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Human exposure assessment. I: Understanding the uncertainties.
G K Whitmyre1, J H Driver, M E Ginevan
1Technology Sciences Group Inc., Washington, D.C. 20036.
Toxicology and Industrial Health
|September 1, 1992
Summary
Predictive exposure assessments have uncertainties due to input variability. This study shows that default values can overestimate population exposure, exceeding the 95th percentile.
Area of Science:
- Environmental Health
- Risk Assessment
- Toxicology
Background:
- Predictive exposure assessment methods involve uncertainties from input parameter variability and methodological assumptions.
- Despite efforts to standardize methods and default values, quantitative analysis of output variability remains insufficient.
- Federal and state agencies often employ worst-case approaches in exposure assessments.
Purpose of the Study:
- To quantitatively illustrate the impact of input parameter variability on exposure estimates.
- To compare exposure estimates derived from variable parameters versus standard default values.
- To highlight the potential for overestimation using default value approaches.
Main Methods:
- Range-sensitivity analysis was conducted on exposure scenarios.
- Monte Carlo simulations were performed using variable input parameters.
- Exposure estimates were compared between simulation and default value approaches.
Main Results:
- Variability in input parameters can lead to output exposure estimates differing by over four orders of magnitude.
- Monte Carlo simulations revealed significant variability in exposure outcomes.
- Default value approaches can generate exposure estimates exceeding the 95th percentile of the exposed population.
Conclusions:
- The variability in predictive exposure assessment outputs is substantial and requires quantitative evaluation.
- Standard default values may not accurately represent population exposure distributions.
- A shift towards more nuanced, data-driven approaches is needed to improve the accuracy of exposure assessments.