Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated data points of measured groundwater arsenic concentration. Our global prediction map includes known arsenic-affected areas and previously undocumented areas of concern. By combining the global arsenic prediction model with household groundwater-usage statistics, we estimate that 94 million to 220 million people are potentially exposed to high arsenic concentrations in groundwater, the vast majority (94%) being in Asia. Because groundwater is increasingly used to support growing populations and buffer against water scarcity due to changing climate, this work is important to raise awareness, identify areas for safe wells, and help prioritize testing.

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

The near juxtaposition of the Makgadikgadi Basin (Botswana), the world's largest saltpan complex, with the Okavango Delta, one of the planet's largest inland deltas (technically an alluvial megafan), has intrigued explorers and scientists since the middle of the 19 th century. It was clear from early observations that the Makgadikgadi Basin once contained a huge lake, paleo-Lake Makgadikgadi. Several authors have since speculated that this lake also covered wide regions to the north and west of the Makgadikgadi Basin. Our interpretation of unusually high-quality helicopter time-domain electromagnetic (HTEM) data indicates that paleo-Lake Makgadikgadi extended northwestward at least into the region presently occupied by the Okavango Delta. The total area of paleo-Lake Makgadikgadi exceeded 90,000 km 2 , larger than Earth's most extensive freshwater body today, Lake Superior (North America). Our HTEM data, constrained by ground-based geophysical and borehole information, also provide evidence for a paleo-megafan underlying paleo-Lake Makgadikgadi sediments.

For about the past eight decades, high concentrations of naturally occurring fluoride have been detected in groundwater in different parts of India. The chronic consumption of fluoride in high concentrations is recognized to cause dental and skeletal fluorosis. We have used the random forest machine-learning algorithm to model a data set of 12 600 groundwater fluoride concentrations from throughout India along with spatially continuous predictor variables of predominantly geology, climate, and soil parameters. Despite only surface parameters being available to describe a subsurface phenomenon, this has produced a highly accurate prediction map of fluoride concentrations exceeding 1.5 mg/L at 1 km resolution throughout the country. The most affected areas are the northwestern states/territories of Delhi, Gujarat, Haryana, Punjab, and Rajasthan and the southern states of Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana. The total number of people at risk of fluorosis due to fluoride in groundwater is predicted to be around 120 million, or 9% of the population. This number is based on rural populations and accounts for average rates of groundwater consumption from nonmanaged sources. The new fluoride hazard and risk maps can be used by authorities in conjunction with detailed groundwater utilization information to prioritize areas in need of mitigation measures.

Groundwater is a critical resource in India for the supply of drinking water and for irrigation. Its usage is limited not only by its quantity but also by its quality. Among the most important contaminants of groundwater in India is arsenic, which naturally accumulates in some aquifers. In this study we create a random forest model with over 145,000 arsenic concentration measurements and over two dozen predictor variables of surface environmental parameters to produce hazard and exposure maps of the areas and populations potentially exposed to high arsenic concentrations (>10 µg/L) in groundwater. Statistical relationships found between the predictor variables and arsenic measurements are broadly consistent with major geochemical processes known to mobilize arsenic in aquifers. In addition to known high arsenic areas, such as along the Ganges and Brahmaputra rivers, we have identified several other areas around the country that have hitherto not been identified as potential arsenic hotspots. Based on recent reported rates of household groundwater use for rural and urban areas, we estimate that between about 18–30 million people in India are currently at risk of high exposure to arsenic through their drinking water supply. The hazard models here can be used to inform prioritization of groundwater quality testing and environmental public health tracking programs.

The Okavango Delta of northern Botswana is one of the world's largest inland deltas or megafans. To obtain information on the character of sediments and basement depths, audiomagnetotelluric (AMT), controlled-source audiomagnetotelluric (CSAMT) and central-loop transient electromagnetic (TEM) data were collected on the largest island within the delta. The data were inverted individually and jointly for 1-D models of electric resistivity. Distortion effects in the AMT and CSAMT data were accounted for by including galvanic distortion tensors as free parameters in the inversions. By employing Marquardt-Levenberg inversion, we found that a 3-layer model comprising a resistive layer overlying sequentially a conductive layer and a deeper resistive layer was sufficient to explain all of the electromagnetic data. However, the top of the basal resistive layer from electromagnetic-only inversions was much shallower than the well-determined basement depth observed in high-quality seismic reflection images and seismic refraction velocity tomograms. To resolve this discrepancy, we jointly inverted the electromagnetic data for 4-layer models by including seismic depths to an interface between sedimentary units and to basement as explicit a priori constraints. We have also estimated the interconnected porosities, clay contents and pore-fluid resistivities of the sedimentary units from their electrical resistivities and seismic P-wave velocities using appropriate petrophysical models. In the interpretation of our preferred model, a shallow ∼40 m thick freshwater sandy aquifer with 85-100 m resistivity, 10-32 per cent interconnected porosity and <13 per cent clay content overlies a 105-115 m thick conductive sequence of clay and intercalated saltwater-saturated sands with 15-20 m total resistivity, 1−27 per cent interconnected porosity and 15-60 per cent clay content. A third ∼60 m thick sandy layer with 40-50 m resistivity, 10-33 per cent interconnected porosity and <15 per cent clay content is underlain by the basement with 3200-4000 m total resistivity. According to an interpretation of helicopter TEM data that cover the entire Okavango Delta and borehole logs, the second and third layers may represent lacustrine sediments from Paleo Lake Makgadikgadi and a moderately resistive freshwater aquifer comprising sediments of the recently proposed Paleo Okavango Megafan, respectively.

Helicopter time-domain electromagnetic (HTEM) surveying has historically been used for mineral exploration, but over the past decade it has started to be used in environmental assessments and geologic and hydrologic mapping. Such surveying is a cost-effective means of rapidly acquiring densely spaced data over large regions. At the same time, the quality of HTEM data can suffer from various inaccuracies. We developed an effective strategy for processing and inverting a commercial HTEM data set affected by uncertainties and systematic errors. The delivered data included early time gates contaminated by transmitter currents, noise in late time gates, and amplitude shifts between adjacent flights that appeared as artificial lineations in maps of the data and horizontal slices extracted from inversion models. Multiple processing steps were required to address these issues. Contaminated early time gates and noisy late time gates were semiautomatically identified and eliminated on a record-by-record basis. Timing errors between the transmitter and receiver electronics and inaccuracies in absolute amplitudes were corrected after calibrating selected HTEM data against data simulated from accurate ground-based TEM measurements. After editing and calibration, application of a quasi-3D spatially constrained inversion scheme significantly reduced the artificial lineations. Residual lineations were effectively eliminated after incorporating the transmitter and receiver altitudes and line-to-line amplitude factors in the inversion process. The final inverted model was very different from that generated from the original data provided by the contractor. For example, the average resistivity of the thick surface layer decreased from ∼1800 to ∼30 Ωm, the depths to the layer boundaries were reduced by 15%-23%, and the artificial lineations were practically eliminated. Our processing and inversion strategy is entirely general, such that with minor system-specific modifications it could be applied to any HTEM data set, including those recorded many years ago.