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DOE OSTI2023

Counter Unmanned Aircraft System Metrics Tool

SAND2023-05387O The Counter Unmanned Aircraft System (CUAS) Metrics Tool consists of three applications: • The Data Collection Tool is designed to be used during testing and captures details that can be imported into the Figure Creator application. • The Unmanned Aircraft System (UAS) Log Converter converts different UAS logs into a standardized hierarchical data format with a timeline of geospatial data. • The Figure Creator develops relevant images for the test event. The application includes logic to ingest and correlate data from the Data Collection Tool and the UAS Log Converter to produce the chosen figures. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Mayle, Ashley↗

DOE OSTI2026

Resilience Metrics for Solar Photovoltaics

This workshop presentation proposes the development of solar photovoltaic (PV) system resilience metrics and a methodology and framework for evaluation of PV resilience metrics. PV resilience metrics are needed to establish a consistent basis for reporting, evaluation, and data collection by industry, evaluate performance of PV systems that have been subject to natural hazards, correlating resilience to system attributes, and predicting resilience for any PV system. PV resilience metrics can guide improved system design, standards, and insurance coverage. Establishing consistent metrics can foster data collection on impacts of natural hazards on PV systems.

14 SOLAR ENERGY↗

DOE OSTI2023

Electrical Infrastructure Cost Model for Marine Energy Systems

The National Renewable Energy Laboratory's Electrical Infrastructure Cost Model is an Excel-based tool designed to estimate the electrical infrastructure costs of marine energy components and subsystems. It incorporates data collected from offshore wind projects, utility projects, and other relevant sources to provide accurate and comprehensive cost projections. With its user-friendly interface, the model allows users to input various parameters related to the system array, electrical cables, and substations. By leveraging industry data, cost trends, and technological advancements, the model generates outputs that include system array sizing, electrical cable specifications and costs, substation specifications and costs, and total electrical infrastructure costs. One of the notable strengths of the model is its flexibility in covering multiple-orders-of-magnitude scaled systems, accommodating projects ranging from proof-of-concept or pilot-scale installations to large-scale offshore systems. By collecting data largely from offshore wind reports and utility projects, the model incorporates real-world conditions and accounts for industry-specific factors. It incorporates cost trends and sizing relationships to deliver cost estimations for electrical infrastructure components, such as electrical cables and substation equipment.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

DEM, DSM, and Cleaned LiDAR Point Cloud Data from the NGEE Arctic UAS Campaigns at the Teller 27 Field Site from 2017 and 2018, Seward Peninsula, Alaska

A Digital Elevation Model (DEM) and Digital Surface Model (DSM) were derived from airborne Light Detection and Ranging (LiDAR) data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) quadcopter and hexacopter platforms operated by Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) scientists from the EES-14 group at LANL. These data were collected in August 2017 and July 2018 at the NGEE Arctic field site near mile marker 27 of the Bob Blodgett Nome-Teller Memorial Highway between Nome, Alaska and Teller, Alaska. A Vulcan Raven X8 Airframe (Mitcheldean, Gloucestershire, UK), DJI Matrice 600 Pro Airframe (Shenzhen, China), and Routescene UAV LiDARSystem (Edinburgh, Scotland, UK) were used to collect LiDAR data. Following pre-processing in Routescene LidarViewer Pro software, the LiDAR point clouds were cleaned and processed using CloudCompare software to separate ground and off-ground points. A high resolution DEM and DSM were then created using ArcGIS Pro software. This data package contains fully cleaned point clouds of ground and off-ground points (.las), a 25 cm DEM (.tif), and a 25 cm DSM (.tif) for the Teller 27 field site. Ancillary aircraft data, flight mission parameters, weather conditions, and raw lidar data and imagery can be found in the L0 datasets for these campaigns: NGA299 (2017) and NGA297 (2018). Minimally processed point clouds and auxiliary files can be found in the L1 dataset: NGA304 (2017 and 2018).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Real-time and Autonomous Water Quality Monitoring System Based on Remotely Operated Vehicle

Existing water quality monitoring systems near hydropower facilities are limited by the lack of mobility of the sensors’ carrier platform. Most systems use a buoy, a mounting fixture attached to a solid structure, or a human worker, which significantly limits the selection of the sampling sites and poses safety risks during data collection and equipment maintenance. To improve on this technology, we developed an autonomous water quality monitoring system that can operate in dangerous water environments near hydropower facilities for water sampling at multiple locations. The goal is to enable safe, timely, and comprehensive water-quality data collection; maximize power generation revenue with improved operational control; and reduce Federal Energy Regulatory Commission and state water quality monitoring costs for compliance. The system incorporates a remotely operated vehicle as the mobile monitoring platform, a dissolved oxygen sensor for monitoring water quality, a tether management system for automatically winding the tether, a solar mobile docking platform for suppling power to the ROV, and a web-based graphical user interface for data post-processing and visualization. In addition, preliminary field research are presented to demonstrate the system capabilities.

Salalila, Aljon L.↗

DOE OSTI2022

Mining G.O.L.D. (Geothermal Opportunities Leveraged Through Data): Exploring Synergies Between the Geothermal and Mining Industries

This report analyzes potential collaborations between the geothermal and locatable mineral industries (focused on the portion of the Basin and Range Province within Nevada, United States of America). The objectives of this study included analyzing: 1. The type and quality of data collected by the locatable mineral industry to determine feasibility for geothermal resource exploration; 2. The regulatory pathways and potential barriers that could prevent development of geothermal resources discovered via a mining claim (and vice versa) in the United States; 3. The historical development of geothermal resources discovered via mineral exploration data in the United States and illustrations of co-located mining and geothermal power projects; 4. The value propositions for both the locatable mineral and the geothermal industries to collaborate. This article concludes that many of the data collected by the mining industry as part of locatable mineral exploration (e.g., copper, gold, lithium) would also be useful for identifying and developing previously unknown geothermal resources (and in some notable cases, already have led to geothermal resource development). In addition, for minimal costs, the mining industry could catalogue these data and potentially monetize the data itself or use the data in the future to develop a geothermal project. Leveraging these locatable mineral data to develop geothermal resources and/or co-located minerals and geothermal resources would represent significant cost savings when compared to developing geothermal resources under a business-as-usual scenario as well as compared to current generating technologies (e.g., diesel-powered generators) employed at remote mining operations. Ultimately, leveraging mining industry data, knowledge, and expertise serves to effectively expand the geothermal exploration workforce, increase the rate of geothermal resource discovery, and potentially reduce geothermal electricity's levelized cost of energy (LCOE) by 23%-29%.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Atmospheric Radiation Measurement (ARM) airborne field campaign data products between 2013 and 2018

Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the US Department of Energy (DOE) Office of Science supported an instrumented Gulfstream 1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final 6 years of G-1 operations (2013 to 2018, https://doi.org/10.5439/1999133; Mei and Gaustad, 2024). The integrated dataset includes data collected from 236 flights (766.4 h), which covered the Arctic, the US Southern Great Plains (SGP), the US West Coast, the eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Córdoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability in the thermodynamic quantities and aerosol and cloud properties for addressing essential science questions in Earth system process studies. This paper describes the DOE ARM merged G-1 datasets, including information on the acquisition, data collection challenges and future potentials, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Electromagnetic Induction (EMI) Data, 2024, Trail Creek, Colorado

This dataset contains Electromagnetic Induction (EMI) data collected at Trail Creek, Colorado, in 2024. EMI surveys were conducted to investigate the spatial distribution of electrical conductivity in the subsurface, providing insights into soil moisture and subsurface geological features. The surveys were performed along multiple transects to capture variations in conductivity influenced by changes in soil composition, moisture content, and underlying geological structures. This dataset complements other geophysical data collected in the region, including Electrical Resistivity Tomography (ERT) and Terrestrial LiDAR Scanning (TLS), providing a detailed understanding of the subsurface and its impact on surface vegetation and hydrological processes. The data are valuable for environmental geophysics, ecological research, and hydrological modeling in mountainous ecosystems. The files include: - data.zip: the raw EMI data (.csv) - inversion.zip: the inverted resistivity model (.csv and .kml) - kriging.zip: the kriging resistivity model (.csv, .tif, .kmz) - flmd.csv: file level metadata file describing all files within this dataset - dd.csv: data dictionary file describing the column headers within CSV files This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

CMD Mini-Explorer↗

DOE OSTI2020

Global Experiences with HPC Operational Data Measurement, Collection and Analysis

As we move into the exascale era, supercomputers grow larger, denser, more heterogeneous, and ever more complex. Operating such machines reliably and efficiently requires deep insight into the operational parameters of the machine itself as well as its supporting infrastructure. To fulfill this need, early adopter sites have started the development and deployment of Operational Data Analytics (ODA) frameworks allowing the continuous monitoring, archiving, and analysis of near realtime performance data from the machine and infrastructure levels, providing immediately actionable information for multiple operational uses. To understand their ODA goals, requirements, and use cases, we have conducted a survey among eight early adopter sites from the US, Europe, and Japan that operate top 50 high-performance computing systems. We have assessed the technologies leveraged to build their ODA frameworks, identified use cases and other push and pull factors that drive the sites' ODA activities, and report on their operational lessons.

Ott, Michael↗

DOE OSTI2022

VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run

This data collection was contributed to the Visualisation Hackathon 2022 (#VisMetHack2022), in conjunction with the Using ECMWF's Forecasts (UEF2022) workshop. The European Center for Medium-Range Weather Forecasts (ECMWF) and the Oak Ridge National Laboratory (ORNL) are pleased to announce access to the data collection from global 1-km nature run (NR) simulations using the Integrated Forecast System (IFS) with explicit convection. We invite you to join us in exploring this precursor to a digital twin of the earth! The NR simulations reveal unprecedented detail of the earth’s atmosphere, and the then outgoing Editor-in-Chief of AGU JAMES commended the project as one of “stunning ambitions,” enabled by computational capacity at scale. The project also won the 2020 HPCwire Readers Choice Award for Best Use of HPC in Physical Sciences. A set of two NR seasonal simulations have been completed, one corresponding to the northern hemispheric winter months (NDJF) and the other for the North Atlantic tropical cyclone season (ASO). The project used the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). The simulations were facilitated with an INCITE award from the US Department of Energy Office of Science. For the first seasonal run of four months (NDJF), the hydrostatic IFS model was initialized at 00Z on 1 November 2018. The NR for the TC season (AS) was initialized at 00Z on 1 August 2019. The NR simulations were constrained only by sea surface temperatures (SST) at the lower boundary. The IFS output was saved every 3 hours. After feedback and interest from the scientific community, the simulations were rerun for four specific extreme events, with output every 15 minutes. The special cases include a tropical cycle and three severe storm events over the continental USA.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Engineering-Scale Test of a Water-Lean Solvent for Post-Combustion Capture

EPRI, Pacific Northwest National Laboratory, RTI International, and their project collaborators developed an engineering-scale test of a new water-lean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (EEMPA or 2-EEMPA) as a post-combustion CO 2 capture solvent for power plant applications. This test was conducted using the Pilot Solvent Test Unit at the National Carbon Capture Center. The primary objective of this test was to collect long-term data operating EEMPA with both coal- and natural gas-representative flue gases at the approximately 0.5 MW e -equivalent scale (5–10 metric tons CO 2 /day captured). This report details the activities preparing for that test, data collected during the test campaign, and analyses and interpretations of that data.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events

Abstract. High-time-resolution measurements of in situ aerosol and cloud properties provide the ability to study regional atmospheric processes that occur on timescales of minutes to hours. However, one limitation to this approach is that continuous measurements often include periods when the data collected are not representative of the regional aerosol. Even at remote locations, submicron aerosols are pervasive in the ambient atmosphere with many sources. Therefore, periods dominated by local aerosol should be identified before conducting subsequent analyses to understand aerosol regional processes and aerosol–cloud interactions. Here, we present a novel method to validate the identification of regional baseline aerosol data by applying a mathematical algorithm to the data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility in the eastern North Atlantic (ENA). The ENA central facility (C1) includes an aerosol observing system (AOS) for the measurement of aerosol physical, optical, and chemical properties at time resolutions from seconds to minutes. A second temporary supplementary facility (S1), located ∼0.75 km from C1, was deployed for ∼1 year during the Aerosol and Cloud Experiments (ACE-ENA) campaign in 2017. First, we investigate the local aerosol at both locations. We associate periods of high submicron number concentration (Ntot) in the fine-mode condensation particle counter (CPC) and size distributions from the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) as a function of wind direction using a meteorology sensor with local sources. Elevated concentrations of Aitken-mode (< 100 nm diameter) particles were observed in correspondence with the wind directions associated with airport operations. At ENA, the Graciosa Airport and its associated activities were found to be the main sources of high-concentration aerosol events at ENA, causing peaks in 1 min Ntot that exceeded 8000 and 10 000 cm−3 at C1, in summer and winter, respectively, and 5000 cm−3 at S1 in summer. Periods with high Ntot not associated with these wind directions were also observed. As a result, the diverse local sources at ENA yielded a poor relationship between Ntot measurements collected at C1 and S1 (R2=0.03 with a slope =0.05±0.001). As a first approach to mask these events, the time periods when the wind direction was associated with the airport operations (west to northwest and southeast to south at C1 and east to south at S1) were applied. The meteorological masks removed 38.9 % of the data at C1 and 43.4 % at S1, and they did not significantly improve the relationship between the two sites (R2=0.18 with a slope =0.06±0.001). Due to the complexity of high-Ntot events observed at ENA, we develop and validate a mathematical ENA Aerosol Mask (ENA-AM) to identify high-Ntot events using 1 min resolution data from the AOS CPC at C1 and S1. After its parameterization and application, ENA-AM generated a high correlation between Ntot in the summer at C1 and S1 (R2=0.87 with a slope =0.84±0.001). We identified the regional baseline at ENA to be 428±228 cm−3 in the summer and 346±223 cm−3 in the winter. Lastly, we compared masked measurements from the AOS with the ARM Aerial Facility (AAF) during flights over C1 in the summer to understand submicron aerosol vertical mixing over C1. The high correlation (R2=0.71 with a slope of 1.04±0.01) observed between C1 and the AAF Ntot collected within an area of 10 km surrounding ENA and at altitudes < 500 m indicated that the submicron aerosol at ENA was well mixed within the first 500 m of the marine boundary layer during the month of July during ACE-ENA. Our novel method for determining a regional aerosol baseline at ENA can be applied to other time periods and at other locations with validation by a secondary site or additional collocated measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Complementarity of neutron, XFEL and synchrotron crystallography for defining the structures of metalloenzymes at room temperature

Room-temperature macromolecular crystallography allows protein structures to be determined under close-to-physiological conditions, permits dynamic freedom in protein motions and enables time-resolved studies. In the case of metalloenzymes that are highly sensitive to radiation damage, such room-temperature experiments can present challenges, including increased rates of X-ray reduction of metal centres and site-specific radiation-damage artefacts, as well as in devising appropriate sample-delivery and data-collection methods. It can also be problematic to compare structures measured using different crystal sizes and light sources. In this study, structures of a multifunctional globin, dehaloperoxidase B (DHP-B), obtained using several methods of room-temperature crystallographic structure determination are described and compared. Here, data were measured from large single crystals and multiple microcrystals using neutrons, X-ray free-electron laser pulses, monochromatic synchrotron radiation and polychromatic (Laue) radiation light sources. These approaches span a range of 18 orders of magnitude in measurement time per diffraction pattern and four orders of magnitude in crystal volume. The first room-temperature neutron structures of DHP-B are also presented, allowing the explicit identification of the hydrogen positions. The neutron data proved to be complementary to the serial femtosecond crystallography data, with both methods providing structures free of the effects of X-ray radiation damage when compared with standard cryo-crystallography. Comparison of these room-temperature methods demonstrated the large differences in sample requirements, data-collection time and the potential for radiation damage between them. With regard to the structure and function of DHP-B, despite the results being partly limited by differences in the underlying structures, new information was gained on the protonation states of active-site residues which may guide future studies of DHP-B.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Mobility Energy Productivity and Equity: E-Bike Impacts for Low-Income Essential Workers in Denver

New mobility technologies such as electrified and shared mobility, combined with polices and incentive programs, are emerging to help address sustainability and equity issues in transportation planning. However, it can be difficult to understand the impacts of novel mobility trends and emerging modes on energy-efficient access. This is owing to a lack of (1) open-source tools enabling rapid data collection, and (2) open-source metrics that consider multimodal, multiactivity access and mobility within the contexts of sustainability and equity. Here, this paper addresses the topic of improving evaluation of transportation modes and incentive programs by integrating an open-source platform for tracking human travel data—the Open Platform for Agile Trip Heuristics (OpenPATH)—with a mobility metric that quantifies the efficiency of a region’s transportation system: Mobility Energy Productivity (MEP). Integration is demonstrated in the context of pilot programs in Colorado, where low-income essential workers were provided with electric bikes (e-bikes). OpenPATH-informed MEP calculations showed that several locations in downtown Denver provided comparable time-, cost-, and energy-efficient access to opportunities using e-bikes compared with driving. Additionally, providing e-bikes to low-income essential workers was found to be meaningful, as they utilized e-bikes the most to commute, despite driving still being their most utilized mode and the mode with highest MEP scores in Denver. We show how data collected from open-source tools coupled with robust metrics such as MEP can help evaluate the impacts of emerging mobility options. This could support developing policies to incentivize novel modes to achieve greater levels of sustainable, equitable, and efficient access.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Continuous soil temperature and soil deformation measurements, Teller road Mile 47, Nome, Alaska.

The dataset comprises Soil Temperature and Deformation monitoring data gathered from a watershed situated along the Nome-Teller road at Mile 47 in Alaska. Its primary objective is to enhance comprehension of deformation mechanisms in permafrost environments by using 51 probes designed following Wielandt et al. (2022). The dataset comprises a description of Probe_ID, Location, start time, end time and probe length in the "ProbesLocations_StartEndDates.csv" file, the 51 data files encapsulated within "Data_files.zip" and the computed deformation for 2023 and 2022 in "Deformation_T47.zip".Each file in Data_files.zip is named as followed "ProbeId_StartMonth_EndMonth.csv". Within each file, the initial column denotes the timestamp in UTC, followed by the sensor’s battery voltage and temperature and acceleration values (X, Y, Z) in subsequent columns. The data collection frequency is set at 30-minute intervals. Each file in Deformation_T47.zip is named as followed "ProbeId_Temp_def_year.csv". Within each file, the initial column denotes the timestamp in UTC, followed by the temperature at each sensor and cumulative deformation from last to first sensor in subsequent columns. The data collection is daily averaged.This dataset forms an integral component of the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Evaluating the feasibility of using downwind methods to quantify point source oil and gas emissions using continuously monitoring fence-line sensors

The dependable reporting of methane (CH 4 ) emissions from point sources, such as fugitive leaks from oil and gas infrastructure, is important for profit maximization (retaining more hydrocarbons), evaluating climate impacts, assessing CH 4 fees for regulatory programs, and validating CH 4 intensity in differentiated gas programs. Currently, there are disagreements between emissions reported by different quantification techniques for the same sources. It has been suggested that downwind CH 4 quantification methods using CH 4 measurements on the fence line of production facilities could be used to generate emission estimates from oil and gas operations at the site level, but it is currently unclear how accurate the quantified emissions are. To investigate the accuracy of downwind methods, this study uses fence-line simulated data collected during controlled-release experiments as input for a non-standard closed-path eddy covariance (EC), the Gaussian plume inverse model (GPIM), and the backward Lagrangian stochastic (bLs) model in a range of atmospheric conditions. This study's EC attempt was unsuccessful due to data collection and instrumentation issues, resulting in invalid results characterized by underestimated emissions, large negative fluxes, and cospectra/ogives that deviated from their ideal shapes. Consequently, the EC results could not be compared with the GPIM and bLS model. The bLs model demonstrated the highest accuracy for single-release single-point emissions, though it exhibited greater uncertainty than GPIM under multi-release conditions. Across the GPIM and bLs model, the most reliable quantification was achieved with 15 min averaging and a narrow 5° wind sector range. Although EC was limited in this context, future studies should consider employing a standard EC system and further optimizing GPIM and bLs approaches – particularly for complex multi-source scenarios – to enhance quantification accuracy and reduce uncertainty.

03 NATURAL GAS↗

DOE OSTI2021

Plug-and-play polymer microfluidic chips for hydrated, room temperature, fixed-target serial crystallography

The practice of serial X-ray crystallography (SX) depends on efficient, continuous delivery of hydrated protein crystals while minimizing background scattering. Of the two major types of sample delivery devices, fixed-target devices offer several advantages over widely adopted jet injectors, including: lower sample consumption, clog-free delivery, and the ability to control on-chip crystal density to improve hit rates. In this study we present our development of versatile, inexpensive, and robust polymer microfluidic chips for routine and reliable room temperature serial measurements at both synchrotrons and X-ray free electron lasers (XFELs). Our design includes highly X-ray-transparent enclosing thin film layers tuned to minimize scatter background, adaptable sample flow layers tuned to match crystal size, and a large sample area compatible with both raster scanning and rotation based serial data collection. The optically transparent chips can be used both for in situ protein crystallization (to eliminate crystal handling) or crystal slurry loading, with prepared samples stable for weeks in a humidified environment and for several hours in ambient conditions. Serial oscillation crystallography, using a multi-crystal rotational data collection approach, at a microfocus synchrotron beamline (SSRL, beamline 12-1) was used to benchmark the performance of the chips. Furthermore, high-resolution structures (1.3–2.7 Å) were collected from five different proteins – hen egg white lysozyme, thaumatin, bovine liver catalase, concanavalin-A (type VI), and SARS-CoV-2 nonstructural protein NSP5. Overall, our modular fabrication approach enables precise control over the cross-section of materials in the X-ray beam path and facilitates chip adaption to different sample and beamline requirements for user-friendly, straightforward diffraction measurements at room temperature.

59 BASIC BIOLOGICAL SCIENCES↗