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The model includes many typical features and processes that would be part of a PA, but is entirely ficticious. This does not represent any particular site and is meant to be a generic example. A practitioner could, however, start with this model and by adding site-specific features and parameter values (distributions) use this model as a starting point for a real model to be used in real decision making.
This method of constructing PAs offers several advantages to the traditional technique, which involves constructing and running many diverse computer programs, which generally do not communicate directly with each other, to run a series of calculations for each part of the model. This common method is not transparent, does not allow for feedback between the different modeled parts, and is difficult to present to the decision maker and the public. In general, past PAs have been solely deterministic in nature as well, meaning that they produce a single result (e.g. dose to a receptor) which does not capture the uncertainty inherent in the process.
New software like GoldSim, combined with ever-increasing computer power,
brings the art of Performance Assessment to new levels of sophistication,
transparency, and utility. Regulators and other decision makers, as well
as the general public and other stakeholders, will benefit from the
improvements offered by this method of modeling. Perhaps the most
significant innovation is the integration of the state of knowledge about
a site (including its radiological inventory and environmental processes at
work) into the model itself. The uncertainties inherent in the analysis
are reflected in the range of results obtained by the model.
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This LLW trench is situated near the ground surface, with a few meters of earthen cover, in a common configuration called shallow land burial (SLB). Water from precipitation and run-on is allowed to infiltrate, mingle with the disposed waste, leaching its radiological constituents, and continue downward through the unsaturated zone to the water table. There the contaminated water mixes with an aquifer which supplies a drinking water well. Biological processes are also modeled, including plant uptake and redistribution of contaminants to the ground surface and animal burrowing, which moves contaminated soils to the surface and back down through the soil column as burrows collapse.
Two human receptors are modeled, a transient occupant and a local resident
who engages in light agriculture in contaminated soil.
The transient is assumed to spend a limited
amount of time walking around the site, exposed to dust and "ground shine" but
not local water or foodstuffs. The resident spends much more time on the site,
eats locally-produced crops and animal products, and drinks water from the
aforementioned well. This resident scenario is a standard for most PAs.
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The CSM is translated into a mathematical representation, focused on following
the fate and transport of the radiological constituents from the waste form
to the receptor. This mathematical abstraction is essentially a large "word
problem", where one translates a narrative description into parameters and
equations. Once the mathematical model is devised, it can be coded into a
computer model. In this case, GoldSim is used to document both. Since GoldSim
is a "whiteboard"-style program, meaning that it is graphically based using
"pages" which can contain text, links to sources of information,
and modeling elements all
together, the model can be used to document itself. Modeling elements include
data inputs (either single-valued or as probability distributions), equations
from the mathematical model, and specialized elements to represent discrete
volumes of environmental media (e.g. soil or rock) or even the waste form
itself. Result elements are used to display results in a variety of formats,
usually as time histories of contaminant concentrations or doses to the
receptors.
The Generic PA GoldSim model can be run using mean (average) values of the
parameters, which will produce an average result, useful for getting the
general idea of the future risk posed by the waste disposal facility.
The model can also be run probabilistically, using any number of realizations
(typically hundreds or perhaps even thousands for a sufficiently complex model)
each of which represents a possible outcome, given the uncertainties in the
input parameter values, and perhaps in parts of the model itself. (This is
called model uncertainty, and it, too, can be implemented using GoldSim).
The probabilistic, or stochastic, results reflect the state of knowledge
about the site and its behavior, and therefore a more honest evaluation of
the potential future risk it poses. In this case, the resulting doses have
a spread of about four orders of magnitude.
These results can be used for sensitivity analysis, which can identify which of the uncertain parameters are most significant in determining the dose, for example. A decision maker can then evaluate whether it makes sense to devote further resources to reducing uncertainties, or if the uncertainty reflected by the model is acceptable for the purposes of decision making (e.g. deciding whether the site requires further engineering or controls in order to be protective of the public).
A log of version upgrades:
Here are links to the full model and software needed to run it:
Here are links to the Player version of the model and software needed to run it:
Send comments regarding these pages to
John Tauxe
Last modified: 9 July 2007