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At least 217 records · Page 12Linked to original sources

USGS2003

Understanding climatic impacts, vulnerabilities, and adaptation in the United States: Building a capacity for assessment

Based on the experience of the U.S. National Assessment, we propose a program of research and analysis to advance capability for assessment of climate impacts, vulnerabilities, and adaptation options. We identify specific priorities for scientific research on the responses of ecological and socioeconomic systems to climate and other stresses; for improvement in the climatic inputs to impact assessments; and for further development of assessment methods to improve their practical utility to decision-makers. Finally, we propose a new institutional model for assessment, based principally on regional efforts that integrate observations, research, data, applications, and assessment on climate and linked environmental-change issues. The proposed program will require effective collaboration between scientists, resource managers, and other stakeholders, all of whose expertise is needed to define and prioritize key regional issues, characterize relevant uncertainties, and assess potential responses. While both scientifically and organizationally challenging, such an integrated program holds the best promise of advancing our capacity to manage resources and the economy adaptively under a changing climate.

Climatic Change↗

USGS2008

ET–The key to balancing the water budget in the Southwest

Throughout the Southwest, state and federal water-resource managers are becoming increasingly concerned about the impacts of future groundwater development on the region’s limited water resources, environmentally sensitive ecosystems, and rural lifestyle. To address their concerns, scientists and engineers are deploying physically based mathematical models to assess and predict the potential effects of increased groundwater pumping. The accuracy of these predictions is directly related to how well water budgets are quantified and balanced at basin and regional scales.

Nevada↗

USGS2018

Soil property and class maps of the conterminous United States at 100-meter spatial resolution

With growing concern for the depletion of soil resources, conventional soil maps need to be updated and provided at finer and finer resolutions to be able to support spatially explicit human–landscape models. Three US soil point datasets—the National Cooperative Soil Survey Characterization Database, the National Soil Information System, and the Rapid Carbon Assessment dataset—were combined with a stack of over 200 environmental datasets and gSSURGO polygon maps to generate complete coverage gridded predictions at 100-m spatial resolution of six soil properties (percentage of organic C, total N, bulk density, pH, and percentage of sand and clay) and two US soil taxonomic classes (291 great groups [GGs] and 78 modified particle size classes [mPSCs]) for the conterminous United States. Models were built using parallelized random forest and gradient boosting algorithms as implemented in the ranger and xgboost packages for R. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100, and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Cross validation results indicated an out-of-bag classification accuracy of 60% for GGs and 66% for mPSCs; for soil properties, RMSE for leave-location-out cross-validation was 0.74 ( R 2 = 0.68), 17.8 wt% ( R 2 = 0.57), 12 wt% ( R 2 = 0.46), 3.63 wt% ( R 2 = 0.41), 0.2 g cm −3 ( R 2 = 0.42), and 0.27 wt% ( R 2 = 0.39) for pH, percent sand and clay, weight percentage of organic C, bulk density, and weight percentage of total N, respectively. Nine independent validation datasets were used to assess prediction accuracies for soil class models, and results ranged between 24 and 58% and between 24 and 93% for GG and mPSC prediction accuracies, respectively. Although mapping accuracies were variable and likely lower than gSSURGO in some areas, this modeling approach can enable easier integration of soil information with spatially explicit models compared with multicomponent map units.

Soil Science Society of America Journal↗

USGS2006

Performance assessments of nuclear waste repositories: A dialogue on their value and limitations

Performance Assessment (PA) is the use of mathematical models to simulate the long-term behavior of engineered and geologic barriers in a nuclear waste repository; methods of uncertainty analysis are used to assess effects of parametric and conceptual uncertainties associated with the model system upon the uncertainty in outcomes of the simulation. PA is required by the U.S. Environmental Protection Agency as part of its certification process for geologic repositories for nuclear waste. This paper is a dialogue to explore the value and limitations of PA. Two “skeptics” acknowledge the utility of PA in organizing the scientific investigations that are necessary for confident siting and licensing of a repository; however, they maintain that the PA process, at least as it is currently implemented, is an essentially unscientific process with shortcomings that may provide results of limited use in evaluating actual effects on public health and safety. Conceptual uncertainties in a PA analysis can be so great that results can be confidently applied only over short time ranges, the antithesis of the purpose behind long-term, geologic disposal. Two “proponents” of PA agree that performance assessment is unscientific, but only in the sense that PA is an engineering analysis that uses existing scientific knowledge to support public policy decisions, rather than an investigation intended to increase fundamental knowledge of nature; PA has different goals and constraints than a typical scientific study. The “proponents” describe an ideal, sixstep process for conducting generalized PA, here called probabilistic systems analysis (PSA); they note that virtually all scientific content of a PA is introduced during the model-building steps of a PSA, they contend that a PA based on simple but scientifically acceptable mathematical models can provide useful and objective input to regulatory decision makers. The value of the results of any PA must lie between these two views and will depend on the level of knowledge of the site, the degree to which models capture actual physical and chemical processes, the time over which extrapolations are made, and the proper evaluation of health risks attending implementation of the repository. The challenge is in evaluating whether the quality of the PA matches the needs of decision makers charged with protecting the health and safety of the public.

Risk Analysis↗

USGS2005

Fine-scale spatial variation in plant species richness and its relationship to environmental conditions in coastal marshlands

Previous studies have shown that variations in environmental conditions play a major role in explaining variations in plant species richness at community and landscape scales. In this study, we considered the degree to which fine-scale spatial variations in richness could be related to fine-scale variations in abiotic and biotic factors. To examine spatial variation in richness, grids of 1 m 2 plots were laid out at five sites within a coastal riverine wetland landscape. At each site, a 5 × 7 array of plots was established adjacent to the river’s edge with plots one meter apart. In addition to the estimation of species richness, environmental measurements included sediment salinity, plot microelevation, percent of plot recently disturbed, and estimated community biomass. Our analysis strategy was to combine the use of structural equation modeling (path modeling) with an assessment of spatial association. Mantel’s tests revealed significant spatial autocorrelation in species richness at four of the five sites sampled, indicating that richness in a plot correlated with the richness of nearby plots. We subsequently considered the degree to which spatial autocorrelations in richness could be explained by spatial autocorrelations in environmental conditions. Once data were corrected for environmental correlations, spatial autocorrelation in residual species richness could not be detected at any site. Based on these results, we conclude that in this coastal wetland, there appears to be a fine-scale mapping of diversity to microgradients in environmental conditions.

Louisiana↗

USGS1984

Habitat Suitability Index Models: Brewer's sparrow

This document is part of the Habitat Suitability Index (HSI) model series, which provides habitat information useful for impact assessment and habitat management for the Brewer's sparrow (Spizella breweri). Several types of habitat information are provided. A Habitat Use Information Section can be used to drive quantitative relationships between key environmental variables and habitat suitability. The HSI Model Section documents a habitat model and information pertinent to its application. The model synthesizes the habitat use information into a framework appropriate for field application and is scaled to produce an index value between 0.0 (unsuitable habitat) to 1.0 (optimum habitat).

FWS/OBS↗

USGS2004

Metadata for ReVA logistic regression dataset

The U.S. Geological Survey in cooperation with the U.S. Environmental Protection Agency's Regional Vulnerability Assessment Program, has developed a set of statistical tools to support regional-scale, ground-water quality and vulnerability assessments. The Regional Vulnerability Assessment Program goals are to develop and demonstrate approaches to comprehensive, regional-scale assessments that effectively inform water-resources managers and decision-makers as to the magnitude, extent, distribution, and uncertainty of current and anticipated environmental risks. The U.S. Geological Survey is developing and exploring the use of statistical probability models to characterize the relation between ground-water quality and geographic factors in the Mid-Atlantic Region. Available water-quality data obtained from U.S. Geological Survey National Water-Quality Assessment Program studies conducted in the Mid-Atlantic Region were used in association with geographic data (land cover, geology, soils, and others) to develop logistic-regression equations that use explanatory variables to predict the presence of a selected water-quality parameter exceeding specified management concentration thresholds. The resulting logistic-regression equations were transformed to determine the probability, P(X), of a water-quality parameter exceeding a specified management threshold. Additional statistical procedures modified by the U.S. Geological Survey were used to compare the observed values to model-predicted values at each sample point. In addition, procedures to evaluate the confidence of the model predictions and estimate the uncertainty of the probability value were developed and applied. The resulting logistic-regression models were applied to the Mid-Atlantic Region to predict the spatial probability of nitrate concentrations exceeding specified management thresholds. These thresholds are usually set or established by regulators or managers at national or local levels. At management thresholds of 1 milligram per liter, and 3 milligrams per liter, the probability of nitrate concentrations exceeding these levels is greater than 50 percent (.50) throughout much of the Mid-Atlantic Region. This includes extensive areas throughout central Maryland, southeastern Pennsylvania, northwestern Pennsylvania and the Delmarva Peninsula. In addition, extensive areas in North Carolina and Virginia also have high probabilities of nitrate concentrations in ground water exceeding management thresholds of 1 milligram per liter and 3 milligrams per liter. The mapped areas showing a high predicted probability of nitrate concentrations in ground water exceeding 1 milligram per liter and 3 milligrams per liter correspond to areas that are mapped as cultivated land cover overlying carbonate rocks. At a management threshold of 10 milligrams per liter (corresponding to the U.S. Environmental Protection Agency standard for nitrate in drinking water of 10 milligrams per liter), the predicted probability of nitrate concentrations in ground water exceeding this level are low for most of the Mid-Atlantic Region except for the Delmarva Peninsula, southeastern Pennsylvania, and areas mapped as carbonate rocks in Virginia, Maryland, and Pennsylvania.

Open-File Report↗

USGS2024

Evaluation of an impulse-response emulator for groundwater contaminant transport modeling

There is a significant need to develop decision support tools capable of delivering accurate representations of environmental conditions, such as ground and surface water solute concentrations, in a timely and computationally efficient manner. Such tools can be leveraged to assess a large number of potential management strategies for mitigating non-point source pollutants. Here, we assess the effectiveness of the impulse-response emulation approach to approximate process-based groundwater model estimates of solute transport from MODFLOW and MT3D over a wide range of model inputs and parameters, with the goal of assessing where in parameter space the assumptions underlying this emulation approach are valid. The impulse-response emulator was developed using the sensitivity analysis utilities in the PEST++ software suite and is capable of approximating MODFLOW/MT3D estimates of solute transport over a large portion of the parameter space tested, except in cases where the Courant number is above 0.5. Across all runs tested, the highest percent errors were at the plume fronts. These results suggest that the impulse-response approach may be suitable for emulation of solute transport models for a wide range of cases, except when high-resolution outputs are needed, or when very low concentrations at plume edges are of particular interest.

Groundwater↗

USGS2017

Incorporating population viability models into species status assessment and listing decisions under the U.S. Endangered Species Act

Assessment of a species' status is a key part of management decision making for endangered and threatened species under the U.S. Endangered Species Act. Predicting the future state of the species is an essential part of species status assessment, and projection models can play an important role in developing predictions. We built a stochastic simulation model that incorporated parametric and environmental uncertainty to predict the probable future status of the Sonoran desert tortoise in the southwestern United States and North Central Mexico. Sonoran desert tortoise was a Candidate species for listing under the Endangered Species Act, and decision makers wanted to use model predictions in their decision making process. The model accounted for future habitat loss and possible effects of climate change induced droughts to predict future population growth rates, abundances, and quasi-extinction probabilities. Our model predicts that the population will likely decline over the next few decades, but there is very low probability of quasi-extinction less than 75 years into the future. Increases in drought frequency and intensity may increase extinction risk for the species. Our model helped decision makers predict and characterize uncertainty about the future status of the species in their listing decision. We incorporated complex ecological processes (e.g., climate change effects on tortoises) in transparent and explicit ways tailored to support decision making processes related to endangered species.

Sonoran Desert↗

USGS2015

Human and bovine viruses and bacteria at three Great Lakes beaches: Environmental variable associations and health risk

Waterborne pathogens were measured at three beaches in Lake Michigan, environmental factors for predicting pathogen concentrations were identified, and the risk of swimmer infection and illness was estimated. Waterborne pathogens were detected in 96% of samples collected at three Lake Michigan beaches in summer, 2010. Samples were quantified for 22 pathogens in four microbial categories (human viruses, bovine viruses, protozoa, and pathogenic bacteria). All beaches had detections of human and bovine viruses and pathogenic bacteria indicating influence of multiple contamination sources at these beaches. Occurrence ranged from 40 to 87% for human viruses, 65–87% for pathogenic bacteria, and 13–35% for bovine viruses. Enterovirus, adenovirus A, Salmonella spp. , Campylobacter jejuni , bovine polyomavirus, and bovine rotavirus A were present most frequently. Variables selected in multiple regression models used to explore environmental factors that influence pathogens included wave direction, cloud cover, currents, and water temperature. Quantitative Microbial Risk Assessment was done for C. jejuni , Salmonella spp. , and enteroviruses to estimate risk of infection and illness. Median infection risks for one-time swimming events were approximately 3 × 10 –5 , 7 × 10 –9 , and 3 × 10 –7 for C. jejuni , Salmonella spp., and enteroviruses, respectively. Results highlight the importance of investigating multiple pathogens within multiple categories to avoid underestimating the prevalence and risk of waterborne pathogens.

Wisconsin↗

USGS2010

Evolution of Ore Deposits and Technology Transfer Project: Isotope and Chemical Methods in Support of the U.S. Geological Survey Science Strategy, 2003-2008

Principal functions of the U.S. Geological Survey (USGS) Mineral Resources Program are providing assessments of the location, quantity, and quality of undiscovered mineral deposits, and predicting the environmental impacts of exploration and mine development. The mineral and environmental assessments of domestic deposits are used by planners and decisionmakers to improve the stewardship of public lands and public resources. Assessments of undiscovered mineral deposits on a global scale reveal the potential availability of minerals to the United States and other countries that manufacture goods imported to the United States. These resources are of fundamental relevance to national and international economic and security policy in our globalized world economy. Performing mineral and environmental assessments requires that predictions be made of the likelihood of undiscovered deposits. The predictions are based on geologic and geoenvironmental models that are constructed for the diverse types of mineral deposits from detailed descriptions of actual deposits and detailed understanding of the processes that formed them. Over the past three decades the understanding of ore-forming processes has benefited greatly from the integration of laboratory-based geochemical tools with field observations and other data sources. Under the aegis of the Evolution of Ore Deposits and Technology Transfer Project (referred to hereinafter as the Project), a 5-year effort that terminated in 2008, the Mineral Resources Program provided state-of-the-art analytical capabilities to support applications of several related geochemical tools to ore-deposit-related studies. The analytical capabilities and scientific approaches developed within the Project have wide applicability within Earth-system science. For this reason the Project Laboratories represent a valuable catalyst for interdisciplinary collaborations of the type that should be formed in the coming years for the United States to meet its natural-resources and natural-science needs. This circular presents an overview of the Project. Descriptions of the Project laboratories are given first including descriptions of the types of chemical or isotopic analyses that are made and the utility of the measurements. This is followed by summaries of select measurements that were carried out by the Project scientists. The studies are grouped by science direction. Virtually all of them were collaborations with USGS colleagues or with scientists from other governmental agencies, academia, or the private sector.

Circular↗

USGS2009

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Journal of Exposure Science and Environmental Epid↗

USGS2025

Living with uncertainty: Using multi-model large ensembles to assess emperor penguin extinction risk for the IUCN Red List

Improved methods for identifying species at risk are needed to strengthen climate change vulnerability assessments, as current estimates indicate that up to one million species face extinction due to environmental changes. Integrating multiple sources of uncertainty enhances the robustness of Red List of Threatened Species assessments, providing a more comprehensive understanding of species’ risks. We present a comprehensive framework that incorporates uncertainties, including measurement error, structural uncertainty, natural variability, future climate emissions scenario, and extreme events of sea ice loss, to evaluate the extinction risk of the emperor penguin ( Aptenodytes forsteri ), currently classified as Near-Threatened. We apply three ecological models, one bioclimatic and two metapopulation models, combined with a multi-model large ensemble (MMLE) of climate projections from general circulation models, to conduct a Red List evaluation at both global, regional and colony levels. Our results show that emperor penguins could be classified under a range of Red List categories depending on the ecological model, Intergovernmental Panel on Climate Change (IPCC) climate emissions scenario, and extreme event frequency. Under Criterion A, global classifications vary from Vulnerable to Critically Endangered. Severe declines are projected in the Indian and East Pacific sectors, Dronning Maud Land and the Amundsen-Bellingshausen Sea, with Criterion E indicating that 24% to 100% of colonies meet Endangered status thresholds, depending on huddling thresholds and ecological models. This study represents the first application of an MMLE coupled with an ecological ensemble approach to project climate change impacts on a species, capturing a comprehensive range of uncertainties and offering a framework for improving forecasting and decision-making under climate change.

Biological Conservation↗

USGS2022

Modelling physiological costs to assess impacts of climate change on amphibians in Yellowstone National Park, U.S.A

Amphibians are vital elements of ecosystems, serving as predator and prey. Their biphasic nature makes them dependent on aquatic and terrestrial habitats; as wet-skinned ectotherms, they are vulnerable to a range of environmental threats, including climate change. Yellowstone National Park (YNP) is becoming warmer and drier, and some wetlands important to amphibians have diminished. Continued climate change is predicted to reduce snowpack, soil moisture, and forest cover. We used data from models of future climate and vegetation cover to mechanistically model how climate change might affect the movements of Western Toads ( Anaxyrus boreas ) across the landscape of three test areas in YNP for the years 2050 and 2090, compared to 2000 as a baseline. Least-cost path analysis produced mixed results: for 2050 and 2090, physiological costs of movement increased in one test area and decreased in another; they were mixed in the third. These changes generally reflect the preference by toads for more open forests. Estimating costs for other species of YNP amphibians produced more negative results. For Columbia Spotted Frogs ( Rana luteiventris ) and Boreal Chorus Frogs ( Pseudacris maculata ) (both more aquatic and less adapted to terrestrial habitats), movement costs increased by about 2–15X. Reduced frequency or duration of rain events might limit the nocturnal movements of Western Tiger Salamanders ( Ambystoma mavortium ). Climate change may not have negative impacts on all amphibians throughout YNP, but increased movement costs for terrestrial habitats will accentuate effects of drying wetlands in at least parts of YNP. Land management actions that preserve habitat structure of both forest and low shrub cover may help mitigate continued drying conditions of climate change.

Montana, Wyoming↗

USGS2014

Linking bovine tuberculosis on cattle farms to white-tailed deer and environmental variables using Bayesian hierarchical analysis

Bovine tuberculosis is a bacterial disease caused by Mycobacterium bovis in livestock and wildlife with hosts that include Eurasian badgers ( Meles meles ), brushtail possum ( Trichosurus vulpecula ), and white-tailed deer ( Odocoileus virginianus ). Risk-assessment efforts in Michigan have been initiated on farms to minimize interactions of cattle with wildlife hosts but research on M. bovis on cattle farms has not investigated the spatial context of disease epidemiology. To incorporate spatially explicit data, initial likelihood of infection probabilities for cattle farms tested for M. bovis , prevalence of M. bovis in white-tailed deer, deer density, and environmental variables for each farm were modeled in a Bayesian hierarchical framework. We used geo-referenced locations of 762 cattle farms that have been tested for M. bovis , white-tailed deer prevalence, and several environmental variables that may lead to long-term survival and viability of M. bovis on farms and surrounding habitats (i.e., soil type, habitat type). Bayesian hierarchical analyses identified deer prevalence and proportion of sandy soil within our sampling grid as the most supported model. Analysis of cattle farms tested for M. bovis identified that for every 1% increase in sandy soil resulted in an increase in odds of infection by 4%. Our analysis revealed that the influence of prevalence of M. bovis in white-tailed deer was still a concern even after considerable efforts to prevent cattle interactions with white-tailed deer through on-farm mitigation and reduction in the deer population. Cattle farms test positive for M. bovis annually in our study area suggesting that the potential for an environmental source either on farms or in the surrounding landscape may contributing to new or re-infections with M. bovis . Our research provides an initial assessment of potential environmental factors that could be incorporated into additional modeling efforts as more knowledge of deer herd factors and cattle farm prevalence is documented.

Michigan↗

USGS2024

Factors contributing to pesticide contamination in riverine systems: The role of wastewater and landscape sources

Wastewater treatment plant (WWTP) discharges can be a source of organic contaminants, including pesticides, to rivers. An integrated model was developed for the Potomac River watershed (PRW) to determine the amount of accumulated wastewater percentage of streamflow (ACCWW) and calculate predicted environmental concentrations (PECs) for 14 pesticides in non-tidal National Hydrography Dataset Plus Version 2.1 stream segments. Predicted environmental concentrations were compared to measured environmental concentrations (MECs) from 32 stream sites that represented a range of ACCWW and land use to evaluate model performance and to assess possible non-WWTP loading sources. Statistical agreement between PECs and MECs was strongest for insecticides, followed by fungicides and herbicides. Principal component analysis utilizing optical fluorescence and ancillary water quality data identified wastewater and urban runoff sources. Pesticides that indicated relatively larger sources from WWTPs included dinotefuran, fipronil, carbendazim, thiabendazole, and prometon whereas imidacloprid, azoxystrobin, propiconazole, tebuconazole, and diuron were more related to urban runoff. In addition, PECs generally comprised a low proportion of MECs, which indicates possible dominant loading sources beyond WWTP discharges. Cumulative potential toxicity was higher for sites with greater ACCWW and/or located in developed areas. Imidacloprid, fipronil, and carbendazim accounted for the largest portion of predicted potential toxicity across sites. The chronic aquatic life toxicity benchmarks for freshwater invertebrates were exceeded for 82 % of the imidacloprid detections ( n = 28) and 47 % of the fipronil detections ( n = 19). These results highlight the ecological implications of pesticide contamination from WWTP discharges and also the potential legacy effects from accumulated soil and groundwater sources. Pesticide management strategies that mitigate both current and historical impacts may improve the health of aquatic ecosystems.

Potomac River watershed↗

USGS1978

Hydrologic data for urban storm runoff from three localities in the Denver metropolitan area, Colorado

Urban storm-runoff data, collected from 1975 to 1977, on three catchment areas in the Denver, Colo., metropolitan area are presented. The catchment are predominantly a single-family residential catchment area in Littleton, a multifamily residential and commercial catchment area in Lakewood, and a high-density residential and commercial catchment area in Denver. Precipitation, rainfall-runoff, snowmelt-runoff, water-quality (common constituents, nutrients, biochemical oxygen demand, coliform bacteria, and solids, trace elements, and pesticides), and catchment-area data are necessary to use the U.S. Environmental Protection Agency 's Storm Water Management Model II. The urban storm-runoff data may be used by planning, water-management, and environmental-protection agencies to assess the impact of urban storm runoff on the hydrologic system. (Woodard-USGS)

Colorado↗

USGS2014

Response to heavy, non-floating oil spilled in a Great Lakes river environment: a multiple-lines-of-evidence approach for submerged oil assessment and recovery

The Enbridge Line 6B pipeline release of diluted bitumen into the Kalamazoo River downstream of Marshall, MI in July 2010 is one of the largest freshwater oil spills in North American history. The unprecedented scale of impact and massive quantity of oil released required the development and implementation of new approaches for detection and recovery. At the onset of cleanup, conventional recovery techniques were employed for the initially floating oil and were successful. However, volatilization of the lighter diluent, along with mixing of the oil with sediment during flooded, turbulent river conditions caused the oil to sink and collect in natural deposition areas in the river. For more than three years after the spill, recovery of submerged oil has remained the predominant operational focus of the response. The recovery complexities for submerged oil mixed with sediment in depositional areas and long-term oil sheening along approximately 38 miles of the Kalamazoo River led to the development of a multiple-lines-of-evidence approach comprising six major components: geomorphic mapping, field assessments of submerged oil (poling), systematic tracking and mapping of oil sheen, hydrodynamic and sediment transport modeling, forensic oil chemistry, and net environmental benefit analysis. The Federal On-Scene Coordinator (FOSC) considered this information in determining the appropriate course of action for each impacted segment of the river. New sources of heavy crude oils like diluted bitumen and increasing transportation of those oils require changes in the way emergency personnel respond to oil spills in the Great Lakes and other freshwater ecosystems. Strategies to recover heavy oils must consider that the oils may suspend or sink in the water column, mix with fine-grained sediment, and accumulate in depositional areas. Early understanding of the potential fate and behavior of diluted bitumen spills when combined with timely, strong conventional recovery methods can significantly influence response success.

Michigan↗