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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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1,663 records · Page 88Linked to original sources
USGS1976

Technique for estimating magnitude and frequency of floods in Kentucky

This report presents flood magnitude and frequency relations applicable to unregulated streams in Kentucky. The relations are based on flood data at 117 gaging stations in Kentucky and 14 in adjacent states having 10 or more years of record not significantly affected by man-made changes. Equations that relate flood magnitude and frequency to contributing drainage area in 16 geographic areas may be used to estimate magnitude of future floods with recurrence intervals of as much as 100 years on gaged and ungaged streams having drainage areas of 10 to 4,300 square miles (25 .9 to 11,100 square kilometers) . Estimating equations are also presented in graphical form for the convenience of the user. Additional graphs are presented to estimate flood magnitude for selected recurrence intervals along the Cumberland, Kentucky, and Ohio Rivers.

Kentucky↗
USGS2025

A time-space model of graphite mineral systems

Increasing demand for graphite in energy storage systems warrants review of graphite ore genesis in a mineral systems framework. Orogenic graphite encompasses the metamorphic and orogenic mineral systems that produce flake graphite and hydrothermal vein (lump and chip) graphite deposits, respectively. A common feature of orogenic graphite deposits is an association with upper amphibolite- to granulite-facies metasedimentary rocks in continent-continent or continent-island arc collisional orogens. Orogenic flake graphite deposits form primarily through graphitization of organic carbon during regional metamorphism, but strain localization and partial melting of pelitic protoliths are likely important processes for graphite grade and quality enrichment. Orogenic vein graphite deposits precipitate from hydrothermal fluids, possibly derived from metamorphism or anatexis at depth. Decarbonation reactions in mixed calcareous-carbonaceous metasedimentary sequences are the most likely carbon sources for the veins. In contrast, intrusion-related graphite includes magmatic-hydrothermal and metamorphic mineral systems that form primarily in continental arc settings via the interaction of magmas with carbonaceous sedimentary packages. Magmatic-hydrothermal flake graphite deposits are hosted in plutonic and volcanic rocks, and result from the exsolution of CO 2 -CH 4 -rich fluids from melts contaminated by such packages. Contact metamorphism of carbonaceous sedimentary rocks by plutons produces some microcrystalline (amorphous) graphite deposits, including many in China. Compilation of geologic data from known graphite deposits globally suggests that pulses of carbon deposition in the Paleoproterozoic, Mesoproterozoic, and Neoproterozoic provided source carbon material. Subsequent supercontinent orogenesis at ca. 2,100 to 1,700 Ma (Columbia), ca. 1,300 to 1,000 Ma (Rodinia), and ca. 650 to 500 Ma (Gondwana) resulted in the genesis of orogenic flake and vein graphite deposits, where favorable geologic components overlapped with organic ± carbonate carbon-rich strata. Cryogenian deposition of graphite protoliths and Cryogenian – Cambrian metamorphic mineralization account for nearly 75% of all known resources globally and coincide with profound carbon isotope excursions and climate variability, implying a link with the global carbon budget. Comparatively few graphite deposits are associated with Pangea-forming orogens, attributed to less exhumation and/or denudation. High-temperature metasedimentary belts containing organic carbon-rich protoliths are most favorable for hosting orogenic flake graphite deposits, whereas sequences that also contain carbonate protoliths are favorable for orogenic graphite veins. Continent-scale orogenic belts may host both deposit types along with vanadium deposits. Use of a time-space mineral systems framework for graphite deposits can improve exploration models needed to ensure future supply of this critical mineral and provide insights into Earth’s long-term carbon cycle.

Mineralium Deposita↗
USGS2026

Estimating paleotemperature using stable isotopes of soil-formed phyllosilicates from paleosols: A review

Fossilized soils, or paleosols, contain soil-formed phyllosilicates whose stable isotopic compositions may be used to calculate paleotemperature and thus reconstruct ancient terrestrial environments. Though paleosols are common in the geologic record, the use of phyllosilicates as paleotemperature proxies is limited in the literature owing to difficulties with selecting optimal paleosols, isolation from non-clay minerals and organic materials, mixtures of phyllosilicates in natural samples, wide variations of chemical compositions for phyllosilicates, and limited to undefined equilibrium fractionation factors between phyllosilicates-water. Here, we address these challenges by examining and comparing methods used for sample selection, mineral isolation, pretreatments, mineral identification, conventional and developing methods for oxygen and hydrogen isotopic analyses, and determination of phyllosilicate-water equilibrium fractionation factors, concluding with recommendations for best approaches for paleotemperature estimation. Additionally, we discuss how to identify and avoid detrital phyllosilicates, the impacts of diagenesis, comparison of stable isotope and non-isotope paleosol paleotemperature proxies, and challenges and opportunities for broadly using paleosols as paleoclimate archives. With ongoing efforts to refine this multi-faceted paleotemperature approach, the stable isotope geochemistry of soil-formed phyllosilicates continues to be an invaluable proxy system, enhancing our understanding of terrestrial paleoenvironments and paleoclimate.

Kansas↗
USGS2025

Fingerprinting magmatic REE deposit sources with zircon petrochronology

Carbonatites and associated alkaline silicate rocks are of considerable economic interest due to their enrichments in rare earth elements. The petrogenesis and source(s) of these complexes, however, are poorly understood. Models propose either mantle plume-derived carbon-rich melts or a mantle source enriched by subduction-related metasomatism. We use zircon trace elements to fingerprint carbonatite-alkaline complex sources, focusing on the economically significant 1.4 Ga Mountain Pass intrusive suite (MPIS). Autocrystic zircon from MPIS alkaline silicate rocks are enriched in Th and U relative to Nb suggesting a subduction influenced, oxidized source region. Lower Sc/Yb, higher Ti concentrations, and the absence of Eu anomalies in autocrystic zircon suggest derivation from a less hydrous, hotter, and deeper mantle source relative to arc-related 1.8–1.6 Ga inherited zircon. These data are best explained by syn- to post-collisional Mesoproterozoic reactivation of a subduction-metasomatized mantle source. The source and tectonic setting of the MPIS contrasts with Mesoproterozoic Gifford Creek and Bayan Obo carbonatites which exhibit plume-like compositions and are associated with rifts, suggesting different sources and tectonic settings for these economically significant deposits. We demonstrate that zircon petrochronology is a robust method for distinguishing carbonatite sources and can inform more targeted exploration strategies for critical mineral resources.

Conference Paper↗
USGS2025

Effect of land cover type on 3D deformation recovery from synthetically deformed high-resolution satellite optical imagery

The limits of detection for earthquake surface deformation in the spatial domain have improved with advances in remote sensing imagery data availability, resolution, and analysis. Sub-pixel correlation and digital elevation model (DEM) differencing from sub-meter, earthquake-spanning satellite optical imagery has enhanced surface rupture mapping and deformation measurements. However, knowledge of measurement accuracy and uncertainty is limited. To address this, we construct orthophotos and digital elevation models (DEMs) from repeat high resolution (∼0.5 m) satellite optical imagery along two sections of the Garlock fault, California with clear fault geomorphology and differing land cover. We deform later sets of DEMs and images with synthetic earthquakes containing both diffuse and discrete horizontal and vertical displacements. Sub-pixel image correlation and DEM differencing demonstrate how vegetation degrades recovered displacement accuracy. In barren land cover, horizontal displacements are detectable to an expected ∼1/10th-pixel size. With shrubs, trees, and grass, detectable displacements increase to >1/2-pixel size, and filtering results by correlation score and using elevation values as input rather than image values improves accuracy. Vertical displacement detection thresholds remain lower in vegetation, at >1-pixel size. Higher slope angles degrade displacement recovery, worsened by vegetation. Diminishing seasonal separation improves accuracy over vegetated regions, though not to the level achieved in barren environments. These results will inform research and operational efforts on the utility of high resolution satellite optical imagery for detecting deformation in varied land cover. Furthermore, they reveal where alternative measurements, such as from LiDAR or radar interferometry, are required to mitigate the effects of vegetation and capture fine-scale crustal deformation.

California↗
USGS2025

Tracing mercury from land to river: Global sources, retention, and implications for sustainability

Mercury (Hg) pollution in river systems is a global sustainability challenge. Yet the transport, transformation, and retention of Hg within global rivers remain poorly quantified, particularly in regions with sparse observations such as Southeast Asia and Africa, hindering effective pollution mitigation and reinforcing geographic inequities in scientific knowledge and environmental governance. Here, we present the first global, high-resolution simulation of riverine Hg dynamics using a process-based model that traces Hg from land-based sources through river networks to the ocean. Under a realistic scenario, we estimate that ~1,900 megagrams per year (Mg/yr) of Hg enters global rivers, including 1,500 Mg/yr from human-induced sources and 400 Mg/yr from soil erosion. Nearly half of this flux (~1,000 Mg/yr) is retained in reservoirs and dams, which act as major sinks. While such retention limits downstream delivery to the oceans, it also heightens in-reservoir Hg methylation risks. By bridging the gap between Hg releases and observed riverine exports, our framework offers a scalable tool for data-limited regions, promotes data access, and supports global freshwater and pollution-management strategies.

EarthArXiv↗
USGS2026

Season and antecedent conditions impact concentration-discharge relationships for dissolved organic carbon and alkalinity in southeast Alaskan watershed

Fluvial export of dissolved carbon plays an important role in watershed-scale biogeochemistry. Predicted changes in climate are expected to impact watershed hydrologic regimes, and in turn, the sources and export of dissolved carbon from watersheds. Here, we utilize high resolution measurements of discharge and dissolved carbon concentration to examine how concentration-discharge (CQ) relationships vary seasonally and during high flow events over the main runoff season (May–October) in a temperate forested watershed in Southeast Alaska. Concentration-discharge relationships for dissolved organic carbon (DOC) and alkalinity demonstrated strong seasonal patterns, with more linear relationships in May and June versus other months. Changing power law model slopes ( b values; the exponent in a power law regression between runoff and carbon yields) indicated potentially shifting watershed sources (biogenic vs. geologic) and contrasting dominant flowpaths (shallow vs. deeper groundwater) for DOC and alkalinity over the sampling period. During the largest storm event of the study, DOC and alkalinity b values shifted from an overall pattern of transport (mean b = 1.58 values >1.0 indicate transport limitation) and source limitation (mean b = 0.48, values <1.0 indicate source limitation) to chemostatic (DOC, b = 0.99; alkalinity, b = 1.019). In June through August, patterns in hysteresis index suggest that CQ relationships were altered when storms followed in close succession to each other. Together, these findings indicate that seasonal and antecedent flow conditions play a role in dissolved carbon export from forested watersheds. Understanding these dynamics, particularly during winter months, will become increasingly important as changes to hydroclimate impact riverine carbon export.

Alaska↗
USGS2026

Tracing mercury from land to river: Global sources, retention, and implications for sustainability

Mercury (Hg) pollution in river systems is a global sustainability challenge. Yet the transport, transformation, and retention of Hg within global rivers remain poorly quantified, particularly in regions with sparse observations such as Southeast Asia and Africa, hindering effective pollution mitigation and reinforcing geographic inequities in scientific knowledge and environmental governance. Here, we present the first global, high-resolution simulation of riverine Hg dynamics using a process-based model that traces Hg from land-based sources through river networks to the ocean. Under a realistic scenario, we estimate that ∼1900 megagrams per year (Mg/yr) of Hg enters global rivers, including 1500 Mg/yr from human-induced sources and 400 Mg/yr from soil erosion. Nearly half of this flux (∼1000 Mg/yr) is retained in reservoirs and dams, which act as major sinks. While such retention limits downstream delivery to the oceans, it also heightens in-reservoir Hg methylation risks. By bridging the gap between Hg releases and observed riverine exports, our framework offers a scalable tool for data-limited regions, promotes data access, and supports global freshwater and pollution-management strategies.

Environmental Science & Technology↗
USGS2025

Physicochemical properties and bioreactivity of sub-10 µm geogenic particles: Comparison of volcanic ash and desert dust

Exposure to ambient particulate matter (PM) with an aerodynamic diameter of <10 μm (PM 10 ) is a well-established health hazard. There is increasing evidence that geogenic (Earth-derived) particles can induce adverse biological effects upon inhalation, though there is high variability in particle bioreactivity that is associated with particle source and physicochemical properties. In this study, we investigated physicochemical properties and biological reactivity of volcanic ash from the April 2021 eruption of La Soufrière volcano, St. Vincent, and two desert dust samples: a standardized test dust from Arizona and an aeolian Gobi Desert dust sampled in China. We determined particle size, morphology, mineralogy, surface texture and chemistry in sub-10 μm material to investigate associations between particle physicochemical properties and observed bioreactivity. We assessed cellular responses (cytotoxic and pro-inflammatory effects) to acute particle exposures (24 hr) in monocultures at the air-liquid interface using two types of cells of the human airways: BEAS-2B bronchial epithelial cells and A549 alveolar type II epithelial cells. In acellular assays, we also assessed particle oxidative potential and the presence of microorganisms. The results showed that volcanic ash and desert dust exhibit intrinsically different particle morphology, surface textures and chemistry, and variable mineralogical content. We found that Gobi Desert dust is more bioreactive than freshly erupted volcanic ash and Arizona test dust, which is possibly linked to the presence of microorganisms (bacteria) and/or nanoscale elongated silicate minerals (potentially clay such as illite or vermiculite) on particle surfaces.

GeoHealth↗
USGS2025

Beyond optimality: Dryland ecosystems infrequently use water efficiently for carbon gain

Optimality theory assumes plants maximize carbon gain per unit water lost and is often implemented to scale leaf-level carbon gain and water use to regional and global scales. Optimality theory is often mathematically represented by assuming plant water-use efficiency (WUE) scales with VPD k , where k = ½ represents expected optimal behavior. It is unclear, however, if this relationship holds in arid and semi-arid ecosystems that are strongly impacted by soil and atmospheric moisture status. We used data from seven flux tower sites along an aridity gradient in New Mexico to answer: how does the relationship between WUE and VPD compare to expectations based on optimality theory? To address this question, we integrated the Dynamic Evapotranspiration Partitioning Approach for Rapid Timescales with a stochastic antecedent model to estimate ecosystem-level WUE (GPP/T) and the net sensitivity of WUE to VPD, or k Dynamic , which we compare to the theoretical optimal sensitivity of k = ½. Our results show that optimality theory is not always appropriate, and k Dynamic often deviates from ½, especially at some of the more arid sites or during periods of low soil moisture. At less arid, higher elevation sites, k Dynamic is most consistent with optimality theory at moderate VPD levels, but not at high VPD. In general, the sensitivity of WUE to VPD is highly variable such that k Dynamic exhibits notable daily and seasonal variability, suggesting highly dynamic stomatal behavior. These results emphasize that representing plant water-use strategies as dynamic in time and space is critical to improving large-scale estimates of plant water use.

New Mexico↗
USGS2026

Rapid earthquake magnitude classification via P-wave strains from borehole strainmeters and Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) offers a promising approach for earthquake early warning (EEW) in settings where seismic networks are costly to maintain. By repurposing fiber-optic cables as dense strainmeter arrays, DAS enables real-time earthquake detection wherever those fibers are accessible. However, poor azimuthal coverage and challenges in estimating magnitude from strain measurements remain key hurdles in applying for earthquake monitoring. Here, we develop a machine learning method to distinguish large (M≥5.4) earthquakes from smaller ones within the first 4 seconds of a strain waveform after a P-wave arrival without determining location. Using ensemble decision tree models trained on borehole strainmeter data (3.5≤M≤7.1) and tested on onshore DAS waveforms (including the 2024 M7 Offshore Cape Mendocino earthquake), we find that low-frequency (0.2–0.5 Hz) continuous wavelet transform coefficients are the strongest predictors of magnitude, in addition to strain amplitude. Both DAS and borehole strainmeters effectively capture long-period strain signals, making these findings valuable for EEW systems. Our method shows high precision compared to the real-time EEW system, ShakeAlert®, supporting the position that DAS is a viable technology for earthquake monitoring and magnitude classification.

California↗
USGS2024

Turbidite correlation for paleoseismology

Marine turbidite paleoseismology relies on the assumption of synchronous triggering of turbidity currents by earthquake shaking to infer rupture extent and recurrence. Such inference commonly depends on age dating and correlation of the physical stratigraphy of deposits carried by turbidity currents (i.e., turbidites) across great distances. Along the Cascadia subduction zone, which lies offshore the Pacific Northwest, USA, turbidite facies in core photographs, X-ray computed tomography images, and magnetic susceptibility (MS) data exhibit differences in character over relatively short distances, which implies that not all deposits can be correlated with confidence. Thus, subjective correlation based on expected similarity over great distances and weak age constraints does not independently support paleoseismic models. We present a new method for correlating turbidites along the Cascadia margin that can yield a more objective and repeatable stratigraphic framework to underpin earthquake recurrence. We use dynamic time warping to correlate MS logs and measure correlation coefficients of core pairs to evaluate correlation strength. We then compare these measures to a distribution of correlation coefficients of randomly generated turbidite sequences and find that only a small number of core pairs can be correlated more confidently than randomly stacked turbidites. This methodology promises a more robust correlation strategy for future stratigraphic studies.

Oregon, Washington↗
USGS2026

SlideDetect: Spatio-temporal landslide detection using a three-dimensional convolutional neural network

Landslides pose a serious and ongoing threat to both human lives and infrastructure worldwide; therefore, it is of interest to predict where and when landslides are likely to occur. Advances in machine learning techniques have spurred numerous studies aimed at estimating relative landslide propensity, but are limited to spatial (as opposed to temporal) prediction due to the sparsity of landslide timing data. We address this data gap by training SlideDetect, a 3-dimensional convolutional neural network (3D CNN), to identify landslides based on their spatial and temporal occurrence within multitemporal image stacks. We use an inventory of landsides triggered by the 2018 Hokkaido earthquake and two years of monthly composite optical imagery spanning this event. The model can identify not only landslide location but also landslide date with an area under the precision-recall curve (PR-AUC) of 0.84. We further present a new standard for presenting PR curve results that explicitly compares model performance at different confidence thresholds, allowing for clearer model evaluation and comparison. Our new approach to constraining landslide timing paired with this more consistent and objective method for evaluating model performance shows considerable promise, and with further application and testing, SlideDetect could enhance the data availability and tools needed to advance landslide hazard and risk assessments.

JGR Machine Learning and Computation↗
USGS2024

Using a time-of-travel sampling approach to quantify per- and polyfluoroalkyl substances (PFAS) stream loading and source inputs in a mixed-source, urban catchment

Understanding per- and polyfluoroalkyl substances (PFAS) mass distribution in surface and groundwater systems can support source prioritization, load reduction, and water management. Thirteen sites within an urban catchment were sampled utilizing a time-of-travel sampling approach to minimize the influence of subdaily fluctuations in mass from PFAS point sources and to quantify PFAS and ancillary chemical loads from various PFAS sources. A larger increase in perfluoroalkyl sulfonate (PFSA) loads (8 to 11 μg/s, up to 618%) than in perfluoroalkyl carboxylate (PFCA) loads (no change to 3.4 μg/s, up to 122%) was observed at sites below tributaries influenced by military bases with known groundwater discharge. Point discharges from two sewage treatment plants (STPs) resulted in increases in PFCA and PFSA loads that were similar (6 and 10 μg/s respectively) below the first STP and greater for PFCA compared to PFSA loads (23 and 13 μg/s respectively) below the second STP. Overall, percent increases in total PFAS load ranged from 20 to 277% for military base inputs and 44 to 77% for STP inputs. A focus catchment that represents only 14% (76.9 km 2 ) of the drainage area at the most downstream site (544 km 2 ) accounted for about 70% of PFSA and 40% of PFCA loads observed at the most downstream site. Results show that by using a time-of-travel sampling approach in mixed, urban settings with several PFAS sources, it is possible to quantify stream loads from individual PFAS sources, thereby improving source attribution and providing actionable data for water-resource managers.

ACS ES&T Water↗
USGS2025

Assessment of density pattern retention of generalized data for 1:100,000-scale United States topographic maps

Cartographic generalization reduces the complexity of geographic data to produce legible, smaller-scale displays that retain essential information and logical geographic patterns. Generalization is a vital process in topographic map production. An important challenge in this process is managing and evaluating consistency across scale in the density and spatial distribution of map features such as buildings, roads, streams, water bodies, and elevation contours. Density patterns in these features reflect underlying physiographic conditions, which include factors such as bedrock geology, tectonics, climate, and landforms. Assessments of an acceptable level of change in feature density patterns are critical to ensuring the readability, usability, and accuracy of generalized maps and data. Preserving realistic density patterns across mapping scales also supports sustainable development goals in cartography, by helping to prioritize and communicate the relative reliability of geospatial data at specific scales.

Conference Paper↗
USGS2024

Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning

Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.

Michigan, Wisconsin↗
USGS2025

Smectite-rich horizons in Inceptisols trigger shallow landslides in tropical granitic terranes

Puerto Rico was affected by >70,000 landslides in the wake of 2017 Hurricane Maria, and landslide prevalence was especially high in the Utuado region in the Cordillera Central. Landslide density was highest where soil parent material is granodiorite; landslide slip surfaces tended to be shallow (<60 cm), and often were mobilized rapidly and with long runout distances. This study combines field observations with soil mineralogy (bulk and clay fractions), soil geochemistry (bulk fraction), and soil strength as determined by field cone penetrometer testing (CPT) to test the hypothesis that clay-rich subsoil horizons function as slip planes when water-logged. Soil pits were excavated to depths of ∼200 cm in Ultisols on an undulating plateau and to ∼100 cm in Inceptisols on steep slopes (36-43 o ) that flank the plateau and cone penetrometer tests (CPT) were done within 2 m of the soil pit. Six pits were located adjacent to scarps from previous landslides, enabling analysis of soil profiles downward through extrapolated slip surfaces. Results from X-ray diffraction (XRD), X-ray fluorescence (XRF) and thermogravimetric analysis (TGA) indicate that soils are heterogeneous, often with subsoil horizons enriched in clay minerals and immobile elements (Al, Fe, Ti). Inceptisols on steep slopes often contain smectite-rich horizons at 30–60 cm depth that appear to function as slip surfaces; in other Inceptisols, such horizons are not present and landslide susceptibility is potentially lower. In Ultisols, soil mineralogy is dominated by kaolinite with minor halloysite, and increased kaolinite content at soil depths ≥80 cm at some sites suggests potential slip surfaces enhancing probability of landslides. The origin of clay-rich horizons appears to be (1) fractures in granodiorite that facilitate water flow and leaching, accelerating mineral dissolution during early weathering stages, and (2) smectite-rich buried soils under permeable colluvium likely deposited by a prior mass wasting event. Where clay-rich layers occur beneath more-permeable horizons, rapid infiltration then absorption of water in clay-rich subsoil horizons causes decreased shear strength and increased landslide susceptibility.

Puerto Rico↗
USGS2025

Statewide cumulative human health risk assessment of inorganics-contaminated groundwater wells, Montana, USA

Across the United States, rural residents rely on unregulated and generally unmonitored private wells for drinking water, which may pose serious health risks due to unrecognized contaminants. We assessed the nature, degree, and spatial distribution of cumulative health risks from inorganic contaminants in groundwater. Our analysis included nearly 84,000 data points from 6500+ wells, across 51 of Montana's 98 watersheds, using a public groundwater database. We compared a drinking water screening level cumulative risk assessment (CRA) for inorganics based on the U.S. Environmental Protection Agency (EPA) protective health thresholds (Maximum Contaminant Level Goals, Health Advisories [MCLG-HAs]) to a CRA based on EPA public supply enforceable Maximum Contaminant Levels (MCLs). Based on median concentrations of 19 inorganics (antimony, arsenic, barium, beryllium, boron, cadmium, chromium, copper, fluoride, manganese, molybdenum, nickel, nitrate, lead, selenium, strontium, thallium, uranium, zinc), 75% of watersheds had MCLG-HA-based cumulative risk values > 1.0; arsenic and uranium contributed the most risk, followed by strontium, fluoride, manganese and boron. Hence, this screening level (Tier I) CRA indicated widespread potential for unrecognized human health risk to private well users from inorganic contaminants considering both carcinogenic and non-carcinogenic risks. Sensitivity analysis showed that benchmarks applied (MCLG-HAs versus MCLs) exerted the largest control on results. Our findings identify priority regions for Tier 2 risk assessments to elucidate local sources and distributions of geogenic versus anthropomorphic contaminants. Our study is the first statewide assessment of cumulative health risk from groundwater that we are aware of, and results support increased statewide drinking water education and testing to reduce human health risks from contaminated private well water.

Montana↗