MCMC methods applied to the reconstruction of the autumn 2017 ruthenium 106 atmospheric contamination source term

In case of an accidental radioactive release, the Institute for Radiological Protection and Nuclear Safety (IRSN) uses atmospheric dispersion models to assess radiological consequences for human health and environment. The accuracy of the models results is highly dependent on the meteorological fields and the source term, including the location, the duration, the magnitude and the isotopic composition of the release. Inverse modeling methods have proven to be efficient in assessing source term. The authors have developed an inverse method based on a variational approach and applied it to the Fukushima accident using dose rate measurements (Saunier et al., 2013) and air concentration measurements (Winiarek et al., 2012; Saunier et al., 2016). The method has been extended to deal with minor detection events where the source location is usually unknown (Saunier et al., 2019). Variational methods are suitable in operational use since they are able of quickly providing an optimal solution. However, unlike Bayesian methods, the quantification of the uncertainties of the reconstructed source term is usually not easily accessible. Indeed, Bayesian inverse methods are developed in order to efficiently sample the distributions of the variables of the source, thus allowing to get a complete characterization of the source. In September 2017, small amounts of ¹⁰⁶Ru have been observed in Europe without knowledge on the origin of the release. Although concentrations levels were too low to pose any health or environmental issues, the widespread detection suggested that the source term must have been quite high. Monte Carlo Markov Chains (MCMC) methods have been applied to reconstruct the ¹⁰⁶Ru source using the Parallel Tempering algorithm based on Bayesian inference. The distributions of the variables associated to the source and the observations errors are presented. Convergence of the MCMC methods has been studied and points out that chains with small number of parameters are drawing distributions consistent with the results obtained using variational methods. Moreover, the computational time required by the method is suitable for operational use.