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At least 667 records · Page 37Linked to original sources
USGS2005

Naturally Occurring Arsenic in Ground Water, Norman, Oklahoma, 2004, and Remediation Options for Produced Water

In 2000, the U.S. Environmental Protection Agency (EPA) reviewed the arsenic drinking water standard for public water supplies. Considering the available research and statistics on the health effects of arsenic ingestion, the EPA reduced the Maximum Contaminant Level (MCL) for public drinking water from 50 micrograms per liter (?g/L) to 10 ?g/L (U.S. Environmental Protection Agency, 2001a). As a result of the more stringent standard, the EPA estimates that about 3,000 public water providers across the United States must take action to meet the new standard before it becomes effective on January 23, 2006 (U.S. Environmental Protection Agency, 2001b). The City of Norman (City) is one of several Oklahoma municipalities affected by the new arsenic standard. About 20 percent of Norman?s water is supplied by wells completed in the Central Oklahoma (Garber-Wellington) aquifer; the rest is supplied by Lake Thunderbird (fig. 1) or purchased from Oklahoma City. The Norman well field is composed of 24 active wells, and water produced from about half of the wells will not be in compliance with the new MCL (figs. 2 and 3). Chemical treatment of water with elevated arsenic is possible, but it is generally cost prohibitive. Another costly solution is simply to abandon the high-arsenic wells and replace them with new wells in low-arsenic areas. In the next phase of well construction beginning in 2005, the City plans to construct as many as 30 new wells in northeast Norman (Bryan Mitchell, City of Norman, oral commun., 2005). The new wells will replace production lost to the new arsenic standard and add new production to keep pace with rapidly growing consumer demand. Well modification to exclude arsenic-bearing water from existing wells is a more cost-effective solution, but it requires a great deal of knowledge about local aquifer properties and individual well dynamics to decide which wells are good candidates for modification. With the goal of determining if well modification can be used to bring some of Norman?s high-arsenic wells into compliance with the new arsenic standard, the EPA Office of Research and Development (ORD) initiated a three-year research project in 2003 with participation from the U.S. Geological Survey (USGS), Oklahoma State University, and the City of Norman. The primary objectives of the project are to: (1) determine where naturally occurring arsenic is entering wells by collecting water samples at different depths, (2) investigate the utility of new methods for collecting water-quality data in a pumping well, (3) better understand the stratigraphy and composition of aquifer rocks, (4) assess 10 wells for the possibility of arsenic remediation by well modification, and (5) evaluate the effectiveness of well modification in bringing marginal wells into compliance with the new arsenic MCL. The purpose of this report is to describe the occurrence of arsenic in ground water near Norman, Oklahoma, and available options for reducing arsenic concentrations in produced ground water.

Fact Sheet↗
USGS2006

Land-based lidar mapping: a new surveying technique to shed light on rapid topographic change

The rate of natural change in such dynamic environments as rivers and coastlines can sometimes overwhelm the monitoring capacity of conventional surveying methods. In response to this limitation, U.S. Geological Survey (USGS) scientists are pioneering new applications of light detection and ranging (lidar), a laser-based scanning technology that promises to greatly increase our ability to track rapid topographic changes and manage their impact on affected communities.

Fact Sheet↗
USGS1975

Water-quality data from oil and gas wells in part of the Permian Basin, southeastern New Mexico and western Texas

Approximately 8,000 chemical analyses of water produced from formations of several geologic ages in south eastern New Mexico and western Texas are tabulated by both geographic location and geologic formation. Empirical and mathematical relationships between (1) the dissolved solids and the measured and computed resistivity of water, the chloride-ion concentration and the density, and (2) the concentration of chloride-ion and the density and the resistivity of water, were established. Resistivities of water computed following a method developed by Moore, Szasz, and Whitney (1966) using the concentration of the six most common constituents dissolved in natural water correlate favorably with the measured resistivities of natural water. The water analyses were obtained chiefly from oil companies and oil-service companies operating in the western part of the Permian basin. The data were associated with the proper well by searching location, operator, and reference number cross indexes prepared from the Permian Basin Well Data System files. The cross indexes were determined to be essential to efficient data processing operations. After preliminary screening for nonrepresentative data, all data were indexed to the Permian Basin Well Data System magnetic tape file scout records by the unique reference number. The data were then coded, punched into tabulating cards, and stored on magnetic tape for computer operations. Data verification steps not normally required when using data received from a laboratory following standardized analytical techniques on samples obtained under standardized and controlled procedures were performed in order to upgrade reliability of the information. Data errors detected included poorly located or identified samples; mistakes including reproducing, or key punching; laboratory errors; inconsistent reporting; and nonrepresentative or contaminated samples.

New Mexico, Texas↗
USGS1999

Baseline water quality and preliminary effects of artificial recharge on ground water, south-central Kansas, 1995–98

To investigate the feasbility of artificial recharge as a method of meeting future water-supply needs and to protect the Equus Beds aquifer from saltwater intrusion from natural and anthropogenic sources to the west, the Equus Beds Ground-Water Recharge from Demonstration Project was begun in 1995. The project is a cooperative effort between the city of Wichita and the Bureau of Reclamation, U.S. Department of the Interior. During the project, high flows from the Little Arkansas River are captured and recharged into the Equus Beds aquifer through recharge basins, a trench, or a recharge well, located at two recharge sites near Halstead and Sedgwick, Kansas. To document baseline concentrations and compatibility of stream (recharge) and aquifer water, the U.S. Geological Survey collected water samples from February 1995 through August 1998. These samples were analyzed for dissolved solids, total and dissolved inorganic constituents, nutrients, organic and volatile organic compounds, radionuclides, and bacteria. Results of baseline sampling indicated that the primary constituents of concern for recharge were sodium, chloride, nitrite plus nitrate, iron and manganese, total coliform bacteria, and atrazine. Chloride and atrazine were of particular concern because concentrations of these constituents in water from the Little Arkansas River frequently exceeded regulatory criteria. The Little Arkansas River is used as the source water for recharge. The U.S. Environmental Protection Agency Secondary Maximum Contaminant Level for chloride is 250 mg/L (milligrams per liter), and the Maximum Contaminant Level for atrazine is 3.0 μg/L (micrograms per liter) as an annual mean. Baseline concentrations of chloride in surface water ranged from 8.0 to 400 μg/L. Baseline concentrations of atrazine in surface water ranged from less than 0.10 to 46 μg/L. Concentrations of chloride and atrazine have increased in water from some of the wells at both the Halstead and Sedgwick recharge sites after recharge began, although concentrations remained within the range of baseline values in the Equus Beds aquifer and are considerably less than U.S. Environmental Protection Agency drinking-water criteria. However, a substantial quantity of water has not been recharged at the Sedgwick site to determine the overall effects of artificial recharge on aquifer quality. Continued monitoring is necessary to determine long-term effects at both sites. Major ion and trace element concentrations in source water and receiving water were analyzed to determine the compatibility of recharge and receiving ground water for artificial recharge. Stiff diagrams of major ions were used to show the similarity or differences between source surface water and receiving ground water. The water from both sources, for the most part, was chemically compatible to the receiving aquifer water at both recharge sites. It may be possible to decrease the monitoring frequency at the Halstead recharge site because water-quality changes in receiving water at this site are very gradual. However, real-time water-quality monitoring of surrogates needs to be site specific for the determination of chloride and atrazine. Real-time water-quality monitoring potentially can be used to more effectively manage the artificial recharge process, enabling project officials to respond more rapidly to changes in water quality.

Water-Resources Investigations Report↗
USGS2013

Structured decision making

Wildlife management is a decision-focused discipline. It needs to integrate traditional wildlife science and social science to identify actions that are most likely to achieve the array of desires society has surrounding wildlife populations. Decision science, a vast field with roots in economics, operations research, and psychology, offers a rich set of tools to help wildlife managers frame, decompose, analyze, and synthesize their decisions. The nature of wildlife management as a decision science has been recognized since the inception of the field, but formal methods of decision analysis have been underused. There is tremendous potential for wildlife management to grow further through the use of formal decision analysis. First, the wildlife science and human dimensions of wildlife disciplines can be readily integrated. Second, decisions can become more efficient. Third, decisions makers can communicate more clearly with stakeholders and the public. Fourth, good, intuitive wildlife managers, by explicitly examining how they make decisions, can translate their art into a science that is readily used by the next generation.

Book chapter↗
USGS2015

Rates and patterns of surface deformation from laser scanning following the South Napa earthquake, California

The A.D. 2014 M6.0 South Napa earthquake, despite its moderate magnitude, caused significant damage to the Napa Valley in northern California (USA). Surface rupture occurred along several mapped and unmapped faults. Field observations following the earthquake indicated that the magnitude of postseismic surface slip was likely to approach or exceed the maximum coseismic surface slip and as such presented ongoing hazard to infrastructure. Using a laser scanner, we monitored postseismic deformation in three dimensions through time along 0.5 km of the main surface rupture. A key component of this study is the demonstration of proper alignment of repeat surveys using point cloud–based methods that minimize error imposed by both local survey errors and global navigation satellite system georeferencing errors. Using solid modeling of natural and cultural features, we quantify dextral postseismic displacement at several hundred points near the main fault trace. We also quantify total dextral displacement of initially straight cultural features. Total dextral displacement from both coseismic displacement and the first 2.5 d of postseismic displacement ranges from 0.22 to 0.29 m. This range increased to 0.33–0.42 m at 59 d post-earthquake. Furthermore, we estimate up to 0.15 m of vertical deformation during the first 2.5 d post-earthquake, which then increased by ∼0.02 m at 59 d post-earthquake. This vertical deformation is not expressed as a distinct step or scarp at the fault trace but rather as a broad up-to-the-west zone of increasing elevation change spanning the fault trace over several tens of meters, challenging common notions about fault scarp development in strike-slip systems. Integrating these analyses provides three-dimensional mapping of surface deformation and identifies spatial variability in slip along the main fault trace that we attribute to distributed slip via subtle block rotation. These results indicate the benefits of laser scanner surveys along active faults and demonstrate that fine-scale variability in fault slip has been missed by traditional earthquake response methods.

California↗
USGS2012

An algal model for predicting attainment of tiered biological criteria of Maine's streams and rivers

State water-quality professionals developing new biological assessment methods often have difficulty relating assessment results to narrative criteria in water-quality standards. An alternative to selecting index thresholds arbitrarily is to include the Biological Condition Gradient (BCG) in the development of the assessment method. The BCG describes tiers of biological community condition to help identify and communicate the position of a water body along a gradient of water quality ranging from natural to degraded. Although originally developed for fish and macroinvertebrate communities of streams and rivers, the BCG is easily adapted to other habitats and taxonomic groups. We developed a discriminant analysis model with stream algal data to predict attainment of tiered aquatic-life uses in Maine's water-quality standards. We modified the BCG framework for Maine stream algae, related the BCG tiers to Maine's tiered aquatic-life uses, and identified appropriate algal metrics for describing BCG tiers. Using a modified Delphi method, 5 aquatic biologists independently evaluated algal community metrics for 230 samples from streams and rivers across the state and assigned a BCG tier (1–6) and Maine water quality class (AA/A, B, C, nonattainment of any class) to each sample. We used minimally disturbed reference sites to approximate natural conditions (Tier 1). Biologist class assignments were unanimous for 53% of samples, and 42% of samples differed by 1 class. The biologists debated and developed consensus class assignments. A linear discriminant model built to replicate a priori class assignments correctly classified 95% of 150 samples in the model training set and 91% of 80 samples in the model validation set. Locally derived metrics based on BCG taxon tolerance groupings (e.g., sensitive, intermediate, tolerant) were more effective than were metrics developed in other regions. Adding the algal discriminant model to Maine's existing macroinvertebrate discriminant model will broaden detection of biological impairment and further diagnose sources of impairment. The algal discriminant model is specific to Maine, but our approach of explicitly tying an assessment tool to tiered aquatic-life goals is widely transferrable to other regions, taxonomic groups, and waterbody types.

Freshwater Science↗
USGS2024

A comparison of eDNA sampling methods in an estuarine environment on presence of longfin smelt (Spirinchus thaleichthys) and fish community composition

The loss of tidal wetlands in the San Francisco Bay estuary have led to declines in native fish presence. Restoration of tidal wetlands in this area has intensified, with a primary goal of increasing the number of native fishes. We compared the presence of longfin smelt in naturally accreted and beneficial dredge reuse wetlands as a measure of successful restoration. We used environmental DNA (eDNA) analyses as our metric for fish presence and fish community composition, employing two different water sampling methods for comparison (standard and high-volume). Longfin smelt were present in multiple sites, but at numbers too low for accurate comparisons across sites. Community composition varied based on the water sampling method, but the presence/absence of longfin smelt was consistent across sampling methods. As this represents a pilot study, further refinement of methodology is necessary, but the use of high-volume water sampling methods is promising.

California↗
USGS2019

Comparison of methods for modeling fractional cover using simulated satellite hyperspectral imager spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions.

Remote Sensing↗
USGS2007

Integrating remote sensing and ground methods to estimate evapotranspiration

Evapotranspiraton (ET) is the second largest term in the terrestrial water budget after precipitation, and ET is expected to increase with global warming. ET studies are relevant to the plant sciences because over 80% of terrestrial ET is due to transpiration by plants. Remote sensing is the only feasible means for projecting ET over large landscape units. In the past decade or so, new ground and remote sensing tools have dramatically increased our ability to measure ET at the plot scale and to scale it over larger regions. Moisture flux towers and micrometeorological stations have been deployed in numerous natural and agricultural biomes and provide continuous measurements of actual ET or potential ET with an accuracy or uncertainty of 10–30%. These measurements can be scaled to larger landscape units using remotely-sensed vegetation indices (VIs), Land Surface Temperature (LST), and other satellite data. Two types of methods have been developed. Empirical methods use time-series VIs and micrometeorological data to project ET measured on the ground to larger landscape units. Physically-based methods use remote sensing data to determine the components of the surface energy balance, including latent heat flux, which determines ET. Errors in predicting ET by both types of methods are within the error bounds of the flux towers by which they are calibrated or validated. However, the error bounds need to be reduced to 10% or less for applications that require precise wide-area ET estimates. The high fidelity between ET and VIs over agricultural fields and natural ecosystems where precise ground estimates of ET are available suggests that this might be an achievable goal if ground methods for measuring ET continue to improve.

Critical Reviews in Plant Science↗
USGS1997

Digital shaded-relief image of Alaska

One of the most spectacular physiographic images of the conterminous United States, and the first to have been produced digitally, is that by Thelin and Pike ( USGS I-2206, 1991 ). The image is remarkable for its crispness of detail and for the natural appearance of the artificial land surface. Our goal has been to produce a shaded-relief image of Alaska that has the same look and feel as the Thelin and Pike image. The Alaskan image could have been produced at the same scale as its lower 48 counterpart (1:3,500,000). But by insetting the Aleutian Islands into the Gulf of Alaska, we were able to print the Alaska map at a larger scale (1:2,500,000) and about the same physical size as the Thelin and Pike image. Benefits of the 1:2,500,000 scale are (1) greater resolution of topographic features and (2) ease of reference to the U.S. Geological Survey (USGS) (1987) Alaska Map E and the statewide geologic map (Beikman, 1980), which are both 1:2,500,000 scale. Manually drawn, shaded-relief images of Alaska's land surface have long been available (for example, Department of the Interior, 1909; Raisz, 1948). The topography depicted on these early maps is mainly schematic. Maps showing topographic contours were first available for the entire State in 1953 (USGS, 1:250,000) (J.H. Wittmann, USGS, written commun., 1996). The Alaska Map E was initially released in 1954 in both planimetric (revised in 1973 and 1987) and shaded-relief versions (revised in 1973, 1987, and 1996); topography depicted on the shaded-relief version is based on the 1:250,000-scale USGS topographic maps. Alaska Map E was later modified to include hypsometric tinting by Raven Maps and Images (1989, revised 1993) as copyrighted versions. Other shaded-relief images were produced for The National Geographic Magazine (LaGorce, 1956; 1:3,000,000) or drawn by Harrison (1970; 1:7,500,000) for The National Atlas of the United States. Recently, the State of Alaska digitally produced a shaded-relief image of Alaska at 1:2,500,000 scale (Alaska Department of Natural Resources, 1994), using the 1,000-m digital elevation data set referred to below. An important difference between our image and these previous ones is the method of reproduction: like the Thelin and Pike (1991) image, our image is a composite of halftone images that yields sharp resolution and preserves contrast. Indeed, the first impression of many viewers is that the Alaskan image and the Thelin and Pike image are composites of satellite-generated photographs rather than an artificial rendering of a digital elevation model. A shaded-relief image represents landforms in a natural fashion; that is, a viewer perceives the image as a rendering of reality. Thus a shaded-relief image is intrinsically appealing, especially in areas of spectacular relief. In addition, even subtle physiographic features that reflect geologic structures or the type of bedrock are visible. To our knowledge, some of these Alaskan features have not been depicted before and so the image should provide earth scientists with a new "look" at fundamental geologic features of Alaska.

Alaska↗
USGS2007

Comparison of four approaches to a rock facies classification problem

In this study, seven classifiers based on four different approaches were tested in a rock facies classification problem: classical parametric methods using Bayes' rule, and non-parametric methods using fuzzy logic, k-nearest neighbor, and feed forward-back propagating artificial neural network. Determining the most effective classifier for geologic facies prediction in wells without cores in the Panoma gas field, in Southwest Kansas, was the objective. Study data include 3600 samples with known rock facies class (from core) with each sample having either four or five measured properties (wire-line log curves), and two derived geologic properties (geologic constraining variables). The sample set was divided into two subsets, one for training and one for testing the ability of the trained classifier to correctly assign classes. Artificial neural networks clearly outperformed all other classifiers and are effective tools for this particular classification problem. Classical parametric models were inadequate due to the nature of the predictor variables (high dimensional and not linearly correlated), and feature space of the classes (overlapping). The other non-parametric methods tested, k-nearest neighbor and fuzzy logic, would need considerable improvement to match the neural network effectiveness, but further work, possibly combining certain aspects of the three non-parametric methods, may be justified. ?? 2006 Elsevier Ltd. All rights reserved.

Computers & Geosciences↗
USGS2009

The Ecology of Parasite-Host Interactions at Montezuma Well National Monument, Arizona - Appreciating the Importance of Parasites

Although parasites play important ecological roles through the direct interactions they have with their hosts, historically that fact has been underappreciated. Today, scientists have a growing appreciation of the scope of such impacts. Parasites have been reported to dominate food webs, alter predator-prey relationships, act as ecosystem engineers, and alter community structure. In spite of this growing awareness in the scientific community, parasites are still often neglected in the consideration of the management and conservation of resources and ecosystems. Given that at least half of the organisms on earth are probably parasitic, it should be evident that the ecological functions of parasites warrant greater attention. In this report, we explore different aspects of parasite-host relationships found at a desert spring pond within Montezuma Well National Monument, Arizona. In three separate but related chapters, we explore interactions between a novel amphipod host and two parasites. First, we identify how host behavior responds to this association and how this association affects interactions with both invertebrate non-host predators and a vertebrate host predator. Second, we look at the human dimension, investigating how human recreation can indirectly affect patterns of disease by altering patterns of vertebrate host space use. Finally - because parasites and diseases are of increasing importance in the management of wildlife species, especially those that are imperiled or of management concern - the third chapter argues that research would benefit from increased attention to the statistical analysis of wildlife disease studies. This report also explores issues of statistical parasitology, providing information that may better inform those designing research projects and analyzing data from studies of wildlife disease. In investigating the nature of parasite-host interactions, the role that relationships play in ecological communities, and how human activities alter these associations, scientists usually make inferences by methods of statistical hypotheses testing. This type of hypothesis testing places additional importance on the analysis and interpretation of parasite-host interactions. We address these ideas in this report, focusing on the following questions: (1) How do two parasites with complex life cycles alter the behavior of a novel amphipod host, and how do host and non-host predators respond to infected amphipod prey? (2) Does human recreation affect spatial patterns of infection in an otherwise natural ecosystem? (3) How is hypothesis-testing applied in studies of wildlife disease? (4) What conclusions can we make about the relative usefulness of these methodologies? and (5) How can the analysis and interpretation of wildlife disease studies be improved? Each chapter of this report contains its own literature-cited section, with tables included in appendixes at the end of the full report.

Open-File Report↗
USGS2020

A guidebook to spatial datasets for conservation planning under climate change in the Pacific Northwest

This guidebook provides user-friendly overviews of a variety of spatial datasets relevant to conservation and management of natural resources in the face of climate change in the Pacific Northwest, United States. Each guidebook chapter was created using a standardized template to summarize a spatial dataset or a group of closely related datasets. Datasets were selected according to standardized criteria based on input through a collaborative process involving researchers and natural-resource managers throughout the Pacific Northwest region. In each chapter, basic spatial and temporal information is provided for the dataset, along with a conceptual overview, glossary of key terms, links to download data and supporting documentation, a brief methods summary describing how the dataset was created, guidelines for dataset interpretation, assessment of uncertainties along with evaluation of caveats and simplifying assumptions, and information about potential and actual conservation applications of the dataset. Collectively, this information provides natural-resource managers with “snapshots” of a variety of datasets representing diverse processes and conditions, including climate projections, changes in hydrologic conditions, vegetation and fire-regime shifts, animal habitat changes, species movements, and topographic and soil conditions relevant to climate change. Along with other types of data and site-specific information, the datasets described in this guidebook have the potential to inform management of valued natural resources throughout the Pacific Northwest region in the context of adaptation to changing climate conditions.

Idaho, Montana, Oregon, Washington↗
USGS2006

Approach to an assessment of volatile organic compounds in the nation's ground water and drinking-water supply wells

The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) developed an approach for a national assessment of the occurrence, status, and distribution of volatile organic compounds (VOCs) in samples of ground water from aquifer studies and in samples from drinking-water supply wells, specifically domestic and public wells. Data on VOCs in samples from aquifers and from domestic and public wells had three sources: (1) NAWQA sampling of ground water in aquifer studies and of shallow ground water in areas of agricultural or urban land use from 1993-2002, (2) retrospective data from other Federal, State, and local agencies that sampled ground water in aquifer studies from 1985-1997, and (3) sampling of ground water from public wells used as a source of drinking water from 1999-2000 (source-water survey). Data for assessing VOCs in ground water came from the NAWQA sampling of aquifers and from retrospective data (1 and 2). Domestic wells were the most commonly sampled well type in these aquifer studies. Data for assessing VOCs in domestic well samples came from the NAWQA sampling of aquifers and shallow ground water and from retrospective data (1 and 2). Data for assessing VOCs in public well samples came from NAWQA sampling of aquifers and shallow ground water and from the source-water survey (1, 2, and 3). Fifty-five VOCs were included in the national assessment. All ground-water samples were collected using consistent, prescribed field protocols and were analyzed using approved analytical methods. All ground-water collection activities and laboratory procedures included the collection of various quality-control samples in order to ensure the quality of the data. Comparisons of detection frequencies and detected concentrations among individual VOCs, groups of VOCs, aquifer studies, and data sets were performed using two assessment levels: (1) an assessment level of 0.2 microgram per liter (?g/L), and (2) an assessment level of 0.02 ?g/L. Selection of the 0.2-?g/L assessment level was based on historical laboratory reporting levels of VOCs used by the USGS National Water-Quality Laboratory (NWQL) prior to April 1996, and selection of the 0.02-?g/L assessment level was based on a new, low-level analytical method developed by the NWQL and used since April 1996. Relational analyses using multivariate logistic regression were performed on VOC data from aquifers, domestic wells, and public wells to better understand the natural and anthropogenic factors that control or influence the occurrence of VOCs. Ancillary data used in the relational analyses represent a variety of anthropogenic and hydrogeologic controls on the occurrence of VOCs. For aquifers, relational analyses were performed only on NAWQA data analyzed using the new low-level analytical method for 10 frequently detected VOCs. For domestic wells, relational analyses were performed at assessment levels of 0.2 ?g/L and 0.02 ?g/L for 6 and 10 frequently detected VOCs at these levels. For public wells, relational analyses were performed at an assessment level of 0.2 ?g/L for nine frequently detected VOCs.

Open-File Report↗
USGS2015

Biogeochemical aspects of uranium mineralization, mining, milling, and remediation

Natural uranium (U) occurs as a mixture of three radioactive isotopes: 238 U, 235 U, and 234 U. Only 235 U is fissionable and makes up about 0.7% of natural U, while 238 U is overwhelmingly the most abundant at greater than 99% of the total mass of U. Prior to the 1940s, U was predominantly used as a coloring agent, and U-bearing ores were mined mainly for their radium (Ra) and/or vanadium (V) content; the bulk of the U was discarded with the tailings (Finch et al., 1972). Once nuclear fission was discovered, the economic importance of U increased greatly. The mining and milling of U-bearing ores is the first step in the nuclear fuel cycle, and the contact of residual waste with natural water is a potential source of contamination of U and associated elements to the environment. Uranium is mined by three basic methods: surface (open pit), underground, and solution mining (in situ leaching or in situ recovery), depending on the deposit grade, size, location, geology and economic considerations (Abdelouas, 2006). Solid wastes at U mill tailings (UMT) sites can include both standard tailings (i.e., leached ore rock residues) and solids generated on site by waste treatment processes. The latter can include sludge or “mud” from neutralization of acidic mine/mill effluents, containing Fe and a range of coprecipitated constituents, or barium sulfate precipitates that selectively remove Ra (e.g., Carvalho et al., 2007). In this chapter, we review the hydrometallurgical processes by which U is extracted from ore, the biogeochemical processes that can affect the fate and transport of U and associated elements in the environment, and possible remediation strategies for site closure and aquifer restoration. This paper represents the fourth in a series of review papers from the U.S. Geological Survey (USGS) on geochemical aspects of UMT management that span more than three decades. The first paper (Landa, 1980) in this series is a primer on the nature of tailings and radionuclide mobilization from them. The second paper (Landa, 1999) includes coverage of research carried out under the U.S. Department of Energy’s Uranium Mill Tailings Remedial Action Program (UMTRA). The third paper (Landa, 2004) reflects the increased focus of researchers on biotic effects in UMT environs. This paper expands the focus to U mining, milling, and remedial actions, and includes extensive coverage of the increasingly important alkaline in situ recovery and groundwater restoration.

Applied Geochemistry↗
USGS1964

Natural carbon-14 activity of organic substances in streams

Carbon-14 measurements made on organic contaminants extracted from streams show percentages of industrial waste and domestic sewage. The method, used previously for studies of the atmosphere, can be used in studies of pollution sources.

Science↗
USGS2014

Methods for estimating magnitude and frequency of floods in Arizona, developed with unregulated and rural peak-flow data through water year 2010

Flooding is among the worst natural disasters responsible for loss of life and property in Arizona, underscoring the importance of accurate estimation of flood magnitude for proper structural design and floodplain mapping. Twenty-four years of additional peak-flow data have been recorded since the last comprehensive regional flood frequency analysis conducted in Arizona. Periodically, flood frequency estimates and regional regression equations must be revised to maintain the accurate estimation of flood frequency and magnitude. Annual peak-flow data collected through water year 2010 were compiled from 448 unregulated streamflow-gaging stations, hereafter referred to as streamgages, in Arizona having a minimum of 10 years of record. Flood frequency estimates were first computed with station (or at-site) skew using the Expected Moments Algorithm with a multiple Grubbs-Beck test to identify multiple potentially influential low flows to fit a Pearson Type III distribution. Next, a multiple step Bayesian least-squares-regression approach was used to determine a new statewide regional skew of −0.09. No basin characteristics analyzed were statistically significant in explaining the variation in skew and as a result, the constant model was chosen as the best regional skew model for the Arizona study area. The mean square error used in Bulletin 17B (B17B) of the Interagency Advisory Committee on Water Data is used to describe the precision of the regional skew. The constant model had a mean square error equal to 0.08, which corresponds to an effective record length of 85 years. This is a marked improvement over a previous Arizona regional skew analysis, with a reported mean square error of 0.31, for a corresponding effective record length of around 17 years. Thus the new regional model had almost five times the information content (as measured by effective record length) of that calculated in USGS Water Supply Paper 2433, published in 1997, or the value of 0.302 reported in the B17B generalized skew map. The flood frequency estimates were recalculated using a weighted skew of the station and regional skew. Station flood frequency estimates for each streamgage are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. Geographical information systems were used to compute basin characteristic information for each streamgage for the purpose of developing regional equations to estimate flood statistics at ungaged basins. Five hydrologic flood regions in Arizona were defined in a multivariate regionalization process based on mean basin elevation, mean annual precipitation, and soil permeability. A regional generalized least-squares-regression analysis was used to develop five sets of equations from 344 nonredundant streamgages, corresponding to five regions, for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities at ungaged basins in Arizona. The regression equations developed for these five regions were based on one or more of the statistically significant explanatory variables: drainage area, mean basin elevation, and mean annual precipitation. Average standard errors of prediction for the regression regions for the five regions ranged from 27 to 122 percent and the pseudo-coefficients of determination (pseudo-R 2 ), a measure of the proportion of peak-flow variation that is explained by the basin characteristics, ranged from 68 to 98 percent. Regression equations for Central Highlands (region 4) had the lowest model error and the greatest pseudo-R 2 metrics. The equations for Colorado Plateau (region 2) regression equations generally had greater model error and lower pseudo-R 2 metrics. The improvement of regional regression equation model error and pseudo-R 2 metrics was related to higher numbers of streamgages, longer period of record, and even spatial coverage within a region. The regional regression equations were integrated into the U.S. Geological Survey’s StreamStats program. The StreamStats program is a national map-based web application that allows the public to easily access published flood frequency and basin characteristic statistics. The interactive web application allows a user to select a point within a watershed (gaged or ungaged) and retrieve flood-frequency estimates derived from the current regional regression equations and geographic information system data within the selected basin. StreamStats provides users with an efficient and accurate means for retrieving the most up to date flood frequency and basin characteristic data. StreamStats is intended to provide consistent statistics, minimize user error, and reduce the need for large datasets and costly geographic information system software.

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