Record Display for the EPA National Library Catalog

RECORD NUMBER: 41 OF 45

OLS Field Name OLS Field Data
Main Title Statistical estimation and visualization of ground-water contamination data {computer file} /
Author Boeckenhauer, R. K. ; Cox, D. D. ; Ensor, K. B. ; Bedient, P. B. ; Holder, A. W.
Other Authors
Author Title of a Work
Boeckenhauer, Rachel K.
CORP Author Rice Univ., Houston, TX. Dept. of Environmental Science and Engineering.;National Risk Management Research Lab., Ada, OK. Subsurface Protection and Remediation Div.
Publisher U.S. Environmental Protection Agency, Office of Research and Development,
Year Published 2000
Report Number EPA/600/R-00/034
Stock Number PB2001-104380
OCLC Number 46632149
Subjects Groundwater--Computer simulation ; Groundwater flow--Computer simulation
Additional Subjects Ground water ; Water pollution ; Soil pollution ; Statistical analysis ; Visualization ; Estimates ; Remediation ; Methods ; Exploratory analysis
Internet Access
Description Access URL
http://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=30002E0S.PDF
http://purl.access.gpo.gov/GPO/LPS9902
http://www.epa.gov/nrmrl/pubs0199.html
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
ERAD  EPA/600/R-00-034 2 copies Region 9 Library/San Francisco,CA 03/04/2013
NTIS  PB2001-104380 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 01/01/1988
Abstract
This work presents methods of visualizing and animating statistical estimates of ground water and/or soil contamination over a region from observations of the contaminant for that region. The primary statistical methods used to produce the regional estimates are nonparametric regression and geostatistical modeling to produce surface estimates with little outside intervention, whereas geostatistical modeling produces prediction errors. Finally, a method is proposed for estimating the total amount of contaminant present in a region. The proposed method models the data as a realization of a lognormal stochastic process and then capitalizes on conditional simulation to generate realizations of the modeled process from which the distribution of the total contaminant (or integral of the process) is estimated.
Notes
Title from title screen. Includes bibliographical references.