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301 records · Page 5Linked to original sources
USGS1997

Nutrients and pesticides in ground water of the Ozark Plateaus in Arkansas, Kansas, Missouri, and Oklahoma

A total of 229 ground-water samples were collected from 215 sites as part of the Ozark Plateaus study unit of the National Water-Quality Assessment Program. These samples were collected from 1993 through 1995 using a network of springs and wells with three scale-dependent components. The first component, the study-unit survey, consisted of 99 randomly selected springs and domestic wells in the Springfield Plateau and Ozark aquifers. The second component, two land-use studies, consisted of 42 springs and domestic wells in a poultry-dominated agricultural area and 40 springs and domestic wells in a cattle-dominated agricultural area overlying the Springfield Plateau aquifer. The third component, the small-watershed study, consisted of 4 springs, 18 domestic wells, and 11 monitoring wells in a small basin within the poultry land-use study area. Samples were analyzed for major ions, nutrients, dissolved organic carbon, methylene blue active substances, tritium, and 88 pesticides and metabolites.The water-quality data from these samples were analyzed with descriptive and statistical methods. Nitrite plus nitrate, which was detected more often and in greater concentrations than any of the other nutrients, ranged from less than 0.05 to 25 milligrams per liter as nitrogen. Nitrite plus nitrate concentrations positively correlated to percent agricultural land use around each site. Median nitrite plus nitrate concentrations generally were greater in samples from springs than in samples from wells. Concentrations of nitrite, ammonia, and ammonia plus organic nitrogen were also affected by land use and also by concentrations of dissolved oxygen in the ground water. Concentrations of phosphorus and orthophosphate probably were affected by land use and also by phosphorus solubility. Pesticides were detected in 80 of 229 samples from 73 of 215 sites. A total of 20 pesticides were detected with a maximum of 5 pesticides detected in any 1 sample. The most commonly detected pesticides were tebuthiuron, atrazine, prometon, desethylatrazine, and simazine. Maximum concentrations ranged from 0.003 to 1.0 microgram per liter. The occurrence and distribution of pesticides were related to land use. Percent agricultural land use was greater for samples with pesticides detected than for samples with no pesticides detected. Pesticides were detected more often in samples from springs than in samples from wells. The occurrence of pesticides also was related to seasonality and chemical characteristics, such as solubility and persistence, of the compounds.

Arkansas, Kansas, Missouri, Oklahoma↗
USGS2025

Geochemistry of quartz and fluid inclusions associated with gold, tungsten, and antimony in the Stibnite-Yellow Pine district, Idaho

Deposits in the historic Stibnite-Yellow Pine district in east-central Idaho were mined for Ag, Au, Hg, Sb, and W. Ore is hosted in breccia and fault zones along NE-striking faults that transect the Atlanta lobe of the Late Cretaceous Idaho batholith and metamorphosed Neoproterozoic to lower Paleozoic rocks. Geochronological studies have delineated five superimposed and zoned hydrothermal events in the mining district which range in age from Late Cretaceous to the Eocene. They formed the following consecutive assemblages: (1) vein and disseminated pre-ore milky quartz (± calcite), muscovite, and brassy pyrite with minor molybdenite and base metal sulfides; (2) vein and disseminated quartz, calcite, sericite, auriferous-pyrite, and arsenopyrite; (3) potassium feldspar, quartz, dolomite, and calcite with vein and disseminated scheelite ± pyrite and arsenopyrite; (4) Au-Ag veins that contain adularia; and (5) breccia, vein, and disseminated quartz, calcite, and stibnite with minor Ag sulfosalts and distal zones of cinnabar and realgar at Fern and Hermes mines. In this study, the pressure, temperature, and composition of ore-forming fluids were measured in Au-, W-, and Sb-bearing samples collected from drill core and surface exposures. Using optical petrography, scanning electron microscopy cathodoluminescence (SEM-CL) and electron microprobe cathodoluminescence (EMPA-CL) imaging, as well as electron microprobe (EMP) and laser ablation-inductively coupled plasma-mass spectrometry trace element analyses, four major overprinting generations of hydrothermal quartz are defined. Fluid inclusion assemblages (FIAs) in each generation of quartz were characterized by microthermometry and laser Raman spectroscopy. Quartz and fluid inclusion paragenesis are as follows: (Qi) altered and overprinted Qi with no coeval FIAs that formed before or during event 1; (Q1) early veins as well as disseminated quartz with dull gray SEM-CL and H 2 O-NaCl-CO 2 FIAs that formed at minimum temperatures and pressures of 281° to 358°C and 1346 to 1789 bar during event 2 (Au); (Q2) vein and disseminated quartz, often euhedral, with H 2 O-NaCl ± CO 2 ± CH 4 FIAs that formed at minimum temperatures and pressures of 183° to 213°C and 72 to 602 bar during event 3 (W); and (Q3) euhedral and disseminated quartz with H 2 O-NaCl FIAs that formed at minimum temperature and pressures of 156° to 161°C and 5 to 6 bar during event 5 (Sb). Stibnite hosts H 2 O-NaCl FIAs trapped at minimum temperatures and pressures of 150° to 189°C and 5 to 12 bar that are similar to those of Q3-hosted FIAs. The quartz paragenetic history in the district follows a progressive temporal path from Tienriched quartz deposited by a relatively deep and higher temperature aqueous-carbonic fluid to a Li, Al, and Sb enriched quartz precipitated from a lower temperature aqueous fluid. Gases in fluid inclusion extracts from ore and gangue minerals were analyzed by mass spectrometry. Noble gas isotope analysis of fluid inclusion extracts revealed three signatures. Pre-ore Qi has R/R A (the isotopic ratio of the sample 3 He/ 4 He to atmospheric 3 He/ 4 He (1.384 × 10 −6 )) values that reflect the composition of the crustal melt-derived Atlanta lobe of the Idaho batholith. Quartz (Q1) and auriferous arsenian pyrite of hydrothermal event 2 have 20 Ne/ 4 He and R/R A values that plot along the air-crust mixing line, and these gases were likely introduced by metamorphic fluids derived from metasedimentary rocks. Stibnite with associated quartz (Q3 ± Q2) have 20 Ne/ 4 He and R/R A values that extend from the air-crust mixing line to the air-mantle mixing line, showing a shift from a crustal signature to an overprinting mantle signature. Late calcite has 20 Ne/ 4 He and R/R A values that extend from air toward the mantle end member, which suggests that magmatic volatiles condensed into meteoric groundwater late in the paragenesis. A mantle volatile source is also supported by high 20 Ne/ 22 Ne ratios. The Stibnite-Yellow Pine district is similar to other Au-W-Sb districts around the world in ore fluid chemistries, with late-stage stibnite being a product of cooling. The fluid pressure-temperature-composition (P-T-X) estimates and the crustal source of He during hydrothermal event 2 gold mineralization are characteristic of orogenic Au deposits, and the observed inclusion reequilibration textures indicate that trapped fluid inclusions were deformed during transitions between the brittle-ductile environment. Scheelite was deposited during event 3 with Q2 quartz and is overgrown by Q3 quartz and stibnite. Our interpretation of the quartz texture, chemistry, and fluid inclusion P-T-X data suggest that Au, W, and Sb mineralization occurred as hydrothermal fluids evolved from H 2 O-NaCl-CO 2 at >300°C and depths of ~4 to 6 km near the ductile to brittle transition to H 2 O-NaCl at ~150°C and epithermal depths as the Idaho batholith was exhumed. Geochronology indicates that from the Late Cretaceous to early Eocene, overprinting hydrothermal events formed deep orogenic Au, intermediate W, and shallow epithermal Au-Ag and Sb. Without comprehensive geochronologic constraints, our fluid inclusion data and textural observations could be interpreted as a single evolving hydrothermal system, as has been done in many other mining districts with similar mineralization.

Idaho↗
USGS2026

Quantitative mineral resource assessment of lithium pegmatite deposits in the northern Appalachian orogen, USA

Lithium demand is projected to increase more than 48 times by 2040 due to electric vehicle production and other energy storage needs. Most lithium production is outside of the USA, thereby increasing supply chain vulnerability. The combined end use importance and heightened supply risk of lithium make this lightest metallic element a critical commodity to the USA. To mitigate this supply risk, the US Geological Survey is actively assessing lithium deposits in the USA. Herein, we detail an assessment for lithium-mineralized pegmatites in the US northern Appalachian Mountains. Permissive tracts were generated by cross-referencing tectonic and geologic maps and mineral occurrence data with mappable criteria derived from generalized and region-specific lithium pegmatite ore deposit models; tracts were then ranked as having high, medium, or low permissibility. Available geophysical and geochemical data were found to be of minimal utility for this deposit type at the scale of the assessment. The number of undiscovered deposits were estimated and integrated into probabilistic simulations, which included an expanded and updated global grade and tonnage model of pegmatite-hosted lithium ore. The estimated total amount of undiscovered resources for the northern Appalachian Orogen has a median value of 1,410,000 metric tons of Li 2 O when considering moderate correlation across sub-regions. At a confidence level of 90%, a resource of at least 90,000 metric tons of Li 2 O remains undiscovered, and at a 10% confidence level, a resource of as much as 7,380,000 metric tons Li 2 O remains undiscovered. After applying an up-to-date economic filter to convert median contained lithium to recoverable material, a correlated total of 900,000 metric tons of Li 2 O may be economically extractable, equating to enough Li 2 O to provide the current annual US lithium supply deficit (presently obtained through net imports) for 127 years at 2025 rates of apparent consumption. This period of provision will inevitably shorten with projected increasing consumption rates, emphasizing that further research could be completed to better delineate regions of high lithium resource potential and support exploration and domestic production.

Connecticut, Delaware, Maine, Massachusetts, New H↗
USGS2025

Rare earth elements on the Moon

Rare earth elements (REEs) are a scarce but vital resource for our modern economies and lifestyles. Since the late 1990s, China has supplied the vast majority of the world’s refined REEs. Increasing global demand has broadened the search for REE deposits to unconventional places, including the Moon. Although most lunar rocks have very low REE concentrations, Apollo samples showed that one type of lunar rock containing potassium (K), REEs, and phosphorus (P)—known by the acronym KREEP—has high concentrations of REEs. Data from orbiting satellites have identified locations where substantial deposits of KREEP are likely. The viability of mining these deposits depends on the evolution of REE economics, the development of the Earth-Moon infrastructure, and the findings from future lunar mineral exploration missions.

Fact Sheet↗
USGS2024

Preparing for today's and tomorrow's water-resources challenges in eastern Long Island, New York

Freshwater is a vital natural resource. Although New York is a water-rich State, the wise and economical use of water resources is needed to ensure that there is enough water of adequate quality for both human and ecological needs—both for today and for tomorrow. Nowhere in New York is this more evident than in Nassau and Suffolk Counties on Long Island, where the public water supply is obtained from the sole-source aquifers located directly beneath the nearly 3 million people who live there. In 2023, in eastern Long Island’s Suffolk County, groundwater was pumped from these aquifers by more than 1,100 public water-supply wells to meet the needs of about 1.5 million people.

New York↗
USGS1976

Trace elements in some rocks and slag from the Yemen Arab Republic and their bearing on the iron ore near Sa'dah

Thirty-seven samples of rocks, saprolite, laterite, gossan, and slag were collected in the Yemen Arab Republic during June-July 1975 when field work was conducted to refine an interpretation of the regional geology made from LANDSAT-1 images by M. J. Grolier. These samples we reanalyzed in the laboratories of the U. S. Geological Survey, and the results were interpreted for use in programs of economic development as a guide for the evaluation of mineral resources in the Yemen Arab Republic. The work was undertaken as part of the program of the U. S. Agency for International Development with the cooperation of the Mineral and Petroleum Authority, Ministry of Economy, Yemen Arab Republic.

Open-File Report↗
USGS2025

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California↗
USGS2026

High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs

We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi 2 ), of which 13,148,345 ha (52,706 mi 2 ) are located in Mexico and 24,770,640 ha (95,639 mi 2 ) in the United States. We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations. Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections. The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable. The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community. This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.

Arizona, Baja California, California, Nevada, Sina↗
USGS2024

Lithium resource in the Smackover Formation brines of Southern Arkansas

Lithium-rich brine deposits occur throughout the United States, including in the Smackover Formation. The concentration of lithium in Smackover Formation brines was predicted across southern Arkansas by using a machine-learning model that incorporated lithium concentration data and geologic information. Between 5.1 and 19.0 million metric tons of lithium are calculated to be present in the brines of the Smackover Formation in southern Arkansas. The range in possible total lithium reflects the uncertainty in machine-learning predictions of lithium concentrations and the range of Smackover Formation porosity. This estimate quantifies the in-place lithium resource and does not consider the technological and economic feasibility of extracting the lithium from the brines.

Arkansas↗
USGS1976

Diamond drilling at the Ma'milah Mine, Kingdom of Saudi Arabia

The Ma'millah gold mine is about 90 km southeast of At Ta'if at lat 21°03'N., long 41°18'E., in southwestern Saudi Arabia. The deposit, which was worked extensively by ancient miners, consists of several veins and massive lenses of quartz along a regional fault. The extensive ancient mine workings, large size of some quartz outcrops, and significant gold content of several samples indicated that subsurface exploration was warranted. Accordingly, two holes were drilled into the deposit during the interval December 1972 to February 1973. The drill findings were disappointing; the grade of vein material was found to be too low to be of economic interest and the quartz bodies were found to pinch out both along strike and downdip. The drilling program therefore was terminated. The deposit is not worth further exploration

Ma'milah Mine↗
USGS1998

Water-quality assessment of the Ozark Plateaus study unit, Arkansas, Kansas, Missouri, and Oklahoma — Nutrients, bacteria, organic carbon, and suspended sediment in surface water, 1993-95

Nutrient, bacteria, organic carbon, and suspended- sediment samples were collected from 1993-95 at 43 surface-water-quality sampling sites within the Ozark Plateaus National Water- Quality Assessment Program study unit. Most surface-water-quality sites have small or medium drainage basins, near-homogenous land uses (primarily agricultural or forest), and are located predominantly in the Springfield and Salem Plateaus. The water-quality data were analyzed using selected descriptive and statistical methods to determine factors affecting occurrence in streams in the study unit. Nitrogen and phosphorus fertilizer use increased in the Ozark Plateaus study unit for the period 1965-85, but the application rates are well below the national median. Fertilizer use differed substantially among the major river basins and physiographic areas in the study unit. Livestock and poultry waste is a major source of nutrient loading in parts of the study unit. The quantity of nitrogen and phosphorus from livestock and poultry wastes differed substantially among the river basins of the study unit's sampling network. Eighty six municipal sewage-treatment plants in the study unit have effluents of 0.5 million gallons per day or more (for the years 1985-91). Statistically significant differences existed in surface-water quality that can be attributed to land use, physiography, and drainage basin size. Dissolved nitrite plus nitrate, total phosphorus, fecal coliform bacteria, and dissolved organic carbon concentrations generally were larger at sites associated with agricultural basins than at sites associated with forested basins. A large difference in dissolved nitrite plus nitrate concentrations occurred between streams draining basins with agricultural land use in the Springfield and Salem Plateaus. Streams draining both small and medium agricultural basins in the Springfield Plateau had much larger concentrations than their counterparts in the Salem Plateau. Drainage basin size was not a significant factor in affecting total phosphorus, fecal coliform bacteria, or dissolved organic carbon concentrations. Suspended-sediment concentrations generally were small and indicative of the clear water in streams in the Ozark Plateaus. A comparison of the dissolved nitrite plus nitrate, total phosphorus, and fecal coliform data collected at the fixed and synoptic sites indicates that generally the data for streams draining basins of similar physiography, land-use setting, and drainage basin size group together. Many of the variations are most likely the result of differences in percent agricultural land use between the sites being compared or are discharge related. The relation of dissolved nitrite plus nitrate, total phosphorus, and fecal coliform concentration to percent agricultural land use has a strong positive 2 Water-Quality Assessment-Nutrients, Bacteria, Organic Carbon, and Suspended Sediment in Surface Water, 1993-95 correlation, with percent agricultural land use accounting for between 42 and 60 percent of the variation in the observed concentrations.

Arkansas, Kansas, Missouri, Oklahoma↗
USGS2024

The 3D Elevation Program—Supporting Ohio's economy

Introduction High-quality elevation data are proving to be a resource of great economic value in dealing with many important issues in Ohio. Current and accurate high-resolution elevation data support flood risk management, water quantity and quality assessment, precision farming, conservation planning, impervious-surface modeling, forest and other natural resources management, abandoned mine and geologic hazard assessment, karst mapping, and siting of wellhead pads for horizontal drilling. These data also support coastal zone management, traffic safety and preliminary engineering site-selection studies for transportation infrastructure, solar potential and other renewable energy planning, aviation safety, and identification of features of interest or concern such as archaeological sites and orphan oil and gas wells. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Ohio↗
USGS2024

Characterization of change in tree cover state and condition over the conterminous United States

Variability in the effects of disturbances and extreme climate events can lead to changes in tree cover over time, including partial or complete loss, with diverse ecological consequences. It is therefore critical to identify in space and time the change processes that lead to tree cover change. Studies of change are often hampered by the lack of data capable of consistently detecting different types of change. Using the Landsat satellite record to create a long time-series of land cover and land cover change, the U.S. Geological Survey Land Change Monitoring Assessment and Projection (LCMAP) project has made an annual time series of land cover across the conterminous United States for the period 1985 to 2018. Multiple LCMAP products analyzed together with map validation reference plots provide a robust basis for understanding tree cover change. In LCMAP (Collection 1.2), annual change detection is based on harmonic model breaks calculated at each Landsat pixel from the Continuous Change Detection and Classification (CCDC) algorithm. The results showed that the majority of CCDC harmonic model breaks (signifying change) indicated partial tree cover loss (associated with management practices such as tree cover thinning) as compared to complete tree cover loss (associated with practices like clearcut harvest or fire disturbance). Substantially fewer occurrences of complete tree cover loss were associated with change in land cover state. The area of annual tree cover change increased after the late 1990s and stayed high for the rest of the study period. The reference data showed that tree harvest dominated across the conterminous United States. The majority of tree cover change occurred in evergreen forests. Large estimates of disturbance-related tree cover change indicated that tree cover loss may have previously been underreported due to omission of partial tree cover loss in prior studies. This has considerable implications for forest carbon accounting along with tracking ecosystem goods and services.

Forests↗
USGS2025

Analysis of the potential effects of Uzbekistan’s mineral endowment on the critical mineral supply of tungsten

Tungsten appears on the 2018 and 2022 U.S. Geological Survey critical mineral lists in part because of a very high global production concentration in China, which produces almost 83 percent of the world’s mined tungsten. Using known parameters and values from other tungsten mining operations, we created hypothetical scenarios in which three tungsten deposits in Uzbekistan are considered for development. Our results show that all three deposits are likely to be economically viable to develop under 2024 market conditions. If the three studied tungsten deposits were put into production, Uzbekistan could become the third-leading tungsten-producing country in the world and increase world output of tungsten by 2.7 percent. Putting these tungsten deposits in Uzbekistan into production could slightly reduce the tungsten global market concentration, therefore reducing the supply disruption potential for tungsten.

Open-File Report↗
USGS2025

Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery

Mapping the land area used for mining in the past is essential for guiding the remediation of affected landscapes and assessing the resource potential of related waste products. Despite significant recent progress delineating footprints of active and inactive mining globally, the known inventory of such mine lands remains incomplete. Here, I describe a new map dataset of footprints of land surface disturbance and waste at sites of past mining in Alaska (USA) based on visual interpretation of satellite imagery. This dataset maps 6–14 times the area of previous regional and global mine footprint maps in Alaska and is the first in the region to explicitly delineate mine waste landforms (e.g., tailings piles). The data are publicly available from the U.S. Geological Survey under a “no rights reserved” Creative Commons (CC0) license agreement.

Alaska↗
USGS2025

Sources and risk factors for nitrate, pathogens, and fecal contamination of private wells in rural southwestern Wisconsin, USA

Household well water can be degraded by contaminants from the land's surface, but private well owners lack means to protect the source water from neighboring disturbances. Rural residents of southwestern Wisconsin, USA, rely on private well water, and the combination of land use and fractured carbonate bedrock makes groundwater vulnerable to contamination. To identify the extent, sources, and risk factors of private well contamination, randomly selected wells sampled during two-day periods in fall (n = 301) and spring (n = 529) were analyzed for nitrate and indicator bacteria, and a subset (n = 138) was sampled across four seasonal events for analysis of pathogens and microbial source tracking markers by quantitative polymerase chain reaction. Risk factors representing land use, hydrology, geology, and well construction were analyzed for associations with contamination in multivariable models. The importance of risk factors varied by contaminant, illustrating the multifaceted nature of rural groundwater quality. Nitrate contamination was associated with agricultural land use, and wells with casings that extended below a shale aquitard accessed less contaminated water than those drawing water from above it. Human fecal microbes were detected in 64 wells (46%), and rainfall was the key risk factor for contamination, indicating that wastewater from septic systems was available to contaminate wells when transport conditions were favorable. Manure microbes from cattle/ruminants and pigs were detected in 33 and 13 wells, respectively, and concentrations increased with the hectarage of cultivated land near wells. Pathogen genes for viruses, bacteria, and protozoa were detected in 66 wells (48%), including more detections of zoonotic than human-specific pathogens, and human Bacteroides , an indicator of wastewater, was an equivocal predictor of pathogen presence in private wells. Characterizing important elements of the setting, like geology, and identifying sources and risk factors for contaminants can inform landscape-level policies to protect groundwater quality.

Wisconsin↗
USGS2025

Estimating the probability of export restrictions to inform mineral criticality

To assess risks associated with advanced technologies’ supply chain disruptions, governmental agencies and others have developed mineral “criticality” assessments, with criticality described using the economic impact and probability of supply chain disruptions. Previous work developed subjective supply risk indicators to approximate this probability, typically combining several factors such as supply diversity and trading partners’ political stability, where indicator weightings can substantially impact results. This work explicitly quantifies export barrier probability using an ensemble of machine learning classifiers, with probability estimates informed by exogenous variables, including prior barrier implementation and global export dominance. Major differences in high-probability countries and commodities are observed across models, but the ensemble method highlights Indonesia, China, Tanzania, and the United States as particularly high risk. The Supplementary Data File provides export barrier probability estimates for each analyzed country-commodity pair, enabling a direct, quantitative, objective contribution to assessing mineral criticality, enhancing risk identification and prioritization for policymakers.

Resources, Conservation, and Recycling↗
USGS2025

Estimating the probability of export restrictions to inform mineral criticality

As demand for advanced technologies rises, mineral commodities will increase in geopolitical importance. To assess risks associated with mineral commodity supply chain disruptions, governmental agencies and others have developed "criticality" assessments, with criticality described using the economic impact and probability of supply chain disruptions. In previous work, subjective supply risk indicators were developed to approximate this probability, typically combining several factors such as supply diversity and political stability of trading partners, where indicator weightings can substantially impact results. This work explicitly quantifies trade barrier probability using an ensemble of several machine learning classifiers, with probability estimates informed by exogenous variables such as prior trade barrier implementation and global export dominance. Major differences in the high-probability countries and commodities are observed across models, but the ensemble method highlights Indonesia, China, Tanzania, and the United States as particularly high risk. This approach enables a direct, quantitative, objective approach to assessing trade barrier probability, enhancing risk identification and prioritization for policymakers.

SSRN↗