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Abstract One of the difficulties encountered in source-term analyses for airborne contaminants is the large computational effort required to predict air concentrations for all possible release scenarios. In some cases, analysts use atmospheric ATM runs with complex models done in the reverse-time direction because one ATM run done backwards in time for each sample can yield as much information as potentially hundreds or thousands of ATM runs done forwards in time. Unfortunately, the effective atmospheric dilution between the source and sampling locations differ depending on the time direction of the ATM run, with runs in the forward time direction being more realistic. No general studies have been published comparing the agreement between runs in the two time directions. Over 18000 ATM runs at 14 release locations were used to explore the agreement between dilution factors for the forward and reversed time directions at distances up to 1000 km from the release point. Ten of the release locations have a correlation below 0.9, with the lowest correlations occurring over mountainous terrain. The release locations were estimated using the time-reversed ATM runs, with 26% of the estimated release points being within 10 km of the modeled release point, 61% within 25 km, and 80% within 50 km. Most of the location differences greater than 50 km occur for two release locations in mountainous terrain. Good time-reversibility cannot be guaranteed for a new analysis, so we recommend any source-term solution using time-reversed ATM runs should include comparisons based on forward time ATM runs. Highlights • Empirical comparison of dilution factors for paired ATM runs in forward and backwards time directions. • Land elevation changes have a large negative impact on the time reversibility of the ATM runs. • Source term estimation using time-reversed ATM runs is very useful. • Source term estimation using time-reversed ATM runs needs confirmation using forward ATM runs. [ABSTRACT FROM AUTHOR]

A new algorithm (Xcounts) is introduced for estimating the activity concentrations of the xenon isotopes 131mXe, 133mXe, 133Xe, and 135Xe using beta-gamma coincidence data. The algorithm simultaneously estimates the decay counts associated with the four xenon isotopes, background, and radon in contrast to the net-counts method that uses sequential residual removal to account for background and interferences. Calibration data for background counts are determined from gas-background measurements and simulation. In Xcounts, the false positive count rates for 131mXe and 133mXe are lower than those for 133Xe and 135Xe. This algorithm appears to reliably detect the metastable isotopes at lower activity levels than the net-counts method and have similar performance for the other isotopes. • New approach for estimating radioxenon activity concentrations in air. • Approach uses simultaneous isotope count estimation using beta-gamma coincidence data. • Background and radon daughter counts are estimated rather than subtracted. • Performance compares well with existing models for 133Xe and 135Xe. • Detection limits for 131mXe and 133mXe are lower than for net-count based algorithms. [ABSTRACT FROM AUTHOR]

Abstract Many source-term estimation algorithms for atmospheric releases assume the measured concentration data are influenced only by the releases of interest. However, there are situations where identifying a short-term release from an unknown location in the presence of long-term releases from a different location is of interest. One such example is determining if part or all of a typical magnitude concentration of a radioactive isotope in a sampler came from a nuclear explosion, such as the explosion announced by DPRK in 2013, while medical isotope facilities and nuclear power plants were also operating in the region. An estimation algorithm has been developed for the case where a short-duration release is confounded by a long-term nuisance signal associated with an additional release location. The technique is demonstrated using synthetic release data for a hypothetical medical isotope production facility and a hypothetical puff release from a different location. The algorithm successfully determines the location (within 30 km) and time-varying release rate (within a factor of 2) for the medical isotope production facility and the location (within 60 km), time (within 6 h), and release magnitude (within a factor of 4) of the puff release. Highlights • New algorithm for source-term estimation for two or more concurrent release locations. • Algorithm quantifies uncertainty in the release time and location. • Algorithm performs successfully using synthetic data for two release locations. [ABSTRACT FROM AUTHOR]

Predicting source or background radionuclide emissions is limited by the effort needed to run gas/aerosol atmospheric transport models (ATMs). A high-performance surrogate model is developed for the HYSPLIT4 (NOAA) ATM to accelerate transport simulation through model reduction, code optimization, and improved scaling on high performance computing systems. The surrogate model parameters are a grid of short-duration transport simulations stored offline. The surrogate model then predicts the path of a plume of radionuclide particles emitted from a source, or the field of sources which may have contributed to a detected signal, more efficiently than direct simulation by HYSPLIT4. Termed the Atmospheric Transport Model Surrogate (ATaMS), this suite of capabilities forms a basis to accelerate workflows for probabilistic source prediction and estimation of the radionuclide atmospheric background. • Atmospheric Transport Model Surrogate (ATaMS) accelerates a Lagrangian ATM. • Short-term sparsely distributed bursts are assimilated into particle distributions. • Forward transport simulation is accelerated 10–100x compared to HYSPLIT4. • ATaMS facilitates accurate retrospective particle transport from a station. [ABSTRACT FROM AUTHOR]

Large networks producing frequent atmospheric radionuclide measurements have additional power in characterizing and localizing radionuclide release events over the analysis performed with four or fewer radionuclide measurements. However, adding unrelated measurements to an analysis dilutes that advantage, unless source-term models are extended to account for this complexity. A key steppingstone to obtaining network power is to select a group of related sample measurements that are associated with a release event. Such collections of measurements can be assembled by an analyst, or perhaps they can be selected by algorithm. The authors explore, using a year of atmospheric transport calculations and realistic sensor sensitivities, the potential for a computed radionuclide association tool. • Using a group of measurements associated with a radionuclide release can yield better event characterization. • Selecting the measurements is currently a subjective and analyst-intensive job. • Several possible automated association schemes exist to objectively select related measurements. • A pre-computed scheme based on recent weather requires only modest computations. [ABSTRACT FROM AUTHOR]

Numerous algorithms have been developed to determine the source characteristics for an atmospheric radionuclide release, e.g., (Bieringer et al., 2017). This study compares three models that have been applied to the data collected by the International Monitoring System operated by the Comprehensive Nuclear-Test-Ban Treaty Organization Preparatory Commission to estimate source event parameters. Each model uses a different approach to estimate the parameters. A deterministic model uses a possible source region (PSR) approach (Ringbom et al., 2014) that is based on the correlation between predicted and measured sample values. A model (now called BAYEST) developed at Pacific Northwest National Laboratory uses a Bayesian formulation (Eslinger et al., 2019, 2020; Eslinger and Schrom, 2016). The FREAR model uses a different Bayesian formulation (De Meutter and Hoffman, 2020; De Meutter et al., 2021a, 2021b). The performance of the three source-location models is evaluated with 100 synthetic release cases for the single xenon isotope, 133Xe. The release cases resulted in detections in a fictitious network with 120 noble gas samplers. All three source-location models use the same sampling data. The two Bayesian models yield more accurate location estimates than the deterministic PSR model, with FREAR having slightly better location performance than BAYEST. Samplers with collection periods of 3, 6, 8, 12, and 24-h were used. Results from BAYEST show that location accuracy improves with each reduction in sample collection length. The BAYEST model is slightly better for estimating the start time of the release. The PSR model has about the same spread in start times as the FREAR model, but the PSR results have a better average start time. The Bayesian source-location algorithms give more accurate results than the PSR approach, and provide release magnitude estimates, while the base PSR model does not estimate the release magnitude. This investigation demonstrates that a reasonably dense sampling grid will sometimes yield poor location and time estimates regardless of the model. The poor estimates generally coincide with cases where there is a much larger distance between the release point and the first detecting sampler than the average sampler spacing. • Compare performance of three source-location algorithms using synthetic atmospheric samples. • Two Bayesian source-location models have quite similar results. • The Bayesian models give more accurate location estimates than a deterministic probable source region model. • Even a reasonably dense sampling grid sometimes yields poor location and release time estimates. • Shorter sample collection periods provide better location accuracy than longer periods. [ABSTRACT FROM AUTHOR]

Algorithms that estimate the location and magnitude of an atmospheric release using remotely sampled air concentrations typically involve a single chemical or radioactive isotope. A new Bayesian algorithm is presented that makes discrimination between possible types of releases (e.g., nuclear explosion, nuclear power plant, or medical isotope production facility) an integral part of the analysis for samples that contain multiple isotopes. Algorithm performance is demonstrated using synthetic data and correctly discriminated between most release-type hypotheses, with higher accuracy when data are available on three or more isotopes. • New algorithm for source-term estimation using two or more isotopes. • The algorithm selects the most likely type (power plant, explosion, etc.) of release event. • More isotopes discriminate between release types better than two isotopes. [ABSTRACT FROM AUTHOR]

Design of an efficient monitoring network requires information on the type and size of releases to be detected, the accuracy and reliability of the measuring equipment, and the desired network performance. This work provides a scientific basis for optimizing or minimizing networks of 133Xe samplers to achieve a desired performance level for different levels of release. The approach of this work varies the density of sampling locations to find optimal location subsets, and to explore the properties of variations of those subsets – how crucial is a specific subset; are substitutions problematic? The choice of possible station locations is arbitrary but constrained to some extent by the location of islands, land masses, difficult topography (mountains, etc.) and the places where infrastructure exists to run and support a sampler. Performance is evaluated using hypothetical releases and atmospheric transport models that cover an entire year. Three network performance metrics are calculated: the probability of detecting the releases, the expected number of stations to detect the releases, and the expected number of samples that detect the releases. The quantitative measures support picking optimal or near-optimal network of a specific station density. If a detection probability of 90% (high) was desired for a design basis release of 1014 Bq (1% of 133Xe production from a 1 kt explosion), then a very high density would be required using today's sampling and measurement technology. If the design basis release were raised to 1015 Bq, then the station density could be lowered by a factor of 3. To achieve a location goal of three station detections on average, posited here for the first time, would also require very high station density for a release of 1014 Bq. • A scientific basis for designing an effective network of 133Xe samplers is presented. • A global approach is used to optimize the performance of regional networks. • Estimated reactor and medical backgrounds of 133Xe affect network sensitivity. • A detection probability metric is tested using current sampler characteristics. • Two potential metrics for the source-location capability of a network are presented. [ABSTRACT FROM AUTHOR]

Aerosol monitoring for radioactivity is a mature and proven technology. However, by improving key specifications of aerosol monitoring equipment, more samples per day can be collected and analyzed with the same minimum detectable concentrations as current systems. This work models hypothetical releases of 140Ba and 131I over a range of magnitudes corresponding to the inventory produced from the fission of about 100 g to 1 kiloton TNT-equivalent of 235U. The releases occur over an entire year to incorporate the natural variability in atmospheric transport. Sampling equipment located at the 79 locations for radionuclide stations identified in the Comprehensive Nuclear-Test-Ban Treaty (CTBT) for the International Monitoring System are used to determine the detections of the individual releases. Alternative collection schemes in next generation equipment that collect 2, 3, or 4 samples per day, rather than the current 1 sample per day, would result in detections in many more samples at more stations with detections for a given release level. The authors posit that next generation equipment will result in increased network resilience to outages and improved source-location capability for lower yield source releases. The application of dual-detector and coincidence measurements to these systems would significantly boost sensitivity for some isotopes and would further enhance the monitoring capability. • Shorter sampling periods improve the IMS network detection probability. • Shorter sampling periods unexpectedly improve detections for releases in the topics. • Improvement in location capability is inferred from a larger number of samples with detections. • Dual-detector and coincidence gamma spectroscopy greatly improve sensitivity to some isotopes. [ABSTRACT FROM AUTHOR]

An overview of the hardware and software developed for the Source Term Analysis of Xenon (STAX) project is presented which includes the data collection from two stack monitoring systems installed at medical isotope production facilities, infrastructure to transfer data to a central repository, and methods for sharing data from the repository with users. STAX is an experiment to collect radioxenon emission data from industrial nuclear facilities with the goal of developing a better understanding of the global radioxenon background and the effect industrial radioxenon releases have on nuclear explosion monitoring. A final goal of this work is to utilize collected data along with atmospheric transport modeling to calculate the contribution of a peak or set of peaks detected by the International Monitoring System (IMS) to provide desired discriminating information to the International Data Centre (IDC) and National Data Centers (NDCs). Types of data received from the STAX equipment are shown and collected data was used for a case study to predict radioxenon concentrations at two IMS stations closest to the Institute for RadioElements (IRE) in Belgium. The initial evaluation of results indicate that the data is very valuable to the nuclear explosion monitoring community. • The STAX experiment collects xenon emission data from industrial nuclear facilities. • Data is used to better understand radioxenon impact on nuclear explosion monitoring. • STAX monitoring systems have been installed at two medical isotope facilities. • STAX Data is being used to develop better tools for discriminating xenon sources. • Collecting data from additional facilities would help refine data analysis methods. [ABSTRACT FROM AUTHOR]

The Source Term Analysis of Xenon (STAX) project has been installing stack detectors at medical isotope production facilities to measure radioxenon emissions to investigate the effect of radioxenon releases on nuclear explosion monitoring. This paper outlines the installation of the first STAX detection system at the National Institute for Radioelements (IRE) in Fleurus, Belgium which has been operating for over three years and transferring collected data to the STAX repository. Information about the equipment installed, the data flow established, and calculations for determination of radioxenon releases from the facility are presented. Data quality was investigated to confirm values reported by STAX automated data processing and in a comparison of collected STAX data with data collected by IRE for regulatory reporting. • A STAX detection system was installed at the National Institute for Radioelements. • The detection system has been operating for over three years. • The system is integral to development of project data transfer, storage, and visualization tools. • STAX data was analyzed to verify calculations for reported release values. • Equipment performance for approximately two years of operation is discussed. [ABSTRACT FROM AUTHOR]

The noble gas collection and measurement stations in the International Monitoring System (IMS) are heavily influenced by releases from medical isotope production facilities. The ability to reliably model the movement of radioxenon from the points of release to these IMS samplers has improved enough that a routine aspect of the analysis of IMS radioxenon data should be the prediction of the effect of releases from industrial nuclear facilities on the sample concentrations. Predicted concentrations at IMS noble gas systems in Germany and Sweden based on measured releases from Institute for Radioelements (IRE) in Belgium and atmospheric transport modeling for a four-month period are presented and discussed. • Radioxenon concentrations at IMS samplers estimated using STAX release data. • STAX release data are useful for predicting impacts at IMS samplers. • Routine analysis of IMS data should include predicted radioxenon concentrations. [ABSTRACT FROM AUTHOR]

The International Monitoring System (IMS) of the Comprehensive-Nuclear-Test-Ban-Treaty monitors the atmosphere for radioactive xenon leaking from underground nuclear explosions. Emissions from medical isotope production represent a challenging background signal when determining whether measured radioxenon in the atmosphere is associated with a nuclear explosion prohibited by the treaty. The Australian Nuclear Science and Technology Organisation (ANSTO) operates a reactor and medical isotope production facility in Lucas Heights, Australia. This study uses two years of release data from the ANSTO medical isotope production facility and 133Xe data from three IMS sampling locations to estimate the annual releases of 133Xe from medical isotope production facilities in Argentina, South Africa, and Indonesia. Atmospheric dilution factors derived from a global atmospheric transport model were used in an optimization scheme to estimate annual release values by facility. The annual releases of about 6.8 × 1014 Bq from the ANSTO medical isotope production facility are in good agreement with the sampled concentrations at these three IMS sampling locations. Annual release estimates for the facility in South Africa vary from 2.2 × 1016 to 2.4 × 1016 Bq, estimates for the facility in Indonesia vary from 9.2 × 1013 to 3.7 × 1014 Bq and estimates for the facility in Argentina range from 4.5 × 1012 to 9.5 × 1012 Bq. [ABSTRACT FROM AUTHOR]

A Bayesian source-term algorithm recently published by Eslinger et al. (2019) extended previous models by including the ability to discriminate between classes of releases such as nuclear explosions, nuclear power plants, or medical isotope production facilities when multiple isotopes are measured. Using 20 release cases from a synthetic data set previously published by Haas et al. (2017), algorithm performance was demonstrated on the transport scale (400–1000 km) associated with the radionuclide samplers in the International Monitoring System. Inclusion of multiple isotopes improves release location and release time estimates over analyses using only a single isotope. The ability to discriminate between classes of releases does not depend on the accuracy of the location or time of release estimates. For some combinations of isotopes, the ability to confidently discriminate between classes of releases requires only a few samples. • Source-term estimation algorithm works at IMS sampler separation distances. • Algorithm selects the most likely type (power plant, explosion, etc.) of release event. • More measured isotopes discriminate between release types better than two isotopes. • Using more isotopes improves location and time estimates over using a singe isotope. [ABSTRACT FROM AUTHOR]

Pacific Northwest National Laboratory (PNNL) staff developed the Radionuclide Aerosol Sampler Analyzer (RASA) for worldwide aerosol monitoring in the 1990s. Recently, researchers at PNNL and Creare, LLC, have investigated possibilities for how RASA could be improved, based on lessons learned from more than 15 years of continuous operation, including during the Fukushima Daiichi Nuclear Power Plant disaster. Key themes addressed in upgrade possibilities include having a modular approach to additional radionuclide measurements, optimizing the sampling/analyzing times to improve detection location capabilities, and reducing power consumption by using electrostatic collection versus classic filtration collection. These individual efforts have been made in a modular context that might constitute retrofits to the existing RASA, modular components that could improve a manual monitoring approach, or a completely new RASA. Substantial optimization of the detection and location capabilities of an aerosol network is possible and new missions could be addressed by including additional measurements. • Improved collection and measurement technology reviewed for legacy aerosol systems. • New capabilities enable the verification responsibilities of aerosol systems in high-level release scenarios. • Electrostatic precipitation improves sample air volume per watt. • Simple MDC analysis shows existing systems could benefit from alternative operations. • Shorter sampling intervals will enable better source location estimates. [ABSTRACT FROM AUTHOR]