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Regular groundwater quality monitoring in resource-constrained regions present formidable challenges in terms of funding, testing facilities and manpower; necessitating the development of easily implementable monitoring techniques. This study proposes a copula-based risk assessment model utilizing easily measurable indicators (e.g., turbidity, alkalinity, pH, total dissolved solids (TDS), conductivity), to monitor the contaminates in groundwater which are otherwise difficult to measure (i.e., iron, nitrate, sulfate, fluoride, etc.). Preliminary correlation between the indicators and the target contaminates were identified using Pearson coefficient. Best representative univariate distributions for these pairs were selected using the Akaike Information Criterion (AIC), which were used in the formulation of the copula model. Validation against observed data showcased the model's high accuracy, supported by consistent Kendall Tau correlation coefficients. Through this model, conditional probabilities of the contaminants not exceeding the permissible limits set by the Bureau of Indian Standards (BIS) were calculated using indicator concentration. Notably, an inverse correlation between iron concentration and conductivity was noted, with the likelihood of iron exceeding BIS limits decreasing from 90 to 50% as conductivity rose from 500 to 2000 micromhos/cm. TDS emerged as a pivotal indicator for nitrate and sulfate concentrations, with the probability of sulfate surpassing 10 mg/l decreasing from 75 to 25% as TDS increased from 250 to 750 mg/l. Likewise, the probability of nitrate exceeding 1 mg/l decreased from 90 to 60% with TDS levels reaching 1500 mg/l. Furthermore, a 63% probability of fluoride concentrations remaining below 1 mg/l was observed at turbidity levels of 0–10 NTU. These findings hold significant implications for policymakers and researchers since the model can provide crucial insights into the risks associated with the contaminates exceeding the permissible limit, facilitating the development of an efficient monitoring and management strategies to ensure safe drinking water access for vulnerable populations. [ABSTRACT FROM AUTHOR]

Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. For effective decontamination, it is pivotal to identify regional pollution hotspots. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, hotspots, and evaluate associated health risks pertaining to nitrate and fluoride in the Al-Hassa region, KSA. The physicochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021–4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate health quotient (HQ) values indicated chronic human health risks ranging from low to severe, with infants being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The hazard index (HI) yielded moderate- to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the total hazard index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources, vulnerable regions, and human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans. [ABSTRACT FROM AUTHOR]

This study explores the extent of leachate contamination and groundwater vulnerability in urban dumpsites, with a specific focus on the Ipata area in Ilorin, Nigeria. The study employs a combination of 2D Electrical Resistivity Tomography (ERT), soil classification, and physicochemical analyses to investigate the percolation of leachate into groundwater and its potential environmental and health implications. The ERT data unveiled subsurface layers, highlighting the presence of decomposed topsoil down to approximately 1.2m. Beneath this layer, a low-resistivity zone (6.53 to 10.7 Om) indicated the potential risk of leachate percolation into groundwater. Soil classification revealed a shallow topsoil layer with insufficient clay content to hinder leachate penetration, emphasizing the need for enhanced containment measures. Physicochemical analysis of leachate, well water, and soil displayed variations in key parameters such as pH, electrical conductivity, total dissolved solids, and anion concentrations. Leachate exhibited high pH and electrical conductivity, suggesting elevated total dissolved solids, while well water remained within acceptable pH limits for drinking water. Heavy metal concentrations exceeded permissible WHO limits in topsoil, leachate, and well water, with cadmium presenting a high ecological risk. The absence of persistent organic pollutants (POPs) in the samples indicates a current focus on heavy metals as a primary concern. In conclusion, this study underscores the urgent need for proactive pollution abatement measures in urban dumpsites like Ipata. Regular monitoring of surface and groundwater quality is essential to safeguard public health and the environment. [ABSTRACT FROM AUTHOR]

In developing countries, the increase in anthropogenic activities has aggravated the possibilities of groundwater resources pollution, which threatens water sustainability. It is, therefore, necessary to have monitoring networks that provide information that could influence decision-making both on the current and the future situation. The objective of this study was to identify priority zones where monitoring points are required for the optimization of the groundwater quality network in the Toluca Valley aquifer (Mexico), both under current (2020) and future conditions (2030). The analytical hierarchical process method was used in a Geographic Information Systems environment. The density of wells, hydraulic gradient, groundwater pollution hazard, water bodies as well as mountain zones were the analyzed criteria, as advised by expert opinion. The results are prioritization maps for every scenario. The low and moderate priority zones show the highest extension percentage in the current scenario but tend to decrease in future ones. The high-priority zones in the metropolitan area of the Toluca Valley show a 5% increase in the period and stand out for both scenarios. Finally, overlapping the current quality network with the high-priority zones for 2020 and 2030 enabled for concluding that 40.5% of wells in the existing network are essential for the current and future monitoring. The adaptation of this methodology, considering future scenarios of the conditions of the aquifer, will enable to have a monitoring network that is adaptable to these conditions over time, which in turn will facilitate the obtaining of timely data and information. [ABSTRACT FROM AUTHOR]

Water resources become more and more threatened by the increasing request related to the population growth, especially in agricultural regions. This article focused on the design of a geo-model monitoring network controlling groundwater quality in the fast-growing agricultural area of Souassi, Central eastern Tunisia. The studied area is known for its dry climate conditions and information scarcity while they are most needed. In this case, the integrated approach based on the Exploratory Factor Analysis coupled to the Confirmatory Factors Analysis and geostatistics were performed in order to detect the most relevant factors intervening in the groundwater quality and their spatial variability in the shallow aquifer of Souassi. This investigation is based on data set of nine parameters (EC, Ca, Mg, Na, K, HCO3, SO4, Cl and NO3) determined in 30 samples from wells tapping the shallow aquifer of Souassi. The PCA results allowed illustrating that the groundwater quality in Souassi shallow aquifer is affected by both natural and anthropogenic factors. Developing the SEM led to identifying the most meaningful factors controlling the water mineralization. The geospatial interpolation using kriging technique enabled us to locate areas with high salinity values corresponding to the natural discharge zone of the aquifer as well as areas with high rate of nitrate coinciding essentially with olive fields in the central zone. Under limited database, the results allow highlighting the risk zones in the aquifer. This could assist in establishing sustainable agricultural activities while helping to solve environmental degradation of water resources in the study area. [ABSTRACT FROM AUTHOR]

Groundwater quality monitoring is a critical part of water management in all groundwater basins. In order to be effective and to meet the required needs, groundwater quality monitoring networks (GQMNs) must be designed to be able to operate long-term and economically without minimal disruption. The analytical hierarchical process (AHP), a multi-criteria decision-making program, was used to design a GQMN for an alluvial aquifer located in the Islam Abad plain west of Kermanshah province, Iran. This semi-arid area is subject to groundwater depletion and water quality changes. The model used 8 primary criteria sub-divided with 5 sub-criteria based on a combination of empirical data and expert opinion. The primary criteria included density of wells, well discharge, well depth, water quality (conductivity), flow direction, annual groundwater extraction, water level declines, and accessibility. The model showed that 59 of 254 production wells in the basin could provide optimal monitoring locations. When a second screening of the wells was used to determine constraints (physical conditions of the wells and pumps, owner permission of use, type of the pump, etc.), the number of wells was reduced to 13 wells. An initial round of water sampling and chemical analysis demonstrated that the design of the GQMN met the goals of the water management agency of the region. [ABSTRACT FROM AUTHOR]

essment of groundwater quality monitoring networks requires methods to determine the potential efficiency and cost-effectiveness of the current monitoring programs. To this end, the concept of entropy has been considered as a promising method in previous studies since it quantitatively measures the information produced by a network. In this study, the measure of transinformation in the discrete entropy theory and the transinformation-distance ( T- D) curves, which are used frequently by other researchers, are used to quantify the efficiency of a monitoring network. This paper introduces a new approach to decrease dispersion in results by performing cluster analysis that uses fuzzy equivalence relations. As a result, the sampling (temporal) frequency determination method also recommends the future sampling frequencies for each location based on certain criteria such as direction, magnitude, correlation with neighboring stations, and uncertainty of the concentration trend derived from representative historical concentration data. The proposed methodology is applied to groundwater resources in the Tehran-Karadj aquifer, Tehran, Iran. [ABSTRACT FROM AUTHOR]

Selection of the number and location of groundwater quality monitoring wells may require the consideration of different aspects such as: monitoring objectives, temporal frequency of monitoring, and monitoring costs. These aspects were examined using observations of groundwater quality variables made twice a year in 124 wells in the northern part of the Gaza Strip, where groundwater is of the best quality. Considering these features the groundwater quality monitoring network was assessed and redesigned using entropy. The assessment procedure was based on expressing transinformation as a function of distance between wells. It was found that the number and location of groundwater quality monitoring wells varied if these aspects were embedded in the assessment and redesign procedure. [ABSTRACT FROM AUTHOR]

What size sample is sufficient for spatially sampling ambient groundwater quality? Water quality data are only as spatially accurate as the geographic sampling strategies used to collect them. This research used sequential sampling and regression analysis to evaluate groundwater quality spatial sampling policy changes proposed by California's Department of Water Resources. Iterative or sequential sampling of a hypothetical groundwater basin's water quality produced data sets from sample sizes ranging from 2.8% to 95% coverage of available point sample sites. Contour maps based on these sample data sets were compared to an original (control), mapped hypothetical data set, to determine at which point map information content and pattern portrayal are not improved by increasing sample sizes. Comparing series of contour maps of ground water quality concentration is a common means of evaluating the geographic extent of groundwater quality change. Comparisons included visual inspection of contout maps and statistical tests on digital versions of these map files, including correlation and regression products. This research demonstrated that, down to about 15% sample site coverage, there is no difference between contour maps produced from the different sampling strategies and the contout map of the original data set. [ABSTRACT FROM AUTHOR]