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.

Potentially toxic metals contamination, health risk, and source apportionment in the agricultural soils around industrial areas, Firozabad, Uttar Pradesh, India: a multivariate statistical approach.

Potentially toxic metals (PTMs) contamination in the soil poses a serious danger to people's health by direct or indirect exposure, and generally it occurs by consuming food grown in these soils. The present study assessed the pollution levels and risk to human health upon sustained exposure to PTM concentrations in the area's centuries-old glass industry clusters of the city of Firozabad, Uttar Pradesh, India. Soil sampling (0-15 cm) was done in farmers' fields within a 1 km radius of six industrial clusters. Various environmental (geo-accumulation index, contamination factor, pollution load index, enrichment factor, and ecological risk index) and health risk indices (hazard quotient, carcinogenic risk) were computed to assess the extent of damage caused to the environment and the threat to human health. Results show that the mean concentrations of Cu (33 mg kg-1), Zn (82.5 mg kg-1), and Cr (15.3 mg kg-1) were at safe levels, whereas the levels of Pb, Ni, and Cd exceeded their respective threshold limits. A majority of samples (88%) showed considerable ecological risk due to the co-contamination of these six PTMs. Health risk assessment indicated tolerable cancer and non-cancer risk in both adults and children for all PTMs, except Ni, where adults were exposed to potential threat of cancer. Pearson's correlation study revealed a significant positive correlation between all six metal pairs and conducting principal component analysis (PCA) confirmed the common source of metal pollution. The PC score ranked different sites from highest to lowest according to PTM loads that help to establish the location of the source. Hierarchical cluster analysis grouped different sites into the same cluster based on similarity in PTMs load, i.e., low, medium, and high.

Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria.

Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R2) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO3, Cl, Mg, and SO4 were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction.

Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach.

Poor irrigation water quality can mar agricultural productivity. Traditional assessment of irrigation water quality usually requires the computation of various conventional quality parameters, which is often time-consuming and associated with errors during sub-index computation. To overcome this limitation, it becomes critical, therefore, to have a visual assessment of the irrigation water quality and identify the most influential water quality parameters for accurate prediction, management, and sustainability of irrigation water quality. Therefore, in this study, the overlay weighted sum technique was used to generate the irrigation water quality (IWQ) map of the area. The map revealed that 29.2% of the area is suitable for irrigation (low restriction), 41.7% is moderately suitable (moderate restriction); and 29.1% is unsuitable (high restriction), with the irrigation water quality declining towards the central-southeastern direction. Multilayer perceptron artificial neural networks (MLP-ANNs) and multiple linear regression models (MLR) were integrated and validated to predict the IWQ parameters using Cl-, HCO3- SO42-, NO3-, Ca2+, Mg2+, Na+, K+, pH, EC, TH, and TDS as input variables, and MAR, SAR, PI, KR, SSP, and PS as output variables. The two models showed high-performance accuracy based on the results of the coefficient of determination (R2 = 0.513-0.983). Low modeling errors were observed from the results of the sum of square errors (SOSE), relative errors (RE), adjusted R-square (R2adj), and residual plots, further confirming the efficacy of the two models; although the MLP-ANNs showed higher prediction accuracy for R2. Based on the sensitivity analysis of the MLP-ANN model, HCO3, pH, SO4, EC, and Cl were identified to have the greatest influence on the irrigation water quality of the area. This study has shown that the integration of GIS and machine learning can serve as rapid decision-making tools for proper planning and enhanced agricultural productivity.

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

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

An integrated approach to the bioavailability, ecological, and health risk assessment of potentially toxic elements in soils within a barite mining area, SE Nigeria.

Monitoring and assessment of soil quality are important in mining areas. In this study, indexical, spatiotemporal, and chemometric models were developed to monitor and assess the pollution level and health risk of potentially toxic elements (PTEs) within the Iyamitet-Okurumutet mine province, SE Nigeria. Surface soils were sampled within the mine area and analyzed for pH, cation exchange capacity, organic matter, and PTEs (Pb, Zn, Cd, Mn, Fe, Ba) following standard techniques. It was revealed that the soils are slightly acidic and the enrichment of PTEs except for Cd (4.08 mg/kg-1) was within recommended standards. Contamination factor, enrichment factor, and pollution index suggest that the soils are moderately polluted. Geospatial maps and ecological risk indices revealed that higher ecological risk imprints seem to increase towards the south-eastern parts of the area. Chemometric analysis revealed that PTE enrichment in the soil is majorly influenced by anthropogenic activities. Further, bioavailability/bioaccessibility risk assessment index (BRAI) and health risk assessment models were developed to quantify the bioavailable/human bioaccessible portion of elements in soils and the associated health risks. The BRAI ranged from high (3 ≥ 5) to very high (> 5) risk of human bioaccessibility; hence, greater amount of PTEs will be bioaccessible for absorption into the human gastrointestinal system than they would for plants uptake. The hazard index and lifetime cancer risk (LCR) revealed that most of the samples present high chronic cancer risks from dermal contact and ingestion for children and adults. The LCR values ranged between 1.0E-6 and 1.0E - 04, with the children population showing greater vulnerability to cancer risks.