Probabilistic assessment of the influence of lake properties in long-term radiation doses to humans

Feasibility of neural network metamodels for emulation and sensitivity analysis of radionuclide transport models

In this paper we compare the outputs of neural network metamodels with numerical solutions of differential equation models in modeling cesium-137 transportation in sand. Convolutional neural networks (CNNs) were trained with differential equation simulation results. Training sets of various sizes (from 5120 to 163,840) were used. First order and total order Sobol methods were applied to both models in order to test the feasibility of neural network metamodels for sensitivity analysis of a radionuclide transport model. Convolutional neural networks were found to be capable of emulating the differential equation models with high accuracy when the training set size was 40,960 or higher. Neural network metamodels also gave similar results compared with the numerical solutions of the partial differential equation model in sensitivity analysis.

On the inclusion of forest exposure pathways into a stylized lake-farm scenario in a geological repository safety analysis

Geological disposal of radioactive waste has been recognized as the 'reference solution' to ensure the safety required for the present and future society and environment. To study the possible exposure pathways from groundwater to humans, radioactive transport modelling is used. One of the ecosystems that may play a significant role when assessing the dose conversion factor (i.e. the dose resulting from a nominal release of 1 Bq/year of each radionuclide) for humans is forest. In this paper we have developed a model of a lake-farm system with a forest component. The biosphere system used in this study represents a typical agricultural scenario in Finland, amended with a typical forest. A lake is assumed to form due to post-glacial land uplift. The main features of this future lake have been obtained from our probabilistic shoreline displacement model. Both deterministic calculations and sensitivity analysis were carried out to simulate the model. The deterministic simulation demonstrates the behaviour of the studied radionuclides (36Cl, 135Cs, 129I, 237Np, 90Sr, 99Tc and 238U) and the proportions of different exposure pathways to humans. Particularly for 135Cs and 129I, forest pathways make a notable contribution to the dose conversion factor. The sensitivity analysis was done using two methods: EFAST and Sobol'. With both methods, the parameters related to the farm contribute the most to the variance of the dose conversion factor for humans. The study demonstrates that the exposure pathways related to forest products may make a considerable contribution to the dose conversion factor in a lake-farm-forest system. It is also confirmed that an advanced sensitivity analysis for a radionuclide transport and dose assessment model on such a landscape scale is feasible even with moderate computational efforts.

Probabilistic assessment of the impact of bottom sediment on doses to humans from a groundwater-mediated radionuclide release in a farm-lake scenario

Radionuclide transport with groundwater flow and subsequent doses to people are an aspect to be studied when assessing the long-term safety of geological nuclear waste repositories. A scenario where the radionuclide release migrates through a three-layer sediment structure of a lake in a farming environment is presented in this paper. The sediment column consists of deep (till), intermediate (glacio-aquatic sediment) and top layers (clay). The radionuclide release is assumed to enter the deep sediment layer from a bedrock fracture system at a rate of 1 Bq yr^-1. The main objectives of the paper are to investigate the most contributing parameters, especially linked to the sediment layers, to the overall dose estimates for humans. The sensitivity analysis was conducted in two phases where the Morris method was used for screening and the Extended Fourier Amplitude Sensitivity Testing and Sobol's methods were used for estimating total-order indices. The studied radionuclides, ³⁶Cl, ¹³⁵Cs, ¹²⁹I, ⁹⁴Nb, ²³⁷Np, ⁹⁰Sr, ⁹⁹Tc and ²³⁸U, exhibit differences in how the sediment layers affect the concentration in the lake water used for drinking, irrigation and watering cattle and subsequently the dose conversion factors for humans through ingestion, inhalation and external radiation.