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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]

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]

This study developed innovative predictive models of groundwater pollution using in situ electrical conductivity (EC) and oxidation–reduction potential (ORP) measurements at livestock carcass burial sites. Combined electrode analysis (EC and ORP) and machine learning techniques efficiently and accurately distinguished between leachate and background groundwater. Two models—empirical and theoretical—were constructed based on a supervised classification framework. The empirical model constructs a classifier with high accuracy, sensitivity, and specificity, utilizing the comprehensive in situ EC and ORP measurements. The theoretical model with only two end members achieves comparable performance by simulating the leachate–groundwater interactions using a geochemical mixing model. Besides enhancing the early detection capabilities, our approach considerably reduces the reliance on extensive hydrochemical analyses, thus streamlining the monitoring process. Moreover, the use of field parameters was found to proactively identify potential pollution incidents, enhancing the efficiency of groundwater monitoring strategies. Our approach is applicable to various waste disposal sites, indicating its extensive potential for environmental monitoring and management. [Display omitted] • EC and ORP measurements enable early identification of leachate impacts. • Supervised machine learning is enhanced for precise leachate pollution prediction. • The geochemical mixing model enables low-cost groundwater pollution monitoring. • This approach supports proactive groundwater management strategies. [ABSTRACT FROM AUTHOR]

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. • There is a need for agile, effective groundwater management and monitoring tools. • AquMADE is a user-friendly, accessible, and comprehensive tool for the experts. • AquMADE combines GIS and MCDM methods, aiding informed decision-making. • Decision-makers' risk-taking attitude can greatly impact groundwater quality results. [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]

A foot-and-mouth disease (FMD) outbreak during 2010 affected the entire country of South Korea and approximately 3.4 million swine and bovine mortalities were disposed of at approximately 4800 on-farm burial sites for a few months following the first outbreak. Furthermore, outbreaks of avian influenza (AI) have struck Korea consistently since 2014. Public concern regarding the deterioration of the surrounding environment has been raised aiming at the enormous infected animal carcass burials. On behalf of the Ministry of Environment (ME) of the Korean government, we conducted groundwater monitoring at approximately 3000 wells around the burial sites for 7 years from 2011 to 2017. The baseline groundwater already had enriched levels of nitrogen and chloride compounds masking the influence of leachate with the pre-existing anthropogenic contamination. Based on the trend analysis, most monitoring wells had no significant trends in NO 3 -N and Cl implying that an abrupt degradation in water quality was not expected across the country. Increasing proportions of total coliform detection in the groundwater of the majority of provinces will cause potential damage to human health around the carcass burial sites. Wells showing frequent upward and downward trends near the carcass burial pits were targeted for principal component analysis (PCA) and the results showed that NH 4 -N, TOC, Cl, and K could be indicators distinguishing the sole impacts of animal leachate on the groundwater. Analyses of the nationwide monitoring data indicated possibly delayed and prolonged impacts of the widespread burials of livestock mortalities on the groundwater environment. The finding provides realistic insight regarding how to manage the mass burial of livestock mortalities to protect groundwater resources. • Nationwide monitoring of groundwater quality near the FMD burial pits was conducted. • Temporal change in NO 3 -N, Cl, total coliforms was frequently observed near burial sites. • Statistic approaches can distinguish impact of animal leachate alone on groundwater. [ABSTRACT FROM AUTHOR]