Dowsing for danger Arsenic is a metabolic poison that present in minute quantities most rock materials and, under certain natural conditions, can accumulate aquifers and cause adverse health effects. Podgorski Berg used measurements of arsenic groundwater from ∼80 previous studies to train machine-learning model with globally continuous predictor variables, including climate, soil, topography (see the Perspective by Zheng). The output global map reveals potential hazard contamination...

Abstract The health of millions people worldwide is negatively impacted by chronic exposure to elevated concentrations geogenic fluoride in groundwater. Due effects including dental mottling and skeletal fluorosis, the World Health Organization maintains a maximum guideline 1.5 mg/L drinking water. As groundwater quality not regularly tested many areas, it often unknown if water given well or spring contains harmful levels fluoride. Here we present state-of-the-art global hazard map based on...

Fifty million to 60 people potentially exposed high levels of groundwater arsenic, which is coincident with areas irrigation.

For about the past eight decades, high concentrations of naturally occurring fluoride have been detected in groundwater different parts India. The chronic consumption is recognized to cause dental and skeletal fluorosis. We used random forest machine-learning algorithm model a data set 12 600 from throughout India along with spatially continuous predictor variables predominantly geology, climate, soil parameters. Despite only surface parameters being available describe subsurface phenomenon,...

Groundwater is a critical resource in India for the supply of drinking water and irrigation. Its usage limited not only by its quantity but also quality. Among most important contaminants groundwater arsenic, which naturally accumulates some aquifers. In this study we create random forest model with over 145,000 arsenic concentration measurements two dozen predictor variables surface environmental parameters to produce hazard exposure maps areas populations potentially exposed high...

Naturally occurring, geogenic manganese (Mn) and iron (Fe) are frequently found dissolved in groundwater at concentrations that make the water difficult to use (deposits, unpleasant taste) or, case of Mn, a potential health hazard. Over 6000 measurements Mn Fe Southeast Asia Bangladesh were assembled statistically examined with other physicochemical parameters. The machine learning methods random forest generalized boosted regression modeling used spatially continuous environmental...

Most people in Ghana have no or only basic access to safely managed water. Especially rural areas, much of the population relies on groundwater for drinking, which can be contaminated with fluoride and lead dental fluorosis. Children under age two are particularly susceptible adverse effects retain 80-90% a dose, compared 60% adults. Despite numerous local studies, spatially continuous picture exists contamination across Ghana, nor is there any estimate what proportion potentially exposed...

Abstract Protection of groundwater resources is essential to ensure quality and sustainable use. However, predicting vulnerability anthropogenic pollution can be difficult where data are limited. This particularly true in the Sahel region Africa, which has a rapidly growing population increasing water demands. Here we use measurements tritium ( 3 H) with machine learning create an aquifer map (of western Sahel), forms important basis for management. Modelling shows that arid areas greater...

Abstract An assessment of global groundwater quality is needed in response to the threats posed by anthropogenic and geogenic contaminants. This essay summarises challenges involved, including a large number potentially relevant water parameters, poor availability data many regions complex nature systems. Direct monitoring can sometimes be augmented indirect methods such as earth observations, involving citizen science. A new web portal being developed complement existing databases.

Changes in climate and anthropogenic activities have made water salinization a significant threat worldwide, affecting biodiversity, crop productivity contributing to insecurity. The Horn of Africa, which includes eastern Ethiopia, northeast Kenya, Eritrea, Djibouti, Somalia, has natural characteristics that favor high groundwater salinity. Excess salinity been linked infrastructure health problems, including increased infant mortality. This region suffered successive droughts limited the...

Abstract Geogenic arsenic contamination in groundwaters poses a severe health risk to hundreds of millions people globally. Notwithstanding the particular risks exposed populations Indian sub-continent, at time writing, there was paucity geostatistically based models spatial distribution groundwater hazard India. In this study, we used logistic regression secondary data with research-informed soil, climate and topographic variables as principal predictors generate maps resolution 1 km across...

Groundwater nitrate (NO3-) pollution is a pressing issue linked to agricultural practices, urbanization, and industrial activities. This study focuses on Taiwan’s groundwater nitrogen (NO3-N) contamination by integrating satellite remote sensing, monitoring, various environmental factors using GIS. Data from 451 monitoring stations, sampled quarterly 2020 2024, reveal that NO3-N concentrations generally range between 1–10 mg/L, while approximately 2% exceed Drinking Water...

Arsenic contamination in topsoil, primarily geogenic origin, presents significant public health risks due to its potential accumulation agricultural products. This study focuses on predicting arsenic concentrations across European topsoil using machine learning. The analysis integrates geochemical data from the GEMAS (Geochemical Mapping of Agricultural and Grazing Land Soil Europe) database 15 environmental variables (climate, geological, soil, hydrological factors) create a map spatial...

Excessive nitrate in groundwater, which is primarily caused by anthropogenic activities, a worldwide problem. Consequently, Goal 6 of the UN Sustainable Development Goals lists as one key indicators groundwater quality. However, most countries, nationwide occurrence unknown, monitoring networks only represent small points space. To bridge this gap, machine learning modelling that predicts concentrations at high spatial resolution promising tool to identify high-risk areas. Here, we use...

Northeastern Iran. In northeastern Iran, water needed for municipal and agricultural activities mainly comes from groundwater resources. However, it is subject to substantial anthropogenic geogenic contamination. We characterize the sources of contamination by employing an integrated approach that can be applied identification large-scale in other regions. An existing dataset georeferenced quality parameters 676 locations northeast Iran was analyzed investigate geochemical properties...