Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title Methodology for characterization of uncertainty in exposure assessments /
Author Whitmore, Roy W.,
Other Authors
Author Title of a Work
Falco, James W.,
Publisher Office of Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency,
Year Published 1985
Report Number EPA/600-S8-85-009
OCLC Number 899244243
Subjects Health risk assessment. ; Environmental health--Evaluation. ; Uncertainty.
Internet Access
Description Access URL
Library Call Number Additional Info Location Last
EJBD ARCHIVE EPA 600-S8-85-009 In Binder Headquarters Library/Washington,DC 11/06/2017
EJBD  EPA 600-S8-85-009 In Binder Headquarters Library/Washington,DC 10/03/2018
Collation 3 pages ; 28 cm.
Caption title. "EPA/600-S8-85-009." "September 1985." At head of title: Project Summary
Contents Notes
Virtually all exposure assessments except those based upon measured exposure levels for a probability sample of population members rely upon a mathematical model to predict exposure. Whenever a model that has not been validated is used, an uncertainty associated with the exposure assessment may be present. The primary characterization of uncertainty is partly qualitative, i.e., it includes assumptions inherent in the model. Sensitivity of the exposure assessment to model formulation can be investigated by replicating the assessment for plausible alternative models. When an exposure assessment is based upon directly measured exposure levels for a probability sample of population members, uncertainty can be greatly reduced and described quantitatively. In this case, the primary sources of uncertainty are measurement errors and sampling errors. A thorough quality assurance program should be designed into the study to ensure that measurement errors can be estimated. The effects of all sources of random error should be measured quantitatively by confidence interval estimates of parameters of interest, e.g., percentiles of the exposure distribution. Moreover, the effect of random errors can be reduced by taking a larger sample. Whenever the latter is not feasible, it is sometimes possible to obtain at least some data for exposure and model input variables. This substantially reduces the amount of quantitative uncertainty for estimation of the distribution of exposure.