Approximate sequential Bayesian filtering to estimate 222Rn emanation from 226Ra sources using spectral time series

A new approach to assess the emanation of 222Rn from 226Ra sources based on ?-ray spectrometric measurements is presented. While previous methods have resorted to steady-state treatment of the system, the method presented incorporates well-known radioactive decay kinetics into the inference procedure through the formulation of a theoretically motivated system model. The validity of the 222Rn emanation estimate is thereby extended to regimes of changing source behavior, potentially enabling the development of source surveillance systems in the future. The inference algorithms are based on approximate recursive Bayesian estimation in a switching linear dynamical system, allowing regimes of changing emanation to be identified from the spectral time series while providing reasonable filtering and smoothing performance in steady-state regimes. The derived method is applied to an empirical ?-ray spectrometric time series obtained over 85 d and is able to provide a time series of emanation estimates consistent with the physics of the emanation process.