A probabilistic approach to the estimation of radioactive contaminant inventories at a nuclear waste disposal site. (April 2023)

• Record Type: Journal Article
• Authors: Hazra, Indranil; Pandey, Mahesh D.; Rahman, Mokhles
• Is Part Of: Journal of environmental radioactivity. Volume 259/260(2023)
• Publication Date: 2023-04
• Subjects: Spatial data modeling -- Kriging -- Conditional simulation -- Nuclear waste disposal -- Beta activity -- Radiation contamination; Radioactivity -- Periodicals; Radiation, Background -- Periodicals; Radioecology -- Periodicals; Radioactive pollution -- Periodicals; Environmental Pollutants -- Periodicals; Radioactive Pollutants -- Periodicals; Radioactivity -- Periodicals; Radioécologie -- Périodiques; Pollution radioactive -- Périodiques; Fond de rayonnement -- Périodiques; 539.752
• DOI: 10.1016/j.jenvrad.2023.107119
• Languages: English
• ISSNs: 0265-931X

Abstract

Abstract: Routine site inspections are often conducted to gather data on radiation contamination on the surface and below ground near nuclear waste disposal areas. These observations are used to calculate total radiation inventory and its spatial delineation. The statistical kriging approach is often used to spatially interpolate contamination data, and it generates predictions at unsampled sites that are then utilized to calculate the contaminated site's radiation inventory. The kriging output, however, creates a point estimate of the inventory that omits the potential uncertainties from other sources. This paper presents a method for assessing the uncertainty of radiation inventories based on the geostatistical conditional simulation method – a simulation methodology that takes into account the observations made at the sampled sites. The radiation inventories' histograms are generated by conducting many conditional simulations of the projection map using a fitted kriging model. A practical implementation of the suggested approach is shown by evaluating total beta inventories and their spatial delineation using groundwater monitoring data at a nuclear waste disposal site. Highlights: Method for estimation of radiation inventories at nuclear waste disposal facilities. Uncertainty analysis using statistical kriging and conditional simulation on ArcGIS. Practical application of total beta inventory estimation at a Canadian site.