Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA, 99354, USA.
J Environ Radioact. 2020 Dec;225:106439. doi: 10.1016/j.jenvrad.2020.106439. Epub 2020 Sep 30.
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.
埃斯林格等人（2019 年）最近发布的贝叶斯源项算法通过纳入区分核爆炸、核电站或医用同位素生产设施等不同释放源类别的能力，对以往模型进行了扩展。该算法利用哈斯等人（2017 年）先前公布的综合数据集的 20 个释放案例，在与国际监测系统中的放射性核素取样器相关的输运尺度（400-1000 公里）上演示了算法性能。与仅使用单一同位素的分析相比，纳入多种同位素可提高释放位置和释放时间的估算精度。区分不同释放源类别的能力并不依赖于释放位置或时间估算的准确性。对于某些同位素组合，仅需少量样本即可有把握地区分不同释放源类别的释放。
