An integrated indexical investigation of selected heavy metals in drinking water resources from a coastal plain aquifer in Nigeria

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.

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.

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.

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

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

WITHDRAWN: Investigation of drinking water quality and population health risk assessment in a poor rural area

Ahead of Print article withdrawn by publisher.

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.