1 Performance Evaluation of the URM-1ATM Modeling System in the Southern Appalachian Mountains

Abstract-- Recently, a comprehensive air quality modeling system has been developed as part of the Southern Appalachians Mountains Initiative (SAMI). The performance of the model in predicting ozone, size- and composition-resolved aerosols, and acid deposition mass fluxes have been evaluated using m...

Full description

Bibliographic Details
Main Authors: James W. Boylan, Mehmet T. Odman, James G. Wilkinson, Armistead G. Russell, Stephen F. Mueller, Robert E. Imhoff, Kevin G. Doty, William B. Norris, Richard T. Mcnider
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.555.7062
http://samiproject.ce.gatech.edu/Documents/Papers/performance.pdf
Description
Summary:Abstract-- Recently, a comprehensive air quality modeling system has been developed as part of the Southern Appalachians Mountains Initiative (SAMI). The performance of the model in predicting ozone, size- and composition-resolved aerosols, and acid deposition mass fluxes have been evaluated using measurements during nine episodes between 1991 and 1995. The daily averaged normalized bias and error for ozone are typically within EPA guidance criteria for urban-scale modeling. The mean normalized error was approximately 40 % for the sulfate, ammonium, elemental carbon, and organic components that constitute over 75 % of the PM2.5 in the region. The error is generally larger for the nitrate and soil components but these components are relatively small. The wet deposition mass fluxes have high spatial variability, but still agree well with observations. The mean normalized errors for sulfate and nitrate wet deposition were approximately 25%. Wet deposition bias was further accentuated by a bias in simulated precipitation. Variations in modeling error with pollutant levels were also examined. Most species showed a systematic overestimation for low levels and an underestimation for high levels.