Uncertainties in atmospheric dispersion modelling during nuclear accidents

Inversion of 137Cs emissions following the fukushima accident with adaptive release recovery for temporal absences of observations

Temporal absences in observation records lead to release losses during the source term inversions of atmospheric radionuclide emissions. Consequently, objectively-estimated source terms for the Fukushima accident contain fewer release details and present large discrepancies when compared with the expert-judged one. This paper describes an objective method that can adaptively recover the missing releases caused by the temporal absences of observations. The proposed method assumes that the accident releases of radionuclides are piecewise-constant and comprise both peaks and constant releases. The missing releases are adaptively recovered as either peaks or constant releases by minimizing the total variation of the estimated source term. The proposed method is applied to the Fukushima accident and evaluated against regional airborne and deposited ¹³⁷Cs observations. The results demonstrate that this method effectively recovers the missing releases, producing a source term that matches the timing of both on-site gamma dose rate peaks and accident events. The retrieved source term improves the simulation of air concentrations and reproduces most of the deposition patterns. This is the first time that an objective method has independently reproduced the details in the expert-judged one for the Fukushima accident.

Atmospheric dispersion modeling for an accidental release from a SLOWPOKE-2 research reactor: a case study

Atmospheric dispersion modeling was performed for a postulated design basis accident at a SLOWPOKE research reactor. The MCNP-5 computer code was used to estimate the neutron flux spectrum which was then used in the ORIGEN-S code to perform core depletion calculations and determine the radiological source term. The HotSpot health physics code was then used to model the atmospheric transport of the radioactive material released to estimate the resulting doses to the population downwind of the reactor. The highest total effective dose (TED) for a release from the reactor's exhaust stack in predominant meteorological conditions, stability class C, was 0.37 mSv, while a maximum TED of 4.29 mSv was estimated for a release at ground level. Ground deposition was estimated to be 3900 kBq/m². It was shown that any hypothetical release of radioactive material resulting from such an accident would have no significant adverse effect on the municipal water reservoir close to the reactor.

Source term determination with elastic plume bias correction

Estimation of a source term, i.e. release rate, of atmospheric radionuclide emissions is of key interest for nuclear emergency response and further accident analysis. The source term estimate is, however, often very inaccurate due to biases in atmospheric transport and used meteorological analysis. We propose a method for atmospheric plume bias correction which uses not only concentrations modeled at a measuring site but also the information on concentration gradient from the neighborhood of each measuring site, i.e. information already available from the atmospheric transport model. To properly regularize the model, we propose an elastic model of the plume bias correction based on regularization with the use of known topology of the measurement network. The proposed plume bias correction method can be coupled with an arbitrary source term estimation algorithm and can be instantly applied to any other atmospheric release of hazardous material. We demonstrate the method in two real cases. First, we use data from the European Tracer Experiment to validate the methodology. Second, we use data from the ¹⁰⁶Ru occurrence over Europe in 2017 to demonstrate the methodology in a more demanding case where agreement with state-of-the-art estimates is shown with much better reconstruction of measurements.

Comparing model skills for deterministic versus ensemble dispersion modelling: The Fukushima Daiichi NPP accident as a case study

Atmospheric dispersion models are crucial for nuclear risk assessment and emergency response systems since they rapidly predict air concentrations and deposition of released radionuclides, providing a basis for dose estimations and countermeasure strategies. Atmospheric dispersion models are associated with relatively large and often unknown uncertainties that are mostly attributed to meteorology, source terms and parametrisation of the dispersion model. By developing methods that can provide reliable uncertainty ranges for model outputs, decision makers have an improved basis for handling nuclear emergency situations. In the present work, model skill of the Severe Nuclear Accident Programme (SNAP) model was quantified by employing an ensemble method in which 51 meteorological realisations from a numerical weather prediction model were combined with 9 source term descriptions for the accidental ¹³⁷Cs releases from Fukushima Daiichi Nuclear Power Plant during 14th-17th March 2011. The meteorological forecast was compared to observations of wind speed from 30 meteorological stations. The 459 dispersion realisations were compared with hourly observations of activity concentrations from 100 air filter stations. Exclusive use of deterministic meteorology resulted in most members of the dispersion ensemble showing too low concentration values, however this was mitigated by applying ensemble meteorology. Ensemble predictions, including both the meteorological and source term ensemble, show an overall higher prediction skill compared to individual meteorology and source term runs, with true predictive rate accuracy increasing from 30%-50% to 70%-90%, with a decrease in positive predictive rate accuracy from 75%-80% to 65%-75%. Skill scores and other ensemble indicators also showed improvements in using ensembles of source terms and meteorology. From the present study on the Fukushima accident there are strong indications that ensemble predictions improve the basis for decision making in the early phase after a nuclear accident, which emphasises the importance of including ensemble prediction in nuclear preparedness tools of the future.