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| Titel |
Inverse modelling of radionuclide release rates using gamma dose rate observations |
| VerfasserIn |
Thomas Hamburger, Andreas Stohl, Christoph von Haustein, Severin Thummerer, Christian Wallner |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250099013
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| Publikation (Nr.) |
 EGU/EGU2014-14750.pdf |
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| Zusammenfassung |
| Severe accidents in nuclear power plants such as the historical accident in Chernobyl
1986 or the more recent disaster in the Fukushima Dai-ichi nuclear power plant
in 2011 have drastic impacts on the population and environment. The hazardous
consequences reach out on a national and continental scale. Environmental measurements
and methods to model the transport and dispersion of the released radionuclides
serve as a platform to assess the regional impact of nuclear accidents – both, for
research purposes and, more important, to determine the immediate threat to the
population.
However, the assessments of the regional radionuclide activity concentrations and the
individual exposure to radiation dose underlie several uncertainties. For example, the
accurate model representation of wet and dry deposition. One of the most significant
uncertainty, however, results from the estimation of the source term. That is, the time
dependent quantification of the released spectrum of radionuclides during the course
of the nuclear accident. The quantification of the source terms of severe nuclear
accidents may either remain uncertain (e.g. Chernobyl, Devell et al., 1995) or rely
on rather rough estimates of released key radionuclides given by the operators.
Precise measurements are mostly missing due to practical limitations during the
accident.
Inverse modelling can be used to realise a feasible estimation of the source term
(Davoine and Bocquet, 2007). Existing point measurements of radionuclide activity
concentrations are therefore combined with atmospheric transport models. The
release rates of radionuclides at the accident site are then obtained by improving the
agreement between the modelled and observed concentrations (Stohl et al., 2012). The
accuracy of the method and hence of the resulting source term depends amongst
others on the availability, reliability and the resolution in time and space of the
observations. Radionuclide activity concentrations are observed on a relatively sparse
grid and the temporal resolution of available data may be low within the order of
hours or a day. Gamma dose rates on the other hand are observed routinely on a
much denser grid and higher temporal resolution. Gamma dose rate measurements
contain no explicit information on the observed spectrum of radionuclides and have
to be interpreted carefully. Nevertheless, they provide valuable information for
the inverse evaluation of the source term due to their availability (Saunier et al.,
2013).
We present a new inversion approach combining an atmospheric dispersion model and
observations of radionuclide activity concentrations and gamma dose rates to obtain the
source term of radionuclides. We use the Lagrangian particle dispersion model FLEXPART
(Stohl et al., 1998; Stohl et al., 2005) to model the atmospheric transport of the released
radionuclides. The gamma dose rates are calculated from the modelled activity
concentrations. The inversion method uses a Bayesian formulation considering
uncertainties for the a priori source term and the observations (Eckhardt et al., 2008).
The a priori information on the source term is a first guess. The gamma dose rate
observations will be used with inverse modelling to improve this first guess and to
retrieve a reliable source term. The details of this method will be presented at the
conference.
This work is funded by the Bundesamt für Strahlenschutz BfS, Forschungsvorhaben
3612S60026.
References
Davoine, X. and Bocquet, M., Atmos. Chem. Phys., 7, 1549–1564, 2007.
Devell, L., et al., OCDE/GD(96)12, 1995.
Eckhardt, S., et al., Atmos. Chem. Phys., 8, 3881–3897, 2008.
Saunier, O., et al., Atmos. Chem. Phys., 13, 11403-11421, 2013.
Stohl, A., et al., Atmos. Environ., 32, 4245–4264, 1998.
Stohl, A., et al., Atmos. Chem. Phys., 5, 2461–2474, 2005.
Stohl, A., et al., Atmos. Chem. Phys., 12, 2313–2343, 2012. |
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