Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 ¹⁰⁶Ru event

Using a Bayesian framework in the inverse problem of estimating the source of an atmospheric release of a pollutant has proven fruitful in recent years. Through Markov chain Monte Carlo (MCMC) algorithms, the statistical distribution of the release parameters such as the location, the duration, and the magnitude as well as error covariances can be sampled so as to get a complete characterisation of the source. In this study, several approaches are described and applied to better quantify these distributions, and therefore to get a better representation of the uncertainties. First, we propose a method based on ensemble forecasting: physical parameters of both the meteorological fields and the transport model are perturbed to create an enhanced ensemble. In order to account for physical model errors, the importance of ensemble members are represented by weights and sampled together with the other variables of the source. Second, once the choice of the statistical likelihood is shown to alter the nuclear source assessment, we suggest several suitable distributions for the errors. Finally, we propose two specific designs of the covariance matrix associated with the observation error. These methods are applied to the source term reconstruction of the 106Ru of unknown origin in Europe in autumn 2017. A posteriori distributions meant to identify the origin of the release, to assess the source term, and to quantify the uncertainties associated with the observations and the model, as well as densities of the weights of the perturbed ensemble, are presented.

High-Speed Visualization of Very Large High-Resolution Simulations for Air Hazard Transport and Dispersion

In the case of an atmospheric release of a noxious substance, modeling remains an essential tool to assess and forecast the impact of the release. The impact of such situations on populated, and hence built-up, areas is of the uttermost importance. However, modeling on such areas requires specific high-resolution approaches, which are complex to set up in emergency situations. Various approaches have been tried and evaluated: The EMERGENCIES and EMED project demonstrated an effective strategy using intensive parallel computing. Large amounts of data were produced that proved initially to be difficult to visualize, especially in a crisis management framework. A dedicated processing has been set up to allow for rapid and effective visualization of the modeling results. This processing relies on a multi-level tiled approach initiated in web cartography. The processing is using a parallel approach whose performances were evaluated using the large amounts of data produced in the EMERGENCIES and EMED projects. The processing proved to be very effective and compatible with the requirements of emergency situations.

Accelerated Time and High-Resolution 3D Modeling of the Flow and Dispersion of Noxious Substances over a Gigantic Urban Area—The EMERGENCIES Project

Accidental or malicious releases in the atmosphere are more likely to occur in built-up areas, where flow and dispersion are complex. The EMERGENCIES project aims to demonstrate the operational feasibility of three-dimensional simulation as a support tool for emergency teams and first responders. The simulation domain covers a gigantic urban area around Paris, France, and uses high-resolution metric grids. It relies on the PMSS modeling system to model the flow and dispersion over this gigantic domain and on the Code_Saturne model to simulate both the close vicinity and the inside of several buildings of interest. The accelerated time is achieved through the parallel algorithms of the models. Calculations rely on a two-step approach: the flow is computed in advance using meteorological forecasts, and then on-demand release scenarios are performed. Results obtained with actual meteorological mesoscale data and realistic releases occurring both inside and outside of buildings are presented and discussed. They prove the feasibility of operational use by emergency teams in cases of atmospheric release of hazardous materials.

Assessment of conservatism of assumptions on the population exposure conditions applied for establishing the permissible atmospheric release levels

The annual permissible atmospheric release levels of radionuclides are established with the condition that the population annual dose should not exceed the dose quota. Two conservative assumptions on the population exposure are applied in handbooks for calculation of the permissible release levels: a person can reside in any point outside the sanitary protection zone of nuclear facility; all exposure pathways, including food consumption, are determined by the place of residence. Calculation of the annual dose for adults living near the Smolensk, Rostov and Bilibino NPP was performed for three scenarios to evaluate the degree of conservatism of these assumptions. Scenario 1 assumed permanent residence in a critical point of the area with production of all food in this point and consumption only local food by the population. Scenario 2 assumed that a certain part of food is imported. In scenario 3 dose assessment was made for a permanent resident of the closest settlement near NPP, taking into consideration realistic characteristics of the exposure conditions. Atmospheric release levels of radionuclides assessed during the radiation and technical surveys of the NPP’s emission sources in 2017 – 2019 were used as the initial data. It is shown that the assumptions on the conditions of population exposure significantly affect the conservatism of the annual dose assessment. The calculated annual dose for the most conservative scenario is higher than for realistic scenario by 6,3 times (for the Smolensk NPP), 8,4 times (Rostov NPP), 883 times (Bilibino NPP). The highest degree of conservatism was shown for the internal radiation dose estimates. The results of calculation of the annual dose from NPP’s emissions strongly depend on the method for dose assessment from releases of 3H and 14C. Use of the regional values of consumption of locally produced food allows reasonable reducing the conservatism of the annual dose estimates from the NPP’s permanent radionuclide releases.

Quantification of the modelling uncertainties in atmospheric release source assessment and application to the reconstruction of the autumn 2017 Ruthenium 106 source

Using a Bayesian framework in the inverse problem of estimating the source of an atmospheric release of a pollutant has proven fruitful in recent years. Through Markov chain Monte Carlo (MCMC) algorithms, the statistical distribution of the release parameters such as the location, the duration, and the magnitude as well as the likelihood covariances can be sampled so as to get a complete characterisation of the source. In this study, several approaches are described and applied to improve on these distributions, and therefore to get a better representation of the uncertainties. First, a method based on ensemble forecasting is proposed: physical parameters of both the meteorological fields and the transport model are perturbed to create an enhanced ensemble. In order to account for model errors, the importance of ensemble members are represented by weights and sampled together with the other variables of the source. Secondly, the choice of the statistical likelihood is shown to alter the nuclear source assessment, and several suited distributions for the errors are advised. Finally, two advanced designs of the covariance matrix associated to the observation error are proposed. These methods are applied to the case of the detection of Ruthenium 106 of unknown origin in Europe in autumn 2017. A posteriori distributions meant to identify the origin of the release, to assess the source term, to quantify the uncertainties associated to the observations and the model, as well as densities of the weights of the perturbed ensemble, are presented.