Assessment Of Groundwater Quality Research Articles

Investigation of Heavy Metal Composition and Associated Health Risks from Selected Groundwater Wells in Temeke, Dar-es-Salaam

Water quality continues to be the challenge to residents in Dar es Salaam due to various anthropogenic activities in the city. This study was conducted to assess groundwater quality by determining concentrations of heavy metals. Both probability and Nonprobability sampling techniques were used to collect samples from 40 groundwater wells in 24 wards of Temeke municipal and analysed using Microwave Plasma Atomic Emission Spectrometer (Agilent 4210 MP-AES) at the Geochemistry Laboratory of the University of Dar es Salaam. Results indicated varying heavy metal concentrations ranging from 0.71 ± 0.08 to 6.64 ± 0.21 mg/l for As, 0.02 ± 0.01 to 0.07 ± 0.01 mg/l for Co and 0.16 ± 0.01 to 0.02 ± 0.01 mg/l for Pb. The As, Co and Pb concentrations exceeded (P < 0.05) the recommended TBS standards of 0.05 mg/l, 0.003 mg/l and 0.01 mg/l respectively. Other concentrations ranged from 0.01 ± 0.01 to 0.88 ± 0.01 mg/l for Cu, 0.02 ± 0.01 to 3.55 ± 0.02 mg/l for Fe, 0.00 ± 0.00 to 4.63 ± 0.03 mg/l for Mn and 0.01 ± 0.01 to 3.00 ± 0.01 mg/l for Zn. The Cu, Fe, Mn and Zn concentrations were below (P > 0.05) the allowable TBS limits of 0.05 mg/l, 0.3 mg/l, 0.1 mg/l and 3 mg/l respectively. Health risk analysis revealed potential adverse health effects from long term consumption of As, Mn and Pb with Hazard Quotient exceeding 1 (HQ > 1). The Hazard Index (HI) for all groundwater samples was also above 1 (HI > 1) indicating a higher likelihood of adverse health effects. The study concluded that groundwater wells in Temeke Municipal are affected through natural and anthropogenic activities leading to high levels of heavy metal concentrations. Also, this study provides valuable information on risk associated by continued usage of groundwater in Temeke region and hence informs respective stakeholders on groundwater quality and potential risks thereby emphasizing the need for remedial measures and stricter regulations to safeguard public health. The study recommends the need for regular groundwater quality and safety monitoring to assess the levels of heavy metals concentrations and implement sustainable groundwater management strategies to address the increasing water demand in Temeke municipal, Dar es Salaam.

Comprehensive assessment of groundwater quality in the Prayagraj District, Ganga Basin.

Urbanization has severely impacted the world water resources especially the shallow groundwater systems. There is a need of a robust method for quantifying the water quality degradation, which is still a challenge for most of the urban centers across the world. In this study, a highly urbanized region of Ganga basin is selected to critically evaluate commonly used WQIs and compare with fuzzy modeling. A total of 28 water samples were collected from diverse sources (surface and groundwaters) in the vicinity of urban region covering an area of 216 km2 during the premonsoon period. TDS, TH, NO3-, and F- values were found to be above the permissible limits in 57%, 89%, 4%, and 7% samples, respectively. The WQIs (entropy and integrated) outputs were found to be similar with 89% of the samples falling under moderate category. Fuzzy modeling was carried out allowing user-defined weighting factors for the most influential ions, and the output suggested 96% of the samples falling under moderate to excellent categories. Based on the chemical results and considering the lithology of the study area, the geochemical reactions controlling the water quality were deduced. This study outlines a systematic approach of evaluating the overall water quality of an urban region highlighting the merits and limitations ofWQIs. It also justifies the immediate need to generate more robust data to achieve the sustainable development goals 6 (clean water and sanitation) and 11 (sustainability of cities and human settlement).

Assessment of groundwater quality using water quality index, multivariate statistical analysis and machine learning techniques in the vicinity of an open dumping yard

Assessment of groundwater quality using water quality index, multivariate statistical analysis and machine learning techniques in the vicinity of an open dumping yard

Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region.

Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML's superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Water Quality Assessment in an Area Degraded by Urban Solid Waste in Southern Brazil

ABSTRACTIn most Brazilian municipalities, urban solid waste (USW) is disposed of in open dumps, with numerous sites now being closed, monitored, and rehabilitated. One location, the “old dump” area in the Portão municipality (southern Brazil), previously served as an irregular repository for USW and industrial solid waste (ISW). Its use continued until 1993 when it was closed for a natural remediation process. For this reason, this study aimed to assess groundwater quality in the Portão “old dump,” employing physicochemical, microbiological, and toxicity quality indicators. The study's scope was extended to encompass the Portão Stream as well. Notably, the second collection campaign revealed elevated dissolved aluminum (Al3+), iron (Fe2+), and nitrate levels in all groundwater samples (from PZi wells, i = 1–6), with the PZ3 monitoring well experiencing the most pronounced impact. Further quality indicators, such as total phosphorus, thermotolerant coliforms, BOD5, as well as Al3+ and copper (Cu2+) levels in the second water collection campaign from Portão Stream, were classified as Class 4, suggesting that the water is suitable only for navigation and aesthetic purposes. Conductivity analysis identified Portão Stream as another impacted area. Moreover, during the second collection campaign, the study uncovered an escalation in micronuclei (MN) frequency and chromosomal alterations in PZ5. Downstream water samples from Portão Stream exhibited similarly heightened MN levels. This study's investigation into water quality is a concrete example of the implications of improper USW management. These findings underscore the pressing need for effective waste management strategies to mitigate environmental degradation and protect public health.

Assessment of Groundwater Quality around a Cement Factory in Ewekoro, Ogun State, Southwest Nigeria

Assessment of Groundwater Quality around a Cement Factory in Ewekoro, Ogun State, Southwest Nigeria

An Imputation Method for Simulating 3D Well Screen Locations from Limited Regional Well Log Data.

In groundwater modeling studies, accurate spatial and intensity identification of water sources and sinks is of critical importance. Precise construction data about wells (water sinks) are particularly difficult to obtain. The collection of well log data is expensive and laborious, and government records of historic well log data are often imprecise and incomplete with respect to the precise location or pumping rate. In many groundwater modeling studies, such as groundwater quality assessments, a precise representation of the horizontal and vertical distribution of well screens is required to accurately estimate contaminant breakthrough curves. The number of wells under consideration may be very large, for example, in the assessment of nonpoint source pollution. In this paper, we propose an imputation framework that allows for proper reconstruction of missing well data. Our approach exploits available information and tolerates data gaps and imprecisions. We demonstrate the value of this method for a subregion of the Central Valley aquifer (California, USA). We show that our framework imputes missing values that preserve statistical properties of available data and that remain consistent with the known spatial distribution of well screens and pumping rates in the three-dimensional aquifer system.

Numerical Verification of Groundwater Suitability for Irrigation Around the Subsurface Dam Area of Miyako Island, Japan

The sustainable management of water resources is essential for agricultural productivity, especially in areas with scarce water availability. This study focused on assessing groundwater quality for irrigation near the subsurface dam area of Miyako Island, Japan. Water samples from three observation points were tested for various parameters, including electrical conductivity (EC), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), residual sodium bicarbonate (RSBC), permeability index (PI), Kelley's ratio (KR), and magnesium adsorption ratio (MAR). EC values ranged from 270 to 800 µS/cm, suggesting water quality ranging from doubtful to good. SAR values between 0.23 and 1.49 suggested excellent quality. SSP ranged from 7.90% to 31.71%, mostly indicating good to excellent quality. RSBC values fluctuated between -1.57 to 1.45 epm, largely within safe limits. PI values varied from 40.34 to 75.83, indicating good permeability. Total hardness (TH) ranged from 105.50 to 326.45 ppm, classifying the water as hard to very hard. MAR values were below 50, suggesting potential soil issues. A numerical model confirmed observed Ca²⁺ concentrations, showing an increasing trend due to enhanced CO₂ emissions and lower pH. The data analysis revealed strong positive relationships between SSP and KR (r = 0.984), SAR and SSP (r = 0.951), and SAR and KR (r = 0.960). Despite generally acceptable values, continuous monitoring is recommended, especially for hardness, to ensure sustainable crop production. This study underscores the need for regular assessment and management of groundwater quality in subsurface dam areas to mitigate potential adverse effects on soil and agricultural productivity.

Groundwater Quality Assessment with Respect to Heavy Metal Content by using Heavy Metal Pollution Index (HPI) of Moradabad District, Uttar Pradesh, India

Heavy metal pollution index (HPI), is a rating method to assess the water quality and to categorize the groundwater pollution with respect to heavy metals content. The aims of the present study are to look over the current status of heavy metals pollution in and around Moradabad city. Heavy metal pollution in water has gained universal consciousness due to its tenacity, accumulation in the food chain and negative effects on ecological as well as human health. Its variation in concentration can cause deterioration of water. The study focuses on examining the content of heavy metal (Zn, Fe, Cd, Mn, Pb, Ni, Cu, and Cr) in the 30 water samples from the surrounding areas of Moradabad city. The concentrations of Cd, Mn, Fe, Cu, Pb, Zn, Ni, and Cr in water samples were recorded in the pre- and post-monsoon season of 2017. The examined samples reveal that the contamination level of heavy metal is in the following sequence Ni>Fe>Pb>Cd>Cr>Cu>Zn>Mn in premonsoon 2017, whereas in postmonsoon season, heavy metals values are shown as Fe>Pb>Ni>Mn>Cr> Cu>Zn>Cd. The HPI average value in pre-monsoon 2017 is 13.07, which recommended that the groundwater quality is good. While in post-monsoon season HPI average values has been 159.26 suggesting that the water quality is poor to inferior. The correlation matrix has been evaluated and shows a positive correlation with the elements. The immense production of industrial solid waste in Moradabad city and its improper disposal in the form of heap piled outside the city area generates leachate. Heavy metal leaching from these disposal points may contaminate the groundwater as well as surface water resources. The study shows that heavy metal concentration has a noticeable rise in water due to different anthropogenic and various natural sources of contamination.

Evaluation of River and Groundwater Quality in the Mzimvubu to Tsitsikamma Water Management Area (WMA7)

This paper assessed and evaluated the river and groundwater quality of five major catchments in the Mzimvubu to Tsitsikamma Water Management Area (WMA7). The river assessment and evaluation considered the present state maximum, minimum and mean seasonal values of physiochemical parameters (EC, NO3 + NO2, Cl, pH, NH4-N, PO4-P and Mg). The study was able to successfully make use of statistical analysis and a water quality index in order to explain seven-year trends and the present state of all five catchments within the study area. The statistical analysis results showed that water quality parameters such as PO4, NH4 and NO3 + NO2 were not complying with the South African Water Quality Guidelines (SAWQG) limits, suggesting that the study area was impacted by anthropogenic activities such as agriculture, sewage works, etc. The application of WQI showed that the Gamtoos catchment was in a good water quality state (49.71) while the other four catchments were in a poor to very poor state, according to the WQI classification. Based on the groundwater quality assessment, the physicochemical parameters (NH4, EC, Ca, NO3 + NO2, pH, Na, Cl, Mg, F, K, SO4 and CaCO3) were selected and the main parameters of concern were EC (salinity), NO3 + NO2 and SO4. However, groundwater assessment revealed that groundwater quality was chemically suitable for drinking purposes in most parts of the study area. Therefore, the study recommended resource-directed measures to improve surface and groundwater resource protection, especially in areas where surface and groundwater quality for other uses remains a challenge. Further, strong water resource compliance monitoring and enforcement to control water resource pollution sources is critical.

Assessment of Surface and Ground Water Quality for Irrigation Purposes of a Subtropical Wetland in South West Coast of India

Assessment of Surface and Ground Water Quality for Irrigation Purposes of a Subtropical Wetland in South West Coast of India

Groundwater quality and health risk assessment of nitrate and fluoride in Al Qaseem area, Saudi Arabia

Abstract Groundwater serves as the lifeline in arid regions, where aquifer overuse and climatic factors can substantially degrade its quality, posing significant challenges. The current study examines the drinking water quality in the Al Qaseem area and assesses the potential health risks from nitrate ( NO 3 − {\text{NO}}_{3}^{-} ) and fluoride (F−) exposure to infants, children, and adults. This evaluation employs parameters such as the daily water intake, hazard quotient, and non-carcinogenic hazard index. Groundwater samples from 38 wells and boreholes were analyzed for major cations and anions. The water quality index and multivariate tools were utilized. The average concentrations of SO 4 2 − {\text{SO}}_{4}^{2-} , Cl–, Ca+, Na+, Mg2+, and K+ exceeded acceptable limits. Among the 38 samples, 8 were unsuitable for drinking, with 5 categorized as very poor quality, 10 as poor, 14 as good, and 1 as excellent. Nitrate levels ranged from 1.30 to 108.00 mg/L, with a mean of 36.56 mg/L. Three wells exceeded World Health Organization (WHO) guidelines (50.00 mg/L). Fluoride ranged from 0.10 to 0.98 mg/L, with a mean of 0.71 mg/L and none surpassing WHO recommendations (1.5 mg/L). The HI values for adults, children, and infants were 0.993, 2.606, and 2.78, respectively. About 57.89% of the water samples exceeded the safety level of 1 for adults and 94.73% for both children and infants. Thus, the groundwater in the study area may pose non-carcinogenic health risks to infants, children, and adults when used as drinking water.

Investigating the effects of surface water recharge on groundwater quality using hydrochemistry and ANFIS model: A case study Minia Governorate, Egypt

Monitoring and assessing groundwater quality and quantity lays the basis for sustainable management. Therefore, this research aims to investigate various factors that affect groundwater quality, emphasizing its distance to the primary source of recharge, the Nile River. To this end, two separate study areas have been considered, including the West and West-West of Minia, Egypt, located around 30 and 80 km from the Nile River. The chosen areas rely on the same aquifer as groundwater source (Eocene aquifer). Groundwater quality has been assessed in the two studied regions to investigate the difference in quality parameters due to the river's distance. The power of machine learning to associate different variables and generate beneficial relationships has been utilized to mitigate the cost consumed in chemical analysis and alleviate the calculation complexity. Two adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the water quality index (WQI) and the irrigation water quality index (IWQI) using EC and the distance to the river. The findings of the assessment of groundwater quality revealed that the groundwater in the west of Minia exhibits suitability for agricultural utilization and partially meets the criteria for potable drinking water. Conversely, the findings strongly recommend the implementation of treatment processes for groundwater sourced from the West-West of Minia before its usage for various purposes. These outcomes underscore the significant influence of surface water recharge on the overall quality of groundwater. Also, the results revealed the uncertainty of using sodium adsorption ratio (SAR), Sodium Percentage (Na%), and Permeability Index (PI) techniques in assessing groundwater for irrigation and recommended using IWQI. The developed ANFIS models depicted perfect accuracy during the training and validation stages, reporting a coefficient of correlation (R) equal to 0.97 and 0.99 in the case of WQI and 0.96 and 0.98 in the case of IWQI. The research findings could incentivize decision-makers to monitor, manage, and sustain groundwater.

Seasonal assessment of groundwater quality in the Al Hoceima region, northern Morocco: Physicochemical parameters and fecal contamination indicators

Seasonal assessment of groundwater quality in the Al Hoceima region, northern Morocco: Physicochemical parameters and fecal contamination indicators

Assessment of Groundwater Quality through Hydrochemistry Using Principal Components Analysis (PCA) and Water Quality Index (WQI) in Kızılırmak Delta, Turkey

Assessment of Groundwater Quality through Hydrochemistry Using Principal Components Analysis (PCA) and Water Quality Index (WQI) in Kızılırmak Delta, Turkey

Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index

This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010–2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH4+-N, Cl-, and NO3--N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.

Machine learning-based ensemble model for groundwater quality prediction: A case study

ABSTRACT Groundwater quality is vital for public health and environmental sustainability. As managing large datasets is challenging for traditional methods, this study combines the hidden Markov model (HMM) and the artificial neural network (ANN), a machine learning-based ensemble model to predict groundwater quality in Kanyakumari District, Tamil Nadu, India. In order to train the model, the acquired data is cleaned and normalized. HMM is used to find hidden patterns while the ANN architecture is used to forecast groundwater quality categories. Accuracy, precision, sensitivity, and F1-scores calculation are necessary to evaluate the model's performance. The effectiveness of the approach can be analyzed by k-fold cross-validation scores. The study demonstrates the effectiveness of the HMM–ANN approach in groundwater quality prediction with an accuracy of 97.41%. Thus, the research contributes to groundwater quality assessment by offering a unique methodology that facilitates informed decision-making for water resource management and environmental conservation.

Groundwater Quality Assessment of a Crystalline Terrain at Semi-arid Region in Southern Parts of India

Groundwater Quality Assessment of a Crystalline Terrain at Semi-arid Region in Southern Parts of India

Groundwater in the coastal areas of Ghana: Quality and associated health risks

Self-supply water sources, particularly groundwater sources, play key roles in the water supply ecosystem of developing countries. Recent studies indicate that groundwater sources in coastal communities in Ghana are under threat from improper waste management practices, seawater intrusion and atmospheric aerosol deposition. In this study, Water Quality Index (WQI) and Nemerow's Pollution Index (NPI) were employed to assess groundwater quality in four coastal communities of Ghana. The health risks associated with metal pollution of groundwater were investigated using incremental life cancer risk and hazard quotient. pH of groundwater in all the studied communities were acidic during the rainy season. Electrical conductivity ranged from 0.44 to 2.61 mS/cm in the rainy season and from 0.43 to 2.45 mS/cm in the dry season for the four studied locations. Results also showed brackish conditions and mineralization of groundwater in Winneba, Accra, and Keta. Mean nitrate concentrations in Winneba and Accra were higher than the WHO standards for both the rainy and the dry season. Arsenic was higher than the acceptable level in Accra and Keta during the dry season, while iron was higher than the acceptable levels in Accra in both the rainy and dry seasons. Principal Component Analyses showed that Pb, As, and Fe had the highest loading in the first component in Essiama, while PO43-and Pb had the highest loading in the second component in Accra. WQI showed that the quality of groundwater in all the studied communities ranged from marginal to poor indicating that groundwater in the coastal communities often or usually departs from desirable quality. NPI revealed that NO3- , As, and Fe contribute to groundwater deterioration. Health risk assessment showed that As posed a high cancer risk in Accra and potential cancer risk in Essiama, Winneba, and Keta during the dry season. As also posed potential cancer risk in Accra during the rainy season. Non-cancer health risk was observed for As in Accra and Keta. The findings of this study suggest urgent regulations and monitoring strategies to improve groundwater quality in the coastal communities of Ghana.

Assessment of Groundwater Quality for Drinking and Irrigation Purposes Using Hydro-Chemical and Water Quality Index Studies in Kurukshetra (Haryana), India

Assessment of Groundwater Quality for Drinking and Irrigation Purposes Using Hydro-Chemical and Water Quality Index Studies in Kurukshetra (Haryana), India