Source type estimation using noble gas samples

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.