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Atmospheric Chemistry in Existing Air Atmospheric Dispersion Models and Their Applications: Trends, Advances and Future in Urban Areas in Ontario, Canada and in Other Areas of the World

Author(s): Barbara Laskarzewska | Mehrab Mehrvar

Journal: International Journal of Engineering
ISSN 1985-2312

Volume: 3;
Issue: 1;
Start page: 21;
Date: 2009;
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Keywords: Air quality data | Air dispersion modeling | Gaussian dispersion model | Dry deposition | Wet deposition (in-cloud | below cloud | snow) | Urban emissions

Air quality is a major concern for the public. Therefore, the reliability in modeling and predicting the air quality accurately is of a major interest. This study reviews existing atmospheric dispersion models, specifically, the Gaussian Plume models and their capabilities to handle the atmospheric chemistry of nitrogen oxides (NOx) and sulfur dioxides (SO2). It also includes a review of wet deposition in the form of in-cloud, below cloud, and snow scavenging. Existing dispersion models are investigated to assess their capability of handling atmospheric chemistry, specifically in the context of NOx and SO2 substances and their applications to urban areas. A number of previous studies have been conducted where Gaussian dispersion model was applied to major cities around the world such as London, Helsinki, Kanto, and Prague, to predict ground level concentrations of NOx and SO2. These studies demonstrated a good agreement between the modeled and observed ground level concentrations of NOx and SO2. Toronto, Ontario, Canada is also a heavily populated urban area where a dispersion model could be applied to evaluate ground level concentrations of various contaminants to better understand the air quality. This paper also includes a preliminary study of road emissions for a segment of the city of Toronto and its busy streets during morning and afternoon rush hours. The results of the modeling are compared to the observed data. The small scale test of dispersion of NO2 in the city of Toronto was utilized for the local hourly meteorological data and traffic emissions. The predicted ground level concentrations were compared to Air Quality Index (AQI) data and showed a good agreement. Another improvement addressed here is a discussion on various wet deposition such as in cloud, below cloud, and snow.
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