Simulating the impacts of household travel on greenhouse gas ...

Transportation (2011) 38:871–887 DOI 10.1007/s11116-011-9362-9

Simulating the impacts of household travel on greenhouse gas emissions, urban air quality, and population exposure Marianne Hatzopoulou • Jiang Y. Hao • Eric J. Miller

Published online: 12 July 2011 Ó Springer Science+Business Media, LLC. 2011

Abstract This paper establishes a link between an activity-based model for the Greater Toronto Area (GTA), dynamic traffic assignment, emission modelling, and air quality simulation. This provides agent-based output that allows vehicle emissions to be tracked back to individuals and households who are producing them. In addition, roadway emissions are dispersed and the resulting ambient air concentrations are linked with individual time-activity patterns in order to assess population exposure to air pollution. This framework is applied to evaluate the effects of a range of policy interventions and 2031 scenarios on the generation of vehicle emissions and greenhouse gases in the GTA. Results show that the predicted increase of approximately 2.6 million people and 1.3 million jobs in the region by 2031 compared to 2001 levels poses a major challenge in achieving meaningful reductions in GHGs and air pollution. Keywords

Activity-based model Air quality Vehicle emissions Policy evaluation

Introduction Urban areas in Canada are no exception to a global trend toward increasing car use, both in terms of vehicle ownership and vehicle kilometres travelled (VKT). According to M. Hatzopoulou (&) Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke St. W., Room 492, Montreal, QC H3A 2K6, Canada e-mail: [email protected] J. Y. Hao Department of Civil Engineering, University of Toronto, 35 St George Street, Toronto, ON M5S 1A4, Canada e-mail: [email protected] E. J. Miller Department of Civil Engineering, Director Cities Centre, University of Toronto, 455 Spadina Ave, Suite 400, Toronto, ON M5S 2G8, Canada e-mail: [email protected]

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Transportation (2011) 38:871–887

Environment Canada’s latest National Pollutant Release Inventory, road transportation is a major contributor to emissions of greenhouse gases (GHGs) and Criteria Air Contaminants (CACs). Among roadway vehicles, light-duty gasoline vehicles and trucks are responsible for a significant portion of emissions (EC 2008a, b). In 2007, the City of Toronto’s Public Health department estimated the contribution of traffic-related air pollution to about 440 premature deaths and 1,700 hospitalizations per year (Toronto Public Health, 2007). The climate change and sustainability agenda have added a new dimension to transport policy both in Canadian urban areas and in most metropolitan areas around the world. The City of Toronto is committed to achieving a significant decrease in transport emissions of air pollutants and GHGs by 2031. This study is motivated by the need to improve transport policy appraisal in the Greater Toronto Area (GTA) by extending an activity-based travel demand model with capabilities for representing transport impacts of interest to policy-makers. For this purpose, the activity-based travel demand model for the GTA, TASHA (Travel Activity Scheduler for Household Agents) developed at the University of Toronto (Miller and Roorda 2003), is linked with the MATSIM (Multi-Agent Transportation Simulation Toolbox) agent-based simulation model for travel demand and traffic flow, as well as models for vehicle emissions (MOBILE6.2C) and atmospheric dispersion (CALMET/CALPUFF). This paper describes the development of this modelling framework which builds upon earlier research linking TASHA with emission modelling (Hao et al. 2010) as well as integrating emission and dispersion modelling for the City of Toronto (Hatzopoulou and Miller 2010). The developed framework is then used to investigate the potential for reducing transport emissions of air pollutants and GHGs in the GTA by 2031 compared to 1991 levels. The main objective is to examine whether significant reductions in emissions can be expected in 2031 with respect to 1991 and assess the types of policies that can achieve such reductions.

Trends in the development of large-scale models addressing urban sustainability Despite significant efforts dedicated towards the development of Integrated Urban Models (IUM) and activity-based models as policy-assessment tools, they still lack the capability of fully modelling air quality impacts. Indeed, few examples exist today where IUM and activity-based models have been extended with capabilities for simultaneous evaluation of emissions, air quality, and population exposure as a result of land-use and transport policy scenarios (Buliung et al. 2006). Shiftan and Suhrbier (2002) have used activity-based models developed for Portland (MBRC and Bowman 1998) to investigate the impacts of Transportation Demand Management measures on mobile emissions, tours, trips, and VMT. AMOS (Activity Mobility Simulator) and PCATS (Prism-Constrained ActivityTravel Simulator) are examples of activity-based microsimulation models that have been used for emission estimation. PCATS has been coupled with a dynamic network simulator (DEBNetS) and used to forecast CO2 emissions in Kyoto, Japan (Kitamura et al. 1996). AMOS has been embedded, as a central component with the Sequenced Activity Mobility Simulator (SAMS) conceptual framework that includes a module for generating air quality emissions (Kitamura et al. 1998). On the IUM side, Kanaroglou et al. (2006) have mapped CO concentrations in the Hamilton area in Canada by linking IMULATE with emission and dispersion models. In the context of the SPARTACUS (System for Planning and Research in Towns and Cities for Urban Sustainability) project conducted within the Environment and Climate Research Program of the European Commission, a land-use and transport model, MEPLAN, was combined with a set of sustainability indicators which

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include transport emissions and air quality (Lautso and Toivanen 1999; Lautso et al. 2004). Recently Beckx et al. (2009), linked the activity-based model ALBATROSS with emission modelling and used the resulting emissions as input for the AURORA air quality model to predict hourly concentrations of different pollutants in the Netherlands. On the other hand, the literature swells with studies conducted by air quality modellers, linking state-of-the-art emission and dispersion modelling. Recently, a European network of researchers developed the OSCAR air quality assessment system which includes a suite of models for studying street-level air quality problems; including traffic, emissions, meteorological pre-processing, and dispersion. Two different dispersion models are integrated in the OSCAR framework: CARII which is a simple model that performs annual statistics and CAR-FMI which is more suited for the estimation of hourly concentrations (Sokhi et al. 2008). The TEMMS (Traffic Emission Modelling and Mapping Suite) project in the UK is an example of a modelling system that integrates traffic emissions and air dispersion. Currently, the model is linked to a traffic assignment model, SATURN (Simulation and Assignment of Traffic to Urban Road Networks); a vehicle emissions model, ROADFAC; and air pollution dispersion models, Airviro or ADMS (Mitchell et al. 2005). Recognizing exposure as a better indicator of the health effects of air pollution than emissions or concentration of air pollutants, the DAPPLE (Dispersion of Air Pollution and Penetration into the Local Environment) research project, developed in the UK, aims at assessing the sustainability of urban road transport in terms of exposure to traffic-related air pollution (Colvile et al. 2004). In Portugal, Borrego et al. (2003) developed a modelling system linking the TREM (Transport Emission Model for Line Sources) emission model with the VADIS Lagrangian dispersion model. The TREM/VADIS system was used mainly to assess dispersion conditions in street canyons. In Finland, Karppinen et al. (2000) developed a modelling system for evaluating traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion in urban areas. The dispersion modelling is based on the Finnish urban dispersion model UDM-FMI and the road network dispersion model CAR-FMI. While there is much to be learnt from these studies, they are not primarily aimed at assessing strategic transport policy scenarios due to their poor sensitivity to travel demand or land-use changes which are best represented within IUMs linked with activity-based travel demand models. This paper presents an integrative process whereby models of travel demand, vehicle emissions and dispersion, are integrated in a modelling framework which aims to respond to policy-makers’ needs in terms of environmental appraisal of transport policy. The approach transcends the traditional methods for coupling models and ensures that advances in transport, emissions, and dispersion modelling are not left in isolation but that each discipline is set within the broader context of the other disciplines.

Description of modelling framework The contribution of household travel to urban air quality in the GTA was estimated through the modelling framework illustrated in Fig. 1. This framework involves six main steps: 1. As a first step, the activity-based travel demand model, TASHA, is used to generate an initial activity schedule including mode choice on a microsimulation basis using interzonal travel times and transit level of service measures generated by EMME/2. 2. The initial TASHA schedules, for auto drivers, are then input into MATSim which in-turn generates Origin–Destination travel times that are used to update the initial

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Fig. 1 Overview of modelling framework

TASHA schedules. The output of this second step is an updated set of auto travel schedules for each person. 3. In third step, the updated schedules are input into a car allocation model which attaches a specific car to every trip; the output is fed back into MATSim which generates a list of events including trip starts and ends, as well as links entered and left for every person, trip, and vehicle.

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4. The MATSim events are then input into an emissions calculator which is supplied with MOBILE6.2C emission factors (EFs). The resulting emissions are computed for start, hot soak, and exhaust (idling and cruising) conditions. 5. Only exhaust emissions occurring on every roadway link, and aggregated on an hourly basis, are input into CALPUFF, a puff-based Gaussian dispersion model driven by its meteorological pre-processor, the CALMET diagnostic meteorological model. Linkbased emissions of nitrogen oxides (NOx) are selected for dispersion. The resulting concentrations are then compared with data from air pollution monitoring stations. 6. The last step involves deriving population exposure to vehicle-induced air pollution by accumulating exposure of individuals throughout their daily activities using output from TASHA (for all individuals, both drivers and non-drivers) and air pollutant concentrations. Generation of vehicle emissions Emission modelling includes steps 1–4 in Fig. 1. It involves linking TASHA with MATSim and a car allocation model to generate travel demand data which is then processed by an emissions calculator, supplied with EFs generated by Mobile6.2C. Both travel demand and input data for emissions were generated for 2001. Travel demand At the core of the generation of travel demand data for emission modelling in the GTA is the integration between TASHA, MATSim, and a car allocation model. TASHA is a microsimulation, activity-based model. It generates activities for each person based on observed joint probability distributions; predicts activity locations based on a series of entropy models; schedules activities following a set of rules; and finally assigns mode through a random utility tour-based mode choice model. It has been validated using 2001 trip diary data (Roorda et al. 2007). MATSim is currently developed jointly at TU Berlin and ETH Zu¨rich. It consists of a variety of microsimulation tools for modelling travel demand and traffic flow. It is a fast agent-based simulation designed to handle large networks and millions of agents. Large scale scenarios have been tested in Zurich, Berlin, and other cities (Balmer 2007). The Iterative Demand Optimization Process—Evolutionary Algorithm (MATSim-EA) within the MATSim framework starts with an initial travel demand and uses an iterative process between travel activity rescheduling and traffic assignment. The traffic assignment component in MATSim uses event-driven queue-based time-step-based microsimulation to model vehicles on the network. In the current configuration for integrating TASHA and MATSim, TASHA is used to generate personal tour information and MATSim in-turn, simulates the tours and attempts different routes to minimize and stabilize travel times. Once the simulation is complete, interzonal travel times are extracted from the simulation results and fed back into TASHA. Since the current version of MATSim can only handle auto trips, TASHA still has to rely on EMME/2 for non-drive related data such as transit travel times and level of service data. TASHA uses household expansion factors to amplify the O-D flows generated based on 5% of the real GTA population. A MATSim simulation with full population is computationally infeasible on the computers used for this research. Therefore, to use the 5% population, MATSim network capacity needs to be reduced. This reduction factor was specified in the simulation configuration file and was applied only during the actual simulation. The reduction in

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storage capacity is simulated by amplifying the car length. After experimenting with different values, a flow capacity factor of 0.05 and a storage capacity of 0.1 were used. The detailed methodology for integrating TASHA and MATSIM is presented in Hao et al. (2010). While TASHA predicts the mode for each conducted trip, it does not attempt to allocate the different cars owned by a household to specific trips. For this purpose, a Car Allocation Model was developed and applied to the TASHA output. The model assigns a vehicle to each auto trip within a person’s activity chain, taking into account the availability of vehicles, as well as scheduling conflicts between different people in the same household. The model was developed in an object-oriented platform using the Python programming language. Based on the number of cars in the household (as determined by TASHA), a pool of cars is developed, containing the household cars as distinct elements. When an individual starts a trip chain, he/she will request a car from the car pool and when that individual finishes the trip chain, the car will be released. Throughout the day; cars are requested and released until the end of the day where the car pool is re-populated for each household. In the absence of information on the types of vehicles owned by households (not available from TASHA), all cars are assumed to be the same (type, age, model, fuel, etc.). Vehicle type distributions for the GTA are then applied to the output of the car allocation model in order to refine vehicle emission estimates. Details regarding the methodology and set of rules behind the car allocation model are presented in Hatzopoulou et al. (2007). Emission calculator Four types of emissions were estimated: exhaust, idling, start, and hot soak. Exhaust and idling emissions account for vehicle emissions on the roads, whereas start and hot soak emissions take place at the start and end of each trip. Emissions of NOx, Carbon Monoxide (CO), Volatile Organic Compounds (VOCs), and Carbon Dioxide (CO2) were estimated. In order to estimate EFs for the four types of emissions, three different look-up tables for EFs were generated using the Canadian version of the Mobile6.2 model developed by the USEPA (2003). Exhaust emissions were estimated while the vehicle was travelling at free-flow speed, and while idling on congested roads and intersections. The idling portion assumes a low-speed drive cycle (average speed of 2.5 mph) which includes acceleration, deceleration, and stopping. A total of 60 speed-roadway type scenarios were modelled for each hour of the day, emission type, and pollutant. A look-up table for start EFs (in grams/ start) was also developed. Start emissions depend on the amount of time an engine was turned off before it is started, i.e. on the soak duration. The look-up table for start EFs was developed for 70 soak duration categories. Finally, a look-up table for hot soak EFs (in grams/min) was generated. Hot soak emissions depend on the length of the soak following an engine shut-down and they typically become negligible within an hour. As such, the look-up table for hot soak EFs was developed for 60 soak duration categories, each of 1 min. These were generated only for VOCs. Following the generation of EFs and travel demand data, the two were combined in order to calculate total emissions for a typical day in 2001. Four types of events in the MATSim output are used to extract travel demand data needed to calculate emissions. These include: departure, enter link, leave link, and arrival. For every vehicle and with every departure, start emissions are generated (depending on the time the engine has been off since the end of the previous trip); entering and leaving links provide information for

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exhaust and stop-and-go emissions occurring on the link; and with every arrival, hot soak emissions are generated (until the end of the hot soak). Emissions were computed and analyzed through various dimensions. These include total emissions in the GTA aggregated by type of emission and pollutant; emissions per trip (time, purpose); emissions per person (age, occupation, gender) and household (household location, dwelling type, number of cars); as well as emissions by roadway link. In terms of the total daily emissions in the GTA, exhaust emissions have the largest percentage share, followed by start, hot soak, and idling emissions (Fig. 2). Total emissions are highest during peak hours and idling emissions are most significant during the morning and afternoon peaks. Household location was found to be a major contributor to the amount of emissions produced. Figure 3 presents daily transportation CO2 emissions per household plotted based on the household zone of residence in the GTA. Clearly, households that generate the highest CO2 emissions throughout the day due to their travel reside outside of the City whereas Toronto residents are associated with relatively low daily CO2 emission loads. Figure 4 illustrates daily link-based CO2 emissions superimposed on the previous map, clearly showing that the highest emissions are indeed occurring within the City. The same pattern is observed for air pollutants. Such a finding has enormous policy and equity repercussions. As the City of Toronto becomes faced with the challenge of reducing its own GHG emissions, it is important to note that changing the travel behaviour of Toronto residents can hardly achieve this goal. Residents of the GTA are all responsible for the GHGs generated within the City. With its ability to estimate emissions on an individual trip basis and link them to specific individuals and households, this modelling framework provides the possibility to conduct equity analyses and becomes an invaluable tool for transport policy appraisal. Modelling ambient air quality and exposure The TASHA-MATSim-Mobile6.2C interface developed in steps 1–4 (Fig. 1) was linked with an air quality model (Step 5 in Fig. 1). For this purpose, hourly link-based emissions of NOx in the City of Toronto were dispersed and the resulting spatial and temporal distributions of NOx levels in the City were assessed. At this stage, only exhaust, linkbased emissions are dispersed, future research will look at the dispersion of evaporative Fig. 2 Distribution of daily VOC emissions in the GTA

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Fig. 3 CO2 emissions (in kg) per household by household location

Fig. 4 CO2 emissions on roadway links superimposed on the daily CO2 emissions per household

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VOC emissions. Among the range of pollutants for which emissions were estimated, NOx were selected due to (1) their contribution to transport emissions in the City of Toronto, in fact 73% of NOx emissions in the City are attributed to the transport network (ICF International 2007); and (2) their role in the formation of tropospheric ozone. The CALMET/CALPUFF modelling system was selected for this application. CALPUFF is a transport and dispersion model that advects puffs of emitted pollutants while simulating chemical transformation processes along the way (Scire et al. 2000a). CALMET is a meteorological model which develops three dimensional hourly gridded fields of wind and temperature (Scire et al. 2000b). Using CALMET, a meteorological model was developed for the GTA for the year 2001 thus simulating daily meteorological conditions over the entire year. For this purpose terrain elevation data, land cover data, hourly meteorological data for six stations within the modelling domain, and three dimensional hourly meteorological data obtained from the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model MM5 (Grell et al. 1994) were processed. In light of the importance of wind fields to the transport of pollutants, wind vectors generated by CALMET were compared to surface observations. For this purpose, time series files for hourly wind speeds and directions for 2001 were extracted at three locations (Hamilton Airport, Pearson Airport, and the Toronto Island Airport). Based on the monthly windroses developed for the three locations, some general trends are observed. In general, CALMET captures well the most frequent winds in the GTA, which are Westerly, Southwesterly, or Northwesterly. Northern winds are somehow under-represented by the model especially the Northern wind gusts which occur in the winter season. CALMET predicted overall lower wind speeds and under-represented winds higher than 10 m/s. These observations are consistent with other studies (Klausmann and Scire 2005). Following the generation of three dimensional meteorological data for the modelling domain, the CALMET output file was used to drive the CALPUFF dispersion model. Road segments were input into CALPUFF as area sources. This was done by (1) dividing every road link into n-segments of equal length, each segment with a length approximately equal to 0.5 km; (2) re-allocating link emissions equally among the generated segments; and (3) drawing buffers around road segments to create small area sources. A total of 10,000 links (around 15,000 areas) in the City of Toronto alone were treated as emission sources for dispersion. Figure 5 illustrates the methodology and data flows for the CALMET/CALPUFF application. The detailed methodology is discussed in Hatzopoulou and Miller (2010). Figure 6 presents 24-h average NO2 concentrations for selected days in 2001 (images to the left) in addition to the time at which the highest concentration of the day occurred (images to the right). Vehicle emissions vary between summer and winter conditions. Travel demand is the same for the different days. While this assumption is not entirely true, the purpose of this exercise is to illustrate the effect of meteorology on air quality under the same travel demand conditions. Clearly, different meteorological conditions at the selected days have lead to different concentration profiles for the same traffic conditions. On January 5th and July 17th, the highest concentrations occur in the evening while the highest concentrations on November 23rd occur in the morning. Despite varying meteorological conditions among the selected days, all of the maps show that areas North, Northeast, and Northwest of the City are mostly affected by road emissions. Observed concentrations in the City of Toronto were obtained from monitoring stations managed by Environment Canada and compared with concentrations predicted at the same locations. Overall, the model captures the general trend in NOx throughout the day.

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Fig. 5 Data flows (inputs and outputs) and processors for meteorological and dispersion modelling using CALMET/CALPUFF

However, predicted concentrations are less than a factor of two of observations and this is in part due to the fact that trucks and other commercial vehicle movements as well as other point sources were not taken into account (Hatzopoulou and Miller 2010). Using hourly concentrations at the 463 Traffic Analysis Zones (TAZs) making-up the City of Toronto, population exposure profiles were constructed (step 6 in Fig. 1). Recall that TASHA generates a list of activities and trips for individuals throughout the day. As such, by keeping track of the location of activities and knowing the distribution of NO2

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Fig. 6 Daily NO2 concentrations at zone centroids (left) and time of day at which the maximum concentration occurs (right)

concentrations within the City, the levels which individuals are exposed to throughout the day, are accumulated thus generating a daily average NO2 exposure for every individual. The highest accumulated concentration by any individual on July 17 was 81 lg/m3 whereby the highest daily concentration at any location was 74 lg/m3. This means that certain individuals accumulated an average daily concentration higher than the highest daily average at any location. This is due to the fact that they may have happened to be at the worst time period in each location they visited. Traditionally, air pollution exposure is estimated based on the home location. This framework demonstrates the importance of examining the ‘‘micro-data’’ especially for individuals at risk since their time-activity

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patterns may cause them to accumulate high concentrations despite the fact that they may live in locations characterized by acceptable air quality.

Scenario analysis of vehicle emissions in Toronto The GTA is composed of the City of Toronto and four regional municipalities (Durham, York, Peel, Halton). The City of Hamilton is also often included within the GTA commuter-shed (Fig. 7). Regional totals for population and employment in the four GTA regional municipalities, the Cities of Toronto and Hamilton for the years 1991, 2001, and 2031, show an expected increase of 48% in the total population of the region in 2031 compared to 2001. Employment is expected to increase by about 51%. This increase corresponds to an additional 2.6 million people and 1.3 million jobs approximately between 2001 and 2031 which entails a significant challenge for the region if it were to commit to a decrease or even stabilization of its GHG and air pollutant emissions from the transport sector. The main objective of this application is to assess the impact of transit expansion and emission-reduction technology on GHGs and air pollutant emissions of road vehicles in the City of Toronto and the GTA as a whole in 2031 as compared to 1991 levels. For this purpose, five different scenarios are developed and modelled using part of the framework described in ‘Description of modelling framework’ section. Development of scenarios A total of five scenarios were selected for this study, these include a 1991 base case scenario with 1991 population and employment as well as emission standards and vehicle technology characterizing the 1991 vehicle fleet. A 2001 base case scenario including 2001

Fig. 7 Greater Toronto and Hamilton area

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population and employment as well as emission standards and vehicle technology characterizing the 2001 vehicle fleet was also developed. For 2031, a do nothing scenario (scenario 3) was developed including population and employment distributions for 2031. This scenario represents the evolution of travel in 2031 assuming no improvements to either roads or transit are implemented. This transportation scenario is coupled with 2007 US CAFE standards including fuel economy values for passenger cars and light-duty trucks up to model year 2007. This scenario assumes that no modifications to these standards are implemented by 2031 and therefore all model year vehicles 2007–2031 follow the fuel economy value for 2007. This scenario will be referred to as 2031_DN_CAFE2007. The 2031 ‘‘transit’’ scenario (scenario 4) includes population and employment distributions for 2031. It also includes a new light-rail network in the City as well as improvements to the interregional public transportation (aka GO Transit) through the addition of new stations thus expanding its reach. This scenario represents the effect of new transit supply. It is coupled with the same fuel economy standards as in scenario 3. It will be referred to as 2031_TC_CAFE2007. The final scenario (scenario 5) includes the same transit developments as in scenario 4 in addition to tightened CAFE standards for passenger cars and light-duty trucks for model years 2020–2031. Beside fuel economy, this scenario includes lower gasoline volatility. This scenario will be referred to as 2031_TC_CAFE?. Results and discussion A look at the evolution of total trips and total transit trips in 1991, 2001, 2031, and 2031 with transit improvements reveals a steady increase in the total number of trips and in transit trips (Table 1). Emissions of air pollutants have been decreasing since 1991 despite an increase in trips and VKT. On the other hand, CO2 emissions have increased in 2001 compared to 1991 and under the 2031 scenario compared to 2001. Under the transit scenario, a small decrease in CO2 emissions is observed; however, it is only under the 2031 transit and improved fuel efficiency scenario that a significant drop in CO2 emissions is observed (Fig. 8). In order to better understand the effect of transit improvements on pollutant emissions, link-based NOx exhaust emissions in the GTA were plotted at 6:00 AM under the 2031_DN_CAFE2007 scenario as well as the 2031_TC_CAFE2007 scenario (Fig. 9). By visually comparing the two maps, it can be seen that while highway emissions have slightly decreased under the effect of transit improvements, the most significant change seems to have occurred within the City of Toronto whereby emissions from arterial roads have decreased.

Table 1 Total daily trips and transit trips in the GTA and for Toronto residents Scenario

1991

2001

2031

2031_transit

2031_transit (minus) 2031

Total trips in GTA

8,981,266

10,495,424

15,659,890

15,679,677

19,787

Total transit trips in GTA

1,633,955

1,863,052

2,278,039

2,359,157

81,119

Total trips Toronto residents

4,318,046

4,596,127

5,723,515

5,730,176

6,662

Total transit trips for Toronto residents

1,254,062

1,392,660

1,713,980

1,725,253

11,273

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Fig. 8 Percent change in emissions compared to 1991 for all scenarios

Fig. 9 Link-based NOx emissions in the GTA at 6:00 AM under the two 2031 scenarios: 2031_DN_CAFE2007 (left) and 2031_TC_CAFE2007 (right) scenarios

By comparing the five different scenarios, results show that the decrease in emissions between 1991 and 2031 is largely the effect of improved vehicle technology and tighter emission standards. The effect of transit improvements, in 2031, on transit mode share and passenger auto CO2 emissions is minimal. Only with very stringent fuel efficiency standards can CO2 levels in 2031 be brought down to approximately 1991 levels. The growth that the region is expected to undergo by 2031 is significant. Most of this growth is projected to occur outside the City of Toronto, in the regions of York, Peel, Halton, and Durham. This situation poses a major challenge in achieving meaningful reductions in GHG emissions within the region. This analysis indicates that transit investments within the City of Toronto alone are not sufficient to achieve this goal. Similarly, improvements in vehicle fuel efficiency will only achieve reductions down to 1991 levels but not below. As a result, more aggressive policy instruments and a reduced reliance on gasoline internal combustion engines are needed. Policies promoting densification and transit expansions beyond the City of Toronto would allow for a more sustainable growth outside of Toronto.

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The lack of a significant increase in transit mode share between the 2031 do nothing scenario and the 2031 transit scenario may also be attributed to various modelling limitations. The 2031 do nothing scenario may have overestimated transit usage because the effect of congestion on transit buses is currently not taken into account within the model. As a result, the effect of dedicated bus lanes as part of the Transit City scenario may not have been captured adequately. Moreover, our level of confidence in current off-peak travel times is lower than that for AM or PM peak periods; this may have led to a poor capture of the effect of light rail lines which typically have a more significant impact during the off-peak. Currently TASHA does not capture changes in vehicle ownership. This means for example, that households cannot respond to transit improvements by reducing the number of cars owned. Finally, no land-use changes were modelled as part of this exercise; as a result, potential densification along transit corridors within the City was not captured.

Conclusion This paper describes the on-going development of an integrated multi-model approach for the assessment of road emissions, ambient air quality and population exposure. It establishes a link between activity-based travel demand modelling, dynamic traffic assignment, and emission as well as dispersion modelling and mapping thus providing a framework for transport policy assessment. The framework developed and calibrated for the year 2001 is then used, in part, to assess the impacts of improved vehicle technology and transit expansions on the generation of vehicle emissions in the GTA in 2031. The motivation for this application derives from the City of Toronto’s commitment to decrease city-wide emissions of air pollutants and GHGs in 2031 below 1991 levels. An increase of approximately 2.6 million people and 1.3 million jobs is predicted to occur in the region by 2031 compared to 2001 levels. Most of this growth would occur outside the City of Toronto, which poses a major challenge in achieving meaningful reductions in GHGs. This study demonstrates that transit investments within the City of Toronto alone (while neglecting commuter transit serving the other municipalities within the GTA) are not sufficient to achieve this goal especially that most of the growth is expected outside the City. Similarly, improvements in vehicle fuel efficiency will only achieve reductions down to 1991 levels but not below. While this study provides an analysis of a limited range of policies and technology fixes and their effects on vehicle emissions in the GTA and Hamilton, further research is needed in order to assess the effectiveness of a broader range of scenarios. These include regional transit expansions capturing a wider commuter base and improved connections with Toronto’s transit network; land-use intensification along major transit corridors; transit improvements at the expense of road capacity; as well as parking policies and pricing. Despite the limitations in the application of the modelling framework, the current study provides a comprehensive treatment of vehicle emissions by explicitly representing most variables affecting the level of emissions in addition to their spatial and temporal variation. The link between an activity-based model and dynamic traffic assignment provides agentbased output that allows emissions on the network to be calculated without losing linkage to each household agent. This enables emissions on the network to be tracked back to those who are producing them, allowing for analysis by household location, or various personal attributes. The treatment of individual link emissions as individual line sources within the dispersion model allows for improved spatial representation of NOx concentrations that

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does not resort to gridding. Indeed, a main drawback of existing modelling approaches for large urban areas is the allocation of emissions to grid cells. This reliance on spatial surrogates runs the risk of underestimating emission density and hence pollutant concentrations along and in the vicinity of roadways. Acknowledgments This research was funded by a Transport Canada TPMI grant, as well as by contributions from the Ontario Ministries of Transportation and Public Infrastructure Renewal and the City of Toronto. This research was conducted while the first author was a post-doctoral fellow at the University of Toronto.

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Author Biographies Marianne Hatzopoulou is Assistant Professor in Transportation Engineering at McGill University. She obtained a PhD from the University of Toronto. Her main research expertise involves modelling of road transport emissions and urban air quality as well as assessing population exposure to air pollution through the integration of travel demand and environmental simulation. She is interested in modelling the interactions between daily activities and travel patterns of urban dwellers and the generation and dispersion of traffic emissions in urban environments. Jiang Y. Hao is a transportation planner for IBI Group, Toronto, Canada, with expertise in land use planning, travel demand forecasting, microsimulation, and vehicle emission modelling. He holds a B.A.Sc. degree in engineering science, infrastructure option, and an M.A.Sc degree in civil engineering from the University of Toronto. Hao was a member of Professor Eric Miller’s research team that developed an operational integrated urban model (IUM) system for Canadian cities, using the Greater Toronto Hamilton Area as the case study. He worked on various sub-models that are responsible for population synthesis, trip generation, and mode assignment. Eric J. Miller is the inaugural Director of the University of Toronto Cities Centre. He has B.A.Sc. and M.A.Sc. degrees from the University of Toronto and a Ph.D. from M.I.T. Prof. Miller is Chair of the Transportation Research Board Committee on Travel Behavior and Values and past-Chair of the International Association for Travel Behaviour Research. His research interests include: integrated land use transportation modelling; analysis of urban form—travel behaviour interactions; modelling transportation system energy use and emissions; and microsimulation modelling.

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