Understanding the Extent of Heavy Metal Pollution in Drinking Water Supplies from Umunya, Nigeria: An Indexical and Statistical Assessment

Hydrogeochemical evolution and heavy metal characterization of groundwater from southwestern, Nigeria: An integrated assessment using spatial, indexical, irrigation, chemometric, and health risk models

This study examines the hydrogeochemical and heavy metal parameters of groundwater in Ojo District to determine its suitability for use, potential sources, and human health implications. Ten groundwater samples were assessed, and hydrogeochemical modelling was performed via the Aquachem software. The chemical ions were in the following order: EC > (107.78-448.65 μS/cm) > TDS (182.02-320.77 mg/l) > TH (46.22-182.45 mg/l) > pH (5.55-6.35); HCO3 - (64.13-125.82 mg/l) > Na+ (36.87-96.49 mg/l) > Ca2+ (47.65-58.88 mg/l) > SO4 2- (19.94-53.67) > NO3 - (15.55-44.25 mg/l) > Cl- (20.43-27.16 mg/l) > Mg2+ (11.09-16.87 mg/l) and K+ (2.55-7.86 mg/l). The concentrations of heavy metals in groundwater were in the range of: Fe (0.11-0.27 mg/l) > Mn (0.003-0.16 mg/l) > Ni (0.05-0.12 mg/l) > Zn (0.003-0.05 mg/l) > Pb (0.001-0.03 mg/l) > As (0.001-0.005 mg/l) > Cr (0.002-0.005 mg/l) > Cd (0.001-0.003 mg/l) and Cu (0.001-0.0002 mg/l), with Pb, Mn, and Ni exceeding their allowable limits. The Schoeller and Gibbs plots revealed that the major mechanisms controlling the aquifer groundwater in Ojo region are geological rock weathering and mineralization, with a minimal influence of saltwater intrusion. The piper trilinear diagram also revealed that none of the cation was dominant while the anions were strongly dominated by HCO3 - (weak acids). The hydrogeochemical facies which describes the geochemical characteristics of the groundwater were classified into 3 types; "Ca2+-Mg+-HCO3 - (65 %)", "mixing zones (30 %)", and "Na+-K+-Cl--HCO3 - (5 %)". The hydrogeochemical modelling revealed that the groundwater is characterized by forward cation exchange, while rock-water interactions (silicate dissolution) were heavily involved in the geochemical processes. The single pollution index showed that Pb, Ni, and Mn contributed significantly to contamination, and the multi-pollution indices showed that the groundwater was slightly-moderately polluted. The integrated groundwater quality index revealed that only 10 % were clean, 50 % were poor or moderately unclean, 30 % were highly unclean, and only 10 % were extremely unclean (unfit for utilization). The water pollution index showed that 70 % of the groundwater was good. The irrigation indices suggest that the groundwater would enhance soil quality and support plant growth. Multivariate analysis revealed that the groundwater is being influenced by geogenic factors and anthropogenic activities. The health risk assessment (Hazard Quotient and Hazard Index) showed that exposure of adults to the investigated groundwaters could result in noncarcinogenic adverse effects. The cancer risk values also exceeded the minimum limit (1.0 x 10-6) and thresholds (1.0 x 10-4) for adults, indicating the carcinogenic potential of the groundwater.

Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management.

Assessing heavy metal and physiochemical pollution load of Danro River and its management using floating bed remediation

River Danro in Garhwa (India) plays a vital role as a significant source of surface water and a crucial tributary of the North Koel River, ultimately joining the Ganga River Basin. Serving both urban-industrial and rural areas, the region faces challenges, including sand mining near Belchampa Ghat. This study aimed to assess physicochemical and heavy metals pollution at nine sampling locations, utilizing the Overall Index of Pollution (OIP), Nemerow Pollution Index (NPI), and Heavy Metal Pollution Index (HPI). OIP values indicated excellent surface water quality (0.71) in non-monsoon and slight pollution (6.28) in monsoon. NPI ranged from 0.10 to 1.74 in non-monsoon and from 0.22 (clean) to 27.15 (heavily polluted) in monsoon. HPI results suggested groundwater contamination, particularly by lead. Principal component analysis (PCA) and geospatial mapping showed similar outcomes, highlighting the influence of adjacent land use on water quality. Recognizing the significance of the Danro River in sustaining life, livelihoods, and economic growth, the study recommends implementing measures like floating bed remediation and regulatory actions for effective river management. The study acknowledges weaknesses in the current practical assessment methods for water contamination. These weaknesses make it difficult to put plans for cleaning up and controlling contamination into action. Because of this, future research on developing new in-place remediation techniques should focus on creating better ways to measure how effective the cleanup is.

Spatial and temporal distribution characteristics and risk assessment of heavy metals in groundwater of Pingshuo mining area

Groundwater pollution in the Pingshuo mining area is strongly associated with mining activities, with heavy metals (HMs) representing predominant pollutants. To obtain accurate information about the pollution status and health risks of groundwater, 189 groups of samples were collected from four types of groundwater, during three periods of the year, and analyzed for HMs. The results showed that the concentration of HMs in groundwater was higher near the open pit, waste slag pile, riverfront area, and human settlements. Except for Ordovician groundwater, excessive HMs were found in all investigated groundwater of the mining area, as compared with the standard thresholds. Fe exceeded the threshold in 13-75% of the groundwater samples. Three sources of HMs were identified and quantified by Pearson's correlation analysis and the PMF model, including coal mining activities (68.22%), industrial, agricultural, and residential chemicals residue and leakage (16.91%), and natural sources (14.87%). The Nemerow pollution index revealed that 7.58% and 100% of Quaternary groundwater and mine water samples were polluted. The health risk index for HMs in groundwater showed that the non-carcinogenic health risk ranged from 0.18 to 0.42 for adults, indicating an acceptable level. Additionally, high carcinogenic risks were identified in Quaternary groundwater (95.45%), coal series groundwater (91.67%), and Ordovician groundwater (26.67%). Both carcinogenic and non-carcinogenic risks were greater for children than adults, highlighting their increased vulnerability to HMs in groundwater. This study provides a scientific foundation for managing groundwater quality and ensuring drinking water safety in mining areas.

Hydrogeochemical assessment and health-related risks due to toxic element ingestion and dermal contact within the Nnewi-Awka urban areas, Nigeria

Awka and Nnewi metropolises are known for intensive socioeconomic activities that could predispose the available groundwater to pollution. In this paper, an integrated investigation of the drinking water quality and associated human health risks of contaminated groundwater was carried out using geochemical models, numerical water quality models, and the HHRISK code. Physicochemical analysis revealed that the groundwater pH is acidic. Predicted results from PHREEQC model showed that most of the major chemical and trace elements occurred as free mobile ions while a few were bounded to their various hydrated, oxides and carbonate phases. This may have limited their concentration in the groundwater; implying that apart from anthropogenic influx, the metals and their species also occur in the groundwater as a result of geogenic processes. The PHREEQC-based insights were also supported by joint multivariate statistical analyses. Groundwater quality index, pollution index of groundwater, heavy metal toxicity load, and heavy metal evaluation index revealed that 60-70% of the groundwater samples within the two metropolises are unsuitable for drinking as a result of anthropogenic influx, with Pb and Cd identified as the priority elements influencing the water quality. The HHRISK code evaluated the ingestion and dermal exposure pathway of the consumption of contaminated water for children and adult. Results revealed that groundwater from both areas poses a very high chronic and carcinogenic risk from ingestion than dermal contact with the children population showing greater vulnerability. Aggregated and cumulative HHRISK coefficients identified Cd, Pb, and Cu, to have the highest health impact on the groundwater quality of both areas; with residents around Awka appearing to be at greater risks. There is, therefore, an urgent need for the adoption of a state-of-the-art waste management and water treatment strategies to ensure safe drinking water for the public.

Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination

Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metals. First, based on the heavy metal sampling data of wheat and the dietary consumption data of residents, a wheat risk level dataset was constructed using the risk evaluation method; a data-driven approach was used to classify the dataset into risk levels using the K-Means++ clustering algorithm; and, finally, on the constructed dataset, Pyraformer was used to predict the risk assessment indicator and, thus, the risk level. In this paper, the proposed model was compared to the constructed dataset, and for the dataset with the lowest risk level, the precision and recall of this model still reached more than 90%, which was 25.38-4.15% and 18.42-5.26% higher, respectively. The model proposed in this paper provides a technical means for hierarchical management and early warning of heavy metal contamination of wheat in China, and also provides a scientific basis for dynamic monitoring and integrated prevention of heavy metal contamination of wheat in farmland.

The concentration of potentially toxic elements (PTEs) in drinking water from Shiraz, Iran: a health risk assessment of samples

The existence of potentially toxic elements (PTEs) in water bodies has posed a menace to human health. Thus, water resources should be protected from PTEs, and their effect on the exposed population should be investigated. In the present investigation, the concentrations of PTEs such as lead (Pb), mercury (Hg), manganese (Mn), and iron(Fe) in the drinking water of Shiraz, Iran, were determined for the first time. In addition, hazard quotient, hazard index, cancer risk, and sensitivity analysis were applied to estimate the noncarcinogenic and carcinogenic impacts of Pb, Hg, Mn, and Fe on exposed children and adults through ingestion. The mean concentrations (µg/L) of Pb, Hg, Mn, and Fe were 0.36, 0.32, 2.28, and 8.72, respectively, in winter and 0.50, 0.20, 0.55, and 10.36, respectively, in summer. The results displayed that Fe concentration was more than the other PTEs. PTE concentrations were lower than the standard values of the Environment Protection Agency and World Health Organization. Values of the degree of contamination and heavy metal pollution index for lead, mercury, manganese, and iron were significantly low (< 1) and excellent (< 50), respectively. Based on the Spearman rank correlation analysis, positive and negative relationships were observed in the present study. The observations of the health risk assessment demonstrated that mercury, lead, iron, and manganese had an acceptable level of noncarcinogenic harmful health risk in exposed children and adults (hazard quotients < 1 and hazard index < 1). The carcinogenic risk of lead was low (< E - 06), which can be neglected. Monte Carlo simulation showed that water intake rate and mercury concentration were the most critical parameters in the hazard index for children and adults. Lead concentration was also the most crucial factor in the cancer risk analysis. The results of the present study proved that the drinking water of Shiraz is safe and healthy and can be confidently consumed by people.

Driving effects and transfer prediction of heavy metal(loid)s in contaminated courtyard gardens using redundancy analysis and multilayer perceptron

The distribution and migration of heavy metal(loid)s in the soil-vegetable systems of courtyard gardens near mining areas have rarely been investigated, leading to potential food safety risks for residents. Moreover, the existing research is mainly focused on the total content of heavy metal(loid)s (tMetals) rather than the bioavailable contents (aMetals). In this study, 26 and 28 pairs of soil and vegetable samples were collected from the courtyard gardens near the Realgar mine in Baiyun Town and the lead-zinc (Pb-Zn) mine in Shuikoushan Town, respectively. The tMetal and aMetal of cadmium (Cd), mercury (Hg), arsenic (As), Pb, chromium (Cr), nickel (Ni), copper (Cu), Zn, manganese (Mn), iron (Fe), and calcium (Ca) in the samples were analyzed in this study. The results showed that courtyard gardens were polluted by various heavy metal(loid)s at varying degrees. The bioavailabilities of different metals varied significantly, among which Cd has the highest bioavailability (> 30%). In the transfer process of heavy metal(loid)s, the transfer rate (Tf) was ranked as soil-roots (1.50) > stems-leaves (1.07) > roots-stems (0.46) > stems-fruits (0.33). Redundancy analysis was used to evaluate the driving effects, and the results revealed that aCa, aZn, and aFe in soil could inhibit the absorption of aCd by plant roots. Soil organic matter was the inhibiting factor regarding the transfer of aAs and aCu, whereas it was also the promoting factor for transferring aPb, aNi, and aCr. Furthermore, the multilayer perceptron (MLP) could effectively predict the Tf of heavy metal(loid)s based on the aMetal. The R² values of the MLP were ranked as follows: 0.91 for As, 0.88 for Zn, 0.85 for Hg, 0.83 for Cu, 0.79 for Cr, 0.66 for Cd, 0.65 for Pb, and 0.52 for Ni. This study emphasizes the aMetal-based ecological characteristics and prediction ability. The study results are significant for guiding residents to strategize appropriate crop planting and ensure the safe production and consumption of vegetables.

Probabilistic health risk assessment for residents exposed to potentially toxic elements near typical mining areas in China

Public health problems caused by toxic elements in mining areas have always been an important topic worldwide. However, existing studies have focused on single exposure routes and common toxic elements, which might underestimate the risks faced by residents. In this study, three typical mining areas in central China were selected to assess the health risks of 14 potentially toxic elements through five exposure routes using Monte Carlo simulations. The results indicated that the 95th percentile non-carcinogenic risk values to humans via rice and vegetable ingestion ranged from 9.8 to 26.0 and 6.2 to 19.0. The corresponding carcinogenic risks ranged from 1.4E-2 to 6.3E-2 and from 2.9E-3 to 2.3E-2, respectively. Therefore, residents face serious health risks. Multi-element analysis showed that cadmium (Cd), boron (B), and arsenic (As) were the main contributors to rice non-carcinogenicity, whereas Cd and nickel (Ni) were the main elements of rice carcinogenicity. B and lead (Pb) played an essential role in the non-carcinogenesis of vegetables, and B, Ni, and Cd played an essential role in carcinogenesis. Accidental ingestion is the main route of soil exposure. In these three areas, the probability of non-carcinogenic risk faced by adults was 40%, 0%, and 1%, respectively, while the probabilities for children were 100%, 62%, and 83%, respectively. Regarding carcinogenicity, the risk for both adults and children was up to 100%. This study emphasizes the overall health risks in polluted areas via multi-route and multi-element analysis. This conclusion is helpful to comprehensively assess the potential health risks faced by residents in mining areas and provide baseline data support and a scientific basis for formulating reasonable risk control measures.

Convolutional neural network-based applied research on the enrichment of heavy metals in the soil-rice system in China

The enrichment of heavy metals in the soil-rice system is affected by various factors, which hampers the prediction of heavy metal concentrations. In this research, a prediction model (CNN-HM) of heavy metal concentrations in rice was constructed based on convolutional neural network (CNN) technology and 17 environmental factors. For comparison, other machine learning models, such as multiple linear regression, Bayesian ridge regression, support vector machine, and backpropagation neural networks, were applied. Furthermore, the LH-OAT method was used to evaluate the sensitivity of CNN-HM to each environmental factor. The results showed that the R² values of CNN-HM for Cd, Pb, Cr, As, and Hg were 0.818, 0.709, 0.688, 0.462, and 0.816, respectively, and both the MAE and RMAE values were acceptable. The sensitivity analysis showed that the concentrations of Cd and Pb, mechanical composition, soil pH, and altitude were the main sensitive features for CNN-HM. Compared with CNN-HM based on all input features, the performance of the quick prediction model that was based on the sensitive features did not degrade significantly, thereby indicating that CNN-HM has stronger stability and robustness. The quick prediction model has extensive application value for timely prediction of the enrichment of heavy metals in emergencies. This study demonstrated the effectiveness and practicability of CNNs in predicting heavy metal enrichment in the soil-rice system and provided a new perspective and solution for heavy metal prediction.

Heavy metal concentrations in rivers and drinking water of Esmeraldas (Ecuador) under an intermittent water supply service

Universal access to safe water is a major global goal, but these efforts could be at stake because drinking water sources are becoming polluted in many developing countries. Chlorine, major ions, and heavy metals were measured in rivers and drinking water of Esmeraldas because potential pollution sources raise concerns about the quality of the water supply, and because users have developed strategies to cope with water shortages including collecting river water and water distributed by tankers, storing water at home, and consuming commercial bottled water. We sampled water from the water distribution system (WDS) and the Esmeraldas and Teaone rivers including the intake to the potabilization plant, water distributed by tankers, and commercial bottled water. Most of the samples collected from the Esmeraldas and Teaone rivers, the WDS, and tankers complied with drinking water standards, but higher concentration of cadmium and other metals in the eastern part of the city is an indication of corrosion inside the WDS. Commercial bottled and WDS water showed similar heavy metal concentrations, but regular consumption of some brands may lead to higher exposure to arsenic and mineral deficiencies. Chlorine concentrations in the water supplied by the WDS were below the values required for safe disinfection, and in-house chlorination is uncommon in the city. Strengthening pollution control in the Esmeraldas river, monitoring corrosion of the WDS, and promoting point-use chlorination and better water handling practices are required to secure a safer water supply in the long term.

Prediction modeling of potentially toxic elements' hydrogeopollution using an integrated Q-mode HCs and ANNs machine learning approach in SE Nigeria

Machine learning techniques have proven to be very useful in environmental and engineering assessments, including water quality studies. This is because they have flexible linear and nonlinear forecasting functions that can efficiently and reliably estimate measurable and continuous variables. The aim of this paper was to classify the water quality and also predict potentially toxic anions (PTAs; e.g., Cl, SO₄, HCO₃, and NO₃) and potentially toxic heavy metals (PTHMs; e.g., Fe, Zn, Ni, Cr, and Pb) in water resources in Ojoto and its surroundings, Nigeria. Q-mode hierarchical clusters (HCs) and artificial neural networks (ANNs) were integrated to achieve the research objectives. Prior to the HCs and ANNs modeling, correlation-, unrotated principal component-, and varimax-rotated factor analyses were performed to flag the level of associations between the input water quality variables. With respect to pH, two water quality cluster groups were identified. However, three and four cluster groups were identified based on the PTAs and PTHMs concentrations, respectively. Four ANN models (two for each group) were used for predicting the PTAs and PTHMs in the waters resources. Using coefficient of determination (R²) and AUC (area under curve) values and direct comparison of parity plots, the performance and accuracy of the ANN models were substantiated. Overall, the results obtained reveal that the proposed ANN models suitably predicted the concentrations of the PTAs and PTHMs. Thus, this paper provides useful information for better monitoring, management, and protection of the water resources. However, more modeling studies are encouraged to validate and/or improve the findings of the current work.

Determination and health risk assessment of trace elements in the tap water of two Sub-Cities of Addis Ababa, Ethiopia

Water is an essential component of all living things on earth and the contamination of water by heavy metals can cause detrimental health effects. This study aimed to determine the health risk posed by trace elements (Fe, Zn, Cu, Mn, Ni, Cr, Cd, Co, Pb, and As) present in the drinking water supplies of Gullele and Akaki-Kality Sub-Cities, upstream and downstream parts of Addis Ababa, respectively. The concentrations of the potentially toxic trace elements in the water samples were determined using inductively coupled plasma optical emission spectroscopy (ICP-OES). The highest concentration of the heavy metals was observed for Iron. Cadmium and cobalt were not detected in any of the tap water samples. Samples from Gullele contained higher levels of Fe and Mn, 220.3 ± 0.17 and 19.78 ± 0.08 μg/L, respectively compared to Akaki-Kality, 38.87 ± 0.14 and 2.08 ± 0.01 μg/L, respectively. Conversely, tap water from Akaki-Kality contained significantly higher levels of As than that from Gullele. Additionally, Cr and Ni were detected only in samples from Akaki-Kality, which might be due to the various industries in the area. The highest incremental lifetime cancer risk was found for arsenic, with values for children and adults in Akaki-Kality 2.50 × 10-4 and 4.50 × 10-4, respectively. Likewise, in Gullele Sub-City, it was 5.00 × 10-5 and 1.00 × 10-4 for adults and children, respectively. The results indicate that carcinogenic risk occurrence is probable from As in both studied areas.

Assessment of microbial and heavy metal contamination in shallow hand-dug wells bordering Ona River, Southwest Nigeria

Ona River is one of the three major rivers draining the city of Ibadan. Groundwater is the major source of drinking water in the metropolis; however, data on quality of shallow aquifers bordering Ona River is relatively scarce. This study aimed to evaluate bacteriological status, heavy metal content, and associated human and ecological health risks in hand-dug wells nearby Ona River. A total of 24 water samples from 12 sampling points were collected for chemical and microbial analyses. Heavy metals and microbial pathogens were analyzed using atomic absorption spectrometry and total plate count methods, respectively. Analyses of microbial and heavy metal (HMs) data showed that shallow hand-dug wells within the vicinity of Ona River were bacteriologically contaminated while most of analyzed heavy metals (except manganese) exceeded the drinking water quality standards. Interpretation of microbial and heavy metal (HMs) data identified predominance of anthropogenic activities as the major source of contamination in drinking water. Further scrutiny of HM data through integrated pollution indices identified two nearby wells (S₇ and S₈) exceed the safe limits and pose considerable risk to inhabitants. In terms of ecological risk index (ER), cadmium exhibited considerable to very high ER in all collected samples while manganese and zinc showed low ER in all analyzed water samples. Potential of non-carcinogenic risk through ingestion pathway in the study area was identified with the order of contributive ratios by HMs as Cd > Pb > Zn > Fe > Mn. The calculated target hazard quotient (THQ) due to ingested HMs for three human population categories exceeds the safe limit in the order of adult < children < infants. The study revealed the deteriorated state of waterside shallow hand-dug wells that need immediate actions by relevant stakeholders in water management. The study recommends improved hygienic practices, pretreatment of water before use, and most importantly, provision of potable pipe-borne water supply to the residents of the study area.

Polydopamine assists the continuous growth of zeolitic imidazolate framework-8 on electrospun polyacrylonitrile fibers as efficient adsorbents for the improved removal of Cr(vi)

PDA coating assists the growth of ZIF-8 particles on PAN fibers to fabricate composite ZIF-8@PDA/PAN fibers as efficient adsorbents for Cr(vi) removal.

Appraising drinking water quality in Ikem rural area (Nigeria) based on chemometrics and multiple indexical methods

The continuous deterioration of drinking water quality supplies by several anthropogenic activities is a serious global challenge in recent times. In this current study, the drinking water quality of Ikem rural agricultural area (southeastern Nigeria) was assessed using chemometrics and multiple indexical methods. Twenty-five groundwater samples were collected from hand-dug wells and analyzed for physicochemical parameters such as pH, major ions, and heavy metals. The pH of the samples (which ranged between 5.2 and 6.7) indicated that waters were slightly acidic. Cations and anions (except for phosphate) were within their respective standard limits. Except for Mn, heavy metals were also found to be below their maximum allowable limits. Factor analysis identified both geogenic processes and anthropogenic inputs as possible origins of the analyzed physicochemical parameters. Modified heavy metal index, geoaccumulation index, and overall index of pollution revealed that all the hand-dug wells were in excellent condition, and hence safe for drinking purposes. However, pollution load index, water quality index (WQI), and entropy-weighted water quality index (EWQI) revealed that some wells (about 8-12%) were slightly contaminated, and hence are placed in good water category. A hierarchical cluster analysis (HCA) was performed based on the integration of the WQI and EWQI results. The HCA revealed two major quality categories of the samples. While the first cluster comprises of samples classified as excellent drinking water by both WQI and EWQI models, the second cluster comprises of about 12% samples which were identified as good water by either the WQI or EWQI.