Impact of effluent-derived heavy metals on the groundwater quality in Ajao industrial area, Nigeria: an assessment using entropy water quality index (EWQI)

Assessing water quality and human health risk near coal mines and industrial area of Singrauli, India: special emphasis on toxic elements

Water contamination is a major environmental issue, especially in rapidly growing industrialized areas like Singrauli. This study addresses research gaps regarding the hydrochemical characterization, health risk assessment, and source identification of contaminants. Hydrochemistry shows the concentrations of Na+, Ca2+, F-, Mn, As, Mo, Sr, and Ni were above the permissible limit for drinking usage. Water quality index (WQI), heavy metal pollution (HMPI), and evaluation indices (HMEI) revealed As, Mn, Cd, Mo, Co, and Ni were the key heavy elements contributing towards aqueous media pollution in the Singrauli area. Additionally, F was also considered one of the major contaminants. In health risk assessment, the higher values of hazard quotient (HQ) for non-carcinogens were associated with Mn, As, Mo, and F; and hazard index (HI) values > 1 were found in 70% and 55% of samples for children and adults, respectively. Carcinogenic risk (CR) for human health was associated with As. CR values in 56.7% (for adults) and 61.7% (for children) of the total samples exceeded 1 × 10-4. Monte Carlo simulation was applied and highlighted the significant risk factors responsible for both carcinogenic and non-carcinogenic health impacts. 19.2%, 7.3%, and 6.9% of the simulated HQ values for adults and 30.1%, 16.9%, and 10.6% for children were above the safe limit for F, As, and Mn, respectively. Additionally, only 43.8% and 24.8% of the simulated HI for adults and children were within the safe limit. Irrespective of age groups, all the simulated values of As in CR were above 1 × 10-6; and 60% (for adults) and 77.1% (for children) of the values were above 1 × 10-4. This outcome emphasizes the urgency of pollution control measures, especially for As, F, and Mn, to safeguard public health. Moreover, a multivariate statistical approach revealed that both geogenic and anthropogenic sources were responsible for contamination. Therefore, regular monitoring, filtration, and purification are mandatory to ensure safe drinking water for human consumption.

Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand

Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use.

Tracing the land use specific impacts on groundwater quality: a chemometric, information entropy WQI and health risk assessment study

Understanding the nexus of land use and water quality can potentially underline the influences within the groundwater management. The study envisages land use-specific qualitative assessment of the groundwater resources in Ghaziabad district, in western Uttar Pradesh, India. For encountering the relative impacts of land use on the groundwater quality, chemometric analysis has been employed to apportion the pollution sources. The integration of quality parameters, in the information entropy index modeling, has segregated the quality classes and visualized the seasonal suitability trends as per potability standards along with non-carcinogenic health hazard risk assessment (HHRA). The qualitative assessment of the groundwater resources, along with spatial distribution, has deciphered a polluting impact, specifically in western and south western parts of district, and observed the linkages with direct and indirect discharges/seepages from densely populated residential and industrial land use types localized in urbanized areas. Statistically significant annual and seasonal variations have been found exclusively for EC, Mg2+, F-, Cd, Cr(total), Ni, and Pb which inferred variable concentrations, whereas land use types showed a non-significant variation within groundwater quality. Chemometric-based source apportioning and hierarchical cluster analysis (HCA) have derived salinization and enrichment of dissolved salts, arising from mixed sources and contributes to metal pollution, i.e., mainly from anthropogenic sources. Information EWQI derived poor to extremely poor category represented degraded potability specifically for fewer sites located within western and southern parts on the Yamuna-Hindon flood plains for limited sites of residential, industrial, and agricultural in an urbanized region. However, majority of the samples fall under excellent to good groundwater quality, recommendable in the north and north-eastern (peri-urban) regions. Non-carcinogenic HHRA has shown that majority of the samples categorized under unsafe value for hazard index (HI > 1), for females and children and thus, presumed probable health hazard risk from metal groundwater pollution in south-western part, eastern, and northern regions.

Bottled water quality assessment through entropy-weighted water quality index (EWQI) and pollution index of groundwater (PIG): a case study in a fast-growing metropolitan area in Türkiye

The aim of the study is to evaluate the quality of water collected during wet and dry seasons of Şanlıurfa and to expand and improve understanding of the pollution status and drinking quality of bottled water used for drinking purposes. To do so, an entropy-weighted water quality index (EWQI) and a groundwater pollution index (PIG) were used to evaluate data on physicochemical parameters related to drinking water quality standards. The parameters related to bottled water quality were evaluated with Piper and Ternary diagrams. The calculated EWQI values ranged from 7.78 to 29.74 in wet season whereas 11.63 to 32.20 in dry season. Overall, EWQI data showed that all of water samples were suitable for drinking. Similarly, the values of PIG varied from 0.09 to 0.3 in wet season but 0.1 to 0.26 in dry season, which also showed that all water samples from the study area were suitable.

Arsenic and fluoride exposure in drinking water caused human health risk in coastal groundwater aquifers

Groundwater (GW) is a precious resource for human beings as we depend on it as a source of fresh drinking water, agricultural practices, industrial and domestic uses, etc. Extreme exposure of arsenic (As) and fluoride (F-) concentrations along the coastal GW aquifers of "South 24 Parganas and East Medinipur" diluted the quality of GW and created serious health issues. Various chronic health disorders such as - black foot disease, fluorosis skin cancer, cardiac problems, and other water borne diseases have been noticed in these two coastal districts. The comprehensive entropy-weighted water quality index (EWQI) and health risk assessment (HRA) were applied to evaluate the quality of GW and probable health risks in the coastal districts. Monte Carlo simulation and sensitivity analysis methods were simultaneously adopted to identify the non-carcinogenic health risk assessment due to regular ingestion of contaminated GW. As the study region is densely populated and part of the Sundarbans Ramsar site, it has greater importance at the international level along with regional importance to address the GWQ of this region. The major findings of the present study highlight that almost 55% of the study area is confronting serious GW quality issues and associated probable health risk (HR) due to the intense accumulation of As and F- in the GW aquifers of the study area. Children's health is more vulnerable due to the consumption of As containing GW, and adults are highly affected due to the intake of F- bearing GW in the coastal districts. The findings of the current study will draw the attention of hydrologists, groundwater management authorities, government bodies, and NGOs to regulate and monitor the GW aquifers routinely, enhance GW quality, minimizing the health hazards and sustainable water management in a more scientific and sustainable way which must be advantageous for coastal people.

Soil heavy metal pollution from waste electrical and electronic equipment of repair and junk shops in southern Thailand and their ecological risk

The waste electrical and electronic equipment (WEEE) stream in Thailand shifted from exporting WEEE to recycling them in domestic enterprises after China's import restrictions on e-waste in 2018. This study aims to investigate the pollution status, pollution sources, and ecological risk of heavy metals from manual WEEE dismantling facilities (12 repair shops and 8 junk shops) in the Nakhon Si Thammarat province of southern Thailand by examining the concentrations of As, Cd, Ni, and Pb in the topsoil (0-15 cm) during the wet and dry seasons. The results revealed that the mean concentrations of all heavy metals were higher during the dry season than in the wet season. The concentrations of analyzed soil heavy metals decreased as the intensity of e-waste dismantling activities increased, with recycling sites > repair sites > control sites (no e-waste recycling activities). Only 10% of WEEE processing workshops (junk shops) had soil Pb and As concentrations that exceeded Thailand's residential soil quality standards. However, ecological indexing models based on the geo-accumulation index found that 75% of electric repair shops were contaminated with the analyzed heavy metals, particularly Pb. Moreover, the Nemerow integrated pollution index indicated that 16.7% of electric repair shops were on the pollution warning line. Our findings suggest that policymakers should promote ecological risk assessment as a method for mitigating the negative environmental impact of electronic repair businesses, which are widely dispersed in residential areas and tend to dominate the WEEE stream because of the circular economy concept of "right to repair", and highlight the decline of junk shops and e-waste dismantling villages for waste export resulting from China's ban.

Evaluating Groundwater Quality in the Asante Akyem Central District of Ghana

Groundwater is the primary water supply source for many people living on the surface of the earth. They depend on this valuable resource for their daily needs. But this natural resource is continuously being threatened by both natural and anthropogenic activities. Therefore, continuous monitoring and assessment of the water quality of this resource is important to keep check of the effects of water contaminants especially on human health. This study assessed the levels of Fe, Cd, Mn, and Hg in hand-dug wells at 4 communities (Ahyiayem, Odumasi Zongo, Apeboaso, and Kwaakyewaso) in the Asante Akyem Central District of Ghana and evaluated its sources and potential health risk associated with their exposure. These communities are known for illegal small-scale mining. The analysis also included other water quality parameters such as pH, nitrate, potassium, sodium, total hardness, calcium hardness, Phosphate, chloride, and total dissolved solids. From the research, there was no carcinogenic health risk to the communities concerning Fe, Mn, and Hg through ingestion and dermal contact because the hazard quotients and health hazard indices recorded in both adults and children were below one (<1). However, from the research, Cd exhibited carcinogenic health risk because its Cancer risk (CR) index exceeded 10-6 for both adults and children. This implies that there is a risk of cancer infection from ingesting water from the hand-dug wells in the study areas. According to the analysis of the Water Quality Index (WQI), the wells at Ahyiayem, Apeboaso, and Kwaakyewaso recorded indexes that were between 15 and 50, signifying that the hand-dug wells in these communities are safe for human consumption. However, about 90% of the wells at Odumasi Zongo recorded WQI values that were between 80 and 320. This means the hand-dug wells at Odumasi Zongo are highly polluted and not safe for human consumption.

Impacts of seasonal variations and wastewater discharge on river quality and associated human health risks: A case of northwest Dhaka, Bangladesh

Surface water pollution caused by the discharge of effluents from industrial estates has become a major concern for Dhaka (Bangladesh). This study aims to have a concise look at the severe river water pollution, mainly from effluents discharged from the tannery village. Effluent samples were collected from five ejected points, including the central effluent treatment plant (CETP), twenty adjacent river water, and two pond water nearby Hemayetpur, Savar. Thirty-one parameters have been observed at these sampling points for three seasons, from April 2021 to January 2022. The results obtained from water quality indices, i.e., water quality index (WQI), entropy water quality index (EWQI), and irrigation water quality index (IWQI), show that most studied surface water samples ranked "unsuitable" for consumption, irrigation, and anthropogenic purposes. The highest health risk was observed downstream of Hemayetpur city at the Savar CETP discharge site, indicating higher levels of heavy metal in the river water following the tannery village. Carcinogenic and non-carcinogenic human health risks could be triggered mainly by water consumption as concentrations of arsenic (As), chromium (Cr), nickel (Ni), and lead (Pb) exceeded the upper benchmark of 1 × 10^-4 for adults and children. The results of the carcinogenic risk assessment revealed that children were more vulnerable to health hazards, and quick corrective action is required to control the increased levels of heavy metals at all sample locations. Therefore, through bioaccumulation, human health and the environment are affected in these areas. Using river water for consumption, household work, or even irrigation purposes is not advisable. This study's result highlighted that properly implementing compatible policies and programs is required to improve effluent treatment methods and provide biodegradability to the Dhaleshwari River.

Fluoride and nitrate enrichment in coastal aquifers of the Eastern Province, Saudi Arabia: The influencing factors, toxicity, and human health risks

Fluoride and nitrate contamination of groundwater is a major environmental issue in the world's arid and semiarid regions. This issue is severe in both developed and developing countries. This study aimed at assessing the concentration levels, contamination mechanisms, toxicity, and human health risks of NO₃^- and F^- in the groundwater within the coastal aquifers of the eastern part of Saudi Arabia using a standard integrated approach. Most of the tested physicochemical properties of the groundwater exceeded their standard limits. The water quality index and synthetic pollution index evaluated the suitability of the groundwater and showed that all the samples have poor and unsuitable quality for drinking. The toxicity of F^- was estimated to be higher than NO₃^-. Also, the health risk assessment revealed higher risks due to F^- than NO₃^-. Younger populations had higher risks than elderly populations. For both F^- and NO₃^-, the order of health risk was Infants > Children > Adults. Most of the samples posed medium to high chronic risks due to F^- and NO₃^- ingestion. However, negligible health risks were obtained for potential dermal absorption of NO₃^-. Na-Cl and Ca-Mg-Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks.

Heavy metal pollution in surface sediments and human health assessment in southern Al-Khobar coast, Saudi Arabia

Surface sediment samples from twenty-nine stations in south Al-Khobar coastline, Arabian Gulf, have been examined to assess the heavy metal contamination and impact on human health through dermal contact. The content in Cu, Zn, Cd, Pb, Fe, Mn, Co, Hg and Ni, was evaluated. Pollution index, modified degree of contamination, and pollution load index agreed to exclude heavy metal pollution. Differently, soil pollution index and Nemerow integrated pollution index pointed out moderate and heavy pollution grade for Hg and Cu, respectively. The human health assessment, according to the low values of the hazard index ˂ 1.0 for both adults and children, excluded any significant impact on the human body. The chronic daily intake and the total lifetime cancer risk also were consistent in excluding any risk to human health. Similarly, the carcinogenic risks for Pb and Cd, and the total cancer risk ˂1 × 10^-6, did not imply significant health hazards.

Hydro-chemical assessment of coastal groundwater aquifers for human health risk from elevated arsenic and fluoride in West Bengal, India

The vulnerability of groundwater in the coastal regions in terms of As, F^-, and NO₃^- exposure is growing rapidly. Hence, the present study focused on assessing groundwater quality, ecological richness, and HR in the coastal districts of West Bengal by applying field-based CD, GWQI, ERI, and HRI techniques. After assessing the GW vulnerability, it is stated that approximately 40-50 % area of the two selected coastal district's GW is poor to very poor in quality, the ecology of GW is threatened, and human health is faced serious risk for both dry and wet season. The Wilcox and USSL diagram verified that nearly 50 % GW aquifers of coastal district of West Bengal are not fit for irrigation and drinking. The findings of this study will be beneficial to manage and control groundwater vulnerability in the coastal regions for water scientists, policy makers, and researchers as well in sustainable way.

Entropy-assisted approach to determine priorities in water quality monitoring process

Effective determination of water quality and water pollution assessment is crucial and challenging processes. Evaluating water quality in rivers, researchers have referred to various statistical, probabilistic and stochastic methods to obtain efficient information from the monitoring network. As data are greatly random, the information content can be obtained by utilizing various methods including but not limited to the "entropy." Monitoring is a difficult process due to high measurement costs, while it is also difficult to optimize the network in terms of time, space, and especially the variable to be monitored. In the presented study, it is aimed to create an effective approach to be used in optimizing the monitoring network by determining the "prior" variables by entropy that measures the uncertainty by using all the data without time difference. The presented study proposes an alternative method to define the water quality variables that should be monitored much more frequently. Study is exemplified for demonstrating its potential use in a case study level, Grand River in Canada, by assessing water quality data obtained from 15 water quality monitoring stations. Results showed that BOD, Cl, and NO₂-N among examined 8 different variables are as the "prior" variables should be monitored. It is being proven that the prior variable that should be monitored for optimization of the network can be easily determined with the information obtained from the data statistically evaluated with entropy, and it can be stated as an effective method for managers to use in the decision-making process.

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.

Hydrogeochemical characterizations and quality evaluation of groundwater in the major river basins of a geologically and anthropogenically driven semi-arid tract of India

Access to clean drinking water has been acknowledged as a human right and assessing the hydrogeochemistry and groundwater quality status plays an important role in proving cleaner and safer water for human consumption. This study evaluated the sources and driving factors of the groundwater facies in the five major river basins (viz. Ajay, Mayurakshi, Kopai, Brahmani and Dwarka) of an agroeconomic semi-arid Indian tract through hydrogeochemical and principal component analyses based on 2200 groundwater samples (N_s = 2200) obtained during the pre- and post-monsoon cycles from 1100 wells (N_w = 1100). The results revealed that minerals weathering, ion/reverse ion exchange, mixing and evaporation processes along with anthropogenic inputs are responsible for the deteriorated groundwater quality of the river basins. The study has considered the cokriging approach that uses geostatistical and multivariate statistical techniques to interpolate a dataset. To determine the spatio-seasonal variabilities of the groundwater facies more accurately, the estimation accuracies of different interpolation techniques viz. inverse distance weighting, kriging/cokriging and splines techniques were compared and kriging/cokriging was found to represent the variability more accurately. Shannon's entropy theory was employed to assess the groundwater quality of the river basins as it eliminates the subjective bias and inherent uncertainties of the groundwater systems. Groundwater in ~37.45-38.42% of the total area was moderate to extremely poor for human consumption where 10.40-12.14%, 9.09-12.40%, 21.18-22.35%, 15.20-19.93% and 6.48-8.80% samples from the Ajay (N_w = 175), Brahmani (N_w = 175), Dwarka (N_w = 180), Kopai (N_w = 350) and Mayurakshi (N_w = 220) river basins exhibited unfit to drink water quality. The sensitivity of the water quality model was analyzed to identify the influences of the individual parameters which revealed that the outcome does not depend solely on one parameter. The study recommends adaptation of the treatment techniques to ensure clean drinking water for the residents. Managed aquifer recharge techniques might also improve the groundwater quality in certain areas.

A Surrogate Water Quality Index to assess groundwater using a unified DEA-OWA framework

In this paper, we introduce a new approach, based on a unified framework incorporating Data Envelopment Analysis (DEA) and Ordered Weighted Averaging (OWA), for assessing water quality in contextual settings that involve a large number of hydrochemical parameters. In order to enhance discrimination among water sources, the DEA model is adopted with data-driven input variables, called "surrogate optimistic closeness values," computed through an aggregation procedure that includes the observed values of the hydrochemical parameters with OWA weights. The proposed DEA-OWA methodology has been employed to assess the quality of 51 water samples, collected from irrigation wells in Sereflikochisar Basin, Turkey, by means of 19 hydrochemical parameters. Using different subjectivity levels, the Surrogate Water Quality Indices (SWQIs) that are produced are proven effective in enhancing discrimination among the water sources while enabling a more robust water quality-based ranking. The k-means analysis has been used for clustering the water quality of the wells into Excellent, Good, Permissible, and Unsuitable rather than using pre-set boundaries. Only one water source has been identified as Excellent, whereas 17.65%, 45.10%, and 35.29% of the sampled wells, respectively, are categorized with Good, Permissible, and Unsuitable water quality. Inferred from wells' location, the results suggest that the groundwater might be drastically affected by saline water intrusion from Lake Tuz. The latter conclusion has been corroborated through a Tobit regression analysis.

Discussion on the existing methodology of entropy-weights in water quality indexing and proposal for a modification of the expected conflicts

The present research focuses on addressing various ambiguities in the existing method of integrating information entropy and water quality, thereby presenting a novel approach for an entropy-weighted water quality index. A three-dimensional water quality dataset is considered in the proposed method, the third dimension being the sampling frequency factor. The probability of observed values adhering to desirable limits prescribed by a standard code is estimated, leading to the computation of information entropy and, eventually, entropy weights. These weights are then used for the computation of the Modified Entropy-weight Water Quality Index (MEWQI) values. To verify the proposed method's applicability, the water quality dataset of Deepor Beel, India, was considered. IS 10500: 2012 was used for estimating MEWQI values. Results showed an excellent correlation with the observed dataset and their uncertainties of occurrence. The reliability and correctness of the proposed methodology were finally confirmed through both cluster analysis and sensitivity analysis. The cluster analysis showed remarkable associations with the computed MEWQI values, while the sensitivity analysis proved that no particular parameter was accountable for the contribution of MEWQI values; instead, all parameters exhibited equal contributions. The proposed methodology was thus found to be the most reasonable and reliable as it considered both factors, i.e., measured values concerning standard limits and the uncertainty, necessary for a consistent water quality monitoring program.

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.

Heavy metals pollution indexing, geospatial and statistical approaches of groundwater within Challawa and Sharada industrial areas, Kano City, North-Western Nigeria

Abstract

The present study focused on pollution status of groundwater in the industrial areas of Challawa and Sharada in Kano city based on pollution indices, statistical and spatial analyses. Twenty groundwater samples representing groundwater of the studied areas (Ten from each area) were analyzed for the presence of Cd, Cr, Ni, Fe, Mn and Zn using atomic absorption spectrophotometer. The result showed 95%, 5%, 60%, 15% and 25% of the analyzed water samples had detectable Cd, Cr, Ni, Fe, and Mn above the drinking water limits of both Nigerian standards for drinking water quality NSDWQ and World Health Organization (WHO) with Cd dominating other analyzed heavy metals in the groundwater. Evaluation of heavy metal pollution revealed a low polluted status based on the contaminant index (Cd), synthetic pollution index, heavy metals evaluation index, and heavy metal pollution index. Metal index categorized the groundwater as seriously polluted. The statistical evaluation gave strong and positive correlations between indices and a moderate one between the metallic ions. Component analysis revealed a strongly positive loading of Fe, Ni and Zn while Cd had a strong negative loading. Cr and Mn were positive and moderately loaded. Statistical analyses suggested both anthropogenic and geogenic sources for the heavy metals mainly from the industrial and agricultural practices and rock weathering processes, respectively. This study is expected to be a useful tool in the planning, monitoring and mitigation of pollution activities in the area.

Article Highlights

Industrial impact on groundwater quality with special reference to Cr2+ and Pb2+ in coastal aquifers

The present investigation has been carried out in the Ottapidaram taluk to evaluate the suitability of groundwater for drinking purposes and to assess the non-carcinogenic health risks. Twenty groundwater samples were collected, and the major physicochemical parameters were measured along with the heavy metals lead (Pb²⁺) and chromium (Cr²⁺). The analyzed anions and cations follow the average dominance order, Cl^-  > PO₄^3-  > SO₄^2-  > NO₃^-  > F^-, and Mg²⁺  > Ca²⁺  > Na⁺  > K⁺, respectively. From the water quality index to know the 45% of the water samples are unsuitable for drinking purposes. The statistical analysis of the data infers that major geochemical process of the region is leaching of salts by contaminated water, followed by industrial pollution and geogenic sources. The spatial representation of the different parameters reveals that the western part of the study area is predominated by geogenic sources and the eastern part is contaminated by industrial effluents. The non-carcinogenic risks of F^-, NO₃^-, Cr²⁺_, and Pb²⁺ were assessed. The findings show 40% of the samples exceeds the chromium hazard quotient, and 50% exceed the lead hazard quotient value of 1 recommended by the US Environmental Protection Agency (USEPA). The present investigation shows that Cr²⁺ and Pb²⁺ highly pollute the groundwater due to the industrial impacts. The present study suggests that the groundwater from this taluk is worse, and people from this taluk have health risks due to groundwater drinking.

Impact of some beverage industries on the physicochemical characteristics of Ajali River in Enugu, Nigeria

Ajali River near some beverage industries was assessed. Eleven physicochemical parameters and six heavy metals (copper, zinc, iron, cadmium, chromium, and lead) were analyzed on water and sediment collected from different locations near three beverage industries. Standard methods were used to determine the physicochemical parameters while heavy metals were determined with atomic absorption spectrophotometer. The health risk assessment of the water samples were estimated by calculating the hazard quotient (HQ), total hazard index (THI), and the probability cancer risk (PCR) of the heavy metals. Results showed that the concentrations of some heavy metal and physicochemical properties were slightly impaired compared to permissible standards. However, cadmium in water (0.56-11.34 mg/L) and sediments (2.81-481.40 mg/kg) samples were above the recommended limit, indicating possible cadmium pollution in the study area. The water quality index values showed that some of the sampled locations had poor (53.43-134.90%) water quality. The risk assessment of the water samples revealed that HQ for Cu, Zn, Fe, and Cr were of no probable risk (HQ < 1) while Cd (HQ = 173-438) were of probable risk. THI of the water samples showed probable risk (THI > 1) resulting from the high percentage contribution of Cd (94.13 to 99.95%). The probability carcinogenic risk assessment for adults (Cd-7.14 × 10^-2, Cr-1.43 × 10^-4) and children (Cd-1.66 × 10^-1 and Cr-3.34 × 10^-4) were significantly harmful. Even though the presence of these industries could encourage more economic activities in the area, there is need for the relevant agencies to enforce effective treatment and proper management of wastes especially cadmium, to safeguard this water source for the rural dwellers.

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.