Groundwater quality assessment for drinking and irrigation in the plains of Oran (northwestern Algeria) using geographic information system, water quality indices and multivariate statistical methods

This study aims to determine the spatial and temporal variations in the water quality of the Seben Taşlıyayla (Bolu) Reservoir and classify its water quality. For this purpose, water samples were taken monthly from three stations between March 2023 and February 2024, and then analyses were conducted; 21 physicochemical and 7 heavy metal parameters were examined, and the compliance of the obtained results with SWQR and WHO standards was tested. The parameters Pb+2 and TA were found to exceed standard limits, restricting water usage. Principal Component Analysis (PCA) was conducted to identify the main components of the water body. Through PCA, five principal factors were found to explain 92.824% of the total variance. It was determined that agricultural activities, heavy metals, and anthropogenic activities influenced the reservoir’s water quality. Hierarchical Cluster Analysis (HCA) categorized the seasons into two clusters and the 28 parameters into three clusters. Regarding irrigation water quality parameters, the SAR value was classified as “Excellent,” the % Na as “Good,” the MH (>50) as “Unsuitable,” the KR (

Integrated assessment of groundwater quality beneath the rural area of R'mel, Northwest of Morocco

This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

An attempt was made to assess the properties of groundwater in the Ibadan Metropolitan area, Nigeria by applying multivariate statistical methods on samples collected from various parts of the study area. Out of all the physio-chemical features of groundwater, the quality is a major concern since the long-term progress of groundwater researches in numerous study fields is reliant on the accessibility to high-quality groundwater. Several quantitative methodologies have been successfully used to analyze groundwater hydrochemistry. One other major way to explaining groundwater quality is multivariate statistical analysis, of which hierarchical cluster analysis (HCA) and principal component analysis (PCA) comprise. The Kruskal–Wallis Test, Pearson Correlation, and the Independent Sample Test were employed in assessing water quality. The groundwater quality index was used to examine the suitability of water for ingestion. pH is significantly correlated with Na+ and Ca2+, (0.480 and 0.257), and negatively correlated with Cl− and HCO3 _, (−416 and −0.398). TDS levels correlated with K+ concentration levels (0.228). Na+ significantly correlated with Ca2+ and Mg2+ (0.849 and 0.968). pH, EC, magnesium and chloride, were significantly influenced by the lithology (p < 0.05). Furthermore pH, K+, Na+, Mg2+, Ca2+, HCO3 −, and NO3 −, all differ significantly from the WHO limits (p < 0.05). The water quality index indicates that the water from the different sampling point fall under the good category of the index. Various statistical analysis assists in determining the spatiality of main explanatory variables and in determining the extent to which planning is required.

Groundwater quality and associated health risk in the arid environment, Rabigh basin, Western Saudi Arabia, was assessed using an integrated approach namely groundwater suitability zone (GWSZ) maps, drinking water quality index (DWQI), irrigation water quality (IWQ) parameters, irrigation water quality index (IWQI), chronic daily index (CDI), and hazard quotient (HQ). Groundwater samples were collected (n = 50) and analysed. Groundwater is alkaline (80%), fresh to brackish, and hard to very hard, and 78% of samples exceeded the international drinking water safe limit. The DWQI indicates that groundwater samples are excellent (24%), good (24%), poor (20%), very poor (10%), and unsuitable (22%) classes for drinking use. Total HQ (HQoral F- + HQoral NO3-) indicated that 68%, 80%, and 72% of samples express non-carcinogenic health threat to adult, children, and infant, respectively, in the study region. IWQ parameters, namely, EC, sodium adsorption ratio (SAR), Kelly's ratio (KR), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH), suggest that 72%, 66%, 64%, 98%, and 92% of samples have SAR < 6, KR < 1, MH < 50, PI > 25%, and Na% < 60%, respectively, which are suitable for irrigation. USSL classification implies that groundwater is suitable only for salt-tolerant crops and high permeability soil. IWQI values suggest that groundwater in 12%, 82%, and 6% of wells are low, medium, and highly suitable, respectively, for irrigation. Furthermore, only 42% of samples are recommended for livestock uses due to high F- (> 2). GWSZ maps, DWQI, and IWQI imply that groundwater in the upstream region is suitable whereas groundwater in the downstream is not recommended for any uses. Hence, this study recommended proper groundwater augmentation methods to reduce the salinity and improve the water quality in the shallow aquifer in the arid environment. The GWSZ, DWQI, and IWQI maps will aid to identify the suitable zones for groundwater development and sustainable management.

Aluminium slag waste is a residue from aluminium recycling activities, classified as hazardous waste so its disposal into the environment without processing can cause environmental problems, including groundwater pollution. There are 90 illegal dumping areas for aluminium slag waste spread in the Sumobito District, Jombang Regency. This study aims to evaluate the quality of shallow groundwater surrounding aluminium slag disposal in the Sumobito District for drinking water. The methods applied an integrated water quality index ( WQI) and heavy metal pollution index ( HPI), multivariate analysis (principal component analysis (PCA) and hierarchical clustering analysis (HCA)), and geospatial analysis for assessing groundwater quality. The field campaign conducted 40 groundwater samples of the dug wells for measuring the groundwater level and 30 of them were analysed for the chemical contents. The results showed that some locations exceeded the quality standards for total dissolved solids ( TDS), electrical conductivity (EC), and Al 2+. The WQI shows that 7% of dug well samples are in poor drinking water condition, 73% are in good condition, and 20% are in excellent condition. The level of heavy metal contamination based on HPI is below the standard limit, but 13.3% of the water samples are classified as high contamination. The multivariate analysis shows that anthropogenic factors and natural sources/geogenic factors contributed to shallow groundwater quality in the study area. The geospatial map shows that the distribution of poor groundwater quality is in the northern area, following the direction of groundwater flow, and is a downstream area of aluminium slag waste contaminants.

In a semi-arid region of Maadher, central Hodna (Algeria), groundwater is the main source for agricultural and domestic purposes. Anthropogenic activities and the presence of climate change's effects have a significant impact on the region's groundwater quality. This study's goals were to use water quality indices to evaluate the groundwater's quality and its suitability for drinking and irrigation, as well as to identify contaminated wells using a geographic information system (GIS) and the spatial interpolation techniques of ordinary kriging and inverse distance weighting (IDW). The results reveal that all water samples exceeded the World Health Organization's standards for nitrate ions and had alarming concentrations of calcium, chlorine, and sulfate (WHO). According to Piper's diagram, the groundwater hydrochemical facies is composed of the elements sulfate-chloride-nitrate-calcium (SO42--Cl-NO3--Ca2+ water type). The majority of samples fall into the poor water category, slightly more than 10% fall into the very poor water category, and less than 10% fall into the good to the excellent quality category, per the water quality indices, which classify samples in a similar manner. According to irrigation water indices, every sample is suitable for irrigation. Depending on the direction of groundwater flow, the spatial distributions of Ca2+, Na+, Mg2+, SO42-, and Cl- show that their concentrations are high north of the area and relatively low south of Maadher village (Fig.3). Nitrate concentrations are high in the majority of samples, particularly those close to the Bousaada wadi. In most samples, particularly those close to the Bousaada wadi, nitrate levels are high. Various water quality models were described, and GIS spatial distribution maps were created using standard kriging and inverse distance weighting (IDW) techniques through selected semi-variograms predicted against measurements. To determine the origin of mineralization and the chemical processes that take place in the aquifer-which include the precipitation and dissolution of dolomite, calcite, aragonite, gypsum, anhydrite, and halite-the groundwater saturation index was calculated.

Groundwater is a major source for the water supply of households in the mining‐intensive area of Khibiny Alkaline Massif, Kola Peninsula, in the Arctic. There are an increasing number of signs of groundwater quality deterioration in the area caused by the presence of elevated aluminum concentrations. Groundwater quality studies using univariate and multivariate statistical methods and the Water Quality Index were conducted to analyze a field dataset including 12 groundwater quality parameters monitored between 1999 and 2012. Descriptive statistics showed that the monitored water did not meet the established drinking water standards for aluminum concentration and pH level. The calculated Spearman correlation coefficient matrix revealed statistically significant associations (α‐level = .05) between Al concentrations and pH values, concentrations of SO42−, NO3−, Cl−, and TDS. Factor analysis using the principal component analysis extraction method (FA/PCA) identified four major influencing factors. Altogether the factors captured 67.53% of the dataset total variance. The outcomes of the hierarchical cluster analysis (HCA) revealed that the 12 monitored groundwater quality parameters can be grouped into three clusters where the concentration of Al and pH level formed a separate cluster. The calculated score values of the Canadian Council of Ministers of the Environment Water Quality Index indicated a deterioration of groundwater quality over the monitoring period.

Groundwater quality assessment is crucial for the sustainable management of water resources in arid regions, where groundwater is the primary source of water supply and increasing demand raises concerns. The study area in Southwest Algeria relies heavily on groundwater as a source of water supply, and the increasing demand for freshwater raises concerns about the quality of groundwater. To assess the hydrochemical characteristics and water quality of groundwater in the Ain Sefra region, multivariate statistical methods, geochemical modeling and water quality indices were employed. The study revealed that the groundwater samples could be classified into four water groups using hierarchical cluster analysis Q mode (HCA), namely Ca–Mg–HCO3, Ca–Mg–Cl–SO4, Ca–SO4 and Na–Cl. Factor analysis was used to identify the main factors controlling the study area’s hydrochemical processes. The results indicated that water–rock interaction, reverse ion exchange and anthropogenic pollution were the main hydrochemical processes affecting groundwater chemistry. The water quality index indicated that the groundwater was suitable for human consumption, with only 2.32% of the samples being unsuitable. Additionally, the groundwater was suitable for agricultural use, but salinity control was necessary. The saturation index values showed that the groundwater was supersaturated with aragonite, calcite, dolomite, anhydrite and gypsum, and undersaturated with halite. Ca-smectite, Mg-smectite and kaolinite were identified as the primary processes controlling the chemical composition of groundwater. The application of multivariate statistical methods, geochemical modeling and water quality indices provided a comprehensive understanding of the hydrochemical characteristics and water quality of groundwater in the Ain Sefra region. The findings of the study can serve as a useful basis for future studies on groundwater quality assessment in the region.

This research employed the groundwater quality index (GWQI), multivariate statistical methods, and human health risk assessment model to investigate the suitability of groundwater for domestic uses in northern Khartoum state, Sudan. The groundwater samples were analyzed for eleven physiochemical parameters, including pH, EC, TDS, TH, Cl−, SO42−, NO3−, Ca2+, Mg2+, Na+, HCO3− and the primary investigation indicated the deviation of these parameters from World Health Organization (WHO) standards. The hydrochemical analysis revealed different groundwater facies with the dominance of Ca–Mg–HCO3 water type. Consequently, the groundwater samples were classified, based on GWQI, into three categories as 76.4% of the samples fall in the excellent water class, 17.6% are projected in the good water class, and 5.9% of groundwater samples are considered unsuitable for human consumption. The multivariate statistical methods were applied, including Pearson’s correlation analysis, principal component analyses (PCA), and hierarchical cluster analysis (HCA). Three principal components (PCs) explaining 86.07% of total variances are extracted. These PCs indicated that rock-water interactions and agricultural practices influence groundwater quality in the study area. Additionally, HCA is used to categorize groundwater samples based on the concentration of the physiochemical parameters. Consequently, three types of groundwater were identified as low, medium, and highly mineralized. In the final stage, the non-carcinogenic human health risk was assessed based on the concentration of NO3− using the United States Environmental Protection Agency (USEPA) models. The obtained hazard quotient for children indicated that 64.7% of groundwater samples are beyond the permissible limit (1 <), and the use of these samples may result in health consequences. Therefore, remedial measures are suggested for the sustainable use of groundwater.

Objective: This study involves application of Geographic Information System (GIS) technique for assessment of the groundwater quality using the features and working of the GIS software for plotting the geospatial data which is very useful in monitoring the groundwater quality for effective management. The groundwater quality in the Gorakhpur district has special significance and needs great attention of all concerned because it is the only source of water for industrial, domestic and irrigation water supply. Method: The groundwater samples were collected manually from the available water sources from 150 locations distributed in Gorakhpur city. Quantum GIS was used for WQI & spatial-distribution data maps of 150 Samples. WQI and weighted overlay maps were produced, which provide a better understanding of the existing water Quality Scenario of Gorakhpur City. WQI classifies water into five categories that are Excellent, Good, Poor, Very Poor & Water unsuitable for drinking purposes. The weighted overlay maps were created in the study area from the spatial distribution of seven water quality parameters. Finding: Quality analysis of the drinking water such as spatial distribution maps of individual water quality parameters, Water Quality Index was found and various stress zones in Gorakhpur City were identified. According to WQI, out of 150 samples, only 3 samples were found of Poor Ground Water for drinking purposes sampled inwards Purdilpur (Ward 42), Dilejakpur (Ward 38) and Alhadadpur (Ward 55) with WQI of 103.54, 100.17 and 100.11, respectively. The best water sample is that whose WQI is the least. Shaktinagar (Ward 37) was found to have the best results with a WQI of 59.05 i.e., good water. None of the samples were found as ‘Excellent’. Novelty: This study proposed a concept of assessment and categorization of the groundwater quality based on WQI, which took 7 parameters into consideration so that proper steps of monitoring, and management can be done to stop the deterioration of the quality of water. Keywords: Ground water; Water Quality Index (WQI); Weighted Overlay; GIS; IDW Surface Interpolation; Spatial Distribution

Jazan province on Saudi Arabia’s southwesterly Red Sea coast is facing significant challenges in water management related to its arid climate, restricted water resources, and increasing population. A total of 180 groundwater samples were collected and tested for important hydro-chemical parameters used to determine its adaptability for irrigation. The principal components analysis (PCA) was applied to evaluate the consistency/cluster overlapping, agglomeration in the datasets, and to identify the sources of variation between the 11 major ion concentrations (pH, K+, Na+, Mg2+, Ca2+, SO42−, Cl−, HCO3−, NO3−, TDS, and TH). The EC values ranged from excellent to unsuitable, with 10% being excellent to good, 43% permissible, and 47% improper for irrigation. The SAR classification determined that 91.67% of groundwater samples were good to excellent for irrigation, indicating that they are suitable for irrigation with no sodium-related adverse effects. Magnesium hazard values showed that 1.67% of the samples are unsuitable for irrigation, while the remaining 98.33% are suitable. Chloro-alkaline indices signify that most groundwater samples show positive ratios indicating that ion exchange is dominant in the aquifer. The Gibb’s diagram reflects that evaporation, seawater interaction, and water–rock interaction are the foremost processes impacting groundwater quality, besides other regional environmental variables. A strong positive correlation was declared between TDS and Na+, Mg2+, Ca2+, Cl−, SO42− in addition to TH with Mg2+, Ca2+, Cl−, SO42−, TDS, and also Cl− with Na+, Ca2+, Mg2+ were major connections, with correlation coefficients over 0.8 and p &lt; 0.0001. The extracted factor analysis observed that TH, Ca2+, TDS, Cl−, and Mg2+ have high positive factor loading in Factor 1, with around 52% of the total variance. This confirms the roles of evaporation and ion exchange as the major processes that mostly affect groundwater quality, along with very little human impact. The spatial distribution maps of the various water quality indices showed that the majority of unsuitable groundwater samples were falling along the coast where there is overcrowding and a variety of anthropogenic activities and the possible impact of seawater intrusion. The results of the hierarchical cluster analysis agreed with the correlations mentioned in the factor analysis and correlation matrix. As a result, incorporating physicochemical variables into the PCA to assess groundwater quality is a practical and adaptable approach with exceptional abilities and new perspectives. According to the study’s findings, incorporating different techniques to assess groundwater quality is beneficial in understanding the factors that control groundwater quality and can assist officials in effectively controlling groundwater quality and also enhancing the water resources in the study area.

The assessment and prediction of water quality are important aspects of water resource management. Therefore, the groundwater (GW) quality of the Nubian Sandstone Aquifer (NSSA) in El Kharga Oasis was evaluated using indexing approaches, such as the drinking water quality index (DWQI) and health index (HI), supported with multivariate analysis, artificial neural network (ANN) models, and geographic information system (GIS) techniques. For this, physical and chemical parameters were measured for 140 GW wells, which indicated Ca–Mg–SO4, mixed Ca–Mg–Cl–SO4, Na–Cl, Ca–Mg–HCO3, and mixed Na–Ca–HCO3 water facies under the influence of silicate weathering, rock–water interactions, and ion exchange processes. The GW in El Kharga Oasis had high levels of heavy metals, particularly iron (Fe) and manganese (Mn), with average concentrations above the limits recommended by the World Health Organization (WHO) for drinking water. The DWQI categorized most of the samples as not suitable for drinking (poor to very poor class), while some samples fell in the good water class. The results of the HI indicated a potential health risk due to the ingestion of water, with the risk being higher for children in only one location. However, for both children and adults, there was a low risk of dermal and ingestion exposure to the water in all locations. The contaminants could be from natural sources, such as minerals leaching from rocks and soil, or from human activities. Based on the results of ANN modeling, ANN-SC-13 was the most accurate prediction model, since it demonstrated the strongest correlation between the best characteristics and the DWQI. For example, this model’s thirteen characteristics were extremely important for predicting DWQI. The R2 value for the training, cross-validation (CV), and test data was 0.99. The ANN-SC-2 model was the best in measuring HI ingestion in adults. The R2 value for the training, CV, and test data was 1.00 for all models. The ANN-SC-2 model was the most accurate at detecting HI dermal in adults (R2 = 0.99, 0.99, and 0.99 for the training, CV, and test data sets, respectively). Finally, the integration of physicochemical parameters, water quality indices (WQIs), and ANN models can help us to understand the quality of GW and its controlling factors, and to implement the necessary measures that prevent outbreaks of various water-borne diseases that are detrimental to human health.

Surface water is used for a variety of purposes, including agriculture, drinking water, and other services. Therefore, its quality is crucial for irrigation, human welfare, and health. Thus, the main objective is to improve surface water quality assessment and geochemical analysis to evaluate anthropogenic activities’ impact on surface water quality in the Oued Laou watershed, Northern Morocco. Thirteen surface water samples were characterized for 26 physicochemical and biological parameters. In this aspect, emerging techniques such as multivariate statistical approaches (MSA), water quality indices (WQI), irrigation water quality (IWQI), and Geographic Information System (GIS) were employed to identify the sources of surface water pollution, their suitability for consumption, and the distribution of surface water quality. The results showed that the major ion concentrations were reported in the following order: Ca2+, Na+, Mg2+, and K+; and HCO3− &gt; CO32− &gt; Cl− &gt; SO42− &gt; NO3− &gt; F− &gt; PO43− &gt; NO2−. It was also demonstrated that almost all parameters had concentrations lower than World Health Organization (WHO) limits, except for bicarbonate ions (HCO3−) and the biochemical oxygen demand for five days (BOD5), which exceeded the WHO limits at 120 mg/L and 3 mg/L, respectively. Furthermore, the types of Ca2+-HCO3− (Calcium-Bicarbonate) and Ca2+-Mg2+-HCO3− (Calcium-Magnesium-Bicarbonate) predominated in surface water. The Principal Component Analysis (PCA) indicates that the Oued Laou river was exposed to two forms of contamination, the first being attributed to anthropogenic activities such as agriculture, while the second reflects the water-sediment interaction. The Hierarchical Cluster Analysis (HCA), reflecting the mineralization in the study area, has classified the samples into four clusters. The Inverse Distance Weighting (IDW) of the WQI indicates that 7.69% and 38.46% of the surface water represent, respectively, excellent and good quality for drinking. At the same time, the IWQI revealed that 92.30% of the water surface is good for irrigation. As a result, the combination of WQIs, PCA, IWQI, and GIS techniques is effective in providing clear information for assessing the suitability of surface water for drinking and its controlling factors and can also support decision-making in susceptible locations such as the Oued Laou river in northern Morocco.

In ground water quality studies multivariate statistical techniques like Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Factor Analysis (FA) and Multivariate Analysis of Variance (MANOVA) were employed to evaluate the principal factors and mechanisms governing the spatial variations and to assess source apportionment at Lawspet area in Puducherry, India. PCA/FA has made the first known factor which showed the anthropogenic impact on ground water quality and this dominant factor explained 82.79% of the total variance. The other four factors identified geogenic and hardness components. The distribution of first factor scores portray high loading for EC, TDS, Na+ and Cl− (anthropogenic) in south east and south west parts of the study area, whereas other factor scores depict high loading for HCO3−, Mg2+, Ca2+ and TH (hardness and geogenic) in the north west and south west parts of the study area. K+ and SO42− (geogenic) are dominant in south eastern direction. Further MANOVA showed that there are significant differences between ground water quality parameters. The spatial distribution maps of water quality parameters have rendered a powerful and practical visual tool for defining, interpreting, and distinguishing the anthropogenic, hardness and geogenic factors in the study area. Further the study indicated that multivariate statistical methods have successfully assessed the ground water qualitatively and spatially with a more effective step towards ground water quality management.