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A Stochastic Assessment of Nuclear Waste Management Practices at the Hanford Site, Washington
Published online by Cambridge University Press: 21 March 2011
Abstract
Waste management and disposal decisions at the Hanford Site, Washington, depend in part on an understanding of the risks and impacts associated with alternate disposal and remedial actions. A proof-of-principle site-wide assessment of the risks and impacts associated with all wastes that will remain at the Hanford Site following cleanup has been performed for the first time. It simulates contaminant release, migration, and fate from the initiation of site operations in 1944 forward, and, thus, illustrates historical and near-term influences on long-term risk and impact.
A stochastic simulation tool capable of addressing 1000 waste discharge and disposal sites and 10 contaminants for a period of 1000 years has been created and applied. Human health and ecological risks as well as impacts to the regional economy and local cultures are estimated. The methodology developed is known as the System Assessment Capability (SAC). It is currently in a Revision 0 state corresponding to a proof-of-principle demonstration.
An initial assessment based on the planning baseline of the U.S. Department of Energy (Richland Operations office and office of River Protection) has been undertaken. Preliminary results of the assessment indicate variability in predictions is most influenced by uncertainty in:
- geochemical adsorption (i.e., distribution coefficients, Kd) for contaminant release, vadose zone, and groundwater models; especially for uranium and iodine which are somewhat but not greatly adsorbed,
- solid waste burial ground inventories of iodine-129, and
- liquid discharge site inventories of technetium-99.
It is apparent that variables governing change in performance are a function of space and time. Initially, these results point to the need for related models and data to be examined, and, if necessary, augmented through future laboratory and field studies to better quantify or reduce uncertainty.
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- Copyright © Materials Research Society 2002