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At least 19 recordsLinked to original sources
USGS2015

Linking state-and-transition simulation and timber supply models for forest biomass production scenarios

We linked state-and-transition simulation models (STSMs) with an economics-based timber supply model to examine landscape dynamics in North Carolina through 2050 for three scenarios of forest biomass production. Forest biomass could be an important source of renewable energy in the future, but there is currently much uncertainty about how biomass production would impact landscapes. In the southeastern US, if forests become important sources of biomass for bioenergy, we expect increased land-use change and forest management. STSMs are ideal for simulating these landscape changes, but the amounts of change will depend on drivers such as timber prices and demand for forest land, which are best captured with forest economic models. We first developed state-and-transition model pathways in the ST-Sim software platform for 49 vegetation and land-use types that incorporated each expected type of landscape change. Next, for the three biomass production scenarios, the SubRegional Timber Supply Model (SRTS) was used to determine the annual areas of thinning and harvest in five broad forest types, as well as annual areas converted among those forest types, agricultural, and urban lands. The SRTS output was used to define area targets for STSMs in ST-Sim under two scenarios of biomass production and one baseline, business-as-usual scenario. We show that ST-Sim output matched SRTS targets in most cases. Landscape dynamics results indicate that, compared with the baseline scenario, forest biomass production leads to more forest and, specifically, more intensively managed forest on the landscape by 2050. Thus, the STSMs, informed by forest economics models, provide important information about potential landscape effects of bioenergy production.

AIMS Environmental Science↗
USGS2015

Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change

State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine ( Pinus albicaulis ). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle, Dendroctonus ponderosae ), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.

AIMS Environmental Science↗
USGS2015

Simulating long-term effectiveness and efficiency of management scenarios for an invasive grass

Resource managers are often faced with trade-offs in allocating limited resources to manage plant invasions. These decisions must often be made with uncertainty about the location of infestations, their rate of spread and effectiveness of management actions. Landscape level simulation tools such as state-and-transition simulation models (STSMs) can be used to evaluate the potential long term consequences of alternative management strategies and help identify those strategies that make efficient use of resources. We analyzed alternative management scenarios for African buffelgrass ( Pennisetum ciliare syn. Cenchrus ciliaris ) at Ironwood Forest National Monument, Arizona using a spatially explicit STSM implemented in the Tool for Exploratory Landscape Scenario Analyses (TELSA). Buffelgrass is an invasive grass that is spreading rapidly in the Sonoran Desert, affecting multiple habitats and jurisdictions. This invasion is creating a novel fire risk and transforming natural ecosystems. The model used in this application incorporates buffelgrass dispersal and establishment and management actions and effectiveness including inventory, treatment and post-treatment maintenance. We simulated 11 alternative scenarios developed in consultation with buffelgrass managers and other stakeholders. The scenarios vary according to the total budget allocated for management and the allocation of that budget between different kinds of management actions. Scenario results suggest that to achieve an actual reduction and stabilization of buffelgrass populations, management unconstrained by fiscal restrictions and across all jurisdictions and private lands is required; without broad and aggressive management, buffelgrass populations are expected to increase over time. However, results also suggest that large upfront investments can achieve control results that require relatively minimal spending in the future. Investing the necessary funds upfront to control the invasion results in the most efficient use of resources to achieve lowest invaded acreage in the long-term.

Arizona↗
USGS2015

Encapsulating model complexity and landscape-scale analyses of state-and-transition simulation models: an application of ecoinformatics and juniper encroachment in sagebrush steppe ecosystems

State-and-transition simulation modeling relies on knowledge of vegetation composition and structure (states) that describe community conditions, mechanistic feedbacks such as fire that can affect vegetation establishment, and ecological processes that drive community conditions as well as the transitions between these states. However, as the need for modeling larger and more complex landscapes increase, a more advanced awareness of computing resources becomes essential. The objectives of this study include identifying challenges of executing state-and-transition simulation models, identifying common bottlenecks of computing resources, developing a workflow and software that enable parallel processing of Monte Carlo simulations, and identifying the advantages and disadvantages of different computing resources. To address these objectives, this study used the ApexRMS® SyncroSim software and embarrassingly parallel tasks of Monte Carlo simulations on a single multicore computer and on distributed computing systems. The results demonstrated that state-and-transition simulation models scale best in distributed computing environments, such as high-throughput and high-performance computing, because these environments disseminate the workloads across many compute nodes, thereby supporting analysis of larger landscapes, higher spatial resolution vegetation products, and more complex models. Using a case study and five different computing environments, the top result (high-throughput computing versus serial computations) indicated an approximate 96.6% decrease of computing time. With a single, multicore compute node (bottom result), the computing time indicated an 81.8% decrease relative to using serial computations. These results provide insight into the tradeoffs of using different computing resources when research necessitates advanced integration of ecoinformatics incorporating large and complicated data inputs and models. - See more at: http://aimspress.com/aimses/ch/reader/view_abstract.aspx?file_no=Environ2015030&flag=1#sthash.p1XKDtF8.dpuf

AIMS Environmental Science↗
USGS2015

An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California

Increased land-use intensity (e.g. clearing of forests for cultivation, urbanization), often results in the loss of ecosystem carbon storage, while changes in productivity resulting from climate change may either help offset or exacerbate losses. However, there are large uncertainties in how land and climate systems will evolve and interact to shape future ecosystem carbon dynamics. To address this we developed the Land Use and Carbon Scenario Simulator (LUCAS) to track changes in land use, land cover, land management, and disturbance, and their impact on ecosystem carbon storage and flux within a scenario-based framework. We have combined a state-and-transition simulation model (STSM) of land change with a stock and flow model of carbon dynamics. Land-change projections downscaled from the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emission Scenarios (SRES) were used to drive changes within the STSM, while the Integrated Biosphere Simulator (IBIS) ecosystem model was used to derive input parameters for the carbon stock and flow model. The model was applied to the Sierra Nevada Mountains ecoregion in California, USA, a region prone to large wildfires and a forestry sector projected to intensify over the next century. Three scenario simulations were conducted, including a calibration scenario, a climate-change scenario, and an integrated climate- and land-change scenario. Based on results from the calibration scenario, the LUCAS age-structured carbon accounting model was able to accurately reproduce results obtained from the process-based biogeochemical model. Under the climate-only scenario, the ecoregion was projected to be a reliable net sink of carbon, however, when land use and disturbance were introduced, the ecoregion switched to become a net source. This research demonstrates how an integrated approach to carbon accounting can be used to evaluate various drivers of ecosystem carbon change in a robust, yet transparent modeling environment.

California↗
USGS2015

Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling

Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC projections, at the 0.5 × 0.5 cell resolution, were downscaled to seven Level III ecoregions in the Pacific Northwest, United States. RCP transition values at each cell were downscaled based on the proportional distribution between ecoregions of (1) cell area, (2) land-cover composition derived from remotely-sensed imagery, and (3) historic LULC transition values from a LULC history database. Resulting downscaled LULC transition values were aggregated according to their bounding ecoregion and “cross-walked” to relevant LULC classes. Ecoregion-level LULC transition values were applied in a STSM projecting LULC change between 2005 and 2100. While each downscaling methods had advantages and disadvantages, downscaling using the historical land-use history dataset consistently apportioned RCP LULC transitions in agreement with historical observations. Regardless of the downscaling method, some LULC projections remain improbable and require further investigation.

AIMS Environmental Science↗
USGS2015

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

AIMS Environmental Science↗
USGS2016

Use of lidar point cloud data to support estimation of residual trace metals stored in mine chat piles in the Old Lead Belt of southeastern, Missouri

Historic lead and zinc (Pb-Zn) mining in southeast Missouri’s ―Old Lead Belt‖ has left large chat piles dominating the landscape where prior to 1972 mining was the major industry of the region. As a result of variable beneficiation methods over the history of mining activity, these piles remain with large quantities of unrecovered Pb and Zn and to a lesser extent cadmium (Cd). Quantifying the residual content of trace metals in chat piles is problematic because of the extensive field effort that must go into collecting elevation points for volumetric analysis. This investigation demonstrates that publicly available lidar point data from the U.S. Geological Survey 3D Elevation Program (3DEP) can be used to effectively calculate chat pile volumes as a method of more accurately estimating the total residual trace metal content in these mining wastes. Five chat piles located in St. Francois County, Missouri, were quantified for residual trace metal content. Utilizing lidar point cloud data collected in 2011 and existing trace metal concentration data obtained during remedial investigations, residual content of these chat piles ranged from 9247 to 88,579 metric tons Pb, 1925 to 52,306 metric tons Zn, and 51 to 1107 metric tons Cd. Development of new beneficiation methods for recovering these constituents from chat piles would need to achieve current Federal soil screening standards. To achieve this for the five chat piles investigated, 42 to 72% of residual Pb would require mitigation to the 1200 mg/kg Federal non-playground standard, 88 to 98% of residual Zn would require mitigation to the Ecological Soil Screening level (ESSL) for plant life, and 70% to 98% of Cd would require mitigation to achieve the ESSL. Achieving these goals through an existing or future beneficiation method(s) would remediate chat to a trace metal concentration level that would support its use as a safe agricultural soil amendment.

Missouri↗
USGS2001

Acidic deposition in the northeastern United States: Sources and inputs, ecosystem effects, and management strategies: The effects of acidic deposition in the northeastern United States include the acidification of soil and water, which stresses terrestrial and aquatic biota

Acidic deposition is the transfer of strong acids and acid-forming substances from the atmosphere to the surface of the Earth. The composition of acidic deposition includes ions, gases, and particles derived from the following: gaseous emissions of sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), ammonia (NH 3 ), and particulate emissions of acidifying and neutralizing compounds. Over the past quarter century of study, acidic deposition has emerged as a critical environmental stress that affects forested landscapes and aquatic ecosystems in North America, Europe, and Asia. This complex problem is an example of a new class of environmental issues that are multiregional in scale and are not amenable to simple resolution by policymakers. Acidic deposition can originate from transboundary air pollution and can affect large geographic areas. It is highly variable across space and time, links air pollution to diverse terrestrial and aquatic ecosystems, and alters the interactions of many elements (e.g., sulfur [S], nitrogen [N], hydrogen ion [H + ], calcium [Ca 2+ ], magnesium [Mg 2+ ], and aluminum [Al]). It also contributes directly and indirectly to biological stress and to the degradation of ecosystems. Despite the complexity of the effects of acidic deposition, North American and European management actions directed toward the recovery of damaged natural resources have resulted in recent decreases in both emissions and deposition of acidic S compounds. Accordingly, acidic deposition presents an instructive case study for coordinating science and policy efforts aimed at resolving large-scale environmental problems.

northeastern United States↗
USGS2022

Bayesian applications in environmental and ecological studies with R and Stan

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

Book↗
USGS2018

Navigating the field of decision analysis

Managers, policy makers, and decision makers with responsibility for environmental decisions have an extraordinarily difficult job. The systems they manage are complex (coupled human-natural systems), with many dimensions and complicated dynamics. Our knowledge of how those systems respond to management actions is often limited, so many of the decisions have to be made in the face of uncertainty. The field of decision analysis provides a comprehensive set of tools for structuring, analyzing, and making decisions. Arising initially as a way to understand and manage risk, the field has expanded in the last 80 years to cover such topics as multipleobjective trade-offs, time-dependent linked decisions, the value of information, and competition among multiple decision makers. At the same time, cognitive psychologists and behavioural economists have studied human decisions to understand when our innate processes work and when they fail. Formal decision analysis is increasingly being used in many sectors, including economic, industrial, manufacturing, agricultural, transportation, and medical sectors, by individuals, corporations, non-profits, and government agencies. Occasional application of decision analysis to environmental decisions began in the mid-1970s, but the environmental science and management world was largely unaware of decision analysis until the late 1990s and early 2000s. (A major aim of the ARC Centre of Excellence for Environmental Decisions – CEED – was to better connect decision analysis and environmental management). Today, the field of environmental decision analysis is coming of age with the maturation of a rich set of tools to help decision makers frame, analyze, and implement decisions. Sometimes the wealth of tools is daunting, but there are core principles that tie decision analysis together. To help the reader navigate this complexity, we summarize the field of decision analysis in four key messages.

Decision Point Online↗
USGS2021

A more representative community of ecologists

Ecologists play a crucial role in providing solutions to the challenges facing the world. For most of the history of the field, however, the science of ecology has been pursued by white men, and increasingly, by white women. This lack of diversity is untenable today, not only because it is socially unjust, but also because solving environmental problems requires diversity. Ecology as a science is an extremely rewarding and fun career choice for many, but how can the field recruit a more diverse workforce to do this important and gratifying work? Attracting and retaining future ecologists of color is the focus of this Forum. How do young people choose a career in ecology and environmental sciences? For many ecologists, environmental scientists and managers, and future natural resource professionals, an early field experience provides a crucial introduction and gateway. A sojourn at a field station, an extended field trip, or field expedition has introduced many of today’s professionals to their fields, but at the same time, a growing literature documents problematic behavior, discrimination, and other forms of harassment that have been far too frequently reported, and little attention has been given to conscious or implicit exclusion of students from diverse backgrounds. In their lead article, Bowser and Cid, two leading researchers and long-time champions of diversity in ecology, build on experience in long-running programs as well as the literature to diagnose challenges facing diverse youth, and present approaches to encourage, rather than discourage, further engagement. They describe affirmative and creative measures that foster a sense of inclusion and community among young scholars; this sense of belonging and empowerment allows many to advance to studies and careers in ecology and environmental science. In the subsequent six papers, authors explore topics either responding to, or inspired by, the lead paper. They explore alternatives to a field experience as a gateway to a career in ecology or environmental sciences through, for example, data science or the social sciences. Other comment papers describe challenges faced in the next phases of an academic career, barriers faced by specific cultural groups and the approaches, challenges, and outcomes of programs aiming to increase the diversity of the environmental STEM workforce. All responses sound a clarion call for change to hear and value different voices and perspectives to be heard and valued. These include (1) structural and cultural change to our institutions and reward structures; (2) developing and nurturing personal relationships among students and their mentors, within teams and in internships; (3) making entry to ecology inviting and making advancement in ecology free from systemic barriers; and (4) broadening our vision of ecology, and the ways we learn about the world’s ecosystems. The Ecological Society of America, the home for Ecological Applications , is committed to the diversity, equity and inclusion needed to tackle environmental challenges in unity (https://www.esa.org/esablog/2020/09/24/time-for-action-esa-initiates-a-diversity-equity-inclusion-and-justice-deij-task-force/). The world needs all available talent and perspectives to meet environmental challenges today. Ecological Applications has long published occasional papers on the profession of ecology and never ones more important or timely than these. The editorial team is proud to provide an outlet for the voices of our field, in all its current, if inadequate, diversity and honored to host the passionate and committed views of our authors.

Ecological Applications↗
USGS2016

U.S. Geological Survey science strategy for highly pathogenic avian influenza in wildlife and the environment (2016–2020)

Introduction Through the Science Strategy for Highly Pathogenic Avian Influenza (HPAI) in Wildlife and the Environment, the USGS will assess avian influenza (AI) dynamics in an ecological context to inform decisions made by resource managers and policymakers from the local to national level. Through collection of unbiased scientific information on the ecology of AI viruses and wildlife hosts in a changing world, the U.S. Geological Survey (USGS) will enhance the development of AI forecasting tools and ensure this information is integrated with a quality decision process for managing HPAI. The overall goal of this USGS Science Strategy for HPAI in Wildlife and the Environment goes beyond document­ing the occurrence and distribution of AI viruses in wild birds. The USGS aims to understand the epidemiological processes and environmental factors that influence HPAI distribution and describe the mechanisms of transmission between wild birds and poultry. USGS scientists developed a conceptual model describing the process linking HPAI dispersal in wild waterfowl to the outbreaks in poul­try. This strategy focuses on five long-term science goals, which include: Science Goal 1—Augment the National HPAI Surveillance Plan; Science Goal 2—Determine mechanisms of HPAI disease spread in wildlife and the environment; Science Goal 3—Characterize HPAI viruses circulating in wildlife; Science Goal 4—Understand implications of avian ecol­ogy on HPAI spread; and Science Goal 5—Develop HPAI forecasting and decision-making tools. These goals will help define and describe the processes outlined in the conceptual model with the ultimate goal of facilitating biosecurity and minimizing transfer of diseases across the wildlife-poultry interface. The first four science goals are focused on scientific discovery and the fifth goal is application-based. Decision analyses in the fifth goal will guide prioritization of proposed actions in the first four goals.

Open-File Report↗
USGS2024

GeoAI for science and the science of GeoAI

This paper reviews trends in GeoAI research and discusses cutting-edge ad- vances in GeoAI and its roles in accelerating environmental and social sciences. It ad- dresses ongoing attempts to improve the predictability of GeoAI models and recent re- search aimed at increasing model explainability and reproducibility to ensure trustworthy geospatial findings. The paper also provides reflections on the importance of defining the "science" of GeoAI in terms of its fundamental principles, theories, and methods to ensure scientific rigor, social responsibility, and lasting impacts.

Journal of Spatial Information Science↗
USGS2011

Towards identifying the next generation of superfund and hazardous waste site contaminants

Background This commentary evolved from a workshop sponsored by the National Institute of Environmental Health Sciences titled "Superfund Contaminants: The Next Generation" held in Tucson, Arizona, in August 2009. All the authors were workshop participants. Objectives Our aim was to initiate a dynamic, adaptable process for identifying contaminants of emerging concern (CECs) that are likely to be found in future hazardous waste sites, and to identify the gaps in primary research that cause uncertainty in determining future hazardous waste site contaminants. Discussion Superfund-relevant CECs can be characterized by specific attributes: They are persistent, bioaccumulative, toxic, occur in large quantities, and have localized accumulation with a likelihood of exposure. Although still under development and incompletely applied, methods to quantify these attributes can assist in winnowing down the list of candidates from the universe of potential CECs. Unfortunately, significant research gaps exist in detection and quantification, environmental fate and transport, health and risk assessment, and site exploration and remediation for CECs. Addressing these gaps is prerequisite to a preventive approach to generating and managing hazardous waste sites. Conclusions A need exists for a carefully considered and orchestrated expansion of programmatic and research efforts to identify, evaluate, and manage CECs of hazardous waste site relevance, including developing an evolving list of priority CECs, intensifying the identification and monitoring of likely sites of present or future accumulation of CECs, and implementing efforts that focus on a holistic approach to prevention.

Environmental Health Perspectives↗
USGS2025

Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems

The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal scales of variable inundation are vast, ranging from sub-meter to whole landscapes and from sub-hourly to multi-decadal. The broad range of system types and scales makes it challenging to predict the hydrology, biogeochemistry, ecology, and physical evolution of VIEs. Despite all experiencing the loss and gain of an overlying water column, VIEs are rarely considered together in conceptual, theoretical, modeling, or measurement frameworks and approaches. Studying VIEs together has the potential to generate mechanistic understanding that is transferable across a much broader range of environmental conditions, relative to knowledge generated by studying any one VIE type. We postulate that enhanced transferability will be important for predicting changes in VIE function in response to global change. Here we aim to catalyze cross-VIE science that studies drivers and impacts of variable inundation across Earth's VIEs. To this end, we complement expert mini-reviews of eight major VIE systems with overviews of VIE-relevant methods and challenges associated with scale. We conclude with perspectives on how cross-VIE science can derive transferable understanding via unifying conceptual models in which the impacts of variable inundation are studied across multi-dimensional environmental space.

Biogeosciences↗
USGS2025

USGS Environmental Health Program—Per- and polyfluoroalkyl substances (PFAS) science activities [postcard]

Introduction The U.S. Geological Survey (USGS) Environmental Health Program conducts various activities related to PFAS (per- and polyfluoroalkyl substances) to understand their impacts on human health and the environment. Through these activities, the program aims to provide critical information and resources to address the challenges posed by PFAS contamination.

General Information Product↗
USGS2021

Earth Resources Observation and Science Center hosting services

The Earth Resources Observation and Science (EROS) Center has a long history of leveraging technology in support of Earth science and business applications including data management, processing, and virtualization and complex solutions to visualize and distribute data. It is the aim of EROS to offer operational excellence and service as a key component to the Federal Cloud-Smart directives and Department of the Interior data center consolidation goals. The EROS Center offers space, network, security, and environmental controls and information technology service management. Included with leveraging technology, the EROS Center has industry-leading staff to assist with supporting Earth science and business applications.

Fact Sheet↗