Water quality appraisal of selected farm provinces using integrated hydrogeochemical, multivariate statistical, and microbiological technique

Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management.

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.

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash-Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit-cost ratio.

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.

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.

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.

Atrazine pollution in groundwater and raw bovine milk: Water quality, bioaccumulation and human risk assessment

Atrazine herbicide can bioaccumulate over time and thus affect humans for generations to come. However, scarce studies have evaluated its bioaccumulation potential in bovine milk, a nutritional staple for children and the elderly both domestically and internationally. This study aimed to determine its concentration in groundwater and bovine milk, as well as the risks it is likely to pose for human health. Eighteen dairy farms in the Pampean plain of Argentina were analyzed. A strong correlation was found between the chemical composition and the geomorphological characteristics of the plain. In addition, increased salinity was observed in the groundwater at greater distances from the aquifer's recharge area. Atrazine was quantified in 50 % of the groundwater samples (at values ranging from 0.07 to 1.40 μg/L), and in 89 % of the bovine milk samples (from 2.51 to 20.97 μg/L). Moreover, atrazine levels in 44.4 % of the groundwater samples and 11.1 % of the bovine milk samples (n = 18) exceeded the limits internationally established as safe for human consumption. The hazard quotient (HQ) values of the compound were negligible for children and adults, both in groundwater (child = 9.7E-4, adult = 4.5E-4) and in milk (child = 1.0E-2, adult = 1.6E-3). The estimated cancer risk (CR) values need further evaluation (child = 7.8E-6, adult = 3.6E-6 in groundwater; child = 6.6E-5, adult = 1.3E-5 in milk). In both types of samples, the HQ and CR of residual atrazine were higher for children than for adults. Nevertheless, bioaccumulation factors suggest that dairy cows have a moderate capacity to incorporate atrazine from abiotic matrices. This is the first report on residual atrazine in bovine milk in Argentina. The results presented here indicate that the status of atrazine contamination in the area should continue to be monitored in order to assess its long-term impact on public health.

Multivariate statistics and hydrogeochemistry of deep groundwater at southwestern part of Bangladesh

Multivariate statistics and GIS alone with geochemical modeling were applied to investigate the hydro-geochemical characteristics of groundwater and their spatial distribution in the deep aquifer system of Bagherpara Upazila, Bangladesh. This Upazila consists of an area of 308.3 km² and local people mostly rely on groundwater to meet the drinking water requirements. Water samples from one hundred randomly selected deep tube wells (152–198 m) were collected and analyzed for 14 water quality parameters to characterize the hydro-geochemical properties. The groundwater shows slight alkaline in nature throughout the study area. Most of the water samples were turbid and 68% of them failed to meet the drinking water quality standard prescribed by the World Health Organization (WHO). TDS concentration ranges between 280 mg/L and 1040 mg/L, with a mean value of 446.20 (±122.19) mg/L. The general order of cation and anion along the study area were Ca²⁺>Mg²⁺>Na⁺>K⁺ and HCO₃^->Cl⁻>PO₄^3->SO₄^2->NO₃^- respectively. Carbonate weathering is the dominant process for releasing ions in groundwater. Besides, the ion exchange process is active in the study area, which leads to the reduction of Na⁺ ions. Gibb's plot suggests a rock dominance inheritance controls the dissolution and precipitation of minerals along with the ion-exchange process, and ultimately dictates the groundwater chemistry. Besides, the Piper diagram showed that Ca²⁺-Mg²⁺-SO₄^2- is the dominant water type in 65% of the samples followed by Ca²⁺-Mg²⁺–HCO₃^- water type (35%). The abundance of Ca²⁺ and Mg²⁺ ions and the alkaline nature of groundwater indicate mixed geochemical facies and reverse exchange reactions. The principal component analysis (PCA) reveals that weathering and leaching of host rocks was the main natural source, while municipal solid waste dumping, sewage discharge, and fertilizer application could be other anthropogenic factors that affect the groundwater geochemistry. In fine, the chemical characteristics of groundwater were acquired through rock dissolution, percolation, and reverse exchange process.

Groundwater quality and vulnerability in farms from agricultural-dairy basin of the Argentine Pampas

Agricultural and livestock activities strongly influence groundwater quality and conditioning its use as water supply in rural areas. The aim was to determine the quality and suitability of the groundwater supply used in dairy farms of an agricultural area of Pampa plain of Córdoba (Argentina). Piper's diagram showed that the groundwater types were sodium bicarbonate, sodium bicarbonate-chloride, sodium chloride-sulfate, and sodium sulfate. Physicochemical parameters revealed that cations and anions showed a high and significant correlation in water samples, indicating a strong water-rock interaction. Nitrate (NO₃^-) content was significantly correlated with pH, water well depth, and distance from contamination sources. A high positive correlation between arsenic (As) and bicarbonate, sulfate, sodium, and chloride (p < 0.05) indicates a similar origin. Among the pesticides monitored, 2,4-D was detected in 25% of groundwater samples (0.4 to 0.8 µg/L) coinciding with the ordinary application practices. In general, most of the groundwater samples did not comply with national and international regulations for drinking water and dairy hygiene, due to the high content of As, NO₃^-, bacteria, and the presence of 2,4-D herbicide. However, the quality of water was suitable for livestock drink. The data obtained in this study contribute to a better understanding of the contamination processes taking place and improve the agricultural and livestock management for an efficient use of this resource by dairy farmers.

Characterisation of Hydro-Geochemical Processes Influencing Groundwater Quality in Rural Areas: A Case Study of Soutpansberg Region, Limpopo Province, South Africa

Groundwater is often the main or only source of fresh water supply in arid to semi-arid rural areas owing to decreasing rainfall patterns, reduced availability of surface water and socioeconomic activities. It is important to understand the hydro-geochemical processes influencing groundwater quality for improved management and sustainability of resources and to improve rural livelihoods. To understand the hydro-geochemical process influencing the hydro-geochemistry of the Soutpansberg region, this study assessed groundwater quality data from 12 boreholes and 2 geothermal springs collected between 1995 and 2017. This study indicated that the majority of the samples were classified as fresh groundwater dominated by Ca-HCO3 and mixed Ca-Mg-Cl types. Gibbs diagrams, Pearson correlations, bivariate plots and saturation indexes suggested that rock dominance processes, such as weathering of silicates, dissolution of carbonates and halite minerals and ion exchange processes, were the main hydro-geochemical processes influencing the groundwater quality in the Soutpansberg region. The high concentration of F− in the geothermal spring was attributed to the dissolution of fluorite mineral. Point source anthropogenic inputs from fertilisers were attributed to the high concentration of NO3− in the groundwater. This study recommends that research outputs should be used to influence and support policy change and groundwater allocation in arid to semi-arid rural environments for improved management of resources and livelihoods. This study further recommends that suitable deflouridation and denitrification techniques be applied to improve the quality of groundwater for drinking purposes.

Health Risk Assessment and Multivariate Statistical Analysis of Heavy Metals in Vegetables of Khyber Pakhtunkhwa Region, Pakistan

The level of heavy metals in vegetables grown in soil irrigated with various sources of water and the health risks associated with the consumption of these vegetables were assessed in this study. Samples of water, soil, and vegetables were collected from farmer fields. After digestion in acidic solution, analytical measurements were made using an atomic absorption spectrophotometer. The mean concentration of Pb, Cr, Cd, Cu, Zn, Ni, Fe, and Mn in the soil of two sampling area were in the range from 60.00 to 84.00 mg kg^-1, 68.00 to 98.00 mg kg^-1, 1.60 to 2.60 mg kg^-1, 26.10 to 33.20 mg kg^-1, 22.60 to 30.80 mg kg^-1, 50.10 to 78.30 mg kg^-1, 420.00 to 471.00 mg kg^-1, and 270.20 to 340.50 mg kg^-1, respectively. Heavy metals in soil varied significantly at (P ≤ 0.001) among sampling area. The nine heavy metals were divided into two clusters for wastewater and soil, according to cluster analysis. The number of variables was reduced using principal component analysis, which yielded three latent factors, one for wastewater and one for soil. Pb, Cr, Cd, Cu, Zn, Ni, Fe, and Mn concentrations were significantly higher at P ≤ 0.001 in nine vegetables grown on soil irrigated with untreated wastewater than in vegetables grown on fresh-tube well-water-irrigated soil. The health risks associated with metal intake were assessed using the estimated daily intake of metals (EDIM), hazard quotients (HQs), and hazard index (HI). The rates of metal transfer to vegetables have been determined. Except for Pb and Cd, all of the elements' EDMI values were found to be lower than their RfD values. The corresponding HRI values of metals in the various vegetables were found to be below 1, implying that vegetable consumption in the studied region poses no carcinogenic risk. Constant determination of heavy metals in all fruits and vegetables is essential for the assessment of health risks associated with dietary metal exposure. The study has provided valuable information to the general public about the use of wastewater for irrigation of vegetables.

Assessment of Maize Drought Risk in Midwestern Jilin Province: A Comparative Analysis of TOPSIS and VIKOR Models

Drought is a major natural disaster that causes a reduction in rain-fed maize yield. Agricultural drought risk assessment is conducive to improving regional disaster management ability, thereby reducing food security risks and economic losses. Considering the complexity of risk assessment research, an increasing number of researchers are focusing on the multiple-criteria decision-making (MCDM) method. However, the applicability of the MCDM method to agro-meteorological disaster risk assessments is not clear. Therefore, this study comprehensively evaluated hazard, exposure, vulnerability, and emergency response and recovery capability using the TOPSIS and VIKOR models to generate a maize drought risk map in mid-western Jilin Province and ranked the drought risk of each county. The results showed that: (1) maize drought risk in the middle and west of Jilin province showed an increasing trend. Spatially, the risk diminished from west to east. The drought risks faced by Tongyu, Changchun, and Dehui were more severe; (2) the evaluation results of the two models were verified using the yield reduction rate. The VIKOR model was found to be more suitable for agrometeorological drought risk assessments; (3) according to the damage degree of drought disaster to maize, the cluster analysis method was used to divide the study area into three sub-regions: safe, moderate drought, and severe drought. Combined with the characteristics of different regions, suggestions on disaster prevention and mitigation are proposed. The results of this study can provide a basis for formulating strategies to alleviate drought, reduce losses, and ensure the sustainable development of agriculture.

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.

Emerging microalgae-based technologies in biorefinery and risk assessment issues: Bioeconomy for sustainable development

Industrial wastewater treatment is of paramount importance considering the safety of the aquatic ecosystem and its associated health risk to humankind inhabiting near the water bodies. Microalgae-based technologies for remediation of environmental pollutants present avenues for bioenergy applications and production of value-added biochemicals having pharmaceutical, nutraceutical, antioxidants, carbohydrate, phenolics, long-chain multi-faceted fatty acids, enzymes, and proteins which are considered healthy supplements for human health. Such a wide range of products put up a good case for the biorefinery concept. Microalgae play a pivotal role in degrading complex pollutants, such as organic and inorganic contaminants thereby efficiently removing them from the environment. In addition, microalgal species, such as Botryococcus braunii, Tetraselmis suecica, Phaeodactylum tricornutum, Neochloris oleoabundans, Chlorella vulgaris, Arthrospira, Chlorella, and Tetraselmis sp., etc., are also reported for generation of value-added products. This review presents a holistic view of microalgae based biorefinery starting from cultivation and harvesting of microalgae, the potential for remediation of environmental pollutants, bioenergy application, and production of value-added biomolecules. Further, it summarizes the current understanding of microalgae-based technologies and discusses the risks involved, potential for bioeconomy, and outlines future research directions.

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.

Ecological and human health risk evaluation of potential toxic metals in paddy soil, rice plants, and rice grains (Oryza sativa) of Omor Rice Field, Nigeria

Potential toxic metals from natural and anthropogenic sources accumulate in soil and plants, and represent important environmental contamination challenges. The ecological and human health risks of the potential toxic metals in rice grain, paddy soil, and rice plants of Omor rice field were assessed. The total metal concentration from the four sampling sections (mg/kg) were soil-Zn (29.51 ± 2.23), Mn (55.27 ± 8.10), Cd (5.49 ± 2.24), Cu (2.94 ± 1.47), Pb (14.35 ± 6.54), and Cr (27.06 ± 8.31); rice grain-Zn (21.70 ± 5.44), Mn (3.30 ± 0.21), Cd (00.14 ± 0.11), Cu (2.80 ± 0.34), Pb (11.98 ± 0.58), and Cr (15.86 ± 2.79); and for rice plant-Zn (5.24 ± 1.93), Mn (4.68 ± 1.91), Cd (0.21 ± 0.11), Cu (4.88 ± 0.61), Pb (15.24 ± 6.16), and Cr (46.5 ± 6.05). The estimated daily intakes for adult showed that Cd and Pb exceeded the safe limit by 1% and 93%, respectively. The metal hazard quotients (Zn-0.0007, Mn-0.00019, Cd-0.16, Cu-0.19, and Cr-0.0000077) were less than 1 indicating no probable health risk originating from their exposure. The total hazard index (0.35) also suggests no probable health risk connected with the rice consumption. The metals' ecological risk indices of the soil showed low-risk (< 40), except Cd in some sections of the rice field which indicated moderate potential ecological risk (40-80).

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.

Assessing the Industrial Effluent Effect on Irrigation Water Quality and Farm Soil near Kombolcha Town, Ethiopia

This study evaluated various industrial effluents’ effect on irrigation water quality and farm soil near Kombolcha town. Several industries such as brewery, steel iron, textile, and tannery have been installed near the Borkena River that crosses Kombolcha town. Representative samples of irrigation water and farm soil were collected from the upper and down part of Borkena river. The upper site was used as a control as it was not contaminated by industrial effluents. The analysis for selected parameters showed that the downstream irrigation water quality had mean concentrations of pH = 8.54, magnesium (Mg+2) = 5.27 mg/l, carbonate (CO3−2) = 1.25 mg/l, bicarbonate (HCO3−) = 9.10 mg/l, copper (Cu) = 0.21 mg/l, chromium (Cr) = 0.31 mg/l, and cadmium (Cd) = 0.03 mg/l which were above the permissible limit of the Food and Agriculture Organization’s (FAO’s) irrigation water quality standard. The mean concentrations of electric conductivity (EC) = 0.96 ds/m, sodium (Na+) = 3.35 mg/l, chloride (Cl−) = 7.67 mg/l, and total dissolved solids (TDS) = 612.98 mg/l were slightly and moderately restricted for irrigation. Moreover, the concentration of heavy metals, calcium (Ca+2) = 16.61 mg/l, iron (Fe) = 4.25 mg/l, manganese (Mn) = 0.18 mg/l, and lead (Pb) = 0.47 mg/l, was below the permissible limit of the FAO and nonrestricted. However, the mean concentration of EC, HCO3−, Cu, Cr, Cd, and TDS for downstream-irrigated farm soil samples was above the permissible limit of the FAO. The concentration of most selected parameters in downstream farm soil was also decreasing along with depth except pH, CO3−2, and HCO3-. Generally, there is a significant quality difference (at P ≤ 0.05) between the upstream and downstream irrigation water quality on the parameters of Mg+2, Cl−, Pb, and Cu.

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.

Impact of informal settlements and wastewater treatment plants on helminth egg contamination of urban rivers and risks associated with exposure

The quality of surface water could be influenced by both anthropogenic and natural factors. This study was designed to determine the impact of informal settlement and wastewater treatment plants on helminth egg contamination of urban rivers and the risks associated with everyday use. We also ascertained the accumulation of these eggs in the river sediments. The study was carried out in two rivers in the eThekwini Municipality of South Africa. Grab samples were taken at different points over a 10-month period. Ascaris spp., hookworm, Toxocara spp., Trichuris spp. and Taenia spp. were the helminth eggs detected in both the water column and sediments, with mean Ascaris spp. eggs of 0-6.3 (± 5.1)/L in the water and 0-6.8 (± 5.2)/kg in sediment samples. The helminth egg concentrations showed seasonal variation, probably due to changes in infection levels of the populations or natural factors, such as rainfall. The informal settlements had a greater impact than treated wastewater. For every 10,000 recreational users of the rivers 19 to 58 may be infected under undisturbed conditions, increasing to 29-88 individuals when the riverbed is disturbed. The risk from agricultural use of the rivers was above the tolerable risk values applicable for wastewater reuse, recommended by the World Health Organization. This calls for a re-evaluation of the policies governing surface water quality assessment, where the inclusion of helminth eggs and sediment monitoring will be critical.

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.