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DOE OSTI2015

Taxonomy of USA east coast fishing communities in terms of social vulnerability and resilience

Increased concern with the impacts that changing coastal environments can have on coastal fishing communities led to a recent effort by NOAA Fisheries social scientists to develop a set of indicators of social vulnerability and resilience for the U.S. Southeast and Northeast coastal communities. A goal of the NOAA Fisheries social vulnerability and resilience indicator program is to support time and cost effective use of readily available data in furtherance of both social impact assessments of proposed changes to fishery management regulations and climate change adaptation planning. The use of the indicators to predict the response to change in coastal communities would be enhanced if community level analyses could be grouped effectively. This study examines the usefulness of combining 1130 communities into 35 relevant subgroups by comparing results of a numerical taxonomy with data collected by interview methods, a process herein referred to as “ground-truthing.” The validation of the taxonomic method by the method of ground-truthing indicates that the clusters are adequate to be used to select communities for in-depth research. - Highlights: • We develop a taxonomy of fishing communities based on vulnerability indicators. • We validate the community clusters through the use of surveys (“ground-truthing”). • Clusters differ along important aspects of fishing community vulnerability. • Clustering communities allows for accurate and timely social impact assessments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Tools for integrating environmental objectives into policy and practice: What works where?

An abundance of approaches, strategies, and instruments – in short: tools – have been developed that intend to stimulate or facilitate the integration of a variety of environmental objectives into development planning, national or regional sectoral policies, international agreements, business strategies, etc. These tools include legally mandatory procedures, such as Environmental Impact Assessment and Strategic Environmental Assessment; more voluntary tools such as environmental indicators developed by scientists and planning tools; green budgeting, etc. A relatively underexplored question is what integration tool fits what particular purposes and contexts, in short: “what works where?”. This paper intends to contribute to answering this question, by first providing conceptual clarity about what integration entails, by suggesting and illustrating a classification of integration tools, and finally by summarising some of the lessons learned about how and why integration tools are (not) used and with what outcomes, particularly in terms of promoting the integration of environmental objectives.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Bottom-up responses to environmental and social impact assessments: A case study from Guatemala

In this article we take a closer look at resistance to the practice of Environmental Impact Assessment (EIA) in mining and energy projects in Guatemala. Collectivities resisting mining and hydropower projects in Guatemala are increasingly using the evaluations of EIAs conducted by international independent professionals. Reaching out to international experts is facilitated by local communities' engagements in transnational networks bringing together activists, NGOs, scientists, journalists and others. We argue that resistance movements resort to international professionals to challenge the limits imposed on them by the national legislation and institutional arrangements as well as by the way in which EIAs are performed in the country. Further, the engagements in networks that facilitate access to knowledge contribute to strengthen the legitimacy of communities' claims. Challenges to and complaints about EIAs are ways in which affected communities try to reclaim their right to participate in decision-making related to their local environment and the development of their communities. Both complaints about EIAs and the use of transnational networks to attain better participation in decision making processes at local levels, illustrated in this study for Guatemala, are common responses to the advancement of extractive industries and hydropower development across Latin America. The widespread of initiatives to challenge EIAs involving international experts in the region show that EIAs have become a sort of a transnational battleground. - Highlights: • Communities’ opposition to extractive projects is rooted in lack of participation in decision-making, including EIAs • Experts’ evaluations of approved EIAs confirm communities’ claims of poor practices in the public sector • Research presented here shows that local communities linked to transnational networks are able to scale up their demands.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Synthesis of rhenium coatings on 316 stainless steel and their electrochemical behavior towards water oxidation in saline environments

The production of electrolytic hydrogen or Green Hydrogen has attracted the attention of scientists as a potential enabler of sustainable energy production. The cleavage of the water molecule requires high energy, in order to produce hydrogen and oxygen through their corresponding half reactions, the hydrogen evolution and oxygen evolution reactions. Further, this latter reaction has been studied in more detail, since its slow kinetics make the water electrolysis less efficient, and, for instance, delay the formation of hydrogen in the counter compartment of the electrolytic cell. In this work, a study of the oxygen evolution reaction is presented. For this, a series of rhenium catalysts deposited onto stainless steel 316 are studied with the aim of analyzing the effect of the pure metal (Re) and the metal with heteroatoms (Re-C, Re-B, and Re-O). As one of the problems worldwide is the scarcity of freshwater, the study focuses on the performance of this series of catalysts in highly saline environments, representative of seawater. The synthesis and electrochemical performance is shown, giving high expectations that these electrocatalysts could be potential electrocatalysts in marine environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Pesticide risk perceptions and the differences between farmers and extensionists: Towards a knowledge-in-context model

A growing body of literature analyzes farmer perceptions of pesticide risk, but much less attention has been given to differences in risk perception between farmers and technical experts. Furthermore, inconsistencies in knowledge have too easily been explained in terms of lack of knowledge rather than exploring the underlying reasons for particular forms of thinking about pesticide risks. By doing this, the division between expert and lay knowledge has been deepened rather than transcended. Objective: This study aims to understand differences and similarities among the perceptions of pesticide risks of farmers, farm workers, and technical experts such as extensionists, by applying a social science approach towards knowledge and risk attitudes. Methods: Semi-structured interviews and field observations were conducted to smallholders, farm workers, extensionists, health professionals and scientists involved in the use and handling of pesticides. Subsequently, a survey was carried out to quantify the farmers and extensionists' acceptance or rejection of typical assertions expressed previously in the semi-structured interviews. Results: Smallholders showed to gain knowledge from their own experiences and to adapt pesticides practices, which is a potential basis for transforming notions of pesticide safety and risk reduction strategies. Though extensionists have received formal education, they sometimes develop ideas deviating from the technical perspective. The risk perception of the studied actors appeared to vary according to their role in the agricultural labor process; they varied much less than expected according to their schooling level. Conclusions: Commitment to the technical perspective is not dramatically different for extensionists on the one hand and farmers as well as farm workers on the other hand. Ideas about a supposed lack of knowledge by farmers and the need of formal training are too much driven by a deficit model of knowledge. Further research on risk perceptions of pesticides and training of rural people will benefit from the development of a knowledge-in-context model. -- Highlights: • Researching perceptions of farmers' extensionists and other professionals. • Experts as well as farmers deviate from the technical perspective. • Blaming who is responsible for pesticide problems creates expert-lay division. • Qualitative and quantitative methods, not as complementary but integrated. • Knowledge-in-context model as an alternative to the knowledge-deficit model.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Environmental contaminants of emerging concern in seafood – European database on contaminant levels

Marine pollution gives rise to concern not only about the environment itself but also about the impact on food safety and consequently on public health. European authorities and consumers have therefore become increasingly worried about the transfer of contaminants from the marine environment to seafood. So-called “contaminants of emerging concern” are chemical substances for which no maximum levels have been laid down in EU legislation, or substances for which maximum levels have been provided but which require revision. Adequate information on their presence in seafood is often lacking and thus potential risks cannot be excluded. Assessment of food safety issues related to these contaminants has thus become urgent and imperative. A database ( (www.ecsafeseafooddbase.eu)), containing available information on the levels of contaminants of emerging concern in seafood and providing the most recent data to scientists and regulatory authorities, was developed. The present paper reviews a selection of contaminants of emerging concern in seafood including toxic elements, endocrine disruptors, brominated flame retardants, pharmaceuticals and personal care products, polycyclic aromatic hydrocarbons and derivatives, microplastics and marine toxins. Current status on the knowledge of human exposure, toxicity and legislation are briefly presented and the outcome from scientific publications reporting on the levels of these compounds in seafood is presented and discussed. - Highlights: • Development of a European database regarding contaminants of emerging concern. • Current status on knowledge of human exposure, toxicity and legislation. • Review on the occurrence of contaminants of emerging concern in seafood.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Climate change and environmental concentrations of POPs: A review

In recent years, the climate change impact on the concentrations of persistent organic pollutants (POPs) has become a topic of notable concern. Changes in environmental conditions such as the increase of the average temperature, or the UV-B radiation, are likely to influence the fate and behavior of POPs, ultimately affecting human exposure. The state of the art of the impact of climate change on environmental concentrations of POPs, as well as on human health risks, is here reviewed. Research gaps are also identified, while future studies are suggested. Climate change and POPs are a hot issue, for which wide attention should be paid not only by scientists, but also and mainly by policy makers. Most studies reported in the scientific literature are focused on legacy POPs, mainly polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), polychlorinated biphenyls (PCBs) and pesticides. However, the number of investigations aimed at estimating the impact of climate change on the environmental levels of polycyclic aromatic hydrocarbons (PAHs) is scarce, despite of the fact that exposure to PAHs and photodegradation byproducts may result in adverse health effects. Furthermore, no data on emerging POPs are currently available in the scientific literature. In consequence, an intensification of studies to identify and mitigate the indirect effects of the climate change on POP fate is needed to minimize the human health impact. Furthermore, being this a global problem, interactions between climate change and POPs must be addressed from an international perspective.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Meeting the imperative to accelerate environmental bioelectromagnetics research

Highlights: • Concerns mount about adverse health effects of low-level radiofrequency (RF) radiation. • Bioelectronics and environmental RF effects research both need an omics platform. • Standard bioelectromagnetic data models would advance dosimetry and omics work. • Steps toward developing standards and a shared research platform are proposed. In this article, the author draws on his experience in the world of geospatial information technology standards to suggest a path toward acceleration of bioelectromagnetics science. Many studies show biological effects of extremely low frequency (ELF) and radiofrequency (RF) radiation despite that fact that the radiation is too weak to cause temperature changes in biological features. Considered together in worst case scenarios, such effects, many of which appear to have long latencies, could have potentially disastrous consequences for the health and safety of humans and wildlife. Other studies show no such effects, and in both cases, often there are significant research quality deficits that make it difficult to draw firm conclusions from the data. The progress of bioelectromagnetics science is retarded by a lack of standard data models and experimental protocols that could improve the overall quality of research and make it easier for researchers to benefit from omics-related bioinformatics resources. {sup C}ertainty of safety{sup }of wireless devices used in digital communications and remote sensing (radar) is impossible without dosimetry standards that reflect the effects of non-thermal exposures. Electrical signaling in biological systems, a poorly funded research domain, is as biologically important as chemical signaling, a richly funded research domain, and these two types of signaling are inextricably connected. Entreprenuerial scientists pursuing bioelectronic innovations have begun to attract new funding. With appropriate institutional coordination, this new funding could equally benefit those investigating environmental effects of ELF and RF radiation. The author proposes a concerted effort among both bioelectronics technology stakeholders and environmental bioelectromagnetics science researchers to collaborate in developing institutional arrangements and standard data models that would give the science a stronger bioinformatics platform and give researchers better access to omics data. What is proposed here is essentially a bioelectromagnetics omics initiative.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

A Scientific Data Processing Framework for Time Series NetCDF Data

ARM Data Integrator (ADI) is a framework to streamline the development of scientific algorithms that analyze time-series NetCDF data, and to improve the content and consistency of the output data products produced by these algorithms. ADI achieves these goals by automating the process of retrieving and preparing data for analysis, supporting the definition of output data products through a graphical interface, and providing a modular, flexible software development architecture. The input data, preprocessing, and output data specifications are defined through a graphical interface and stored in a database. ADI also includes a workflow for data integration, a library of software modules to support the workflow, and a source code generator that produces C, IDL and Python templates. Data preparation support includes automated retrieval of data from input files, merging the retrieved data into appropriately sized chunks, and transformation of the data onto a common coordinate system grid. Through the graphical interface, users can view the details of both their data products and those in the ARM catalog. The variable and attribute definitions of the existing data products can be used to build new output data products. In addition, the rules that make up the ARM archive’s data standards are laid on top of the view of the new data product providing the user with a visual cue indicating where their output violates an archive standard. The necessary configurations are stored in a database that is accessed by the ADI libraries. This paper discusses the ADI framework, its supporting components, and how ADI can significantly decrease the time and cost of implementing scientific algorithms while improving the ability of scientists to disseminate their results.

Atmospheric Science↗

DOE OSTI2016

Modeling fuels and fire effects in 3D: Model description and applications

Scientists and managers critically need ways to assess how fuel treatments alter fire behavior, yet few tools currently exist for this purpose. In this work, we present a spatially-explicit-fuel-modeling system, FuelManager, which models fuels, vegetation growth, fire behavior (using a physics-based model, FIRETEC), and fire effects. FuelManager's flexible approach facilitates modeling fuels across a wide range of detail. Large trees or shrubs with specific coordinates are modeled as individual “Plants”, while understory plants are modeled as collections of plants called “LayerSets”. Both Plants and LayerSets contain various fuel particles (leaves, needles, twigs) with various properties including shape, size and surface area to volume ratio. A wide range of vegetation and treatments can be modeled, analyzed quantitatively and visualized in a 3D viewer. We describe the modeling approach and demonstrate fuel modeling at different levels of detail, fuel treatment and fire effects capabilities. Lastly, detailed model equations are provided in the Appendices.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A systematic review of machine learning in groundwater monitoring

With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This includes using hybrid models that combine traditional and new techniques. Despite the use of machine learning in many scientific areas, there is a lack of comprehensive reviews focusing on its use in environmental monitoring, especially groundwater monitoring. We aim to fill this gap by exploring machine-learning applications in groundwater monitoring. We discuss relevant methods, their limitations, and future potential. We summarize research on automating data processing and model training using groundwater sensor data. Our research underscores the transformative potential of machine learning to revolutionize long-term groundwater monitoring and contamination detection, providing valuable insights for future research and practical applications.

AI/ML↗

DOE OSTI2016

Creating a ‘Collaboratory’ environment to transcend traditional research barriers: Insights from the United States

Today’s scientific endeavors often resemble a large lecture hall partitioned into separate sectors. Scientists, engineers and students study big questions largely in isolation. Here, local factors drive their activities, but the groups have overlapping interests and capabilities for tackling big questions about our planet’s energy future.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Can we have clean energy and grow our crops too? Solar siting on agricultural land in the United States

We report a record amount of large-scale solar development is proposed and under construction in the US Midwest, much of it on agricultural land. This article contributes to the social science literature on renewable energy and public acceptance by addressing stakeholder perspectives on developing utility-scale solar power on agricultural land and complex institutional dynamics that shape these siting decisions. How do stakeholders’ interactions coproduce solar siting decisions? How are the existing complex contexts of energy and agricultural systems affecting solar siting? Who are the stakeholders involved in agricultural solar siting, and how do their epistemic paradigms about using agricultural land, especially prime farmland, differ? To answer these questions, interviews were conducted and analyzed across energy, agriculture, government, and expert stakeholder groups, focusing on Michigan, with comparison to other states. The results provide a conceptual map of stakeholder interaction related to solar development on farmland and characterize the main epistemic paradigms shaping stakeholder conflict. Issues shaping agricultural community perceptions include identity and farmland, the decisions of local farmland renters and non-operating landlords, prime versus lower quality farmland, farm financial viability, and farmland as private property or a public good. Some scientists and engineers seek to alleviate conflicts and drawbacks of agricultural land conversion for solar use by combining farming and solar energy generation: a practice called agrivoltaics. We argue that agrivoltaics are currently treated as a technological fix by not accounting for stakeholder interaction in complex policy, community, and scientific institutions. We conclude by identifying interdisciplinary research priorities to develop socially robust designs.

14 SOLAR ENERGY↗

DOE OSTI2025

Siting bioenergy facilities in the United States: Measuring participation in decisions and distribution of effects

Scientists and stakeholders can inform the process of siting renewable energy facilities in ways that do not perpetuate socioeconomic disparities associated with fossil fuel industries or create new ones. Procedural justice indicators and distributional justice indicators that incorporate environmental, social, and economic objectives can be used to site energy facilities in ways that increase benefits and reduce negative impacts to disadvantaged and underserved populations. A generic list of potential energy justice indicators for siting bioenergy facilities was developed collaboratively between U.S. bioenergy researchers and diverse agriculture, energy, and energy and environmental justice stakeholders and experts. From this list smaller numbers of indicators can be selected or modified with communities for local siting of bioenergy facilities. Groups of indicators can be used to guide biorefinery or biopower siting and permitting decisions, e.g., to compare siting options, to draw early attention to key problems, or to track progress toward justice-related targets.

09 BIOMASS FUELS↗

DOE OSTI2015

Generalized gas-solid adsorption modeling: Single-component equilibria

Over the last several decades, modeling of gas–solid adsorption at equilibrium has generally been accomplished through the use of isotherms such as the Freundlich, Langmuir, Tóth, and other similar models. While these models are relatively easy to adapt for describing experimental data, their simplicity limits their generality to be used with many different sets of data. This limitation forces engineers and scientists to test each different model in order to evaluate which one can best describe their data. Additionally, the parameters of these models all have a different physical interpretation, which may have an effect on how they can be further extended into kinetic, thermodynamic, and/or mass transfer models for engineering applications. Therefore, it is paramount to adopt not only a more general isotherm model, but also a concise methodology to reliably optimize for and obtain the parameters of that model. A model of particular interest is the Generalized Statistical Thermodynamic Adsorption (GSTA) isotherm. The GSTA isotherm has enormous flexibility, which could potentially be used to describe a variety of different adsorption systems, but utilizing this model can be fairly difficult due to that flexibility. To circumvent this complication, a comprehensive methodology and computer code has been developed that can perform a full equilibrium analysis of adsorption data for any gas-solid system using the GSTA model. The code has been developed in C/C++ and utilizes a Levenberg–Marquardt’s algorithm to handle the non-linear optimization of the model parameters. Since the GSTA model has an adjustable number of parameters, the code iteratively goes through all number of plausible parameters for each data set and then returns the best solution based on a set of scrutiny criteria. Data sets at different temperatures are analyzed serially and then linear correlations with temperature are made for the parameters of the model. The end result is a full set of optimal GSTA parameters, both dimensional and non-dimensional, as well as the corresponding thermodynamic parameters necessary to predict the behavior of the system at temperatures for which data were not available. It will be shown that this code, utilizing the GSTA model, was able to describe a wide variety of gas-solid adsorption systems at equilibrium.In addition, a physical interpretation of these results will be provided, as well as an alternate derivation of the GSTA model, which intends to reaffirm the physical meaning.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Assessing Single-Source Reproducibility of Human Head Hair Peptide Profiling from Different Regions of the Scalp

Neither microscopical hair comparisons nor mitochondrial DNA sequencing alone, or together, constitutes a basis for personal identification. Due to these limitations, a complementary technique to compare questioned and known hair shafts was investigated. Recently, scientists from Lawrence Livermore National Laboratory's Forensic Science Center and other collaborators developed a peptide profiling technique, which can infer non-synonymous single nucleotide polymorphisms (SNPs) preserved in hair shaft proteins as single amino acid polymorphisms (SAPs). In this study, peptide profiling was evaluated to determine if it can meet forensic expectations when samples are in limited quantities with the possibility that hair samples collected from different areas of a single donor’s scalp (i.e., single source) might not exhibit the same SAP profile. The average dissimilarity, percent differences in SAP profiles within each source, ranged from 0% difference to 29%. This pilot study suggests that more work is needed before peptide profiling of hair can be considered for forensic comparisons.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors—including NVIDIA, Intel, AMD, and IBM—have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. Here, this article provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling—sometimes encouraged by restricted GPU memory—NVLink is less important.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows

Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportional–integral–derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows—data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24 TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when utilizing our proposed method, and faults are detected and mitigated far in advance of their occurrence.

97 MATHEMATICS AND COMPUTING↗

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