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At least 1,135 records · Page 63Linked to original sources
USGS2020

Energy development and production in the Great Plains: Implications and restoration opportunities

Energy is an integral part of society. The major US energy sources of fossil fuels (coal, oil, natural gas); biofuels (ethanol); and wind are concentrated in grassland ecosystems of the Great Plains. As energy demand continues to increase, mounting pressures will be placed on North American grassland systems. In this review, we present the ecological effects of energy development and production on grassland systems. We then identify opportunities to mitigate these effects during the planning, construction, and production phases by using informed methodology and improved technology. Primary effects during energy development include small- and large-scale soil disturbance and vegetation removal as small patches of grasslands are used to host oil or gas wells, wind turbine pads, associated roadways, and pipelines or through the conversion of large grassland areas to biofuel croplands. Direct habitat loss or habitat fragmentation can affect wildlife directly through increased mortality or indirectly through reduction in habitat quantity and quality. During energy production, air and water quality can be affected through regular emissions or unplanned spills. Energy development can also affect the economy and health of local communities. During planning, energy development and production effects can be reduced by carefully considering effects on grasslands during siting and even by selecting different energy source types. During construction, effects on soil and plant systems can be minimized by eliminating weed populations before disturbance, salvaging and stockpiling topsoil for future revegetation, and harvesting native local seed for postsite restoration. During energy production operations, noise and road traffic reduction plans and atmospheric monitoring will enable more informed mitigation measures. Continued research on energy development effects and mitigation measures is necessary to establish best management practices beneficial to grassland health while providing needed energy for the United States.

Colorado, Illinois, Indiana, Iowa, Kansas, Minneso↗
USGS2023

Future direction of fuels management in sagebrush rangelands

Sagebrush ecosystems in the United States have been declining since EuroAmerican settlement, largely due to agricultural and urban development, invasive species, and altered fire regimes, resulting in loss of biodiversity and wildlife habitat. To combat continued conversion to undesirable ecological states and loss of habitat to invasive species fueled by frequent fire, a variety of fuel treatments, including networks of fuel breaks, are being implemented or proposed in sagebrush ecosystems, particularly in and around the Great Basin. In this forum paper we briefly review current knowledge of common fuel treatment approaches, their intended benefits, potential risks, and limitations. We additionally discuss challenges for fuel treatment strategies in the context of changes in climate, invasive species, wildlife habitat, and human population, and we explore how advances in geospatial technologies, monitoring, and fire behavior modeling, as well as accounting for social context, can improve the efficacy of fuels management in sagebrush ecosystems. Finally, given continued potential for ecosystem transformation, we describe approaches to future fuels management by considering the applicability of the Resist-Accept-Direct (RAD) framework. The intent of the paper is to provide scientists and land managers with key information and a forward-thinking framework for fuels science and adaptive management that can respond to both expected and unexpected changes in sagebrush rangelands.

Rangeland Ecology and Management↗
USGS2004

Recycling of construction debris as aggregate in the Mid-Atlantic Region, USA

Reclaimed asphalt pavement (RAP) and portland cement concrete (RPCC) are abundant and available substitutes for natural aggregate in many areas. This paper presents an overview of factors that affect recycled aggregate cost, availability, and engineering performance, and the results of a survey of business practices in the Mid-Atlantic region. For RAP, processing costs are less than those for virgin natural aggregate. Use of efficient asphalt pavement stripping technology, on-site reclamation, and linked two-way transport of asphalt debris and processed asphalt paving mix between asphalt mix plants and paving sites has led to extensive recycling of asphalt pavement in the Mid-Atlantic region of the US. Most of the sites that recycle asphalt pavement (RAP) are located in or near urban areas close to important transportation corridors. RPCC is a viable aggregate source in urban settings where unit costs for processed aggregate from RPCC and natural aggregate are comparable. Disposal fees charged at RPCC recycling sites help defray processing costs and the significantly lower tipping fees at recycling sites versus landfill disposal sites encourage recycling of construction debris as aggregate. Construction contractors and construction debris recycling centers, many of which have the ability to crush and process concrete debris at the job site, produce most RPCC. Production of RPCC aggregate from construction debris that is processed on site using portable equipment moved to the construction site eliminates transportation costs for aggregate and provides an economic incentive for RPCC use. Processing costs, quality and performance issues, and lack of large quantities where needed limit RPCC use. Most RPCC suppliers in the Mid-Atlantic area are located in counties with population densities greater than 400 people/km2 (1036 people/mile2) and that have high unit-value costs and limited local availability of natural aggregate. ?? 2004 Published by Elsevier B.V.

Conference Paper↗
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↗
USGS2022

One hundred years of cobalt production in the Democratic Republic of the Congo

Cobalt is an indispensable element for the manufacture of strategic technologies including advanced batteries, jet engines, rare-earth permanent magnets, petroleum catalysts, and tool parts that enable construction, manufacturing, and mining. Cobalt routinely scores high in mineral supply risk assessments due to the concentration of cobalt mine production in the Democratic Republic of the Congo (DRC). This stands in contrast to the fact that DRC cobalt mine production had a 20% compound annual growth rate from 1995 through 2020—in large part due to investments by Chinese firms beginning in the mid-2000s. Given this continuous growth, one may ask why this supply is perceived to be so risky. This analysis illuminates the causes of historic disruptions to DRC cobalt mine and refinery production by analyzing country-level production, historical reports, and cobalt prices back to 1924. The results indicate that the main causes of supply disruptions were damage to transportation routes, underinvestment in maintaining nationalized mining assets, and the disintegration of the DRC economy during the early 1990s. On the other hand, cobalt mine production increased 50% from 1977 to 1979 despite two secessionist conflicts in DRC's cobalt producing region and increased seven-fold from 1996 to 2003 despite two African wars over the DRC and its resources. These results indicate that—barring another economic disintegration or mining industry nationalization—DRC mine production will likely continue to be the dominant supplier of the world's growing demand for cobalt in lithium-ion batteries. These results also indicate that sustained development of transportation (and other) infrastructure in Africa, as well as support for good governance in the DRC may prove key to the continued stability of DRC cobalt mine supplies.

Resources Policy↗
USGS2005

Multispectral imaging contributions to global land ice measurements from space

Global Land Ice Measurements from Space (GLIMS) is an international consortium established to acquire satellite images of the world's glaciers, analyse them for glacier extent and changes, and assess change data for causes and implications for people and the environment. Although GLIMS is making use of multiple remote-sensing systems, ASTER (Advanced Spaceborne Thermal Emission and reflection Radiometer) is optimized for many needed observations, including mapping of glacier boundaries and material facies, and tracking of surface dynamics, such as flow vector fields and supraglacial lake development. Software development by GLIMS is geared toward mapping clean-ice and debris-covered glaciers; terrain classification emphasizing snow, ice, water, and admixtures of ice with rock debris; multitemporal change analysis; visualization of images and derived data; and interpretation and archiving of derived data. A global glacier database has been designed at the National Snow and Ice Data Center (NSIDC, Boulder, Colorado); parameters are compatible with and expanded from those of the World Glacier Inventory (WGI). These technology efforts are summarized here, but will be presented in detail elsewhere. Our presentation here pertains to one broad question: How can ASTER and other satellite multispectral data be used to map, monitor, and characterize the state and dynamics of glaciers and to understand their responses to 20th and 21st century climate change? Our sampled results are not yet glaciologically or climatically representative. Our early results, while indicating complexity, are generally consistent with the glaciology community's conclusion that climate change is spurring glacier responses around the world (mainly retreat). Whether individual glaciers are advancing or retreating, the aggregate average of glacier change must be climatic in origin, as nonclimatic variations average out. We have discerned regional spatial patterns in glaciological response behavior; these patterns are best attributed to climate-change variability and to regional differences in glacier size and response times. In many cases, glacier length changes under-represent the magnitude of glacier ablation, because thinning (sometimes without immediate length changes) is also important. An expanded systematic, uniform analysis of many more glaciers is needed to isolate the glacier response components due to climatic and nonclimatic perturbations, to produce quantitative measures of regional variation in glacier changes, and to predict future regional glacier trends relevant to water resources, glaciological hazards, and global sea level. This comprehensive assessment (to be completed in stages) is expected to lend a critically needed filter to identify successful climate models that explain recent glacier changes and change patterns (and hence, are apt to describe future changes) and to eliminate unsuccessful models. ?? 2005 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2011

Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat–lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, “young” forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS–VCT disturbance products. The height of “young” forest is then mapped based on the derived relationships and the LTSS–VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS–VCT method yielded the lowest cross validation error. The R 2 and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS–VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.

Remote Sensing of Environment↗
USGS2012

State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill

The vast and persistent Deepwater Horizon (DWH) spill challenged response capabilities, which required accurate, quantitative oil assessment at synoptic and operational scales. Although experienced observers are a spill response's mainstay, few trained observers and confounding factors including weather, oil emulsification, and scene illumination geometry present challenges. DWH spill and impact monitoring was aided by extensive airborne and spaceborne passive and active remote sensing. Oil slick thickness and oil-to-water emulsion ratios are key spill response parameters for containment/cleanup and were derived quantitatively for thick (> 0.1 mm) slicks from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data using a spectral library approach based on the shape and depth of near infrared spectral absorption features. MODIS (Moderate Resolution Imaging Spectroradiometer) satellite, visible-spectrum broadband data of surface-slick modulation of sunglint reflection allowed extrapolation to the total slick. A multispectral expert system used a neural network approach to provide Rapid Response thickness class maps. Airborne and satellite synthetic aperture radar (SAR) provides synoptic data under all-sky conditions; however, SAR generally cannot discriminate thick (> 100 μm) oil slicks from thin sheens (to 0.1 μm). The UAVSAR's (Uninhabited Aerial Vehicle SAR) significantly greater signal-to-noise ratio and finer spatial resolution allowed successful pattern discrimination related to a combination of oil slick thickness, fractional surface coverage, and emulsification. In situ burning and smoke plumes were studied with AVIRIS and corroborated spaceborne CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation) observations of combustion aerosols. CALIPSO and bathymetry lidar data documented shallow subsurface oil, although ancillary data were required for confirmation. Airborne hyperspectral, thermal infrared data have nighttime and overcast collection advantages and were collected as well as MODIS thermal data. However, interpretation challenges and a lack of Rapid Response Products prevented significant use. Rapid Response Products were key to response utilization—data needs are time critical; thus, a high technological readiness level is critical to operational use of remote sensing products. DWH's experience demonstrated that development and operationalization of new spill response remote sensing tools must precede the next major oil spill.

Remote Sensing of Environment↗
USGS2015

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls ( Strix occidentalis caurina ), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map. Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors. We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped. We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps. Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

Remote Sensing of Environment↗
USGS2022

Fifty years of Landsat science and impacts

Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and programmatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.

Remote Sensing of Environment↗
USGS2026

On-demand global Landsat evapotranspiration product: Development, evaluation, and dissemination

Global actual evapotranspiration (ET) is one of the essential climate variables needed to understand and manage the relationships among food, energy, and water resources. The U.S. Geological Survey Earth Resources Observation and Science (EROS) Center launched a provisional ET product in 2020, offering on-demand, field-scale global coverage derived from Landsat data through the EROS Science Processing Architecture (ESPA) platform. The ESPA interface provides ET data for cloud-free Landsat overpasses starting in 1982 with Landsat 4 through the current Landsat 9. The ET data are delivered as a Provisional Level-3 Science product created using the Operational Simplified Surface Energy Balance (SSEBop) model. Landsat surface temperature and reference ET are the main model drivers along with vegetation index and net radiation for model parameterization. A large volume of Landsat-based ET orders (e.g., over 1,200,000 images from June 2020 through December 2025) around the world indicate increasing awareness and application of the ET data. The ESPA platform enables land and water resource managers and researchers to access a first-order ET product without requiring advanced knowledge of remote sensing technology or evapotranspiration modeling. We present the methodology and workflow of the on-demand Landsat ET product and its performance evaluations over diverse hydro-climatic settings. The product can help estimate field-scale consumptive water use and thus quickly and consistently assess historical water use, allocation, and budget to inform water management under changing environments. Future ET data aggregated to monthly and seasonal time scales are expected to enhance integration with decision-making tools and procedures.

Remote Sensing of Environment↗
USGS2016

Regional monitoring programs in the United States: Synthesis of four case studies from Pacific, Atlantic, and Gulf Coasts

Water quality monitoring is a cornerstone of environmental protection and ambient monitoring provides managers with the critical data they need to take informed action. Unlike site-specific monitoring that is at the heart of regulatory permit compliance, regional monitoring can provide an integrated, holistic view of the environment, allowing managers to obtain a more complete picture of natural variability and cumulative impacts, and more effectively prioritize management actions. By reviewing four long-standing regional monitoring programs that cover portions of all three coasts in the United States – Chesapeake Bay, Tampa Bay, Southern California Bight, and San Francisco Bay – important insights can be gleaned about the benefits that regional monitoring provides to managers. These insights include the underlying reasons that make regional monitoring programs successful, the challenges to maintain relevance and viability in the face of ever-changing technology, competing demands and shifting management priorities. The lessons learned can help other managers achieve similar successes as they seek to establish and reinvigorate their own monitoring programs.

California, Delaware, Florida, Maryland, New Jerse↗
USGS2012

Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k -fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k -fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

Science of the Total Environment↗
USGS2017

The precipitation of indium at elevated pH in a stream influenced by acid mine drainage

Indium is an increasingly important metal in semiconductors and electronics and has uses in important energy technologies such as photovoltaic cells and light-emitting diodes (LEDs). One significant flux of indium to the environment is from lead, zinc, copper, and tin mining and smelting, but little is known about its aqueous behavior after it is mobilized. In this study, we use Mineral Creek, a headwater stream in southwestern Colorado severely affected by heavy metal contamination as a result of acid mine drainage, as a natural laboratory to study the aqueous behavior of indium. At the existing pH of ~ 3, indium concentrations are 6–29 μg/L (10,000 × those found in natural rivers), and are completely filterable through a 0.45 μm filter. During a pH modification experiment, the pH of the system was raised to > 8, and > 99% of the indium became associated with the suspended solid phase (i.e. does not pass through a 0.45 μm filter). To determine the mechanism of removal of indium from the filterable and likely primarily dissolved phase, we conducted laboratory experiments to determine an upper bound for a sorption constant to iron oxides, and used this, along with other published thermodynamic constants, to model the partitioning of indium in Mineral Creek. Modeling results suggest that the removal of indium from the filterable phase is consistent with precipitation of indium hydroxide from a dissolved phase. This work demonstrates that nonferrous mining processes can be a significant source of indium to the environment, and provides critical information about the aqueous behavior of indium.

Colorado↗
USGS2017

The history of mercury pollution near the Spolana chlor-alkali plant (Neratovice, Czech Republic) as recorded by Scots pine tree rings and other bioindicators

We assessed > 100 years of mercury (Hg) pollution recorded in the tree rings of Scots Pine near a Czech chlor-alkali plant operating since 1941. Hg concentrations in tree rings increased with the launching of plant operations and decreased when Hg emissions decreased in 1975 due to an upgrade in production technology. Similar to traditional bioindicators of pollution such as pine needles, bark and forest floor humus, Hg concentrations in Scots Pine boles decreased with distance from the plant. Mean Hg in pine bole in the 1940s ranged from 32.5 μg/kg Hg at a distance of 0.5 km from the plant to 5.4 μg/kg at a distance of > 4.7 km, where tree ring Hg was the same as at a reference site, and other bioindicators also suggest that the effect of the plant was no longer discernible. Tree ring Hg concentrations decreased by 8–29 μg/kg since the 1940s at all study sites including the reference site. The lack of exact correspondence between changes at the plant and tree ring Hg indicated some smearing of the signal due to lateral translocation of Hg from sapwood to heartwood. Bole Hg concentrations reflected local and regional atmospheric Hg concentrations, and not Hg wet deposition.

Neratovice↗
USGS2021

Groundwater discharges as a source of phytoestrogens and other agriculturally derived contaminants to streams

Groundwater discharge zones in streams are important habitats for aquatic organisms. The use of discharge zones for thermal refuge and spawning by fish and other biota renders them susceptible to potential focused discharge of groundwater contamination. Currently, there is a paucity of information about discharge zones as a potential exposure pathway of chemicals to stream ecosystems. Using thermal mapping technologies to locate groundwater discharges, shallow groundwater and surface water from three rivers in the Chesapeake Bay Watershed, USA were analyzed for phytoestrogens, pesticides and their degradates, steroid hormones, sterols and bisphenol A. A Bayesian censored regression model was used to compare groundwater and surface water chemical concentrations. The most frequently detected chemicals in both ground and surface water were the phytoestrogens genistein (79%) and formononetin (55%), the herbicides metolachlor (50%) and atrazine (74%), and the sterol cholesterol (88%). There was evidence suggesting groundwater discharge zones could be a unique exposure pathway of chemicals to surface water systems, in our case, metolachlor sulfonic acid (posterior mean concentration = 150 ng/L in groundwater and 4.6 ng/L in surface water). Our study also demonstrated heterogeneity of chemical concentration in groundwater discharge zones within a stream for the phytoestrogen formononetin, the herbicides metolachlor and atrazine, and cholesterol. Results support the hypothesis that discharge zones are an important source of exposure of phytoestrogens and herbicides to aquatic organisms. To manage critical resources within the Chesapeake Bay Watershed, more work is needed to characterize exposure in discharge zones more broadly across time and space.

Delaware, Maryland, New Jersey, New York, Pennsylv↗
USGS2021

An approach for decomposing river water-quality trends into different flow classes

A number of statistical approaches have been developed to quantify the overall trend in river water quality, but most approaches are not intended for reporting separate trends for different flow conditions. We propose an approach called FN 2Q , which is an extension of the flow-normalization (FN) procedure of the well-established WRTDS (“Weighted Regressions on Time, Discharge, and Season”) method. The FN 2Q approach provides a daily time series of low-flow and high-flow FN flux estimates that represent the lower and upper half of daily riverflow observations that occurred on each calendar day across the period of record. These daily estimates can be summarized into any time period of interest (e.g., monthly, seasonal, or annual) for quantifying trends. The proposed approach is illustrated with an application to a record of total nitrogen concentration (632 samples) collected between 1985 and 2018 from the South Fork Shenandoah River at Front Royal, Virginia (USA). Results show that the overall FN flux of total nitrogen has declined in the period of 1985–2018, which is mainly attributable to FN flux decline in the low-flow class. Furthermore, the decline in the low-flow class was highly correlated with wastewater effluent loads, indicating that the upgrades of treatment technology at wastewater treatment facilities have likely led to water-quality improvement under low-flow conditions. The high-flow FN flux showed a spike around 2007, which was likely caused by increased delivery of particulate nitrogen associated with sediment transport. The case study demonstrates the utility of the FN 2Q approach toward not only characterizing the changes in river water quality but also guiding the direction of additional analysis for capturing the underlying drivers. The FN 2Q approach (and the published code) can easily be applied to widely available river monitoring records to quantify water-quality trends under different flow conditions to enhance understanding of river water-quality dynamics.

Virginia↗
USGS2023

Changes in chemical occurrence, concentration, and bioactivity in the Colorado River before and after replacement of the Moab, Utah wastewater treatment plant

Long-term (2010–19) water-quality monitoring on the Colorado River downstream from Moab Utah indicated the persistent presence of Bioactive Chemicals (BC), such as pesticides and pharmaceuticals. This stream reach near Canyonlands National Park provides critical habitat for federally endangered species. The Moab wastewater treatment plant (WWTP) outfall discharges to the Colorado River and is the nearest potential point-source to this reach. The original WWTP was replaced in 2018. In 2016–19, a study was completed to determine if the new plant reduced BC input to the Colorado River at, and downstream from, the outfall. Water samples were collected before and after the plant replacement at sites upstream and downstream from the outfall. Samples were analyzed for as many as 243 pesticides, 109 pharmaceuticals, 20 hormones, 51 wastewater indicator chemicals, 20 metals, and 8 nutrients. BC concentrations, hazard quotients (HQs), and exposure activity ratios (EARs) were used to identify and prioritize contaminants for their potential to have adverse biological effects on the health of native and endangered wildlife. There were 22 BC with HQs >1, mostly metals and hormones; and 23 BC with EARs >0.1, mostly hormones and pharmaceuticals. Most high HQs or EARs were associated with samples collected at the WWTP outfall site prior to its replacement. Discharge from the new plant had reduced concentrations of nutrients, hormones, pharmaceuticals, and other BC. For example, all 16 of the hormones detected at the WWTP outfall site had maximum concentrations in samples collected prior to the WWTP replacement. The WWTP replacement had less effect on instream concentrations of metals and pesticides, BC whose sources are less directly tied to domestic wastewater. Study results indicate that improved WWTP technology can create substantial reductions in concentrations of non-regulated BC such as pharmaceuticals, in addition to regulated contaminants such as nutrients.

Utah↗