Record Display for the EPA National Library Catalog

RECORD NUMBER: 10 OF 11

OLS Field Name OLS Field Data
Main Title Sensitivity of Critical Load Estimates for Surface Waters to Model Selection and Regionalization Schemes.
Author Holdren, G. R. ; Strickland, T. C. ; Shaffer, P. W. ; Ryan, P. F. ; Ringold, P. L. ;
CORP Author ManTech Environmental Technology, Inc., Corvallis, OR. ;Oak Ridge National Lab., TN. Environmental Sciences Div. ;Oak Ridge National Lab., Grand Junction, CO. Environmental Sciences Div.;Corvallis Environmental Research Lab., OR.;Department of Energy, Washington, DC.
Publisher c1992
Year Published 1992
Report Number EPA-68-C-0006 ;DE-AC-0584OR21400; EPA/600/J-93/412;
Stock Number PB93-236495
Additional Subjects Air pollution ; Air water interactions ; Water pollution ; Surface waters ; Mathematical models ; Acidification ; Deposition ; Study estimates ; Ecosystems ; Regional analysis ; Sulfates ; Acid neutralizing capacity ; Comparison ; Performance evaluation ; Reprints ; Critical loads
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB93-236495 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 11/22/1993
Collation 13p
Abstract
A critical load is the amount of atmospheric pollutant that can be deposited on a sensitive ecosystem without causing measurable, long-term degradation in ecosystem form or function. The authors compare several methods for making critical load estimates of SO4 deposition to lakes. The models were originally developed to address surface water acidification issues. Critical loads are computed here as the levels of deposition required to reduce surface water acid neutralizing capacities (ANC) from present values to either 25 microeq/L or 0 microeq/L. These end points were selected to provide a common basis for comparing critical load values obtained from the various models and are not intended to provide definitive critical load estimates. Differences in results are observed not only between models, but within models when slightly different approaches are used for estimating certain parameters. The interpretation of model results depends strongly on how systems are grouped regionally.