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At least 379 records · Page 21

Multidimensional dosimetry of {sup 106}Ru eye plaques using EBT3 films and its impact on treatment planning

Purpose: The purpose of this study was to establish a method to perform multidimensional radiochromic film measurements of {sup 106}Ru plaques and to benchmark the resulting dose distributions against Monte Carlo simulations (MC), microdiamond, and diode measurements. Methods: Absolute dose rates and relative dose distributions in multiple planes were determined for three different plaque models (CCB, CCA, and COB), and three different plaques per model, using EBT3 films in an in-house developed polystyrene phantom and the MCNP6 MC code. Dose difference maps were generated to analyze interplaque variations for a specific type, and for comparing measurements against MC simulations. Furthermore, dose distributions were validated against values specified by the manufacturer (BEBIG) and microdiamond and diode measurements in a water scanning phantom. Radial profiles were assessed and used to estimate dosimetric margins for a given combination of representative tumor geometry and plaque size. Results: Absolute dose rates at a reference depth of 2 mm on the central axis of the plaque show an agreement better than 5% (10%) when comparing film measurements (MCNP6) to the manufacturer’s data. The reproducibility of depth-dose profile measurements was <7% (2 SD) for all investigated detectors and plaque types. Dose difference maps revealed minor interplaque deviations for a specific plaque type due to inhomogeneities of the active layer. The evaluation of dosimetric margins showed that for a majority of the investigated cases, the tumor was not completely covered by the 100% isodose prescribed to the tumor apex if the difference between geometrical plaque size and tumor base ≤4 mm. Conclusions: EBT3 film dosimetry in an in-house developed phantom was successfully used to characterize the dosimetric properties of different {sup 106}Ru plaque models. The film measurements were validated against MC calculations and other experimental methods and showed a good agreement with data from BEBIG well within published tolerances. The dosimetric information as well as interplaque comparison can be used for comprehensive quality assurance and for considerations in the treatment planning of ophthalmic brachytherapy.

60 APPLIED LIFE SCIENCES↗

Evaluation of Deep Subsurface Resistivity Imaging for Hydrofracture Monitoring

This report describes the results of the first of its kind monitoring of a hydrofracture operation with electromagnetic measurements. The researchers teamed with oil and gas producer Encana Corporation to design and execute a borehole to surface monitoring of three fracture stages at a well pad in central Colorado. The field project consisted of an equipment upgrade, a survey design and modeling phase, several weeks of data collection, and data processing and interpretation. Existing Depth to Surface Resistivity (DSR) instrumentation was upgraded to allow for continuous high precision recording from downhole sources. The full system can now collect data continuously for up to 72 hours, which is sufficient to measure data for 10 frac stages. Next we used numerical modeling and frac treatment data supplied by Encana to design a field survey to detect EM signal from induced fractures. Prior to modeling we developed a novel technique for using well casing as an antenna for a downhole source. Modeling shows that 1) a measurable response for an induced fracture could be achieved if the facture fluid was of high salinity 2) an optimum fracture response is created when the primary source field is parallel to the well casing but perpendicular to the fracture direction. In mid-July, 2014 we installed an array of more than 100 surface sensors, distributed above the treatment wells and extending for approximately 1 km north and 750 m eastward. We applied a 0.6 Hz square wave signal to a downhole current electrode located in a horizontal well 200 m offset from the treatment well with a return electrode on the surface. The data were transmitted to a recording trailer via Wi-Fi where we monitored receiver and transmitter channels continuously in a 72-hour period which covered 7 frac stages, three of which were high salinity. Although the background conditions were very noisy we were able to extract a clear signal from the high salinity stages. Initial data interpretation attempts consisting of trying to directly invert collected data using a simple background starting model did not produce reasonable models. We next used a simulated data set to develop a constrained inversion workflow that places the downhole fracture in the correct location. Finally, we used a combination of forward and inverse models to fit the collected data to a model that incorporated 3 frac stages. This project provided a first-of-its-kind application of EM technology to map an induced fracture. The results demonstrated a clear anomaly during the operation that can be fit to a reasonable model of an induced fracture.

42 ENGINEERING↗

Application of activity pencil beam algorithm using measured distribution data of positron emitter nuclei for therapeutic SOBP proton beam

Purpose: Recently, much research on imaging the clinical proton-irradiated volume using positron emitter nuclei based on target nuclear fragment reaction has been carried out. The purpose of this study is to develop an activity pencil beam (APB) algorithm for a simulation system for proton-activated positron-emitting imaging in clinical proton therapy using spread-out Bragg peak (SOBP) beams.Methods: The target nuclei of activity distribution calculations are {sup 12}C nuclei, {sup 16}O nuclei, and {sup 40}Ca nuclei, which are the main elements in a human body. Depth activity distributions with SOBP beam irradiations were obtained from the material information of ridge filter (RF) and depth activity distributions of compounds of the three target nuclei measured by BOLPs-RGp (beam ON-LINE PET system mounted on a rotating gantry port) with mono-energetic Bragg peak (MONO) beam irradiations. The calculated data of depth activity distributions with SOBP beam irradiations were sorted in terms of kind of nucleus, energy of proton beam, SOBP width, and thickness of fine degrader (FD), which were verified. The calculated depth activity distributions with SOBP beam irradiations were compared with the measured ones. APB kernels were made from the calculated depth activity distributions with SOBP beam irradiations to construct a simulation system using the APB algorithm for SOBP beams.Results: The depth activity distributions were prepared using the material information of RF and the measured depth activity distributions with MONO beam irradiations for clinical therapy using SOBP beams. With the SOBP width widening, the distal fall-offs of depth activity distributions and the difference from the depth dose distributions were large. The shapes of the calculated depth activity distributions nearly agreed with those of the measured ones upon comparison between the two. The APB kernels of SOBP beams were prepared by making use of the data on depth activity distributions with SOBP beam irradiations that were made from the depth activity distributions with MONO beam irradiations and sorted in terms of energy, SOBP width, and thickness of FD. The data on APB kernels of SOBP beams were determined as installment data for the simulation system using the APB algorithm for SOBP beam irradiations.Conclusions: A method of obtaining the depth activity distributions and the APB algorithm for clinical use of SOBP beams have been developed. It is suggested that the simulation system for imaging the clinical irradiated volume with the APB algorithm can be used in clinical proton therapy using SOBP beams by preparing and investigating the data on APB kernels of SOBP beams.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Walker Ranch 3D seismic images

Amplitude images (both vertical and depth slices) extracted from 3D seismic reflection survey over area of Walker Ranch area (adjacent to Raft River). Crossline spacing of 660 feet and inline of 165 feet using a Vibroseis source. Processing included depth migration. Micro-earthquake hypocenters on images. Stratigraphic information and nearby well tracks added to images. Images are embedded in a Microsoft Word document with additional information. Exact location and depth restricted for proprietary reasons. Data collection and processing funded by Agua Caliente. Original data remains property of Agua Caliente.

15 GEOTHERMAL ENERGY↗

Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability: Supporting Data and Code

This repository contains R code and associated datasets for reproducing the analysis described in the manuscript titled “Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability” (DOI: 10.1029/2024JG008604). The provided scripts facilitate a comprehensive analysis of snow depth variability influenced by microtopography and vegetation distribution in a subarctic watershed. Included datasets are high-resolution spatial maps of snow depth, terrain elevation, vegetation height, and distance from shrubs taller than 1 meter, all formatted as text files (.txt). These data are fully describe in doi:10.15485/2316038. Users can adapt the provided R scripts to accommodate different data formats or larger spatial domains, noting that some output files may require modification due to their size.The code includes implementations for boosted regression tree analysis adapted from methods outlined in Elith et al. (2008). Users interested in understanding or modeling landscape-scale snow distribution patterns, particularly in Arctic or subarctic ecosystems, will find this package useful. 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↗

Predicting variations of the least principal stress with depth: Application to unconventional oil and gas reservoirs using a log-based viscoelastic stress relaxation model

Knowledge of layer-to-layer variations of the least principal stress, S hmin , with depth is essential for optimization of multi-stage hydraulic fracturing in unconventional reservoirs. Utilizing a geomechanical model based on viscoelastic stress relaxation in relatively clay rich rocks, we present a new method for predicting continuous S hmin variations with depth. The method utilizes geophysical log data and S hmin measurements from routine diagnostic fracture injection tests (DFITs) at several depths for calibration. We consider a case study in the Wolfcamp formation in the Midland Basin, where both geophysical logs and values of S hmin from DFITs are available. We compute a continuous stress profile as a function of the well logs that fits all of the DFITs well. We utilized several machine learning technologies, such as bootstrap aggregation (or bagging), to improve the generalization of the model and demonstrate that the excellent fit between predicted and observed stress values is not the result of over-fitting the calibration points. The model is then validated by accurately predicting hold-out stress measurements from four wells within the study area and, without recalibration, accurately predicting stress as a function of depth in an offset pad about 6 miles away.

58 GEOSCIENCES↗

Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System

Abstract Enhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.

15 GEOTHERMAL ENERGY↗

Fixed Bit Rotary Drilling Failure Criteria Effect on Drilling Vibration

Polycrystalline diamond compact (PDC) bit use has been expanded to geothermal drilling applications due to their improved performance in drilling compared to roller cone bits. Multiple vibration models have been developed yet and there are limited use of field data to study the ability of numerical vibration model to predict recorded vibration in the field. The objective of this paper is to evaluate the capabilities of two different rock-bit interaction models with two different ROP formulations for predicting the actual behavior seen in the field. The first model (Model-I) considers the rock-bit interaction, incorporating the effect of subsurface formation characteristics. The second model (Model-II) is a PDC bit model that uses calculated threshold weight-on-cutter (WOCt) to study the interaction of the bit with the rock surface. The models, which couple the drill stem axial and torsional motions, are solved numerically using the fourth-order Runge-Kutta method and compared with recorded downhole drilling data from the Chocolate Mountain Aerial Gunnery Range (CMAGR) geothermal well located in California. An 8–½" PDC bit with 8 blades was modeled and vibration simulation was performed to investigate stick-slip vibrations at 1400 ft. depth. Simulation results using field data indicate that Model-II encounters a low range of stick-slip severity (SSV) as compared to Model-I, which is in the moderate range. Comparison with the field value for SSV shows Model-II is the best representative of the field case for this depth. The sensitivity analysis shows that results are sensitive to model constants making prediction of SSV from models uncertain.

15 GEOTHERMAL ENERGY↗

SPRUCE Deep Peat Microbial Diversity, CO2 and CH4 Production in Response to Nutrient, Temperature, and pH Treatments during Incubation Studies

This dataset contains the results of a microcosm incubation study on deep peat collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA in September 2014. Microcosms were monitored for carbon dioxide (CO2) and methane (CH4) production, and microbial community dynamics were assessed using quantitative polymerase chain reaction (qPCR) and amplicon sequencing. The experiment was designed with a full factorial design with elevated temperature, nitrogen (N), (P), and pH treatments was used with samples from each transect serving replicates. In all, 96 microcosms were constructed to account for the 16 treatment combinations (Nitrogen x Phosphorous x pH x temperature), 2 time points, and 3 replicates. Temperature treatments were 6 degrees C, to mimic the SPRUCE ambient plot temperatures, and 15 degrees C to mimic the SPRUCE +9 degrees C treatment. These data show that one year of deep peat warming, microbial community structure and abundance of bacterial, archaeal, fungal, and methanogenic populations showed strong vertical stratification across the peat depth profile yet no clear response to the temperature treatments (Kluber et al, 2020). In an effort to identify factors that may be limiting decomposition and microbial community change in deep peat, we conducted an anerobic microcosm incubation of deep peat (150-200 cm depth). This dataset contains one data file in comma-separate values (*.csv) format. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗

Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs

Context. The South Pole Telescope third-generation camera (SPT-3G) has observed over 10,000 square degrees of sky at 95, 150, and 220 GHz (3.3, 2.0, 1.4 mm, respectively) and will significantly overlap the ongoing 14,000 square-degree Euclid Wide Survey. The Euclid collaboration recently released Euclid Deep Field South (EDF-S) observations of 23 square degrees at wide field depths in the first quick data release (Q1). Aims. With the goal of releasing complementary millimeter-wave data and encouraging legacy science, we performed dedicated observations of a 57-square-degree field overlapping the EDF-S. Methods. The observing time totaled 20 days, and we reached noise depths of 4.3, 3.8, and 13.2 $μ$K-arcmin at 95, 150, and 220 GHz, respectively. Results. In this work we present the temperature maps and two catalogs constructed from these data. The emissive source catalog contains 601 objects (334 inside EDF-S) with 54% synchrotron-dominated sources and 46% thermal dust emission-dominated sources. The 5$σ$ detection thresholds are 1.7, 2.0, and 6.5 mJy in the three bands. The cluster catalog contains 217 cluster candidates (121 inside EDF-S) with median mass $M_{500c}=2.12 \times 10^{14} M_{\odot}/h_{70}$ and median redshift $z$ = 0.70, corresponding to an order-of-magnitude improvement in cluster density over previous tSZ-selected catalogs in this region (3.81 clusters per square degree). Conclusions. The overlap between SPT and Euclid data will enable a range of multiwavelength studies of the aforementioned source populations. This work serves as the first step toward joint projects between SPT and Euclid and provides a rich dataset containing information on galaxies, clusters, and their environments.

Archipley, M. [Chicago U., Astron. Astrophys. Ctr.↗

MODEL-INDEPENDENT LIMITS ON THE LINE-OF-SIGHT DEPTH OF CLUSTERS OF GALAXIES USING X-RAY AND SUNYAEV-ZEL'DOVICH DATA

We derive a model-independent expression for the minimum line-of-sight extent of the hot plasma in a cluster of galaxies. The only inputs are the 1-5 keV X-ray surface brightness and the Comptonization from Sunyaev-Zel'dovich (SZ) data. No a priori assumptions regarding equilibrium or geometry are required. The method applies when the X-ray emitting material has temperatures anywhere between 0.3 keV and 20 keV and metallicities between 0 and twice solar-conditions fulfilled by nearly all intracluster plasma. Using this method, joint APEX-SZ and Chandra X-ray Observatory data on the Bullet Cluster yield a lower limit of 400 {+-} 56 kpc on the half-pressure depth of the main component, limiting it to being at least spherical, if not cigar-shaped primarily along the line of sight.

79 ASTRONOMY AND ASTROPHYSICS↗

Occurrences of REE and CM in volcanic ash beds associated with coals in SW Wyoming & NW Colorado

Rare earth elements (REEs) and critical minerals (CMs) are vital components in the nation’s economy, national security, and the further strengthening of our renewable energy capabilities and infrastructure. Recent work has revealed more insight into previously unknown modes of occurrence for REEs, particularly in coal seams and adjacent noncoal sediments, but many data gaps persist. Temporal relationships between coal beds and adjacent tonstiens, ash beds, or other partings are widely recognized but not fully understood. Variations in REE concentration and their direct or indirect association with coal beds are not predictable. This study presents findings from examining lateral and potentially stratigraphic variation of REE and CM occurrences in coals of late Cretaceous and Paleocene age. In this study we collected and examined samples of differing ages both core and coal mine exposures for qualitative x-ray fluorescence (XRF), and multi-element geochemistry. Preliminary XRF data indicates variable levels of light REEs in a core from the Yampa coalfield of Moffat County in northwest Colorado. An absence of similar enrichments in slightly older coals in the Adaville Formation of the Fold and Thrust belt in southwest Wyoming is described from XRF and ICP-MS evaluation. Previous work has shown an association with the Yampa Bed and REE enrichment in coals of the Upper Cretaceous Williams Fork Formation. While not common and widespread, this association suggests that leaching or reworking of the ash bed could lead to elevated REEs in the adjacent coal. We investigate differences between the two data occurringas geologic compositions and post-depositional histories. In-depth petrography and precise geochemistry should reveal data that at the very least will help improve some aspects of the geologic models of coal basins and the effect of an influx of non-coal sediments.

REE, CM, ash, volcanic ash, coal↗

RECONSTRUCTING THE {gamma}-RAY PHOTON OPTICAL DEPTH OF THE UNIVERSE TO z {approx} 4 FROM MULTIWAVELENGTH GALAXY SURVEY DATA

We reconstruct the {gamma}-ray opacity of the universe out to z {approx}< 3-4 using an extensive library of 342 observed galaxy luminosity function (LF) surveys extending to high redshifts. We cover the whole range from UV to mid-IR (0.15-25 {mu}m) providing for the first time a robust empirical calculation of the {gamma}{gamma} optical depth out to several TeV. Here, we use the same database as Helgason et al. where the extragalactic background light was reconstructed from LFs out to 4.5 {mu}m and was shown to recover observed galaxy counts to high accuracy. We extend our earlier library of LFs to 25 {mu}m such that it covers the energy range of pair production with {gamma}-rays (1) in the entire Fermi/LAT energy range, and (2) at higher TeV energies probed by ground-based Cherenkov telescopes. In the absence of significant contributions to the cosmic diffuse background from unknown populations, such as the putative Population III era sources, the universe appears to be largely transparent to {gamma}-rays at all Fermi/LAT energies out to z {approx} 2 whereas it becomes opaque to TeV photons already at z {approx}< 0.2 and reaching {tau} {approx} 10 at z = 1. Comparing with the currently available Fermi/LAT gamma-ray burst and blazar data shows that there is room for significant emissions originating in the first stars era.

79 ASTRONOMY AND ASTROPHYSICS↗

Planck 2018 results: III. High frequency instrument data processing and frequency maps

This paper presents the High Frequency Instrument (HFI) data processing procedures for the Planck 2018 release. Major improvements in mapmaking have been achieved since the previous Planck 2015 release, many of which were used and described already in an intermediate paper dedicated to the Planck polarized data at low multipoles. These improvements enabled the first significant measurement of the reionization optical depth parameter using Planck-HFI data. This paper presents an extensive analysis of systematic effects, including the use of end-to-end simulations to facilitate their removal and characterize the residuals. The polarized data, which presented a number of known problems in the 2015 Planck release, are very significantly improved, especially the leakage from intensity to polarization. Calibration, based on the cosmic microwave background (CMB) dipole, is now extremely accurate and in the frequency range 100-353 GHz reduces intensity-to-polarization leakage caused by calibration mismatch. The Solar dipole direction has been determined in the three lowest HFI frequency channels to within one arc minute, and its amplitude has an absolute uncertainty smaller than 0.35 μK, an accuracy of order 10 -4 . This is a major legacy from the Planck HFI for future CMB experiments. The removal of bandpass leakage has been improved for the main high-frequency foregrounds by extracting the bandpass-mismatch coefficients for each detector as part of the mapmaking process; these values in turn improve the intensity maps. This is a major change in the philosophy of "frequency maps", which are now computed from single detector data, all adjusted to the same average bandpass response for the main foregrounds. End-to-end simulations have been shown to reproduce very well the relative gain calibration of detectors, as well as drifts within a frequency induced by the residuals of the main systematic effect (analogue-to-digital convertor non-linearity residuals). Using these simulations, we have been able to measure and correct the small frequency calibration bias induced by this systematic effect at the 10 -4 level. There is no detectable sign of a residual calibration bias between the first and second acoustic peaks in the CMB channels, at the 10 -3 level.

79 ASTRONOMY AND ASTROPHYSICS↗

Oregon Rivers Characterization Tool

DHS’s Regional Resiliency Assessment Program (RRAP) undertook the Oregon State Transportation Systems RRAP project between 2018-2021 to assess the seismic vulnerabilities of the state’s transportation system to a Cascadia Subduction Zone (CSZ) earthquake (CISA 2021). The project primarily focused on the effects of a CSZ earthquake on the mobility of emergency response efforts into the region. In the event that bridges located at crucial river crossings fail during a CSZ earthquake, rivers will become natural barriers affecting the ability to support response and recovery efforts. The characteristics of rivers (e.g., width, depth, and flow) are critical data to inform planning for the recovery of transportation systems across river barriers and determine reopening times following a CSZ earthquake. As part of the transportation system assessment, an Argonne National Laboratory (Argonne) project team developed a River Characterization Tool (RCT) to quantify the physical characteristics of the river system in Oregon, which can be used to inform a system-level analysis of transportation recovery and reopening times. This report mainly discusses approaches, methods, development, and applications of the RCT. The use of the river data generated from this tool for a system-level analysis of transportation recovery is discussed in the Oregon Transportation Systems RRAP Resiliency Assessment report (CISA 2021). The primary goal of the RCT is to generate statewide dataset of widths, depths, and flows for all rivers across Oregon to assess the effects of river barriers on emergency response and recovery efforts following a CSZ earthquake. The RCT leverages remote sensing data that multispectral sensors collect from satellites, employs a deep learning model to characterize river predictions, and computes river channel geometric dimensions with hydraulic relationships and river predictions. The entire process of river characterization is automated and implemented through the RCT.

58 GEOSCIENCES↗

Challenges and Lessons Learned from an Analysis of Three Zero Energy Buildings

Zero energy buildings, or zero energy ready buildings, which are designed and operated by public or private commercial property owners can play an important role in reducing carbon emissions. This paper discusses lessons learned and key takeaways from an in-depth analysis of three zero energy buildings that took an integrated design and construction approach to significantly reduce energy use. Two of these projects are new construction and one is a retrofit to zero energy. Findings are based on project literature review, data analysis, and in-depth interviews with the building design teams and staff who were involved during the design, construction, and operation of the buildings. The paper addresses value proposition and cost data in such a way that other building owners can replicate the strategies and technology solutions in response to regulatory mandates or organizational goals. It also provides details on operational improvements taken at each zero energy building to enhance energy performance and increase the potential for load flexibility, and discusses challenges and lessons gleaned from design teams and building staff. The findings serve as a reference for building owners, designers, engineers, contractors, or others interested in, or involved with, the design, construction, or use of new or existing buildings. The paper also includes recommended pathways for widespread adoption of zero energy strategies that can be applied in various locations.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

GIS-Based Modeling of Contaminated Soil Volumes at Multiple Sites in the Formerly Utilized Sites Remedial Action Program - 20149

The remediation of hazardous, toxic, and radioactive waste (HTRW) sites produces cost-related risks associated with the estimation of contaminated soil or debris volumes. Historical risk-management techniques include cost contingencies to cover volume uncertainties that affect project budgeting and decision-making. The Buffalo District teamed with project partners to lessen volume uncertainty and reduce project risks at multiple HTRW sites managed under the Formerly Utilized Sites Remedial Action Program (FUSRAP). Historical remedial investigations under FUSRAP commonly identified the presence of radiological material in site media, the associated human health risk, and then areas of remediation. To manage remedial execution and reduce risk, pre-design or remediation-phase sampling essentially 'chased' contamination, which was not conducive to efficient predictive budgeting derived from Feasibility Study (FS) cost analyses. The Buffalo District first optimized their approach to better understand volume uncertainty by utilizing the Argonne National Laboratory's Bayesian Approaches for Adaptive Spatial Sampling (BAASS) software [1]. BAASS processed soft data (e.g., gamma walk-over data) and spatial sampling data to estimate the lateral extent of contaminated soil irrespective of depth (i.e., gross contamination extent) and define areas of contaminant uncertainty. The software performed a binary transformation of contaminant concentrations at all sampling points based upon remedial action goals or a sum of ratios approach (i.e., clean, impacted, or range of impacts in soil). The model produced two-dimensional (horizontal) contaminant probability contours and statistical uncertainty in the sampling coverage and resulting contaminant extents. This method was translated vertically by partitioning the sampling data into depth brackets that produced a stacked representation of contaminant extents and uncertainty in the subsurface (i.e., similar to construction lifts). The results commonly led to a better understanding of project uncertainty and the need for sampling strategies that produce high-confidence soil volumes, which control costs. The BAASS-based delineations were eventually replaced by Empirical Bayesian Kriging (EBK) methods available in ArcGIS Spatial or 3D Analysts [2]. The EBK method calculates contaminant probability zones derived from user-controlled semivariograms of the spatial datasets. The resulting probability zones (e.g., 50% or 80% of contaminant probability) represent the two-dimensional surface delineation of the overall horizontal remedial area, similarly to BAASS. However, unlike BAASS, the vertical sampling data within these probability zones became vertical control points to contour a subterranean surface that connects subsurface points to the land-surface delineations of contamination. The resulting representation of horizontal and vertical impacts within an enclosed envelop (volume) of soil included uncertainty distributions that are used to plan uncertainty-reduction sampling. These data-driven and math-based models of three-dimensional sampling results produced well-bounded remedial volumes for project planning and better uncertainty predictions during project budgeting. The EBK method was applied to several FUSRAP sites managed by the Buffalo District and compared to less rigorously modeled sites previously remediated by the District. The comparison of modeled to actual remediated volumes provide a basis for validating the volume-estimation method. This comparison is important to ensure modeled volumes match physical boundaries of site remediation. FUSRAP sites with denser investigative sampling and lesser volume uncertainty proved useful in remedial planning and contracting. The Buffalo District noted that historical sites with sparser sampling arrays had greater disparity between estimated volumes and final remedial volumes. The benefit achieved over the cost of detailed soil sampling appears positive for FUSRAP projects, especially where impacts vary widely and appear unbounded by investigation-phase sampling. The subsequent Empirical Bayesian Kriging of contamination coupled with vertical contouring for soil estimations reduces uncertainty in soil volumes or indicates where sampling is required to reduce uncertainty, which together optimize remedial planning and budgeting. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Thaw depth, soil moisture, and vegetation height, Teller and Kougarok sites, Seward Peninsula, Alaska, 2022

Thaw depth, soil moisture, and vegetation height sampled from locations on the Teller MM27, Kougarok MM80, and Kougarok MM82 NGEE-Arctic sites, Seward Peninsula, Alaska. These data were collected in support of the ongoing NGEE-Arctic and NASA ABoVE data synthesis work. Samples were collected in July 2022, including 6 transects covering the entire Teller MM27 watershed and 4 transects covering 4 thaw ponds at Kougarok MM80 and MM82. This data package includes sample information and thaw depth, soil moisture, vegetation height data (.csv). Metadata files include data descriptions (_dd.csv) for tabular data and file level metadata (.csv). 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↗