Exposure estimates produced using predictive exposure assessment methods are associated with a number of uncertainties that relate to the inherent variability of the values for a given input parameter (e.g., body weight, ingestion rate, inhalation rate) and to unknowns concerning the representativeness of the assumptions and methods used. Despite recent or ongoing consensus-building efforts that have made significant strides forward in promoting consistency in methodologies and parameter default values, the potential variability in the output exposure estimates has not been adequately addressed from a quantitative aspect. This is exemplified by remaining tendencies within federal and state agencies to use worst-case approaches for exposure assessment. In this study, range-sensitivity and Monte Carlo analyses were performed on several different exposure scenarios in order to illustrate the impact of the variability in input parameters on the total variability of the exposure output. The results of this study indicate that the variability associated with the example scenarios range up to more than four orders of magnitude when just some of the parameters are allowed to vary. Comparison of exposure estimates obtained using Monte Carlo simulations (in which selected parameters were allowed to vary over their observed ranges) to exposure estimates obtained using standard parameter default assumptions demonstrate that a default value approach can produce an exposure estimate that exceeds the 95th percentile exposure in an exposed population.
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