlled by the weathering of rocks, respectively. This research pinpoints the irrigation spatial pattern for regional water resource practices, identifies novel suitable areas, and improves sustainable agricultural uses in tribal-prone areas.

Groundwater quality appraisal and zone mapping for agriculture utilities in Wadi Fatima, Saudi Arabia using water quality indices, boron and trace metals.

Groundwater quality in Wadi Fatimah is evaluated and demarcated for agriculture utilities using comprehensive approaches namely, international standards, agricultural water quality (AWQ) indices, irrigation water quality index (IWQI), and trace metals. Groundwater samples were collected (n = 59) and analysed for EC, pH, major and minor ions and trace metals. According to FAO recommendations, 42% of samples (EC > 3000 µS/cm) are inappropriate for agricultural uses. AWQ indices including salinity hazard, Kelly's ratio and Na% show that 50%, 19% and 37% of samples, respectively, are unsuitable for agricultural uses. USSL classification reveals that groundwater is preferable only for high-permeability soils and salt-tolerant crops. IWQI suggests that 88% of samples are moderately usable for agriculture. The interrelationship between water salinity and crop yield justified that 73%, 59%, 51% and 25% of samples are desirable to yield 90% in date palm trees, sorghum, rice and citrus fruits, respectively. Groundwater is appropriate for date palm trees except in downstream regions. Boron concentration suggests that 52%, 81% and 92% of samples are suitable for sensitive, semi-tolerant and tolerant crops, respectively. Groundwater in the central part (suitable for sensitive crops), central and upstream regions (semi-tolerant crops) and all regions except downstream (tolerant crops) are suitable for cultivation. Trace metals contents illustrate that 36%, 34%, 22%, 8%, 5% and 100% of samples are inappropriate for agriculture due to high concentrations of Cr, Cu, Ni, V, Mn and Mo, respectively in the groundwater. Further, AWQ indices, IWQI, USSL classifications and trace metals ensure that groundwater in the downstream, and a few pockets in the upstream are unfit for agricultural uses. This study recommends that groundwater in this basin is more suitable for tolerant crops (ie. date palm, sorghum) followed by semi-tolerant and sensitive crops.

Investigating the effects of surface water recharge on groundwater quality using hydrochemistry and ANFIS model: A case study Minia Governorate, Egypt

Monitoring and assessing groundwater quality and quantity lays the basis for sustainable management. Therefore, this research aims to investigate various factors that affect groundwater quality, emphasizing its distance to the primary source of recharge, the Nile River. To this end, two separate study areas have been considered, including the West and West-West of Minia, Egypt, located around 30 and 80 km from the Nile River. The chosen areas rely on the same aquifer as groundwater source (Eocene aquifer). Groundwater quality has been assessed in the two studied regions to investigate the difference in quality parameters due to the river's distance. The power of machine learning to associate different variables and generate beneficial relationships has been utilized to mitigate the cost consumed in chemical analysis and alleviate the calculation complexity. Two adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the water quality index (WQI) and the irrigation water quality index (IWQI) using EC and the distance to the river. The findings of the assessment of groundwater quality revealed that the groundwater in the west of Minia exhibits suitability for agricultural utilization and partially meets the criteria for potable drinking water. Conversely, the findings strongly recommend the implementation of treatment processes for groundwater sourced from the West-West of Minia before its usage for various purposes. These outcomes underscore the significant influence of surface water recharge on the overall quality of groundwater. Also, the results revealed the uncertainty of using sodium adsorption ratio (SAR), Sodium Percentage (Na%), and Permeability Index (PI) techniques in assessing groundwater for irrigation and recommended using IWQI. The developed ANFIS models depicted perfect accuracy during the training and validation stages, reporting a coefficient of correlation (R) equal to 0.97 and 0.99 in the case of WQI and 0.96 and 0.98 in the case of IWQI. The research findings could incentivize decision-makers to monitor, manage, and sustain groundwater.

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model.

An in-depth understanding of nitrate-contaminated surface water and groundwater quality and associated risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices, and human health risk model. Groundwater samples were categorized into two clusters by SOM analysis. Cluster I including three samples were Ca-SO4 type and cluster II of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for cluster I samples and (2) calcite dissolution, silicate weathering, and positive cation exchange for cluster II samples. Nitrate contamination occurred in both cluster I and II samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis. Compared with the two clusters of groundwater, surface water has a shorter circulation cycle and lower ion concentrations resulting in better water quality. This study can provide scientific basis for groundwater quality evaluation in other parts of the world.

Hydrogeochemical Evaluation of Groundwater Quality and Its Applicability for Various Purposes in the Drought-Prone Konso Zone, Rift Valley, Southern Ethiopia

The present study area, the Konso Zone, is in Rift Valley, Southern Ethiopia, and highly depends on rivers and groundwater for economic and social development. Water availability in this area faces multiple challenges, including scarcity, quality concerns, and seasonal variations. In the Rift Valley region, including the present study area, groundwater quality is particularly problematic due to high fluoride levels. Moreover, the high concentrations of total dissolved solids in Konso Zone groundwater pose challenges for industrial, agricultural, and drinking purposes. GIS-based spatial analysis techniques, hydrogeochemical analyses, and water quality indices were used in the current study. The evaluation of water quality involved the utilization of both groundwater quality and irrigation water quality indices. In the study area, the cations and anions exhibited a general trend with Na+ &gt; Mg2+ &gt; Ca2+ &gt; K+ &gt; Fe2+ and HCO3− &gt; Cl−, SO42− &gt; NO3− &gt; F−. Multiple water types were identified within the research area, including Na-HCO3, Ca-Na-HCO3, NaCl, and Ca-Mg/Cl, with Na-HCO3 being the most prevalent. The formation of the NaHCO3 water type is primarily attributed to ion exchange processes, with siliciclastic aquifers being linked to cation exchange in sodium bicarbonate groundwater. Total Dissolved Solids and SO42− contaminants significantly impact the water quality, resulting in deterioration. Gibbs’s figure indicates that 50% of the water samples were found where rocks were weathered, demonstrating the importance of water-rock interactions in groundwater composition. Water samples containing more fluoride than the WHO’s limit of 1.50 mg/L were deemed unsuitable for consumption. Regarding irrigation suitability, most water samples from the Konso area were acceptable, except for the sodium percentage in 66.6% of the samples, which was unsuitable for irrigation. There are 26.67% of samples that are of questionable quality, 66.67% that are deemed inappropriate, and 26.67% that are deemed unacceptable. These findings underscore the pressing need for comprehensive strategies and interventions to address water quality issues in the Konso Zone, emphasizing the critical importance of monitoring and managing groundwater resources to ensure sustainable and safe water access for various regional uses.

Classification of Groundwater Quality for Irrigation Purposes in Wetland Region by Irrigation Water Quality Index

Classification of Groundwater Quality for Irrigation Purposes in Wetland Region by Irrigation Water Quality Index