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Under the Copernicus programme, an operational CO2 Monitoring Verification and Support system (CO2MVS) is being developed and will exploit data from future satellites monitoring the distribution of CO2 within the atmosphere. Methods for estimating CO2 emissions from significant local emitters (hotspots; i.e. cities or power plants) can greatly benefit from the availability of such satellite images that display the atmospheric plumes of CO2. Indeed, local emissions are strongly correlated to the size, shape, and concentration distribution of the corresponding plume, which is a visible consequence of the emission. The estimation of emissions from a given source can therefore directly benefit from the detection of its associated plumes in the satellite image. In this study, we address the problem of plume segmentation (i.e. the problem of finding all pixels in an image that constitute a city or power plant plume). This represents a significant challenge, as the signal from CO2 plumes induced by emissions from cities or power plants is inherently difficult to detect, since it rarely exceeds values of a few parts per million (ppm) and is perturbed by variable regional CO2 background signals and observation errors. To address this key issue, we investigate the potential of deep learning methods and in particular convolutional neural networks to learn to distinguish plume-specific spatial features from background or instrument features. Specifically, a U-Net algorithm, an image-to-image convolutional neural network with a state-of-the-art encoder, is used to transform an XCO2 field into an image representing the positions of the targeted plume. Our models are trained on hourly 1 km simulated XCO2 fields in the regions of Paris, Berlin, and several power plants in Germany. Each field represents the plume of the hotspot, with the background consisting of the signal of anthropogenic and biogenic CO2 surface fluxes near to or far from the targeted source and the simulated satellite observation errors. The performance of the deep learning method is thereafter evaluated and compared with a plume segmentation technique based on thresholding in two contexts, namely (1) where the model is trained and tested on data from the same region and (2) where the model is trained and tested in two different regions. In both contexts, our method outperforms the usual segmentation technique based on thresholding and demonstrates its ability to generalise in various cases, with respect to city plumes, power plant plumes, and areas with multiple plumes. Although less accurate than in the first context, the ability of the algorithm to extrapolate on new geographical data is conclusive, paving the way to a promising universal segmentation model trained on a well-chosen sample of power plants and cities and able to detect the majority of the plumes from all of them. Finally, the highly accurate results for segmentation suggest the significant potential of convolutional neural networks for estimating local emissions from spaceborne imagery.

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.

In the event of a release of radioactive pollutants into the atmosphere, one of the missions of the authorities is to evaluate the consequences of this release in order to implement, if necessary, measures to protect the population. These may include evacuation or sheltering in the very short term, and restrictions on the consumption or marketing of contaminated foodstuffs in the longer term. For this purpose, numerical models are used to simulate the dispersion of radionuclides in the atmosphere.The accuracy of the results obtained from these models strongly depends on the knowledge of the source term, i.e. the location, duration and magnitude of the release as well as its distribution between radionuclides. However, knowledge of the source term is generally subject to significant uncertainties. In addition to the source term, other uncertainties arise from the transport model, meteorological fields, measurement data and the representativeness of the model with respect to the measurements.In this PhD, we have developed and applied inverse modelling methods to improve the evaluation of the source term and to quantify the uncertainties.Among the inverse modelling methods, variational deterministic approaches are effective in providing a rapid estimate of the source term, but the quantification of uncertainties associated with this estimate is generally difficult.We therefore propose to address the problem within the probabilistic framework of Bayesian inference, which is part of a formalism that allows a more complete assessment of uncertainties.Several Markov chain Monte Carlo (MCMC) sampling methods are implemented in order to reconstruct the variables describing the source: the Metropolis Hastings (MH) algorithm, the Parallel tempering algorithm and finally the Reversible-Jump MCMC.These algorithms are first applied and validated on the ruthenium 106 detection event that occurred in Europe in the fall of 2017. The probability densities of the source variables are reconstructed in order to identify the geographical origin of the detections as well as the quantities of ruthenium 106 released into the atmosphere.Then, in a second step, several methods are developed in order to incorporate and quantify different sources of errors within the Bayesian problem and thus enable us to obtain a better reconstruction of the distribution of the release.The second case study is dedicated to the Fukushima Daiichi plant accident which led to long releases associated with time-varying kinetics. The reconstruction of these releases with more complex characteristics required the development of a new MCMC algorithm, the Reversible-Jump MCMC, which was adapted from previous sampling methods.Applied to the Fukushima case, the Reversible-Jump MCMC shows its ability to sample more finely and efficiently the distribution of the source term and uncertainties., En cas de rejet de polluants radioactifs dans l’atmosphère, une des missions des autorités est d’évaluer les conséquences de ce rejet afin de mettre en œuvre, si nécessaire, des mesures de protection des populations. Il peut s’agir d’évacuation ou de mise à l’abri à très court terme et de restrictions de consommation ou de commercialisation des denrées alimentaires contaminées à plus long terme. Pour cela, des modèles numériques sont utilisés pour simuler la dispersion des radionucléides dans l’atmosphère.La précision des résultats obtenus à partir de ces modèles dépend fortement de la connaissance du terme source, c’est-à-dire de la localisation, de la durée, de l’ampleur du rejet ainsi que de sa distribution entre radionucléides. Or, la connaissance du terme source est généralement soumise à d’importantes incertitudes. En plus du terme source, d’autres incertitudes proviennent du modèle de transport, des champs météorologiques, des données de mesure et de la représentativité du modèle par rapport aux mesures.Dans cette thèse, nous avons développé et appliqué des méthodes de modélisation inverse permettant d’améliorer l’évaluation du terme source et de quantifier les incertitudes.Parmi les méthodes de modélisation inverse, les approches déterministes variationnelles sont efficaces pour fournir une estimation rapide du terme source, mais la quantification des incertitudes associée à cette estimation est généralement difficile.Nous proposons donc d'aborder le problème dans le cadre probabiliste de l'inférence bayésienne qui s’inscrit dans un formalisme permettant d’obtenir une évaluation plus complète des incertitudes.Plusieurs méthodes d’échantillonnage de Monte Carlo à chaîne de Markov (MCMC) sont mises en œuvre afin de reconstruire les variables décrivant la source : l’algorithme de Metropolis Hastings (MH), l’algorithme du Parallel tempering et enfin le Reversible-Jump MCMC.Ces algorithmes sont tout d’abord appliqués et validés sur l’évènement de détection de ruthénium 106 survenu en Europe à l’automne 2017. Les densités de probabilité des variables de la source sont reconstruites afin d’identifier l’origine géographique des détections ainsi que les quantités de ruthénium 106 rejetées dans l’atmosphère.Puis, dans un second temps, plusieurs méthodes sont développées afin d’incorporer et de quantifier différentes sources d’erreurs au sein du problème bayésien et ainsi permettre une meilleure reconstruction de la distribution du rejet.Le second cas d’étude est dédié à l’accident de Fukushima qui a conduit à des rejets longs associés à une cinétique variable dans le temps. La reconstruction de ces rejets aux caractéristiques plus complexes a nécessité le développement d’un nouvel algorithme MCMC, le Reversible-Jump MCMC, qui a été adapté à partir des méthodes d’échantillonnage précédentes.Appliqué au cas de Fukushima, le Reversible-Jump MCMC montre sa capacité à échantillonner plus finement et plus efficacement la distribution du terme source et des incertitudes.

International audience; In October 2017 unusual Ruthenium-106 detections across most of Europe prompted the Institut de Radioprotection et de Sûreté Nucléaire the magnitude of the release. This paper presents the inverse modeling techniques used during the event to achieve this goal. The method is based on a variational approach and consists of using air concentration measurements with the ldX long-range dispersion model included in the IRSN’s C3X operational platform. The method made it possible to quickly identify the southern Urals as the most likely geographical origin of the release. Despite uncertainties regarding the starting date of the release, calculations show that it potentially began on 23 September, while most of the release was emitted on 26 September. Among the nuclear plants identified in the southern Urals, the Mayak complex is that from which the dispersion of the Ruthenium-106 plume is most consistent with observations. The reconstructed Ruthenium-106 source term from Mayak is ∼250 TBq. In total, it was found that for 72% of the measurements simulated and observed air concentration agreed within a factor of 5. In addition, thesimulated deposition of 106Ru agrees with the observed deposition. Outside the southern Urals, the simulations indicate that areas with highest deposition values are located in southern Scandinavia and southeastern Bulgaria and are explained by rainfall events occurring while the plume was passing over.

International audience; One dimensional interpolatory rules with n to n+2 nesting are first derived by local and global minimization of the classical bounds of integration and polynomial interpolation error. The proposed 1D rules are then tested for different classes of functions. The most efficient 1D quadratures are then extended to multi-dimension using Smolyak's method and tested on basic multivariate functions. The proposed rules and their nD extension appear to be as efficient as their counterparts based on the classical Leja sequences.