\Axions Beyond Gen 2", Proceedings of a Workshop held January 25-27, 2021, Seattle, Washington, USA
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Physics - Physics of elementary particles and fiel↗
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Final Scientific/Technical Report: Characterizing the Response of the Cascadia Margin Gas Hydrate Reservoir to Bottom Water Warming Along the Upper Continental Slope
The objective of this project is to understand the response of the WA margin gas hydrate system to contemporary warming of bottom water along the upper continental slope. Through pre-cruise analysis and modeling of archive and recent geophysical and oceanographic data, we (1) inventoried bottom simulating reflectors along the WA margin and defined the upper limit of gas hydrate stability, (2) refined margin-wide estimates of heat flow and geothermal gradients, (3) characterized decadal scale temporal variations of bottom water temperatures at the upper continental slope of the Washington margin, and (4) used numerical simulations to provide quantitative estimates of how the shallow boundary of methane hydrate stability responds to modern environmental change. These pre-cruise results provided the context for a systematic geophysical and geochemical survey of methane seepage along the upper continental slope from 48° to 46°N during a 10-day field program on the R/V Thompson from October 10-19, 2014. This systematic inventory of methane emissions along this climate-sensitive margin corridor and comprehensive sediment and water column sampling program provided data and samples for Phase 3 of this project that focused on determining fluid and methane sources (deep-source vs. shallow; microbial, thermogenic, gas hydrate dissociation) within the sediment, and how they relate to contemporary intermediate water warming. During the 2014 research expedition, we sampled nine seep sites between ~470 and 520 m water depth, within the zone of predicted methane hydrate retreat over the past 40 years. We imaged 22 bubble plumes with heights commonly rising to ~300 meters below sea level with one reaching near the sea surface. We collected 22 gravity cores and 20 CTD/hydrocasts from the 9 seeps and at background locations (no acoustic evidence of seepage) within the depth interval of predicted downslope retreat of the methane hydrate stability zone. Approximately 300 pore water samples were extracted from the gravity cores, and the pore water was analyzed for a comprehensive suite of solutes, gases, and stable isotope ratios. This comprehensive geochemical dataset was used to characterize the fluid and gas source(s) at each of the seep sites surveyed. The primary results of this project are: 1) Bottom simulating reflector-derived heat flow values decrease from 95 mW/m2 10 km east of the deformation front to ~60 mW/m2 60 km landward of the deformation front, with anomalously low values of ~25 mW/m2 on a prominent mid-margin terrace off central Washington. 2) The temperature of the incoming sediment/ocean crust interface at the deformation front ranges between 164-179 oC off central Washington, and the 350 oC isotherm at the top of the subducting ocean crust occurs 95 km landward of the deformation front. Differences between BSR-derived heat flow and modeled conductive heat flow suggest mean upward fluid flow rates of 0.4 cm/yr across the margin, with local regions (e.g. fault zones) exhibiting fluid flow rates up to 3.5 cm/yr. 3) A compilation of 2122 high-resolution CTD, glider, and Argo float temperature profiles spanning the upper continental slope of the Washington margin from the years 1968 to 2013 show a long-term warming trend that ranges from 0.006-0.008 oC/yr. Based on this long-term bottom water warming, we developed a 2-D thermal model to simulate the change in sediment temperature distribution over this period, along with the downslope retreat of the methane hydrate stability field. Over the 43 years of the simulation, the thermal disturbance propagated 30 m into the sediment column, causing the base of the methane hydrate stability field to shoal ~13 m and to move ~1 km downslope. 4) A preliminary analysis of seafloor observations and mid-water column acoustic data to detect bubble plumes was used to characterize the depth distribution of seeps along the Cascadia margin. These results indicate high bubble plume densities along the continental shelf at water depths <180 m and at the upper limit of methane hydrate stability along the Washington margin. 5) The majority of the seeps cored during the 2014 research expedition on the R/V Thompson contained abundant authigenic carbonate indicating that they are locations of long-lived seepage rather than emergent seep systems related to methane hydrate dissociation. Despite the evidence for enhanced methane seepage at the upper limit of methane hydrate stability along the Washington margin, we found no unequivocal evidence for active methane hydrate dissociation as a source of fluid and gas at the seeps surveyed. The pore fluid and bottom water chemistry shows that the seeps are fed by a variety of fluid and methane sources, but that methane hydrate dissociation, if occurring, is not widespread and is only a minor source (below the detection limit of our methods). Collectively, these results provide a significant advance in our understanding of the thermal structure of the Cascadia subduction zone and the fluid and methane sources feeding seeps along the upper continental slope of the Washington-sector of the Cascadia margin. Though we did not find unequivocal evidence for methane hydrate dissociation as a source of water and methane at the upper pressure-temperature limit of methane hydrate stability at present, continued warming of North Pacific Intermediate Water in the future has the potential to impact the methane hydrate reservoir in sediments at greater depths along the slope. Thus, this study provides a strong foundation and the necessary characterization of the background state of seepage at the upper limit of methane hydrate stability for future investigations of this important process.
54 ENVIRONMENTAL SCIENCES↗
Energy Northwest - Horn Rapids Solar and Storage: An Assessment of Battery Technical Performance
Chartered in 1957 as a joint action agency of the state, Energy Northwest (ENW) is a consortium of 27 public utility districts and municipalities across Washington state. ENW takes advantage of economies of scale and shared services to help utilities run their operations more efficiently and at lower cost, to the benefit of more than 1.5 million customers. ENW develops, owns, and operates a diverse mix of electricity generating resources, including hydro, solar, and wind projects – and the Northwest’s only active nuclear energy facility. These projects provide enough reliable, affordable, and environmentally responsible energy to power more than a million homes each year, and that carbon-free electricity is provided at the cost of generation. The agency continually explores new generation projects to meet its members’ needs. In 2017, as part of the second round of funding from the Washington state Clean Energy Fund, the Washington State Department of Commerce granted up to $3 million in matching funds to develop an estimated $6.5 million project that deployed a 4-MW, 20-acre solar generating array of photovoltaic (PV) panels coupled with a 1 MW/5.5 MWh lithium-iron-phosphate battery energy storage system (BESS) in Richland, Washington. The combination of PV and BESS will provide a predictable, renewable generating source and will also serve as a training ground for solar and battery technicians throughout the nation. The City of Richland will purchase the power from the project and utilize the benefits of the energy storage. The project provides Washington state with its first opportunity to integrate a large-scale solar and storage facility into its clean mix of hydro, nuclear, and wind resources. This first-of-its-kind facility combines solar generation with battery storage and technician training. In 2019, Pacific Northwest National Laboratory (PNNL) worked with ENW to assess the integrated PV and BESS in representative use cases that could benefit the City of Richland. Between March and May 2022, extensive testing was conducted, and the results were used to assess the technical performance of the BESS subjected to actual field operations. Both reference performance and use case tests were performed: (A) Reference performance tests assess the general technical capabilities of the BESS, such as energy capacity, round-trip efficiency (RTE), ramp rate, and signal tracking capability. These are the first tests performed (baseline), and they are repeated after use case tests (post cycle). A standardized U.S. Department of Energy (DOE) energy storage performance protocol was used to characterize the BESS, including representative duty cycle profiles, test procedure guidance, and calculation guidance for determining key characteristics. (B) Use case tests examine the performance of the BESS for specific use cases using duty cycles developed by PNNL in collaboration with ENW. Five use cases were selected for testing: 1) demand charge reduction, 2) load shaping, 3) transmission charge reduction, 4) Volt-VAR service, and 5) outage mitigation. The use case duty cycles were developed based on utility and site-specific characteristics in addition to the technical characteristics and physical capabilities of the BESS. Use case tests were performed between the baseline and post cycle tests. This report describes the BESS and its components, presents testing and performance analysis results, and shares key insights and lessons learned from this project. Outcomes of the tests and analyses will help ENW understand the performance of the Horn Rapids BESS in its current state and design appropriate operational strategies for this and other BESSs over the long term.
14 SOLAR ENERGY↗
Short-Depth QAOA circuits and Quantum Annealing on Higher-Order Ising Models (Rev.2)
The Quantum Alternating Operator Ansatz (QAOA) and Quantum Annealing (QA) are quantum algorithms that are both based on the adiabatic theorem and both have the goal of sampling the optimal solution(s) of combinatorial optimization problems. Quantum annealing has been physically instantiated on D-Wave devices using superconducting flux qubits, and QAOA can be programmed on digital gate-model quantum computers such as the programmable superconducting transmon qubits devices of the IBMQ series, for instance ibm washington. QAOA and QA address the same types of problems, but it is unclear how they will scale to large problem sizes and to larger and higher-fidelity quantum computers. In this article, we present a direct comparison between QAOA, one and two rounds, run on all 127 qubits of ibm washington and QA run on D-Wave Advantage system4.1 and Advantage system6.1. The problems which allow for this comparison are random Ising model problems whose connectivity matches the heavy hexagonal lattice topology of ibm washington and the Pegasus graph connectivity of the two D-Wave devices. We create two classes of problem instances for this comparison: one with higher order terms (ZZZ variable interactions), linear terms, and quadratic terms, and a separate problem type with only linear and quadratic terms. Our QAOA circuits are novel and extremely short depth, with a CNOT depth of 6 per round, which allows whole chip usage of ibm washington’s heavy hexagonal lattice and can be applied to future heavy-hex chips. We also test the effectiveness of the error suppression technique digital dynamical decoupling on the QAOA circuits. The QAOA circuits compiled to ibm washington are composed of several thousand circuit instructions, approximately 3, 000 depending on the details of the circuit, making these some the largest quantum circuits ever executed on a digital quantum processor. QAOA and QA are compared against the classical heuristic algorithm of simulated annealing and all problem instances are exactly solved using CPLEX in order to evaluate which samplers, if any, correctly found the ground state solution(s) of the problem instances. We find that (i) QA outperforms QAOA on all problem instances, (ii) QAOA samples the problems better than random sampling, and (iii) QAOA angle computation exhibits clear parameter concentration across the ensemble of Ising models.
127 Qubits↗
Integrated Disposal Facility Performance Assessment: Modeling Consistency to Support Regulatory Decision Making at Hanford - 17398
Under DOE Order 435.1, 'Radioactive Waste Management', Washington River Protection Solutions, LLC and INTERA, Inc are currently preparing a performance assessment for the Hanford Integrated Disposal Facility, a near-surface low-activity waste disposal facility located on the Hanford Site in southeast Washington State. The disposal facility is expected to receive vitrified low-activity waste produced at the Hanford Waste Treatment and Immobilization Plant, as well as other secondary solid wastes generated during the treatment process. The performance assessment performs the analyses that will be relied upon to receive the Disposal Authorization Statement, establish waste acceptance criteria, modify the RCRA permit for the facility and make a waste-incidental-to-reprocessing determination for the vitrified tank waste. In January 2006 in the case State of Washington v. Bodman (Civil No. 2:03-cv-05018-AAM), DOE, the Washington State Department of Ecology (Ecology) and the State of Washington Attorney General's office reached a settlement agreement in Ecology's claim regarding off-site waste shipments to Hanford and the adequacy of modeling in the environmental impact statement. In response to the agreement, the DOE Office of Environmental Management set a policy to establish a consistent use of commercially available and public domain software for a vadose zone and groundwater model platform to support regulatory decisions at Hanford. In October 2012, the Associate Principal Deputy Assistant Secretary for Environmental Management released a memorandum to the manager of the Richland Operation Office directing how future environmental modeling activities to support regulatory decision making on the Hanford Site would be undertaken to be consistent with the 2006 settlement agreement. The preparation of the performance assessment must meet DOE Order 435.1 requirements and the additional direction provided by DOE. This paper will identify the approach taken to comply with the requirements in the 2012 memorandum. The paper will describe how environmental modeling performed for the 2017 Integrated Disposal Facility Performance Assessment was planned in a phased approach and how the current modeling is tied to the analyses that supported the decisions made in the environmental impact statement published in 2012. Additionally, the selection and qualification of the software selected to perform modeling activities for the performance assessment will be described. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Cougar response to a gradient of human development
Abstract Human populations continue to increase and transform Earth's ecosystems. For large carnivores, human development reduces habitat abundance, alters predator–prey dynamics, and increases the risk of mortality, which may threaten the viability of many populations. To investigate how the cougar ( Puma concolor ) responds to a gradient of human development in four areas in Washington, USA, we used utilization distributions, county tax parcel data, Weibull modeling analysis, and multiple comparison techniques. Cougars used wildland areas the majority of the time (79% ± 2%, n = 112 cougars), with use decreasing as housing densities increased. When present in human‐developed areas in eastern Washington, 99% of the habitat that cougars used had housing densities ≤76.5 residences/km 2 , which was <846.0 residences/km 2 observed in western Washington ( P < 0.01). Cougars used areas in western Washington with greater housing density likely because of the clustered nature of housing developments, the connectivity with greenbelts and forested corridors, and security cover of dense maritime vegetation. Our findings suggest a consistent, albeit nuanced response by cougars to human development that may be used by wildlife managers, landscape planners, and environmental educators to guide and enhance their efforts to minimize the impacts of human development on cougars and reduce the potential for conflicts with people. Our model may also provide guidance for thresholds of human development for other adaptable large carnivores.
Washington↗
Data and scripts associated with “Non-random processes impacting organic matter chemistry are maximized in mid-order streams”
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Non-random processes impacting organic matter chemistry are maximized in mid-order streams” submitted to Limnology and Oceanography (L&O) by Danczak et al. (in review). This package contains data and scripts used to investigate dissolved organic matter (DOM) molecular chemistry and diversification processes across 47 surface-water sampling sites in the Yakima River Basin, Washington, USA, during an August 2021 sampling campaign. The package contains analyses of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), geochemical measurements, geospatial attributes, molecular diversity, and meta-metabolome ecological null models needed to reproduce the main manuscript results. The underlying field data were pulled from exising data packages at https://doi.org/10.15485/1892052 (Fulton et al., 2022) and https://doi.org/10.15485/1898914 (Grieger et al., 2022). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. We thank the following organizations for providing access to field locations for sample collection: the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, the Confederated Tribes and Bands of the Yakama Nation, and the Cowiche Canyon Conservatory. Research was conducted under Washington State Parks and Recreation Commission Scientific Research Permit #210901. We are grateful to the Yakama Nation Tribal Council and Yakama Nation Fisheries for their collaboration in facilitating sample collection and ensuring data usage aligns with their values and worldview. This data package contains an R-Markdown file for analyses and five folders: (1) Data, (2) Geospatial Data, (3) Supplemental_Files, (5) Figures_pdf, (4) and src. The Data folder contains tabular inputs and derived files used in the manuscript analysis. The Geospatial Data folder contains climate and water-balance, hydrologic, land-cover, population/regional water-use, stream, topographic, and stream-order attribute CSV files. The src folder contains scripts used to process data, run analyses, and generate figures. The Figures_pdf folder contains manuscript figure outputs. The Supplemental_Files folder contains supplemental analysis products. All files are .csv, .pdf, .html, .png, .R, .Rmd, .svg, or .tre. This data package is associated with the rcfsa-RC2-SPS_Null_Modeling repository found at https://github.com/river-corridors-sfa/rcfsa-RC2-SPS_Null_Modeling.
54 ENVIRONMENTAL SCIENCES↗
A New Gold Mine? Achieving HVAC Energy Efficiency Through a System Metric
Washington State's Commercial Energy Code adopted a new energy metric called HVAC Total System Performance Ratio (TSPR) in 2019, a first in the codes world to regulate HVAC system efficiency. TSPR is a ratio of annual heating and cooling loads to the annual carbon emissions associated with the energy consumed by the HVAC system. TSPR provides a performance-based solution to evaluate and improve the overall HVAC design. The TSPR metric and its companion calculation tool were developed by Pacific Northwest National Laboratory (PNNL) with support from U.S. Department of Energy (DOE), Northwest Energy Efficiency Alliance (NEEA) and the City of Seattle. The new metric represents a significant shift in how HVAC design will meet code requirements. Utility programs can also leverage TSPR as a measure to determine energy savings and incentive amounts for HVAC retrofits. This paper describes the efforts by NEEA and its collaborators to prepare the market for TSPR adoption in code. This paper provides the pilot projects led by University of Washington Integrated Design Lab (IDL) to understand potential issues that could be faced by early adopters. This paper also covers how training and outreach provide engagement opportunities that can streamline code compliance, help address issues faced by early adopters and promote participation in utility programs. As Washington State works on the goals of 70% energy reduction and zero fossil-fuel greenhouse gas emission homes and buildings by the year 2031 , system level performance metrics will likely become increasingly more necessary and prevalent. This paper concludes that the HVAC TSPR requirement helps familiarize the HVAC industry with this approach and helps Washington achieve its long-term goals.
Liu, Bing↗
The practice and economics of hybrid poplar biomass production for biofuels and bioproducts in the Pacific Northwest
Hybrid poplar demonstration-scale farms were managed in Oregon, Washington, Idaho, and California to establish management practices, yields, harvesting methods, and the economics of biomass production. Yield during the 2-year establishment cycle averaged 3.5 dry Mg ha –1 year –1 increasing to 11.6 Mg ha –1 year –1 in the ensuing 3-year coppice cycle. Populus deltoides (Bartram ex Marsh.) × P. maximowiczii (Henry) varieties preformed best in Oregon during the coppice cycle with the best variety producing 18.1 Mg ha –1 year –1 , while P. ×generosa (Henry) varieties maximized yields in Washington at 22.1 Mg ha –1 year –1 . P. ×canadensis (Moench) varieties excelled in Idaho and California with upper yields of 13.6 Mg ha –1 year –1 and 12.9 Mg ha –1 year –1 , respectively. Stands were cut with a single-pass harvester 2 years after planting and a second time after 3 years of coppice growth; material capacity, limited by poor ground conditions, varied between 21.7 to 31.3 green Mg h –1 . Chemical composition averaged 1.87% inorganics, 7.74% extractives, 26.90% lignin, 38.07% glucan, 13.66% xylan, 1.61% galactan, 1.14% arabinan, and 2.76% mannan. Production costs (USD) projected over a 20-year rotation of six coppice cycles were $71.81 Mg –1 in Washington, $89.91 Mg –1 in Oregon, $98.76 Mg–1 in Idaho, and 179.07 Mg–1 in California. Here, land rental, establishment, crop care, harvest, transportation, and land restoration, respectively, accounted for 23%, 5%, 19%, 30%, 17%, and 6% of total feedstock cost. Farms were successfully restored to conditions existing before poplar conversion. In the absence of fertilization, increases in soil pH and decreases in nitrate-nitrogen, zinc, iron, and organic matter were consistently noted but could not be associated with poplar production alone.
09 BIOMASS FUELS↗
Characteristics and Variability of the Nearshore Wave Resource on the U.S. West Coast
Characterizing the nearshore wave resource at a regional scale poses a great challenge because high-quality wave data are required. This paper presents a detailed analysis of nearshore wave climate and resource characterization on the U.S. West Coast based on a 32-year, regional wave hindcast. Resource characterization closely followed the International Electrotechnical Commission standards. The wave hindcast data were generated from an unstructured-grid SWAN model with approximately 300 m grid resolution in the nearshore region. Statistics of six IEC wave resource parameters at the 50 m water depth were calculated for four sub-regions along the coast, including Washington, Oregon, and Northern and Southern California. Results indicate that the Washington and Oregon coast has a similar nearshore wave resource. The wave resource in Southern California is significantly less than that of the Washington and Oregon coast. Alongshore and cross-shore variabilities of wave power were assessed. Temporal variability, at seasonal, inter-annual, and decadal scales were analyzed. Overall, strong seasonal variation is observed; high wave energy occurs in the winter months and calm seas in the summer. Finally, extreme environmental conditions, i.e., the 100-year extreme environmental contours, for different locations along the West Coast were investigated.
wave resource characterization, nearshore, west co↗
1994 Household Travel Survey
The Metropolitan Washington Council of Governments/National Capital Region Transportation Planning Board (COG/TPB) periodically conducts regional household travel surveys to monitor changes in daily travel and to gather information on the demographic, socioeconomic, and trip-making characteristics of Washington, D.C.-area residents. Information collected in the 1994 Household Travel Survey was an important component in the development of regional travel demand forecasting models used to predict changes in daily travel in response to current development trends and changes in regional transportation policies and programs. In two waves during the spring and fall of 1994, consultants for COG/TPB conducted a survey of daily travel by persons living in area households. The survey file contains travel information associated with 4,863 households residing in 13 jurisdictions comprising the greater Washington, D.C., region (approximately a 1-in-300 sample). The survey file contains 39,800 internal trip records with respect to the expanded cordon.
1Hz data↗
WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)
This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.
54 ENVIRONMENTAL SCIENCES↗
Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.
54 ENVIRONMENTAL SCIENCES↗
Final Report for Phase II Study: Prototyping the Sketch Planning Visualization Tool for Non-Motorized Travel
To further examine how factors such as those identified from the Phase I NMT study, and the modeling framework developed under that effort could be applied to local/regional level planning activities, FHWA decided to pursue a Phase II study. It was determined that a small geographic area with more detailed local data would be necessary. Although Washington D.C. was not one of the 2009 NHTS add-ons, it did conduct a household travel survey of 11,000 households in 2007-2008. The National Capital Region Transportation Planning Board at the Metropolitan Washington Council of Governments (MWCOG) conducted the household travel survey. The data coverage under the MWCOG survey is much higher than that of the NHTS. As a part of the Phase II study, a prototype of a Geographic Information System (GIS)-based sketch planning visualization tool was also to be developed. The intent was to use a neighborhood in the Washington D.C. region as a case study for this prototype application.
97 MATHEMATICS AND COMPUTING↗
A Mulit-State Model for Catalyzing the Home Energy Efficiency Market
The RePower Kitsap partnership sought to jump-start the market for energy efficiency upgrades in Kitsap County, an underserved market on Puget Sound in Washington State. The Washington State Department of Commerce partnered with Washington State University (WSU) Energy Program to supplement and extend existing utility incentives offered by Puget Sound Energy (PSE) and Cascade Natural Gas and to offer energy efficiency finance options through the Kitsap Credit Union and Puget Sound Cooperative Credit Union (PSCCU). RePower Kitsap established a coordinated approach with a second Better Buildings Neighborhood Program project serving the two largest cities in the county – Bainbridge Island and Bremerton. These two projects shared both the “RePower” brand and implementation team (Conservation Services Group (CSG) and Earth Advantage).
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Transactive Campus Energy Systems: Final Report
Transactive energy refers to the combination of economic and control techniques to improve grid reliability and efficiency. The fundamental purpose of transactive energy management is to seamlessly coordinate the operation of large numbers of new intelligent assets—such as distributed solar, energy storage and responsive building loads—to provide the flexibility needed to operate the power grid reliably and at minimum cost, particularly one filled with intermittent renewable generation such as the Pacific Northwest. It addresses the key challenge of providing smooth, stable, and predictable “control” of these assets, despite the fact that most are neither owned nor directly controlled by the power grid. The Clean Energy and Transactive Campus (CETC) work described in this report was done as part of a Cooperative Research and Development Agreement (CRADA) between the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) and the Washington State Department of Commerce (Commerce) through the Clean Energy Fund (CEF). The project team consisted of PNNL, the University of Washington (UW) and Washington State University (WSU), to connect the PNNL, UW, and WSU campuses to form a multi-campus testbed for transaction-based energy management—transactive—solutions. Building on the foundational transactive system established by the Pacific Northwest Smart Grid Demonstration (PNWSGD), the purpose of the project was to construct the testbed as both a regional flexibility resource and as a platform for research and development (R&D) on buildings/grid integration and information-based energy efficiency. This report provides a summary of the various tasks performed under the CRADA.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Determining Unabated Airborne Radionuclide Emissions Monitoring Requirements Using Inventory-Based Methods
Compliance with the airborne radionuclide emission monitoring requirements in the National Emission Standards for Hazardous Air Pollutants (NESHAP; Title 40 of the U.S. Code of Federal Regulations Part 61, Subpart H) and State requirements in Washington Administrative Code 246–247: Radiation Protection – Air Emissions and 173-480: Ambient Air Quality Standards and Emission Limits for Radionuclides were evaluated for Pacific Northwest National Laboratory (PNNL) operations. Additional guidance may be found in the U.S. Department of Energy Handbook, Environmental Radiological Effluent Monitoring and Environmental Surveillance. To meet regulatory requirements, reviews of planned or proposed projects and activities provide the basis for implementing necessary monitoring adjustments or for implementing changes to projects and activities in a timely manner. Potential unabated offsite doses were evaluated for emission locations managed by PNNL and licensed to the Department of Energy. These locations were at facilities in Richland, Washington (i.e., the Hanford Site and PNNL Richland Campus) and in Sequim, Washington, (PNNL Marine Sciences Laboratory). This report describes the inventory-based methods and provides the results for the NESHAP assessment performed in 2017.
61 RADIATION PROTECTION AND DOSIMETRY↗
Development and Analysis of Control Strategies for a 1 MW/3.2 MWh Energy Storage System at Avista Utilities
This report documents the work performed by Pacific Northwest National Laboratory (PNNL) and Washington State University (WSU) supporting an effort to develop control strategies for a 1 MW/3.2 MWh energy storage system (ESS) at Avista Utilities (Avista). The ESS project is funded though the Washington Clean Energy Fund (CEF) program administered by the Washington State Department of Commerce (DOC). A tri-party Cooperative Research and Development Agreement (CRADA), namely the CRADA 360, was created in 2015 to facilitate this work.
25 ENERGY STORAGE↗