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Main Title Quality assurance of multi-media model for predictive screening tasks /
Author Chen, J., ; Beck, M. B.
Other Authors
Author Title of a Work
Beck, M. B.,
CORP Author Imperial Coll. of Science, Technology and Medicine, London (England). Dept. of Civil Engineering. ;Georgia Univ., Athens. School of Forest Resources.;Environmental Protection Agency, Athens, GA. National Exposure Research Lab.
Publisher U.S. Environmental Protection Agency, Office of Research and Development,
Year Published 1999
Report Number EPA 600-R-98-106
Stock Number PB2007-106207
OCLC Number 966688482
Subjects Groundwater--Pollution--Mathematical models--Evaluation ; Hazardous waste sites--Leaching--Mathematical models--Evaluation
Additional Subjects Quality assurance ; Soil types ; Classification ; Cost effectiveness ; Site characteristics ; Tests ; Sensitivity analysis ; Model sensitivity ; Tables(Data) ; Contamination ; Contaminants ; Impact ; Multi-media model ; Predictive screening tasks ; Predictive performance ; Output uncertainty ; Discriminating power ; EPAMMM model
Internet Access
Description Access URL
Library Call Number Additional Info Location Last
ELBD ARCHIVE EPA 600-R-98-106 Received from HQ AWBERC Library/Cincinnati,OH 10/04/2023
ELBD RPS EPA 600-R-98-106 repository copy AWBERC Library/Cincinnati,OH 12/27/2016
ELBD  EPA 600-R-98-106 AWBERC Library/Cincinnati,OH 12/27/2016
NTIS  PB2007-106207 Some EPA libraries have a fiche copy filed under the call number shown. 07/26/2022
Collation viii, 63 pages : illustrations ; 28 cm
Priorities must be determined for the ways in which limited resources can be deployed in the most cost-effective manner. In the case of potential contamination of groundwater by leachates from facilities for storing hazardous materials, there are many more sites where action might be taken to reduce risks of exposure than there are funds to support all such actions. There is a need to rank the sites of potential action in terms of achieving the greatest reduction in the risk of exposure for a given sum of money. In situations such as this, which are characterized by gross uncertainty, assessing the reliability of a model in performing the task of a screening analysis is especially important. The risks of ranking the sites for remedial action in an erroneous order are significant. The paper explores three groups of tests that might be formulated to determine model reliability. The first of these is concerned with establishing whether the uncertainties surrounding the parameterization of the model render it impotent in discriminating between which of two sites, say, gives the significantly higher predicted receptor concentration of contaminant, in conditions where this result would generally be expected. The second test is a straightforward form of regionalized sensitivity analysis designed to identify which of the model's parameters are critical to the task of predicting exceedance, or otherwise, of prescribed (regulatory) receptor-site concentrations. The third test is designed to achieve a more global form of sensitivity analysis in which the dependence of selected statistical properties of the distributions of predicted concentrations (mean, variance, and 95th-percentile) on specific model parameters can be investigated. The results of these tests suggest that it may be possible to develop a novel form of statistic for assisting in judging the trustworthiness of a candidate model for performing predictive exposure assessments.
"The U.S. Environmental Protection Agency through its Office of Research and Development partially funded and collaborated in the research described here ... with Imperial College of Science, Technology, and Medicine, London, UK"--Page ii. "August 1999." Includes bibliographical references (pages 60-63). "EPA 600-R-98-106." "Cooperative agreement # CR 816572-010, 'Analysis of uncertainty in environmental simulation'." "Project officer: Thomas O Barnwell Jr., Assistant Laboratory Director, National Exposure Research Laboratory."