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DOE OSTI2016

Health benefits and costs of filtration interventions that reduce indoor exposure to PM2.5 during wildfires

Increases in hospital admissions and deaths are associated with increases in outdoor air particles during wildfires. This paper estimates the health benefits expected if interventions had improved particle filtration in homes in Southern California during a 10-day period of wildfire smoke exposure. Economic benefits and intervention costs are also estimated. The six interventions implemented in all affected houses are projected to prevent 11% to 63% of the hospital admissions and 7% to 39% of the deaths attributable to wildfire particles. The fraction of the population with an admission attributable to wildfire smoke is small, thus, the costs of interventions in all homes far exceeds the economic benefits of reduced hospital admissions. However, the estimated economic value of the prevented deaths exceed or far exceed intervention costs for interventions that do not use portable air cleaners. For the interventions with portable air cleaner use, mortality-related economic benefits exceed intervention costs as long as the cost of the air cleaners, which have a multi-year life, are not attributed to the short wildfire period. Finally, cost effectiveness is improved by intervening only in the homes of the elderly who experience most of the health effects of particles from wildfires.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Automated characterization of perceptual quality of clinical chest radiographs: Validation and calibration to observer preference

Purpose: The authors previously proposed an image-based technique [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)] to assess the perceptual quality of clinical chest radiographs. In this study, an observer study was designed and conducted to validate the output of the program against rankings by expert radiologists and to establish the ranges of the output values that reflect the acceptable image appearance so the program output can be used for image quality optimization and tracking. Methods: Using an IRB-approved protocol, 2500 clinical chest radiographs (PA/AP) were collected from our clinical operation. The images were processed through our perceptual quality assessment program to measure their appearance in terms of ten metrics of perceptual image quality: lung gray level, lung detail, lung noise, rib–lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm–lung contrast, and subdiaphragm area. From the results, for each targeted appearance attribute/metric, 18 images were selected such that the images presented a relatively constant appearance with respect to all metrics except the targeted one. The images were then incorporated into a graphical user interface, which displayed them into three panels of six in a random order. Using a DICOM calibrated diagnostic display workstation and under low ambient lighting conditions, each of five participating attending chest radiologists was tasked to spatially order the images based only on the targeted appearance attribute regardless of the other qualities. Once ordered, the observer also indicated the range of image appearances that he/she considered clinically acceptable. The observer data were analyzed in terms of the correlations between the observer and algorithmic rankings and interobserver variability. An observer-averaged acceptable image appearance was also statistically derived for each quality attribute based on the collected individual acceptable ranges. Results: The observer study indicated that, for each image quality attribute, the averaged observer ranking strongly correlated with the algorithmic ranking (linear correlation coefficient R > 0.92), with highest correlation (R = 1) for lung gray level and the lowest (R = 0.92) for mediastinum noise. There was a strong concordance between the observers in terms of their rankings (i.e., Kendall’s tau agreement > 0.84). The observers also generally indicated similar tolerance and preference levels in terms of acceptable ranges, as 85% of the values were close to the overall tolerance or preference levels and the differences were smaller than 0.15. Conclusions: The observer study indicates that the previously proposed technique provides a robust reflection of the perceptual image quality in clinical images. The results established the range of algorithmic outputs for each metric that can be used to quantitatively assess and qualify the appearance quality of clinical chest radiographs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Regional differences in tobacco smoking and lung cancer in Portugal in 2018: a population-based analysis using nationwide incidence and mortality data

Objectives: This study aims to estimate the proportion of lung cancer cases and deaths attributable to tobacco smoking in Portugal in 2018, complemented by trends in incidence and mortality, by sex and region. Design: Cancer cases for 1998–2011 and cancer deaths for 1991–2018 were obtained from population-based registries and Statistics Portugal, respectively. We projected cases for 2018 and used reported deaths for the same year to estimate, using Peto’s method, the number and proportion of lung cancer cases and deaths caused by tobacco smoking in 2018. We calculated the age-adjusted incidence and mortality rates in each year of diagnosis and death. We fitted a joinpoint regression to the observed data to estimate the annual percentage change (APC) in the rates. Setting: Portugal. Results: In 2018, an estimated 3859 cases and 3192 deaths from lung cancer were attributable to tobacco smoking in Portugal, with men presenting a population attributable fraction (PAF) of 82.6% (n=3064) for incidence and 84.1% (n=2749) for mortality, while in women those values were 51.0% (n=795) and 42.7% (n=443), respectively. In both sexes and metrics, the Azores were the region with the highest PAF and the Centre with the lowest. During 1998–2011, the APC for incidence ranged from 0.6% to 3.0% in men and 3.6% to 7.9% in women, depending on region, with mortality presenting a similar pattern between sexes. Conclusion: Exposure to tobacco smoking has accounted for most of the lung cancer cases and deaths estimated in Portugal in 2018. Differential patterns of tobacco consumption across the country, varying implementation of primary prevention programmes and differences in personal cancer awareness may have contributed to the disparities observed. Primary prevention of lung cancer remains a public health priority, particularly among women.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Stand validation of lidar forest inventory modeling for a managed southern pine forest

We evaluated area-based approaches (ABAs) to light detection and ranging (lidar) predictions of plot- and stand-level forest attributes (tree count, height, basal area, volume, aboveground biomass, broadleaf/conifer, and diameter at breast height — “diameter”). ABA methods included post-stratification (PS), ordinary least squares (OLSs) regression, k nearest neighbors ( kNN), and random forest (RF). This study was conducted on the Savannah River Site in South Carolina, USA. Plot- and stand-level predictions were validated against fixed-radius 0.04 ha (0.1 acre) plots in 49 ≈2.0 ha (5 acre) stands. Our findings demonstrate that lidar can be incorporated operationally into forest inventory systems to provide stand-level inferences for a wide range of forest attributes. Volume predictions for specific diameter classes, however, often fared poorly (root mean squared error (RMSE) > 100%) for the methods we explored, especially for larger (less common) diameter trees. Stand-level results were consistently better than pixel-level results (10–200+ percentage points). kNN and RF performed similarly and better than OLS and PS, but RF was the most robust to model configurations, while kNN has practical advantages such as simultaneous predictions of many attributes.

Forestry↗

DOE OSTI2014

SCaLeM: A Framework for Characterizing and Analyzing Execution Models

As scalable parallel systems evolve towards more complex nodes with many-core architectures and larger trans-petascale & upcoming exascale deployments, there is a need to understand, characterize and quantify the underlying execution models being used on such systems. Execution models are a conceptual layer between applications & algorithms and the underlying parallel hardware and systems software on which those applications run. This paper presents the SCaLeM (Synchronization, Concurrency, Locality, Memory) framework for characterizing and execution models. SCaLeM consists of three basic elements: attributes, compositions and mapping of these compositions to abstract parallel systems. The fundamental Synchronization, Concurrency, Locality and Memory attributes are used to characterize each execution model, while the combinations of those attributes in the form of compositions are used to describe the primitive operations of the execution model. The mapping of the execution model’s primitive operations described by compositions, to an underlying abstract parallel system can be evaluated quantitatively to determine its effectiveness. Finally, SCaLeM also enables the representation and analysis of applications in terms of execution models, for the purpose of evaluating the effectiveness of such mapping.

Exascale↗

DOE OSTI2025

Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software

Scientific software is essential to scientific innovation and in many ways it is distinct from other types of software. Abandoned (or unmaintained), buggy, and hard to use software, a perception often associated with scientific software can hinder scientific progress, yet, in contrast to other types of software, its longevity is poorly understood. Existing data curation efforts are fragmented by science domain and/or are small in scale and lack key attributes. We use large language models to classify public software repositories in World of Code into distinct scientific domains and layers of the software stack, curating a large and diverse collection of over 18,000 scientific software projects. Using this data, we estimate survival models to understand how the domain, infrastructural layer, and other attributes of scientific software affect its longevity. We further obtain a matched sample of non-scientific software repositories and investigate the differences. We find that infrastructural layers, downstream dependencies, mentions of publications, and participants from government are associated with a longer lifespan, while newer projects with participants from academia had shorter lifespan. Against common expectations, scientific projects have a longer lifetime than matched non-scientific open-source software projects. We expect our curated attribute-rich collection to support future research on scientific software and provide insights that may help extend longevity of both scientific and other projects.

Malviya Thakur, Addi [ORNL] (ORCID:000000022681999↗

DOE OSTI2016

MapRE GIS Tools & Data (MapRE)

The MapRE (Multi-criteria Analysis for Planning Renewable Energy) GIS (Geographic Information Systems) Tools are a set of ArcGIS tools to a) conduct site suitability analysis for wind and solar resources using inclusion and exclusion criteria, and create resource maps, b) create project opportunity areas and compute various attributes such as cost, distances to existing and planned infrastructure. and environmental impact factors; and c) calculate and update various attributes for already processed renewable energy zones. In addition, MapRE data sets are geospatial data of renewable energy project opportunity areas and zones with pre-calculated attributes for several countries. These tools and data are available at mapre.lbl.gov.

Deshmukh, Ranjit↗

DOE OSTI2017

Data Informed Vehicle Trip Energy Estimation [SWR-17-30]

There is a need to accurately estimate trip energy consumption for vehicle trips where high resolution GPS data is not available, or where the trips are simply proposed and have not been driven. This need is most apparent in green routing algorithms, it is critical to accurately estimate the energy consumption for a route that has not been driven. The Data Informed Energy Estimation Software addresses this need by identifying the driving and roadway attributes most impactful on fuel consumption, and using large amounts of high resolution GPS data from real world driving to determine the average fuel consumption for all combinations of attributes. These attributes can include traffic conditions, vehicle speed, and road gradient among others. This aggregation technique results in a lookup table that provides energy consumption values for combinations of road and driving techniques. This lookup table is an extremely lightweight and versatile result that can be applied in a number of applications.

Holden, Jacob↗

DOE OSTI2025

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Tropospheric Waveguide Teleconnections and Their Seasonality

Abstract One-point correlation maps of the subseasonal variability of 200-hPa meridional wind in nature and an atmospheric general circulation model are systematically analyzed to quantify the impact of the climatological-mean jets on tropospheric covariability as a result of the jets acting as waveguides for the propagation of Rossby waves. As anticipated by linear theory, signatures of jet influence are detected in terms of (i) the geographical position of the strongest teleconnections, (ii) the zonal orientation and extent of prominent patterns of variability, and (iii) the scale of the features that make up those patterns. Further evidence of jet waveguide influence comes from examining the seasonality of these teleconnection attributes. During winter, covariability can be essentially circumglobal, while during summer it tends to be confined within two separate sectors of the globe where the jets are especially strong. Experiments with a multilevel linear planetary wave model confirm that the analyzed characteristics of teleconnections in the waveguides can be attributed to the action of the mean state; no organization to the anomalous forcing of the atmosphere is required to produce these properties. Some attributes, however, depend on the presence of zonal variations in the climatological-mean state that are of similar scale to the teleconnection patterns themselves.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

End-of-Century Changes in Orographic Precipitation with the Intermediate Complexity Atmospheric Research Model over the Western United States

Abstract Downscaled precipitation projections were created using the Intermediate Complexity Atmospheric Research (ICAR) model over the western United States to increase the physical realism in orographic precipitation changes. End-of-century simulations from eight models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) were downscaled with ICAR and compared to the widely utilized statistically downscaled dataset, localized constructed analogs (LOCAs), to understand where and why projections of cool-season (September–May) precipitation differed. ICAR and LOCA precipitation projections were similar, but their sign differed in hydrologically relevant regions likely due to ICAR’s simulation of microphysics and mesoscale dynamics with high-resolution topography (6 km). In the Pacific Northwest, cool-season precipitation projections from ICAR showed an increase on the windward side of the Cascades and no significant change within the lee. This difference between the windward and leeward side was attributed to reduced zonal wind speeds, allowing more time for microphysical processes within ICAR. This contrast is enhanced by rain’s faster fall speed compared to snow, limiting transport into the lee. Meanwhile, LOCA projected an increase in precipitation across the Cascades. In the Upper Colorado River basin, LOCA projected an increase in precipitation in high elevation regions (>3000 m), but ICAR projected no significant change or a decrease in precipitation. High elevation differences were most evident in the spring and fall and were also attributed to a snow-to-rain transition and dynamical processes that impacted orographic enhancement within ICAR. Idealized, controlled studies are needed to better isolate individual processes, but these results underscore the importance of including microphysics and mesoscale dynamics within regional-scale precipitation projections. Significance Statement A set of global climate model simulations was downscaled using an atmospheric model that contains key physical equations, referred to as Intermediate Complexity Atmospheric Research (ICAR). ICAR was used to examine projected changes in end-of-century cool-season precipitation over mountains in the western United States. Precipitation projections from ICAR were similar to projections that used statistical relationships to downscale climate projections. However, projections differed between ICAR and statistically downscaled datasets in whether they increased, decreased, or stayed the same in specific, hydrologically relevant regions such as the eastern Cascades and high elevation areas of the Upper Colorado River basin. These differences were attributed to the simulation of physical processes in ICAR. The results highlight the importance of kilometer-scale atmospheric processes in regional climate projections.

Currier, William Ryan [NOAA/Physical Sciences Labo↗

DOE OSTI2021

Impact Mapping for Geospatial Reasoning and Decision Making

Objective The reported study evaluated a novel approach to aiding geospatial reasoning and decision making. Background Impact mapping aims to alleviate the cognitive demands of geospatial tasks in part by externalizing data in the form of an integrated decision surface. This is achieved by aggregating data across multiple sources of information and visualizing their combined utility rather than objective measurements or individual utility. Previous research has shown that geospatial decisions improve when aided in this manner, but it remains unknown if dynamic decision making, often plagued by fatigue and anchoring bias, would benefit similarly. Method The experiment implemented a systematic manipulation of the presence of a composite impact map and the number of attributes present in a two-stage disaster relief, resource allocation task to investigate when and how impact mapping is beneficial or deleterious to decision makers. Results The presence of the composite impact map increased the utility of selected sites, increased re-planning decisions, reduced information display views, and reduced workload. Generally, the effect of the composite impact map was greater when participants were asked to evaluate more attributes. Conclusion Composite impact maps appear to improve repeated geospatial reasoning and minimize anchoring bias because they alleviate the cognitive demands otherwise necessary to interpret and maintain information from multiple attributes. Application Data visualization techniques, such as impact mapping, can improve repeated geospatial decision making in environments that include high cognitive demand.

Illingworth, David A.↗

DOE OSTI2019

Identification and manipulation of Neurospora crassa genes involved in sensitivity to furfural

Background: Biofuels derived from lignocellulosic biomass are a viable alternative to fossil fuels required for transportation. Following plant biomass pretreatment, the furan derivative furfural is present at concentrations which are inhibitory to yeasts. Detoxification of furfural is thus important for efficient fermentation. Here, we searched for new genetic attributes in the fungus Neurospora crassa that may be linked to furfural tolerance. The fact that furfural is involved in the natural process of sexual spore germination of N. crassa and that this fungus is highly amenable to genetic manipulations makes it a rational candidate for this study. Results: Both hypothesis-based and unbiased (random promotor mutagenesis) approaches were performed to identify N. crassa genes associated with the response to furfural. Changes in the transcriptional profile following exposure to furfural revealed that the affected processes were, overall, similar to those observed in Saccharomyces cerevisiae . N. crassa was more tolerant (by ~ 30%) to furfural when carboxymethyl cellulose was the main carbon source as opposed to sucrose, indicative of a link between carbohydrate metabolism and furfural tolerance. We also observed increased tolerance in a Δ cre-1 mutant (CRE-1 is a key transcription factor that regulates the ability of fungi to utilize non-preferred carbon sources). In addition, analysis of aldehyde dehydrogenase mutants showed that ahd-2 (NCU00378) was involved in tolerance to furfural as well as the predicted membrane transporter NCU05580 ( flr-1 ), a homolog of FLR1 in S. cerevisiae . Further to the rational screening, an unbiased approach revealed additional genes whose inactivation conferred increased tolerance to furfural: (i) NCU02488, which affected the abundance of the non-anchored cell wall protein NCW-1 (NCU05137), and (ii) the zinc finger protein NCU01407. Conclusions:We identified attributes in N. crassa associated with tolerance or degradation of furfural, using complementary research approaches. The manipulation of the genes involved in furan sensitivity can provide a means for improving the production of biofuel producing strains. Similar research approaches can be utilized in N. crassa and other filamentous fungi to identify additional attributes relevant to other furans or toxic chemicals.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

“Candidatus Paraporphyromonas polyenzymogenes” encodes multi-modular cellulases linked to the type IX secretion system

Background In Nature, obligate herbivorous ruminants have a close symbiotic relationship with their gastrointestinal microbiome, which proficiently deconstructs plant biomass. Despite decades of research, lignocellulose degradation in the rumen has thus far been attributed to a limited number of culturable microorganisms. Here, we combine metaomics and enzymology to identify and describe a novel Bacteroidetes family (UMH11) composed entirely of uncultivated strains that are predominant in ruminants and only distantly related to previously characterized taxa. Results The first metabolic reconstruction of UMH11-affiliated genome bins, with a particular focus on the provisionally named UParaporphyromonas polyenzymogenes, illustrated their capacity to degrade various lignocellulosic substrates via comprehensive inventories of singular and multi-modular carbohydrate active enzymes (CAZymes). Closer examination revealed an absence of archetypical polysaccharide utilization loci found in human-gut microbiota. Instead, we identified many multi-modular CAZymes putatively secreted via the Bacteroidetes-specific Type 9 secretion system (T9SS). This included cellulases with two or more catalytic domains, which are modular arrangements that are unique to Bacteroidetes species studied to date. Core metabolic proteins from UP. polyenzymogenes were detected in metaproteomic data and were enriched in rumen-incubated plant biomass, indicating that active saccharification and fermentation of complex carbohydrates could be assigned to members of this novel family. Biochemical analysis of selected UP. polyenzymogenes CAZymes further iterated the cellulolytic activity of this hitherto uncultured bacterium towards linear polymers, such as amorphous and crystalline cellulose as well as mixed linkage β-glucans. Conclusion We propose that UP. olyenzymogenes genotypes and other UMH11 members actively degrade plant biomass in the rumen of cows, sheep, and most likely other ruminants, utilizing singular and multi-domain catalytic CAZymes secreted through the T9SS. The discovery of a prominent role of multi-modular cellulases in the Gramnegative Bacteroidetes, together with similar findings for Gram-positive cellulosomal bacteria (Ruminococcus flavefaciens) and anaerobic fungi (Orpinomyces sp.), suggests that complex enzymes are essential and have evolved within all major cellulolytic dominions inherent to the rumen.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2014

Estimation and Uncertainty Analysis of Impacts of Future Heat Waves on Mortality in the Eastern United States

Background: It is anticipated that climate change will influence heat-related mortality in the future. However, the estimation of excess mortality attributable to future heat waves is subject to large uncertainties, which have not been examined under the latest greenhouse gas emission scenarios. Objectives: We estimated the future heat wave impact on mortality in the eastern United States (~ 1,700 counties) under two Representative Concentration Pathways (RCPs) and analyzed the sources of uncertainties. Methods Using dynamically downscaled hourly temperature projections in 2057-2059, we calculated heat wave days and episodes based on four heat wave metrics, and estimated the excess mortality attributable to them. The sources of uncertainty in estimated excess mortality were apportioned using a variance-decomposition method. Results: In the eastern U.S., the excess mortality attributable to heat waves could range from 200-7,807 with the mean of 2,379 persons/year in 2057-2059. The projected average excess mortality in RCP 4.5 and 8.5 scenarios was 1,403 and 3,556 persons/year, respectively. Excess mortality would be relatively high in the southern and eastern coastal areas. The major sources of uncertainty in the estimates are relative risk of heat wave mortality, the RCP scenarios, and the heat wave definitions. Conclusions: The estimated mortality risks from future heat waves are likely an order of magnitude higher than its current level and lead to thousands of deaths each year under the RCP8.5 scenario. The substantial spatial variability in estimated county-level heat mortality suggests that effective mitigation and adaptation measures should be developed based on spatially resolved data.

climate change↗

DOE OSTI2014

Land-Use History and Contemporary Management Inform an Ecological Reference Model for Longleaf Pine Woodland Understory Plant Communities.

Ecological restoration is frequently guided by reference conditions describing a successfully restored ecosystem; however, the causes and magnitude of ecosystem degradation vary, making simple knowledge of reference conditions insufficient for prioritizing and guiding restoration. Ecological reference models provide further guidance by quantifying reference conditions, as well as conditions at degraded states that deviate from reference conditions. Many reference models remain qualitative, however, limiting their utility. We quantified and evaluated a reference model for southeastern U.S. longleaf pine woodland understory plant communities. We used regression trees to classify 232 longleaf pine woodland sites at three locations along the Atlantic coastal plain based on relationships between understory plant community composition, soils lol(which broadly structure these communities), and factors associated with understory degradation, including fire frequency, agricultural history, and tree basal area. To understand the spatial generality of this model, we classified all sites together. and for each of three study locations separately. Both the regional and location-specific models produced quantifiable degradation gradients–i.e., progressive deviation from conditions at 38 reference sites, based on understory species composition, diversity and total cover, litter depth, and other attributes. Regionally, fire suppression was the most important degrading factor, followed by agricultural history, but at individual locations, agricultural history or tree basal area was most important. At one location, the influence of a degrading factor depended on soil attributes. We suggest that our regional model can help prioritize longleaf pine woodland restoration across our study region; however, due to substantial landscape-to-landscape variation, local management decisions should take into account additional factors (e.g., soil attributes). Our study demonstrates the utility of quantifying degraded states and provides a series of hypotheses for future experimental restoration work. More broadly, our work provides a framework for developing and evaluating reference models that incorporate multiple, interactive anthropogenic drivers of ecosystem degradation.

Ecological Reference Models↗

DOE OSTI2019

GeoRePORT Case Studies - Coso, WSMR, Dixie Valley, and Chena Hot Springs

The Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) was developed with funding from the U.S. Department of Energy Geothermal Technologies Office to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. GeoRePORT provides scientists and nonscientists a comprehensive and quantitative means of reporting: (1) features intrinsic to geothermal sites (project grade) and (2) maturity of the development (project readiness). Because geothermal feasibility is not determined by any single factor (e.g., temperature, permeability, permitting), a site?s project grade and readiness are evaluated on 12 attributes pertaining to geological, technical, or socio-economic feasibility. In this paper, we present case studies showing how GeoRePORT can be used to compare geological, technical, and socio-economic attributes between geothermal systems. The consistent and objective assessment protocols used in GeoRePORT allow for comparison of project attributes across unique locations and geological settings. GeoRePORT case studies presented here outline the geological, socio-economic, and technical features of four individual geothermal sites: Coso, Chena, Dixie Valley, and White Sands Missile Range. The case studies illustrate the usefulness of GeoRePORT in evaluating project risk and return, identifying gaps in reported data, evaluating R&D impact, and gathering insights on successes and failures as applicable to future projects.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

New reference materials for trace-levels of actinide elements in plutonium

In this report two plutonium oxides were prepared as unique reference materials for measurement of actinide elements present as trace constituents. Each reference material unit is approximately 200 mg of PuO 2 powder in a quartz glass bottle. Characterized attributes of the oxides included mass fractions of plutonium, americium, neptunium, and uranium. Isotope-amount ratios were also determined for plutonium and uranium, but neptunium and americium were observed to be monoisotopic 237 Np and 241 Am. Measurements for characterization and verification of the attributes show that plutonium and trace actinides are homogeneous with the exception of limited heterogeneity for uranium, primarily observed for the 238 U isotope. Model purification ages calculated from measured americium and uranium attribute values are consistent with material histories and indicate that these impurities are predominantly due to the decay of plutonium isotopes.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗