Investigating the hydrogeochemical processes and quality of water resources in Ojoto and environs using integrated classical methods

Hydrogeochemical evolution processes, groundwater quality, and non-carcinogenic risk assessment of nitrate-enriched groundwater to human health in different seasons in the Hawler (Erbil) and Bnaslawa Urbans, Iraq

The main objectives of this research are to assess groundwater, a primary source of drinking water in the urban areas of Hawler (Erbil) and Bnaslawa in northern Iraq, and the non-carcinogenic human health risks of nitrate contamination associated with drinking water quality. For this purpose, twenty-seven groundwater samples were collected from wells to assess the hydrogeochemical characteristics and groundwater quality for both natural and anthropogenic purposes during the wet (May 2020) and dry (September 2020) seasons. During the wet and dry seasons, NO3- in groundwater ranged from 14.00 to 61.00 mg/L and 12.00 to 60.00 mg/L, with an average value of 35.70 and 29.00 mg/L, respectively. Approximately 25.92% of the samples exceeded the permissible limit of the WHO (2011) drinking water standard. The ratios of NO3-/Na+ vs. Cl-/Na+ and SO42-/Na+ vs. NO3-/Na+ indicate the effect of agricultural activities and wastewater leaking from cesspools or septic tanks on the quality of groundwater during the wet and dry seasons. The entropy weighted water quality index method ranked 62.5% and 75% of the urban groundwater as not recommended for drinking, and the remaining samples are moderately suitable in both wet and dry seasons. The non-carcinogenic human health risk assessment displayed that during the wet and dry seasons, 29.6% and 25.9% of adults, 48% and 30% of children, and 48.1% and 29.6% of infants were exposed to increased concentrations of nitrate in groundwater. Due to high nitrate in drinking water, non-carcinogenic human health risk levels vary as infant > child > adults. The main findings obtained from this study can assist policymakers in better understanding the hydrogeochemical properties of groundwater in terms of drinking water safety, thereby facilitating the management of water resources to take the necessary measures.

A review of the status, challenges, trends, and prospects of groundwater quality assessment in Nigeria: an evidence-based meta-analysis approach

With the imminent industrial growth and population increase, Nigeria will continue to experience significant shifts in the quality of water, with a rise in emerging contaminants. This will increase the irregularity and complexity of the water quality information. Therefore, using the PRISMA meta-analysis approach, this review systematically identified the commonly used water quality assessment techniques in Nigeria, the drawback in the application of these techniques as well as the gaps in the area of water quality assessment and monitoring from 2003 to 2023. Recommendations were also made based on the evaluation of a new research direction; through the review of the effectiveness of advanced techniques for monitoring water quality in Nigeria. Sixty-eight published articles were chosen for the meta-analysis while the VOSviewer program was used to perform bibliographic coupling and visualization. The review revealed that the application of machine learning in water quality prediction has not been well explored in Nigeria. This is attributed to limited data availability and poor funding by the government. It was found that southwestern Nigeria has a greater amount of research on groundwater quality monitoring and evaluation than other regions. The variability was explained by variations in the underlying geology, aquifer features; variability in anthropogenic activities, and level of literacy among various geopolitical zones. Further studies should focus on the application of soft-computing and integrated biomonitoring techniques for effective prediction and monitoring of emerging contaminants for improved water quality. Effective collaboration between environmental stakeholders and government agencies is recommended for effective water resource sustainability.

Analysis of self-organizing maps and explainable artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor region

Water contamination undermines human survival and economic growth. Water resource protection and management require knowledge of water hydrochemistry and drinking water quality characteristics, mechanisms, and factors. Self-organizing maps (SOM) have been developed using quantization and topographic error approaches to cluster hydrochemistry datasets. The Piper diagram, saturation index (SI), and cation exchange method were used to determine the driving mechanism of hydrochemistry in both surface and groundwater, while the Gibbs diagram was used for surface water. In addition, redundancy analysis (RDA) and a generalized linear model (GLM) were used to determine the key drinking water quality parameters in the study area. Additionally, the study aimed to utilize Explainable Artificial Intelligence (XAI) techniques to gain insights into the relative importance and impact of different parameters on the entropy water quality index (EWQI). The SOM results showed that thirty neurons generated the hydrochemical properties of water and were organized into four clusters. The Piper diagram showed that the primary hydrochemical facies were HCO3--Ca2+ (cluster 4), Cl---Na+ (all clusters), and mixed (clusters 1 and 4). Results from SI and cation exchange show that demineralization and ion exchange are the driving mechanisms of water hydrochemistry. About 45 % of the studied samples are classified as "medium quality"," that could be suitable as drinking water with further refinement. Cl- may pose increased non-carcinogenic risk to adults, with children at double risk. Cluster 4 water is low-risk, supporting EWQI findings. The RDA and GLM observations agree in that Ca2+, Mg2+, Na+, Cl- and HCO3- all have a positive and significant effect on EWQI, with the exception of K+. TDS, EC, Na+, and Ca2+ have been identified as influencing factors based on bagging-based XAI analysis at global and local levels. The analysis also addressed the importance of SO4, HCO3, Cl, Mg2+, K+, and pH at specific locations.

Groundwater chemistry and entropy weighted water quality index of tsunami affected and ecologically sensitive coastal region of India

Quality groundwater is the most essential prerequisite for the better livelihood of the coastal villages and a vital resource for a safe living. Seawater interaction and coastal inundation modify hydro geochemical cycles leading to gross utility as a challenge. Poor quality water intake causes diseases and seriously affects human health. In this study, the suitability of shallow drinking water sources (10-15 m) has been studied with a focus on coastal village in south west of India (Alappad coast, Kollam, Kerala) which is a host of huge placer mineral reserve of the country. This coastal stretch has good deposition of Late Quaternary sediments of heavy mineral placers subjected to severe seawater interactions. Mineralogically, garnet and heavy minerals comprises the beaches and most coastal plains of the Alappad. A concerted geological process where moving water and waves causes erosion, leads to lowering of the earth's surface -is prominent in this fragmented land. This study critically evaluates the temporal-spatial impact of these interactions in an age of varying climatic conditions and hence for reference beyond. Water quality index analysis has been attempted using the entropy weighted water quality index (EWQI) method for a total of 45 samples (15 samples season-wise). It aims to ascertain better choices of groundwater sources for domestic uses for isolated settlers endowed with estuaries, and old coastal plains with barrier beaches. Irrigation suitability was evaluated using sodium adsorption ratio (SAR) and Na%. Observed EWQ Indices (38.2 ± 14.5) for post-monsoon (80% samples), (66.1 ± 77.7) for monsoon (66% samples), and (71.4 ± 71.3) for pre-monsoon (53% samples) fall in excellent category. Post-monsoon is most favoured for a better quality groundwater as evidenced by WQI of 80% among the samples tested. Ca-HCO3 is the dominant hydrochemical type observed. The mean value of iron (0.9 ± 1.3 mg/L) exceeded the permissible limit of 0.3 mg/L during monsoon season due to mineral-water interactions. In pre-monsoon season the parameters Na+ (95.9 ± 200.7 mg/L), Cl- (173.4 ± 510.2 mg/L), EC (1559.3 ± 2510.6 μS/cm), and TDS (492.5 ± 629.7 mg/L) were observed in higher ranges. Significant correlation (p < 0.05) prevailed between EWQI, and parameters-conductivity (0.75), TDS (0.75), Iron (0.59), Ca2+ (0.66), and Mg2+ (0.74). Principal component analysis (PCA) on chemical parameters accounted for the total variance of 84.2% in pre-monsoon, 89.9% in monsoon and 82.9% in post-monsoon. Groundwater quality is influenced by geochemical processes, salt intrusion, and human activities like fertiliser application and domestic sewage discharge. Hierarchical cluster analysis (HCA) grouped the samples into three clusters. Cluster 3 represents poor quality water (13%) in pre-monsoon (EWQI ranged 32.2-192.7), and monsoon (EWQI ranged 171.8-309.7). Cluster 3 in post-monsoon (20%) indicating good water quality (EWQI ranged 51.4-72.6). Ultimate finding is that post-monsoon groundwater is more suitable for drinking and domestic purposes for the selected coastal area.

Ecological pollution and health risk monitoring assessment of polycyclic aromatic hydrocarbons and heavy metals in surface water, southeastern Nigeria

Polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) are predominant pollutants linked with anthropogenic activities across a host of environmental mediums. The level of pollution, ecological and health risk were assessed in surface water from Ekulu in Enugu metropolis, Nigeria for 17 PAHs and selected HMs (As, Cd, Cr, Cu, Pb, Ni, Zn) components. PAHs and HMs were determined using a gas chromatography-flame ionization detector (GC-FID) and atomic adsorption spectrophotometer (AAS). The total PAHs in station A (3.17mg/l), B (1.51mg/l), and C (1.83mg/l) were due to high molecular weight (HMW) PAHs than low molecular weight (HMW) PAHs. HMs contents were within USEPA and WHO minimum contamination levels (MCL) except Cr and Pb. The molecular diagnostics of PAHs showed that incomplete combustion of carbonaceous compounds was dominant, while petrogenic was insignificant across all samples. The ecological indices of PAHs and HMs varied from medium to high pollution due to anthropogenic activities that pose a threat to the ecosystem. The non-carcinogenic models showed that hazard index (HI) ranged from PAHs (0.027 - 0.083) and HMs (0.0067 - 0.087) which is less than unity implying no adverse health issues. The lifetime cancer risk (LCR) for PAHs (4.21×10-4 - 9.61×10-4) and HMs (1.72×10-5 - 3.98×10-5) suggested significant cancer risk is possible over some time for a population of 1 in 10,000 and 100,000 for both PAHs and HMs exposure for 70 years. Therefore, there is an urgent need for proper pollution control and mitigation plan to preserve both age groups from being continuously exposed to anthropogenic activities in the Ekulu River and further study should be carried out to monitor the available toxicants.

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 evolution induced by long-term mining activities in a multi-aquifer system in the mining area

The hydrogeochemical evolution of groundwater is related to and affected by long-term mining activities, which may deteriorate the quality of groundwater. The Fengfeng mine in Handan, North China has a 30-y history of coal mining with long-term mining activities and complex geological conditions, resulting in a complex hydrogeochemical environment in the mining region. In this study, the hydrogeochemical evolution mechanism of groundwater in a multi-aquifer system in the Fengfeng Mining Area was investigated using machine learning (self-organizing maps combined with K-means clustering) and sulfur and oxygen isotopes (δ34SSO4 and δ18OSO4). The hydrogeochemical characteristics of different aquifers in the mining area changed to different degrees after mining compared with the characteristics before mining. The spatiotemporal variations in groundwater components were found to be controlled by pyrite oxidation, gypsum dissolution, and carbonate dissolution, which are affected by mining activities. Pyrite oxidation primarily occurred in the Carboniferous thin-layer limestone aquifer (CLA) and Permian sandstone aquifer (PSA). The hydrogeochemical evolution in the Ordovician limestone aquifer (OLA), the main aquifer in the study area, was affected by leakage recharge from CLA and PSA caused by mining activities. The results showed that owing to the effects of long-term mining, the altered groundwater flow system affected the evolution of groundwater components in each aquifer, particularly the sulfate concentration. This study reveals a distinct hydrogeochemical evolution induced by mining activities, which can provide a basis for groundwater resource management in mining areas.

Investigation of Wabe River water fitness for agricultural and industrial purposes

The Wabe River is bordered by 74.84% agricultural area, and farmers rely solely on rainfall. The present research made an attempt to investigate the suitability of the Wabe river water for Agricultural and Industrial purpose. The suitability of river water for agricultural use was evaluated using the sodium adsorption ratio (SAR), potential salinity (PS), magnesium ratio (MR), Kelly index (KI), permeability index (PI), residual sodium carbonate (RSC), sodium percentage (%Na), and heavy metal pollution index (HPI). Additionally, the Ryznar Stability Index (RSI), Aggressive Index (AI), and Langelier Saturation Index (LSI) were used to evaluate the river water's suitability for industrial uses. Furthermore, plot the Gibb's diagrams to identify the sources of pollution and Piper diagrams to determine the hydrochemical composition of Wabe water. According to the HPI, pollution levels in the wet and dry seasons ranged from 53.34 (low) to 317.58 (medium) and 32.24 to 102.42 (low), respectively. The results showed that the Wabe River has very acceptable water quality characteristics and that the trace elements identified did not surpass thresholds that made them dangerous for agricultural usage. The findings showed that domestic wastewater and leachate contamination at sampling point W4 is the cause of the water quality deterioration in the downstream zone. The Ryznar Stability Index (RSI), Aggressive index, and Langelier saturation index readings were less than 9 at all sampling locations during both seasons, suggesting that the river water was corrosive, highly aggressive and unusable for industrial use without treatment.

Use of graphical and multivariate statistical methods to show a marine intrusion and salinization of a coastal water table: case study of the township of Abomey-Calavi, Benin

A robust classification scheme for partitioning water chemistry samples into homogeneous groups is an important tool for the characterization of hydrologic systems. In this paper we test the performance of the many available graphical and statistical methodologies used to classify water samples including: Piper diagram and diagram of Schoeller and Berckaloff. All the methods are discussed and compared as to their ability to cluster, ease of use, and ease of interpretation.

The combination of graphical and statistical techniques provides a consistent and objective means to show that groundwater in the district of Godomey in the municipality of Abomey-Calavi is contaminated by sea water. This study reveal Togbin to be the area of intrusion of seawater into groundwater. This study has helped in setting the boundaries where seawater intrusion into groundwater occurs in the township of Abomey-Calavi. These results confirm the findings of Boukari et al., in 1996, who detected an early saline intrusion in this Godomey pumping area.

Hydrochemistry and an appraisal of surface water and groundwater quality for domestic and irrigation use in parts of Southern Benue Trough, Nigeria

This research attempts to assess the hydrochemistry of major ions, the quality of surface and subsurface water, as well as its suitability for domestic and agricultural uses, in parts of the Southern Benue Trough. A total of thirty water samples were collected and analyzed in the laboratory using standard practices. Results revealed that the concentration of major cations for both surface and groundwater is in the order: Na⁺ > Ca²⁺ > Mg²⁺ > K⁺, while major anionic constituents are, respectively, present in the order: Cl^- > HCO₃ ^- > CO₃ ^2- > NO₃ ^- > SO₄ ^2- and HCO₃ ^- > CI^- > CO₃ ^2- > NO₃ ^- > SO₄ ^2- for surface and groundwater. The water quality index (WQI) model revealed that 100% of the surface water is of good quality for domestic and other uses, while 29.17% and 70.83% of the groundwater are of excellent and good quality, respectively. Three water types were identified, namely Na-Cl, Ca-Mg-HCO₃, and Na-HCO₃. Lastly agricultural indices (total hardness, TH, percent sodium, %Na, sodium absorption ratio, SAR, residual sodium carbonate, RSC, permeability index, PI and magnesium hazard, MH) computed along with various plots, revealed that the analyzed surface and subsurface water are suitable for irrigation purposes. The application of plots, tables, and models based on the major ionic constituents, gives fast and effective visualization of the quality and chemistry of surface water and groundwater.

Incorporation of information entropy theory, artificial neural network, and soft computing models in the development of integrated industrial water quality index

Keeping purpose and targeted end-users in perspective, several water quality indices have been developed over the past decades to summarily convey water quality information to decision-makers and the general public. Industrial water quality is often analyzed based on the corrosion and scaling potentials (CSPs) of water. The commonly used CSP index parameters are chloride-sulfate mass ratio, Langelier index, Larson-Skold index, aggressive index, Ryznar stability index, and Puckorius scaling index. Simultaneous application of these index parameters often classifies a sample into multiple water quality categories, thereby introducing bias in assessment and decision-making. No previous numerical model integrated the CSP indices to provide a single, composite index value for a more unbiased interpretation of industrial water quality. Therefore, this paper proposes an integrated industrial water quality index (IIWQI) that integrates the six CSP index parameters for direct and concise assessment of industrial water resources. To achieve its aim, this research incorporated information entropy theory and soft computing techniques. The developed IIWQI was applied to water samples from southeastern Nigeria. Different classification groups were observed based on the six CSP indices. However, the IIWQI summarized the classifications of the water samples into three categories: Class 1 (28.57%, slight-medium corrosivity, significant scaling potential); Class 2 (46.43%, medium-high corrosivity, no scaling); and Class 3 (25.00%, high-very high corrosion, no scaling). Correlation analysis revealed the relationships between the physicochemical variables, CSP index parameters, IIWQI, and the entropy-based variability of the IIWQI. The spatiotemporal water quality groups were revealed by Q-mode hierarchical dendrograms. Multiple linear regression and two multilayer perceptron neural networks accurately predicted the IIWQI. The findings of this paper could help in better evaluation, interpretation, and management of industrial water quality.

Groundwater quality evaluation and human health risks assessment using the WQI, NPI and HQnitrate models: case of the Sfax intermediate aquifer, Sahel Tunisia

Groundwater is a vital natural resource required to satisfy the domestic and agricultural needs. In general, human health is linked to the quality of the consumed water. For instance, long-term exposure to high nitrate levels in groundwater may cause problems. Hence, the present study was conducted to assess the nitrate contamination of groundwater as well as its related health risks for the inhabitants of the Sfax region, Sahel Tunisia. Irrigation groundwater suitability has been evaluated with sodium content (%Na), electrical conductivity (EC), magnesium hazard (MH), sodium adsorption ratio (SAR), permeability index (PI), Kelly's ratio (KR) and soluble sodium percent (SSP). The results indicate that the selected groundwater is characterized by low to moderate quality for irrigation. Furthermore, the drinking water quality index (DWQI) was assessed using potential of hydrogen (pH), total dissolved solids (TDS), magnesium (Mg²⁺), calcium (Ca²⁺), sodium (Na⁺), chloride (Cl^-), sulfate (SO₄^2-), potassium (K⁺), bicarbonate (HCO₃^-) and nitrate (NO₃^-). The results indicate that 3.63% of samples have good quality of water, while 41.82% have poor to very poor water quality and the rest (54.55%) are unfit for drinking. The nitrate pollution index (NPI) model revealed that about 42% of the samples present significant to very significant type of pollution. Based on human health risk assessment, the children are at higher risks compared to the other affected groups. The obtained results could be used as a basic document for realistic management of groundwater quality and to provide an overview for decision-making authorities to take necessary actions.

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.

Evaluation of the water quality of some sulfur springs in Hammam Al-Alil sub-district in Nineveh governorate

The research assessed the quality of mineral water in some sulfur springs in the Nineveh governorate, which are located in the Hammam Al-Alil sub-district on the right bank of the Tigris River. For that, monthly samples were collected from each site (45) water samples at a rate of (9) samples every month from three springs (3 samples \ Appointed) for five months from November (2020) until March (2021). The research aims to conduct a monthly study of water quality by conducting physical and chemical tests for the water of these springs, which included Temperature, Electrical Conductivity, Total Dissolved Solids, pH, Total Hardness, Total Alkalinity, Sulfate Ions, Phosphate Ions, Oils and Greases, as well as the tests. Biological measurement of the Total Count of bacteria. Where the temperature values ​​ranged between (30.6-36.6) m, the Electrical Conductivity ranged between (2050.9-2688.4) μmoz/cm, while the values ​​of Total Dissolved Solids were between (1025.4-2761) mg/liter, and the pH was between (6.8-7.3), concerning Total Hardness, its concentrations ranged between (820-1120) mg/liter during the study period, and the results showed that the Total Alkalinity concentrations were between (552-912) mg/liter in terms of calcium carbonate CaCO3, while the Sulfate and Phosphate ions were Between (1485 - 1712.1) and (0.020-0.069) mg/liter, respectively during the study period, the results indicated that the concentrations of oils and grease were between (0.08-0.27) mg/liter and finally the Total Count of bacteria ranged during the study period between (1.5 - 28) 10x5 cells. Ml -1.

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.

Geochemical characteristics and quality of groundwater evaluation for drinking, irrigation, and industrial purposes from a part of hard rock aquifer of South India

The present study is a part of hard rock aquifer of Telangana, South India, where the groundwater is withdrawn heavily for drinking, irrigation, and small-scale industrial purposes. Geochemical characteristics explain the chemical processes, which control the groundwater chemistry and consequently the groundwater quality, while the chemical quality of groundwater is adversely affected by anthropogenic activities, which damage the water environment. The focus of the present study was, thus, to know the origin of geochemical characteristics and also to evaluate the quality of groundwater for various purposes for taking the suitable remedial measures to provide safe water to the local community. Geochemical relations (GR) and hierarchical cluster analysis (HCA) were used to assess the geochemical characteristics. Entropy weighted groundwater quality index (EWGQI), United States Soil Salinity Laboratory Staff (USSLS)'s diagram, and groundwater quality criteria for water supply pipes (GQCW) were used to evaluate the groundwater quality for drinking, irrigation, and industrial purposes, respectively. The study found that the water-rock interactions associated with ion exchange and evaporation were the prime geochemical factors controlling the geochemical characteristics and the anthropogenic activities as the secondary factor. These observations were further supported by HCA. According to the EWGQI, 34.97% of the spatial area was found to have the poor and very poor groundwater quality zones for drinking purpose, because of the dominance of TDS, Na⁺, Cl^-, [Formula: see text], [Formula: see text], and F^- contents in the groundwater system. Based on the USSLS's diagram, 79.55% of the present study area was observed to be poor and very poor water quality type for irrigation utilization due to salinity hazard. The GQCW demonstrated that the 7.91% and 8.82% of the areas were not suitable for industrial purpose due to influence of incrustation based on [Formula: see text] and [Formula: see text], respectively, and 1.85%, 12.32%, and 1.25 of the areas are unfit due to influence of corrosion based on pH, TDS, and Cl^-, respectively. Therefore, boiling, activated carbon filter, rainwater harvesting, suitable coatings on metal surfaces of water supply pipes, etc. are the important suggested effective strategic measures to provide safe water for drinking, irrigation, and industrial purposes.

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.