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A comparison of atmospheric composition using the Carbon Bond and Regional Atmospheric Chemistry MechanismsChemistry Mechanisms
Sarwar, G., J. Godowitch, B. Henderson, K. Fahey, G. Pouliot, Bill Hutzell, R. Mathur, D. Kang, W. Goliff, AND W. Stockwell. A comparison of atmospheric composition using the Carbon Bond and Regional Atmospheric Chemistry MechanismsChemistry Mechanisms. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, Germany, 13(19):9675-9712, (2013).
We incorporate the recently developed Regional Atmospheric Chemistry Mechanism (version 2, RACM2) into the Community Multiscale Air Quality modeling system for comparison with the existing 2005 Carbon Bond mechanism with updated toluene chemistry (CB05TU). Compared to CB05TU, RACM2 enhances the domain-wide monthly mean hydroxyl radical concentrations by 46% and nitric acid by 26%. However, it reduces hydrogen peroxide by 2%, peroxyacetic acid by 94%, methyl hydrogen peroxide by 19%, peroxyacetyl nitrate by 40%, and organic nitrate by 41%. RACM2 predictions generally agree better with the observed data than the CB05TU predictions. RACM2 enhances ozone for all ambient levels leading to higher bias at low (<60 ppbv) concentrations but improved performance at high (>70 ppbv) concentrations. The RACM2 ozone predictions are also supported by increased ozone production efficiency that agrees better with observations. Compared to CB05TU, RACM2 enhances the domain-wide monthly mean sulfate by 10%, nitrate by 6%, ammonium by 10%, anthropogenic secondary organic aerosols by 42%, biogenic secondary organic aerosols by 5%, and in-cloud secondary organic aerosols by 7%. Increased inorganic and organic aerosols with RACM2 agree better with observed data. While RACM2 enhances ozone and secondary aerosols by relatively large margins, control strategies developed for ozone or fine particles using the two mechanisms do not differ appreciably.
The National Exposure Research Laboratory′s (NERL′s)Atmospheric Modeling Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
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Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LAB
ATMOSPHERIC MODELING DIVISION