SLIM

SLIM: Source Localization by Inverse Methods

In early October 2017, the International Atomic Energy Agency (IAEA) was informed by Member States that low concentrations of Ru-106 were measured in high-volume air samples in Europe from routine monitoring networks. However, no information was given that an accidental release of Ru-106 had taken place. Such events signify that there is a need for prompt and accurate responses from national radiation protection authorities in such cases. This requires that methodologies, suited for operational use, are developed for localization of the source of contamination based on available monitoring data, and furthermore, that the source term is characterized as well as possible in terms of source strength and time dependence of the release.
 
For operational use, nuclear decision-support systems (DSSs) should be extended with modules handling such monitoring data automatically and conveying them to the national meteorological centre accompanied by a request to run an atmospheric dispersion model in inverse mode. The aim would be to determine a geographical area in which to find the potential release point as well as the release period. The DSS user should subsequently have the ability to request forward calculations from the potential release sites. These forward runs would involve fitting the dispersion model results to the monitoring data, and the resulting source characterization data should be returned to the DSS. Obviously, the latter facility can be applied also in cases where the release location is in fact known, and hence, the objective is to estimate the source term and the timing of the release.
 
In the previous NKS-B project MUD, a methodology was developed for quantitative estimation of the uncertainty of atmospheric dispersion modelling stemming from the inherent uncertainties of meteorological model predictions. Subsequently, in the projects MESO and FAUNA, the implications for nuclear emergency preparedness and management were studied also for short-range models and by applying the methodology to the Fukushima Daiichi emergency, and ways to implement the uncertainties in DSSs, and the impacts on real-time emergency management, were described.
 
In the proposed project SLIM, the inherent meteorological uncertainties will be taken into account by applying the MUD methodology to the inverse modelling approach both with respect to localizing the source, and to deriving the source characteristics, the source term. Previously, due to lack of computational power, such methods could not be applied in operational real-time decision support. However, with modern supercomputing facilities available e.g. at national meteorological centres the proposed methodology is feasible for real-time use, thereby adding value to decision support.