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\"Find out why water is important, and how people can protect it\"--Provided by publisher.

Tracer methods have been widely used in many fields of environmental and natural sciences, and also in human health sciences. In particular, tracers are used in the study of karst hydrogeology, typically focusing on phenomena such as sinkholes, sinking rivers and large karst springs. It is known that tracers have been used since antiquity. The aim of tracer tests has been to investigate underground flow paths, transport processes and water–rock interactions, and to get an insight into the functioning of a karst aquifer. In karst hydrogeology, tracer methods are the most important investigation tools beside conventional hydrological methods. In early times, tracer methods were applied only to investigate underground flow-paths. Later they were also used to elucidate transport processes associated with water flow, and today they are often the basis, together with detailed hydrological information, of groundwater protection investigations and aquifer modelling. Many substances (spores, microspheres, bacteriophages, salt tracers, fluorescent dyes, radioactive substances) have been investigated for their properties and potential usage in environmental investigations, in particular the often unknown and inaccessible underground systems of karst areas. A great number of analytical techniques is available. This includes instrumentation for laboratory applications and direct online, on-site or in-situ field measurements. Modern instruments have a high capability for data acquisition, storage and transmission in short intervals, as a basis for quantitative evaluation and modelling. This enables research on the hydrological and hydrochemical dynamics of aquifers and their response to different natural or anthropogenic impacts.

Teaches young readers the importance of keeping water clean and gives practical ideas on things they can do to pitch in.

Groundwater quantity and quality may be affected by climate change through intricate direct and indirect mechanisms. At the same time, population growth and rapid urbanization have made groundwater an increasingly important source of water for multiple uses around the world, including southern Africa. The present study investigates the coupled human and natural system (CHANS) linking climate, sanitation, and groundwater quality in Ramotswa, a rapidly growing peri-urban area in the semi-arid southeastern Botswana, which relies on the transboundary Ramotswa aquifer for water supply. Analysis of long-term rainfall records indicated that droughts like the one in 2013–2016 are increasing in likelihood in the area due to climate change. Key informant interviews showed that due to the drought, people increasingly used pit latrines rather than flush toilets. Nitrate, fecal coliforms, and caffeine analyses of Ramotswa groundwater revealed that human waste leaching from pit latrines is the likely source of nitrate pollution. The results in conjunction indicate critical indirect linkages between climate change, sanitation, groundwater quality, and water security in the area. Improved sanitation, groundwater protection and remediation, and local water treatment would enhance reliable access to water, de-couple the community from reliance on surface water and associated water shortage risks, and help prevent transboundary tension over the shared aquifer.

\"America's wettest state is running out of water, Florida--with its swamps, lakes, extensive coastlines, and legions of life-giving springs--faces a drinking water crisis. Drying Up is a wake-up call and a hard look at what the future holds for those who call Florida home.\"--Dust jacket.

Groundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution. Plain Language Summary Groundwater contamination caused by human activities is a longstanding global challenge. Accurately characterizing and modeling the movement of contaminants is crucial for the protection and management of groundwater resources. However, the complexity of the processes, limitations in data availability, and high computational demands pose significant challenges. In the age of big data, machine learning offers new avenues for exploring contaminant transport in groundwater. In this study, we introduce a novel machine learning model called an attention‐based graph neural network (aGNN) designed to model contaminant transport with sparse monitoring data and to analyze the causal relationships between contaminant sources and observed concentrations at specific locations. We conducted five synthetic case studies across diverse aquifer systems with varying monitoring setups, where aGNN demonstrated superior performance over models based on other approaches. It also proved highly capable of making inferences about pollution levels at unmonitored sites. Moreover, an explanatory analysis using aGNN effectively quantified the impact of each contaminant source, with results validated by a physics‐based model. Overall, this study establishes aGNN as an efficient and robust method for complex spatiotemporal learning in subsurface contaminant transport, making it a valuable tool for groundwater management and contaminant source identification. Key Points A novel graph‐based deep learning method is proposed for modeling contaminant transport constrained by monitoring data The proposed model quantifies the contribution of each potential contaminant source to the observed concentration at an arbitrary location The deep learning method substantially reduces the computational cost compared with a physics‐based contaminant transport model

Groundwater vulnerability mapping is one of the tools most often applied to analyse the sensitivity of karst aquifers to pollution. These maps aim to support stakeholders in decision-making and to promote land-use management compatible with water protection; however, the validation of these maps is still a challenge in many cases, triggering high uncertainty. For karst media, due to the strong heterogeneity in recharge mechanisms and hydraulic characteristics, validation is a significant stage and it must be inherent within the groundwater vulnerability assessment process. This work aims to assess the implementation of tools used for protecting the quality of water discharging or extracted from the Ubrique karst system in southern Spain, which supplies drinking water that is threatened by periodical pollution/turbidity episodes. A groundwater vulnerability map, attained by application of the COP method and validated by multiple in-situ observations, shows an extremely vulnerable system due to the absence of protective overlayers and the significant development of exokarst landforms, including shallow holes. This map could constitute the basis for defining protection zones for the Ubrique springs; however, their comprehensive protection requires the implementation of monitoring tools and an effective management strategy, through an early warning system that assures stable environmental and hydrogeological conditions and improves operational procedures associated with the drinking water service. This research establishes the strong relationship of the different methods applied to protect the source from contamination events, ranging from classical hydrodynamic and hydrochemical approaches to the implementation of protection zones and early warning groundwater quality monitoring networks.

Protection of groundwater quality from various natural and anthropogenic forces is a prime concern in Bangladesh. In this study, we utilized groundwater geochemistry of shallow and deeper aquifers to investigate the hydrogeochemical processes controlling water quality, and the sources and mechanism of Arsenic (As) release to water and associated human health risks in the Faridpur district, Bangladesh. Analysis of hydrochemical facies indicated that groundwaters were Ca–Mg–HCO3 type and that water–rock interactions were the dominant factors controlling their major-ion chemical composition. The dissolution of calcite, dolomite, and silicates, as well as cation exchange processes regulated the major ions chemistry in the groundwater. Dissolved fluoride (F−) concentrations (0.02–0.4 mg/L) were lower than the drinking water standard of 1.5 mg/L set by the World Health Organization (WHO). Arsenic contamination of groundwater is among the biggest health threats in Bangladesh. The measured As concentration (0.01–1.46 mg/L with a mean of 0.12 mg/L) exceeded the maximum permissible limit of Bangladesh and WHO for drinking water. The estimated carcinogenic risk of As exceeded the upper benchmark of 1 × 10–4 for both adult and children, and health threats from shallow groundwater were more severe than the deeper water. The vertical distribution of As resembled Fe and Mn with their higher concentrations in shallow Holocene aquifers and lower in deeper Pleistocene aquifers. Speciation calculation indicated the majority of groundwater samples were oversaturated with respect to siderite, calcite, and dolomite, while undersaturated with respect to rhodochrosite. The saturation state of the minerals along with other processes may exert kinetic control on As, Fe, and Mn distribution in groundwater and lead to their lack of statistically significant correlations. Microbially mediated reductive dissolution of Fe and Mn oxyhydroxides is envisaged as the primary controlling mechanism of As mobilization in Faridpur groundwater. Pyrite oxidation was not postulated as a plausible explanation of As pollution.