Groundwater Quality Monitoring Research Articles

GIS-based groundwater potential zonation and assessment of groundwater quality and suitability for drinking and irrigation purposes in the Shanmughanadhi river basin, south India

To develop a specific watershed scenario, the quantity and quality of groundwater resources must be assessed and monitored locally. In the present study, an attempt has been made to identify possible groundwater potential zones, determine the quality of groundwater resources in this region, and evaluate their suitability for drinking and irrigation purposes. This goal has been achieved with the combined use of the weighted index overlay analysis (WIOA), groundwater quality index (GWQI), Remote Sensing (RS), and Geographic Information System (GIS) techniques. The proposed study area, Shanmuganadhi, is marked with superior rainfall, oscillating temperature, and runoff with litho-units encompassing charnockite (CHK) and hornblende biotite gneiss (HBG). Over-abstraction of groundwater and intensive agricultural practices have resulted in declining and degrading water quantity and quality in this area. The resulting integrated thematic map has been classified into five groundwater potential zones, namely poor, low, moderate, high, and very high, covering 3.2, 45.49, 15.3, 27.9, and 8.1% area, respectively. Sixty groundwater samples were collected during the pre-monsoon (PRM) and analyzed for major Physico-chemical parameters. From the water quality index assessment, over 28.0% of the samples fall within the “excellent,” 8.0% “good” and 4.0% “poor” in CHK, followed by 5.71% “excellent,” 48.57% “good,” 37.14% “poor,” 5.71% “very poor” and 2.87% “unsuitable” for drinking purposes in HBG terrain of this region. Irrigation indices also demonstrated that most of the groundwater samples in the CHK and HBG exposed zones exhibited good classes for irrigation purposes, as evidenced by the SAR and %Na+. The outcome, which shows the research area's groundwater potential zones, is beneficial for improved groundwater resource planning and management. It is concluded that for larger-scale groundwater-quality investigation and assessment, the groundwater quality index-making approach (GWQI) is more practical and dependable. Water planners and decision-makers may find it helpful in effectively controlling and monitoring groundwater quality at the basin or watershed scale. Reducing urban land use and natural runoff from farms can help prevent water contamination in some regions. To ensure the safe use of groundwater, long-term monitoring of these groundwater was suggested.

A metagenome-derived artificial intelligence modeling framework advances the predictive diagnosis and interpretation of petroleum-polluted groundwater

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution

Spatial design of groundwater quality monitoring network using multicriteria analysis based on pollution risk map

Spatial design of groundwater quality monitoring network using multicriteria analysis based on pollution risk map

Spatial and seasonal groundwater quality assessment for drinking suitability using index and machine learning approach

Spatial and seasonal evaluation and monitoring of groundwater (GW) quality is essential for the sustainable management of this priceless resource and the provision of safe drinking water. Nevertheless, GW quality appraisal was not given due attention in the current study area (flat terrain part of the Tana sub-basin). This study sought to assess the seasonal and regional physicochemical GW quality parameters for drinking appropriateness using the drinking water quality index (DWQI) and support vector machine (SVM). The main cations in both the dry and wet study seasons were, in decreasing order, Na+, Ca2+, Mg2+, K+, and Fe2+, according to the results. Conversely, the main anions were HCO3−, CO32−, Cl− or NO3−, SO42− and PO43−, ordered from higher to lower. During the two research seasons, Ca–HCO3 and Na–HCO3 were the predominant water types based on Piper diagram results. Reverse ion exchange and evaporation were the principal hydrogeochemical processes that control the hydrogeochemistry identified by Durov and Gibbs diagrams, respectively. Excellent GW quality class for drinking was demonstrated by the majority of geographical and seasonal DWQI readings over the two seasons. Nevertheless, during the rainy season, there was a noticeable decline in the GW quality condition around the northern shores of Lake Tana. Therefore, it is advised to implement comprehensive GW quality protection measures and improve system management to mitigate pollution to reduce health hazards in the examined region.

Urbanization impact on groundwater quality of selected rural and urban areas in Ondo State, Nigeria using Water Quality Index

Groundwater is the primary source of water in Ondo state, Nigeria, the study area for the research. However, there has been a rising concern in recent times with regard to the contamination of this important water source as a result of urbanization. This study quantified 13 physicochemical parameters (Turbidity, Conductivity, pH, Mg2+ Hardness, Ca2+ Hardness, Total hardness, Alkalinity, Chloride, Nitrate, Sulphate, Phosphate, Total Dissolved Solids (TDS) and Total Suspended Solids (TSS)) in ten groundwater samples collected from dug wells in urban and rural study areas and compared them with the WHO drinking water standards. The comparison reveals that 30.8% of the parameters had levels above the World Health Organisation (WHO) standard limits in the study areas, while 69.2% were within the recommended limits. Water Quality Index (WQI) computations were also carried out which revealed that 60% of samples from the urban study area were of good quality and 40% were poor, while in the rural, 80% of the samples were good and the remaining 20% were poor. Based on the statistical and WQI results, groundwater in the urban study area is generally of lower quality than that of the rural. Possible factors influencing this include surface sealing, seepage of domestic and industrial effluents into aquifers and over-exploitation of groundwater, which characterize urbanized areas. The study thus recommends regular monitoring of groundwater quality, integration of effective systems for safe sewage disposal and strategic city planning of urbanized zones to maximize runoffs for groundwater recharge to improve groundwater quality.

Fiber optics passive monitoring of groundwater temperature reveals three-dimensional structures in heterogeneous aquifers

Alluvial aquifers often exhibit highly conductive embedded formations that can act as preferential pathways for the transport of solutes. In this context, a detailed subsurface characterization becomes crucial for an effective monitoring of groundwater quality and early detection of contaminants. However, small-scale heterogeneities are seldom detected by traditional nondestructive investigations. Heat propagation in porous media can be a relatively inexpensive tracer for groundwater flow, potentially offering valuable information in various applications. In this study, we applied passive Fiber Optics Distributed Temperature Sensing (FO-DTS) to a group of observation wells in a highly heterogeneous phreatic aquifer to uncover structures with different hydraulic conductivity, relying on their response to temperature fluctuations triggered by natural and anthropogenic forcings. A comprehensive data analysis approach, combining statistical methods and physics-based numerical modeling, allowed for a three-dimensional characterization of the subsurface at the experimental site with unprecedentedly high resolution.

Application of selected groundwater quality indices in anthropogenically transformed areas

Correctly assessing the impact of waste landfills on groundwater quality requires the development and use of comprehensive tools that consider the content of individual components in water, as well as those relating to the hydrochemical background. The article presents a comparative analysis of selected water quality indices used around the world to assess the environmental threat caused by sources of pollution. The paper includes the following indicators: Water Quality Index, Landfill Water Pollution Index, Nemerow Pollution Index, Backman Index, Canadian Water Quality Index, Horizontal Ratio, Enrichment Factor and Fuzzy Water Quality. Attention was paid to the values of individual indices, their applicability in municipal and industrial waste landfills, and limitations in their use. A literature review has proven that some indices, such as the Landfill Water Pollution Index and Nemerow Pollution Index, are used very often as they provide reliable results. Index methods help conduct rational water management, complement groundwater quality monitoring, and serve as a tool for making decisions in various water protection areas.

Simulation of Groundwater Quality in Xi 'an,Shaanxi Province Based on Visual Modflow

The so-called groundwater quality simulation is to study the spatial and temporal variation law of the concentration of a certain soluble substance (element or other component) when it is dispersed and transported by simulation method, to predict the instantaneous dynamics and expansion range of polluted groundwater, and to provide scientific basis for formulating reasonable and effective prevention measures. In recent years, the environmental hydrogeology survey results of some provinces, cities and regions in China show that the groundwater in most cities in China has been polluted to varying degrees. This situation directly affects industrial and agricultural production and people's health. However, in the past, environmental hydrogeology work was mostly limited to surface investigation, lacking quantitative and predictive evaluation research. Monitoring groundwater quality dynamics and forecasting the development trend of groundwater pollution is one of the main tasks of groundwater pollution investigation and research. Relevant information shows that some countries in the world have already carried out this research and established a predictive groundwater quality model. Among them, the United States has established 22 material transport models and 5 heat transport models; France has established 5 models of material transport and 4 models of heat transport. The following countries have only established material transport models: 7 in Israel; 2 in the UK; 2 in Canada; 1 in West Germany and 1 in Japan. Therefore, we believe that the study of groundwater quality simulation experiment, which is still blank in China's scientific field, has become an important subject that needs to be carried out urgently to ensure the four modernizations and people's health. The hydrogeological conditions and dynamic characteristics of groundwater resources have been comprehensively analyzed and studied. It points out that the continuous exploitation of groundwater resources in 2018 has led to the deterioration of groundwater quality, land degradation, underground, cracks and depletion of groundwater resources, and puts forward some preventive measures.

Assessing groundwater quality in Nand Samand catchment, Rajasthan, India: a GIS-based multi indices approach for drinking and irrigation water suitability

Abstract Consistently monitoring groundwater quality (GWQ) is essential to reduce the risk of geochemical contaminants and ensure its suitability for agriculture and human consumption. The current investigation aims to assess groundwater (GW) acceptability in the Nand Samand catchment (NSC), utilizing the water quality index (WQI) and irrigation water quality indices (IWQIs) for domestic and irrigation purposes. To achieve this, GW samples were collected from 95 open wells which were located spatially in the catchment, during the pre-monsoon (PRM) and post-monsoon (POM) seasons of 2019 and 2020, and subsequently analysed for 11 physico-chemical parameters. Electrical conductivity (EC) varied from 1.25 to 6.61 dS/m and 0.58 to 7.42 dS/m during the PRM and POM seasons, respectively. Total dissolved solids of the study area ranged between 180 and 1,180 (27%) to 1,180 and 2,180 (62%) during PRM 1,180 (63%) to 1,180–2,180 (31%) during POM, respectively. The study also computed the ‘sodium adsorption ratio’ (SAR) and ‘residual sodium carbonate’ (RSC) to estimate GW's appropriateness for agriculture, finding it suitable in most locations due to its balanced composition. Based on WQI, varying percentages of samples were classified as ‘good’ and ‘poor’ for potable water quality in both seasons.

Nitrate contamination in groundwater and its evaluation of non-carcinogenic health hazards from Arjunanadi River basin, south India

Nitrate (NO3-) contamination of groundwater is increasingly getting common in many places across India. Here, we present new data about nitrate pollution and the associated health consequences for infant, children, and adult population by evaluating 94 groundwater samples from the Madurai and Virudhunagar regions within the Arjunanadi River basin, situated at the southern region of India. Nitrate (9–221 mg/L) remained surpassed the WHO's recommended limit (>45 mg/L) in 34% samples. The piper diagram results specified that 75% of the samples fall in Mixed CaMgCl type. The correlation of nitrate results showed that groundwater of the region degraded by agricultural and industrial activities. The Nitrate Pollution Index (NPI) results of samples revealed significant pollution class, n = 18 (19%) and very significant pollution class, n = 26 (28%) in the study region. Its spatial distribution covered an area of 456.3 km2 in the risk zone with more than permissible nitrate. Among the shallow aquifers, about 16% of whole samples were in the high risk and 9% in very high-risk. Similarly, about 6% samples from deep aquifers were also in the category of high risk. The Water Quality Index (WQI) of groundwater samples indicateed 39% as good, 48% as poor and 9% as very poor to unsuitable. The health risk assessment from intake (HQintake) and skin contact (HQdermal) suggested 64%, 45% and 32% of samples with THI >1 pose hazard for infant, children, and adult population, respectively. The health risks and assessing groundwater quality indicated the necessity of regular groundwater quality monitoring for better long-term health management in this region.

Analyzing Spatial Groundwater Salinity Using Multivariate Analysis and Multiple Linear Regression Models

The increase in the amount of groundwater withdrawal will inevitably pose a threat of seawater intrusion. The purpose of this research was to identify the distribution of shallow groundwater salinity in North Jakarta, West Jakarta and Central Jakarta and to develop a regional model of shallow groundwater salinity distribution. The data used in this study was that of the groundwater quality monitoring, obtained from the Regional Environment Status Book (SLHD), published by The Environment office of Greater Jakarta released in 2022, involving a total of 121 sample points in North Jakarta, West Jakarta, and Central Jakarta. The primary data was taken at 6 (six) sampling locations for model validation purposes. The study began with data grouping, using the Hierarchical Cluster Analysis (HCA) method. The results of identifying the highest distribution of salinity are in cluster 3 (three). A model was subsequently developed, after removing the outliers, with multiple linear analysis methods using the variable the distance from the coastline (X1), well depth (X2) and hardness (X3), to determine the influence of EC, TDS and salinity distribution in shallow groundwater. The results obtained are as follows; EC Models: YEC3 = -1.879+ (1.19.X1) + (5.08.X3). TDS models: YTDS3 = -2.211.30 + (0.81.X1) + (101.41.X2) + (4.07.X3). Salinity models: Ysalinity3 = -0.07+ (6.75×10-5.X1) + (2.4×10-4.X3). Model verification results for R2EC3 = 0.70; R2TDS3 = 0.92; R2salinity3 = 0.88. Validation results produce 21.14% for EC, 8.21% for TDS, and 22.87% for Salinity. This needs further research by increasing the number of primary samples.

AquMADE: A GIS-based web application to assess groundwater quality by introducing a risk-based irrigation water quality index (RB-IWQI)

Groundwater quality monitoring plays a vital role in effective water management across all groundwater basins. The provision of decision support systems can assist decision-makers in strategic planning and the conservation of groundwater resources. In this study, AquMADE—a user-friendly and comprehensive web application—is developed to integrate GIS and multi-criteria decision-making methods seamlessly for modifying the irrigation water quality index. AquMADE serves multiple functionalities. Firstly, it empowers decision-makers to import, interpolate, and visualize collected data, enabling a holistic view of the information at hand. Secondly, it offers the flexibility for decision-makers to adjust the weights of various parameters through the utilization of the analytical hierarchy process, taking into consideration their risk attitudes during the aggregation process using ordered weighted averaging method. Finally, the incorporation of sensitivity analysis methods allows for the evaluation of the impact of changes in inputs, such as criteria weights and aggregation methods, on the resulting outputs. The system underwent user testing in Khoy City, Iran, where it received positive feedback. The assessment revealed that the system performed satisfactorily in terms of functionality, interactivity, and ease of use.

Irrigation impact on water quality and aquifer vulnerability in Kala Oya basin, Sri Lanka

The impact of precipitation and irrigation return flow on water quality and groundwater vulnerability was investigated in the Kala Oya basin, Sri Lanka using the Weighted Arithmetic Water Quality Index, DRASTIC Index, and isotope hydrology. Groundwater and surface water were analyzed for water quality parameters and stable isotopes (18O and 2H) in dry and wet seasons. In both seasons, neutral to alkaline water was observed. Electrical conductivity (EC) and major ions were higher in shallow (EC: 718.3 ± 323.8 μS/cm; Ca2+: 49.54 ± 32.99 mg/L) and deep groundwater (EC: 708.1 ± 321.5 μS/cm; Ca2+: 37.06 ± 11.21 mg/L) than in surface water (EC: 371.7 ± 154.4 μS/cm; Ca2+: 29.66 ± 11.64 mg/L), while similar values of EC and major ions were reported in both shallow and deep groundwater samples. Ca–Mg–HCO3 was the dominant groundwater type which was derived from silicate and carbonate rock weathering. Observations in the wet season revealed δ2H ranged from −47.97 to −16.71‰ and δ18O ranged from −7.56 to −2.87‰, while in the dry season, δ2H ranged from −44.90 to −14.06‰, and δ18O ranged from −7.69 to −2.08‰. The enriched δ18O and δ2H groundwater values were mainly the result of progressive evaporative concentration of the heavier isotopes before water infiltration and groundwater recharge. The solute concentrations were enriched in groundwater by agricultural irrigation returns and associated evaporation indicating the influence of irrigation return flow on groundwater. It is suggested that agricultural practices increase groundwater vulnerability, which recommends special attention to long-term groundwater quality and quantity monitoring, hydrological modelling, land-use planning, and the development of sustainable policies in the basin.

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

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.

Water Quality Assessment of Groundwater Using Multivariate Statistical Techniques: A Case Study of Mogadishu, Banadir Region, Somalia

This study was carried out to assess the groundwater quality around the Mogadishu area, Banadir region, Somalia. Multivariate statistical techniques such as factor analysis (FA), principal component analysis (PCA) and cluster analysis (CA)were applied to 22 groundwater samples collected from boreholes and dug wells in the coastal line districts namely: Wadajir, Kaxda and Dharkinley districts of Banadir region, Somalia. Correlations among 14 hydrochemical parameters were statistically examined. A two-factor model is suggested and explains over 82.4% of the total groundwater quality variation. Factor Analysis (FA) revealed significant variables including electrical conductivity (EC), pH value and other parameters such as K<sup>+</sup>, Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup>, SO<sub>4</sub><sup>2-</sup> and hardness expressed as CaCO<sub>3</sub>, Chlorine, Fluoride, B, S, Si, and NO<sub>3</sub> which are responsible for variations in groundwater quality and affect water chemistry. The results were compared with the World Health Organization (WHO) standard guidelines. Geographic Information System (GIS) was used to create the spatial distribution maps of water quality parameters. Cluster Analysis (CA) grouped all sites into three zones based on spatial similarities and dissimilarities of physiochemical properties. The pH value and Boron, fluoride calcium, magnesium, sulfide, potassium, and Silica are well within the desirable limit at all locations. However, the concentration of conductivity chloride, hardness as CaCO<sub>3</sub>, sulfate, nitrate, and Sodium in all samples exceeded the desirable WHO maximum permissible limit. The study reveals that the groundwater quality changed due to anthropogenic and natural influences such as natural weathering processes. As a result of this the qualities of the boreholes and dug well water samples were therefore not suitable for human consumption without adequate treatment. Regular monitoring of groundwater quality, abolishing unhealthy waste disposal practices, and introducing modern techniques are recommended.

Equipping for risk: Lessons learnt from the UK shale-gas experience on assessing environmental risks for the future geoenergy use of the deep subsurface

Summary findings are presented from an investigation to improve understanding of the environmental risks associated with developing an unconventional-hydrocarbons industry in the UK. The EQUIPT4RISK project, funded by UK Research Councils, focused on investigations around Preston New Road (PNR), Fylde, Lancashire, and Kirby Misperton Site A (KMA), North Yorkshire, where operator licences to explore for shale gas by hydraulic fracturing (HF) were issued in 2016, although exploration only took place at PNR. EQUIPT4RISK considered atmospheric (greenhouse gases, air quality), water (groundwater quality) and solid-earth (seismicity) compartments to characterise and model local conditions and environmental responses to HF activities. Risk assessment was based on the source-pathway-receptor approach. Baseline monitoring of air around the two sites characterised the variability with meteorological conditions, and isotopic signatures were able to discriminate biogenic methane (cattle) from thermogenic (natural-gas) sources. Monitoring of a post-HF nitrogen-lift (well-cleaning) operation at PNR detected the release of atmospheric emissions of methane (4.2 ± 1.4 t CH4). Groundwater monitoring around KMA identified high baseline methane concentrations and detected ethane and propane at some locations. Dissolved methane was inferred from stable-isotopic evidence as overwhelmingly of biogenic origin. Groundwater-quality monitoring around PNR found no evidence of HF-induced impacts. Two approaches for modelling induced seismicity and associated seismic risk were developed using observations of seismicity and operational parameters from PNR in 2018 and 2019. Novel methodologies developed for monitoring include use of machine learning to identify fugitive atmospheric methane, Bayesian statistics to assess changes to groundwater quality, a seismicity forecasting model seeded by the HF-fluid injection rate and high-resolution monitoring of soil-gas methane.The project developed a risk-assessment framework, aligned with ISO 31000 risk-management principles, to assess the theoretical combined and cumulative environmental risks from operations over time. This demonstrated the spatial and temporal evolution of risk profiles: seismic and atmospheric impacts from the shale-gas operations are modelled to be localised and short-lived, while risk to groundwater quality is longer-term.

Monitoring groundwater quality using principal component analysis

Monitoring groundwater quality using principal component analysis

Assessing nitrate contamination in groundwater for public supply: A study in a small Brazilian town

Monitoring groundwater quality is essential for drinking water, avoiding risks to human health, and making it possible to identify and prevent environmental contamination. In Tupã, west of São Paulo-Brazil, the drinking water supply is carried out exclusively by underground water through the public service or alternative solutions using private tubular wells. In this context, this study evaluated the quality of drinking water from 25 private tubular wells in all areas of Tupã. Of these wells evaluated, 12% were contaminated with E. coli and 44% with total coliforms, indicating that the sanitary quality of the water is inadequate and may be directly associated with waterborne diarrheal diseases. The results of the physical-chemical analyses referring to water potability parameters, such as apparent color, turbidity, total dissolved solids, hardness, iron, ammonia, nitrite, chloride, and fluorine, met the requirements of Brazilian legislation. However, all samples showed NO3− contamination, whose concentrations ranged from 1.9 to 23.2 mg L−1NO3−-N, where 32% of the wells exceeded the maximum allowable limit of 10 mg L−1NO3−-N. The consumption of water contaminated with high concentrations of NO3− can cause health risks, especially to children due to their low body weight, causing methemoglobinemia or blue baby syndrome. In the study of the evolution of urban growth, the city's central area exhibited the most significant NO3− contamination because it is an older area with leakage problems and insufficient sewage system maintenance. The non-carcinogenic health risk associated with NO3− contamination was assessed using the Environmental Protection Agency (USEPA) methodology applied to oral ingestion and dermal contact in children and adults. The results revealed negligible risks from dermal contact. However, the assessment highlighted the susceptibility of children to nitrate contamination through oral ingestion, mainly from water from wells in the city's central area. This study emphasizes the importance of monitoring groundwater in Tupã, identifies potential sources of contamination, and provides information to improve water resources management. Implementing appropriate measures based on these findings can mitigate waterborne diseases, safeguard consumer health, and protect the environment.

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta

Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.