Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title A Probabilistic methodology for analyzing water quality effects of urban runoff on rivers and streams : final report.
CORP Author Environmental Protection Agency, Washington, DC. Office of Water.
Publisher U.S. Environmental Protection Agency, Office of Water,
Year Published 1989
Report Number PB2000-104946; EPA 841-R-89-101
Stock Number PB2000-104946
OCLC Number 40058467
Subjects Water--Pollution--United States.
Additional Subjects Water quality ; Urban runoff ; Water pollution effects ; Streams ; Rivers ; Discharge(Water) ; Stream flow ; Ecological concentration ; Rainfall-runoff relationship ; Monte Carlo method ; Gaussian quadrature ; Probability density functions ; Water pollution monitoring ; Regression analysis ; Water pollution control ; Simulation ; Mathematical models ; Probability Dilution Model ; Derived distribution method ; Nationwide Urban Runoff Program
Internet Access
Description Access URL
Library Call Number Additional Info Location Last
ELBD  EPA 841-R-89-101 AWBERC Library/Cincinnati,OH 10/28/2011
ESAD  PB 2000-104946 Region 10 Library/Seattle,WA 05/11/2009
NTIS  PB2000-104946 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 02/27/2020
Collation 1 v. (various pagings) : ill. ; 30 cm.
The Probability Dilution Model provides a screening level methodology for determining the effect of intermittent pollutant discharges on the water quality of streams and rivers. The processes analyzed in the model (urban runoff, streamflow, and water quality) are probabilistic by nature and are treated as such by the model. The method uses a deterministic mass balance approach and accounts for the nature variabilities in the model inputs into the stream. This model is an extremely useful screening level tool for initial planning purposes to help determine problem areas and to screen possible control alternatives. Three methods, the Analytical Derived Distribution Approach, a numerical Gaussian Quadrature method and the Monte Carlo simulation approach, are presented.
"July, 1989."