ICEM 2023® International Conference on Environmental Remediation and Radioactive Waste Management

The Relative Importance of Destructive Analyses for Radiological Characterization With Geostatistics

Session: 02-03 Modeling, Work Management and Data Analysis Software
Wednesday, October 4, 2023 – 1:20 PM

Dismantling and decommissioning 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. Geostatistics 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.

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 much money, depending on the radionuclide. Therefore, the entire sampling strategy should be optimized to reduce useless samples and unnecessary measures.

This paper deals with feedback experience over the years in using geostatistics about the smart use of the variogram to explore spatial data, break down variance contributions, and model radiological contaminations. Before performing any modeling and estimation calculation, the first and foremost part of any geostatistical study is to work intensively on the dataset to explore and validate it. In addition to classical statistics tools such as basemap, histogram, and scatter plot, the variogram strengthens this analysis by identifying spatial inconsistencies, decomposing the different variability contributions (between sample duplicates, measurement replicates, and spatial variability), and consequently interpreting and modeling the joint spatial structure with in situ measurements to improve estimates and reduce uncertainties. Then, contaminated volume classification proves robust even with significant bias on lab data resulting from a nonlinear operation (probability of exceeding a threshold). The waste end-state perspective significantly modifies the classic statistics approach, as radiological data distributions are generally skewed. Appropriate data processing tools need to be used accordingly.

Geostatistics Alara Principle for Waste Classification During Radiological Characterization

Session: 02-02 Risk Assessment and Waste Optimization
Wednesday, October 4, 2023 – 3:35 PM

Radiological characterization aims to find an appropriate 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.

Geostatistics processing of data considers all available pieces of information: historical data, non-destructive measurements, and laboratory analyses of samples. The spatial structure modeling is then used to produce maps and estimate the extent of the 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 various radiological thresholds, estimate source terms, and so on.

This paper deals with feedback experience over the years using geostatistics for cost-benefit analyses about material segregation integrating estimation uncertainty and decision support impact. This approach emphasizes one interesting geostatistics output: the probability of exceeding a threshold.

From a global point of view, geostatistics provides risk curves for surfaces or volumes and accumulation (radiological inventory). The key issue is to limit false negative risk (leaving in place activity levels above the threshold) while handling false positive one (extra volumes due to over-conservatism).

When considering an objective function as the sum of the inverse cumulated frequency for probability levels and the normalized cost for remediation/decontamination, the corresponding curve generally presents a global minimum that balances the two misclassification risks. That can be seen as the ALARA adaptation for waste classification, providing an objective and defendable balance between removed contamination and related treated volumes.