Record Display for the EPA National Library Catalog


Main Title Reliability-Based Uncertainty Analysis of Groundwater Contaminant Transport an Remediation.
Author Hamed, M. M. ; Bedient, P. B. ;
CORP Author Rice Univ., Houston, TX.;National Risk Management Research Lab., Ada, OK. Subsurface Protection and Remediation Div.
Publisher Jun 1999
Year Published 1999
Report Number EPA-CR-821906; EPA/600/R-99/028;
Stock Number PB2007-107081
Additional Subjects Ground water ; Environmental transport ; Water pollution ; Remediation ; Contaminate plumes ; Cleanup ; Downgradient wells ; Estimates ; Reliability-based uncertainty analysis ; Analytical groundwater models ; Probabilistic modeling tools
Internet Access
Description Access URL
Library Call Number Additional Info Location Last
NTIS  PB2007-107081 Some EPA libraries have a fiche copy filed under the call number shown. 07/26/2022
Collation 82p
Failure to rigorously accommodate physical parameter uncertainty in groundwater transport models casts serious doubts on our ability to accurately delineate the contaminant plume at a given site. This failure could also considerably reduce the possibility of success of the remediation scheme intended to clean up a plume within the specified time. In this research, the probability that a contaminant leaking from a waste source will exceed some predetermined target level at a down-gradient well is estimated, along with the sensitivity of this probably to the basic uncertainty in input parameters. The relevant parameters are assumed random with prescribed probability distributions. We present a probabilistic modeling tool based on first- and second-order reliability methods (FORM and SORM) to account for parameter uncertainty in groundwater contaminant transport and remediation. The methodology is applied to analytical groundwater models to provide a simple screening tool for the assessment of contamination and remediation. In addition, numerical-based reliability models are developed to account for aquifer spatial heterogeneity and correlation structure in more complex systems.