Measurements made in situ with gamma detectors and ex situ measurements of soil samples in a laboratory can have complementary roles in the assessment of radioactively contaminated land on decommissioning nuclear sites. Both in situ and ex situ methods were used to characterize (137)Cs contamination within an area at the Dounreay site in Scotland. The systematic difference (bias) between estimates of the mean activity concentration was found to be non-significant when in situ measurements were interpreted using a linear depth model, based on ex situ measurements made at two different depths. An established method of evaluating the random components of measurement uncertainty was used. The random component of analytical uncertainty in the in situ measurements, made in field conditions, was found to exceed that for the ex situ measurements, made in the controlled conditions of a laboratory. However, contamination by the target radionuclide was found to be heterogeneous over small spatial scales. This resulted in significantly higher levels of random sampling uncertainty in individual ex situ measurements. As in situ measurements are substantially less costly, a greater number of measurements can be made, which potentially reduces the uncertainty on the mean. Providing the depth profile of contaminants can be modelled with confidence, this can enable estimates of mean activity concentration over an averaging area to be made with lower overall uncertainties than are possible using ex situ methods.
There is an increasing use of analytical macro‐beam techniques (such as portable XRF, PXRF) for geochemical measurements, as a result of their convenience and relatively low cost per measurement. Reference materials (RMs) are essential for validation, and sometimes calibration, of beam measurements, just as they are for the traditional analytical techniques that use bulk powders. RMs are typically supplied with data sheets that tabulate uncertainties in the reference values by element, for which purpose they also specify a minimum recommended mass of material to be used in the chemical analysis. This minimum mass may not be achievable using analytical beam techniques. In this study, the mass of the test portion interrogated by a handheld PXRF within pellets made from three silicate RMs (SdAR L2, M2 and H1) was estimated using a theoretical approach. It was found to vary from 0.001 to 0.3 g for an 8 mm beam size and 0.0001 to 0.045 g for a 3 mm beam. These test portion masses are mainly well below the recommended minimum mass for these particular RMs (0.2 g), but were found to increase as a function of atomic number (as might be expected). The uncertainties caused by heterogeneity (UHET) in PXRF measurements of the three RMs were experimentally estimated using two different beam diameters for eighteen elements. The elements showing the highest levels of heterogeneity (UHET > 5%) seem generally to be those usually associated with either an accessory mineral (e.g., Zr in zircon, As in pyrite) or low test portion mass (associated with low atomic number). When the beam size was changed from nominally 8 to 3 mm, the uncertainty caused by heterogeneity was seen to increase for most elements by an average ratio of 2.2. These values of UHET were used to calculate revised uncertainties of the reference values that would be appropriate for measurements made using a PXRF with these beam sizes. The methods used here to estimate UHET in PXRF measurements have a potential application to other analytical beam techniques.
Uncertainties arising at different stages of a measurement process can be estimated using analysis of variance (ANOVA) on duplicated measurements. In some cases, it is also desirable to calculate confidence intervals for these uncertainties. This can be achieved using probability models that assume the measurement data are normally distributed. However, it is often the case in practice that a set of otherwise normally distributed measurement values is contaminated by a small number of outlying values, which may have a disproportionate effect on the variances calculated using the 'classical' form of ANOVA. In this case, robust ANOVA methods are able to provide variance estimates that are much closer to the parameters of the underlying normal distributions. A method using bootstrapping to calculate confidence intervals from robust estimates of variances is proposed and evaluated and is shown to work well when the number of outlying values is small. The method has been implemented in a visual basic program.
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