Abstract
Air quality is a major concern for the public; therefore, the reliability of models in predicting the air quality accurately is of a major interest. The objective of this study was to develop an air dispersion model and demonstrate that it can be successfully used in place of or in conjunction with ambient air monitoring stations in determining the local Air Quality Index (AQI). This thesis begins with a review of existing atmospheric dispersion models, specifically, the Gaussian Plume models and their capabilities to handle the atmospheric chemistry of nitrogen oxides (NOx) and sulphur 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 representing atmospheric chemistry, specifically in the context of NOx and SO₂x substances and their applications to urban areas. A review was completed of previous studies where Gaussian dispersion models were applied to major cities around the world such as London, Helsinki, Kanto, and Prague, to predict ground level concentrations NOx and SO₂. For the purpose of this thesis, Gaussian air dispersion model was developed, known as the Air dispersion model for the Road Sources in Urban areas (ARSUS) model, which is capable of predicting ground level concentrations for a contaminant of interest. The ARSUS model was validated against the US EPA ISC3 model before it was used to conduct the 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 ARSUS model to predict ground level concentrations of CO from the tailpipe emissions of CO 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 located in the City of Toronto. The modelled concentrations were then compared to the Toronto West ambient air monitoring station. ARSUS model’s results indicate that air quality in the immediate vicinity of roads or highways is highly impacted by the tailpipe emissions. Higher concentrations are observed for the areas adjacent to the road and highway sources. The tailpipe emissions of CO from highways have a higher contribution to the local air quality. The predicted ground level concentration from the ARSUS model do under-predict when compared to the observed data from the monitoring station; however, despite this a predictive model is viable.
Highlights
Over the past few years, the smog days in Ontario, Canada have been steadily increasing
The air quality in Ontario is on a decline and causes the province additional cost in health care as well as limits enjoyment of the outdoors
By super-imposing the hourly concentrations over the modeled area, the results showed locations of localized hot spots
Summary
Over the past few years , the smog days in Ontario, Canada have been steadily increasing. Air pollution limits the enjoyment of the outdoors and increases the cost of the health care [1] and [2]. To combat this problem , the Ontario Ministry of Environment (MOE) introduced new tools to reduce emissions as well as improved communication with the public on the state of the air quality. The communication policy has been implemented by the introduction of an Air Quality Index (AQI) based on actual pollutant concentrations reported by various monitoring stations across Ontario. One major concern is the spatial distribution of pollutants not captured by monitoring stations
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.