Search

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]

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 ?m) 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.

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.

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]

The present study was carried out near an industrial area with a high-density urban population and large-scale agricultural activities. These anthropogenic activities lead to groundwater pollution and depletion of the water table. This study attempted to classify pollution sources and hydrochemical facies that help to ensure the suitability of water for agriculture and drinking. Irrigation suitability indexes, water quality index (WQI), principal component analysis (PCA), and hierarchical cluster analysis (HCA) were applied to twenty-six groundwater samples that were analysed during May 2018 for major cations and anion concentrations. The results revealed that the mechanism of groundwater chemistry has been controlled by the evaporation process with the dominance of hydrochemical facies viz., Ca-Mg-HCO3, Na-K-Cl-SO4, Ca-Mg-Cl, and Na-K-HCO3. The mean dominant concentration for cations is in the order of Ca2+ > Na+ > Mg2+ > K+ while anions are HCO3− > SO4− > Cl− > NO3− > CO32− > F−. Irrigation suitability indexes indicated that groundwater in the study area is high in saline and low to medium alkali hazards due to industrial activities. The PCA and HCA also recognized that most of the variations are elucidated by anthropogenic processes, predominantly due to excessive population, industrial emissions, and agricultural activities. Further, the WQI of the study area suggested that 15% of the samples were unsuitable, 69% poor, and the remaining 16% only suitable for drinking purposes. The present article helps to understand the suitability and hydrochemical processes of groundwater for irrigation and drinking, which will help policymakers in water supply planning and management.

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]

Rural and peri-urban communities in the developing world typically rely on boreholes and wells for drinking water, since freshwater resources on the land surface are heavily polluted and groundwater is perceived to be safe. Nonetheless, in these areas wastewater is managed onsite, often close to the groundwater abstractions points. Unbeknownst to most members of these local communities, this poses an unprecedented threat to their health, since chemicals and pathogens are leaking from onsite systems to groundwater. Polluted groundwater could also act as an environmental reservoir for bacteria and viruses, including new and emerging infectious diseases. In this study, groundwater from rural and peri-urban South Africa was assessed in terms of drinking water quality. Indicators of faecal pollution were identified across the examined boreholes, with E. coli and nitrates concentrations as high as 195 ​cfu 100 ​mL−1 and 104 ​mg ​L−1, respectively, being identified. This suggests that onsite wastewater systems have grossly impacted groundwater quality. Elevated concentrations of fluoride and chloride (as high as 8.6 and 392 ​mg ​L−1, respectively) were also identified, hence suggesting that the local geological setting also affects groundwater quality. Overall, groundwater was found in need of treatment before consumption, which is currently not the case. To inform local communities and the water industry about the problem, a simple classification model was developed. This could help to assess water quality, identify the nature of pollution, suggest the type of treatment required, and prioritise the need for water and wastewater infrastructure investment in the developing world. It can also inform local communities about the impact of their current wastewater management practices on water resources and the pressing need to upgrade them.

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]

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]

This paper presents a methodology for optimal design of groundwater monitoring networks using the criteria of maximizing information and minimizing monitoring cost. The measure of Transinformation in the Discrete Entropy Theory is used for quantifying the efficiency of the monitoring network. The existing uncertainty in the Transinformation-Distance (T-D) curve is incorporated using the fuzzy set theory. The fuzzy T-D curve is then used in a multi-objective GA-based optimization model, which provides the best locations for monitoring stations. The proposed methodology is applied to groundwater resources in the southern part of Tehran, Iran. The results show the applicability and the efficiency of the model for the optimal design of groundwater monitoring systems.

Abstract: This paper presents a methodology for groundwater quality monitoring network design. This design takes into account uncertainties in aquifer properties, pollution transport processes, and climate. The methodology utilizes a statistical learning algorithm called relevance vector machines (RVM), which is a sparse Bayesian framework that can be used for obtaining solutions to regression and classification tasks. Application of the methodology is illustrated using the Eocene Aquifer in the northern part of the West Bank, Palestine. The procedure presented in this paper utilizes a Monte Carlo (MC) simulation process to capture the uncertainties in recharge, hydraulic conductivity, and nitrate reaction processes through the application of a groundwater flow model and a nitrate fate and transport model. This MC modeling approach provides several thousand realizations of nitrate distribution in the aquifer. Subsets of these realizations are then used to design the monitoring network. This is done by building a best-fit model of nitrate concentration distribution everywhere in the aquifer for each Monte Carlo subset using RVM. The outputs from the RVM model are the distribution of nitrate concentration everywhere in the aquifer, the uncertainty in the characterization of those concentrations, and the number and locations of “relevance vectors” (RVs). The RVs form the basis of the optimal characterization of nitrate throughout the aquifer and represent the optimal locations of monitoring wells. In this paper, the number of monitoring wells and their locations where chosen based on the performance of the RVM model runs. The results from 100 model runs show the consistency of the model in selecting the number and locations of RV‘s. After implementing the design, the data collected from the monitoring sites can be used to estimate nitrate concentration distribution throughout the entire aquifer and to quantify the uncertainty in those estimates. [Copyright &y& Elsevier]

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]

Abstract: This paper presents a new methodology for estimating location and amount of leakage from an unknown pollution source using groundwater quality monitoring data. The proposed methodology includes a multi-objective optimization model, namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) which is linked with MODFLOW and MT3D groundwater quantity and quality simulation models. The main characteristics of an unknown groundwater pollution source are estimated using two probabilistic simulation models, namely Probabilistic Support Vector Machines (PSVMs) and Probabilistic Neural Networks (PNNs). In real-time groundwater monitoring, these trained probabilistic simulation models can present the probability mass function of an unknown pollution source location and the relative error in estimating the amount of leakage based on the observed concentrations of water quality indicator at the monitoring wells. The efficiency of the proposed methodology is demonstrated through a real-world case study. [Copyright &y& Elsevier]

Abstract: A groundwater monitoring network can provide quantity and quality data necessary to make informed decisions regarding the state of the environment. A properly designed monitoring system provides a representative understanding of the state of the monitored area. The selection of the optimum number of monitoring sites and their spatial distribution is a major challenge for the hydrogeologist. On the one hand, improper distribution of monitoring sites or insufficient number of sites will not provide a representative view of the state of the environment. On the other hand, if the sampled sites are too many, the information obtained is redundant and the monitoring network is costly and inefficient. A new methodology combining vulnerability mapping and geostatistics is proposed to help define the most efficient groundwater quality monitoring network on a regional scale. Vulnerability mapping identifies areas with high pollution potential, and in turn, prioritises for monitoring. A geostatistics methodology is then used to interpret the obtained data and to examine the spatial distribution of monitored parameters at different sites. The accuracy of spatial mapping reflects the effectiveness of the distribution of the monitoring sites. The methodology was applied to assess the nitrate monitoring network in the Heretaunga basin, Hawke''s Bay, New Zealand. The DRASTIC approach was used to prepare a vulnerability map for the area of study, and kriging variance was used to check the spatial distribution of the sites. Based on this study, it was found that some areas with high vulnerability are not covered within the existing network indicating the number of monitoring sites and their distribution is not efficient. Some sites should be dropped and some others need to be added to the existing network. [Copyright &y& Elsevier]

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]

Changes in agricultural practices are expected to affect groundwater quality by changing the loads of nutrients and salts in recharging groundwater, but regional monitoring networks installed to register the changes often fail to detect them and interpretation of trend analysis results is difficult. This study aims to improve the detection and understanding of groundwater quality changes with time, combining time series information, concentration–depth profiles, age dating and concentration–depth prognoses based on the historical inputs of solutes. For trend detection, a combination of trend analysis on time series at specific depths and time-averaged concentration–depth profiles was used. To reveal trends that have become obscured by chemical reactions, additional conditionally conservative indicators were introduced that are insensitive to those reactions under specific conditions. Detected trends were matched with prognoses of conservative and reactive transport to aid the understanding of trends. Data of the regional networks in 2 area-types with intensive livestock farming in the Dutch province of Noord–Brabant were used to illustrate the approach. The downward movement of the agricultural pollution front was demonstrated for the 2 area-types. However, many targeted contaminants have become retarded or delayed and quality changes were hard to detect for many reactive solutes, including nitrate. Pollution fronts of these targeted chemical components are still limited to the first 15 m of the subsoil. At deeper level, about 20–25 m, the effects of agricultural pollution and acidification were indicated by chemical indicators that have not been considered by others: oxidation capacity, the sum of cations and chloride. Increasing trends of the conditionally conservative indicators ‘oxidation capacity’ and ‘sum of cations’ were found at a depth of 18–25 m below surface. Increasing trends for potassium were found at shallower depth (7–13 m), which is explained by retardation of potassium due to cation-exchange with calcium and magnesium. The modelled cation-exchange explained the shape of the concentration–depth profile and the increasing trends at shallow depth in the aquifer. [Copyright &y& Elsevier]

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]