In this paper the planning of radionuclide and other environmental contaminant studies is discussed. This discussion is based on a study of the relationship between soil aliquot size and the variability between aliquot 241Am concentrations. The study consisted of drawing multiple soil aliquots of size 1, 10, 25, 50, and 100 g from a large composite soil sample collected at a nuclear weapons testing area on the Nevada Text Site. These aliquots were then analyzed for 241Am. The between aliquot standard deviation (s) of the data increased ninefold as aliquot size declined from 100 to 1 g. This implies that about 70 times as many 1‐g aliquots than 100‐g aliquots would be needed to achieve the same precision in the estimated mean of a single field sample. The data suggest a linear relationship between logarithms of s and aliquot weight (w). This relationship is used to determine the optimum w and number, n, of aliquots per field sample that should be used. These results are also extended to a second contaminant (239,240Pu in this case) that is in a ratio relationship to 241Am. Also, an approach is given for obtaining optimum numbers of field samples and number of aliquots per field sample to minimize either total sampling and analysis costs or the variance of the estimated mean. The aliquot data from the 241Am study indicate increasing median and geometric mean concentrations with increasing aliquot size. This happens because as aliquot weight increases there is a decrease in skewness of the frequency distribution of aliquot concentrations. Since the arithmetic mean is not affected (on the average) by a change in skewness, it may be preferred to the geometric mean or median when comparing results from studies that have used different aliquot sizes.
Geostatistical data analysis techniques were used to stochastically model the spatial variability of groundwater pore velocity in a potential waste repository site. Kriging algorithms were applied to Hanford Reservation data to estimate hydraulic conductivities, hydraulic head gradients, and pore velocities. A first-order Taylor series expansion for pore velocity was used to statistically combine hydraulic conductivity, hydraulic head gradient, and effective porosity surfaces and uncertainties to characterize the pore velocity uncertainty. Use of these techniques permits the estimation of pore velocity uncertainties when pore velocity measurements do not exist. Large pore velocity estimation uncertainties were found to be located in the region where the hydraulic head gradient relative uncertainty was maximal.1.
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