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Black, Paul

Publications and source records attributed to Black, Paul.

Panel Session 80: Interagency Community of Practice in Risk and Performance Assessment

This panel focused on the status of the Inter-agency Performance and Risk Assessment Community of Practice (P and RA COP). Representatives from the P and RA COP and subject matter experts discussed lessons learned and provided feedback on building the P and RA COP to support risk-informed environmental decision making. Panelists with presentations: Understanding Mechanisms to Manage Uncertainty and Risk in Waste Containment Systems (Craig Benson, University of Virginia - CRESP (USA) Scaling for Performance Assessments (Paul Black, Katie Catlett, Doug Anderson, Paul Duffy, Tom Stockton, Sam Van Sickle, John Carson); Interagency Community of Practice in Rick and Performance Assessment (Horst Monken Fernandes); Institutional Controls as a Risk Management Tool (David Esh); Superfund: Explanation of Screening and PRG's for Risk and Dose Assessment (Stuart Walker)

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Modeling Streamflow and Sediment Transport Under Current and Future Climates at the West Valley Site - 20231

Performance Assessment models often require inputs representing specific hydrologic processes such as streamflow and sediment flux. This paper details the development of a process-level hydrologic model and its use in a Probabilistic Performance Assessment (PPA) system-level model for the West Valley Site in western New York. For this PPA model, a watershed model was developed using SWAT (Soil and Water Assessment Tool) and used to evaluate water and sediment fluxes in nearby streams draining the Site. A particular challenge of probabilistic modeling over long periods of time is scaling parameters appropriately so that outcomes are not grossly over- or underestimated. This challenge is especially important when the spatial and temporal scales of process and system level models differ. In this instance, the SWAT watershed model and GoldSim PPA model have similar spatial scales of stream reaches and sub-catchments within a single watershed (approximately hundreds to tens of thousands of square meters, but the temporal scales differ considerably. The SWAT model calculates streamflow and sediment transport rates at the daily scale, while the PPA model requires long-term average values that are applied over hundreds to thousands of year-long periods. As such, distributions of daily climatic parameters such as average precipitation rate, temperature, wind speed, and relative humidity were developed to force a transient SWAT model that captures the dynamic behavior of the watershed in response to daily changes in the weather. Output from the model, which includes stream flow (m{sup 3}/y) and sediment transport rate (Mg/y) is then averaged over a 100-year period as an approximation for the long-term average values which can be applied in GoldSim. A single SWAT model was run 5000 times with varying climatic inputs and deterministic soil, land use, and elevation data, and the range of outputs were compiled into a distribution which can be implemented stochastically into the PPA Model. Additionally, this paper includes a high-level discussion of how changes in future climate affect weather input distributions, and a summary of how those changes in weather affect the outcomes from the SWAT model and the inputs into the PPA model. In this analysis, future climate distributions were developed based on climate research documenting the expected changes to precipitation and temperature in western New York. The 5,000 SWAT simulations were then repeated with these future climate distributions, and the resulting output was implemented into the PPA Model as hydrologic conditions under a future climate. In the current version of the PPA Model, hydrologic parameters change linearly from the 'current climate' conditions to the 'future climate' conditions over a period of 100 years, after which they remain constant. (authors)

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Distribution Development for the RDX Regional Model at Los Alamos National Laboratory - 20385

Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)

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Distribution Development for Residual Inventory at the New York West Valley Site - 20400

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351 ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Operations led to contamination in a number of facilities and locations. Some of that contamination has migrated from waste disposal zones to other layers, formations, and features on and off the WNYNSC. Phase I decommissioning activities are ongoing and involve the removal of a number of areas and structures that have been associated with contamination. The purpose of this work is to outline the approach for characterizing contamination not associated with disposed wastes, contaminated structures, or specific releases. In this work, the term, residual radiological activity, is used to describe environmental contamination that exists subsequent to the completion of Phase I decommissioning activities, that is not associated with disposed wastes, contaminated structures, or specific releases. Contamination from the Site was quantified relative to data that characterize the concentrations of radionuclides that exist in background. Background concentrations are those present in the area but having no influence from Site related activities. The existence of residual radiological activity that is elevated relative to background has the potential to contribute to future risks to human health and the environment. As a consequence, the residual inventory information is used to inform the West Valley Probabilistic Performance Assessment (PPA) model to characterize potential future risks to human health and the environment. The centralized West Valley Data Management System (DMS) was the source of information for the data assembled in this analysis. The DMS is a fairly large compilation consisting of thousands of records from investigation studies, with sample dates ranging from 1990 to present. Samples from monitoring wells, boreholes, geoprobe studies, surface water, surface soils, storm water outfalls, ventilation stack filters, plant and animal tissues, and more are included in the DMS. Results are typically reported in units of activity per unit volume. For the purpose of the analyses presented here, all results were converted into consistent units of pCi per unit volume. Since 1990, data have been collected from various locations across the WNYNSC at different times with varying frequency over the course of several decades. As a consequence, a number of potential issues can arise with respect to the assembly of a dataset that is deemed adequate for the characterization of residual radiological activity. These issues were assessed and resolved to the extent possible through careful consideration of the properties of the distributions. The intent was to use data which characterize the current state of the Site. Radionuclides can be designated to one of several groups depending on their origin. In this work the groups considered were 1) Naturally Occurring Radioactive Material (NORM), 2) fallout, and 3) Other (including power plant, medical research, etc). This grouping is a useful construct with respect to the interpretation of fixed laboratory results. For example, NORM radionuclides that exist within a decay chain should have approximately equivalent distributions of concentrations if they are representative of background conditions. Insights such as these can be used as a check to identify sample results that need to be further investigated or omitted due to issues associated with reported results from fixed laboratory analyses. This type of analysis provided a foundation for the assessment of the adequacy of sample results for use in subsequent components of an assessment. The general process for the assessment of residual radiological contamination at the Site consists of a sequence of several steps. First, for each analyte, several statistical tests were performed to assess the weight of evidence against the null hypothesis that the mean of the distribution of concentrations was equal to zero. If the mean of the distribution of concentrations for a given radionuclide was not found to be greater than zero, then it was removed from consideration as a component of the residual radiological contamination. If there was significant evidence to reject the hypothesis of the mean being equal to zero, the second step was to compare the distribution of the data from the Site to that of the corresponding background. A suite of tests was used to compare the distributions of the site and background data. The results of these tests were collectively used to determine if site data are elevated relative to background. The third step was to develop distributions using a Bayesian framework to characterize the distribution of mean of the increment present above background for each of the radionuclides. The Bayesian model implemented allowed for the comparison of site-specific records to background concentrations to better approximate contamination attributed to the Site. A final screening step was employed for radionuclides that exceed background. This screening step compared 95% upper confidence limits (UCLs) from the increment distribution developed in the previous step to the risk screening levels. This approach yields a list of analytes that were determined to be elevated relative to background.

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Incorporating the Impacts of Climate Change on Hydrology in a Performance Assessment Model - 20403

The evidence of climate change is increasingly well-documented and impacts should be incorporated in performance assessment studies. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. Probabilistic modeling is a core requirement for quantifying uncertainty and evaluating its impacts. Not evaluating future climate states in a performance assessment because of the existence of uncertainty is contradictory to good modeling practices - the most uncertain issues and parameters require the most attention in effective probabilistic modeling. Excluding climate change limits development of modeling information that could aid in effective decision making. In this work we develop methods to use the output from hydrologic models and analysis of historical aerial imagery to quantify and implement the impacts of climate change on hydrologic processes at a nuclear waste site in West Valley, New York. Specifically, we used the HELP (Hydraulic Performance of Landfill Performance) model to characterize key hydrologic processes under both current and future climate conditions to assess the impacts of changing climate on hydrology. A suite of previous climatic models were reviewed and synthesized to produce a cohesive representation of the current state of knowledge of the impact of climate change on important model inputs such as precipitation. Output from the HELP simulations was coupled to the GoldSim model that was used to develop the Probabilistic Performance Assessment (PPA) approach through the application of a novel 'nearest neighbor' technique. First, several thousand realizations were generated from the HELP model using a Latin Hypercube experimental design to ensure adequate coverage of the parameter space of explanatory variables used to drive HELP. For example, porosity is a physical parameter that is used as an input to both HELP and the GoldSim PA model. We then ran sensitivity analysis (SA) algorithms on the output of HELP for each of the responses of interest. For each predictor, each time we build an SA model we get a different value for the sensitivity index (SI). From the collection of all the SA models, the average was computed among all of the SIs to represent the predictor within the context of the nearest neighbor approach. That is, we conduct SA on each HELP outcome for each scenario. This gives us parameter sensitivity indices for the outcomes. We average the parameter sensitivity indices across the outcomes to get the average SI for a scenario. For each realization that is generated from the Goldsim PA model, Goldsim generates random values for physical/empirical parameters that HELP uses as well. For each vector of physical/empirical parameters that Goldsim generates, the vector from the 5,000 HELP runs that is most 'similar' to the Goldsim vector is computed using the nearest neighbor approach. In this context 'similar' means minimization of the SA-weighted sum of the absolute differences among the 5,000 values computed for this statistic, where each value corresponds to a different HELP realization. In order to account for the impacts of climate change, this process was repeated using the spatially downscaled future climate projections. For each of the key parameters of interest, it was assumed that a linear change depicted the relationship between the values for the present day and those for 2100. In this way, the climatically-driven changes in key parameters used to inform the GoldSim model are quantified and incorporated into the PA model output for the future. (authors)

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Approach to Quality Assurance for Complex Environmental Modeling - 20407

A strong Quality Assurance (QA) program for complex environmental modeling is essential for regulatory and public acceptance and trust, but it does not have to be onerous. Neptune and Company, Inc. (Neptune) has developed a strong QA program that improves transparency, traceability, reproducibility, and therefore, defensibility and trust. Neptune's QA program has evolved over the past 27 years, transitioning from an ad hoc QA program, to a program that is currently Nuclear Quality Assurance-1 (NQA-1) compliant, and will soon be NQA-1/DOE approved for DOE EM modeling work. In addition, Neptune is a qualified laboratory assessor and qualified auditor for reference materials, a proficiency testing provider, and fully complies with the Environmental Protection Agency (EPA) QA program. Neptune's President and CEO, Kelly Black, was recently named the Chairperson of the International Organization for Standardization (ISO) Technical Committee 69, Application of Statistical Methods. A strong QA program has been developed for Neptune's radiological performance assessment (PA) program. Although pieces of their QA program are currently in development, their current program includes document control using Subversion, issue tracking and work flow tracking using JIRA, and transparency and traceability via a system of 'calc sheets' for documentation of all data analysis and modeling combined with 'check print' documentation of QA checking, and rigorous model testing and configuration control. All work is reviewed by an independent subject matter expert who is not associated with the collection and assembly of information, for an internal peer review. The program is enforced with a handful of Standard Operation Procedures (SOPs), Work Instructions, QA Project Plans (QAPPs), and Quality Management Plans (QMPs) that are updated frequently, with required annual training and acknowledgment. In addition, effective communication of modeling approaches and results to clients and stakeholders are integral to their QA program. Many of their models are built using the GoldSim modeling platform, for which their models are well-known for their level of transparency, documentation, and QA traceability. This level of QA is also applied to their process-level models. Neptune has taken some lessons learned from the extremely rigorous QA program required for the Yucca Mountain Project (YMP), and imposed the YMP requirements of complete traceability, transparency, and reproducibility, but avoided the inflexibility of a QA program that likely contributed to the suspect e-mails in 2005 that resulted in loss of trust in the YMP, and in nuclear waste disposal (and nuclear power) in general. There are far too many examples of loss of public trust due to poor QA that could have been easily avoided with a simple and straight-forward QA program combined with stakeholder engagement. The purpose of this paper is to share features of Neptune's Radiological Performance Assessment program QA program, and some of their QA related lessons learned over the past 27 years of QA for complex environmental modeling. (authors)

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Bayesian Approach to Estimation of Water Table Elevations Using Historical Rasters as Prior Information 2019 - 20430

In cases of complex but only partially known geology and a lack of spatial control in observation well locations, water table elevation estimation is very challenging. In some cases, auxiliary information, such as observations of the movement of tracers, operation of injection and extraction wells, and calibration of groundwater models against the historical elevation data, can be combined with expert judgement to estimate flow directions in areas of sparse data and to aid in the production of more reliable contour maps (and associated rasters) than could be produced by relying on sparse well elevation data alone. Given a historical sequence of these raster maps, the question arises how to automate, to the extent possible, the process of producing new raster maps to reflect data from previous times, the current data and the operation of expert judgement. One solution is to adopt a Bayesian point of view and to regard the historical well elevation data, auxiliary information and historical raster maps as prior information. The well elevations for water table wells, as well as those for injection/extraction wells and the data associated with other relevant variables, can be viewed as predictors for the raster surface. From this prior information, we can, conditional on the values of these predictors for a new time period, compute an expected value map and a standard deviation map for the new raster. These then can be taken to specify a prior predictive distribution for the pixels in the new raster map. Then we condition the pixels, corresponding to water level observation wells within the raster, on the observed values in those wells (which in general will differ from the regression estimate) for the new time period. Given the smoothness of the water table surface, we then smooth the surface of deviations from the mean surface, based on the variograms of the historical rasters, and add this smoothed surface to the regression mean surface. The error structure of the produced raster map is defined by the regression error structure and the error due to smoothing based on the estimated variograms. This methodology has been developed and is being further refined for groundwater monitoring and remediation at LANL. It is a very flexible method that can also be applied with a variety of other predictors applied to model the water level wells in the area of interest over the historical record. The smoothness of the spatial process and its possible evolution over time can then be estimated from the residuals from this regression. This can be augmented by expert hydrogeological opinion based on site topography and hydrogeology. (authors)

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Stakeholder-Engaged Structured Decision Making for the Los Alamos Legacy Cleanup Mission - 20501

The Los Alamos National Laboratory (LANL) environmental legacy cleanup program requires decisions to be made for environmental remediation, decommissioning and disposal or management of radioactive waste. This legacy cleanup program was established to address groundwater contamination, material disposal areas (MDAs) that have been used to dispose of radioactive and other waste material, and 'aggregate areas' that might produce radioactive or other chemical waste as a result of remediation activities. The LANL site is regulated for environmental concerns under the Resource Conservation and Recovery Act (RCRA). However, some parts of LANL, such as material disposal area G (MDA G), have disposed of radioactive waste under DOE Order 435.1, and are subject to other regulations. For example, decommissioning the remote-handled TRU material stored in 33 shafts at MDA G falls under EPA's 40 CFR 191. Collectively, the regulations are all aimed in the same direction of finding the best solution, either through constructs such as 'as low as reasonably achievable' (ALARA), considering balancing factors as opposed to only cost and human health risk, and, under EPA regulations such as RCRA and NEPA, evaluating impact from all chemicals and both human health and ecological endpoints. Despite the basic goals and objectives of the regulations or their guidance, the main challenge is in their implementation. Arguably perhaps, but really in principle, all of these (and similar) regulations are asking for a decision analysis to be performed. Implementation challenges encountered have included lack of understanding of decision analysis in the industry, lack of effective stakeholder engagement in the decision analysis process, and lack of appreciation of the need to separate value judgments from science, the latter leading to developing conservative, or protective, science-based models. Conservative models lead to poor solutions, lack of effective stakeholder engagement leads to long drawn out protracted approaches to finding a solution (which also might never be found with this approach), and lack of understanding of decision analysis and Bayesian statistics causes poor models to be built, which creates unfortunate situations of 'garbage in, garbage out' becoming the basis for decision making. Stakeholder-engaged structured decision making (SDM) is an approach to solving problems that relies on the theory of decision science to involve stakeholders in the decision-making process. This approach incorporates stakeholder values using a scientifically rigorous methodology that separates value judgments from science in a way that helps avoid the pitfalls of biased, protective, or conservative modeling. This approach has its foundation in Keeney's 1992 treatise on value-focused thinking [1]. Keeney advocated a paradigm shift in decision making based on the idea that the standard way of thinking about decisions is backwards. The standard approach of focusing first on identifying alternatives rather than on articulating values results in a reactive approach with the emphasis on mechanics and fixed choices instead of the core values that have meaning to stakeholders. This paradigm shift effectively engages all stakeholders in the decision-making process while using a values focused thinking approach that can lead to the identification of decision opportunities and the creation of better alternatives. The intent is to be proactive and generate solutions that are related directly to values and objectives as identified by stakeholders. There are, perhaps, two overarching reasons why SDM can be used to benefit LANL's environmental legacy cleanup. Some of LANL's remaining waste management and environmental management problems are challenging and complex (for example, the Cr and RDX plumes, and MDAs) and while the traditional approach has, arguably, worked well for relatively simple risk-based problems, it cannot, or should not, be applied to more complex problems if the most effective and efficient solutions are desired. The second reason is cost. This has perhaps become more critical since publication of the Government Accountability Office (GAO) reports that DoE's environmental liability is considered a high-risk concern for the nation [2]. The focus of SDM is on structuring solutions to decision risk problems by first addressing stakeholder and decision maker values and subsequently developing decision objectives and ways to measure those objectives, preference weighting across objectives, identifying decision alternatives that best achieve those values, and characterizing uncertainty in predictions of the measures. Because a complete decision model is created using SDM, it can be evaluated to find the main elements of the model that drive, or predict, the best solution. This approach creates complete decision models that are transparent, traceable, reproducible and technically defensible. The science behind SDM, or decision analysis, is well founded, yet it is not unusual to see ad hoc approaches to decision making implemented under various environmental regulations that are pertinent to the LANL site, including NEPA, RCRA and DOE Order 435.1. Such ad hoc approaches are often not transparent or traceable, and lack reproducibility and technical defensibility. The LANL legacy cleanup program has embarked on using SDM to address the complex problems that remain. Stakeholder meetings have been held, and a prototype version of the stakeholder value system has been developed. Further meetings are expected in the future to address specific project needs. This is a long-term endeavor considering the complex environmental problems faced by DOE EM in Los Alamos (EM-LA), and careful planning, consideration of stakeholder value systems, and engagement with stakeholders throughout the SDM process is expected to lead to a successful endpoint. (authors)

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Panel Session 132: Risk-Informed Approach for Decision Making in WM, D and D and SNF Management: Reasonable Assurance for Safety

Mr. Larry Camper organized a panel of experts to discuss approaches to better make risk-informed decisions in waste management, decommissioning, and the management of Spent Nuclear Fuel (SNF). The audience heard the perspectives from four panelists that addressed issues ranging from the technical basis used to make risk-informed decisions to for developing cleanup criteria and promulgating regulations and safety standards both domestically and abroad. A summary of each of the presentations given by the panelists is provided herein. This WMS BOD featured panel focused on the Risk-Informed Approach for Decision Making in WM, D and D and SNF Management and the Reasonable Assurance for Safety. The panelists addressed and discussed with the audience different approaches used for decision-making, summarizing ongoing probabilistic vs. deterministic approaches, including IAEA graded approach, and discussed policies/approaches to achieve reasonable assurance for safety rather than using absolute assurance. Panelists with presentations: Risk-Informed Decision Making - More than a Motto? (Paul Black); NRC Staff Perspective on Risk-Informed Approach and Reasonable Safety Assurance in D and D and LLW (Rateb (Boby) Abu Eid); Risk-Informed Decision-Making and Illustrative National Academies Studies (Charles Ferguson); IAEA's Revised Safety Guidance on Remediation (Michelle Roberts); NDA Radioactive Waste Strategy - A Risk Informed Approach (James McKinney)

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A Preliminary Radiological Risk Assessment Model for Disposition of Remote-Handled Transuranic Wastes at Los Alamos National Laboratory Area G - 20116

The U.S. Department of Energy (DOE) operates a low-level radioactive waste (LLW) disposal site at Material Disposal Area G, in Los Alamos, New Mexico, USA. Area G has been the primary LLW disposal site for Los Alamos National Laboratory (LANL) since the 1960's. In addition to LLW, Area G is host to a variety of other wastes, the disposition of which must be determined before closure of the site. A probabilistic Radiological Risk Assessment (RRA) for Area G is used in order to support decision making regarding some wastes that are not addressed in the extant Area G Performance Assessment (PA) and Composite Analysis (CA). Between 1979 and 1987, 33 special shafts were augered into the Bandelier Tuff at Area G. This volcanic tuff is present across Pajarito Plateau on the eastern slopes of the Jemez Mountains, and varies widely in its consistency, from weakly indurated non-welded layers to welded layers that uphold the mesa cliffs of the Plateau. These mesas are home to LANL, Area G, and the townsites of Los Alamos and White Rock, with residences about 1400 m from Area G. The 33 Shafts were lined with steel casing, and contain remote-handled (RH) transuranic wastes (TRU) resulting from experiments and analysis performed in special glove boxes at the Chemistry and Metallurgy Research (CMR) facility at LANL. Some of these wastes originated as used nuclear fuel. The purpose of the Area G RRA is to evaluate the potential future risk to humans and the environment from the RH TRU in the 33 Shafts in the context of the risk associated with the surrounding wastes at Area G. The analysis is responsive to expectations outlined in DOE Order 458.1, Radiation Protection of the Public and the Environment, and is informed by the Manual and Guidance accompanying DOE O 435.1, Radioactive Waste Management. Because the waste meets the definition of TRU, the regulatory context necessarily takes into consideration the regulation governing the disposal of TRU from the U.S. Environmental Protection Agency (EPA): 40 CFR 191, Environmental Radiation Protection Standards for Management and Disposal of Spent Nuclear Fuel, High-Level and Transuranic Radioactive Wastes. Given the broader regulatory context for the RRA, the analysis is subject to different assumptions from those made in the existing DOE O 435.1 PA and CA, such as allowing for future occupation of the site. The analysis begins with a comprehensive evaluation of features, events, processes, and exposure scenarios (FEPS) for Area G and the wastes it contains. These FEPSs are screened to eliminate from further consideration those of extremely low probability and/or consequence, and a conceptual site model (CSM) is subsequently developed. The scope and structure of the Area G RRA Model is informed by this CSM, and the Area G RRA Model is developed using the GoldSim systems analysis modeling platform. This paper presents the initial version of a defensible, transparent, and reasonably realistic model, which is based on the state of knowledge of the wastes, the site, and the FEPSs that govern contaminant transport from wastes into the environment and subsequent exposures to humans and other biota. Probabilistic model input distributions represent uncertainties inherent in the real and modeled systems. The results of the Area G RRA Model inform decisions regarding the disposition of the RH TRU in the 33 Shafts. (authors)

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Probabilistic Groundwater Modeling of the RDX Plume at Los Alamos National Laboratory to Support Risk Assessment - 20359

A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)

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Quantifying the impact of climate change on erosion - 20397

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha (3,338-ac) site located approximately 48 km (30 mi) south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (metric tons, or 705 short tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Erosion can play an important role in the fate and transport of waste at sites where disposal of long-lived waste is anticipated. The statistical characterization of key processes related to erosion is essential to the understanding of site stability through time. One of the key processes governing erosion is extreme precipitation and it is critical that trends in the distribution of extreme precipitation events through time be represented. The evidence of climate change is increasingly well documented and projected impacts on extreme precipitation events should be incorporated in performance assessment studies when relevant. Not evaluating future climate states in a performance assessment is contradictory to good modeling practice. Specifically, excluding climate change limits development of modeling information that could aid in effective decision making. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. In this work, this information was used to assess the impacts of potential changes in climate on erosion processes. The goal was to understand how projections of future climate relate to the performance of the WNYNSC through time. In the first stage of this work multi-temporal historical aerial images were analyzed in conjunction with orthophotography and Lidar data to develop probability distributions for variables representing important erosion processes. The information from these analyses was then used in conjunction with simulated data from the West Valley Erosion Working Group (EWG). Analysis of EWG simulations provides estimates for the change in erosion rates through time that is driven by changes in climate. The time-varying rates of erosion change were applied to the historical aerial imagery data in order to inform time-varying rates of erosion that are driven by changes in climate. Ultimately, this process resulted in the identification of locations for features like gully heads using both the Lidar dataset and projection of the estimated location from the historical aerial photo under consideration back to the Lidar dataset. The distance between the estimate of the location from the historical image and that of the Lidar dataset was the estimated distance the feature has moved. This distance was then divided by the number of years between the Lidar dataset and the year of the historical aerial image of interest to get a rate of movement through time. This was done for several dozen points on each historical aerial image. The analyses from the EWG were used to characterize the relative impact of climate change on the erosion rates. This relative impact was quantified by comparing the LEM based estimates of erosion that were derived using historical climate data with LEM based estimates of erosion that were derived using projections of future climate. The relative increase in the erosion rates was then applied to the historical estimates of erosion derived from the analysis of the historical aerial imagery. This approach was used since the LEM-based estimated of the historical erosion rates from the past century have a bias towards underestimating erosion. This underestimation is hypothesized to be a consequence of an inability of the LEMs to account for the impacts of land-use change (i.e. deforestation) which had been shown to significantly increase erosion in similar Northern hardwood forest ecosystems. In summary, gully head retreat rate and gully widening rate were characterized using statistical probability distributions from the historical aerial image analysis. Simulated data from the EWG was used to estimate the climatically-driven changes in erosion rates through time. The changes through time from the EWG were applied to the gully head retreat rate and gully widening rate characterized using the historical aerial image analysis. This information can be used to inform PPA models to project future risks from a given site. (authors)

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Updates to a Preliminary Probabilistic Performance Assessment Model for Radiological and Chemical Contamination at the West Valley Site, New York - 20420

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States at the WNYNSC. NFS built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (640 metric tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations halted in 1972 and never restarted, leaving behind radioactive and chemical wastes. The U.S. Department of Energy (DOE) was required to complete certain waste management activities under the West Valley Demonstration Project (WVDP) Act of 1980 including decommissioning of WVDP facilities. As collaborating agencies, NYSERDA and the DOE are tasked with making decisions about decommissioning and risk reduction for the West Valley Site. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) model to assist the agencies in their decision making process for decommissioning the WVDP and WNYNSC. One important tool that is needed in order to inform the decision-making process is a science-based model of the West Valley Site that evaluates potential future consequences for human health and the environment. This forms the core of the spatial domain of the West Valley PPA Model. The PPA Model, developed using the GoldSim system modeling software, is a tool intended to provide support for decision making that evaluates uncertainty, in a manner that is transparent, defensible, and robust. The PPA Model includes contaminant transport and health effects components, and is organized around geographically-grouped contaminated facilities. These include the waste disposal areas licensed by the U.S. Nuclear Regulatory Commission and the State of New York, a waste tank farm for storage of high level radioactive waste resulting from reprocessing operations, and several areas contaminated with radioactive and chemical constituents. The PPA Model evaluates contaminant transport from these sources to points of exposure across the site and into receiving surface waters and sediments. Hypothetical people and wildlife could be exposed to contamination at these locations, and the effects of these exposures are evaluated. Contaminant transport processes to be evaluated in the PPA Model include groundwater and surface water transport, contaminant translocation by plants and animals, diffusion, and erosion. The evaluation of exposures to people in this preliminary model is limited to a resident farmer scenario, and ecological assessment is performed at the level of a screening analysis. The results of these preliminary evaluations inform future model developments. PPA Model results are subjected to sensitivity analysis in order to determine those pathways and parameters that are most significant in influencing the results. This information allows analysts and decision makers to focus on those aspects of Site behavior and processes. With this information, the decision makers can drive informed, defensible decisions regarding decommissioning of the Site. This paper includes an update of the information presented at WM2019 [1]. (authors)

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Choosing the Best Modeling Platform for Radiological Risk Assessment Models - 20468

Radiological risk assessments, in the form of performance or safety assessments, are often required under regulations or guidance for remediation of contaminated land, decommissioning of contaminated buildings or structures, and radioactive waste disposal. These risk assessments are usually supported by fate and transport models that address decay and ingrowth of radionuclides, as well as their movement through engineered systems and the natural environment. These models are often projected thousands, or more, years into the future, largely because the radioactive species change through decay and ingrowth, and hence the magnitude of the radioactive effect changes with time. There are many computer codes that are available to address this type of modeling. They range from addressing specific pathways or processes such as infiltration of water, groundwater, surface water, air, biota, diffusion and advection of water and gases, to those that try to couple all processes together to evaluate the impact of fate and transport through the entire system to places in space and time to which access is assumed. These different types of codes are sometimes separated with the monikers process-level and systems-level codes, although it is often not clear that this separation does justice to the capabilities of the many codes that are available to evaluate fate and transport of radionuclides. The focus of this paper is the latter group of modeling codes. Several systems level modeling codes exist and are used. There are differences between these codes in terms of utility, flexibility, complexity and cost. The purpose of this paper is to compare a few of these codes in the context of work currently being performed by the International Atomic Energy Agency (IAEA) Modeling and Data for Radiological Impact Assessments (MODARIA) II Working Group 1 (WG1). The MODARIA II WG1's main focus is how stakeholder engaged decision analysis can, or should, be applied to radiological contamination problems so that better, longstanding, sustainable, solutions are reached. However, the WG1 also recognizes the potential impact of the modeling tools that are chosen to address radiological risk, which is often a primary objective of decision making for radiological problems. Other objectives might also be important, such as constraining costs, obtaining financing, minimizing impact on ecosystems, saving cultural resources, saving jobs, farmland, environmental justice, etc., in a full decision analysis for a given radiological contamination problem, but none of these other objectives have the same types of complex modeling needs as minimize radiological dose. Consequently, a further focus of WG1 is to evaluate the potential impacts on decision making of the choice of fate and transport, and risk assessment, modeling codes that are used to support decision making. The WG1 will produce a report at the end of 2020 that will focus on an approach to effective decision making and stakeholder engagement. The report will also consider the role that performance assessment modeling should play in the decision-making process, including the impact of the choice of modeling tools or computer codes on risk-informed decision making. Several sites around the World have been made available by Member States for these model comparisons, and several modeling tools have been considered. However, the focus of this paper is on two of the sites, one in Belgium and one in Ukraine, and on three of the tools: NORMALYSA (NORM And Legacy Site Assessment); GoldSim{sup C}, and AMBER{sup C}. The final report from this working group will also cover other modeling tools, including RESRAD, and PC-Cream{sup R}. (authors)

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Low Count and Background Radionuclides Analysis - 20488

The US Department of Energy (DOE) is often faced with the need to evaluate radionuclides at low concentrations. When site sample data are likely to be close to threshold activity concentrations of interest, then the means by which the radiochemical analysis is performed and reported is critical. This situation can occur when differentiating from zero (presence/absence) for radionuclides that do not occur naturally, close comparison with environmental background for naturally occurring radionuclides, close comparison with a risk- or dose-based threshold concentrations of interest, or even comparisons across studies. There are several analytical issues that are of concern, but the two that appear to cause incorrect decisions to be made most often involve establishing detection limits and subtracting ambient background conditions in the laboratory. These issues are not critical when radionuclide activity concentrations are large relative to thresholds of concern, but they seem to be poorly understood when it matters. When the comparisons are important and are likely to be close to a threshold of interest, then the general contract with the analytical laboratories needs to be changed so that the right or appropriate data are obtained. The concern is that important decisions are made incorrectly more often as greater scrutiny is placed on DoE's radionuclide cleanup or monitoring decisions by the public and other stakeholders. Examples are presented of problems that have been observed for different projects, both within and outside the realm of DOE and NRC remediation and radioactive waste disposal problems, and solutions are offered that should lead to better data from which important decisions need to be made. The first example is from Los Alamos National Laboratory (LANL) and involves radionuclide concentrations in soil and rock beneath LANL's Material Disposal Area (MDA) G. An initial review of the data led to a conclusion that americium and plutonium are a long way present beneath MDA G. A more thorough review of the data that accounted properly for ambient background and the detection limits that had been established led to the opposite conclusion. Another example is from the Nevada National Security Site where tritium results from one of the wells were unexpectedly high. Proper understanding and analysis of ambient background led to the conclusion that the increased concentrations were not so obvious, and that a different contract with the analytical laboratory was needed to provide more appropriate data to support a better determination. Other examples are used from regulatory review of projects in Nevada, where background levels and secular equilibrium for naturally occurring radionuclides are not established correctly because of analytical issues. The same basic issues have also been found to create difficulties analyzing historical data from the West Valley Demonstration Project. There is evidence in the data that the apparent lack of secular equilibrium where it is expected to exist is related to ambient background subtraction or other analytical issues. A final example is presented for analysis of Tc-99 in samples of depleted uranium. In this case, two different studies that were performed only three months apart provide quite different results. The US Environmental Protection Agency (EPA) established the data quality objectives (DQO) process in the mid-1980's to establish decision performance criteria for data collection. EPA guidance (EPA G-4, for example) clearly distinguishes between DQOs and measurement performance objectives (MQOs) that should be addressed for laboratory analysis of samples. The language of DQOs and MQOs has become confused over time it seems, and the subsequent effects seem to include a lack of attention to decision performance and a routine approach to measurement quality. In order to better address radionuclide sample analysis when the concentrations are close to thresholds of concern, which might be zero for some radionuclides, background for others, and risk-based thresholds for yet others, it is important that routine laboratory analysis methods are adjusted, and that the project team and the laboratory work closely together to ensure that the data meets the MQO requirements of laboratory analysis and reporting of results, and that the MQOs effectively support project-specific DQOs. This basic approach will be applied in Los Alamos in the coming year to the collection of moisture data from underneath MDA T that will be analyzed for americium and neptunium isotopes. Proper understanding of the radiochemistry methods and reporting, and of appropriate statistical methods is critical to the success of such projects, ensuring that the right decisions are made. (authors)

07 ISOTOPE AND RADIATION SOURCES↗