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RECORD NUMBER: 6 OF 6

OLS Field Name OLS Field Data
Main Title Using CMAQ-AIM to Evaluate the Gas-Partitioning Treatment in CMAQ (Community Multiscale Air Quality).
Author Nolte, C. G. ; Bhave, P. V. ; Dennis, R. L. ; Zhang, K. M. ; Wexler, A. S. ;
CORP Author Environmental Protection Agency, Research Triangle Park, NC. National Exposure Research Lab. ;National Oceanic and Atmospheric Administration, Silver Spring, MD. Atmospheric Sciences Modeling Div. ;California Univ., Davis. Dept. of Mechanical, Aeronautical and Materials Engineering.
Publisher 2004
Year Published 2004
Report Number EPA/600/A-04/107 ;NERL-RTP-AMD-04-083;
Stock Number PB2005-101224
Additional Subjects Air pollution ; Air quality models ; Computerized simulation ; Regional-scale ; Photochemistry ; Gases ; Meteorological data ; Aerosols ; Model representations ; Chemical composition ; Fine-particle mass ; Gas partitioning ; Community Multi-Scale Air Quality Model(CMAQ)
Holdings
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Status
NTIS  PB2005-101224 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 05/03/2005
Collation one CD-ROM contains 4 page document
Abstract
The Community Multi-scale Air Quality model (CMAQ) aerosol component utilizes a modal representation, where the size distribution is represented as a sum of three lognormal modes. Though the aerosol treatment in CMAQ is quite advanced compared to other operational air quality models, various members of the CMAQ community have commented that improvements to the aerosol module should be made in order to bring it to the state of the science found in research-grade air quality models. Among the shortcomings noted are that (1) a trimodal representation is insufficient to resolve the aerosol size and composition distribution; (2) interactions of the gas phase with coarse particles, such as sea-salt, are neglected; and (3) the assumption of instantaneous equilibrium between gas-phase species and fine-mode aerosol is inaccurate. In the present study, we seek to understand the effects of the latter two assumptions on predicted fine-particle mass and chemical composition in regional-scale air quality models.