An air dispersion model for the city of Toronto, Ontario, Canada

Abstract

Air quality is a major concern for the public; therefore, the reliability of accurate models in predicting the air quality is of a major interest. In this study, a Gaussian air dispersion model, known as the Air dispersion model for Road Sources in Urban areaS (ARSUS), was developed to predict the ground level concentrations for a contaminant of interest. It was demonstrated that this model could be used successfully in place of or in conjunction with ambient air monitoring stations in determining the local Air Quality Index (AQI). The ARSUS model was validated against the US EPA ISC3 model before it was used to conduct two studies in this investigation. These two studies simulated weekday morning rush-hour tailpipe emissions of CO and predicted ground level concentrations. The first study used the ARSUS model to predict ground level concentrations of CO from the tailpipe emissions for roads and highways located in the vicinity of the Toronto West ambient air monitoring station. The second study involved an expansion of the domain to predict ground level concentrations of CO from tailpipe emissions from highways in the City of Toronto, Ontario, Canada. The predicted concentrations were then compared to the data collected from the Toronto West ambient air monitoring station. The results of the ARSUS model indicated that the air quality in the immediate vicinity of roads or highways is highly impacted by the tailpipe emissions. Higher concentrations were observed for the areas adjacent to the road and highway sources. The tailpipe emissions of CO from highways had a higher contribution to the local air quality. The predicted ground level concentrations from the ARSUS model under-predicted when compared to the observed data from the monitoring station; however, despite this, the predictive model is viable.

Keywords:

• City of Toronto
• air dispersion modeling
• tailpipe emissions
• air quality
• urban emissions

Acknowledgments

The support of Ryerson University is greatly appreciated.