[en] Dismantling and decommissioning of nuclear facilities or remediation of contaminated sites are industrial projects with huge challenges. Precise knowledge of the contamination state is required. Radiological evaluations have multiple objectives to be considered: determination of average activity levels, to allow the categorization of surfaces or volumes (sorted into different radioactive waste categories); location of hot spots (small areas with significant activity levels); and estimation of the source term (total activity) contained in soils or building structures. In addition there are radiation protection and other logistics considerations. Estimates are essential for the proper management of these projects. Currently, characterization remains relatively empirical. Accumulated approximations often have serious consequences that threaten the project’s successful completion, for example through over-categorization or unexpected contamination. Radioactive contamination is generally complex and involves numerous parameters: radiological fingerprint, transfer path, type of contaminated materials, presence of different matrices (soils, concrete), and so on. Numerical modelling often turns out to be very difficult. The characterization phase should be efficient and the sampling strategy has to be rational. However, investigations also represent capital expenditure; the cost of radiation protection constraints and laboratory analysis can represent a large amount of money, depending on the radionuclide. Therefore the entire sampling strategy should be optimized to reduce useless samples and unnecessary measures.

[en] The presented methodological study illustrates a Geo-statistical approach suitable for radiological evaluation in nuclear premises. The waste characterization is mainly focused on floor concrete surfaces. By modeling the spatial continuity of activities, Geo-statistics provide sound methods to estimate and map radiological activities, together with their uncertainty. The multivariate approach allows the integration of numerous surface radiation measurements in order to improve the estimation of activity levels from concrete samples. This way, a sequential and iterative investigation strategy proves to be relevant to fulfill the different evaluation objectives. Waste characterization is performed on risk maps rather than on direct interpolation maps (due to bias of the selection on kriging results). The use of several estimation supports (punctual, 1 m², room) allows a relevant radiological waste categorization thanks to cost-benefit analysis according to the risk of exceeding a given activity threshold. Global results, mainly total activity, are similarly quantified to precociously lead the waste management for the dismantling and decommissioning project. This paper recalled the geo-statistics principles and demonstrated how this methodology provides innovative tools for the radiological evaluation of contaminated premises. The relevance of this approach relies on the presence of a spatial continuity for radiological contamination. In this case, geo-statistics provides reliable activity estimates, uncertainty quantification and risk analysis, which are essential decision-making tools for decommissioning and dismantling projects of nuclear installations. Waste characterization is then performed taking all relevant information into account: historical knowledge, surface measurements and samples. Thanks to the multivariate processing, the different investigation stages can be rationalized as regards quantity and positioning. Waste characterization is finally obtained through the analysis of probability maps of exceeding activity levels. The estimation support must be taken into account to discriminate punctual issues, such as hot spot identification, and waste production issues on larger areas. The main goal of this data processing remains an easier radiological waste management and the best waste categorization with acceptable investigation costs. Ongoing research deals with the implementation of the Geo-statistical methodology on nuclear systems and equipments and on former radiological waste storages. (authors)

[en] Radiological characterisation plays an important role in the process to recycle contaminated or potentially contaminated metals. It is a platform for planning, identification of the extent and nature of contamination, assessing potential risk impacts, cost estimation, radiation protection, management of material arising from decommissioning as well as for the release of the materials as well as the disposal of the generated secondary waste as radioactive waste. Key issues in radiological characterisation are identification of objectives, development of a measurement and sampling strategy (probabilistic, judgmental or a combination thereof), knowledge management, traceability, recording and processing of obtained information. By applying advanced combination of statistical and geostatistical in the concept better performance can be achieved at a lower cost. This paper will describe the benefits with the usage of the available methods in the different stages of the characterisation, treatment and clearance processes aiming for reliable results in line with the data quality objectives. (authors)

[en] The objective of radiological characterization is to find a suitable balance between gathering data (constrained by cost, deadlines, accessibility or radiation) and managing the issues (waste volumes, levels of activity or exposure). It is necessary to have enough information to have confidence in the results without multiplying useless data. Geo-statistics processing of data considers all available pieces of information: historical data, non-destructive measurements and laboratory analyses of samples. The spatial structure modelling is then used to produce maps and to estimate the extent of radioactive contamination (surface and depth). Quantifications of local and global uncertainties are powerful decision-making tools for better management of remediation projects at contaminated sites, and for decontamination and dismantling projects at nuclear facilities. They can be used to identify hot spots, estimate contamination of surfaces and volumes, classify radioactive waste according to thresholds, estimate source terms, and so on. The spatial structure of radioactive contamination makes the optimization of sampling (number and position of data points) particularly important. Geo-statistics methodology can help determine the initial mesh size and reduce estimation uncertainties. Several show cases are presented to illustrate why and how geo-statistics can be applied to a range of radiological characterization where investigated units can represent very small areas (a few m"2 or a few m"3) or very large sites (at a country scale). The focus is then put on experience gained over years in the use of geo-statistics and sampling optimization. (author)

[en] The problem of site characterization is quite complex, especially for deep radiological contamination. This article illustrates the added value of the geo-statistical processing on a real application case dealing with grounds of facilities partially dismantled at the end of the 1950's in Fontenay-aux-Roses CEA Center (France). 12 years ago, a first exploratory drill-hole confirmed the presence of a deep radiological contamination (more than 4 m deep). More recently, 8 additional drill-holes failed to delineate the contamination extension. The integration of the former topography and other geological data led to the realization of 10 additional drill holes. This final stage significantly improved the characterization of the radiological contamination, which impacted the remediation project and the initially estimated volumes. (authors)

No abstract available

[en] Before nuclear facilities are dismantled, and contaminated sites remediated, their initial radiological state must be characterized. Combining historical information, non-destructive measurements and lab analysis results, the statistical method of geo-statistics allows all available data for 2D or 3D contamination mapping to be integrated and enhanced, and helps quantify estimation uncertainties. (authors)

[en] Full text: Sellafield is the UK facility for Nuclear Fuel Reprocessing and Waste Management. It is a compact coastal site with an area of around 3 km2. It is currently operational and is expected to remain licensed until 2120. Radioactive material has entered the sub-surface environment during operations following accidental leaks. This material is currently under active risk management prior to a final hazard reduction and site remediation phase. Sellafield Ltd has to understand and control the legacy of ground contamination to ensure protection of the workforce, the public and the environment. The main control exercised over this material is through an extensive monitoring and risk modelling programme. This work generates a quantity of important environmental data gathered at public cost. Features of the data that make its interpretation difficult include: • Very large data sets • Many different parameters • Long time span (several decades) • Variable quality (changes in analytical methods over time) • Spot 3-D data (as opposed to continuous or 2-D plots) To ensure that the best use is being made of appropriate methods for the gathering and understanding of these data, Sellafield Ltd has commissioned geostatistical analysis. Soils and groundwater data are necessarily spatially correlated and require dedicated geostatistics data processing. Different spatial anisotropies are observed in the saturated and non-saturated zones and integrated in the model. Uncertainty quantification of contaminated volume estimates according to several radiological waste thresholds is addressed to improve risk analysis (remediation feasibility, costs, waste management…). Finally, a critical review of the sampling effort identifies under- or over-sampled areas based on the spatial auto-correlation description. Improvements have been achieved in the following areas through this work: • Inventory; – better quantification of contaminated land volumes with an understanding of uncertainty; • Characterisation – greater resolution of contaminant distribution with an understanding of uncertainty; • Modelling - development of predictive transport models reflecting heterogeneous conditions; • Management – improved use of data visualisation to support communication, planning & risk management. The paper will address: • The background to Sellafield land quality data interpretation challenges; • A description of the use of geostatistics to analyse Sellafield datasets; • A discussion on how results could be used in the future and on potential applications of geostatistics to other nuclear site challenges. (author)

[en] The TruPro"R process enables to collect a significant number of samples to characterize radiological materials. This innovative and alternative technique is experimented for the ANDRA quality-control inspection of cemented packages. It proves to be quicker and more prolific than the current methodology. Using classical statistics and geo-statistics approaches, the physical and radiological characteristics of two hulls containing immobilized wastes (sludges or concentrates) in a hydraulic binder are assessed in this paper. The waste homogeneity is also evaluated in comparison to ANDRA criterion. Sensibility to sample size (support effect), presence of extreme values, acceptable deviation rate and minimum number of data are discussed. The final objectives are to check the homogeneity of the two characterized radwaste packages and also to validate and reinforce this alternative characterization methodology. (authors)

[en] The Bauzot site, located in Burgundy, France, was initially an underground mining site for uranium extraction in the 1950's. Then for a dozen of years, very low level radiological waste and mining residues were stored on the former pit-head. Later in the 1980's, mining works came back with an open pit at the edge of the storage. This site is now Areva Mines' property and is a Classified Installation for Environmental Protection. According to recent environmental regulation, the physicochemical and radiological knowledge of the 50 years old storage site has to be significantly improved to complete the existing monitoring. In addition to new piezometers and the update of the hydrogeological model beneath the site for potential transfer to the surrounding environment, it is then necessary to carry out a sampling campaign to characterize the storage itself. Despite a sizeable budget, the possible number of drill-holes (water-free, confined) is limited and their locations have to be thoroughly selected. That is why a better prior understanding of the storage setting up is necessary. This paper presents first the different sources of information and their exploitation to build a 3D reconstitution of the storage. Based on this improved historical knowledge, an expertise sampling strategy is initially proposed and adapted taking on site drilling results into account. Improving the physical and radiological knowledge of Bauzot site was a quite big challenge. A unique and complex site characterisation has been successfully implemented to confirm the presence of three drum origins and to validate areas of interest. The final characterisation is on the process as laboratory results are just arriving at the end of 2012 (gamma spectrometry on all samples and advanced analyses on targeted samples). The global synthesis will be available soon. The adaptive sampling strategy is very efficient in combination with on line data interpretation to refine interactively the sampling plan. Based on a high level historical reconstitution, it proves to be the best approach to optimize the collected data due to the limited number of drillings. That way, the waste family representing only 1% of all stored drums has been properly delineated with only a very limited drum sampling yield at the storage scale (only 80 drums over 80,000 are passed through). After 6 months of preparation, 3 months of drilling operations and 6 months of laboratory analyses and data interpretation, the Bauzot site is on the point of been correctly characterized thanks to an outstanding cooperation between all stakeholders. Furthermore, no radiological incident has occurred toward drilling workers (despite the extreme climatic conditions) nor toward the environment. The sampling preparation, notably the historical reconstitution, has been very valuable to optimize the investigation effort. In the light of this updated site characterization, Areva Mines is now in position to decide on the storage future, in agreement with regulation authorities.