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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, 133 Xe. 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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 133 Xe 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 10 14 Bq (1% of 133 Xe 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 10 15 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 10 14 Bq., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Battelle Memorial Institute. Published by Elsevier Ltd.. All rights reserved.)

A new algorithm (Xcounts) is introduced for estimating the activity concentrations of the xenon isotopes 131m Xe, 133m Xe, 133 Xe, and 135 Xe 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 131m Xe and 133m Xe are lower than those for 133 Xe and 135 Xe. 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

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 140 Ba and 131 I over a range of magnitudes corresponding to the inventory produced from the fission of about 100 g to 1 kiloton TNT-equivalent of 235 U. 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2022 Battelle Memorial Institute. Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2022. Published by Elsevier Ltd.)

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., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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., (Published by Elsevier Ltd.)

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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The International Monitoring System (IMS) is part of the verification regime for the Comprehensive Nuclear-Test-Ban-Treaty Organization (CTBTO). At entry-into-force, half of the 80 radionuclide stations will be able to measure concentrations of several radioactive xenon isotopes produced in nuclear explosions, and then the full network may be populated with xenon monitoring afterward. An understanding of natural and man-made radionuclide backgrounds can be used in accordance with the provisions of the treaty (such as event screening criteria in Annex 2 to the Protocol of the Treaty) for the effective implementation of the verification regime. Fission-based production of (99)Mo for medical purposes also generates nuisance radioxenon isotopes that are usually vented to the atmosphere. One of the ways to account for the effect emissions from medical isotope production has on radionuclide samples from the IMS is to use stack monitoring data, if they are available, and atmospheric transport modeling. Recently, individuals from seven nations participated in a challenge exercise that used atmospheric transport modeling to predict the time-history of (133)Xe concentration measurements at the IMS radionuclide station in Germany using stack monitoring data from a medical isotope production facility in Belgium. Participants received only stack monitoring data and used the atmospheric transport model and meteorological data of their choice. Some of the models predicted the highest measured concentrations quite well. A model comparison rank and ensemble analysis suggests that combining multiple models may provide more accurate predicted concentrations than any single model. None of the submissions based only on the stack monitoring data predicted the small measured concentrations very well. Modeling of sources by other nuclear facilities with smaller releases than medical isotope production facilities may be important in understanding how to discriminate those releases from releases from a nuclear explosion., (Published by Elsevier Ltd.)

Collision-induced dissociation (CID) is widely used in mass spectrometry to identify biologically important molecules by gaining information about their internal structure. Interpretation of experimental CID spectra always involves some form of in silico spectra of potential candidate molecules. Knowledge of how charge is distributed among fragments is an important part of CID simulations that generate in silico spectra from the chemical structure of the precursor ions entering the collision chamber. In this chapter we describe a method to obtain this knowledge by machine learning.

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 (133)Xe data from three IMS sampling locations to estimate the annual releases of (133)Xe 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 × 10(14) 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 × 10(16) to 2.4 × 10(16) Bq, estimates for the facility in Indonesia vary from 9.2 × 10(13) to 3.7 × 10(14) Bq and estimates for the facility in Argentina range from 4.5 × 10(12) to 9.5 × 10(12) Bq., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Motivation: Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissociation tandem mass spectrometry., Results: A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity.