The population and economic growth experienced by Canadian metropolitan areas in the past twenty years, has been associated with increased levels of car ownership and vehicle kilometres travelled leading to a deterioration of air quality and public health and an increase in greenhouse gas emissions. The need to modify urban growth patterns has motivated planning agencies in Canada to develop a broad range of policies aiming at achieving a more sustainable transportation sector. The challenge however, remains in the ability to test the effectiveness of proposed policy measures. This situation has led to a renewed interest in integrated land-use and transport models to support transport policy appraisal. This research is motivated by the need to improve transport policy appraisal through the use of integrated land-use and transport models linked with a range of sub-models that can reflect transport externalities. This research starts with an exploration of the transport policy environment in Canada through a questionnaire-based survey conducted with planners and policy-makers. The survey results highlight the need for tools reflecting the sustainability impacts of proposed policies. While the second part of this research explores sustainability indicators and recommends a set of social, economic, and environmental measures, linked with integrated land-use and transport models; effort is dedicated to estimate the environmental indicators as part of this thesis. As such, the third part of this research involves the development of an emission-dispersion-exposure modelling framework. The framework includes a suite of sub-models including an activity-based travel demand model (TASHA), an emission factor model (Mobile6.2C), a meteorological model (CALMET), and a dispersion model (CALPUFF). The framework is used to estimate link-based emissions of light-duty vehicles in the Greater Toronto Area under a base scenario for 2001. Dispersion of emissions is then conducted and linked with population in order to estimate exposure to air pollution.

The population and economic growth experienced by Canadian metropolitan areas in the past twenty years, has been associated with increased levels of car ownership and vehicle kilometres travelled leading to a deterioration of air quality and public health and an increase in greenhouse gas emissions. The need to modify urban growth patterns has motivated planning agencies in Canada to develop a broad range of policies aiming at achieving a more sustainable transportation sector. The challenge however, remains in the ability to test the effectiveness of proposed policy measures. This situation has led to a renewed interest in integrated land-use and transport models to support transport policy appraisal. This research is motivated by the need to improve transport policy appraisal through the use of integrated land-use and transport models linked with a range of sub-models that can reflect transport externalities. This research starts with an exploration of the transport policy environment in Canada through a questionnaire-based survey conducted with planners and policy-makers. The survey results highlight the need for tools reflecting the sustainability impacts of proposed policies. While the second part of this research explores sustainability indicators and recommends a set of social, economic, and environmental measures, linked with integrated land-use and transport models; effort is dedicated to estimate the environmental indicators as part of this thesis. As such, the third part of this research involves the development of an emission-dispersion-exposure modelling framework. The framework includes a suite of sub-models including an activity-based travel demand model (TASHA), an emission factor model (Mobile6.2C), a meteorological model (CALMET), and a dispersion model (CALPUFF). The framework is used to estimate link-based emissions of light-duty vehicles in the Greater Toronto Area under a base scenario for 2001. Dispersion of emissions is then conducted and linked with population in order to estimate exposure to air pollution.

Air quality forecasts are of great value since several pollutants in our environment effect both human health, global climate, vegetation, crop yields, animals, materials and acidification of forests and lakes. Air-quality forecasts help to make people aware of the presence and the quantity of pollutants, and give them a chance to protect themselves, their business and the Earth. Many different air-quality models are in daily use all over the world, providing citizens with forecasts of air quality and warnings of unhealthy air quality if recommended highest concentrations are exceeded. This study adapts the WRF meteorological model (Weather research and Forecasting model) to be a driver of the CMAQ air-quality model (models-3 Community Multiscale Air Quality model). Forecasts of ozone and nitrogen dioxide concentrations from this coupled WRF/CMAQ modelling system are tested against observed data during a four-day period in May, 2006. The Lower Fraser Valley study area is a fertile valley surrounded by mountain chains in southwest British Columbia, Canada. The valley stretches from the Pacific coast eastwards towards the Rocky Mountains. This valley hosts more than 2 million people and it is west Canada’s fastest growing region. The Lower Fraser Valley holds a big city, Vancouver, several suburbs, numerous industries and a widespread agricultural production. During the analysed four-day period in May, a synoptic high-pressure built over the region, favoring high concentrations of pollutants as ozone and nitrogen dioxide. The created WRF/CMAQ model forecasted an acceptable magnitude of nitrogen dioxide but the daily variations are not recreated properly by the model. The WRF/CMAQ model forecasts the daily variation of ozone in a satisfying way, but the forecasted concentrations are overestimated by between 20 and 30 ppb throughout the study. Factors that could contribute to the elevated ozone concentrations were investigated, and it was found that the weather forecasting model WRF was not generating fully reliable meteorological values, which in turn hurt the air-quality forecasts. As the WRF model usually is a good weather forecasting model, the short spin-up time for the model could be a probable cause for its poor performance. / Prognoser över luftkvaliteten är mycket värdefulla, då flera luftföroreningar i vår närmiljö påverkar människans hälsa, det globala klimatet, vegetation, djur, material och bidrar till försurning av skog och vattendrag. Luftkvalitetsprognoser gör människan mer medveten om närvaron av luftföroreningar och i vilken mängd de finns. De ger människan en chans att vidta skyddsåtgärder för att skydda sig själv, sitt eventuella levebröd, och Jorden. Många olika luftkvalitetsmodeller används idag dagligdags över hela världen och förser invånare med prognoser för luftkvaliteten och varningar om koncentrationerna av föroreningar överstiger rekommenderade värden. I denna studie används väderprognosmodellen WRF (Weather Research and Forecasting model) för att driva luftkvalitetsmodellen CMAQ (models-3 Community Multiscale Air Quality model). Prognoser av ozon- och kvävedioxidhalterna i luften från den kopplade WRF/CMAQ modellen analyseras mot observerade data under en fyra dagars period i maj, 2006. Studieområdet Lower Fraser Valley är en bördig dalgång som är omgiven av bergskedjor i sydvästra British Columbia, Kanada. Dalen sträcker sig från Stilla havskusten och österut mot Klippiga bergen. I denna dalgång bor mer än 2 miljoner människor och det är västra Kanadas snabbast växande region. Lower Fraser Valley rymmer en storstad, Vancouver, flera förorter, många industrier och även stora jordbruksområden. Den fyra dagars period i maj som analyseras karaktäriseras av ett högtrycksbetonat synoptiskt väderläge med lokala variationer, vilka tillsammans är gynnsamma för att uppmäta höga koncentrationer av luftföroreningar som ozon och kvävedioxid. Den skapade WRF/CMAQ modellen prognostiserar godtagbar magnitud hos kvävedioxid men den dagliga variationen återskapas inte av modellen. Modellen prognostiserar den dagliga variationen av ozonkoncentration på ett tillfredsställande sätt, men storleksmässigt ligger koncentrationerna en faktor 20-30 ppb för högt rakt av under hela studien. Kringliggande faktorer som kan påverka koncentrationen ozon studeras närmare och det framkommer att den meteorologiska prognosmodellen WRF inte genererar fullt tillförlitliga värden för en rättvisande luftkvalitetsprognos. Då WRF modellen vanligtvis är en bra prognosmodell kan den korta initialiseringstiden för modellen vara en trolig orsak till dess otillräckliga prestation.

A base case modelling investigation was conducted to explore the chemical and physical behaviour of ground-level ozone (O3) and its precursor nitrogen dioxide (NO2) in Ontario using the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model. Two related studies were completed to evaluate the performance of CMAQ in reproducing the behaviour of these species in both rural and urban environments by comparing to surface measurements collected by the Ontario Ministry of the Environment (MOE) network of air quality stations. The first study was a winter examination and the second study was conducted for a period during the summer of the same year. The municipality of North Bay was used to represent a rural setting given its smaller population relative to the city of Ottawa which was the base of the urban site. Statistical and graphical analyses were used to validate the model output. CMAQ was found to replicate the spatial variation of O3 and NO2 over the domain in both the winter and summer, but showed some difficulty in simulating the temporal allocation of the species. Validation statistics for North Bay and Ottawa showed overall O3 mean biases (MB) of 3.35 ppb and 2.25 ppb, respectively, and overall NO2 MB of -8.75 ppb and -4.37 ppb, respectively for the winter. Summer statistics generated O3 MB of 4.66 ppb (North Bay) and 10.05 ppb (Ottawa) while both MB for NO2 were between -2.20 ppb to -2.55 ppb. Graphical analysis showed that the model was not able to reproduce the lower levels of O3, especially at night, or the higher levels of NO2 during the day at the North Bay site for either season. This was expected since the comparisons were made between point measurements and 36 km grid-averaged model results. The presence of high amounts of NO2 emissions local to the monitoring sites compared to the levels represented in the emissions inventory may also be a contributing factor. The simulations for Ottawa demonstrated better agreement between model results and measurements as CMAQ provided a more accurate reproduction of both the higher and lower mixing ratios of O3 and NO2 during the winter and summer seasons. Results indicate that CMAQ is able to simulate urban environments better than rural ones.

A base case modelling investigation was conducted to explore the chemical and physical behaviour of ground-level ozone (O3) and its precursor nitrogen dioxide (NO2) in Ontario using the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model. Two related studies were completed to evaluate the performance of CMAQ in reproducing the behaviour of these species in both rural and urban environments by comparing to surface measurements collected by the Ontario Ministry of the Environment (MOE) network of air quality stations. The first study was a winter examination and the second study was conducted for a period during the summer of the same year. The municipality of North Bay was used to represent a rural setting given its smaller population relative to the city of Ottawa which was the base of the urban site. Statistical and graphical analyses were used to validate the model output. CMAQ was found to replicate the spatial variation of O3 and NO2 over the domain in both the winter and summer, but showed some difficulty in simulating the temporal allocation of the species. Validation statistics for North Bay and Ottawa showed overall O3 mean biases (MB) of 3.35 ppb and 2.25 ppb, respectively, and overall NO2 MB of -8.75 ppb and -4.37 ppb, respectively for the winter. Summer statistics generated O3 MB of 4.66 ppb (North Bay) and 10.05 ppb (Ottawa) while both MB for NO2 were between -2.20 ppb to -2.55 ppb. Graphical analysis showed that the model was not able to reproduce the lower levels of O3, especially at night, or the higher levels of NO2 during the day at the North Bay site for either season. This was expected since the comparisons were made between point measurements and 36 km grid-averaged model results. The presence of high amounts of NO2 emissions local to the monitoring sites compared to the levels represented in the emissions inventory may also be a contributing factor. The simulations for Ottawa demonstrated better agreement between model results and measurements as CMAQ provided a more accurate reproduction of both the higher and lower mixing ratios of O3 and NO2 during the winter and summer seasons. Results indicate that CMAQ is able to simulate urban environments better than rural ones.