Jens Havskov Sørensen (co-ordinator), Henrik Feddersen, Kasper Skjold Tølløse, Rostislav Kouznetsov, Mikhail Sofiev, Heiko Klein, Magnus Ulimoen, Lennart Robertson, Jan Pehrsson, Bent Lauritzen, Dan Bohr, Agnieszka Hac-Heimburg, Carsten Israelson, Anna Maria Blixt Buhr, Jonas Lindgren, Tero Karhunen, Tuomas Peltonen,
In early October 2017, the IAEA was informed 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 the 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 spatial and temporal localization of the source of contamination based on available monitoring data.
For operational use, nuclear decision-support systems should be extended with modules handling such monitoring data automatically, e.g. by employing EURDEP, and conveying selected data 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 following results are obtained:
1. Two case studies are identified and selected, viz. the European Tracer Experiment (ETEX-1) and the October 2017 case of Ru-106 in Europe.
2. Methods for temporal and spatial source localization are developed, implemented and described.
3. Deterministic NWP model data are derived from the ECMWF corresponding to the selected cases.
4. Quality-controlled measurement data of ground-level concentration are obtained from filter stations.
5. The inverse methods for source localization are applied by using the DERMA, MATCH and SNAP atmospheric dispersion models to both cases using the deterministic meteorological data.
6. A high-resolution limited-are ensemble prediction system based on the Harmonie NWP model has been set up and applied to the two selected cases.
7. The inverse methods for source localization are applied by using the DERMA, MATCH, SILAM and SNAP atmospheric dispersion models to both cases using the ensemble-statistical meteorological data.