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At least 559 records · Page 31

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗

Disproportionate impacts of COVID-19 in a large US city

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5–24.8%) infection rate and 29.4% (95% CrI: 28.0–31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3–12.0%] vs 25.1% [95% CrI: 23.7–26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49–57%] vs 28% [95% CrI: 27–30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0–3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC’s social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

60 APPLIED LIFE SCIENCES↗

“When I talk about it, my eyes light up!” Impacts of a national laboratory internship on community college student success

Participation in technical/research internships may improve undergraduate graduation rates and persistence in science, technology, engineering, and mathematics (STEM), yet little is known about the benefits of these activities a) for community college students, b) when hosted by national laboratories, and c) beyond the first few years after the internship. We applied Social Cognitive Career Theory (SCCT) to investigate alumni perspectives about how CCI at Lawrence Berkeley National Laboratory (LBNL) impacted their academic/career activities. We learned that alumni had low confidence and expectations of success in STEM as community college students. Participation in CCI increased their professional networks, expectations of success, and STEM skills, identity, and self-efficacy/confidence. Hispanic/Latinx alumni recalled the positive impact of mentors who prioritized personal connections, and women valued "warm" social environments. We propose several additions to the SCCT model, to better reflect the supports and barriers to STEM persistence for community college students.

careers↗

Meteorological Conditions in Urban Sites in Knoxville, USA. 2023

This 2023 dataset, which contains four csv files, reports hourly air temperature, wind speed, solar radiation, incident short wave, reflected short wave, incident long wave, emitted long wave, net radiation, and photosynthetically active radiation (PAR) data measured in urban parks in Knoxville, Tennessee, USA. The sites include Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEEED), West Hills (WHP), and Victor Ashe Park (VAP). Air temperature, wind speed and direction, and solar radiation data were obtained from a METER ATMOS 41 All-in-One Weather Station (Pullman, Washington, USA). Incident and emitted radiation (shortwave and longwave) measurements were made using an Apogee (Logan, Utah, USA) net radiometer (Model SN-500-SS). The SQ-521 Full-Spectrum Quantum sensor (Apogee Instruments, Inc) recorded the PAR. The measurement data for CEP, SEEED, and VAP started on July 1, 2023, while WHP started on August 26, 2023. All measurement ended on December 31, 2023. This work is a part of a larger study which investigates the impact of soil moisture and plant evapotranspiration on ambient temperature and relative humidity in several city parks in Knoxville, Tennessee.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗

Soil Characterizations of Five Urban Sites in Knoxville, Tennessee. 2024-2025

This dataset includes soil characterizations from five urban parks in Knoxville, Tennessee, USA: Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEED), West View Park (WVP), Victor Ashe Park (VAP), and West Hills Park (WHP). The dataset consists of seven CSV files reporting the data from the measurements of gravimetric moisture content, pH, total carbon and nitrogen, soil texture, dissolved organic carbon and nitrogen, and microbial biomass carbon and nitrogen derived from these soil cores. During five separate sampling events conducted in 2024 and 2025, five soil cores were collected at each site within 3 meters of the remote soil monitoring equipment. In 2025, an additional three soil cores were collected adjacent to the monitoring equipment at each site to assess soil bulk density. This dataset is part of a larger study investigating the effects of soil moisture and plant evapotranspiration on ambient temperature and relative humidity across multiple urban parks in Knoxville, Tennessee.

Mayes, Melanie A [ORNL] (ORCID:0000000163689210)↗

Differences in urban plant community compositions across an urban-rural gradient in Knoxville, TN

Urban forests, or vegetation in areas under heavy human influence, provide many ecosystem services to urban residents such as localized cooling via evapotranspiration, shade, filtering of air pollution, and the associated health benefits of natural spaces. In order to quantify the magnitude of localized cooling by trees growing in varying levels of urbanization (based on % impervious surfaces, e.g., buildings, pavement), urban forest species composition, tree size, and tree density must be characterized. As a part of Oak Ridge National Laboratory’s (ORNL) urban forest temperature study, we conducted tree censuses in five Knoxville city parks where ORNL meteorological stations are deployed. Moreover, we measured every woody plant ≥ 5 cm diameter at breast height (DBH) within a 50 m radius of each site’s meteorological station for its DBH and species identification. When possible, individuals were identified down to species. Certain genera (Quercus spp., Carya spp., Pinus spp.) were identified down to genera in interest of time. Individual and total site basal area were calculated from measured DBH data. Results show notable differences in urban plant community compositions and total woody plant basal area across sites, with more urban sites closer to downtown (West View and SEEED) having lower tree basal area than the more suburban sites (West Hills, Cumberland Estates, and Victor Ashe). We identified 54 species across all sites, with West Hills and Victor Ashe having the highest species diversity. Our results show differences in forest compositions and sizes across Knoxville, which are currently informing ORNL’s evapotranspiration estimates for each site. Data Summary: Census data for West Hills (WH), Cumberland Estates (CE), Victor Ashe (VA), West View (WV), and Socially Equal Energy Efficient Development or SEEED (SD) urban forests in Knoxville, TN, USA, including tree size based on diameter at breast height (DBH; 1.3 m), species identification (Latin and common names), and basal area per stem (BA=π×[.5*DBH]^2). Field data are summarized in this file: “Community_Composition_Data.CSV”. Site-specific data detailing each site’s coordinates, number of stems measured at DBH, average tree DBH, α-diversity (number of species present), and total site basal area (sum of individual basal areas per site) are in this file: “Site_Comparisons.CSV”.

Warren, Jeffrey [ORNL] (ORCID:0000000206804697)↗

Meteorological Conditions in Urban Sites in Knoxville, USA. 2025

This 2025 dataset, which contains five csv files, reports hourly air temperature, wind speed, solar radiation, incident short wave, reflected short wave, incident long wave, emitted long wave, net radiation, and photosynthetically active radiation (PAR) data measured in urban parks in Knoxville, Tennessee, USA. The sites include Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEEED), West Hills Park (WHP), West View Park (WVP), and Victor Ashe Park (VAP). Air temperature, wind speed, and solar radiation data were obtained from a METER ATMOS 41 All-in-One Weather Station (Pullman, Washington, USA). Incident and emitted radiation (shortwave and longwave) measurements were made using an Apogee (Logan, Utah, USA) net radiometer (Model SN-500-SS). The SQ-521 Full-Spectrum Quantum sensor (Apogee Instruments, Inc) recorded the PAR. The measurement data for all the sites started on January 1, 2025. and all measurement ended on December 31, 2025. This work is a part of a larger study which investigates the impact of soil moisture and plant evapotranspiration on ambient temperature and relative humidity in several city parks in Knoxville, Tennessee.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗

Soil Temperature Sensor Data, 2025, Five sites in Knoxville, Tennessee

This dataset contains surface soil temperature measurements from five urban parks in Knoxville, Tennessee: Cumberland Estates Park (CE), Socially Equal Energy Efficient Development (SD), West View Park (WV), Victor Ashe Park (VA), and West Hills Park (WH). The dataset includes 16 CSV files documenting soil temperature measurements recorded by HOBO Pendant MX Water Temperature Data Loggers. Data collection for all sites began on January 1, 2025. The end time for each sensor is provided in the End Time_2025.csv file. Each logger was installed at a depth of 10 inches and positioned approximately 3 to 6 feet from the weather station at each site. This dataset is part of a broader study examining the effects of soil moisture and plant evapotranspiration on ambient temperature and relative humidity across multiple urban parks in Knoxville.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗

Security Constrained Distributed Transaction Model for Multiple Prosumers

Massive access of renewable energy has prompted demand-side distributed resources to participate in regulation and improve flexibility of power systems. With large-scale access of massive, decentralized, and diverse distributed resources, demand-side market members have transformed from traditional “consumers” to “prosumers”. To explore the distributed transaction model of prosumers, in this paper, a multi-prosumer distributed transaction model is proposed, and the Conditional Value-at-Risk (CVaR) theory is applied to quantify potential risks caused by the stochastic characteristics inherited from renewable energy. First, a prosumer model under constraints of the distribution network including photovoltaic units, fuel cells, energy storage system, central air conditioning and flexible loads is established, and a multi-prosumer distributed transaction strategy is proposed to achieve power sharing among multiple prosumers. Second, a prosumer transaction model based on CVaR is constructed to measure risks inherited from the uncertainty of PV output within the prosumer and ensure safety of system operation in extreme PV output scenarios. Then, the alternating direction multiplier method (ADMM) is utilized to solve the constructed model efficiently. Finally, distributed transaction costs of prosumers are distributed fairly based on the generalized Nash equilibrium to maximize social benefits. Simulation results show the multi-prosumer distributed transaction mechanism established under the proposed generalized Nash equilibrium method can encourage power sharing among prosumers, increasing their own income and social benefits. Also, the CVaR can assist decision making of prosumers in weighting the risks and benefits, improving system resilience through energy management of prosumers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Energy-Community-Geo-Database

Fundamental to understanding evolving infrastructure and technology is understanding the drivers for energy communities that affect where and how infrastructure is placed and used. An Energy Community Geo Database aligned to carbon storage systems will aid stakeholders in decision making around key future infrastructure (e.g., CCS injection locations) placement while helping contextualize these analyses against past and present-day social and environmental attributes. Much of the data required to support analyses are available from authoritative, largely governmental sources, but at present are disparate and require time-consuming acquisition, integration, and upkeep for currency. The CCS-EC-GB v2.0 integrates datasets from various federal agencies and authoritative sources to aid stakeholders and decision makers of the social and environmental factors that might impact the viability of CCS and energy related project implementation. There are 5 categories in the CCS-EC-GB v2 database. Most of the layers within each category have been updated in this version. As compared to the old database, there is 1 new category in the CCS-EC-GB v2 database: infrastructure. This is an evolving project and application will be updated periodically with new datasets and information. Notes for consideration: This database/web map will be updated with additional as new data and information becomes available and has been processed, reviewed, and approved for release. Additional state and federal entity data are planned to be integrated and included in future revisions. Summary layers provided in this application are derived from proprietary layers and do not always contain key features (age, status, or TVD) and therefore might not be shown when data are queried for those features.

CCS,Carbon capture and storage,Carbon storage,U.S.↗

Generating Mixed Patterns of Residential Segregation: An Evolutionary Approach

The Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. Finally, this work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.

97 MATHEMATICS AND COMPUTING↗

Perspectives on future research directions in green manufacturing for discrete products

With the increasing concern due to climate change caused by a higher atmospheric concentration of CO 2 and other greenhouse gases, reducing environmental impact is becoming more important for every part of society. Manufacturing is responsible for a significant amount of energy/material consumption and environmental burden and, therefore, has a great opportunity to reduce its impact through green manufacturing. Green manufacturing presents opportunities across the manufacturing enterprise to increase the efficient usage of energy and material resources. These opportunities include designing products to consume fewer materials and energy during manufacturing and use, incorporating more efficient manufacturing processes, streamlining and optimizing manufacturing schedules and plans, and circularizing products. The goal of this paper will be to provide a perspective from the authors on the opportunities that exist within green manufacturing for discrete products through a review of pertinent topics and future directions. The paper will focus on processes, manufacturing equipment, manufacturing systems, recovering value at a product’s end-of-life, and additional thoughts that include metrics and indicators, techno-economic assessment, and a discussion of efficiency and effectiveness. Key findings from this review include a need for social indicators and renewable energy considerations in scheduling and process planning, integrating Industry 4.0 into circular economy along with social and institutional dimensions, consistency in the ability to measure and conceptualize metrics and indicators, a detailed evaluation of the life cycle impacts and cost of Addit Manuf, and more human and environment-oriented considerations for smart manufacturing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Quantifying Graph Uncertainty from Communication Data

Graphs are a widely used abstraction for representing a variety of important real-world problems including emulating cyber networks for situational awareness, or studying social networks to understand human interactions or pandemic spread. Communication data is often converted into graphs to help understand social and technical patterns in the underlying communication data. However, prior to this project, little work had been performed analyzing how best to develop graphs from such data. Thus, many critical, national security problems were being performed against graph representations of questionable quality. Herein, we describe our analyses that were precursors to our final statistically grounded technique for creating static graph snapshots from a stream of communication events. The first analyzes the statistical distribution properties of a variety of real-world communication datasets generally fit best by Pareto, log normal, and extreme value distributions. The second derives graph properties that can be estimated given the expected statistical distribution for communication events and the communication interval to be viewed node observability, edge observability, and expected accuracy of node degree. Unfortunately, as that final process is under review for publication, we can't publish it here at this time.

97 MATHEMATICS AND COMPUTING↗

Adaptive Recovery Model: Designing Systems for Testing Tracing and Vaccination to Support COVID-19 Recovery Planning.

This report documents a new approach to designing disease control policies that allocate scarce testing, contact tracing, and vaccination resources to better control community transmission of COVID19 or similar diseases. The Adaptive Recovery Model (ARM) combines a deterministic compartmental disease model with a stochastic network disease propagation model to enable us to simulate COVID-19 community spread through the lens of two complementary modeling motifs. ARM contact networks are derived from cell-phone location data that have been anonymized and interpreted as individual arrivals to specic public locations. Modeling disease spread over these networks allows us to identify locations within communities conducive to rapid disease spread. ARM applies this model- and data-derived abstractions of community transmission to evaluate the effectiveness of disease control measures including targeted social distancing, contact tracing, testing and vaccination. The architecture of ARM provides a unique capacity to help decision makers understand how best to deploy scarce testing, tracing and vaccination resources to minimize disease-spread potential in a community. This document details the novel mathematical formulations underlying ARM, presents a dynamical stability analysis of the deterministic model components, a sensitivity analysis of control parameters and network structure, and summarizes a process for deriving contact networks from cell-phone location data. An example use case steps through applying ARM to evaluate three targeted social distancing policies using Bernalillo County, New Mexico as an exemplar test locale. This step-by-step analysis demonstrates how ARM can be used to measure the relative performance of competing public health policies. Initial scenario tests of ARM shows that ARMs design focus on resource utilization rather than simple incidence prediction can provide decision makers with additional quantitative guidance for managing ongoing public health emergencies and planning future responses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Project "Cropland Abandonment Mapping" (Final Report)

Cropland abandonment is a common type of land-use change across the globe and can be caused by a range of social, economic, and environmental factors. Cropland abandonment has wide-ranging effects on the environment, especially on biodiversity and carbon storage. Despite the importance of cropland monitoring, cropland abandonment is difficult to map and not routinely monitored. In this project, we propose to investigate cropland abandonment using remote sensing technique for the application of carbon storage in Eurasia, where experienced extensive cropland abandonment because of drastic social-economic changes since the 1990s.

54 ENVIRONMENTAL SCIENCES↗

Human Performance Improvement Task Group Task 21-1 Best Practice: Using virtual capabilities or options for HPI application (to reduce errors, strengthening defenses, strengthening the organization, and/or increasing capacity)

In 2020 the world was impacted by COVID-19. How work is done was dramatically changed for a large portion of the workforce. Many have made the abrupt shift to working from their home while others have adapted to COVID protocols (such as social distancing and wearing facemasks). Fundamentally, Human and Organizational Performance Improvement concepts and science did not change – only the delivery method changed (in some cases). The primary effect has been to accelerate dynamics already present in society – from e-commerce to online education to remote healthcare. Change is never easy for humans. However, remote work should be viewed as an opportunity; not a fix to a problem. In this Best Practice paper, you’ll get practical suggestions to help you reduce errors and improve Human Performance in remote, socially-distanced “work-from-home” environments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Advancing Solar Innovation for Low and Moderate-Income Households (Final Technical Report)

The overarching goal of this project was to deepen the understanding of the social and economic aspects of solar adoption, with a specific eye towards any pathways or barriers that could be identified that directly impact low and moderate-income households. Specific objectives included (1) identifying and assessing significant non-economic barriers that complicate the ability of LMI households to adopt and benefit from solar, even under scenarios with low economic barriers; (2) assessing the differential ability of groups to benefit from solar adoption based on their ability to invest in different solar innovations; (3) explaining why some LMI communities nonetheless experience significantly higher rates of solar adoption and benefit than others; (4) developing a methodological model for generating data addressing key issues to the project; and (5) designing and developing an interactive web portal that aggregates existing datasets, provides researchers trend analysis capabilities, standardizes project protocols for other states and communities; provides the opportunities for researchers using the same protocol in other communities to upload their results; and engages with the general public by providing them the ability to upload their own perspectives and stories of solar technology adoption. Through a common framework for conducting semi-structured field interviews. In partnership with cooperative extension services, in conjunction with survey research and the aggregation of existing solar adoption datasets, this study has identified that currently ,there is a strong values alignment and knowledge access challenge that continues to affect solar adoption. Participants in this study – many of whom are either low or moderate-income and/or live in underserved rural communities do not believe solar is an option where they live or believe solar is appropriate for their lifestyle. This perception challenge is further exacerbated by the absolute dearth of adopters in most communities and a total lack of local marketing for solar energy. What marketing that does exist is geared towards audiences other than those in the participant's community, reinforcing a perception that 22% of survey respondents (n = 1,551) have: that solar energy is not meant for them and their community. Overcoming these serious human challenges pertaining to knowledge and marketing are critical to reducing the customer acquisition costs incurred amongst LMI communities and especially those in rural America where the opportunities solar provides to increase local energy resilience, security, all while engaging in personal stewardship of the planet could align with local values. Community-Based Social Marketing, built in partnership with community leaders and geared towards meeting local visions of ideal life, is a near term opportunity to bridge the gap between local priorities and national market and policy imperatives for solar while moving towards the administration's goals for renewable energy as part of a just and equitable decarbonization program. The tools and methods developed for this study can serve as a template for identifying other specific local challenges for solar adoption in other state, and provide the opportunity for a common data framework for understanding system-wide trends and barriers to achieving uptake of residential solar in communities that may, ultimately, benefit the most from the technology.

14 SOLAR ENERGY↗

Position Papers for the ASCR Workshop on the Science of Scientific-Software Development and Use

Software is an increasingly important component in the pursuit of scientific discovery. Both its development and use are essential activities for many scientific teams. At the same time, very little scientific study has been conducted to understand, characterize, and improve the development and use of software for science. Computational science teams have diversified over time to include contributions from domain scientists who provide expertise in scientific and engineering disciplines, applied mathematicians and computer scientists who provide optimal algorithms and data structures, and software and data engineers who provide methodologies and tools adapted and adopted from other software domains. These diverse contributions have enabled tremendous advances in the pursuit of scientific discovery, even as models, computer architectures, and software environments have become more complicated. With this increasing diversity, we believe the next opportunity for qualitative improvement comes from applying the scientific method to understanding, characterizing, and improving how scientific software is developed and used. We believe that this pursuit requires expertise from computational scientists themselves, and from the cognitive and social sciences as well as the software engineering research community. As we look to increase the productivity and sustainability of the scientific-software-development-and-use cycle, a more systematic application of the scientific method to understand processes for software development and use will be a valuable tool to guide future work and result in more usable and sustainable software. This workshop will bring together computer scientists, software engineering researchers, computational scientists, applied mathematicians, social scientists, cognitive scientists, and others, to explore how we can conduct such systematic investigations, what can be learned, and how doing so will benefit the scientific enterprise. The workshop will be structured around a set of breakout sessions, with every attendee expected to participate actively in the discussions. Afterward, workshop attendees — from DOE, industry, and academia — will produce a report for ASCR that summarizes the findings of the workshop.

42 ENGINEERING↗