Multidimensional Analysis of the Contamination Status, Corrosivity and Hydrogeochemistry of Groundwater from Parts of the Anambra Basin, Nigeria

Graphical, statistical and index-based techniques integrated for identifying the hydrochemical fingerprints and groundwater quality of In Salah, Algerian Sahara

Groundwater, a predominant reservoir of freshwater, plays a critical role in providing a sustainable potable water and water for agricultural and industry uses in the In Salah desert region of Algeria. This research collected 82 underground water samples from Albian aquifers to assess water quality and identify hydrogeochemical processes influencing mineralization. To achieve this objective, various methods were employed to evaluate water quality based on its intended uses. The drinking water quality index utilized revealed the water potability status, while the indicators of irrigation potability were employed to evaluate its quality for agricultural purposes. Additionally, an assessment of groundwater susceptibility to corrosion and scaling in an industrial context was conducted using several indices, e.g., Langelier index, Larson-Skold index, Ryznar index, chloride-sulfate mass ratio, Puckorius index, aggressiveness index, and the Revelle index. The findings of this study revealed that the groundwater quality for consumption fell into four categories: good (2.44%), fair (29.27%), poor (65.85%), and non-potable (2.44%). Concerning agricultural irrigation, the indexical results indicated that 15.85% of the waters exhibited adequate quality, while 84.15% were questionable for irrigation. Calculations based on various corrosion and scaling evaluation indices showed that most wells were prone to corrosion, with a tendency for calcium bicarbonate deposit formation. Furthermore, the hydrochemical study identified three water types: Na-Cl (53.66%), Ca-Mg-Cl (37.80%), and Ca-Cl (8.54%) waters. Analyses of correlation matrices, R-type clustering, factor loadings, Gibbs diagrams, scatterplots, and chloro-alkaline indices highlighted that the chemistry of the Albian groundwater is fundamentally impacted by a number of processes such as silicate weathering, evaporite dissolution, ionic exchange, and anthropogenic inputs, that played impactful role in the aquifer's water chemistry.

A correlational study of uranium in groundwater with other physicochemical parameters using GIS mapping in Godda district of Jharkhand, India

The present research concentrates on the cumulative use of GPS and GIS technologies, which are excellent resources for analyzing and monitoring divergent physicochemical parameters in groundwater, including pH, TDS, EC, ORP, Ca+2, Mg+2, NO3-, F-, SO4-2, Cl- and PO4-3 with explicit regard to uranium. Garmin GPS is used to record the locations of the sampling points in the Godda study area. The research aims to offer a thorough understanding of the relationship between soil and water, its impact on public health and the extent to which water can be used in various ways based on its quality. Utilizing the inverse distance weighted (IDW) technique, it is examined how these groundwater parameters and the Water Quality Index (WQI) can be estimated spatially. Additionally, a correlation analysis of the water quality parameters is computed to estimate the local population's cancer risk living in the study area. Except for calcium and magnesium, which are present in excess concentrations throughout the study area with the highest values of 325 and 406 mg/l, respectively at Amediha and Meherma, the results showed that the maximum concentration parameters are within limits with the standard. The main reason might be the area's predominance of Alfisol soil type. The radioactive element uranium is found to be in a limited  range. Chemo-toxicity and radiological risk assessment of the whole area lie far below the restricted cancer risk limit i.e., 30 ppb with the highest concentration of 14 ppb in the 'Sunderpahari' region, following the results obtained. The WQI for the area ranges from 'good' to 'very poor.' The results were favorable but a few sites such as 'Boarijor' and its surroundings, require additional attention to enhance groundwater quality. Given uranium's low availability in groundwater the region's cancer risk assessment is below average.

Seasonal variation of the quality of groundwater resources for human consumption and industrial purposes in the central plain zone of Punjab, India

Due to environmental pollution, climate change, and anthropogenic activities, the judicious use and regular assessment of the quality of groundwater for industrial, agricultural, and drinking purposes had gained a lot of attention across the globe. To assess the seasonal suitability of groundwater based on hydrochemistry and different quality indices, groundwater samples were collected and analyzed for different physicochemical parameters. Our findings indicated that the pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), and calcium ion (Ca2+) content of groundwater were within acceptable limits of WHO and Bureau of Indian Standards (BIS) guidelines for drinking water. However, chloride content exceeded the acceptable levels, accounting for about 29.1% during the pre-monsoon and 15.3% during the post-monsoon period. Based on the water quality index (WQI), none of the water samples were deemed unsuitable for drinking purposes. However, when considering the synthetic pollution index (SPI), 100% of the samples were categorized as moderately polluted during both the pre-monsoon and post-monsoon periods. For industrial purpose suitability, 39.8 and 30.6% of the water samples had high corrosion tendency for pre-monsoon and post-monsoon seasons, respectively. Additionally, 77.5-93.4% of the total water samples were slightly affected by salinization on the basis of Revelle index. Generally, the groundwater quality for drinking purposes meets the WHO and BIS guidelines, with high corrosion potential for industrial use and slight salinization concerns in the area.

Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)

Drinking water quality assessment is a major issue today, as it is crucial to supply safe drinking water to ensure the well-being of society. Predicting drinking water quality helps strengthen water management and fight water pollution; technologies and practices for drinking water quality assessment are continuously improving; artificial intelligence methods prove their efficiency in this domain. This research effort seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system is applied with different defuzzification methods. The proposed model includes three fuzzy intermediate models and one fuzzy final model. Each model consists of three input parameters and 27 fuzzy rules. A water quality assessment model is developed with a dataset that considers nine parameters (alkalinity, hardness, pH, Ca, Mg, fluoride, sulphate, nitrates, and iron). These nine parameters of drinking water are anticipated to be within the acceptable limits set to protect human health. Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; they are an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The proposed method can provide an effective solution for complex systems; this method can be modified easily to improve performance.

Assessing of drinking water quality in Al-karak province in central Jordan; based on water saturation indices

Jordan is renowned for having limited water resources. The demand for water will increasing rapidly as the country's population grows and the number of refugees increases. In order to maintain the highest water quality for consumers, the Ministry of Water and Irrigation and other governmental agencies are striving to manage Jordan's water resources through continuous monitoring. The main objective was to evaluate the drinking water quality at storage mixing tanks at Al-Karak province, besides, assessing its suitability for safe consumption. The investigation scheme was to monitor Al-Karak's drinking water system for three successive months. The fourteen principal storage tanks for the water distribution system in the area of investigation were sampled. The pH, electrical conductivity (EC), major cations, major anions, total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), and heavy metals were measured. The scaling and originality of the dissolved salt elements in the collected water samples and geochemical processes were examined using Piper and Durov diagrams. The indices used in all samples over the period of investigation, are Langelier Saturation Index (LSI), Ryznar Stability Index (RSI), Aggressive Index (AI), Puckorius Scaling Index (PSI), and Water Quality Index (WQI). The results showed that scale development is high in all storage tanks, as the water is calcium carbonate supersaturated, evident from LSI values that ranges 0.5-2. According to the range of RSI values (5.91-6.6), all water tanks are resistant to corrosion. Throughout the period of study (October-December), the estimated WQIs of all samples upon average were found to be less than 50, indicating excellent water quality. Finally, the collected water samples are analyzed and found to be within the acceptable levels of Jordan's drinking water standards.

A multi-model study for understanding the contamination mechanisms, toxicity and health risks of hardness, sulfate, and nitrate in natural water resources

Several water quality contaminants have attracted the attention of numerous researchers globally, in recent times. Although the toxicity and health risk assessments of sulfate and water hardness have not received obvious attention, nitrate contamination has gained peculiar research interest globally. In the present paper, multiple data-driven indexical, graphical, and soft computational models were integrated for a detailed assessment and predictive modeling of the contamination mechanisms, toxicity, and human health risks of natural waters in Southeast Nigeria. Majority of the tested physicochemical parameters were within their satisfactory limits for drinking and other purposes. However, total hardness (TH), SO₄, and NO₃ were above stipulated limits in some locations. A nitrate health risk assessment revealed that certain areas present a chronic health risk to children, females, and males due to water intake. However, the dermal absorption route was found to have negligible health risks. SO₄ in some locations was above the 100 mg/L Nigerian limit; thus, heightening the potential health effects due to intake of the contaminated water resources. Most samples had low TH values, which exposes users to health defects. There are mixed contamination mechanisms in the area, according to graphical plots, R-mode hierarchical dendrogram, factor analysis, and stoichiometry. However, geogenic mechanisms predominate over human-related mechanisms. Based on the results, a composite diagrammatic model was developed. Furthermore, predictive radial basis function (RBF) and multiple linear regression (MLR) models accurately predicted the TH, SO₄, and NO₃, with the RBF outperforming the MLR models. Insights from the RBF and MLR models were useful in validating the results of the hierarchical dendrogram, factor, stoichiometric, and graphical analyses.

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.

Hydrochemical evaluation of groundwater for drinking and irrigation purposes using multivariate indices along Indus Suture Zone, North Pakistan

The present study is aimed to investigate the hydrochemical characteristics, spatial distribution and suitability of groundwater for drinking and irrigation purposes along the Indus Suture Zone (ISZ), north Pakistan. Physicochemical parameters and hazardous trace elements (HTEs) like Cd, Co, Cu and Mn were determined following standard methods. The mean and median concentrations were found below the World Health Organization (WHO) drinking water guidelines values. Hydrochemical results indicate that groundwater sources were mainly attributed to rock-water interaction category. Piper diagram shows that most of the groundwater samples fall in Ca-HCO₃^─ class presenting weak-alkaline proportion type. The drinking water quality index (DWQI) ranking was categorized as good to excellent, indicating the overall quality of the groundwater may pose no health hazard concern. Based on irrigation WQI (SAR, Na%, MAR, KR), the groundwater was found fit for irrigation except SAR whereas 36% of the groundwater samples fall within the poor class. The total HI values through dermal contact exceeded the safe non-carcinogenic threshold of HI = 1. Therefore, there is required an effective groundwater monitoring and management facility in the study area to safeguard residents from various illnesses associated with varying HTEs concentrations in drinking water. The major response actions needed for groundwater bodies restoration are including the installation of a continuous groundwater monitoring network and control of agricultural fertilizers that seems to be the most effective and tangible for immediate action.

Water chemistry and stable isotope characteristics of subsidence lakes in coal mining areas, Eastern China

Many subsidence lakes have formed in eastern China as a result of underground coal mining. These coal mining-related subsidence lakes vary in their formation time and connectivity with rivers. These factors may influence the water chemistry and hydrogen and oxygen stable isotope characteristics of the lake water. This study collected and tested subsidence lake water, atmospheric precipitation, river water, and shallow groundwater in the study area. The results showed that the water chemical types of the subsidence lake water and river water are Cl-Na and HCO₃·Cl-Na and that the water chemical types of the shallow groundwater are mainly HCO₃·Cl-Na and HCO₃·Cl-Ca. There are no significant differences in the water chemical characteristics of subsidence lakes with different subsidence ages and types. The major ions in each water body mainly come from evaporite dissolution and silicate weathering, and ion exchange occurs. Reverse ion exchange occurs in some shallow groundwater samples. The stable isotopes of hydrogen and oxygen in the subsidence lake water, river water, and shallow groundwater are distributed along a straight line with a slope less than that of the LMWL, indicating that these water bodies have a common source, namely, precipitation. With increases in the formation time of the subsidence lakes, the heavy isotopes in the lake water gradually become depleted, and the d value gradually increases, mainly driven by precipitation dilution, weakening evaporation, river recharge, and groundwater recharge. The isotopic values of different types of lakes with the same subsidence time differ little. The research results may provide scientific guidance for the rational development and utilization of water resources in coal mining subsidence areas, enrich the study of the hydrological cycle in the area, and are of great significance for the protection of the local water balance and water environment.

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria, works that have used data-driven algorithms in predicting PTEs in groundwater are scarce. In addition, only a few works have simulated water quality indices using machine learning modelling methods in the region. Therefore, in this study, physicochemical analyses were carried out on groundwater samples in southeastern Nigeria. The laboratory results were used to compute two water quality indices: pollution index of groundwater (PIG) and the water pollution index (WPI), to ascertain groundwater quality. In addition, the physicochemical parameters served as input variables for multiple linear regression (MLR) and artificial neural network (ANN) modelling and prediction of Cr, Fe, Ni, NO3−, Pb, Zn, WPI, and PIG. The results of WPI and PIG computation showed that about 30–35% of the groundwater samples were unsuitable for human consumption, whereas 65–70% of the samples were deemed suitable. The insights from the PIG and WPI model also revealed that lead (Pb) was the most influential PTE that degraded the quality of groundwater resources in the research area. The findings of the MLR and ANN models indicated strong positive prediction accuracies (R 2 = 0.856–1.000) with low modeling errors. The predictive MLR and ANN models of the PIG and WPI generally outperformed those of the PTEs. The models produced in this study predicted the PTEs better compared to previous studies. Thus, this work provides insights into effective water sustainability, management, and protection.

Hydrogeochemistry and groundwater quality assessment in Ambagarh Chowki, Chhattisgarh, India

The current study was focused on hydrogeochemistry of Ambagarh chowki groundwater. The main aim of the study was to evaluate the water quality for drinking and irrigation uses, hydrogeochemistry of groundwater. For this purpose, various physicochemical parameters like pH, electrical conductivity (EC), total hardness (TH), chloride (Cl^-), fluoride (F^-), calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), bicarbonate (HCO₃^-), and sulfate (SO₄^2-) were analyzed. The major dominating ions in groundwater was found as cation Ca²⁺  > Mg²⁺  > Na⁺  > K⁺ and as anions in order of HCO₃^-  > Cl^-  > SO₄^2-  > F^-. The pH of sampled groundwater was ranged from neutral to alkaline in nature (7.0-8.8). Among, all-analyzed parameters in groundwater were safe for drinking purpose except few ions and bicarbonate content was also exceeding the permissible limit which was not suitable for drinking purpose. The potential health hazard element fluoride was varied from 0.1 to 2.2 mg/l and positively associated with the pH, Na⁺, and HCO₃^- content in groundwater. The drinking water quality index (WQI) was fluctuated between 12.22 and 185.56 and reported that most of the groundwater was suitable for drinking purpose except only one sample was unsuitable for drinking. Irrigation water quality assessment of the area was performed by evaluating as sodium adsorption ratio (SAR), permeability index (PI), Kelly ratio (KR), magnesium hazard ratio (MHR), percent sodium (%Na), potential salinity (PS), and residual sodium carbonate (RSC). Whereas most of the groundwater were good and suitable for irrigation use excluding PI (3.70%), MHR (40.74%), RSC (22.22%) and PS (7.41%) were unsuitable for continuous irrigation. Hydrogeochemistry of groundwater evaluated with correlation, Piper, Gibbs, and other geochemical analysis. The Piper trilinear diagram reflects the Ca (Mg)-HCO₃^- type water was mainly contributed approximately 90% of entire sampled groundwater. The different ions in groundwater were originated from the rock water interaction through silicate and carbonate weathering of minerals.

Combining data-intelligent algorithms for the assessment and predictive modeling of groundwater resources quality in parts of southeastern Nigeria

Machine learning algorithms have proven useful in the estimation, classification, and prediction of water quality parameters. Similarly, indexical modeling has enhanced the evaluation and summarization of water quality. In Nigeria, works that have incorporated machine learning modeling in water quality analysis are scarce. Although studies across the globe have utilized overall index of pollution (OIP) and water quality index (WQI), works that have simulated and predicted them using machine learning algorithms seem to be scarce. Studies have not simulated nor predicted OIP. In this paper, several physicochemical parameters were analyzed and used for groundwater quality modeling in southeastern Nigeria based on integrated data-intelligent algorithms. Standard methods were followed in all the analysis and modeling performed in this work. OIP and WQI were computed, and their results revealed that 80% of the groundwater resources are suitable for drinking whereas 20% are highly polluted and unsuitable. Pearson's correlation analysis and R-mode hierarchical clustering revealed the possible sources of contamination. Meanwhile, agglomerative Q-mode hierarchical clustering and K-means (partitional) clustering were used to show the spatial demarcations of water quality in the area. Both clustering algorithms identified two main water quality classes-the suitable and unsuitable classes. Furthermore, multiple linear regression (MLR) model and multilayer perceptron neural networks (MLP-NN) were used for the estimation and prediction of the water quality indices. With low modeling errors, both MLR and MLP-NN showed very strong predictions, as their determination coefficient ranged between 0.999 and 1.000. However, MLR slightly outperformed the MLP-NN in the prediction of OIP. The findings of this paper would enhance sustainable water management in the study region and also contribute great insights to the national and global water quality prediction literatures.

Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms

In recent decades, the simulation and modeling of water quality parameters have been useful for monitoring and assessment of the quality of water resources. Moreover, the use of multiple modeling techniques, rather than a standalone model, tends to provide more robust and reliable insights. In this present paper, several soft computing techniques were integrated and compared for the modeling of groundwater quality parameters (pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), modified heavy metal index (MHMI), pollution load index (PLI), and synthetic pollution index (SPI)) in Ojoto area, SE Nigeria. Standard methods were employed in the physicochemical analysis of the groundwater resources. It was found that anthropogenic and non-anthropogenic activities influenced the concentrations of the water quality parameters. The PLI, MHMI, and SPI revealed that about 20-25% of the groundwater samples are unsuitable for drinking. Simple linear regression indicated that strong agreements exist between the results of the water quality indices. Principal component and Varimax-rotated factor analyses showed that Pb, Ni, and Zn influenced the judgment of the water quality indices most. Q-mode hierarchical and K-means clustering  algorithms grouped the water samples based on their pH, EC, TDS, TH, MHMI, PLI, and SPI values. Multiple linear regression (MLR) and artificial neural network (ANN) algorithms were used for the simulation and prediction of  the pH, EC, TDS, TH, PLI, MHMI, and SPI. The MLR performed better than the ANN model in predicting EC, TH, and TDS. Nevertheless, the ANN model predicted the pH better than the MLR model. Meanwhile, both MLR and ANN performed equally in the prediction of PLI, MHMI, and SPI.

Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality

Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the "poor" to "very poor" bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.

Modeling the impact of potentially harmful elements on the groundwater quality of a mining area (Nigeria) by integrating NSFWQI, HERisk code, and HCs

With excess potentially harmful elements (PHEs), drinking water is marked unsuitable and could pose some health risks when ingested or absorbed by humans. Different age groups are exposed to varied risk levels of PHEs. Analyzing the health risks of PHEs for several age groups could provide detailed insights for effective water resources management. No known study in Ameka Pb-Zn mine province (Nigeria) investigated the health risks of PHEs in water resources for several age groups. Therefore, in this paper, the carcinogenic and non-carcinogenic health risks (due to ingestion and dermal contact) of PHEs in groundwater resources of this area were investigated for nine age groups. To achieve its aim, this study integrated novel HERisk code, NSFWQI (national sanitation foundation water quality index), and hierarchical clusters (HCs) in modeling the groundwater quality. Standard elemental composition analysis revealed that the groundwater is polluted with PHEs. The NSFWQI indicated that 15% of the analyzed water samples have moderate water quality whereas 85% are unsuitable for drinking. The HERisk code, which considered nine age groups (1 to < 2 years, 2 to < 3 years, 3 to < 6 years, 6 to < 11 years, 11 to < 16 years, 16 to < 18 years, 18 to < 21 years, 21 to < 65 years, and > 65 years), revealed that all the samples pose high chronic and cancer risks to all the age groups due to oral ingestion. However, it was realized that age groups 1 to < 16 and > 65 are posed with higher risks than age groups 18 to < 65. Overall, it was realized that all the age groups are far more exposed to ingest or absorb Se, Co, Cd, Se, As, Ni, and Pb than Cu, Fe, and Zn. Nevertheless, the health risks due to dermal absorption are far lower than the risks due to oral ingestion. Conclusively, children and aging people are more predisposed to the health threats than middle-aged populations. HCs and geospatial maps aided the spatiotemporal analysis of the groundwater quality.

Hydrogeochemical facies and pollution status of groundwater resources of Owerri and environs, Southeastern Nigeria

The aim of the study was to assess the status of groundwater quality of Owerri and environs, for drinking and irrigation purposes. Twenty-two (22) groundwater samples were collected and analyzed for both chemical and physical compositions. The result of the study showed that groundwater in the area is of good quality for drinking purposes, except for pH and Fe, which had higher concentrations in some areas. A weak correlation matrix within the sampled parameters of the groundwater was observed. Hydrogeochemical studies revealed that 91% of the samples are within the geochemical zone of 4 (strong acids (SO₄ + Cl) exceed weak acids (CO₃ + HCO₃)), while 9% are of the geochemical zone of 3 (weak acids (CO₃ + HCO₃) exceed strong acids (SO₄ + Cl)). The study shows an ionic trend of Cl^-  > Ca²⁺  > HCO₃^-  > Na⁺  + K⁺  > Mg²⁺  > SO₄^2- and hydrogeochemical facies of Na-Cl, Ca-Cl, Ca-CO₃, Mg-Cl, and Mg-HCO₃ of 45.5%, 36.4%, 4.5%, 4.5%, and 9.1% respectively. Chloro-alkaline values were negative except for B4 which was positive. The water quality index (WQI) revealed water quality status of excellent (4.5%), good (27.3%), poor (40.9%), and very poor (27.3%). Contamination factor (CF) reveals that the groundwater is slightly polluted while the pollution load index (PLI) revealed no noticeable pollution. Gibbs diagram revealed that the entire samples are within the rock dominance zone. Irrigation suitability studies showed that SAR of the groundwater was of excellent quality; %Na had good quality (27.3%), permissible quality (45.4%), and doubtful quality (27.3%); MH had 86.4% of the groundwater suitable, while 13.6% are not suitable; KR had suitable groundwater (59.1%) and unsuitable (40.9%); while the Wilcox diagram had 72.7% excellent water for irrigation and 27.3% permissible for irrigation. A routine check of groundwater in the study area is recommended.

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.