Influencing Groundwater Quality Research Articles

Environmental Factors Influencing Groundwater Quality and Health Risks in Northern Henan Plain, China

Environmental Factors Influencing Groundwater Quality and Health Risks in Northern Henan Plain, China

Assessing groundwater quality in the northwestern coastal area of Sri Lanka using multivariate statistical techniques

The objective of the present study was to assess groundwater quality in the northwestern coastal region of Sri Lanka by analyzing hydrogeochemical data using multivariate techniques. The study examined the relationship between water quality and sources influencing groundwater quality and proposed useful statistical tools for water resource management. Descriptive, comparative, correlation, factor analysis (FA), and discriminant analysis (DA) were among the techniques used. The water quality in terms of electrical conductivity (EC), total dissolved solids (TDS), and dissolved nitrate and phosphate concentrations of the northwestern coastal area, where different aquifer conditions prevailed, were compared, and differences in the mean concentrations of the above parameters were identified. The study also employed ridge regression and support vector machines (SVM) to predict nitrate levels and validate the results using a 70:30 train/test split. The analysis found that the mean concentrations of EC, TDS, and HCO3- are higher in the sandy aquifer area, while Fe2+ and PO43- are higher in the regolith aquifer area. EC, TDS, PO43-, and HCO3- in the hard-rock aquifer area exceed permissible limits. Factor analysis identified four factors contributing to pollution, and a linear discriminant model classified all observations with 99.9% accuracy. The water quality index for Kalpitiya, Vanathavillu, and Katana-Negombo areas was 68.1, 99.2, and 287.9, respectively, while the World Health Organization (WHO) cutoff was 50. The use of multivariate statistical techniques demonstrated the value of analyzing hydrogeochemical data to gain valuable insights into the complex interplay of factors affecting groundwater quality. The SVM model, using a linear kernel, was identified as the best model to predict nitrate levels. A combination of statistical analyses reveals that, with the exclusion of a few wells, the groundwater in the study area is polluted and inappropriate for human consumption or culinary purposes. Nonetheless, the inhabitants persist in utilizing it as their primary source of drinking water. This study reveals the combination of statistical analyses is useful for the management of groundwater resources.

Geogenic and anthropogenic factors influencing groundwater quality for irrigation purposes from a volcano-sedimentary aquifer in the Puebla State, Mexico.

This study investigates the hydrogeochemical characteristics and water quality of the Puebla Valley aquifer, a volcano-sedimentary system predominantly influenced by water-rock interactions and ion exchange processes. The research assesses the suitability of groundwater for agricultural irrigation by applying various water quality indices, including salinity and sodicity indices and Wilcox diagrams and USSL. Seventy-one water samples were analyzed to determine key physicochemical parameters and dominant ion concentrations, revealing that the primary hydrogeochemical facies are HCO -Mg and HCO -Ca. The results indicate a heightened risk of salinization, particularly in agricultural and urban areas, underscoring the need for ongoing monitoring. Natural processes such as water-rock interaction and ion exchange are the main factors influencing groundwater quality. However, anthropogenic activities, particularly the use of fertilizers and irrigation return flows from the Atoyac River, exacerbate groundwater quality degradation. This study emphasizes the necessity of sustainable water resource management to ensure the long-term viability of the aquifer.

Integration of multivariate statistical analysis, geochemical modeling, and irrigation water quality assessment in the aquifers of the South-Atlas Tinghir-Errachidia-Boudenib basin (Pre-African Trough, Morocco)

Irrigation development commonly results in the onset of salinization and soil alkalinization, to different degrees, and the irrigation water quality index (IWQI) is the main method utilized globally to assess water quality. This study aims to investigate the hydrogeochemical characteristics and irrigation suitability of groundwater in the South-Atlas region, one of the arid regions in the southeastern part of Morocco, using Principal Component Analysis (PCA) was applied to identify the main factors influencing groundwater quality and Hierarchical Cluster Analysis (HCA) was then used to classify the water samples into distinct groups based on their hydrogeochemical properties. The research also evaluates groundwater suitability for irrigation using several irrigation water quality indices (IWQIs) such as irrigation water quality index (IWQI), Sodium Adsorption Ratio (SAR), Percent Sodium (Na%), Permeability Index (PI), magnesium hazard (MH), and Potential salinity (PS) assisted by geochemical modeling and the Piper trilinear diagram. Fifty groundwater samples were systematically collected in February 2023 from various aquifers (Senonian, Infracenomanian, Quaternary, and Paleozoic) based on their geographical distribution and hydrogeological significance. The Piper diagram reflecting Ca-Mg-Cl-SO4 followed by Na-K-Cl-SO4 and Ca-Mg-HCO3 water facies under the stress of evaporation, saltwater intrusion and reverse ion exchange processes.The results of the IWQI indicated that, roughly speaking, 0% of the samples fell into the unrestricted category, 46% had leaching restrictions ranging from low to moderate, and 54% of the samples were classified as high to severely limited, meaning that only high salt tolerance plants can be irrigated with groundwater.The Senonian samples demonstrated excellent SAR, Na%, and PI, values, whereas most groundwater samples from other aquifers showed good to permissible suitability for irrigation. The study identified water-rock interactions, particularly in the Infracenomanian aquifer, and the use of agricultural fertilizers as significant factors affecting groundwater composition.

The effect of geology and agricultural development on the groundwater, West Minia, Egypt

Fifty groundwater samples were collected from the Oligocene sand and Middle Eocene limestone aquifers and analyzed for major ions and stable isotopes. Aquifer sediment samples were examined to determine hydraulic properties, and sixty-six well logs were investigated to understand the subsurface geology. The study reveals that lithofacies, mineral composition, and geologic structures significantly influence groundwater quality and behavior. Karst features in the Middle Eocene limestone aquifer create subsurface caves, leading to challenges such as abrupt salinization and well failure. In the Oligocene aquifer, groundwater is predominantly of the sodium chloride type. In the Middle Eocene aquifer, 80 % of samples are sodium chloride, with variations of sodium bicarbonate (10 %) and sodium sulfate (10 %) types. Remote sensing from 2013 to 2021 shows significant agricultural expansion, with vegetation increasing by over 227 km². This growth, driven by large agricultural projects, has led to substantial groundwater consumption, resulting in a 5-meter decline in water levels within just one year (from 2020 to 2021). The over-extraction poses a risk to the aquifers, threatening long-term sustainability. Understanding the region's geology is crucial for mitigating these issues and ensuring sustainable groundwater management.

Pollution source characterization and evaluation of groundwater quality utilizing an integrated approach of Water Quality Index, GIS and multivariate statistical analysis

ABSTRACT The Mid-Gangetic Plain, a vital farmland in India, faces increasing groundwater quality deterioration due to anthropogenic activities. This study aimed to assess groundwater quality and contamination sources in the region utilizing statistical methods. A total of 78 groundwater samples were collected and analyzed using standard methods. The hydrochemistry analysis of samples revealed that several parameters such as Ca2+, Mg2+, HCO3−, NO3−, F− and PO43− surpassed the limits prescribed by the Bureau of Indian Standards (BIS). The principal component analysis yielded three significant factors, explaining 68.96% variation, highlighting geogenic and anthropogenic influences on groundwater chemistry. Hierarchical cluster analysis categorized groundwater into three clusters based on the parameters with similar trends of variation. Furthermore, discriminant analysis identified four significant variables (Mg2+, F−, Cl− and NO3−) responsible for creating the distinction among the identified clusters. Hydrogeochemical categorization and multivariate statistical analyses indicated that rock–water interaction, weathering, leaching and anthropogenic activities collectively influenced groundwater quality throughout the studied region. The Water Quality Index reveals that 59% of samples have good water quality, while 41% exhibit poor quality predominantly concentrated in the south-western, south-eastern and central regions. This study demonstrates the efficacy of statistical techniques to interpret complex datasets and grasp water quality dynamics, enhancing groundwater management.

Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods

Precise estimation of water quality is essential for managing water pollution effectively and improving water management practices. Although, numerous recent investigations have documented inconsistencies in conventional water quality indexing methods owing to inherent subjective nature of its computation. Therefore, this study introduced a novel approach that integrates ENTROPY and CRITIC methods and applied sensitivity analysis-driven machine learning (ML) algorithms for groundwater quality prediction. The aim was to enhance prediction accuracy and robustness while gaining valuable insights into hydrochemical processes influencing groundwater quality. Six ML models, i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN), Linear Regression Model (LRM), Regression Tree (RT), Gaussian Process Regression (GPR), and Boosted Regression Tree (BRT) were applied to forecast water quality across Tamil Nadu, India. This study addresses a significant gap, as systematic ML framework has been lacked to evaluate and predict drinking water quality across the same state, covering 619 monitoring sites. Calibration of the proposed Integrated Water Quality Index (IWQI) models was conducted using 75 % of the dataset (465 training wells) and validated with 25 % (154 monitoring wells), considering 13 physicochemical parameters. Rigorous statistical evaluations employing metrics such as RMSE, MSE, and MAE were performed to identify most effective prediction model, accounting for both uncertainty and predictive performance. This study suggest that ANN model is the most suitable, exhibiting the lowest uncertainty with R2 = 1 and the best predictive performance statistics, including RMSE (training = 0.0264, testing = 0.5638), MSE (training = 0.0007, testing = 0.3179), and MAE (training = 0.0083, testing = 0.0729). Sensitivity analysis identified electrical conductivity (EC) as highest responsive input parameter, while fluoride (F−) showed the lowest sensitivity. The robustness of the ANN model was evident, maintaining accuracy even with perturbations up to 10 %, thereby enabling precise IWQI score forecasting across all monitoring stations.

Anthropogenic Drivers of Spatial Trends in Groundwater Quality in the Upper Athi River Basin of Kenya, East Africa

Rapid urbanization and population growth in the upper Athi River basin in Kenya have increased the strain on the sub catchments water supply and sanitation situation. Due to increasing demand, inadequate supply of drinking water and lack of sanitation facilities, people in the sub-catchment are increasingly reliant on groundwater as a primary or supplementary water source. However, the use of on-site wastewater systems and agricultural pollution, mainly from runoff containing fertilizers, pesticides, herbicides, and faeces, pose a threat to groundwater in the sub catchment. Subsequently, the current study was conducted in the Thiririka sub catchment, Kiambu County, Kenya, to determine the safety of groundwater sources and to examine the factors influencing groundwater quality in the catchment area. This study assessed the influence of anthropogenic activities on the physical, chemical, and bacteriological quality of groundwater in the Upper Athi River basin of Kenya between April and June 2022. Twenty variables were analyzed and compared with water quality standards to determine hydro chemical characteristics, evidence of contamination, and suitability of groundwater. Shallow wells (SW) had higher concentrations of major ions and key parameters than boreholes (BH), such as alkalinity (7%), turbidity (96%), nitrates (92%), sulfates (48%), phosphates (93%), chlorides (77%), potassium (84%), sodium (30%) and fecal coliforms (99%) significant at p < 0.01. Concentrations of eleven water quality variables however were comparable in both systems. Farming, animal husbandry, and pit latrines were negatively but significantly correlated with the water quality of SW explaining substantial amounts of variation (≤ 45%) in concentrations of water quality variables. Ionic and coliform levels increased with decreasing distance and vice versa. IDW interpolation maps were generated in ArcGIS software to determine the spatial variability of groundwater quality in the basin. Anthropogenic activities such as pit latrines and animal husbandry impaired the quality of groundwater which in most cases was not potable.

Groundwater assessment for sustainable development in the Wadi Al-Hamd Basin, Al-Madinah Al-Munawarah, KSA

This study relied on well monitoring many drilled wells data in the Wadi Al-Hamd Basin to determine water level and chemical properties from field measurements. The characteristics of groundwater must be assessed in terms of present and future eases and stresses to ensure sustainable development. Hydrogeological, hydrochemical, groundwater quality indices and Geographic Information Systems (GIS) data sets have been integrated to assess the groundwater aquifer in the study area. To implement this work, we measured the depth to the water table from eighty-eight drilled wells. Also, the major ions were determined from 82 water samples to evaluate the physicochemical properties, groundwater facies, and controlling mechanisms using diagrams such as the modified Piper proposed by Chadha, Total Ionic Salinity (TIS), and Gibbs. In this study, five irrigation water quality indicators (IWQI), including total dissolved solids (TDS), sodium adsorption ratio (SAR), potential salinity (PS), magnesium hazard (MH), and residual sodium carbonate (RSC) have been determined to assess the suitability of groundwater for irrigation. Based on the findings of this study, groundwater flows from the southeast and northwest to the west and south, respectively. In this basin, 56% of water samples have TDS greater than 2000 mg/L, while 62% of water points are categorised as Na–Cl water type, which indicates that evaporation and rock–groundwater interaction influenced groundwater quality evolution. Almost 87% of samples were rated good to excellent for irrigation use with respect to SAR. On the other hand, all water points are suitable for irrigation based on RSC. Likewise, groundwater is suitable for irrigation purposes despite the relatively high PS levels (>5 meq/L in about 49% of samples) and some small spots with high levels of MH (>50% in about 12% of samples), especially in the southern and central portions of the studied basin.

Appraisal of groundwater suitability and hydrochemical characteristics by using various water quality indices and statistical analyses in the Wadi Righ area, Algeria

ABSTRACT This assessment research focuses on the hydrochemical characteristics and groundwater suitability in the Wadi Righ region, in southern Algeria. The statement of the problem revolves around determining water quality using various indices including Permeability Index (PI), Residual Sodium Carbonate (RSC), Water Quality Index (WQI), Sodium Percentage (Na%), Sodium Adsorption Ratio (SAR), Canadian Council of Ministers of the Environment's Water Quality Index (CCME WQI), Magnesium Hazard (MH), Irrigation Water Quality Index (IWQI), and Kelly Index (KR). Additionally, statistical methods were utilized to establish correlations between these indices and chemical elements. The working method involved investigating hydrochemical parameters in Wadi Righ's groundwater and analyzing 52 samples. The results indicate that water quality, as assessed by the water quality indices, was categorized as very poor and unsuitable overall, with lower quality observed particularly in the central and southern regions. However, groundwater demonstrated excellence and suitability for irrigation purposes. Qualitatively, the findings suggest that there are significant relationships among irrigation suitability indices, as indicated by Pearson correlation analysis. These relationships stem from shared inputs and hydrogeochemical characteristics of groundwater. This analysis reinforces the quantitative findings and provides insights into the underlying factors influencing groundwater quality and suitability for irrigation in the Wadi Righ region.

Hydrogeochemical characteristics of groundwater contamination in Guwahati city, Assam, India: Tracing the elemental Threads

Groundwater serves as an important resource for drinking and agriculture in many countries, including India. Assessing the quality of groundwater is essential for understanding its chemical characteristics and suitability for consumption. This study aims to explore the factors affecting the hydrogeochemical changes in groundwater within Guwahati City, Assam, India. Groundwater samples were collected and analyzed for major and trace elements, as well as anion concentrations. Concentrations of As, Al, Ba, Cu, F−, Fe, Mn, and Pb exceeded the permissible limits set by both World Health Organization (WHO) and Bureau of Indian Standards (BIS), indicating serious health concerns for the local inhabitants. The distribution pattern of trace elements exceeding the guideline values is intricate, suggesting widespread contamination of groundwater throughout the study area. The Heavy Metal Pollution Index (HPI) and Water Quality Index (WQI) revealed that, except for the central zone, groundwater across the entire study area requires intervention. Piper plot illustrated that the groundwater is predominantly of Ca–HCO3 type, indicating the dominance of alkaline earth and weak acids. Groundwater hydrogeochemistry is mainly controlled by rock-water interaction and evolves through silicate weathering, carbonate weathering, and cation exchange processes. Multivariate statistical analysis identified distinct groups of groundwater based on chemical characteristics, emphasizing the role of both natural processes and anthropogenic activities in influencing groundwater quality. Regular monitoring, management, and intervention of groundwater sources throughout the study area are crucial for long-term use. The findings of this study will assist stakeholders, regulators, and policymakers in formulating strategies for the sustainable use of groundwater.

Analysing the factors influencing groundwater quality with different pollution indices and PLS-SEM approach in the vicinity of an open dumping yard in Saduperi, Vellore, Tamil Nadu, India.

Open dumping is the prevailing municipal solid waste (MSW) disposal technique in India. Unsanitary landfill releases leachate that contaminates valuable groundwater. Hence, the present study was carried out in the vicinity of the Saduperi open dumpsite, Vellore, Tamil Nadu, India, to explore the key factors that influences groundwater contamination. A total of 216 groundwater samples were collected between May 2021 and April 2022. These samples were categorised into four different seasons such as summer, southwest monsoon (SWM), northeast monsoon (NEM), and winter. Pollution indices such as the Leachate Pollution Index (LPI) and the Heavy Metal Pollution Index (HPI) were used to evaluate the contamination potential. The calculated LPI > 35 in all seasons indicates the prevailing poor environmental condition. It was observed that about 56% of the sampling site was affected by heavy metal concentrations such as Cd, Cr, and Ni. The HPI value was found to be more than the critical value of 100 in the 10 sampling wells for all seasons. Partial least squares-structural equation modelling (PLS-SEM) has also been carried out in this study to create a link between latent variables such as 'IOT Parameters', 'Leachate Parameters', 'Heavy Metal', and 'Groundwater Quality' which were quantified by the yield of R2 value. The R2 value of the sampling well ahead of the dumpsite and along the direction of the groundwater flow values ranges from 24.7 to 86.5% in comparison to the wells located behind the dumpsite, which are prone to more contamination due to migration of leachate. Hence, this present study shows various influencing factors that affect the groundwater quality.

Evaluating Groundwater Quality Using Multivariate Statistical Analysis and Groundwater Quality Index

Under pressure from surface water pollution and climate change, groundwater becomes a critical water source. Information on groundwater quality could contribute to effective groundwater management. This study was carried out to utilize multivariate statistical analysis and the groundwater quality index (GWQI) to evaluate groundwater quality in Ca Mau Province, Vietnam. Twenty-five groundwater samples from residential-urban areas, cemetery areas, landfill areas, and saline intrusion areas were collected for this study. Groundwater quality was evaluated using the National Technical Regulation on Groundwater Quality (QCVN 09-MT:2015/BTNMT) and GWQI. Principal component analysis (PCA) was used to identify potential polluting sources and key variables influencing groundwater quality. Cluster analysis (CA) was applied to cluster groundwater quality, and the sites were recommended for future monitoring. The results revealed that NH4+-N contaminated groundwater in the landfill area, while the saline intrusion area was polluted by TDS and NH4+-N. The groundwater quality classified as excellent, good, poor, and very poor accounted for 44, 40%, 12%, and 4%, respectively. Cluster analysis divided groundwater quality into four groups, mainly based on the presence of NH4+-N and TDS. Nine groundwater sampling locations could be removed from the current groundwater quality program but still ensuring representativeness as a result of CA. PCA proposed two main sources of variation in groundwater quality at each residential-urban area: the cemetery area, the landfilling area, and the saline intrusion area. The groundwater parameters (i.e., pH, TDS, permanganate index, NH4+-N, NO3--N, and Fe) should be continued to monitor. Domestic and industrial wastewater discharge, leachate from cemeteries and landfills, the nature of groundwater aquifers, and seawater intrusion could be potential sources of groundwater variation. The current findings provide scientific information for local environmental authorities to manage and monitor groundwater quality in the study area. Doi: 10.28991/CEJ-2024-010-03-03 Full Text: PDF

Investigating the Impact of Sanitation Infrastructure on Groundwater Quality and Human Health in Peri-Urban Areas

This research explores the intricate relationships between sanitation infrastructure, groundwater quality, and human health outcomes in the peri-urban areas of Gwazunu and Suleja, Suleja Local Government Area, Niger State. It focuses on developing evidence-based strategies to mitigate waterborne disease risks. By employing a mixed-methods approach, combining cross-sectional surveys of 200 households and water quality analysis of 10 sources, the study reveals alarming correlations between inadequate sanitation infrastructure and groundwater contamination. Notably, 75% of water sources exceed World Health Organization guidelines for E. coli, with 94.5% of samples containing over 100 CFU/100mL, indicating severe contamination. This has devastating consequences, as 63% of households reported typhoid fever episodes over the past three months. Further analysis highlights the critical role of sanitation infrastructure conditions and population density in influencing groundwater quality. Investing in improved sanitation infrastructure can significantly mitigate the risk of cholera and typhoid fever, reducing it by 35%. To address these concerns, this study recommends upgrading sanitation infrastructure, implementing decentralized wastewater treatment, and promoting community-led total sanitation initiatives. Integrating water safety planning and transition management frameworks can also protect groundwater quality and public health in peri-urban areas. This research contributes to sustainable solutions for safeguarding groundwater quality and public health, emphasizing evidence-based strategies to mitigate waterborne disease risks. The study's findings have significant implications for policymakers, practitioners, and researchers tackling peri-urban water management and public health challenges.

Evaluation of non-cancer risk owing to groundwater fluoride and iron in a semi-arid region near the Indo-Bangladesh international frontier.

Groundwater quality in Hili, a semi-arid border region at Indo-Bangladesh border, was investigated in the post-monsoon season of 2021, succeeded by assessment of probabilistic health risk arising from fluoride (F-) and iron (Fe) intake, with the hypothesis that groundwater quality of the region was not satisfactory for human consumption and health, considering earlier reports on high groundwater F- and Fe in few of the neighboring districts. All water samples were found to be potable in terms of Ca2+, Mg2+, Cl-, SO42- andNO3-, ,but F- and Fe exceeded prescribed safe limits for drinking water in about 48% and 7% samples. Almost all water samples were found to be good for irrigation in terms of sodium adsorption ratio (SAR), soluble sodium percentage (SSP), Kelly's index (KI), %Na and magnesium ratio (MR). The principal component analysis (PCA) identified three major factors influencing groundwater quality, explaining about 71.8% of total variance and indicated that groundwater quality was primarily influenced by geochemical factors. Carbonate and silicate weathering were mainly responsible for dissolution of minerals in groundwater. Non-carcinogenic risk due to cumulative impact of F-and Fe intake was in the order of THIChildren > THIInfant > THIAdult. As per Monte Carlo simulation run with 5000 trials to ascertain the order of probabilistic health risk, the most dominant governing factors behind non-carcinogenic risk caused by F-and Fe intake were their concentration (Ci) followed by ingestion rate (IR), and exposure duration (ED).

Improving the efficiency of water use in agriculture by modelling the classification of groundwater quality

This study investigates the potential of machine learning for classifying groundwater quality in Telangana, India, to optimize water resource utilization in agriculture. The study aims to develop and evaluate a decision tree model capable of accurately predicting groundwater quality based on chemical composition data. The objective is to identify key factors influencing water quality and provide insights for improving water management practices and enhancing agricultural productivity. The study utilizes a dataset of groundwater quality parameters collected over three years (2018-2020) and employs a decision tree algorithm for model development. The results demonstrate the effectiveness of the model, achieving an accuracy of 95.7%. The analysis highlights the significance of sodium content, dissolved salts ratio, total dissolved solids, and total water hardness as key factors influencing groundwater quality. This research underscores the potential of machine learning for enhancing water resource management in agriculture and suggests further exploration of temporal dynamics, predictive modeling, and broader geographic application to further refine and extend the model’s impact.

Groundwater quality, human health risks and major driving factors in arid and semi-arid regions of Rajasthan, India

Evaluating status, variability and identifying the factors influencing groundwater quality is essential to manage and conserve groundwater resources. The present study aims to examine groundwater quality for 15 water quality parameters from 84 stations in arid and semi-arid districts of Rajasthan, India, using annual data from 2000 to 2018. Statistical methods such as descriptive test statistics, Mann-Kendall (MK) test, Sen’s slope estimation, and Principal component analysis (PCA) were used to analyze the hydrochemical parameters. While World Health Organisation (WHO) and Bureau of Indian Standards (BIS) guidelines were used to assess the suitability of groundwater for domestic purposes, sodium adsorption ratio (SAR), electrical conductivity (EC), and United States Salinity (USSL) diagrams were used for irrigation suitability. Further Generalized Additive Model (GAM) was used to explore the effect of climatic (precipitation, temperature) and anthropogenic (net irrigated area (NIA), fertilizer usage, industrialization, and population) variables on groundwater quality. The empirical results reveal that in groundwater of the study area sodium (Na+) and potassium (K+) were the dominating cations, while chloride (Cl−) and bicarbonate (HCO3−) were the dominating anions. The groundwater was predominantly Na–Cl type. Further, most stations witnessed an increasing trend in calcium, magnesium, sodium, chloride, bicarbonate and sulphate and a decreasing trend in potassium, fluoride and nitrate. The SAR, EC, and USSL indicate that the groundwater in the region is suitable for irrigation if salinity control measures are implemented. Chloride, fluoride and sodium concentrations exceeded the prescribed guideline limits in most stations, jeopardizing the suitability of water for drinking and posing significant health hazards. The GAM analysis revealed that anthropogenic variables significantly impact the groundwater quality parameters compared to the climatic variables. Accordingly, it is suggested that anthropogenic parameters must be addressed while formulating groundwater resource management policies. Thus, policies should focus on the stringent enactment of regulations and guidelines controlling the excessive use of fertilizers, implementing proper disposal of municipal solid and liquid waste, and managing industrial pollutants.

Hydro-chemometrics of the Nubian Aquifer in Sudan: an integration of groundwater quality index, multivariate statistics, and human health risk assessment

This research employed the groundwater quality index (GWQI), multivariate statistical methods, and human health risk assessment model to investigate the suitability of groundwater for domestic uses in northern Khartoum state, Sudan. The groundwater samples were analyzed for eleven physiochemical parameters, including pH, EC, TDS, TH, Cl−, SO42−, NO3−, Ca2+, Mg2+, Na+, HCO3− and the primary investigation indicated the deviation of these parameters from World Health Organization (WHO) standards. The hydrochemical analysis revealed different groundwater facies with the dominance of Ca–Mg–HCO3 water type. Consequently, the groundwater samples were classified, based on GWQI, into three categories as 76.4% of the samples fall in the excellent water class, 17.6% are projected in the good water class, and 5.9% of groundwater samples are considered unsuitable for human consumption. The multivariate statistical methods were applied, including Pearson’s correlation analysis, principal component analyses (PCA), and hierarchical cluster analysis (HCA). Three principal components (PCs) explaining 86.07% of total variances are extracted. These PCs indicated that rock-water interactions and agricultural practices influence groundwater quality in the study area. Additionally, HCA is used to categorize groundwater samples based on the concentration of the physiochemical parameters. Consequently, three types of groundwater were identified as low, medium, and highly mineralized. In the final stage, the non-carcinogenic human health risk was assessed based on the concentration of NO3− using the United States Environmental Protection Agency (USEPA) models. The obtained hazard quotient for children indicated that 64.7% of groundwater samples are beyond the permissible limit (1 <), and the use of these samples may result in health consequences. Therefore, remedial measures are suggested for the sustainable use of groundwater.

Multivariate Statistical Analysis and Geospatial Mapping for Assessing Groundwater Quality in West El Minia District, Egypt

The primary goal of this study is to analyze the hydrogeochemical properties and assess the groundwater quality for drinking, domestic, and irrigation purposes in West El Minia, Egypt. Major components were determined in 49 groundwater samples to evaluate water quality in the study area. Principal component analysis (PCA), hierarchical cluster analysis (HCA), geostatistics, and spatial mapping were used to identify the chemical components and processes that influence groundwater quality and highlight areas of health risks. According to the TDS values, about 22% of the groundwater samples are suitable for drinking. Due to the elevated values of hardness in the examined water, none of the water samples are suitable for use in a household. The majority of groundwater samples are acceptable for irrigation based on the sodium adsorption ratio (SAR), residual sodium carbonate (RSC), Kelley ratio (KR), magnesium hazard (MH), and permeability index, and some can be adequately treated. The study indicated that different groundwater characteristics (such as TDS, Na+, K+, HCO3−, Cl−, and SO42−) do not comply with WHO requirements in some regions, which may pose a threat to human health.

Effects of cattle manure and soil parent material on shallow groundwater quality

AbstractLivestock are a critical agricultural commodity in the Midwestern United States and are directly linked to row crop production. Chemical and manure nutrient application for row crop production has resulted in significant losses of nitrogen (N) and phosphorus (P) to downstream ecosystems causing environmental crises like seasonal hypoxia in the Gulf of Mexico. However, the use of cattle manure has multiple benefits over commercial fertilizer including increased soil carbon levels and improved nutrient cycling and should be considered a critical component for future agricultural production in Iowa. In this study, we evaluated influences of bedded cattle manure and commercial fertilizer on groundwater quality in a field with varying soil parent material of glacial till and colluvium in eastern Iowa. We found that soil parent material predominately influenced groundwater quality. Multiple water quality parameters including nitrate varied significantly (p &lt; 0.0001). Nitrate concentrations were nearly double in colluvium compared to glacial till (13.2 ± 6.7 compared to 7.0 ± 3.6 mg/L, respectively). Further, nitrate concentrations averaged 12.2 ± 8.1 mg/L in areas receiving bedded cattle manure and were significantly higher (p &lt; 0.0001) compared to areas receiving commercial fertilizer only, which averaged 8.6 ± 4.7 mg/L. Dissolved P was significantly lower (p &lt; 0.05) in areas receiving bedded cattle manure averaging 0.13 ± 0.12 mg/L compared to 0.07 ± 0.04 mg/L in commercial fertilizer‐only areas. However, higher nitrate concentrations were likely linked to 70 kg/ha of additional N applied to manure areas by farm managers above corn removal rates. Overall, our results stress the importance of appropriate nutrient application rates and placement over nutrient source.