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At least 343 records · Page 19

Idaho National Laboratory CY 2024 National Emission Standards for Hazardous Air Pollutants Analysis, Methodology, and Results for Radionuclides

This report documents the methodology and results for calculating the effective dose equivalent (EDE) to the maximally exposed individual (MEI) from atmospheric radionuclide emissions from Idaho National Laboratory (INL) sources in Calendar Year (CY) 2024. The calculations were performed in accordance with requirements in Code of Federal Regulations (CFR), Title 40, “Protection of the Environment,” Part 61, “National Emission Standards for Hazardous Air Pollutants (NESHAPs),” Subpart H, “National Emission Standards for Emissions of Radionuclides Other than Radon from Department of Energy Facilities” (40 CFR 61, Subpart H). UDFs were calculated using the computer model CAP88-PC for unit (1 Ci/yr) emission rates at INL Site facilities and INL in-town (Idaho Falls) facilities and stored in Microsoft Access databases. The UDFs—in this case, mrem/Ci—were then combined with radionuclide-specific release (emission) rates for each facility-specific source to compute doses at predetermined public receptor locations, including the MEI locations. This report contains the dose results and a description of the methodology and tools used to compute the doses.

07 - ISOTOPES AND RADIATION SOURCES↗

Total Effective Dose from Radiologic Emissions from INL Facilities for Calculation of Population Dose for the INL 2024 Annual Site Environmental Report

Total effective radiation dose from airborne releases was calculated using air dispersion modeling performed by the National Oceanic and Atmospheric Administration (NOAA) Idaho Falls Office using their HYSPLIT computer model (Stein et al. 2015; Draxler et al. 2013), and the Dose Multi-Media (DOSEMM) dose assessment model (Rood 2019) . The objective of these calculations was to provide a grid of total effective dose across a model domain that encompasses a 50-mile (80-km) radius from any Idaho National Laboratory (INL) Site source. In addition to INL Site sources, releases from the Radiological and Environmental Sciences Laboratory (RESL) (IF-683) and IF-603 located at the INL Research Center (IRC) within the Idaho Falls city limits were also included. Due to tracking limitations, radionuclides released from IF-611 and IF-603 are modeled as released from IF-603. The dose results will be combined with GIS software to compute a total population dose for the calendar year (CY) 2024 and will be reported in the INL Annual Site Environmental Report (ASER). This report does not cover the population dose calculation and only documents generation of the gridded dose file.

42 - ENGINEERING↗

Statistical and Machine Learning Approaches to Analyzing Pipeline Incidents in the United States (2010–2024)

This study applies machine learning methods to analyze natural gas pipeline incidents in the United States using the Pipeline and Hazardous Materials Safety Administration (PHMSA) Gas Distribution Incident Dataset (2010–2024). The dataset includes over 600 variables describing incident characteristics, infrastructure attributes, and contributing factors associated with unintentional gas releases. The objective is to assess whether these features can reliably predict the underlying cause of pipeline failures. Multinomial logistic regression and Random Forest models were developed to classify incident causes, including excavation damage, corrosion, equipment failure, and natural forces. Results show that excavation damage is both the most frequent and most predictable cause, with models achieving strong performance for this category. However, when excavation damage is excluded, model accuracy declines significantly, with some models performing near random levels. Across all approaches, severe class imbalance and limited variability in key predictors constrain predictive performance. Pipeline age and diameter emerge as the most influential variables, but they provide insufficient discriminatory power to distinguish among less frequent failure types. These findings indicate that non-excavation-related incidents are rare, heterogeneous, and weakly represented in the dataset, limiting the effectiveness of machine learning classification. Overall, this study highlights the structural limitations of the PHMSA dataset for predictive modeling and underscores the need for improved data balance and feature enrichment. The results reinforce excavation damage prevention as the most impactful strategy for reducing pipeline incidents.

03 NATURAL GAS↗

U.S. ESCO Industry Report: Industry Size and Recent Market Trends, 2022- 2024

The latest edition of the U.S. Energy Service Company (ESCO) Industry Report by Lawrence Berkeley National Laboratory (LBNL) finds that the U.S. ESCO industry continues to show strong growth. The report draws from ESCO industry reported revenue data for the 2022-2024 period, detailing the current size and characteristics of the U.S. ESCO industry. Following 20 years of ESCO industry reports, the 2024 report explores significant revenue trends across market segments, geographic regions, ESCO size, financing structures, and business activities. New analysis in this report outlines customer priorities and non-energy benefit drivers of Energy Savings Performance Contract projects, adjusted revenue analysis detailing the impacts of inflation on industry growth, and project challenges by market segment.

Chelminski, Kathryn↗

The 2024 National Hydropower Map

A visualization of the geospatial distribution and characteristics of operational hydropower plants in the United States in 2024.

13 HYDRO ENERGY↗

2024 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies

These data provide the 2024 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

Array↗

Utility-Scale Solar, 2024 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2024 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2023, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, net value, and interconnection queue data.

analysis↗

The propagation of seismic waves, misinformation, and disinformation from the 2024-10-05 M 4.5 Iran earthquake

The 2024-10-05 Iran M 4.5 earthquake took place at a time of heightened tensions in the Middle East. We perform a discrimination and moment tensor analysis and identify a shallow-dipping, reverse fault source commensurate with the compressional setting of the Iranian interior. Nonetheless, the event's aftermath saw widespread dissemination of misinformation, and potentially active disinformation, concluding that it was in fact a test of an Iranian nuclear weapon. The 'evidence' for many of these claims was based on inaccurate interpretation of seismic data. In this paper, we analyze how geophysical 'fake news' propagated through social media (mainly Twitter/X) following this event, eventually gaining traction in mainstream, earned media. This event is an illustrative warning of how seismic data can be misinterpreted and/or manipulated in public discourse.

58 GEOSCIENCES↗

Editorial: Visualizing offline and live data with AI (VOLDA) workshop first edition Princeton 11-13th June 2024

The first edition of the Visualizing Offline and Live Data with AI’ (VOLDA) Workshop took place at the Princeton University Campus, Mader Hall from 11 to 13 June 2024. This annual workshop held for the first time aims at bringing together the fusion community to discuss the challenges brought by Artificial Intelligence (AI) and visualizing large datasets in fusion experiment and simulation.

artificial intelligence↗

Unlocking Manufacturing Sustainability: Energy Efficiency Opportunities through the US Department of Energy’s Better Plants Program Energy Treasure Hunts (2023–2024)

The US manufacturing sector faces critical challenges: improving sustainability, reducing energy consumption, and reducing greenhouse gas emissions. Energy Treasure Hunt (ETH) training, a service provided by the US Department of Energy’s Better Plants program, offers a compelling solution. Although ETHs have traditionally focused on energy and cost savings, data indicate that ETHs can be used to identify opportunities to reduce emissions and water use and to support a sustainable and circular operation. These 3-day on-site events engage employees in a collaborative search for operational and maintenance efficiency improvement opportunities. The success of ETHs lies in a comprehensive methodology. Each phase in an ETH uses various tools and resources to empower employees to identify practical solutions. This study presents data from 13 ETHs conducted between 2023 and 2024 across diverse manufacturing subsectors in the United States and demonstrates that the events can help create a pragmatic decarbonization pathway. Through the events, a total of 234 energy and emissions reduction opportunities were identified, and the potential impact is significant. Implementing the recommendations could translate to annual savings of 497,299 MMBtu of energy, 64,374 kgal of water, and 4.85 million tCO 2 e of emissions. The fiscal savings from the proposed recommendations translate into nearly $\$$5 million annually. This study identifies the opportunities by energy system type and by the specific actions recommended, while also analyzing the identified opportunities, presenting the most established sustainability recommendations. Case studies from participating partners are presented to further demonstrate that ETHs provide a practical and impactful approach to reducing energy consumption, emissions, and operating costs and promote a more sustainable future for the industrial sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

LandScan Global 2024

The LandScan program is excited to share LandScan 2024, the latest annual update of a global gridded population dataset at 30 arc-second resolution that serves as foundational GEOINT Human Geography data. Substantial changes were continued from last year in the new ML methodological approach with the goal to retain the knowledge and expertise represented through improvements with each annual release over the past quarter century. Through these annual releases, Oak Ridge National Laboratory (ORNL) has consistently produced the most accurate global gridded population data, reflecting both ambient and unwarned population patterns that meet the United States Department of Defense (U.S. DoD) requirements. Additionally, the dataset is used widely across various U.S. government programs and is released publicly through an NGA and ORNL collaborative open portal (https://LandScan.ornl.gov) to expand its availability to researchers, humanitarian organizations, and the public at large.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗

2024 NSUF Annual Program Review

The Department of Energy - Office of Nuclear Energy’s (DOE-NE) Nuclear Science User Facilities (NSUF) program held its annual review on Monday, April 15 through Thursday, April 18, 2024. The meeting involved in-person participation at Idaho National Laboratory as well as a virtual component. The NSUF program review included overview presentations by the NSUF program office and select partner facilities, as well as technical highlights from NSUF supported research projects and the user community. For questions regarding this event, please contact our program office at nsuf@inl.gov.

Author, Unknown↗

2024 NSUF User Development Workshop on Irradiation Testing

Monday, May 20 – Thursday, May 23, 2024 at Center for Advanced Energy Studies at Idaho National Laboratory, Idaho Falls, Idaho. This pilot workshop was hosted to expand the NSUF user community and introduce irradiation testing of nuclear fuel and materials to new researchers, and to build long-term relationships between the NSUF program office, NSUF partner facilities, and NSUF university-affiliated users to provide better utilization of NSUF resources in addressing United States energy challenges.

Starks, Alexis↗

NOvA 2024 official data release (26.61E20 neutrino + 12.5E20 antineutrino)

This data release corresponds to the Bayesian 2024 analysis of NOvA $\nu_e$ appearance and $\nu_{\mu}$ disappearance data, corresponding to analysis described in https://arxiv.org/abs/2509.04361. File `NOvA_2024_data_histograms.root` contains data histograms for all the NOvA data samples. Exposure: * neutrino-enhanced beam: 26.61E20 protons on target * antineutrino-enhanced beam: 12.5E20 protons on target External constraints: * ss2th12=0.851, dm21=7.53e-5 are fixed at 2019 PDG values, with negligible effect on NOvA predictions. * ss2th13 & dm32: * RCDB1D: 1D constraint from Daya Bay for ss2th13, 0.0851+/-0.0024 * RCDB2D: Correlated 2D constraint from Daya Bay on ss2th13 & dm32, available in their official 2023 data release: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.161802#supplemental An additional file containing the predictions (`NOvA_2024_prediction_with_systs_histograms.root`) for all channels’ signal and background components computed at the NOvA best-fit oscillation parameters and systematic pull terms. Numu samples include the no-oscillation case as well. Please note that any fits performed with these histograms are not expected to exactly reproduce the official NOvA results as parameterizations of the numerous systematic uncertainties considered in the official fits are not included in this release. The zip file also contains the 2D credible interval contours, with details in a README.md

Sztuc, Artur [University Coll. London] (ORCID:0000↗

Aerosol Microphysics and Chemical Measurements at Mt. Soledad and Scripps Pier during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) from February 2023 to February 2024 UCSD Library Collection

This dataset includes guest instrument measurements and other PI products for aerosol microphysics and chemical measurements collected at Mt. Soledad and Scripps Pier during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) from February 2023 to February 2024. The measurements include the following instruments at Mt. Soledad: High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS, Aerodyne), Scanning Electrical Mobility Spectrometer (SEMS, Brechtel Manufacturing Inc.), Aerodynamic Particle Sizer (APS, Droplet Measurements Technologies), Single Particle Soot Photometer (SP2, Drople Measurements Technologies), Meteorological Station (WXT520, Vaisala), Ozone (Teco), and trace gas proxies (Teledyne). In addition, the analyses of particle filters collected at Mt. Soledad for three dry-diameter size cuts (<1 micron, <0.5 micron, <0.18 micron) and at Scripps Pier for one dry-diameter size cut (<1 micron) by Fourier Transform Infrared (FTIR) and X-ray Fluorescence (XRF) are reported. A differential mobility analyzer operated as a scanning mobility particle sizer (SMPS, TSI Inc.), a printed particle optical spectrometer (POPS, Grimm), and a continuous flow diffusion cloud condensation nuclei (CCN, DMT) counter provide the mobility aerosol size distribution (30-360 nm), optical size distribution (150 - 6000 nm), size-resolved CCN distribution (30-360 nm) at 0.2, 0.4, 0.6, 0.8, and 1.0% supersaturation. Measurements are reported for both sampling from an isokinetic aerosol inlet and from a Counterflow Virtual Impactor (CVI, Brechtel Manufacturing Inc.). The data are available at the following link: https://library.ucsd.edu/dc/collection/bb0898306q

54 ENVIRONMENTAL SCIENCES↗

Mobile measurement of CH4 around the SGP ARM site during June 23-26, 2024

A mobile measurement campaign from June 23-26, 2024 was conducted to detect and quantify CH₄ emissions within about 8 km of the SGP site. Using a LI-COR LI-7810 tracer gas analyzer mounted on a vehicle, we sampled CH₄ along predefined routes surrounding a suspected source area around the SGP ARM site. Measurements were collected between 04:30 and 09:00 local time when the PBL was shallow, increasing sensitivity to surface emissions. Driving speeds were kept below 5 mph to minimize pressure-related artifacts.

{"Trace Gas Analyzer for methane","Measured mixing↗

Snow ALbedo eVOlution (SALVO) Campaign Qualitative Characterization of Snowpack Stratigraphy from April - June, 2024 in Utqiagivk, AK

The physical structure of the snowpack is impacted by and impacts the exchange of energy with the atmosphere. Qualitative characterization of snowpack stratigraphy in snow pits provides context for what physical processes are changing the snow (e.g., melt, wind-packing, hoar formation). 36 snowpits were dug as part of the 2024 Snow Albedo Evolution (SALVO) field campaign. Snow grain stratigraphy was recorded using standard International Association of Cryospheric Sciences (IACS) snow grain classification ( https://cryosphericsciences.org/publications/snow-classification/) and hand hardness tests (in order of least hard to hardest: F (fist), 4F (four fingers), 1F (one finger), P (pencil), K (knife), I (ice)). These snowpits were generally co-located with library site albedo measurements and are given the same index (Date, Site, Location, Position) when appropriate (see broadband and spectral albedo data archived from the SALVO II project).

Depth to bottom of snow layer↗

KCG Baseline Air Pollution Station LIFSO2 v1 Data from December 2024 to March 2025

Sulfur Dioxide is a key precursor to the formation of new particles within the marine environment, yet commercial instrumentation lack sufficient precision or sensitivity to resolve the levels present in these environments. As such, the LIFSO2, a custom built fibre laser from the University of York, UK, was developed (based on the one developed by Rollins et al 2016). We are able to resolve down to the ppt level with this instrument. We present version 1 (v1) data for Dec 2024 to Mar 2025 for SO2 (1min time average) from the Cape-k precursors field campaign.

Sulfur dioxide (SO2) mixing ratio↗