Sensitivity analysis in a radiological impact assessment of a nuclear power plant discharge. A comparison of the Morris, Spearman and Sobol' approaches.

This paper compares the Morris, Spearman and Sobol' methods of sensitivity analysis in radiological risk assessment. The determination of the most influential parameters on model with regards to the propagation of their uncertainties to output variables, is of greatest interest. This study aims to determine the relative importance of parameters uncertainties on the dose calculation uncertainty in the framework of a scenario of routine discharges discussed in the context of an IAEA working group. The scenario considers atmospheric and liquid discharges of three different types of radionuclides (14C, tritium as HTO and 110mAg) from a nuclear power plant located by the side of a river. It is concluded that the most reliable and practical method according to the ability of ranking influential parameters and the easiness of its application is the Spearman method. As key result, the three first influential variables for annual total dose for all pathways and all radionuclides were the water dissolved inorganic carbon concentration, the volatilisation rate constant and the soil layer solid liquid distribution in 14C.

Improving transfer functions to describe radiocesium wash-off fluxes for the Niida River by a Bayesian approach.

This paper proposed methodological refinements of the generic transfer function approach to reconstruct radiocesium wash-off fluxes from contaminated catchments, by the integration of hydrological descriptors (passed volume of water, flow rate fluctuations and antecedent flow conditions). The approach was applied to the Niida River (Fukushima prefecture, Japan) for the period 03/2011-03/2015, for which daily flow rate (m3/s) and infrequent total radiocesium concentration (Bq/L) values were available from literature. Three models were defined, generic TF (Φ0), flow-corrected time variant (Φ1) and antecedent-flow corrected variant (Φ2). Calibration of these models' parameters was performed with a Bayesian approach because it is particularly adapted to limited datasets and censored information, and it provides parameters distributions. The model selection showed strong evidence of model Φ2 (indicated by marginal likelihood), which integrates current and recent hydrology in its formulation, and lower prediction errors (indicated by RMSE and ME). Models Φ1 and Φ2 better described wash-off dynamics compared to model Φ0, due to the inclusion of one or several hydrological descriptors. From March 2011 to March 2015, model Φ2 estimated 137Cs export from Niida catchment between 0.32 and 0.67 TBq, with a median value of 0.49 TBq, which represents around 0.27% of the initial fallout and could represent a significant source-term to the Ocean compared to the direct release from Fukushima Dai-ichi Nuclear Power Plant (FDNPP). Moreover the remaining 99% of the initial radiocesium fallout within the catchment may constitute a persistent contamination source for wash-off. Although the proposed methodology brought improvements in the assessment of wash-off fluxes, it remains an empirical interpolation method with a limited predictive power, particularly for recent low activities. To improve predictions, modelling approaches require more observed data (particularly more activity values corresponding to more hydrological conditions), and the inclusion of more hydrological descriptors.

Identifiability of sorption parameters in stirred flow-through reactor experiments and their identification with a Bayesian approach.

This paper addresses the methodological conditions -particularly experimental design and statistical inference- ensuring the identifiability of sorption parameters from breakthrough curves measured during stirred flow-through reactor experiments also known as continuous flow stirred-tank reactor (CSTR) experiments. The equilibrium-kinetic (EK) sorption model was selected as nonequilibrium parameterization embedding the Kd approach. Parameter identifiability was studied formally on the equations governing outlet concentrations. It was also studied numerically on 6 simulated CSTR experiments on a soil with known equilibrium-kinetic sorption parameters. EK sorption parameters can not be identified from a single breakthrough curve of a CSTR experiment, because Kd,1 and k- were diagnosed collinear. For pairs of CSTR experiments, Bayesian inference allowed to select the correct models of sorption and error among sorption alternatives. Bayesian inference was conducted with SAMCAT software (Sensitivity Analysis and Markov Chain simulations Applied to Transfer models) which launched the simulations through the embedded simulation engine GNU-MCSim, and automated their configuration and post-processing. Experimental designs consisting in varying flow rates between experiments reaching equilibrium at contamination stage were found optimal, because they simultaneously gave accurate sorption parameters and predictions. Bayesian results were comparable to maximum likehood method but they avoided convergence problems, the marginal likelihood allowed to compare all models, and credible interval gave directly the uncertainty of sorption parameters θ. Although these findings are limited to the specific conditions studied here, in particular the considered sorption model, the chosen parameter values and error structure, they help in the conception and analysis of future CSTR experiments with radionuclides whose kinetic behaviour is suspected.