Institute for Radiation Protection and Nuclear Safety (IRSN)
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Nouveaux radionucléides en médecine nucléaire pour des actes à visée diagnostique, théranostique et thérapeutique
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Une approche hiérarchique bayésienne pour évaluer l'impact des incertitudes dosimétriques sur l'estimation du risque de cancer induit par l'exposition au scanner pendant l'enfance
No full textNational audienceThe French CT cohort includes almost 100,000 children who received at least one computed tomography (CT) scan between 2000 and 2011 in one of the 21 participating university hospitals. A previous analysis of this cohort showed statistically significant dose-response relationships between Xray exposure and cerebral tumors and leukemia. However, several sources of uncertainty coming from CT acquisition parameters and patient's morphology exist but have never been accounted for in risk estimates. This may lead to biased risk estimates and a misquantification of the width of confidence intervals. Bayesian hierarchical models were proposed and compared to simultaneously account for several sources of dose uncertainty when estimating the risk of childhood cancer following CT scans. An excess hazard ratio survival model with time-dependent covariates was considered as disease submodel. Berkson and misclassification errors were assumed to describe the discrepancy between observed and true CT scan parameters on the one hand and estimated and true organ doses on the other hand. The Bayesian inference was performed using an Hamiltonian Monte Carlo algorithm implemented in the R package RStan. A transfer learning approach based on joint modelling was developed to quantify dose uncertainties from an external sample of data containing all CT examinations information
Updated Mortality Analysis of SELTINE, the French Cohort of Nuclear Workers, 1968–2014
No full textInternational audienceCohorts of nuclear workers are particularly relevant to study the health effects of protracted exposures to low doses at low dose-rates of ionizing radiation (IR). In France, a cohort of nuclear workers badge-monitored for external IR exposure has been followed-up for several decades. Its size and follow-up period have recently been extended. The present paper focuses on mortality from both cancer and non-cancer diseases in this cohort. The SELTINE cohort of nuclear workers employed by CEA, Orano, and EDF companies was followed-up for mortality from 1968 to 2014. Mortality in the cohort was compared to that in the French general population. Poisson regression methods were used to estimate excess relative rates of mortality per unit of cumulative dose of IR, adjusted for calendar year, age, company, duration of employment, and socioeconomic status. The cohort included 80,348 workers. At the end of the follow-up, the mean attained age was 63 years, and 15,695 deaths were observed. A strong healthy worker effect was observed overall. A significant excess of pleural cancer mortality was observed but not associated with IR dose. Death from solid cancers was positively but non-significantly associated with radiation. Death from leukaemia (excluding chronic lymphocytic leukaemia), dementia, and Alzheimer’s disease were positively and significantly associated with IR dose. Estimated dose–risk relationships were consistent with those from other nuclear worker studies for all solid cancers and leukaemia but remained associated with large uncertainty. The association between IR dose and dementia mortality risk should be interpreted with caution and requires further investigation by other studies
Augmented quantization : a general approach to mixture models
No full textInternational audienceQuantization methods classically provide a discrete representation of a continuous set. This type of representation is relevant when the objective is thevisualisation of weighted prototype elements representative of a continuous phenomenon. Nevertheless, more complex descriptions may be investigated. In this sense, mixture models identify subpopulations in a sample, arising from different distributions. The Gaussian mixture model is particularly popular and relies on the Expectation-Maximisation (EM) algorithm [1] for maximum likelihood estimation. The computation of the likelihood limits the type of distributions in the mixture; more specifically, for the Dirac distributions and even uniform components despite their high interest in practice for processing computer experiments. Their visualization is convenient and can lead to a sensitivity analysis where variables with largest marginals are least sensitive and vice versa, as shown by our application to a flooding real case in [2].The objective of our study is to build a very general method to provide a mixture model that approximates a sample (xi) n i=1 ∈ X n ⊂ R n from a random variable X. The representatives of the sample are the calculated components of the mixture, chosen in a parameterized family of laws denoted R. We investigate, for a given number of representatives ℓ ∈ N, the mixture X˜ ℓ = R(J) approximating X. The representatives (R(j) ) ℓ j=1 and the discrete random variable J ∈ {1, . . . , ℓ} need to be optimised
Living Labs and other participatory approaches applied to research on multiple environnemental exposures and chronic risks
No full textInternational audienceThe objectives of environmental health research are diverse (e.g.: identifying situations at potential risk, estimating exposures and effects, testing the effectiveness of preventive actions) and related methods are diverse as well. Opportunities for greater implication of the civil society and related challenges differ at each step of research activities. These aspects need to be better known and shared collectively
Methodology for the investigation of undeclared atmospheric releases of radionuclides: Application to recent radionuclide detections in Northern Europe from 2019 to 2022
No full textInternational audienceTraces of radionuclides have been frequently detected in the European atmosphere for several years. The measured concentrations are usually very low, ranging from 0.1 to 10 μBq m-3, and do not pose any health or environmental problems. This study aims to diagnose the origin of small undeclared radionuclide releases into the atmosphere. An inverse modelling approach that combines environmental measurements and atmospheric transport modelling is first used to assess the source location of the release. In addition, the type and process of the nuclear facility from which the release could originate are investigated by identifying the isotope production pathways and comparing them with known typical inventories. These two parts of the proposed method are complementary and allow us to extract as much information as possible from a set of radionuclide measurement data.In a previous study, the origins of detections of various radionuclides (60Co, 134Cs, 137Cs, 103Ru, 106Ru, 141Ce, 95Nb, 95Zr) in Finland, Sweden and Estonia in June 2020 have been investigated. In this paper, the previous investigation is extended by analysing two additional events that occurred in northern Europe in July 2019 and May 2022, as well an overview of other unknown releases detected in Finland over the last decade. A more detailed analysis of the 2020 event is also provided by analysing new available environmental measurements. The calculations indicate that the source location of the three events appears to be in the same region, in Russian Federation. The most probable origin of the June 2020 release seems to be a primary ion exchange resin, after 2 to 5 months of decay, of a pressurized water reactor with fuel cladding failure, and dispersion of fissile material in the primary.The July 2019 and May 2022 events are of particularly noteworthy due to the simultaneous presence of 46Sc, which is neither produced nor in the fuel, nor in the primary loop of PWR or RBMK nuclear power plants, and typical corrosion-activated products from power plants (60Co). Two hypotheses are proposed to explain this source term: a mixture of various solid wastes or recently irradiated graphite from a RBMK reactor. The reliability of the methodology is demonstrated, in particular in the section dedicated to atmospheric transport modelling, and the successful association with source term analysis provides a valuable tool for future studies and assessments of both minor and major radionuclide releases
Uncertainty quantification for a severe accident sequence in a SFP in the frame of the H-2020 project MUSA: First outcomes
No full textInternational audienceThe Management and Uncertainties of Severe Accidents (MUSA) project, funded in HORIZON 2020 and coordinated by CIEMAT (Spain), aims at consolidating a harmonized approach for the analysis of uncertainties and sensitivities associated with Severe Accidents (SAs) focusing on Source Term (ST). In this framework, the objectives of the Innovative Management of Spent Fuel Pool Accidents (IMSFP – WP6), led by IRSN (France), are to quantify and rank the uncertainties affecting accident analyses in a Spent Fuel Pool (SFP), to review existing and contemplated SA management measures and systems and to assess their possible benefits in terms of reduction of radiological onsequences.To quantify the propagation of the uncertainties of the input parameters to the output uncertainties of severe accident codes (ASTEC, MELCOR, RELAP/SCDAP), a diverse set of uncertainty quantification (UQ) tools (DAKOTA, RAVEN, SUNSET, SUSA) are used. The statistical framework used by the different UQ-tools is similar e.g. pure random (Monte Carlo) and Latin hypercube sampling (LHS).Fourteen partners from three different world regions are involved in the WP6 activities. The target of this paper is to describe the achievements during the first three years ofthe project. In a first part, a description is given of the SFP accidental scenario, of the key target variables and radionuclides chosen as ST Figure of Merits (FoM) and of theidentified uncertainty sources in models and input parameters. A key element when defining the SFP scenario has been the consideration (or not) of the reactor building,as it is expected to significantly affect analyses. In a second part, the first insights coming out from the calculation phase of the project are presented. The review of existing SA management measures is also exposed, as well as systems whose benefits will be assessed in the second phase of the project. Finally, challenges that arise from such an exercise are discussed, as well as major difficulties found when applying UQ methodologies to SFP scenarios and solutions adopted
Shapley effects and proportional marginal effects for global sensitivity analysis: application to computed tomography scan organ dose estimation
No full textConcerns have been raised about possible cancer risks after exposure to computed tomography (CT) scans in childhood. The health effects of ionizing radiation are then estimated from the absorbed dose to the organs of interest which is calculated, for each CT scan, from dosimetric numerical models, like the one proposed in the NCICT software. Given that a dosimetric model depends on input parameters which are most often uncertain, the calculation of absorbed doses is inherently uncertain. A current methodological challenge in radiation epidemiology is thus to be able to account for dose uncertainty in risk estimation. A preliminary important step can be to identify the most influential input parameters implied in dose estimation, before modelling and accounting for their related uncertainty in radiation-induced health risks estimates. In this work, a variance-based global sensitivity analysis was performed to rank by influence the uncertain input parameters of the NCICT software implied in brain and red bone marrow doses estimation, for four classes of CT examinations. Two recent sensitivity indices, especially adapted to the case of dependent input parameters, were estimated, namely: the Shapley effects and the Proportional Marginal Effects (PME). This provides a first comparison of the respective behavior and usefulness of these two indices on a real medical application case. The conclusion is that Shapley effects and PME are intrinsically different, but complementary. Interestingly, we also observed that the proportional redistribution property of the PME allowed for a clearer importance hierarchy between the input parameters
Tracing airborne micro-particles released by Al-industry using actinides
No full textInternational audienceIndustry remains an important source of environmental contamination by heavy metals and metalloids which may pose a serious threat to the ecosystems and the population. Especially the alumina production from bauxite ore gives rise to large amount of liquid and solid wastes since the production of 1 kg of alumina involves same weight of solid residue, the well-known red muds enriched in iron and heavy metals. Up to now, without any valorization of by-products, the red muds are stored in artificial ponds which are a potential source of pollutants for the surroundings due to the uplift of red dust under strong wind conditions. The study of plants allows filling the gap of knowledge concerning air quality over a wide potentially contaminated area. Thus, the current work aims to compare the activity concentration of natural radionuclides (238U, decay products and 232Th) in plants leaves (quercus robur and lettuces), in grains (wheat) and leaf vegetables with those of aerosols and the potential sources of atmospheric particles from soils and above all the red dust emitted from the dried ponds and from the piles of bauxite. The activity concentration of natural radionuclides such as 238U, some decay products and 232Th was determined in plants samples taken according to the two dominant wind directions, at plots located 100 to 1,500 m from the basins. Furthermore, the atmospheric particles were taken in the same areas, using a high-volume aerosol sampler. The increase of 232Th and 238U activity concentration in a few trees leaves (a factor 9 and 4, respectively) and in some aerosols samples is accompanied by a decrease of 238U/232Th activity ratio of these matrices. Such low ratio suggests that the airborne particles emitted by bauxite piles and red mud basins - those latter’s are also characterized by low 238U/232Th - first contaminate the atmosphere and then the leaves surface, after deposition. Available at distance greater than 1,000 m from the ponds, locally produced foodstuffs do not show any excess of radionuclides, suggesting low influence of airborne micro particles from the alumina production. Thus, the actinides measured in leaf vegetables rather derived from the soil micro-particles deposited onto leaves than from an anthropogenic source
Operational Method to Easily Determine the Available Fraction of a Contaminant in Soil and the Associated Soil-Solution Distribution Coefficient
No full textInternational audienceWell understanding the solid/solution partitioning of a contaminant is of first importance to determine its residence time in the environment, environmental availability, or bioavailability. Currently, parameters of contaminant transfer models are derived from two conceptually different approaches: one considering that the totality of the contaminant is in equilibrium between the solid and the solution and the other one considering that only a part of the contaminant can be transferred from the solid to the solution without considering equilibrium. Our work offers to reconcile these two approaches by assuming that the contaminant associated with the solid is present under two fractions: one available at equilibrium with the solution, and a second one not available and nontransferable to the solution. We propose to use simple operational batch methods (successive desorption batch experiments, or batch desorption conducted at different volume of solution/mass of solid (V/M) ratios) to check this assumption and to determine the real available contaminant fraction (i.e., the contaminant in the solid which can be at equilibrium with the solution) and its associated solid/solution distribution coefficient. The robustness of the proposed method was tested on simulated conditions, on experiments performed to validate the approach, and on the reinterpretation of literature data. Finally, the use of the available contaminant fraction and its associated solid/solution distribution coefficient in transfer models can improve the predictive modeling of contaminant transfer in the soil/solution/plant system