Impacts of future nuclear power generation on the international monitoring system.

Many countries are considering nuclear power as a means of reducing greenhouse gas emissions, and the IAEA (IAEA, 2022) has forecasted nuclear power growth rates up to 224% of the 2021 level by 2050. Nuclear power plants release trace quantities of radioxenon, an inert gas that is also monitored because it is released during nuclear explosive tests. To better understand how nuclear energy growth (and resulting Xe emissions) could affect a global nonproliferation architecture, we modeled daily releases of radioxenon isotopes used for nuclear explosion detection in the International Monitoring System (IMS) that is part of the Comprehensive Nuclear Test-Ban Treaty: 131mXe, 133Xe, 133mXe, and 135Xe to examine the change in the number of potential radioxenon detections as compared to the 2021 detection levels. If a 40-station IMS network is used, the potential detections of 133Xe in 2050 would range from 82% for the low-power scenario to 195% for the high-power scenario, compared to the detections in 2021. If an 80-station IMS network is used, the potential detections of 133Xe in 2050 would range from 83% of the 2021 detection rate for the low-power scenario to 209% for the high-power scenario. Essentially no detections of 131mXe and 133mXe are expected. The high growth scenario could lead to a 2.5-fold increase in 135Xe detections, but the total number of detections is still small (on the order of 1 detection per day in the entire network). The higher releases do not pose a health issue, but better automated methods to discriminate between radioactive xenon released from industrial sources and nuclear explosions will be needed to offset the higher workload for people who perform the monitoring.

Comparison of source-location algorithms for atmospheric samplers.

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.