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OLS Field Name OLS Field Data
Main Title Uncertainty and the Johnson-Ettinger Model for Vapor Intrusion Calculations.
Author Weaver, J. W. ; Tillman, F. D. ;
CORP Author Environmental Protection Agency, Athens, GA. Ecosystems Research Div.
Publisher Sep 2005
Year Published 2005
Report Number EPA/600/R-05/110;
Stock Number PB2006-101094
Additional Subjects Vapors ; Mathematical models ; Measurement ; Contamination ; Residential buildings ; Air quality ; Greenhouse gases ; Environmental exposure pathway ; Environmental impacts ; Soil properties ; Hydraulic conductivity ; Risk assessment ; Humans ; Ecosystems ; Porosity ; Moisture ; Hazardous materials ; Johnson-Ettinger Model
Internet Access
Description Access URL
http://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1000BEM.PDF
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB2006-101094 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 09/04/2006
Collation 46p
Abstract
The Johnson-Ettinger Model is widely used for assessing the impacts of contaminated vapors on residential air quality. Typical use of this model relies on a suite of estimated data, with few site-specific measurements. Software was developed to provide the public with automated uncertainty analysis applied to the model. An uncertainty analysis was performed on the model, that accounted for synergistic effects among variable model parameters. This analysis showed that a simple one-at-a time parameter uncertainty analysis provides a rough guide for the uncertainty generated by individual parameters and allowed their ranking. The one-at-a-time analysis, however, underestimated the uncertainty in the model results when all or groups of parameters were assumed to be uncertain. An apparent increase in simulated cancer risk caused by the uncertainty introduced from the input parameters was as much as 1285%. The model response to the input parameters showed that for the example studied, there was a positive skew in the model response to parameter variation.