Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title Evaluation of Long-Term Sulfur Deposition Models.
Author Clark, T. L. ; Voldner, E. C. ; Dennis, R. L. ; Seilkop, S. K. ; Alvo, M. ;
CORP Author Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Sciences Research Lab. ;Atmospheric Environment Service, Downsview (Ontario). ;Analytical Sciences, Inc., Research Triangle Park, NC. ;Ottawa Univ. (Ontario).
Publisher c1989
Year Published 1989
Report Number EPA/600/J-89/345;
Stock Number PB90-216102
Additional Subjects Atmospheric models ; Sulfur ; Sulfur dioxide ; Deposition ; Precipitation(Meteorology) ; Sulfates ; Mathematical models ; Performance evaluation ; Sites ; Spatial variations ; Seasonal variations ; Statistical analysis ; Reprints ; Air pollution sampling ; Dry methods ; Wet methods ; Acid rain ; Regional analysis ; Eastern Region(North America)
Library Call Number Additional Info Location Last
NTIS  PB90-216102 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 08/27/1990
Collation 24p
The International Sulfur Deposition Model Evaluation (ISDME) project, jointly conducted by the U.S. Environmental Protection Agency and Atmospheric Environment Service of Environment Canada, assessed the performance of eleven linear chemistry atmospheric models in predicting amounts of sulfur wet deposition. Standardized model input data sets were distributed to the participating modelers, who later submitted seasonal and annual 1980 model predictions of dry/wet deposition and air concentrations of sulfur dioxide and sulfate at up to 66 sites across eastern North America. The models were evaluated in an operational mode using new, more rigorous approaches, as well as the more conventional distribution statistics recommended by the American Meteorological Society. The new approaches focused on the ability of the models to replicate features of the spatial patterns of sulfur wet deposition, as determined by an interpolation technique known as kriging. The technique quantified the uncertainties in the observations which were used in the evaluation process to identify areas where interpolated predictions were statistically significantly different from the interpolated observations. To supplement the evaluation, predictions of dry deposition amounts and air concentrations of each model were intercompared to identify apparent peculiarities. Finally, a scoring system based on criteria for six model performance measures was devised to compare seasonal, annual and overall performances of the models. Three clusters of models, each with similar overall scores, were identified.