Individual and Environmental Determinants of Traffic Emissions and Near-Road Air Quality

Junshi Xu

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Civil Engineering University of Toronto

Individual and environmental determinants of traffic emissions and near-road air quality

Junshi Xu

Doctor of Philosophy

Department of Civil Engineering University of Toronto

2020 Abstract

On-road motor vehicles are responsible for a considerable proportion of near-road air pollution.

While background levels of air pollutants are continuously tracked by regional monitoring networks, assessing near-road air quality remains a challenge in urban areas with complex built environments, traffic composition, and meteorological variation, leading to significant spatiotemporal variability in air pollution. This research addresses current gaps in the literature on local traffic emissions and near-road air quality.

This thesis first investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Traffic emissions are estimated using radar data as well as simulated traffic based on various speed aggregation methods. It provides recommendations for project-level analysis and particulate matter (PM) hotspot analysis.

We further compare fleet averaged emission factors (EFs) derived from a traffic emission model, the Motor Vehicle Emissions Simulator (MOVES), with EFs using plume-based measurements.

This second module stresses the need to collect local traffic information for a better understanding of on-road traffic emissions. Besides, we validate default drive cycles in MOVES against representative drive cycles derived based on real-world GPS data. The validation results

ii are helpful for transportation planners to quantify uncertainties in emission estimation and employ appropriate methods to improve the estimation of on-road emission inventories.

The third module develops eco-score models and evaluates the effect of various factors such as driver and trip characteristics on emission intensities. The results shed light on the impact of driving style on emissions and identify the most important factors affecting the amount of emissions generated by every individual driver.

The fourth module focuses on the impact of traffic emissions on near-road air quality and presents the results of two different experiments. First, it explores the effect of various factors on near-road ultrafine particle (UFP) concentrations based on short-term fixed monitoring, which stresses the significance of using local traffic characteristics to improve near-road air quality prediction. In addition, it captures the distribution of truck movements in urban environments and investigates the impacts of land-use variables and detailed traffic information on near-road

Black Carbon (BC) concentrations.

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Acknowledgments

First of all, I would like to express my deepest and sincere gratitude to my supervisor, Professor Marianne Hatzopoulou, for her tremendous support, inspiring guidance, and motivational mentorship which has encouraged me to grow as a researcher throughout my studies at the University of Toronto. I have been so lucky to have you as a mentor since my Master’s degree. Your mentorship has been an invaluable gift over the past years. One day, I hope to inspire someone else as you've inspired me. Thank you for being such a great role model.

I would like to further express my appreciation to my Ph.D. supervisory committee members, Professor Matthew Roorda, Professor Amer Shalaby, and Professor Heather MacLean, for providing me with their great support and invaluable advice. I would also like to thank my external examiner Professor Guohua Song, for his thoughtful comments and suggestions.

My experience would not have been the same without the friends I met throughout my studies. I would like to thank Soheil Alizadeh, Sina Bahrami, Md Sami Hasnine, Ahmadreza Faghih Imani, Ehab Diab, Albert Lo, Dengbo He, and Ariel Hu for their support and friendship. I have been extremely fortunate to work with incredibly talented friends and colleagues in the Transportation and Air Quality Research Group (TRAQ) for making my Ph.D. adventure at UofT such an incredible and unforgettable experience: An Wang, Ran Tu, Laura Minet, Christos Stogios, Marc Saleh, Jessie Gai, Arman Ganji, Maryam Sherkarrizfard, Ahsan Alam, and Sabreena Anowar. Thank you for all your support and help over the years. Thank you for providing invaluable feedback and helping with data collection.

I am truly indebted to the Mitacs organization for its confidence in my ability and awarding me the Globalink Graduate Fellowship throughout my graduate studies.

I am forever grateful to my parents, Lilan Tao and Fangqing Xu, for their unwavering support in all my endeavours. I thank them both for their unconditional support and for all the sacrifices they have made for me throughout all those years.

Lastly, special thanks to Mingqian Zhang for her endless love and support, who is more than a girlfriend, she is a lifelong partner. Thank you for being by my side through this whole adventure every step of the way. You are a constant source of joy and happiness and never stop believing in me. I cannot imagine this journey without you in my life. iv

Table of Contents

Acknowledgments ...... iv

Table of Contents ...... v

List of Tables ...... x

List of Figures ...... xi

List of Abbreviations ...... xv

Author Contributions ...... xvii

Publication Details ...... xix

Chapter 1 Introduction and Objectives ...... 1

Chapter overview ...... 1

1.1 Background and motivation ...... 1

1.2 Problem statement ...... 3

1.3 Research questions ...... 5

1.4 Research significance ...... 6

1.5 Dissertation structure and overview of chapters ...... 7

1.6 Note on the use of units in this document ...... 9

Chapter 2 Context around Traffic Emissions and Near-Road Air Quality Modelling ...... 10

Chapter overview ...... 10

2.1 Existing efforts in validating traffic emission models ...... 10 2.1.1 Traffic emission model validation based on emission factors ...... 11 2.1.2 Traffic emission model validation based on vehicle dynamics ...... 15

2.2 Refining near-road air quality models ...... 17 2.2.1 Evolutions in data sources and collection methods ...... 17 2.2.2 Improvements in air quality modelling ...... 22

2.3 Identified gaps in the current literature ...... 26

Chapter 3 Contrasting the Direct Use of Data from Traffic Radars and Video-Cameras with Traffic Simulation in the Estimation of Road Emissions and PM Hotspot Analysis ... 28

Chapter overview ...... 28

3.1 Introduction ...... 28

3.2 Materials and methods ...... 31 3.2.1 Study area and data collection sites ...... 31 3.2.2 Data collection and processing ...... 32 3.2.3 Traffic simulation ...... 33 3.2.4 Emission modelling ...... 35 3.2.5 Air quality model ...... 36 3.2.6 Speed input scenarios ...... 37

3.3 Results and discussion ...... 38 3.3.1 Comparison of data streams ...... 38 3.3.2 Validation and calibration of traffic simulation model ...... 40 3.3.3 Comparison of emissions based on different speed input scenarios ...... 42 3.3.4 Comparison of hourly PM based on different speed input scenarios ...... 48

3.4 Conclusion ...... 50

Chapter 4 Comparing Emission Rates Derived from a Model with Those Estimated Using a Plume-Based Approach ...... 52

Chapter overview ...... 52

4.1 Introduction ...... 52

4.2 Materials and methods ...... 55 4.2.1 Study area and data collection sites ...... 55 4.2.2 Generation of modelled emissions ...... 56 4.2.3 Generation of plume-based emissions ...... 59 4.2.4 Air quality model ...... 61 4.2.5 Comparison of modelled and measured results ...... 62

4.3 Results and discussion ...... 62 4.3.1 Descriptive analysis of traffic data ...... 63 4.3.2 Validation and calibration of traffic simulation model ...... 66 4.3.3 Comparison of emission factors ...... 67 4.3.4 Contribution of trucks, transit and other vehicles to total emissions ...... 69 4.3.5 Comparison of the networks with transit bus and streetcar service ...... 71 4.3.6 Contribution of truck, transit and other vehicles to near-road concentrations ...... 72 4.3.7 Effect of transit buses on total emissions ...... 74 4.3.8 Comparison of near-road air quality results ...... 76 vi

4.4 Conclusions and Recommendations for Future Studies ...... 78

Chapter 5 Embedding Local Driving Behaviour in Regional Emission Models to Increase the Robustness of On-Road Emission Inventories ...... 82

Chapter overview ...... 82

5.1 Introduction ...... 82

5.2 Materials and methods ...... 85 5.2.1 Study area and data collection ...... 85 5.2.2 Data Cleaning and Processing ...... 87 5.2.3 Drive Cycle Methods ...... 88 5.2.4 Segment-based method ...... 91 5.2.5 Testing the effect of sample size...... 93

5.3 Results and discussion ...... 95 5.3.1 Descriptive analysis ...... 95 5.3.2 Development of drive cycles and comparison with MOVES ...... 96 5.3.3 Comparison of drive cycles derived from micro-trip method and segment method ...... 98 5.3.4 Comparison of emission estimates for the city of Toronto ...... 102 5.3.5 Testing effect of sample size ...... 104

5.4 Conclusion ...... 105

Chapter 6 A Machine Learning Approach Capturing the Effects of Driving Behaviour and Driver Characteristics on Trip-Level Emissions ...... 107

Chapter overview ...... 107

6.1 Introduction ...... 107

6.2 Materials and methods ...... 110 6.2.1 Study area and data collection ...... 110 6.2.2 Data processing...... 111 6.2.3 Database development ...... 113 6.2.4 Eco-score evaluation model ...... 118 6.2.5 Machine learning model and feature attribution method ...... 118

6.3 Results and discussion ...... 121 6.3.1 Altitude data quality ...... 121 6.3.2 Descriptive analysis ...... 121 6.3.3 Model results ...... 125

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6.4 Discussion and conclusions ...... 136

Chapter 7 A Gradient Boost Approach for Predicting Near-Road Ultrafine Particle Concentrations using Detailed Traffic Characterization ...... 138

Chapter overview ...... 138

7.1 Introduction ...... 138

7.2 Materials and methods ...... 140 7.2.1 Study area and data collection ...... 140 7.2.2 Data processing...... 142 7.2.3 Machine learning model ...... 147 7.2.4 Cross-validation ...... 147 7.2.5 Feature attribution method ...... 149

7.3 Results ...... 149 7.3.1 Traffic data validation...... 149 7.3.2 Descriptive analysis ...... 150 7.3.3 Model results ...... 154

7.4 Discussion and conclusions ...... 161

Chapter 8 Data-Driven Approach to Capture the Association Between Local Truck Movements and Near-Road Black Carbon Concentrations ...... 163

Chapter overview ...... 163

8.1 Introduction ...... 163

8.2 Materials and methods ...... 165 8.2.1 Study area and data collection ...... 165 8.2.2 Data processing...... 167 8.2.3 Machine learning model and feature attribution method ...... 171

8.3 Results ...... 173 8.3.1 Traffic data validation...... 173 8.3.2 Descriptive analysis ...... 173 8.3.3 Model results ...... 177

8.4 Discussion and conclusions ...... 181

Chapter 9 Summary and Conclusion ...... 184

Chapter overview ...... 184

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9.1 Summary of Chapters ...... 184

9.2 Research Contributions ...... 187

9.3 Recommendations for Future Research ...... 189

References ...... 191

Appendices ...... 217

APPENDIX A: VALIDATION RESULTS OF SIMULATED TRAFFIC VOLUMES AGAINST MANUAL COUNTS ...... 217

APPENDIX B: CALIBRATION OF TRAFFIC SIMULATION MODEL ...... 218

List of Tables

Table 2-1 Summary of traffic emission model validation based on emission factors ...... 11

Table 2-2 Summary of evolutions in data sources and collection methods ...... 21

Table 3-1 Comparison of vehicle classification between radar and webcam/manual counts WB and EB refer to westbound and eastbound ...... 40

Table 4-1 Ratios of EFs between Transit and Fleet Average, and Ratios of per Passenger Emissions between Transit and Private Vehicles (In the First Line of the Cell, the Number in Bold indicates the Average Value, and the Second Number indicates the Median. The Numbers in the Second Line are the Ranges)...... 75

Table 5-1 Speed bin definitions for grouping drive cycles ...... 89

Table 5-2 Procedure for extracting micro-trips and corresponding justification ...... 89

Table 6-1 Description of 51 independent variables potentially associated with trip emissions . 115

Table 6-2 Descriptive statistics for dependent variables and independent features across all trips ...... 122

Table 7-1 Correlation matrix of the mean UFP as well as land use and built environment variables ...... 153

Table 7-2 Descriptive statistic for cluster centroids ...... 155

List of Figures

Figure 1.1 Overview of thesis structure ...... 6

Figure 3.1 Study site and data collection points ...... 32

Figure 3.2 The VISSIM base network ...... 35

Figure 3.3 Comparison of traffic volume between radar records and manual counts WB and EB refer to westbound and eastbound...... 40

Figure 3.4 Comparison of speeds between radar (dark grey) and calibrated VISSIM outcomes (light grey)...... 42

Figure 3.5 Emission estimates based on radar data and traffic simulation: a) NOx, b) PM10, and b)

PM2.5...... 44

Figure 3.6 Distribution of hourly proportion of exhaust emissions to total PM emissions: a)

PM10, and b) PM2.5...... 47

Figure 3.7 Distribution of hourly estimated and measured concentrations: a) PM10, and b) PM2.5...... 49

Figure 4.1 Study area and data collection sites ...... 56

Figure 4.2 The traffic simulation network (using the PTV VISSIM platform) ...... 58

Figure 4.3 (a) Comparison of hourly traffic volume between radar records and manual counts in the WB direction, and (b) Comparison of hourly traffic volume between radar records and manual counts in the EB direction...... 65

Figure 4.4 Hourly truck volume and truck proportion on Thursday and Sunday (based on webcam/manual results)...... 66

Figure 4.5 Box plots of hourly EFs derived from emission model and plumed-based method for weekday and weekend ...... 68

Figure 4.6 Box plots of hourly segment emissions (left) and stacked bar charts of average proportions of transit, truck and other vehicles (right) for transit bus and streetcar network ...... 70

Figure 4.7 Stacked bar charts of relative contribution of vehicle classes to air quality ...... 73

Figure 4.8 Box plots of hourly concentrations derived from emission model and plumed-based method, as well as measured concentrations at street level for two pollutants ...... 77

Figure 5.1 An example illustrating drive cycles derived by the micro-trip method and segment- based method ...... 85

Figure 5.2 Methodology for developing distributions of GHG emissions based on the micro-trip method and segment method, as well as calculating total emissions based on MOVES ...... 93

Figure 5.3 Methodology for testing the effect of sample size ...... 94

Figure 5.4 Collected GPS data points in the GTHA region ...... 95

Figure 5.5 Comparison of the target opmode distribution, the Toronto-specific cycle opmode distribution, and the default MOVES opmode distribution ...... 98

Figure 5.6 Comparison of EF distributions derived from segment method and micro-trip method ...... 100

Figure 5.7 Average RMSE between the median cumulative opmode distributions and the other collected observations, as well as the number of observations associated with each speed bin . 102

Figure 5.8 Comparison of daily GHG emissions for the City of Toronto based on two methods (segment method and micro-trip method)...... 104

Figure 5.9 Minimum duration (in seconds) of the sample size needed to generate drive cycles based on two methods (segment method and micro-trip method) ...... 105

Figure 6.1 Number of trips that took place on the road network across the GTHA ...... 122

Figure 6.2 Scatter plot of predicted and observed eco-scores and EFs ...... 127

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Figure 6.3 SHAP summary plot of top 20 features ranked by global feature importance in the XGBoost eco-score model ...... 128

Figure 6.4 SHAP summary plot of top 20 features ranked by global feature importance in the XGBoost eco-score model with discrete features ...... 131

Figure 6.5 SHAP feature attributions explained the output of the model as a sum of the effects of each feature being introduced into a conditional expectation for a specific trip ...... 134

Figure 6.6 The XGBoost model was used to predict the score. Each prediction was explained using SHAP values...... 135

Figure 7.1 Sampling intersections and mid-block locations along Hurontario Street displayed on Google Maps...... 141

Figure 7.2 Video processing system for traffic detection, tracking, and counting ...... 146

Figure 7.3 Boxplot of one-minute UFP concentrations at each location...... 151

Figure 7.4 Boxplot of one-minute UFP concentrations in different levels of temperature, humidity and wind speed as well as different wind directions ...... 152

Figure 7.5 SHAP summary plot of features ranked by global feature importance in the XGBoost UFP model ...... 157

Figure 7.6 Scatter plots of measured and modelled UFP for different test sets ...... 160

Figure 8.1 Distribution of 19 corridors in the City of Toronto ...... 166

Figure 8.2 Video processing system for traffic detection, tracking, and counting ...... 171

Figure 8.3 Boxplot of BC concentrations along each corridor ranked by mean and highest values ...... 175

Figure 8.4 Distribution of mean truck along each corridor ...... 177

Figure 8.5 SHAP summary plot of features ranked by global feature importance in the XGBoost model for BC maximum concentration...... 179

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Figure 8.6 SHAP summary plot of features ranked by global feature importance in the XGBoost model for BC mean concentration...... 181

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List of Abbreviations

NH3 = Ammonia AADT = Annual Average Daily Traffic ANN = Artificial Neural Network BPNN = Backpropagation Neural Network BC = Black Carbon BRT = Boosted Regression Trees CO = Carbon Monoxide CV = Coefficient of Variation CMEM = Comprehensive Modal Emissions Model EB = Eastbound EC = Elemental Carbon EFs = Emission Factors XGBoost = Extreme Gradient Boosting GDI = Gasoline Direct Injection GAM = Generalized Additive Model GTHA = Greater Toronto and Hamilton Area GTA = Greater Toronto Area GHG = Greenhouse Gas HDVs = Heavy-Duty Vehicles ICEV = Internal Combustion Engine Vehicles IVE = International Vehicle Emission IQR = Interquartile Range LUR = Land Use Regression LOS = Level of Service LDVs = Light-Duty Vehicles LDGVs = Light-Duty Gasoline Vehicles LDTs = Light-Duty Trucks LDSA = Lung Deposited Surface Area MCS = Monte Carlo Simulation MOVES = Motor Vehicle Emissions Simulator MLR = Multiple Linear Regression NPRI = National Pollutant Release Inventory MTO = Ontario Ministry of Transportation ONA = Optimized Noise-Reduction Algorithm OD = Origin-Destination

NOx = Oxides of Nitrogen PM = Particulate Matter PKT = Passenger Kilometer Travelled PEMS = Portable Emissions Measurement System PCA = Principal Component Analysis RF = Random Forest RPA = Relative Positive Acceleration RMSD = Root-Mean-Square Deviation RR = Rural Restricted RU = Rural Unrestricted SRS = Satellite Remote Sensing STP = Scaled Tractive Power

SHAP = Shapley Additive Explanation SOCAAR = Southern Ontario Center for Atmospheric Aerosol Research USEPA = The United States Environmental Protection Agency THC = Total Hydrocarbon TRAP = Traffic-Related Air Pollution TTS = Transportation Tomorrow Survey UFP = Ultrafine Particles UTTRI = University of Toronto Transportation Research Institute UAVs = Unmanned Aerial Vehicles UR = Urban Restricted UU = Urban Unrestricted VKT = Vehicle Kilometers Travelled VSP = Vehicle Specific Power VOCs = Volatile Organic Compounds WB = Westbound WHO = World Health Organization

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Author Contributions

This dissertation includes three manuscripts that have been published in peer-reviewed journals and three manuscripts that have been submitted to peer-reviewed journals and are currently under review. Some of the manuscripts were completed with co-authors; details of author contributions are provided below:

Chapter 3: “Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis.” by myself as the first author, with Nathan Hilker, Matheus Truchet, Mohamad-Kenan AI-Rijleh, Ran Tu, An Wang, Dr. Masoud Fallahshorshani, Dr. Greg Evans, and Dr. Marianne Hatzopoulou, who contributed intellectually, provided comments, and edited the manuscript. I was responsible for the entire analysis and paper write-up, all other co-authors helped with data collection.

Chapter 4: “Comparing emission rates derived from a model with those estimated using a plume-based approach and quantifying the contribution of vehicle classes to on-road emissions and air quality.” by myself as the first author, Dr. Jonathan Wang, Nathan Hilker, Dr. Masoud Fallahshorshani, Marc Saleh, Ran Tu, An Wang, Laura Minet, Christos Stogios, Dr. Greg Evans, and Dr. Marianne Hatzopoulou, who contributed intellectually, provided comments, and edited the manuscript. I was responsible for the entire analysis and paper write-up, all other co-authors helped with data collection. Dr. Jonathan Wang helped with generating plume-based emission factors.

Chapter 5: “Embedding local driving behaviour in regional emission models to increase the robustness of on-road emission inventories.” by myself as the first author, Marc Saleh, An Wang, Ran Tu, and Dr. Marianne Hatzopoulou, who contributed intellectually, provided comments, and edited the manuscript. I was responsible for the entire analysis and paper write- up, all other co-authors helped with executing the multiple model runs needed to support the work in this paper.

Chapter 6: “A machine learning approach capturing the effects of driving behaviour and driver characteristics on trip-level emissions.” by myself as the first author, Marc Saleh, and Dr. Marianne Hatzopoulou, who contributed intellectually, provided comments, and edited the

xvii manuscript. I was responsible for the entire analysis and paper write-up, the co-author helped with the literature review.

Chapter 7: “A gradient boost approach for predicting near-road ultrafine particle concentrations using detailed traffic characterization.” by myself as the first author, An Wang, Nicole Schmidt, Dr. Matthew Adams, and Dr. Marianne Hatzopoulou, who contributed intellectually, provided comments, and edited the manuscript. I was responsible for the entire analysis and paper write-up, all other co-authors helped with data collection on the side of the road.

Chapter 8: “Data-driven approach to capture the association between local truck movements and near-road black carbon concentrations using mobile measurements.” by myself as the first author, An Wang, Ran Tu, Marc Saleh, Nicole Schmidt, and Dr. Marianne Hatzopoulou, who contributed intellectually, provided comments, and edited the manuscript. I was responsible for the entire analysis and paper write-up, all other co-authors helped with data collection while I was driving the vehicle to collect mobile data.

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Publication Details

This dissertation is comprised of six papers. The details of the publications are listed below:

Chapter 3: Xu, J., Hilker, N., Turchet, M., Al-Rijleh, M.K., Tu, R., Wang, A., Fallahshorshani, M., Evans, G., Hatzopoulou, M., 2018. Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis. Transp. Res. Part D Transp. Environ. 62, 90–101.

Chapter 4: Xu, J., Wang, J., Hilker, N., Fallah-Shorshani, M., Saleh, M., Tu, R., Wang, A., Minet, L., Stogios, C., Evans, G., Hatzopoulou, M., 2018. Comparing emission rates derived from a model with those estimated using a plume-based approach and quantifying the contribution of vehicle classes to on-road emissions and air quality. J. Air Waste Manag. Assoc. 68, 1159–1174.

Chapter 5: Xu, J., Saleh, M., Wang, A., Tu, R., Hatzopoulou, M., 2019. Embedding local driving behaviour in regional emission models to increase the robustness of on-road emission inventories. Transp. Res. Part D Transp. Environ. 73, 1–14.

Chapter 6: Xu, J., Saleh, M., Hatzopoulou, M., 2019. A machine learning approach capturing the effects of driving behaviour and driver characteristics on trip-level emissions. Presented at the 98th Annual Meeting of Trans. Res. Board, Washington, D.C., January 13–17, 2019. (This paper is currently under review in a journal Atmospheric Environment)

Chapter 7: Xu, J., Wang, A., Saleh, M., Schmidt, N., Adams, M., Hatzopoulou, M., 2019. A gradient boost approach for predicting near-road ultrafine particle concentrations using detailed traffic characterization. Accepted to be presented at a lectern session at the 99th Annual Meeting of Trans. Res. Board, Washington, D.C., January 12–16, 2020. (This paper is currently under review in a journal Environmental Pollution)

Chapter 8: Xu, J., Wang, A., Tu R., Saleh, M., M., Schmidt, Hatzopoulou, M., 2019. Data- driven approach to capture the association between local truck movements and near-road black carbon concentrations using mobile measurements. Accepted to be presented at a poster session at the 99th Annual Meeting of Trans. Res. Board, Washington, D.C., January 12–16, 2020.

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Chapter 1 Introduction and Objectives Chapter overview

This chapter starts with a discussion of the negative health impact of near-road air pollution and the contribution of different vehicle types to traffic emissions. It also introduces various approaches that have been employed to quantify spatiotemporal variability in near-road air pollutant concentrations. Section 1.2 outlines the current challenges in improving the estimation of microscopic traffic emissions and near-road air quality. The scope of work and research questions are proposed in Section 1.3, and research significance is discussed in Section 1.4. Finally, Section 1.5 presents the dissertation structure and how different chapters are organized and connected to address the research objectives and questions.

1.1 Background and motivation

Urban air quality is a primary environmental and public health concern around the world. According to the World Health Organization (WHO), over 80% of the population living in urban areas that monitor air quality are exposed to air pollution concentrations exceeding the WHO guidelines (WHO, 2017). In Canada, more than 32% of the population is estimated to live in the elevated exposure zones, which are defined as 500m on either side of highways (with an average daily traffic volume of 100,000 vehicles or more) or 100m on either side of major urban roads (with an average daily traffic volume of 15,000 vehicles or more), where they are exposed to traffic-related air pollution (Brauer et al., 2013). Based on air pollution levels from 2007 to 2012, it is estimated that in Canada, the total mortality caused by air pollution is 14,400 premature deaths per year (Health Canada, 2017). Emerging evidence has explicitly linked traffic-related air pollution (TRAP) to adverse acute and chronic health outcomes, such as cardiovascular and respiratory mortality and morbidity (Bard et al., 2014; Brook et al., 2018; Chaloulakou et al., 2008; Hoek et al., 2013; Picornell et al., 2019; Weichenthal et al., 2015). Besides, some previous studies have indicated that no threshold concentrations for health effects can be established and ambient concentration levels of zero are used to calculate ‘no harmful health effects’ (Stedman, 2004; Zhang et al., 2008).

1 2

On-road motor vehicles are responsible for a considerable proportion of air pollution and fuel consumption in urban areas (Environment and Climate Change Canada, 2019). The toxic chemicals released by traffic flow that deteriorate near-road air quality include gases and primary particles. After being released from the exhaust pipe, the mixture of gases and particles may experience numerous transformations, including chemical reactions, coagulation and condensation of gases, aerosols and particles, which are caused by local conditions such as temperature, turbulence, sunlight, and humidity (Forehead and Huynh, 2018). Gasoline vehicles are mainly responsible for the emissions of carbon monoxide (CO), volatile organic compounds

(VOCs) and ammonia (NH3) (Borken-Kleefeld and Chen, 2015; Pang et al., 2014). Diesel vehicles generate most of the oxides of nitrogen (NOx) and particulate matter (PM) (Carslaw and Rhys-Tyler, 2013; Johnson, 2013). Re-suspension of road dust due to traffic movement and tire and brake wear also contribute to PM concentrations (Gulia et al., 2019). Recently, studies on other characteristics of PM have emerged, including the characterization of ultrafine particles (UFP) and black carbon (BC) (Jeong et al., 2017; Tobías et al., 2018). UFP have a higher potential for detrimental health impacts than larger particles (>100 nm) due to their small size and increased surface area for adsorption and deposition, making them easy to translocate through organs (Geiser et al., 2008; Kumar et al., 2014). Besides, BC has been identified as a marker for exposure to local traffic, especially diesel exhaust in urban environments, as diesel vehicles generally have BC emission intensities several times higher than gasoline vehicles (Brimblecombe et al., 2015; Lau et al., 2015).

Due to the adverse effects of traffic-related air pollution, various approaches have been employed to quantify spatiotemporal variability in near-road air pollutant concentrations (Klompmaker et al., 2015; Lin et al., 2011; Pepe et al., 2016a; Quaassdorff et al., 2017). Some studies have integrated emission inventory and meteorology data in air quality dispersion models or chemical transport models to simulate the transport, diffusion, dispersion, and chemical reactions of air pollutants in urban built environments and to predict concentrations at selected receptor locations (Fallah-Shorshani et al., 2017; Hood et al., 2018; Sayegh et al., 2016). The accuracy of this approach mainly relies on the quality of emission inventories. Vehicle emission models have been used to estimate traffic-induced emissions at different scales, ranging from a small road network (microscopic) to an entire urban area (mesoscopic) or even at a national level (macroscopic) (Yasmin et al., 2017). Given that traffic influences on local networks have drawn

3 increased attention, microscopic emission models have become of great interest, which can take second-by-second speed profiles of each vehicle into account and estimate emissions at high spatial resolution. These models are usually coupled with traffic microsimulation models that can simulate driver reaction time, route selection, car following, and lane changing behaviours in a short time step (Abou-Senna and Radwan, 2013; Lejri et al., 2018). However, modelling second- by-second emissions for each vehicle entering and leaving the network commonly requires high- level computation and detailed input data (Hülsmann, 2014).

Other studies have employed empirical models and geostatistical models, such as land use regression (LUR) models, to estimate the fine-scaled variations in near-road air pollution (Hoek et al., 2008). Traditionally, they were developed to understand the impact of different land-use variables on air quality based on data collected at fixed-site monitoring stations (Xie et al., 2017). However, near-road air pollution levels have been shown to vary dramatically over short spatial distances due to a variety of factors such as fleet characteristics, meteorological conditions, and built environment, which affect the accuracy of population exposure estimation (Hagler et al., 2010; Hilker et al., 2019; Kim, 2011; Wu et al., 2016). Thus, numerous recent studies have employed mobile monitoring campaigns to cover a large spatial domain and increase the spatial resolution of measurements (Hankey et al., 2019; Messier et al., 2018; Quaassdorff et al., 2017; Van den Bossche et al., 2018; Weichenthal et al., 2015). The contribution of traffic to air pollution concentrations in urban areas is generally captured through indirect estimates, such as the distance to road types (major arterial roads, minor roads, highways) or traffic intensity variables such as annual average daily traffic (AADT) (Liu et al., 2019).

1.2 Problem statement

The Motor Vehicle Emissions Simulator (MOVES), developed by the United States Environmental Protection Agency (USEPA), is a state of the science emission inventory model based on emission data from inspection and maintenance programs and dynamometer tests in Chicago, Phoenix, St Louis, and New York (U.S. Environmental Protection Agency, 2011). In the Canadian context, there are no emission models currently endorsed by Environment Canada, and therefore the MOVES model is often used with default US nation-wide data. However,

4 previous studies have demonstrated that it needs to be validated and calibrated against real-world data (Alam et al., 2014; Liu and Frey, 2015; Perugu, 2019; Song et al., 2012b).

The accuracy of the emission inventory mainly relies on the quality of the vehicle emission factors (EFs), indicating the quantity of a pollutant emitted or amount of energy used per distance driven (Franco et al., 2012). It is not uncommon to find that vehicle EFs derived by real- world measurements and those estimated by traffic emission models have significant differences (Bernard et al., 2018; Borge et al., 2012; Kousoulidou et al., 2013; Peitzmeier et al., 2017; Smit and Bluett, 2011). For instance, Liu and Frey (2015) tested 100 light-duty gasoline vehicles (LDGVs) in North Carolina and found that the MOVES modelled emission rates were up to three times higher than measured rates. This is due to the fact that EFs are associated with many factors such as vehicle characteristics, fuel specifications, emission control system, ambient and operating conditions, while EFs in the traffic emission model are derived under controlled conditions by a chassis dynamometer over predetermined cycles in test centers or laboratories (Franco et al., 2013). Thus, it is essential to understand how emission estimates are affected by different levels of input data in MOVES and the extent to which emission intensities could differ from those collected in real-world measurements.

For estimating emissions at a regional scale, emission inventories employ drive cycles that are specific for various road types, vehicle classes, and traffic conditions in the region. When average hourly speeds are used as input to estimate emissions, the MOVES model relies on default operating mode (Opmode) distributions derived from embedded driving schedules collected in the US nationwide with various speeds for different roads and vehicle types (Xu, 2018). However, two identical vehicles operating with the same average speed can generate different emission intensities due to diverse driving behaviour patterns, such as events of idling, cruising, deceleration, and acceleration (Liu, 2015). For example, Farzaneh et al. (2014) developed local drive cycles for different classes of vehicles by road type based on GPS data collected in five metropolitan areas in Texas, and found the most substantial differences between MOVES default drive cycles and real-world drive cycles were for low and high speeds. Thus, there is a need for collecting representative drive cycles in the Canadian context and embedding them in regional emission models to improve the estimation of on-road emission inventories. Besides, research efforts should include the investigation of the effects of various factors such as driver and trip characteristics on trip-level emission intensities.

Despite the fact that background levels of air pollutants are well monitored by regional monitoring networks, it remains particularly challenging to estimate near-road air quality in urban areas that have complex built environments, land use, road networks, traffic composition, and meteorological variation, leading to a large degree of spatial and temporal dynamics of air pollution concentration (Dias and Tchepel, 2018). There is also evidence to suggest that downwind distance from pollution sources can have substantial impacts on individual exposures in urban areas (Zhang and Batterman, 2013). For instance, Hankey et al. (2015a) have found that the presence of nearby trucks to a bicycle-based monitoring platform was significantly correlated to acute and high concentration exposure events. The average concentration-increase per truck -3 -3 -3 was estimated to be 31,000 cm for particle number, 1.0 µg m for PM2.5, and 1.6 µg m for BC (Hankey and Marshall, 2015a). Therefore, detailed local traffic information is needed to identify the hotspots (e.g., signalized intersections) of air pollution in urban environments, and it is crucial to employ advanced statistical models to better quantify the influences of different factors on the spatial and temporal variation in near-road air pollution (Gulia et al., 2015).

1.3 Research questions

This research addresses the following questions: (1) Do project-level emissions estimates generated by a traffic emission model (MOVES) vary with different aggregation levels for vehicle activity data? To what extent will these emission estimates differences influence air quality estimation in hot-spot analysis? (2) What are the differences between default databases, such as emission intensities and drive cycles, in an emission model (MOVES) and those collected based on real-world measurements? How can we improve the robustness of on-road emission inventory estimation using locally collected data? (3) How can we quantitatively evaluate the effects of different variables including driver and trip characteristics on emission intensities at a trip level? (4) How can we refine near-road air quality models with a data-driven approach using local traffic characteristics to capture the association between different factors such as built environment, meteorological conditions, traffic, and near-road measurements?

This dissertation addresses the current gaps in the literature on local traffic emissions and near- road air quality by (i) understanding the impacts of vehicle activity data on emissions estimates

6 and air quality estimation (ii) validating a traffic emission model against data collected from real- world measurements (iii) quantifying the effects of different factors on trip-level emission intensities (iv) refining estimation of near-road air quality using local traffic characteristics. Figure 1.1 presents the structure of this thesis.

Figure 1.1 Overview of thesis structure

1.4 Research significance

This research addresses four novel aspects that are significant for better understanding and improving local traffic emission estimation and near-road air quality modelling: 1) understanding the effect of speed data at different aggregation levels on the simulation of vehicle emissions and

7 air quality estimation, 2) validating a traffic emission model, including emission intensities and drive cycles, against locally collected data based on real-world measurements, 3) capturing the effect of different variables, such as trip characteristics and driver characteristics, on trip-level EFs, and 4) exploring the association between near-road air pollution and different variables, such as built environment, local meteorology, and detailed traffic characteristics, based on mobile monitoring and short-term fixed monitoring.

This research highlights the significance of collecting real-world data to validate and calibrate traffic emission models, which are relevant for project-level emission inventories and hot-spot analysis; caution must be exercised when using default emission intensities and drive cycles for emission modelling purposes. It would be valuable for transportation planners to consider uncertainties in emission estimation and employ appropriate methods to improve the estimation of on-road emission inventories. Besides, this thesis quantitatively analyses the effects of driver and trip characteristics on emission intensities at a trip level. The results could be useful for policymakers to evaluate strategies (e.g. congestion pricing) for moderating on-road traffic emissions and would also be useful for individuals to make correct route choices and improve their driving behaviours. Also, this research investigates the impacts of various factors on near- road air pollution, and it presents the methods of deriving detailed traffic information. It stresses the importance of collecting local traffic characteristics to capture the spatiotemporal variability of near-road pollutants. The results could be crucial for improving the assessment of individual exposure to ambient air pollution at the intra-urban scale.

1.5 Dissertation structure and overview of chapters

This dissertation consists of six manuscripts that address four research objectives presented in Figure 1.1. Chapter 2 discusses various validation methods for traffic emission models using EFs and vehicle dynamics derived from real-world measurements. It also describes recent evolutions in data sources and collection methods as well as improvements in modelling capabilities for refining near-road air quality estimation. It highlights some crucial gaps in the current literature on validating traffic emission models and refining air quality models and illustrates how this research addresses these issues. Chapter 3 investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Traffic emissions were estimated using radar data as well as simulated traffic based on various speed

8 aggregation methods. We also compared near-road concentrations derived from emissions based on simulated speeds and concentrations based on emissions derived using radar data (Objective 1). It highlights the significance of collecting local real-world data to validate and calibrate traffic emission models which motivates the research described in the succeeding two chapters. Chapter 4 presents a comparison of fleet average EFs derived from a traffic emission model with EFs using plume-based measurements, including an investigation of the contribution of vehicle classes to different traffic emissions. We conducted a field campaign over one week in June 2016 on an arterial road in Toronto, Canada. Traffic data were collected using a traffic camera and a radar, whereas air quality was measured using two monitoring stations. Besides, we employed a dispersion model to identify the extent to which differences in emissions translate to differences in near-road concentrations (Objective 2). Chapter 5 presents the development of opmode distributions derived from local drive cycle construction methods based on real-world GPS data collection and their influences on average-speed EFs. A data collection campaign was conducted between March and July 2018 and 82 research participants were recruited to record daily driving behaviours in the Greater Toronto and Hamilton Area (GTHA) for one week. We constructed representative drive cycles and compared them with the interpolated drive cycles derived from the default database of the MOVES model. Besides, we embedded the local drive cycles in regional emission models to increase the robustness of on-road emission inventories (Objective 2). The findings demonstrate the significant difference between MOVES and local data, and there is a need for a methodology for understanding the effects of different factors on emission intensities estimated based on real-world drive cycles. Chapter 6 investigates the effects of different variables, including meteorology, trip characteristics (such as time of day), driving characteristics (such as the frequency of extended idling), and driver characteristics (such as driving experience) on trip-level EFs. We employed a machine learning approach to develop prediction models for traffic emissions at a trip level. Besides, we employed the Shapley additive explanation (SHAP) measures to quantitatively reveal the importance of various features affecting trip emissions (Objective 3). After understanding the influences of different variables on traffic emissions, there is a need for quantifying the impacts of local traffic characteristics, built environment, and meteorological data on near-road air pollution. Chapter 7 explores the influences of meteorology, built environment, and traffic characteristics on near-road UFP concentrations. We measured minute-level UFP concentrations at various locations along a major arterial road in the Greater Toronto Area (GTA) from February to May 2019. Computer

9 vision techniques were employed to derive detailed local traffic information, including vehicle class and turning movement. Besides, we used various cross-validation strategies to evaluate machine learning models for air quality predictions (Objective 4). Chapter 8 captures the influence of land-use variables and local traffic characteristics on near-road concentrations using mobile measurements. We conducted a data collection campaign along 19 corridors in the City of Toronto during the period extending from March to June 2019. Traffic information was recorded by a camera placed on the dashboard. We used a computer vision method to derive vehicle counts, which were used to estimate traffic flow, density, and average speed. Truck counts were also estimated along each corridor. We employed a machine learning model to evaluate the influence of different variables on BC mean and maximum concentration (Objective 4). Chapter 9 summarizes all findings and highlights the research contributions of this dissertation.

1.6 Note on the use of units in this document

It is important to note that throughout this dissertation, we use a combination of the International System of Units (SI) and Imperial units. This is done on purpose to remain faithful to the convention of the model MOVES (MOtor Vehicle Emission Simulator) that we use in each chapter. The model MOVES uses units of grams to reflect emissions, miles for distance and mile per hour for speed.

Chapter 2 Context around Traffic Emissions and Near-Road Air Quality Modelling Chapter overview

In this chapter, a review of different validation methods for vehicle emission models is presented in Section 2.1. In Section 2.2, we describe various field measurement methods and state-of-the- art empirical models for refining near-road air quality estimates. We illustrate the need for conducting data collection campaigns to derive local traffic characteristics. Finally, in Section 2.3, we present crucial gaps in the current literature on the validation of traffic emission models and refinement of air quality models and illustrate how this research addresses these gaps.

2.1 Existing efforts in validating traffic emission models

Precise high-resolution vehicle emission inventories are critical as they are commonly used in air quality modelling to quantify impacts of on-road vehicle emissions and evaluate the effectiveness of air pollution control strategies. However, several studies have shown that vehicle emission models do not always provide accurate estimates of real-world traffic emissions (Perugu, 2018; Smit et al., 2010; Turkensteen, 2017; Wang et al., 2017). Traffic emission model validation can be defined as the process of determining the degree to which a model can accurately represent the real world from the perspective of the intended use of the model based on independent datasets (Smit and Somervell, 2015).

Different methods have been employed to validate and improve traffic emission models. For instance, measurements of real-world EFs are usually employed to identify and evaluate the improvement of traffic emission models (Smit et al., 2010). The use of the most recent and locally derived EFs for the studied area generally can lead to better traffic emission estimates (Brimblecombe et al., 2015). To date, various methods have been employed to estimate real- world vehicle EFs, including chassis dynamometer, on-road, on-board, tunnel studies, and remote sensing measurements (Kousoulidou et al., 2013; Liu et al., 2019; Park et al., 2011; Zhang et al., 2017).

10 11

Besides, improving estimates of vehicular emissions and fuel use over conventional traffic emission models also entails a better understanding of vehicle driving patterns, which are influenced by many factors such as traffic characteristics, weather conditions, driving style, and roadway characteristics (Nesamani et al., 2017). The significant improvements in the quantity, quality and level of resolution of traffic data have enabled researchers (e.g. using GPS devices and intelligent sensors) to collect large scale field data on vehicle movements in time and space (Reis et al., 2015). Real-world driving conditions can be used to validate traffic emission models and improve predictions of local emissions inventories (Franco et al., 2013; Heidari and Marr, 2015; Ježek et al., 2015).

Moreover, Monk et al. (2015) have described several approaches to link emission inventories and ambient measurements. The advantage of validating traffic emission models using ambient measurements is that it can capture an extensive range of driving conditions and a large number of vehicles, while the integration of emission and dispersion models increases estimation uncertainties due to the environmental simplifications and the influence of other non-traffic factors (Ježek et al., 2018).

2.1.1 Traffic emission model validation based on emission factors

The instantaneous emission rates of an individual vehicle can be measured using chassis dynamometer, on-road, and onboard measurements; the fleet average emission intensities can be quantified using near-road measurements such as tunnel studies and remote sensing. Table 2-1 provides a summary of traffic emission model validation based on emission factors.

Table 2-1 Summary of traffic emission model validation based on emission factors Measurement Advantage Disadvantage Previous study examples methods of real- world EFs Chassis It can relate instantaneous It has a limited range of test (Huang et al., 2017; Zhang dynamometer emission data to corresponding conditions and cannot represent et al., 2017) kinematic parameters. the actual driving behaviour. On-road It can collect a large amount of It is best conducted on a test (Ho et al., 2014; Ježek et al., measurements emission data under various track due to traffic safety 2015) ambient and operating conditions. considerations.

Onboard It can simulate real-world driving It has a relatively long turnover (Delavarrafiee and Frey, measurements conditions and provide time of vehicle tests. 2018; Jaikumar et al., 2017; instantaneous emission rates. Liu and Frey, 2015) Tunnel studies It can measure not just exhaust It cannot represent typical (Brimblecombe et al., 2015; emissions but also non-exhaust vehicle emissions under Smit et al., 2017; Zhang et emissions such as tire and brake complex urban environments. al., 2018) wear. Remote sensing It can monitor the exhaust It cannot provide insight (Peitzmeier et al., 2017; emissions from a large number of regarding how emissions vary Smit and Somervell, 2015; vehicles at a specific location. at the trip level. Wang et al., 2015)

The advantage of deriving vehicle EFs through chassis dynamometer testing is that it can relate instantaneous emission data to corresponding kinematic parameters in the emission models to simulate fuel consumption and emissions for any driving pattern and vehicle configuration (Kousoulidou, 2011). Zhang et al. (2017) investigated the effects of deterioration and vehicle technology on pollutant EFs for light-duty gasoline trucks (LDGTs) according to the inspection and maintenance (I/M) data using a chassis dynamometer method. The results indicated that EFs of CO, HC, and NOx varied substantially with accumulated mileage and emission standards, and median values of EFs were higher than those default EFs in the International Vehicle Emissions (IVE) model (Zhang et al., 2017). Huang et al. (2017) also employed a chassis dynamometer to measure emissions of 51 LDGVs and results showed that measured EFs of CO, Total

Hydrocarbon (THC), NOx, and PM were 55%, 25%, 32%, and 46% higher than those calculated based on an on-road vehicle emission inventory (EI) guidebook released by the Ministry of Environmental Protection of People’s Republic of China. The authors found that malfunctions of catalytic converters after high strength use was the primary reason for the high emissions (Huang et al., 2017). However, these studies could only conduct chassis dynamometer tests for the limited range of test conditions and their results may not represent vehicle emissions caused by the actual driving behaviour in varying situations (Dixit et al., 2017).

Real-world EFs of an individual vehicle can be measured using mobile platforms, which include on-road and on-board measurements. On-road measurements are conducted by following individual vehicles with a van or trailer instrumented with pollution measurement equipment. This method develops fuel-based EFs by measuring the ratios of concentrations of pollutants and carbon-containing species (as a measure of fuel burned) in the exhaust plumes. Ježek et al.

(2015) first collected real-world BC and NOx EFs for 139 individual vehicles of different types encountered on the roads using an on-road chasing method and then used these EFs to calibrate a bottom-up traffic emission model EMISENS. The authors further evaluated the calibrated emission model results with in situ BC and NOx measurements, and found excellent agreement between the results of the traffic emission/dispersion model and the in situ measurements (Ježek et al., 2018). These studies can collect a large amount of instantaneous emission data under various ambient and operating conditions, while this method is best conducted on a test track due to traffic safety considerations (Franco et al., 2013).

Other studies have used on-board measurements to collect real-world EFs. Liu and Frey (2015) evaluated the variability of tailpipe emission rates of 100 gasoline vehicles, including passenger cars, passenger trucks (such as SUVs), and hybrid electric vehicles, using a portable emissions measurement system (PEMS). This study assessed concordance between empirical and predicted emission rates using the USEPA’s MOVES model and found that emission rates of NOx and HC based on empirical data were generally lower than from the MOVES estimates except for CO2 (Liu and Frey, 2015). Delavarrafiee and Frey (2018) used a PEMS to measure tailpipe emission rates of five flex-fuel vehicles, which operate on gasoline or E85 (an 85%/15% volume blend of ethanol and gasoline), and compared measured emission rates with estimates from MOVES. The results suggested that MOVES did not consider higher energy efficiencies for E85 versus gasoline and demonstrated that adequate sample size was the key to the robustness of estimates of differences in tailpipe emission rates given inter-vehicle variability (Delavarrafiee and Frey, 2018). Jaikumar et al. (2017) developed real-time exhaust emission prediction models for CO,

HC, and NOx using artificial neural network (ANN) model and found its estimates were over the Automotive Research Association of India and COPERT model, as the real-world emissions were several times higher when compared to the modelled conventional EFs.

Validating traffic emission models using on-board measurements has the advantage of simulating real-world driving conditions and providing instantaneous emission rates, while the relatively long turnover time of vehicle tests limits its application for testing a large number of vehicles (Bishop et al., 2017; Lau et al., 2015). Near-road measurements, such as tunnel studies and remote sensing, can measure the emissions of thousands of vehicles daily and can estimate the emissions for both individual vehicles and specific vehicle classes. Thus, it is an ideal method for monitoring real-world fleet emissions, identifying high emitters for inspection and

14 maintenance (I/M) programs, and assessing the effectiveness of emission control strategies and vehicle engine technologies (Huang et al., 2018).

Tunnel studies develop fleet average EFs by associating measured pollutants with traffic flows at the entrance and exit of tunnels, and it requires additional vehicle information such as vehicle license plates and vehicle operating conditions to develop disaggregate EFs (Ait-Helal et al., 2015; Zhang et al., 2018). Smit et al. (2017) conducted a tunnel emissions study to validate the Australian vehicle emissions software COPERT Australia and PIARC EFs. The results showed that PIRAC EFs were conservative and exhibited the most significant prediction errors, and COPERT Australia consistently underestimated emissions by 7% - 37%, depending on the pollutant, probably due to underpresentation of high emitting vehicles and lack of empirical emissions data for Australian diesel cars (Smit et al., 2017). Brimblecombe et al. (2015) collected NOx and BC concentrations in three tunnels in Hong Kong, combined with the tunnel traffic volume and composition. The average tunnel EFs were analyzed using a linear regression model. The authors found that estimated EFs from observed NOx and BC were much higher (typically two or three times) than the ones estimated from EMFAC-HK model, which could be caused by the difference in the model assumptions and the representativeness of the vehicle base emission data (Brimblecombe et al., 2015). Tunnel studies can measure not just exhaust emissions but also non-exhaust emissions such as tire and brake wear, while their results cannot represent typical vehicle emissions under complex urban environments.

The on-road remote sensing system generally includes source detector, speed and acceleration detectors, and license plate reader, and is placed either at the side of or above a roadway using infrared or ultraviolet light sources to measure the pollutant concentration. Smit and Somervell (2015) validated the New Zealand vehicle emission model, known as VEPM, designed for emission estimation at a road network level, using the New Zealand remote sensing database for

CO, THC, and NOx. The authors further developed a proof-of-concept hybrid model specific to New Zealand that can provide higher spatial and temporal resolution, as well as more accurate predictions with increasing confidence (Smit and Somervell, 2015). Peitzmeier et al. (2017) conducted a 60-day roadside measurement campaign on a busy street and measured high- temporal resolution (10HZ) of the emission ratios of NOx and CO2 for more than 70,000 exhaust plumes as well as the emission ratios for size-resolved particle numbers per CO2 for approximately 100,000 plumes. The results showed that median ratios using a conservative

estimate based on measured data exceeded the HBEFA data by more than 65% for NOx/CO and by a factor of about 100 for PN/CO2 (Peitzmeier et al., 2017). These studies can monitor the exhaust emissions from large number of vehicles at a specific location, while they cannot provide insight regarding how emissions vary at trip level.

2.1.2 Traffic emission model validation based on vehicle dynamics

In recent years, real-world vehicle dynamics have become useful for emission model validation. Several studies have found that emission models based on mean driving speeds are less successful with congested situations, which is because these models cannot take specific driver behaviours into account, including acceleration, deceleration, cruising, and idling time. Such detailed speed profiles can be collected either using traffic microsimulation models or onboard GPS devices.

Some authors have proposed alternatives to incorporate fluctuations in speed and generate more accurate emissions estimation. Samaras et al. (2018) improved fuel consumption and CO2 emissions calculations by calibrating a microscopic traffic model (AIMSUN) to replicate regular driving patterns over an urban corridor in Turin, Italy, operating under adaptive urban traffic control. Lejri et al. (2018) accounted for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at road segments for an area of Paris covering 3 km2. The results revealed that using a degraded speed definition with COPERT model led to an underestimation ranging from -17% to -36% for NOx emissions and including speed distribution with COPERT led to higher emission estimates (+16%) for NOx emissions under congested conditions (Lejri et al., 2018). Turkensteen (2017) investigated the influence of fixed speeds computations in green vehicle routing problems on the accuracy of carbon emission estimates using the CMEM model. The results suggested that the CMEM results under fixed speeds were sometimes less than half of those computed under realistic driving conditions (Turkensteen, 2017).

Many studies have validated traffic emission models by comparing traffic emissions estimated based on average speeds and instantaneous speeds collected using GPS devices. Ryu et al. (2015) collected GPS data from probe vehicles and compared average emissions and instantaneous emissions per link calculated by an instantaneous emission model CMEM. The study showed that the average speed model tended to underestimate CO2 emissions by about 16% with an increase in acceleration and idle time for a speed range of 20 km/h and below, which was

16 typically for traffic congestion (Ryu et al., 2015). Based on the results, a ‘corrected average emission model’ was developed to improve the accuracy of CO2 estimation (Ryu et al., 2015). Dias et al. (2018) proposed a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation in urban areas and compared estimated emissions with results calculated by a mesoscopic emission model TREM, which determined EFs based on the average speed and vehicle classes. The authors compared the magnitude of the 5th and 95th emission values estimated by the detailed GPS-based modelling approach and the TREM for the entire urban area and results shown a considerable variation of emissions, ranging from -15% to 49% for CO, -14% to 31% for VOC, -19% to 46% for NOx, and -22% to 52% for PM10 (Dias et al., 2018). Zhai et al. (2019) collected extensive vehicle activity data in Beijing using GPS and attempted to develop a comprehensive database of the facility- and speed-specific opmode mode distributions of different vehicle types. The results indicated that time fractions for restricted access and unrestricted access were significantly different at the same average speeds, which could lead to an error of 20% in the emission estimation (Zhai et al., 2019). Besides, results suggested that the driving patterns of taxis and private cars should be estimated separately as taxi drivers had more skillful driving behaviours, and omitting it could cause an estimation error of over 10% (Zhai et al., 2019).

Some studies have quantified the gaps between current drive cycles in emission models and drive cycles in real-world conditions for different vehicle types. Alam and Hatzopoulou (2016a) collected instantaneous bus speeds of 96 buses operating over 3700 road links and estimated emissions using the MOVES2014. The validation results suggested that emissions derived using the locally developed operating mode (opmode) distributions were better than and significantly different from the estimates derived using the MOVES default opmode mode distributions (Alam and Hatzopoulou, 2016a). Lu et al. (2018) investigated the uncertainty of HBEFA for estimating GHG emissions using second-by-second vehicle activity data of LDVs, and results demonstrated that the uncertainties were from the adoption of selected driving cycles and the parameter of relative positive acceleration (RPA). The authors also found different driving characteristics corresponded to the same RPA, indicating the potential inability of RPA to capture specific traffic scenarios such as intersections and congestion (Lu et al., 2018). Sun et al. (2015) proposed a trajectory-based emissions estimation for signalized arterials, which first estimated the trajectories for the entire traffic population including traffic state (e.g. queuing and free-flowing)

17 and vehicle’s driving mode (e.g. idle, deceleration and acceleration) and then estimated vehicle- based emissions using the CMEM. The results indicated that adding random noise to the cruise mode and employing a state-dependent acceleration process could limit the estimation errors within 10-20% (Sun et al., 2015). Perugu (2019) collected local vehicle trajectory data using an Android application ‘Speed Tracker’ and replaced the default drive cycle Federal Test Procedure (FTP-75) in the MOVES with modified Indian drive cycles for India specific vehicles, like auto- rickshaws (3-wheelers). The estimated drive cycles showed that LDVs in India generally spend more time idling due to worse traffic congestion, slower vehicle speeds and local road conditions, which led to a higher fleet average emission rate (Perugu, 2019).

2.2 Refining near-road air quality models

The reliability of predicted air quality estimates is directly dependent upon the detail and quality of the information and analysis method used in the air quality models. It is challenging to predict urban air quality since it is affected by numerous complex factors such as traffic flow, meteorology, as well as land use, making it change over location and time significantly. Besides, there are some high and acute observations caused by factors such as the presence of high emitters (e.g. diesel trucks). Their short-term and extreme influences on near-road air quality are hard to be captured using a traditional air quality model. Thus, there is a high need for refinement, calibration, and evolution of existing air quality models and their data inputs to explain the spatiotemporal variability of near-road air pollution.

2.2.1 Evolutions in data sources and collection methods

Due to the high variability in near-road air pollution in urban environments, mobile monitoring is used to collect air quality data at a high spatial resolution. Nonetheless, a limited number of mobile measurements may only represent a snapshot and cannot capture the temporal variability of urban air quality. Van den Bossche et al. (2015) conducted a broad set of black carbon measurements using a bicycle equipped with a portable BC monitor in Antwerp, Belgium. The study investigated the impact of the temporal variability on the representativeness of the measurements and evaluated a method to map urban air quality based on mobile monitoring (Van den Bossche et al., 2015). Kerckhoffs et al. (2017) explored the robustness of intra-urban LUR models for UFP based on mobile monitoring in three Dutch cities. The authors found that the mobile model for UFP was stable over different settings as the predicted concentrations

18 levels were highly correlated with the predictions derived from a previously developed LUR model with another spatial extent and in a different year at 1500 random addresses (R2 = 0.80) (Kerckhoffs et al., 2017). Lin et al. (2018) used an electric mobile platform with a Fast Mobility Particle Sizer and a sound level meter to examine the spatiotemporal variability of noise and UFP concentrations and identify UFP hotspots. The proposed UFP model included noise parameters, wind speed, temperature, and street canyon index and had a coefficient of determination of 0.77 (Lin et al., 2018). Liu et al. (2019) conducted mobile monitoring to assess the BC concentrations on three sampling routes in Shanghai, China, with a total length of 116 km. A LUR model was established to examine the spatial variability of BC measurements with various predictors, such as meteorology, socio-economics, and the distance to BC point-sources; the model was able to explain 68% of the variability of BC levels (Liu et al., 2019).

Low-cost air pollution sensors have become an essential tool for dense urban air monitoring, with their cost up to three orders of magnitude lower than reference monitoring stations (Morawska et al., 2018). Recent studies have examined the potential applications of wireless sensor networks as potential air quality monitoring alternatives for improving the spatiotemporal resolution of air pollution. Borrego et al. (2016) evaluated several air quality microsensors against reference methods and found significant differences in the results depending on the platform and on the sensors. Besides, promising results were found for some pollutants, including ozone, CO and NO2 (Borrego et al., 2016). Guanochanga et al. (2018) presented preliminary results of the construction of a low-cost wireless monitoring system as an Internet of Things application to visualize the levels of air pollution through web dissemination. Morawska et al. (2018) conducted a comprehensive literature review on the applications of low-cost sensing technologies for air quality monitoring and exposure assessment based on an analysis of 17 large projects. The authors found that 30% of these projects were commercial or crowdfunded and that the practice of machine learning or other advanced data processing approaches to improve the sensor agreement with reference instruments was increasing (Morawska et al., 2018). Cieplak et al. (2019) demonstrated a concept of the air quality monitoring system using low-cost sensors in the City of Lublin to detect data outliers based on machine learning methods. Besides, Grace and Manju (2019) provided a comprehensive review of air pollution monitoring systems based on wireless sensor networks.

To fill in spatial gaps in air pollution measured by ground-level measurements, recent studies have employed satellite remote sensing (SRS) methods to estimate air pollution at locations where direct measurement is impossible (Just et al., 2015; Kloog et al., 2012; Xiao et al., 2018). Aerosol optical depth is a measure of the attenuation of solar radiation by aerosols in the atmosphere and has been widely used to predict PM concentrations at ground level in many regions (Guo et al., 2017). Yang et al. (2017) developed PM2.5 and NO2 high-resolution prediction models using a LUR framework incorporating ground-based measurement, SRS, and air quality model data in the Pearl River Delta region, China. The results indicated that the NO2 model with the SRS estimate performed best, explaining 60.5% of the spatial variation (Yang et al., 2017). Chen et al. (2018) developed a generalized additive model (GAM) to link ground- level PM1 data with Aerosol optical depth data, meteorology, and land use information to estimate temporal and spatial variations of PM1 concentrations in China during 2005 - 2014. The results of 10-fold cross-validation suggested that R2 for monthly and seasonal prediction was 71% and 77%, respectively (G. Chen et al., 2018). Geng et al. (2018) investigated the sensitivity of satellite-based PM2.5 exposure models to its inputs by simulating various data-poor scenarios based on factors including temporal and sampling frequency of the monitors, the number of ground monitors, the accuracy of the simulation of PM2.5 concentrations based on chemical transport model, and the different combinations of the additional predictors. The results indicated that the model was more sensitive to total observed days than the number of monitors, and a model without land-use data still was able to capture the daily variation of PM2.5 (Geng et al., 2018). Sarafian et al. (2019) evaluated the model performance of two spatiotemporal statistical models, namely the Gaussian Markov random field model and linear mixed model, for satellite- based PM2.5 assessment. The study proposed a new cross-validation scheme (leave-p-out-h-block method) for spatially structured data and demonstrated that the Gaussian Markov random field model was statistically superior to the linear mixed model, under various cross-validation schemes (Sarafian et al., 2019).

Since satellite measurements are generally inadequate for local applications (scale < 1km), small Unmanned Aerial Vehicles (UAVs) equipped with various sensors have been introduced for in- situ air quality monitoring, providing new approaches and research opportunities in air pollution and emission monitoring (Ghosh et al., 2019). Brady et al. (2016) explored the utility of a quadrotor aircraft system for characterizing vertical (± 0.5 m) and horizontal (± 1.0 m)

20 concentration gradients of trace gases and aerosol particles within the boundary layer (0 - 100 m). The study found a maximum aerosol plume height of 40 m above the surf zone (Brady et al., 2016). Kuuluvainen et al. (2018) measured the vertical profile of lung deposited surface area (LDSA) concentration using a drone in an urban street canyon with an aspect ratio of 0.45 in Helsinki, Finland. The study results emphasized the role of turbulence mixing caused by traffic compared to the street canyon vortex as a driving force of the dispersion (Kuuluvainen et al., 2018). Besides, the vertical profiles above the rooftop exhibited a similar exponential decay trend compared to the profiles measured inside the street canyon (Kuuluvainen et al., 2018). Lu et al. (2019) evaluated the impact of meteorological data on the vertical variation in PM2.5 using UAV measurement following a designed route from ground level up to an altitude of 1000 m.

PM2.5 distribution was found to exhibit distinct stratification in the morning but more homogeneity in the afternoon, and meteorological induced changes in the boundary layer height and inversion layer significantly influenced the vertical distribution of PM2.5 concentrations in the lower troposphere (Lu et al., 2019).

Given advances in various sensors and information systems in recent years, big data has become a strong focus of global interest that increasingly attracts attention from academia, industry, and government (Li et al., 2016). It has excellent potential to provide temporal and spatial variability of traffic data as well as near-road air pollution. For instance, real-time high-resolution taxi trip data can provide insight regarding the trip distributions and traffic conditions for the regional road network, which are valuable for traffic management and traffic emission estimation. Zheng et al. (2015) employed a data-driven method that uses current meteorological data, weather forecasts, and air quality data of the station and that of other stations within a few hundred kilometres to forecast the reading of an air quality monitoring station over the next 48 hours. Apte et al. (2017) equipped Google Street View vehicles with an air quality monitoring platform and took measurements on every street in a 30-km2 area of Oakland, CA, and developed maps of annual daytime of NO, NO2, and BC at 30 m-scale. The results provided new insights into the spatial variability of near-road air pollution (Apte et al., 2017). Yu et al. (2017) employed approximately one million raw ridesharing trip data, provided by DiDi Chuxing company, to evaluate the environmental benefits of ridesharing resulted from the travel mode shift and the attitude change towards car purchase behaviour. The results indicated that the annual emission

reductions of CO2 and NOx in Beijing were 46.2 thousand tons and 253.7 tons, respectively (Yu et al., 2017).

Besides, big data can provide spatially resolved demographic or epidemiological information, which can be combined with high-resolution air pollution data to facilitate targeted risk analyses. Tao et al. (2016) examined 112 million posts on Weibo (a popular microblogging system) from 2011-2013 to identify terms whose frequency was most correlated with PM levels in Chinese megacities and used them to generate an Air Discussion Index for representing daily PM. The authors found a strong correlation (R = 0.88) between the Air Discussion Index and PM concentrations measured by U.S Embassy monitor stations (Tao et al., 2016). Dewulf et al. (2016) used mobile phone data of approximately 5 million mobile phone users, collected and stored by telecom operators, living in Belgium to estimate the daily exposure to air pollution. The results suggested that ignoring individual travel patterns can result in a bias in air pollution exposure assessment (Dewulf et al., 2016). Nyhan et al. (2019) estimated population exposure to air pollution using an Aerosol Optical Depth- and Land Use Regression-combined model based on the daily home and work locations of over 400,000 mobile phone users whose positions were recorded. These exposures were compared to the residence-only exposure metric, and the study highlighted the significance of considering daily mobility in health effect estimates (Nyhan et al., 2019). Table 2-2 summarizes evolutions in data sources and collection methods.

Table 2-2 Summary of evolutions in data sources and collection methods Evolutions in data Advantage Disadvantage Previous study sources and examples collection methods Mobile monitoring It can collect near-road air It may only represent a snapshot and (Kerckhoffs et al., 2017; quality data at a high spatial may not be able to capture the Lin et al., 2018; Van den resolution. temporal variability. Bossche et al., 2015) Low-cost sensors It can be an essential tool for It requires the calibration of air (Borrego et al., 2016; dense urban air monitoring quality sensors in the lab or through Guanochanga et al., (e.g. wireless sensor networks). a series of collocation campaigns 2018; Morawska et al., against reference monitoring 2018) stations. Satellite Remote It can fill in spatial gaps in air It is generally inadequate for local (Chen et al., 2018; Geng Sensing (SRS) pollution measured by ground- applications (scale <1 km). et al., 2018; Yang et al., level measurements. 2017)

Small Unmanned It provides research Its usage is limited by the battery (Brady et al., 2016; Aerial Vehicles opportunities in characterizing capacity and aviation regulations. Kuuluvainen et al., 2018; (UAVs) vertical and horizontal Lu et al., 2019) concentration gradients. Big data (e.g. real- It is valuable for traffic It may have a privacy issue and (Apte et al., 2017; Li et time high-resolution management and traffic demands high computing power. al., 2016; Yu et al., 2017; traffic data) emission estimation, as well as Zheng et al., 2015) targeted risk analyses.

2.2.2 Improvements in air quality modelling

Air quality modelling generally can simulate air pollutant concentrations either on a regional or a local scale. Several studies have attempted to develop hybrid models by simulating the dispersion of traffic-related air pollution in near-road environments as well as the transport of air pollution throughout the region. Chang et al. (2015) developed a hybrid modelling framework for providing high resolved spatial fields of traffic-related air pollutants at community scales. The framework could rapidly capture spatial concentration gradients in the near-road environment and could reduce the computation burden by 88-fold to obtain annual averages (Chang et al., 2015). Pepe et al. (2016b) established a hybrid modelling system using the combination of a meteorological model (WRF), a chemical and transport eulerian model (CAMx), and a Lagrangian dispersion model (AUSTAL2000) to simulate the dispersion of traffic-related air pollution within the urban area. The study did not find significant improvements in the overall performance of the hybrid model when compared to stand-alone CAMx at the monitoring stations in Milan city center (Pepe et al., 2016a). Fallah-Shorshani et al. (2017) developed a hybrid configuration, including a street canyon model and a Gaussian puff model to take into account traffic emissions, the regional background, and the transport of pollutants within the urban canopy. The results demonstrated that the hybrid approach decreased the RMSE value between observed and predicted concentrations by 16% - 25% compared to each model on its own (Fallah-shorshani et al., 2017). Bates et al. (2018a) evaluated the application of two model fusion approaches, including additive and multiplicative method, to use publicly available chemical transport simulations, dispersion model simulations, and observations to estimate air pollution at a neighbourhood-level (250m) spatial resolution. The model was able to present comprehensive estimates taking into account spatial gradients near

23 roadways, secondary formation and loss, as well as the effects of regional sources that can influence daily ambient pollutant concentrations (Bates et al., 2018b).

Other studies have also developed hybrid air quality models by integrating local and regional models. Hood et al. (2018) developed a regional-to-local modelling system, which includes a regional chemistry-climate model with 5km horizontal resolution (EMEP4UK) and an urban dispersion and chemistry model with detailed road source emissions (ADMS-Urban), to estimate different air pollutants including NOx, NO2, PM10 and PM2.5 in 2012 across London. The performance of the coupled model was evaluated against the measurements from the background and near-road sites in London, and results indicated that the model had excellent performance at both sites (Hood et al., 2018). Parvez and Wagstrom (2019) presented a hybrid modelling framework combining a regional model CAMx, and a local-scale dispersion model, R-LINE, to estimate hourly concentrations of both primary and secondary species at high spatial resolution (40m) in three major cities in Connecticut. When compared to the regional CAMx estimates, the hybrid model had better agreement with the LUR model results and mixed agreement with satellite-based estimates (Parvez and Wagstrom, 2019). Lai et al. (2019a) integrated different air quality models, including a diffusion model (AERMOD) and a grid model (CMAQ), to investigate the influences of the coal-fired power plant and the traffic source on the PM2.5 levels in the Taichung area. The results indicated that if the coal-fired power load or the traffic source were reduced by 20%, the concentrations of PM2.5 would decline by 0.5% or 4.5%, respectively (Lai et al., 2019b).

Given the LUR model as a standard tool for estimating spatial variation in air pollution, there is an increasing interest in incorporating temporal information from air pollution dispersion models into LUR models to improve model performance and interpretability. Michanowicz et al. (2016a) incorporated hourly Caline3QHCR dispersion information into existing winter-time LUR models, which were initially developed to explore the effects of multiple sources (e.g. legacy industry and vehicle traffic) on intra-urban NO2 across Pittsburgh, PA. The results indicate that the integrated model improved cross-validated R2 by 0.10, 0.03, and 0.05 for the weekday, full- week, and year models, respectively (Michanowicz et al., 2016a). In another study, Michanowicz et al. (2016b) integrated PM2.5 estimates from a dispersion model (AERMOD) into LUR models derived from ambient PM2.5 measurements from 36 sites to represent the different source and elevation profiles. The results revealed that LUR models generally underestimated PM2.5 levels

24 when the monitoring sites were in the downwind side, and the hybrid model could improve prediction accuracy by 2-10% (Michanowicz et al., 2016b). Korek et al. (2017) developed a hybrid model including land use data, meteorological data, and air quality estimates derived from the SIMAIR modelling system as well as the Gaussian dispersion model based on 93 biweekly measurements of NOx at 31 sites in greater Stockholm, Sweden. The model results had a 2 significant improvement in predicting NOx (R = 0.89) compared to the results estimated by the dispersion model alone (R2 = 0.68) (Korek et al., 2017). Tripathy et al. (2019) developed hybrid

AERMOD LUR models for estimating spatiotemporal variation in PM2.5, BC, and metal components (lead, manganese, zinc, and iron) based on fine-scale air pollution records from 37 locations across the Pittsburgh area of complex terrain and industrial sources. The hybrid models were developed specifically for application in epidemiology studies, and models explained more 2 variation in PM2.5 than in BC or metals, with R of 0.79, 0.59 and 0.34, respectively (Tripathy et al., 2019).

Other studies have also attempted various alternatives to enhance the LUR model performance. Wu et al. (2018) first derived pollutant gradient surfaces spatially interpolated using a leave-one- out kriging method based on PM2.5 concentrations at 71 EPA monitoring stations and then used the predicted concentration at the targeted site as a variable in LUR modelling. The results suggested that this hybrid kriging and LUR model could achieve better model performance (R2 of annual model: 0.85; R2 of monthly model: 0.88) compared to the conventional LUR models (R2 of annual model: 0.66; R2 of monthly model: 0.70) (Wu et al., 2018). Xu et al. (2019a) developed national PM2.5 and NO2 empirical models for China in high resolution (on the 1km grid) by incorporating LUR, satellite measurements, and universal kriging. The best models employed forward variable selection and universal kriging, and the R2 values of 10-fold cross- validation were 0.89 and 0.78, for PM2.5 and NO2, respectively (Xu et al., 2019b). Ghassoun et al. (2019) introduced Pseudo dynamic parameters, representing the interaction between wind and the urban morphology as a function of wind direction, into twelve LUR models based on 27 monitoring spots in Braunschweig, Germany. The results indicated that the LUR models including Pseudo dynamic parameters could explain 82% and 89% of the variance of UFP levels, while LUR model alone could explain 68% and 79% of the variance of UFP levels for both Southeast and Southwest directions, respectively (Ghassoun et al., 2019). Hong et al. (2019) extended the spatial scale of land-use regression models by pairing LUR model estimates with

25 satellite images based on deep convolutional neural networks (CNNs). The results demonstrated that using built environment characteristics captured in digital images could extend the spatial scale of LUR models when the GIS predictors were not available (Hong et al., 2019).

In recent years, there is a growing interest in the use of ANN to handle the complex nonlinear association in near-road air pollution, traffic characteristics, local meteorological conditions, and built environment. Adams and Kanaroglou (2016) developed ANN models based on mobile air pollution data with various predictors including surrounding land use information, meteorological data, air pollution concentrations from fixed monitoring stations, and traffic characteristics to map real-time air pollution health risk for environmental management. The 2 2 results showed that model performance was R = 0.78 for PM2.5, and R = 0.34 for NO2 (Adams and Kanaroglou, 2016). Bai et al. (2016) proposed a backpropagation neural network (BPNN) model with a wavelet transformation technique, which could decompose historical time series of air pollution into different scales, to forecast daily air pollutant concentrations including PM10,

SO2 and NO2. The study found that meteorological variables had significant impacts on the daily variability of air pollutants (Y. Bai et al., 2016). Rahimi (2017) developed both ANN models and multiple linear regression (MLR) models to predict short-term NO2 and NOx concentrations as a function of meteorological conditions. The results demonstrated that ANN models had 2 significantly higher R values of 0.92 and 0.94 for NO2 and NOx compared to MLR model 2 results with R values of 0.41 and 0.44 for NO2 and NOx concentrations (Rahimi, 2017). Alimissis et al. (2018) also compared the performance of ANN models and MLR models for spatial estimation of five regulated air pollutants based on data from an urban air quality monitoring network in the greater area of metropolitan Athens in Greece. The results indicate that ANN models were significantly superior to MLR models, mainly where the air quality network density was limited (Alimissis et al., 2018). Biancofiore et al. (2017) developed three empirical models, including an MLR model, an ANN model with and without recursive architecture, to forecast the PM2.5 concentration using various input variables, namely meteorological data, PM10, and CO concentration. All simulation results indicated that the neural network with recursive architecture had better performance compared to both the MLR model and the ANN model without the recursive architecture (Biancofiore et al., 2017). Cabaneros et al. (2019) provided a review of ANN models for ambient air pollution prediction and highlighted the significance of developing systematic protocols for developing powerful ANN models.

Due to the black-box nature of ANN models, various studies have explored other machine learning methods such as tree-based model that can capture the effects of different factors on near-road air pollution. Sayegh et al. (2016) used a Boosted Regression Trees (BRT) model to examine the influence of background concentrations, traffic density, and prevailing meteorological conditions on roadside NOx concentrations at the urban, open motorway, and motorway tunnel sites in the UK. Different site-specific traffic states (free-flow, busy-flow, congested, and severely congested) were found to influence roadside NOx differently, and the relationships between NOx concentrations and other variables were significantly influenced by the quality and resolution of background NOx (Sayegh et al., 2016). Kamińska (2018) employed a random forest (RF) model to investigate the effects of meteorological conditions, temporal conditions, and traffic conditions on near-road air pollution including NO2, NOx and PM2.5 measured at an air quality monitoring station located a short distance from a large intersection. The study found the traffic flow was the most significant variable in explaining the variation of

NO2 and NOx, and meteorological conditions, especially temperature and wind, were the most important predictors for PM2.5 concentrations (Kamińska, 2018). Gao et al. (2019) developed a generalized additive model (GAM) and a LUR model to determine the effects of urban form and meteorology on the air pollution distribution at a neighbourhood scale (2km * 2km) using mobile measurements in two communities in Shanghai, China. The modelling results suggested that the GAM outperformed LUR with a higher adjusted R2 and a lower RMSE, and the GAM-based

PM2.5 concentration surface indicated that the heterogeneous variation of PM2.5 in the central urban area mainly resulted from the nearby highway (Gao et al., 2019). Di et al. (2019) developed a geographically weighted generalized additive model as an ensemble model incorporating three machine learning algorithms, including neural network, random forest, and gradient boosting, to predict PM2.5 concentrations in the 1km * 1km and 100m * 100m grid cells based on a number of predictors such as satellite-derived measurements, chemical transport model predictions, land-use variables, and meteorological variables. The ensemble model of annual PM2.5 estimates was found to have the best performance compared to individual base learners, with the 10-fold cross-validated R2 values ranging from 0.75 to 0.90 (Di et al., 2019).

2.3 Identified gaps in the current literature

Based on the literature discussed in this section, we identify significant gaps that should be addressed to improve the understanding of local traffic emission estimates and near-road air

27 quality modelling. There are mainly four aspects that need to be considered: 1) investigation of the influence of vehicle activity data with different aggregation levels on project-level emissions estimation and hotspot analysis, 2) validation of traffic emission models regarding emission intensities and drive cycles based on locally collected data, 3) investigation of the influence of driver and trip characteristics on trip-level emission intensities, and 4) investigation of the influence of local traffic characteristics, meteorological data, and built environment on near-road air quality.

While a breadth of literature exists for traffic emission model validation, no studies have been conducted to compare the emission rates in MOVES, the most commonly used emission inventory model in North America, against real-world emission rates derived from roadside measurements in Canada. Besides, there is a need to evaluate the effect of speed data on project- level emission estimation and hotspot analysis. Although a recent study developed representative drive cycles for the Toronto waterfront area based on simulated data (Amirjamshidi, 2015), there is a need for developing representative drive cycles based on real-word GPS records and incorporating passenger vehicle driving behaviour in regional emission models to improve the estimation of emission inventories. We also identify the need for investigating the effects of various factors such as driving behaviour, driver characteristics, trip purpose, and time of day on trip-level emissions.

While there is intense research on near-road air quality modelling, very few studies have quantified the impact of detailed traffic characteristics, namely vehicle types and counts, built environment, and meteorological data on high-resolution air pollution concentrations in short- term fixed monitoring studies. Moreover, the impact of high-emitters such as diesel trucks on street-level air pollution is significant, especially short-term exposure, although no studies have collected real-time traffic information during mobile measurements. Thus, it highlights there is a need for collecting local traffic characteristics and developing empirical models to quantitatively evaluate the impact of traffic as well as other factors on near-road air pollution.

Chapter 3 Contrasting the Direct Use of Data from Traffic Radars and Video- Cameras with Traffic Simulation in the Estimation of Road Emissions and PM Hotspot Analysis Chapter overview

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and a video camera were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle volumes and drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6-9am), midday (11-2pm), and afternoon peak (3-6pm) on a weekday (June 23, 2016) along a high- volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the farther lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds at the same point, and their average speeds were closer after calibration. Traffic emissions of nitrogen oxides and particulate matter were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NOx: 4.0 - 27.0g; PM10: 0.3 - 4.8g; PM2.5: 0.2 - 1.3g) for the corridor. The estimates based on radar speeds were at least 3 times as low as emissions derived from simulated vehicle trajectories, cautioning against the use of raw radar data for emission modelling purposes. Finally, estimated PM10 and PM2.5 near-road concentrations derived from emission results based on simulated speeds were two or three times higher than concentrations based on emissions derived from using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis.

3.1 Introduction

While a number of traffic-related air pollutants are often found in near-road environments, nitrogen oxides (NOx) are considered as markers of traffic-related air pollution and have been associated with various chronic and health effects (Chaloulakou et al., 2008). Besides, particulate

29 matter (PM) from vehicle emissions has been identified as a major public health risk, particularly in urban areas (Weinmayr et al., 2016). Two size ranges, PM2.5 and PM10, are widely monitored in ambient air. Vehicles mainly emit fine particles (PM2.5) from the tailpipe exhaust and coarse particles (PM10) from tirewear and brakewear (Ferm and Sjoberg, 2015). The development of modelling tools able to simulate the contribution of road traffic on near-road air quality is crucial in order to assist project-level analysis and in investigating the impacts on air pollution of various policies, such as changes in road capacity or development of bicycling infrastructure.

Vehicle emission models have been developed to predict traffic-induced emissions at macroscopic, mesoscopic, and microscopic levels (Abo-Qudais and Qdais, 2005). Macroscopic models provide network-wide emissions using average aggregated network variables, such as density, flow, and network speed (Jiang et al., 2015). Mesoscopic models provide emission estimates according to average link speeds. These models can take into account spatial and temporal variability across the network although they cannot represent explicitly the vehicle behaviour (Sider et al., 2014). Given that traffic impacts on local networks have drawn increased attention, microscopic emission models have become of great interest (Abou-Senna and Radwan, 2013). They are able to predict vehicular emissions at a second-by-second resolution by taking vehicle operating conditions as inputs including acceleration, deceleration, idling, and cruising (Song et al., 2012a).

In recent years, various studies have sought to simulate traffic emissions through the integration of traffic and emission models. In the city of London, a combination of a microscopic traffic simulation model (VISSIM) and the Comprehensive Modal Emissions Model (CMEM) was applied to evaluate the effect of changes in available road capacity on vehicle emissions (Noland and Quddus, 2006). In Greenville, the traffic model PARAMICS and the Motor Vehicle Emission Simulator (MOVES) were linked to investigate the impact of alternative fuelled vehicles on traffic emissions (Xie et al., 2011). PARAMICS was also integrated with CMEM in a study evaluating the impact of intelligent speed adaptation on energy and emissions (Servin et al., 2006). Various studies have also integrated the USEPA emission model MOVES with the traffic simulator VISSIM (Ghafghazi and Hatzopoulou, 2015). For instance, various scenarios of land configuration at intersections have been studied by integrating these two models to analyze potential designs that can mitigate traffic congestion and reduce emissions (Bing et al., 2014).

Signalized road intersections have been identified as pollution hotspots in urban environments since commuter exposures to traffic-related air pollution such as NOx and PM tend to be higher than average as vehicle acceleration, deceleration, and idling occur more frequently in such microenvironments (Goel and Kumar, 2014). The USEPA has recommended several air quality models for estimating near-road air quality, such as AERMOD, CALINE 4, and CAL3QHC model. A comprehensive analysis was conducted to compare these three models, noting that CALINE 4 and CAL3QHC performed moderately well for an intersection in Sacramento,

California, while AERMOD under-predicted PM2.5 concentrations. Besides, for a busy road in London, CAL3QHC was observed to perform better than other models (Chen et al., 2008). Another study in India employed three air quality models including the ‘modified General Finite Line Source Model’ (M-GFLSM), CALINE3, and CAL3QHC to evaluate the PM concentrations at one of the busiest traffic intersections in the city of Guwahati. The authors found that the

CAL3QHC model performed better than CALINE3 in predicting PM2.5 and PM10 (Gokhale and Raokhande, 2008).

While the resources and capability to develop traffic simulation models for the purpose of modelling emissions are available in academic environments, planning agencies are often limited to the data collected by municipal traffic operation departments. These data often include video- camera recordings and data from traffic radars scattered at various sites across an urban area. As such, in practice, detailed traffic activities across a network are non-existent or difficult to gather therefore assumptions must be made (Sider et al., 2016).

The primary objective of this study is to explore the effects of data derived from 1) video- cameras, 2) radars, and from a 3) traffic simulation model on the resulting traffic emissions and near-road air quality across a busy road segment in Toronto, Canada. While radars can provide speed data for every vehicle (or ensemble of vehicles) passing through a specific location, traffic simulation models are able to simulate the entire vehicle kinematics along a road segment. Are these two approaches comparable in terms of the final emission estimates? What is the effect of speed aggregation on the resulting emissions and hotspot analysis?

This study was motivated by the need to identify the most important inputs for project-level analysis of traffic emissions and near-road air quality. Traffic microsimulation is generally not available as a tool for government agencies to derive project-level traffic emissions. As cities

31 invest in infrastructure for traffic counting, our study investigates the effect of using traffic counts to derive estimates of emissions along a road segment using a coarse estimate for vehicle speed. This approach is compared with an emission estimate generated using a full analysis of individual vehicle drive-cycles derived from a calibrated traffic simulation model. Our study is significant because it quantifies the variability in emissions and concentrations obtained using various sources of traffic data and provides recommendations for project-level analysis and PM hotspot analysis.

3.2 Materials and methods

3.2.1 Study area and data collection sites

Our study consists in estimating traffic emissions for a single road segment on College Street, a four-lane major arterial roadway, with a daily traffic volume ranging from 15,000 to 20,000 (Sabaliauskas et al., 2012). It crosses downtown Toronto from the west to the east ends and goes through various land-uses (residential on both ends and commercial/institutional as it crosses downtown Toronto). Around our study site, College Street is bordered by the University of Toronto buildings on the north side and commercial/restaurant establishments on the south side. Our chosen segment on College Street spans two major intersections: College and St George on the west and College and McCaul on the east. Four-story buildings are present on both sides of the road.

Two sites were identified to collect traffic data: 1) a wide-angled webcam was installed at the intersection of College and St George, capturing all the movements eastbound and westbound on our study segment as well as all turning movements, 2) a webcam and radar, both owned and operated by the Southern Ontario Centre for Atmospheric Aerosol Research (SOCAAR) and located mid-block on College street. Figure 3.1 illustrates our study segment on College Street, the intersection of College and St George, as well as the data collection sites.

Figure 3.1 Study site and data collection points

3.2.2 Data collection and processing

Traffic data were collected continuously for one week from June 17 to 24, 2016. Typically, electric-powered streetcars (light rail) run in the middle lanes of College St. providing public transportation service. However, during the study period we selected for data collection, streetcars were substituted by diesel-fuelled transit buses running along College St. without dedicated lanes. At the midblock location, continuous traffic volume was recorded by a webcam (LifeCam HD-3000, Microsoft, Redmond, WA, U.S.) located on the north side of College Street, right outside the SOCAAR facilities. At the same time, a microwave radar system (SmartSensor HD, Wavetronix, Provo, UT, U.S.) was located in close proximity to the camera. The camera and radar were positioned at a height of 6m and 15m away from the curbside. Simultaneously, a second webcam (Logitech C930E, Logitech, Lausanne, Switzerland) was affixed on the rooftop of a University of Toronto building located at the intersection of College and St George at a distance of 50m to the SOCAAR site, recording traffic information including vehicle types, vehicle counts in all directions and their corresponding route decisions. All instruments’ clocks were synchronized.

Traffic information was manually extracted from rooftop webcam videos by watching footage for 168hours (1 week). Different vehicle classes were recorded, including passenger cars, passenger trucks, light commercial trucks, refuse trucks, intercity buses, school buses, transit buses, single unit short-haul trucks (or medium trucks), and single unit long-haul trucks (or large trucks). Other information such as fleet composition, vehicle counts in all directions and their corresponding routing decisions were also processed from the footage as the inputs of a traffic simulation model. The traffic data extracted at the street level were employed to validate traffic simulation outputs. Despite the availability of image recognition software, we opted for manual processing of webcam data because of the need for detailed vehicle classification which most processing software do not yet perform successfully.

Since traffic data logged by the radar system included the length of each passing vehicle and its corresponding speed, the data were averaged in every minute and every hour for the purpose of estimating vehicle emissions, this resolution resembles what municipal traffic departments normally adopt. Besides, hourly traffic information was employed to compare traffic volumes and composition against webcam data and for comparing speeds against the output of the traffic simulation model. The four lanes of College Street were numbered from 1 to 4, with lane 1 the closest to the SOCAAR webcam and radar and lane 4 the furthest. Traffic flow on lane 1 and lane 2 is in the westbound direction, and on lanes 3 and 4 in the eastbound direction.

Diurnal trends of the traffic volume on the study segment were obtained by organizing the webcam/manual counts at the intersection based on the vehicle movements from and to the westbound and eastbound directions. Out of the five weekday diurnal profiles for traffic volumes, a single day was selected as most representative, based on the root-mean-square deviation (RMSD) method, which aims to measure the differences between two different sets of values. The weekday with the lowest total distance to the other weekdays was selected as the representative weekday; it was Thursday June 23. Traffic data in three different periods namely morning peak (6-9am), midday (11-2pm), and afternoon peak (3-6pm) for Thursday were used to estimate vehicle emissions and near-road air quality.

3.2.3 Traffic simulation

A microscopic traffic simulation model for the study area was developed using the PTV VISSIM platform (VISSIM5.40, PTV, Karlsruhe, Germany). The road network geometry was built based

34 on satellite images from Google Map; and Google Maps Street View was employed to identify turning restrictions, road configurations, and public transit stop locations. The width of each lane was assigned as 3.3m; roadway links were coded with 0% gradient. The speed limits were set at 40km/h for all streets according to the open data portal of the city of Toronto.

Traffic inputs including traffic volume, routing decisions, and fleet composition were derived from the rooftop webcam. The upstream and downstream signalized intersections with respect to the St. George Street and College Street intersection were also included in the network in order to account for vehicle behaviour at the intersection, such as acceleration, slowing down and queuing. The signal timings for the three intersections in the network were collected through field visits. Figure 3.2 presents the VISSIM network, illustrating that although we are simulating emissions for a single road segment on College Street (spanning between St George and McCaul), our simulation network is more extensive in order to ensure that vehicle behaviours are appropriately replicated. The study period covered three periods with a total of 9hours. The warm-up time of the traffic simulation was chosen as 900s, which was long enough for loading the vehicles onto the network.

A data collection point was located along the segment of interest in the traffic simulation model, at the same location where the radar is positioned on the real segment. Two types of outputs were extracted from the traffic simulation. The first was vehicle records at the data collection point including vehicle counts, vehicle composition, and instantaneous speed. These data were used to calibrate the parameters and validate the performance of the traffic simulation model in order to appropriately capture vehicle speeds. The second output included sec-by-sec speed profiles for each vehicle from the moment it enters the network to the moment it exists, which would then be used for estimating emissions in MOVES.

A number of parameters in the model describing car-following and lane-changing behaviours were selected for calibrating the simulation in order to improve the model’s ability to replicate traffic conditions observed. Two hours on Thursday (6pm-8pm) were selected for calibrating the model parameters. The calibrated parameters were then maintained for the rest of the simulation. Simulated vehicle records in the remaining hours were validated against radar/webcam data. Given that the radar detected the instantaneous speed of each passing vehicle, the variability of

35 speed data for all vehicles was chosen as the measure of effectiveness for model calibration and subsequent validation.

Figure 3.2 The VISSIM base network

3.2.4 Emission modelling

The USEPA MOVES model (MOVES2014a, EPA, Washington, D.C., U.S.) was employed in this study to simulate NOx, PM2.5 and PM10 exhaust emissions, brakewear and tirewear emissions, using instantaneous speed data extracted from the traffic simulation or input from recorded data. Emission models that account for variations in speed profiles provide more accurate results than models which only consider an average speed (Liu and Christopher, 2012; Misra et al., 2013). These models account for acceleration, deceleration, cruising, and idling, also known as the drive cycle, which represents actual driving conditions.

MOVES requires information on link activity, link length, road grade, fleet composition, vehicle age, fuel information, as well as meteorology. In particular, link activity refers to the second-by- second speeds generated by the traffic simulation or speed profiles interpolated based on the radar records. The link length and road gradient for each lane were derived from Google Earth reflecting local conditions. Link type was set to ‘urban unrestricted road’. Data on vehicle model years for the City of Toronto were obtained from the Drive Clean program, the provincial inspection and maintenance program (Melorose et al., 2015). Meteorological data were extracted in the form of hourly temperature (°F) and relative humidity (%) from a nearby fixed station

36 operated by Environment Canada (Toronto City Centre Airport), located approximately 3.3 kilometers from our study corridor. Vehicle types were extracted from the VISSIM simulation or from webcam data for the corresponding hour. All passenger cars, passenger trucks and light commercial trucks were assumed to run on gasoline, while other types of vehicles were assumed to run on diesel (Natural Resources Canada, 2009).

When second-by-second drive cycles are input in MOVES, instantaneous emissions are estimated by a) calculating a vehicle specific power (VSP) or a scaled tractive power (STP) for light-duty vehicles and heavy-duty vehicles respectively and b) identifying the corresponding operating mode (opmode). Both VSP and STP are calculated based on a vehicle’s speed and acceleration but differ in how they are scaled. For heavy-duty vehicles, the tractive power is scaled by a constant to bring its values into the same range as the VSP for the light-duty vehicles. (U.S. Environmental Protection Agency, 2015a). The opmode distribution specifies the percentage of time that a vehicle has spent under different operating modes, such as idling, braking, and cruising. Each opmode is associated with a particular emission rate (g/h) which depends on a number of variables such as fuel type, meteorology, and vehicle age (Alam and Hatzopoulou, 2014). On the other hand, when only hourly average speeds are used as input to estimate emissions, MOVES relies on default opmode distributions derived from embedded driving schedules with various average speeds for different roads and vehicle types.

3.2.5 Air quality model

The CAL3QHC model, recommended by the USEPA to evaluate transportation project-level air quality, was employed in this study to estimate concentrations of PM2.5 and PM10 at the College and St George intersection. It is important to note that the CAL3QHC model does not predict

NOx concentrations.

This model enhances the steady-state Gaussian dispersion model CALINE-3 by employing a traffic algorithm for estimating queue lengths and the contribution of emissions from idling vehicles, so it can estimate concentrations from both moving and idling vehicles (U.S. Environmental Protection Agency, 1995). CAL3QHC is considered as a reliable tool for predicting concentrations of inert air pollutants near signalized intersections (Gokhale and Raokhande, 2008).

To estimate near-road PM2.5 and PM10 concentrations, CAL3QHC requires input data such as vehicle-related data, meteorological conditions, signal timing data, and configuration of the intersection. A link can be defined as either a free flow or a queue link, and the program sums the contributions from each link to the receptor. The receptor is located at the same position as the radar on College Street.

Vehicle-related data include traffic volume (vehicle/hr), arrival rate condition (e.g. worse, average, and best progression), composite running emission factor (EF) (g/veh-mile), and idle EF (g/veh-hr). Running and idle EFs were derived from MOVES. Meteorological conditions including wind speed (m/s) and direction (degree) were measured by a meteorological station (WXT520, Vaisala, Vantaa, Finland), located at rooftop level. The signal timings for the intersection were collected through field visits. A default value 1,600 was used for the saturation flow rate. Configuration of the intersection was derived based on Google Map.

3.2.6 Speed input scenarios

In order to investigate the effect of speed aggregation on emission and air quality estimates, five methods for using speed data were adopted in this study. The speed input scenarios have two different definitions: the radar records of one-minute averages and one-hour averages are the time mean speed; all the simulated drive cycles are the space mean speed.

(1) Radar records of one-minute averages: Using one-minute average speed and volume data, sec-by-sec speed profiles across the study segment were derived by assuming vehicles’ speeds were constant for each minute. This assumption was made to test the effect of deriving a drive cycle using one-minute average data.

(2) Radar records of one-hour averages: Hourly average speeds were calculated by averaging radar speeds in one-minute intervals. In this case, MOVES relies on default operating mode (opmode) distributions to estimate emissions.

(3) Simulated link drive cycles: Sec-by-sec speed profiles across the study link were extracted from VISSIM using the link evaluation function. This means that for every second, an average speed of all vehicles on the link was generated. This profile is used by MOVES to generate an opmode distribution for the link. Since such an approach may lead to records of zero speeds when there are no vehicles on

38 the link, these records were deleted. This method does not lead to a reasonable drive cycle when sec-by-sec speeds are examined as a sequence and neither is it treated by MOVES as such. In fact, an opmode distribution is generated which summarizes the fractions of time the road segment experiences various driving modes.

(4) Simulated link drive cycles (seg): As a supplement to generating a drive cycle for a whole link (method 3), in this method the study link was divided into two sub-segments. Then, sec-by-sec speed profiles for each sub-segment were extracted from VISSIM. This method captures to a certain extent, the different driving behaviours along the link. For instance, vehicles move at a higher speed in the middle of a link, and will slow down or stop upstream of an intersection. The emissions on each sub segment were summed up to obtain total emissions on the link.

(5) Simulated vehicle trajectories: Sec-by-sec speed profiles were derived by keeping track of each vehicle from the moment it enters the network to the moment it exits. The treatment of vehicle trajectories ensures that the acceleration and deceleration profiles are consistent across each vehicle’s path. Emissions of each vehicle were then reconstructed to generate road segment emissions. We hypothesize that this method would lead to the most realistic emission estimates, yet it is the most computationally demanding.

3.3 Results and discussion

In this section, we first begin to compare the data streams in terms of vehicle counts and vehicle composition. The radar and rooftop webcam were used to derive traffic volume on the segment of interest spanning College and St George to College and McCaul intersections. Traffic simulation was then conducted and the VISSIM results were calibrated and validated against the radar for vehicle speeds. Then, emissions were simulated based on radar data only and compared against emissions simulated using the VISSIM outputs. Finally, the simulated emissions were employed as input to the air quality model to evaluate near-road PM concentrations.

3.3.1 Comparison of data streams

Figure 3.3 presents a comparison of traffic volumes between webcam data (also referred to as manual counts) and radar records on the segment of interest for the three study periods (morning

39 peak, midday, and afternoon peak) on June 23, 2016. For the same period, traffic volumes obtained from the radar were always lower than the manual counts. In fact, the radar performance varied by lane. The radar had a better correspondence with manual counts on westbound lanes (located closer to the radar) than eastbound lanes. This finding is consistent with a previous study which indicates that radar performance degrades as the distance between a radar and a traffic lane increases (Gorjestani et al., 2008). Besides, we found a large difference between radar records and manual counts during the afternoon peak period (3-6PM) on eastbound lanes due to the occlusion issue caused by high traffic volumes on the closer westbound lanes.

We further investigated the radar performance in terms of vehicle classification by comparing radar data and manual counts over the study period. The radar detected the length of each passing vehicle, so we followed the same classification when we conducted manual counts. The radar records were categorized based on the length range of different vehicle classes and were compared with webcam/manual counts as shown in Table 3-1.

According to the webcam/manual counts, private vehicles (passenger cars and passenger trucks) account for the largest proportion of total vehicles (approximately 95%) and medium-size vehicles (6.7-12.8m) encompass 4% of the total volume, while vehicles in other categories only account for 1% or less. This outcome is consistent with the findings from the national Canadian Survey Report: 96.3%, 2.1% and 1.5% of the total vehicles are light vehicles, medium trucks and heavy trucks respectively (Natural Resources Canada, 2009). In contrast, radar records indicate that 86% of the vehicles are light-duty vehicles. A larger proportion of vehicles are classified by the radar as medium-size (6.7-12.8m) and large-size (over 12.8m) compared to the manual counts. This indicates a weakness in the classification accuracy of the radar which has been demonstrated by others to be influenced by factors such as traffic conditions, distance from traffic lane, and weather (Yu et al., 2010).

2500

2000

1500

1000 Traffic Volume Traffic Volume 500

0 6am - 9am 11am - 2pm 3pm - 6pm 6am - 9am 11am - 2pm 3pm - 6pm WB direction EB direction

Radar records Manual counts

Figure 3.3 Comparison of traffic volume between radar records and manual counts WB and EB refer to westbound and eastbound

Table 3-1 Comparison of vehicle classification between radar and webcam/manual counts WB and EB refer to westbound and eastbound Vehicle category Vehicle Length Radar records Manual counts WB EB WB EB Private Vehicles < 6.7m 85% 88% 96% 95% (small cars, passenger cars, passenger trucks) Light Commercial Truck + School Bus (6.7, 12.8m] 10% 9% 4% 4% + Short Haul +Refuse Truck + Transit Bus Long Haul + Intercity Bus (12.8, 18.3m] 3% 2% 0% 1% Street Cars + Combination of Short Haul >18.3m 2% 1% 0% 0% + Combination of Long Haul

3.3.2 Validation and calibration of traffic simulation model

VISSIM is a time step and behaviour based microscopic traffic simulation model, which includes a number of independent parameters to describe traffic control operation, traffic flow characteristics, and driver behaviour. The simulation model includes default values for each variable, but it also allows users to modify the parameters within their corresponding reasonable ranges based on field-measured conditions.

Traffic information including vehicle volumes, turning decisions, and vehicle composition, obtained from the rooftop webcam were used to load the VISSIM network. The simulation results were first validated based on a comparison of hourly total number of vehicles on the study segment between simulation output and webcam/manual counts. The validation results (provided in appendix A) reveal that simulated volumes are consistent with manual counts.

The simulated vehicle speeds were compared with radar data. For this purpose, a data collection point was set in VISSIM at the same position as the radar on College Street. The data extracted from traffic simulation at that point includes the instantaneous speeds of all passing vehicles, which were compared with radar records for each lane. The calibration results for the network indicated that an improvement in model speeds with respect to radar speeds was achieved when the desired speed distribution parameter was changed from its default range of 48-58km/h to a higher range of 50-65km/h. This reflects the fact that drivers on College St. were moving at faster speeds than the default distribution in the model. Other parameters such as look ahead distance, standstill distance, and minimum headway were not found to significantly change the mean and variance of simulated vehicle speeds. Further information on the calibration of the traffic simulation model is provided in appendix B. It is important to note that although this study did not use queue length to calibrate the traffic simulation model, this variable could have significant influence on simulated vehicle behaviours including idling, acceleration, and deceleration at signalized intersections.

Figure 3.4 presents the distribution of vehicle speeds derived from the calibrated model compared with radar records, across the four lanes over the period extending from 6am to 6pm on June 23, 2016. We observed that the speeds of vehicles on lanes 1 and 2 (westbound direction) were notably lower than those on lanes 3 and 4 (eastbound direction), as there was a transit bus stop in the westbound direction.

Radar speeds were observed to exhibit much larger variability than traffic simulation data. This is especially the case on lanes 3 and 4 whereby VISSIM simulates most vehicles as running very close to the speed limit whereas the radar picks up on the various types of drivers existing on the road. This is not surprising since we expect a traffic simulation model to be able to reflect average conditions characterizing a roadway rather than picking up on the diversity of speeds.

Figure 3.4 Comparison of speeds between radar (dark grey) and calibrated VISSIM outcomes (light grey). Boxes represent the inter-quartile range (25th to 75th percentile), and whiskers indicate the minimum and maximum values. Dots indicate outliers, and crosses refer to mean values.

3.3.3 Comparison of emissions based on different speed input scenarios

As discussed in Section 2.6, five different scenarios for speed input were identified: radar records of one-minute averages, radar records of one-hour averages, simulated link drive cycles, simulated link drive cycles after segmenting the road into smaller links, and simulated vehicle trajectories. Figure 3.5 presents a comparison of hourly NOx, PM2.5 and PM10 total emissions (in grams) occurring on the study link over the three study periods (9 hours). PM emissions include exhaust emissions, brakewear, and tirewear emissions.

a) Distribution of hourly NOx total emissions

b) Distribution of hourly PM10 total emissions

c) Distribution of hourly PM2.5 total emissions

Figure 3.5 Emission estimates based on radar data and traffic simulation: a) NOx, b) PM10, th th and b) PM2.5. Boxes represent the inter-quartile range (25 to 75 percentile), and whiskers indicate the minimum and maximum values. Dots indicate outliers, and crosses refer to mean values.

For the three pollutants (NOx, PM10, and PM2.5), emission results derived from radar data were much lower than the results derived from the simulated speeds. The vehicle trajectory method takes into account sec-by-sec driving behaviour of each vehicle, which is a more realistic representation of the operating mode distributions. The NOx mean value for emissions based on the vehicle trajectory method was 23.1g, which is 13.4% and 16.1% higher than emission results estimated based on the link drive cycles methods with and without segmentation, respectively.

Besides, the NOx emissions derived from radar one-minute and radar hourly methods were 68.1% and 61.5% lower than the emissions derived from the vehicle trajectory method. In addition, the average PM10 emissions based on the vehicle trajectory method was 3.94g, which is 26.6%, 27.7%, 65.2%, and 87.8% higher than emissions based on link drive cycles with and without segmentation, radar hourly method, and radar one-minute method, respectively. Finally, the mean emission derived from the vehicle trajectory method for PM2.5 was 1.07g, which is considerably higher than radar results with relative differences of 75.0% (radar one-minute method) and 65.4% (radar hourly method). The PM2.5 results based on link drive cycles and link drive cycles (with segmentation) were 10.7% and 4.8% lower than results derived from the vehicle trajectory method.

The results indicate that emission estimates based on radar methods were always noticeably lower than estimates derived from vehicle trajectory methods, as radar records of instantaneous speed of passing vehicles at a single point cannot appropriately represent the real drive cycles of vehicles travelling on a link. Furthermore, a large difference in estimated emissions was observed between the two radar methods. This is due to the fact that radar speeds are essentially records of speeds of passing vehicles at a single point on the road. The location of the radar is at midblock and therefore does not pick up on the formation of queues at the downstream intersection. It is therefore incapable of capturing idling vehicles and with a single reading for each vehicle, and it is impossible to capture acceleration and deceleration behaviour. Therefore, in the first approach where we used the radar minute averages to build a drive cycle for the road, we observed that MOVES underestimated emissions compared to the use of the radar hourly averages as link-average speeds. In the latter method, MOVES uses built-in driving schedules based on predefined speed bins and an interpolation algorithm to produce a default operating mode distribution.

We also compared the use of the traffic simulation model to generate a drive cycle for the link (link-level drive cycle) against the use of radar hourly average speeds (which entail MOVES to allocate a default operating mode distribution). Emission estimates based on sec-by-sec speed profiles across the link were higher than those based on radar hourly average speeds. The primary reason for this divergence in emissions is related to the fact that the default operating mode distributions used by MOVES assume a lower proportion of idling and moving at lower speeds for the same average cycle speed. This difference would be more substantial when modelling emissions for a signalized intersection approach link where vehicles tend to idle, decelerate, and accelerate.

In order to capture the different driving behaviors along the link, the link drive cycle segmentation method divides the link into two sub-segments. We separately estimated the emissions of vehicles at a higher speed in the middle of a link, and lower speed upstream of the intersection. The emissions on each sub segment were summed up to obtain total emissions on the link. Emission results of PM2.5 and PM10 using this method were slightly higher than the link drive cycle method.

Since both speed and acceleration are available in the microsimulation output for every vehicle for every second of simulation in the vehicle trajectory method, operating mode distributions based on the calculation of vehicle specific powers were computed for each vehicle. This is thought to be a more accurate way of estimating emissions as it is able to capture all sec-by-sec drive cycle patterns of every vehicle as it crosses every link. This method is different from the link drive cycle where a single profile is derived representing all vehicles on the link. The link drive cycle method averages the speeds of all vehicles on a single link, which generally omits detailed vehicle activity such as acceleration and deceleration, especially at small speed differentials. Meanwhile, the vehicle trajectory method takes into account sec-by-sec driving behaviour of each single vehicle, which is essential to calculate the most accurate operating mode distributions and develop the most accurate emissions. The vehicle trajectory method estimated 36% and 5% higher total PM10 and PM2.5 emissions than the link drive cycles segmentation method.

We further estimated the proportion of PM2.5 and PM10 exhaust emissions to total emissions. The mobile source PM emissions include exhaust emissions and non-exhaust emissions, such as brakewear and tirewear. PM from brakes and tires mainly comes from abrasion, corrosion, and turbulence. Figure 3.6 presents a comparison of the hourly percentage of the exhaust emissions to the total emissions.

a) Distribution of hourly proportion of exhaust emissions to total PM10 emissions

b) Distribution of hourly proportion of exhaust emissions to total PM2.5 emissions

Figure 3.6 Distribution of hourly proportion of exhaust emissions to total PM emissions: a) th th PM10, and b) PM2.5. Boxes represent the inter-quartile range (25 to 75 percentile), and whiskers indicate the minimum and maximum values. Dots indicate outliers, and crosses refer to mean values

Figure 3.6 a) and b) illustrate that the mean proportion of exhaust emissions estimated based on the radar one-minute method were approximately 55% and 87% for PM10 and PM2.5, which are considerably higher than the proportions estimated based on other methods. This result is expected since the one-minute method assumes that the vehicle speed is constant over each minute, so it does not take into account deceleration and braking. Therefore, the proportion of brakewear and tirewear emissions estimated using one-minute method would be much less than the other methods.

The mean proportions of exhaust emissions to the total emissions estimated based on vehicle trajectory method and radar hourly method for both PM2.5 and PM10 were similar, while the results from the vehicle trajectory method exhibited a larger variability than that from the radar hourly method. This indicates that the vehicle trajectory method could better capture real-world situations where vehicles tend to have more decelerations than average when the vehicles are near a signalized intersection.

The two link drive cycle methods estimated a lower proportion of brake and tirewear emissions than the radar hourly method and vehicle trajectory method, due to the fact that link drive cycles only represented the average drive cycle conditions for all vehicles on a link. This method generally omits detailed vehicle activity such as acceleration and deceleration, especially at small speed differentials. The link drive cycle segmentation method estimated slightly higher exhaust emissions than the link drive cycle method. This could be explained by the fact that there was a higher proportion of low speeds in the link drive cycle segmentation method since the average speed of all vehicles in the sub segment close to the intersection was very low.

3.3.4 Comparison of hourly PM based on different speed input scenarios

We further compared hourly PM based on traffic emission estimates derived from the five speed input scenarios. All hourly input data for CAL3QHC model were the same such as meteorological conditions, signal timing data, and configuration of the intersection except for the running EF and idle EF, which were derived from MOVES based on the different speed scenarios. Figure 3.7 presents the estimated hourly concentrations for PM10 and PM2.5.

The mean values for PM10 derived from the link drive cycles method, link drive cycles segmentation method, and vehicle trajectory method were 3.1µg/m3, 3.3µg/m3, and 3.5µg/m3 respectively. They were approximately 2 and 3 times higher than the mean results derived from the radar hourly method, and radar one-minute method.

The mean values for PM2.5 concentrations derived from link drive cycles method, link drive cycles segmentation method, and vehicle trajectory method were 1.6µg/m3, 1.7µg/m3, and 1.8µg/m3 respectively. The results derived from vehicle trajectory method always had the highest variability. The mean PM2.5 concentrations estimated based on radar one-minute and radar hourly methods were close to 0.6µg/m3, and 0.7µg/m3.

Figure 3.7 also presents hourly PM2.5 concentrations measured by a rooftop level air quality station at the intersection during the study period. Its mean and median values were 4.2 µg/m3 and 4.6 µg/m3 respectively, which were approximately 2.5 times as high as concentrations measured by the simulated speed methods, and approximately 6.5 times as high as the two radar methods. Clearly, our analysis does not include the effect of urban background concentrations for PM2.5 and we are solely estimating the effect of local traffic on PM2.5.

It is important to note that, as shown in section 3.3, traffic emission results had larger differences between the different methods compared to the concentration estimates, which are influenced by meteorology.

a) Distribution of hourly PM10 concentrations

b) Distribution of hourly PM2.5 concentrations

Figure 3.7 Distribution of hourly estimated and measured concentrations: a) PM10, and b) th th PM2.5. Boxes represent the inter-quartile range (25 to 75 percentile), and whiskers indicate the minimum and maximum values. Dots indicate outliers, and crosses refer to mean values.

3.4 Conclusion

This study aimed at investigating the effect of traffic data resolution on the simulation of local emissions. In addition, we also investigated the impact of the different methods for estimating emissions on the resulting near-road air quality in an effort to inform the development of project- level analysis, especially PM hot-spot analysis.

The comparison of traffic flows detected by the radar against traffic counts based on video recording with the rooftop camera indicates that radar records were always lower than camera outcomes. A further investigation of radar counts for two directions illustrated that the radar performance decreased with increasing distance from the traffic lane. Besides, the radar exhibited poor performance in terms of vehicle classification and length detection.

The comparison of vehicle speeds simulated by VISSIM and extracted at a fixed point against radar records illustrated that mean speeds were closer after the calibration, and the range of speeds from radar records was wider than that from the traffic simulation.

In order to investigate the effect of speed aggregation on the emission estimates, five different methods for using speed data were adopted in this study. We observed that the simulated drive cycles generated were largely different from the radar records thus leading to large differences in the resulting emission estimates. In addition, the use of the hourly average speed from radar forced MOVES to use built-in drive cycles and default operating mode distributions, which could not appropriately represent drive cycles near a signalized intersection and the emission results were approximately 2 times lower than results from the vehicle trajectory method.

Moreover, the results of vehicle trajectory method indicated that obtaining sec-by-sec driving behaviour of each vehicle from a traffic simulation model is essential to achieve the most accurate operating mode distributions and presumably the most accurate emission estimates. Finally, the link drive cycles segmentation method generated slightly higher emission estimates than the link drive cycles method.

Emissions vary largely with the method adopted to structure the data inputs for emission modeling purposes. This finding is of crucial importance in the development of project-level emissions and hot-spot analysis. We further examined the effect of data inputs on air quality

51 estimates, and the result indicated that PM concentrations estimated based on simulated speed profiles were similar, while they were approximately 2 or 3 times higher than the results derived from radar data.

Further investigation of local-level emission inventories is needed. In particular, we should collect real-world drive cycles so that the VISSIM network can be better calibrated based on the parameters, such as acceleration and VSP distribution, derived from sec-by-sec speed profiles. Besides, queue length could be used in future work to calibrate the simulated vehicle behaviours at signalized intersections. In addition, development of more extensive networks, including behaviour of pedestrians and cyclists would allow us to analyze specific interactions among different road users and investigate their air pollution exposure in near-road environments.

Chapter 4 Comparing Emission Rates Derived from a Model with Those Estimated Using a Plume-Based Approach and Quantifying the Contribution of Vehicle Classes to On-Road Emissions and Air Quality Chapter overview

This study presents a comparison of fleet averaged emission factors (EFs) derived from a traffic emission model with EFs estimated using plume-based measurements, including an investigation of the contribution of vehicle classes to carbon monoxide (CO), nitrogen oxides (NOx), and elemental carbon (EC) along an urban corridor. To this end, a field campaign was conducted over one week in June 2016 on an arterial road in Toronto, Canada. Traffic data were collected using a traffic camera and a radar, while air quality was characterized using two monitoring stations: one located at ground-level and another at the rooftop of a four-storey building. A traffic simulation model was calibrated and validated and sec-by-sec speed profiles for all vehicle trajectories were extracted to model emissions. In addition, dispersion modelling was conducted to identify the extent to which differences in emissions translate to differences in near- road concentrations. Our results indicate that modelled EFs for CO and NOx are twice as high as plume-based EFs. Besides, modelled results indicate that transit bus emissions accounted for

60% and 70% of the total emissions of NOx and EC. Transit bus emission rates in g/passenger.km for NOx and EC were up to 8 and 22 times the emission rates of passenger cars. In contrast, the Toronto streetcars, which are electrically fuelled, were found to improve near- road air quality despite their negative impact on traffic speeds. Finally, we observe that the difference in estimated concentrations derived from the two methods is not as large as the difference in estimated emissions due to the influence of meteorology and of the urban background given that the study network is located in a busy downtown area.

4.1 Introduction

Through a combination of regulation and improved technology, tailpipe emissions from light- duty vehicles have declined substantially over the past several decades. Important milestones included the introduction of fuel injection, positive crankcase ventilation, exhaust gas recirculation, catalytic converters, and on-board diagnostic systems. However, despite these

53 reductions and the large improvements in urban air quality, epidemiological evidence continues to reveal significant associations between traffic-related air pollution (TRAP) and health (Fuertes et al., 2013).

The challenge of reducing TRAP remains ever present (and increasingly complex) due to the emergence of new trends facing urban transportation systems such as the increasing popularity of Gasoline Direct Injection (GDI) vehicles (Zhao et al., 2010). Recent research on GDI engines demonstrated high fuel efficiency at the expense of increased emissions of black carbon (or soot) and toxic substances (Zimmerman et al., 2016). Moreover, the complexity of individual mobility needs and the rise of sharing economies (on-demand transit, ride-sharing, autonomous vehicles) will affect the spatio-temporal distribution of emissions and introduce new vehicle types into the fleet. Finally, the rise in urban freight movements will replace some passenger trips but increase the proportion of small and medium trucks on the road.

In order to anticipate the effects of these trends on air quality and health and inform the development of policies and standards governing vehicles and fuels, accurate emission inventories are essential. Efforts in integrating traffic models with emission models for the purpose of generating emission inventories or for evaluating traffic-related air pollution are in constant evolution. The scale of emission simulation ranges from a single road, to regional networks, and further to state and national inventories (Thunis et al., 2016). Furthermore, different types of dispersion models have been used to quantify the concentrations of air pollutants in near-road environments, and estimate personal exposures, such as Gaussian dispersion models and street-canyon models (Soulhac et al., 2011).

The accuracy of emission inventories or near-road air pollution concentrations mostly rely on the accuracy of the emission estimates, which depend on the quality of the emission factors (EFs). To date, various methods have been used to estimate EFs. The emission profiles of vehicles can be measured under real-world conditions, such as through remote sensing and tunnel studies. An analysis of remote sensing data across the UK indicated that roughly half the total NOx emissions in an urban road were due to heavy duty vehicles (HDVs) and buses (Carslaw et al., 2011). In Sao Paulo, measurements conducted in two tunnels revealed that fleet averaged EFs were heavily influenced by the fraction of HDVs, as the EFs for HDVs were 3.6±1.5g/veh.km and

9.2±2.7g/veh.km, for carbon monoxide (CO) and NOx (Pérez-Martínez et al., 2014). In another

54 study, elemental carbon (EC) with an EF of 131±14mg/veh.km was found to be the most abundant portion of PM2.5 from on-road diesel-fuelled vehicles estimated in a tunnel in Hong Kong (Cheng et al., 2010).

Other real-world EFs measurements have been conducted using mobile platforms. A recent field study in Beijing used a mobile fast response instrument for conducting on-road chasing studies and found that 20% of diesel trucks were responsible for 50% of total CO emissions and over 70% of EC emissions (Wang et al., 2011b). In addition, the chasing method was used in Slovenia to emphasize a disproportionate contribution of high emitters to the fleet’s total emissions: the top 25% of diesel vehicles produced 63, 47 and 61% of EC, NOx and particle number emissions respectively (Fuzzi et al., 2015). A portable emission measurement system (PEMS) was used in on-board measurements in Beijing and determined bus EFs for CO and NOx, in the range of 1.3- 12.7 and 3.0-12.8 g/veh.km, respectively (Wang et al., 2011a). A study in Italy used PEMS to analyze on-road emissions and found that NOx emissions of diesel vehicles were 0.93±0.39g/veh.km (Weiss et al., 2011).

In the US, where emission inventories are conducted at the metropolitan, state, and national level, much of the work is conducted using the USEPA model MOVES (MOtor Vehicle Emissions Simulator). The MOVES model, which is also adopted in Canada, is based on emission data from inspection and maintenance programs and dynamometer tests in various US cities, including but not limited to Chicago, Phoenix, New York, Maryland (U.S. Environmental Protection Agency, 2015b; U.S Enviromental Protection Agency, 2015). These data are based on test cycles that are limited in terms of the range of speed and tractive power that they cover. In fact, findings from recent studies suggest that NOx emissions are overestimated in the model MOVES. These studies performed chemical transport modeling and found that better agreement with ambient monitoring data, satellite observations, and/or aircraft measurements could be achieved by reducing NOx emissions by 50 to 100% (S. Bai et al., 2016). Few studies have compared MOVES results with PEMS data. The most noteworthy is a recent comparison with PEMS conducted by Liu et al. (Liu and Frey, 2015) who tested approximately 100 light-duty gasoline vehicles in North Carolina and found that the MOVES modelled emission rates were up to three times higher than measured rates. This vehicle sample did not capture high-emitters and cold starts which can account for part of the disagreement.

In the Canadian context, there are no emission models currently endorsed by Environment Canada, and therefore the MOVES model is often used with default US nation-wide data. While similarities between new vehicles sold in Canada and the US are present, the differences in driving and meteorological conditions will lead to different deterioration rates of the in-use vehicle fleet. There is a need for comparing MOVES predictions with independent data and assess whether trends in bias appear across multiple sources.

This study aims to compare emission estimates generated from the emission model MOVES with detailed local inputs and plume-based measurements of fleet averaged EFs for vehicles running along a busy road in Toronto, Canada. The plume-based analysis could only generate fleet averaged EFs which could be dominated by high emitters. As such, modelling was further employed to examine the contributions of different vehicle classes and shed light on the differences between the two methods. Finally, EFs derived from MOVES and plume measurements were used as input in a street-canyon dispersion model to capture the effects of difference in EFs on the resulting estimates of near-road air quality.

4.2 Materials and methods

4.2.1 Study area and data collection sites

Our study area includes a single road segment on College Street, a four-lane arterial road in downtown Toronto, Canada. The study segment is located between two major signalized intersections: College St. and Saint George St. on the west, as well as College St. and McCaul St. on the east. Along the segment are multiple-storey buildings on both sides of the road: University of Toronto buildings on the north side, and a mix of commercial and residential buildings on the south side, representing a typical urban canyon environment.

Typically, electric-powered streetcars (light rail) run in the middle lanes of College St. offering public transportation service. However, during the week we selected for data collection (June 17 to 24, 2016), streetcars were substituted by diesel-fuelled transit buses running along College St. without dedicated lanes. This offered a unique possibility to study the effect of transit buses on total emissions as well as compare the effect of transit buses with the effect of streetcars.

Traffic and air quality data collection occurred over a week in the month of June, 2016 (June 17 to 24) at two sites: 1) rooftop level: on the rooftop of a four-storey University of Toronto

56 building at the intersection between College St. and Saint George St., where traffic, meteorology, and air pollution were collected; 2) street level: at the Southern Ontario Center for Atmospheric Aerosol Research (SOCAAR) located on the north side, where traffic data were recorded by a webcam as well as detected by a microwave radar system (SmartSensor HD, Wavetronix, Provo, UT, U.S.). At the same point, ambient air was drawn through inlets located 15m from the roadway and 3m aboveground. All data in this study were provided by SOCAAR. Figure 4.1 presents our study area and two data collection sites.

Figure 4.1 Study area and data collection sites

4.2.2 Generation of modelled emissions

The modelled EFs for CO, NOx, and EC were developed using an integrated modelling approach, which involves a traffic microsimulation model and a microscopic emission model. The traffic microsimulation model was calibrated based on traffic data extracted from raw footage of vehicles at the intersection; then the simulated driving behaviours along the study corridor were processed to provide inputs to the emission model, which generated distance-based EFs (in g km-1) for various pollutants based on the vehicle types.

4.2.2.1 Traffic simulation

In this study, a microscopic traffic simulation model was developed for the study area using the PTV VISSIM platform (VISSIM 9.0, PTV, Karlsruhe, Germany). As shown in Figure 4.2, the simulated network was expanded to include two additional signalized intersections upstream and

57 downstream of the intersection between College St. and Saint George St., in order to capture driving behaviours at the intersection, such as acceleration, deceleration, and queuing. The road configuration was gathered based on Google maps and confirmed through field visits. The width of each lane was set as 3.3m; roadway gradients were assigned as 0%.

Traffic information including vehicle volumes, vehicle types, and their corresponding routing decisions were manually derived from the recorded video images at the intersection. Vehicles were noted as passenger cars, passenger trucks, transit buses, intercity buses, school buses, refuse trucks, single unit short-haul trucks (or medium trucks), and single unit long-haul trucks (or large trucks). Traffic signal timings and turning restrictions were obtained through field visits. Besides, the public transport routes were included in the network by locating transit bus stops on the two sides of the road. The dwell time distribution was defined by estimating the distribution of time difference for each transit bus passing through two webcams at street level and rooftop level. For each simulation run, a 900s warm-up time was used to load vehicles onto the network.

A data collection point was located in the simulation network at the same place where the radar is positioned on the study segment. Two types of outputs were derived from the microsimulation model: 1) vehicle records at the data collection point, including vehicle counts, vehicle types, and instantaneous speeds; 2) sec-by-sec trajectories of each vehicle from the moment it enters the network until it exits the network. The traffic simulation model was first validated against the radar records in terms of the vehicle counts, and then calibrated using a number of parameters to improve the model’s ability to replicate the real-world distribution of speeds as detected by the radar. Two hours (6pm-8pm) on Thursday were used for the calibration. The calibrated parameters were maintained for the rest of the simulation hours. Further information on the calibration of the traffic simulation model is provided in appendix B.

Figure 4.2 The traffic simulation network (using the PTV VISSIM platform)

4.2.2.2 Emission modelling

The USEPA MOVES model (MOVES2014a, EPA) was adopted to estimate the emissions of different pollutants including CO, NOx, and EC based on instantaneous speed data from the simulation model. The use of sec-by-sec speed profiles as the link activity enables the emission model to take into account detailed driving behaviours, including acceleration, deceleration, cruising, and idling, making the results more realistic compared to the aggregated method of using hourly average speeds for all vehicles on the link.

Besides link activity, MOVES needs information on link characteristics, vehicle types, model years, fuel information, and meteorology. The road type was set as “urban unrestricted road”. Vehicle types were obtained from the simulated traffic outputs. Vehicle model year data were derived from the Drive Clean program, the provincial inspection and maintenance program (Melorose et al., 2015). Meteorological data were extracted from the local meteorology station on the rooftop of the University of Toronto building, including hourly temperature (°F) and relative humidity (%). Moreover, all passenger cars, passenger trucks and light commercial trucks were assumed to run on gasoline, while other vehicles were assumed to run on diesel (Natural Resources Canada, 2009). Only “running exhaust emissions” were estimated.

The fleet averaged EF was calculated as a weighted value based on the estimated emissions from different vehicle classes and the proportion of each vehicle type. Total emissions on the study segment were calculated by summing the estimated emissions of each vehicle passing through the link.

4.2.2.3 Comparing segment emissions with transit bus vs. streetcar service

To investigate the effect of public transit on segment emissions, another traffic data collection was conducted on two weekdays in the month of April 2016 (April 28 and 29). During this period, electric-powered streetcars ran along the dedicated middle lanes of College Street.

Traffic data were manually extracted from the webcams, and were then employed as inputs for the traffic simulation model. Unlike the network with transit bus service, vehicles need to wait behind the streetcars when the streetcars stop at the stations, to allow passengers to cross the right lane of the road. Traffic simulation was also calibrated and validated by identifying the parameters and parameter values, which ensure that the influence of streetcars on traffic conditions was appropriately represented.

The selected parameters were then used to conduct the same simulation as the one with transit bus service on Thursday and Sunday in June 2016, and all traffic inputs were kept the same while transit stops were moved from two sides of the road to the central lanes representing street car service. The aim of this exercise is to compare segment emissions with transit bus vs. streetcar service using the same traffic demand for the same time period.

4.2.3 Generation of plume-based emissions

Plume-based EFs were developed under real-world conditions using an automated identification and integration method to analyze exhaust plumes of passing vehicles based on high-time- resolution air pollution measurements (Wang et al., 2015). During the study period (June 17 to

24, 2016), various air pollutants including CO and NOx were continuously measured.

4.2.3.1 Measurement technique

Ambient air at street level was drawn through inlets located 15m from the roadway and 3m above ground. Gaseous pollutants were drawn through 2m fluorinated ethylene propylene (FEP)

60 tubing and were distributed among various instruments. CO was measured using a gas filter correlation infrared analyzer, and NOx was measured using a chemiluminescence analyzer (410i and 48C, respectively; Thermo Scientific, Waltham, MA). The time resolution for CO measurements was 10s, and for NOx was 1s.

Simultaneously, local meteorology data including temperature, humidity, as well as wind speed and direction were measured by a meteorological station (WXT520, Vaisala, Vantaa, Finland), located at rooftop level. The passing vehicles were detected by a SmartSensor HD (SS-125, Wavetronix, Provo, UT) dual radar system, and vehicles were categorized as light-duty (1.5-7m) or heavy-duty vehicles (7-18m) based on the detected length.

4.2.3.2 Data Analysis

All measured pollutants were time corrected to match with the CO2 signal by conducting a sensitivity analysis that shifted the time series of each pollutant in 1s increments in order to find the most appropriate lag time (typically by 0s to 10s). Then, an automated identification method was used to detect vehicle exhaust plumes based on the inflection points before and after a plume estimated from the slope of the CO2 signal averaged over 10s (Wang et al., 2015). Measured plumes that were shorter than 10s or with an average response over the integration period below -1 an effective integration sensitivity (5ppmv s CO2) were considered as erroneous or uncaptured (Wang et al., 2015). The plumes represented emissions generated from a certain number of vehicles passing through the data collection point.

Measured emissions were processed to develop fuel-based emission factors (EFs), which can be defined as grams of pollutant emitted for every kilogram of fuel burned. The EFs for various pollutants were estimated using CO2 and CO as a measure of fuel burned (4.1), given that the conversion efficiency of carbon in fuel to CO2 is close to 99% when gasoline and diesel engines are under normal operation (Franco et al., 2013).

∆[P] EFp = ( ) wc (4.1) ∆[CO2]+ ∆[CO]

-1 where EFp is the fuel-based EF of pollutant p (in g kg-fuel ) per kilogram of fuel burned, ∆[P] is the integrated change in mass concentration of pollutant P (μg m−3) above roadway concentrations for the duration of the plume capture, similarly, ∆[CO2] and ∆[CO] are the

integrated changes in the CO2 and CO concentrations above the roadway concentrations, and wc is the carbon weight fraction (0.86 for gasoline-dominated fleet) (Wang et al., 2015). The background level was determined as the minimum level at the beginning or end of the plume period (Wang et al., 2015).

In addition, the EFs of plumes captured with detectable CO2 while pollutant signals were lower than the instrument sensitivity were classified as below threshold (BT) (Wang et al., 2015). The fleet averaged EFs were calculated based on total captured plumes including BT plumes for the entire detectable vehicle fleet.

4.2.4 Air quality model

An operational urban dispersion model was employed using the software SIRANE developed at

Ecole Centrale de Lyon to calculate CO, NOx, and EC concentrations based on estimated emission results. The dispersion model simulates the pollutant transfer phenomena spatially and temporally within and out of the urban canopy by adopting parametric relations for a simplified urban geometry (Soulhac et al., 2011). The model takes three main mechanisms into consideration for estimating concentrations in the urban canyon: 1) convective mass transfer due to the mean wind direction along the street axis, 2) turbulent transfer across the interface between the street and the overlying air flow, and 3) convective transfer at the street intersections (Soulhac et al., 2011). In addition, a Gaussian plume model was adopted for simulating the dispersion of pollutants diffused out of the streets.

Our study area was modelled as a network of connected street segments. Each segment was simulated as a box model, indicating that the flow and the pollutant was assumed to be uniformly mixed within the street. The main inputs for the dispersion model include the emissions within each street in the network, the local meteorology data, urban geometry, and background concentrations. Meteorological data including temperature (°C), wind speed (m/s), and wind direction (degree) were extracted from the weather station on the rooftop of the University of Toronto building. Hourly cloud cover data were obtained from a weather station on the Toronto Island. Data on building heights and road width were collected from Google Earth, and the building heights were assumed to be homogeneous along each road segment.

The contributions of different vehicle classes to CO, NOx, and EC concentrations were quantified for transit bus and streetcar service based on modelled emission results. The measured concentration of ozone (O3) at rooftop level was used as background for simulating the dispersion of NOx in order to consider the chemical transformation among nitric oxide (NO), nitrogen dioxide (NO2) and O3. Dispersion modelling was conducted for five weekdays under different meteorology conditions with the diurnal profile of traffic emissions.

4.2.5 Comparison of modelled and measured results

4.2.5.1 Comparison of emission factors

The EFs of CO and NOx estimated from the emission model were compared against EFs calculated based on vehicle plumes detected from near-road measurements. It is important to note that the modelled EFs were expressed as a mass of pollutant released per unit distance travelled, while EFs calculated by the plume-based method were defined as a mass of pollutant released per unit mass of fuel used. For this purpose, fuel-based EFs (in g kg-fuel-1) from the plume-based method were converted into distance-based EFs (in g km-1) according to a weighted arithmetic mean fuel consumption rate, which was based on the latest Canadian Fuel Consumption Guide and Canadian Vehicle Survey, which estimate that in-use mean fuel consumption rates for light duty vehicles (LDVs) and heavy duty vehicles (HDVs) were 10.7L/100km and 30.9L/100km, as well as the fleet ratios of LDVs and HDVs were 97% and 3% respectively (Natural Resources Canada, 2017, 2009).

4.2.5.2 Comparison of near-road air quality results

We employed an air quality model to compare concentrations of CO and NOx calculated from network emissions estimated based on modelled and plume-based EFs. The total traffic emissions for each segment were calculated by multiplying the fleet averaged EFs with the total number of vehicles on the segment and the segment length. The concentrations measured at rooftop level were treated as the urban background. We compared simulated concentrations using modelled emissions and plume-based emissions with street-level measured concentrations.

4.3 Results and discussion

This study presents a comparison between fleet averaged EFs derived using the emission model MOVES and EFs generated from plume measurements. The emission model MOVES was then

63 employed to estimate the contributions of different vehicle classes to discuss the potential reasons for the discrepancy. In the end, the air quality analysis was conducted to identify the extent to which differences in emissions translate to differences in estimates of near-road air quality.

4.3.1 Descriptive analysis of traffic data

Figure 4.3 a) and b) present comparisons of hourly traffic volumes between radar records and webcam/manual counts in the westbound (WB) and eastbound (EB) directions on the segment of interest for the study period extending from June 17 to 24, 2016. Each direction includes two lanes, with vehicles in the WB direction closer to the microwave radar compared to those in the EB direction.

For the same hour in the WB direction, traffic volumes obtained from radar records were generally lower than the webcam/manual counts except for a few hours on Saturday and Sunday. There were some hours during the week when radar records plunged substantially and were much lower than the webcam/manual counts.

For the same hour in the EB direction, traffic volumes detected by the radar were considerably lower than those extracted from the webcam/counts. In fact, there were a number of missing radar records, which accounted for approximately 25% of the total hours. The radar performance regarding vehicle detection was better on WB lanes than EB lanes. This finding is consistent with the result in a previous study that there is a negative relationship between the radar’s detection performance and the distance from the traffic lane (Gorjestani et al., 2008).

The traffic volumes exhibited similar diurnal trends on weekdays. Thursday was selected as the most representative weekday based on the root-mean-square deviation (RMSD) method, since it has the lowest differences in hourly traffic volumes to the other weekdays. Sunday was chosen to represent a typical weekend. Hourly traffic data on Thursday and Sunday were employed to estimate emissions and concentrations for the typical weekday and weekend.

The radar records were categorized based on the length range of different vehicle classes and were compared with webcam/manual counts. The webcam/manual count results indicate that the largest proportion of the vehicle fleet (approximately 95%) comes from private vehicles

64 including passenger cars and passenger trucks, while radar records indicate that a lower proportion (approximately 86%) of total vehicles are private vehicles. Given that 96.3% of the total vehicles were reported as light vehicles in the national Canadian Survey Report, the radar performance in terms of detecting vehicle length contains errors, which could be caused by factors such as traffic conditions, and the distance from traffic lane (Yu et al., 2010).

We further investigated the hourly truck counts and their proportions using manual counts. Trucks include light commercial trucks, refuse trucks, short-haul trucks, long-haul trucks, and combination trucks. Figure 4.4 reveals that truck counts and their corresponding proportions vary noticeably over different hours on Thursday and Sunday. In general, there were more trucks observed during the daytime, but the proportion of trucks during the night-time was roughly two to three times higher than the daytime.

Figure 4.4 Hourly truck volume and truck proportion on Thursday and Sunday (based on webcam/manual results)

4.3.2 Validation and calibration of traffic simulation model

The traffic simulation results were first validated against webcam/manual results in terms of hourly total number of vehicles on the study segment for the transit bus and streetcar networks. The validation results indicate that simulated traffic volumes are consistent with manual counts. We further calibrated the model based on comparisons of traffic speeds between simulated results and radar records. The simulation time resolution was also included since it has an influence on the response to traffic controls.

Adjusting the desired speed distribution of the parameter improved model performance, as the 25th percentile, median, and 75th percentile of the simulated speeds became closer to the radar results. Take transit bus network as an example, the average speed of passing vehicles at the data collection point on Sunday increased from 35.4km/h to 38.8km/h after the calibration, which was closer to the average speed (37.2km/h) detected by the radar. Similarly, the traffic simulation performance was improved on Thursday, as the simulated average speed increased from 30.9km/h to 31.9km/h, making it closer to the average speed (32.4km/h) based on radar records. Other parameters were not found to significantly influence the output of the model.

4.3.3 Comparison of emission factors

The fleet averaged EFs of NOx and CO derived from the emission model were compared with the EFs calculated using real-world measurements. Figure 4.5 presents the distributions of EFs for each pollutant estimated using both methods.

The variability in EFs estimated from the emission model for all pollutants is similar between weekday and weekend. Specifically, the range of EFs on the weekend are within the range of EFs on weekdays.

In contrast, the EFs calculated by the plume-based method are different between weekday and weekend for NOx. In fact, the plume-based EFs for NOx (0.13-0.85g/km) on weekday are approximately three times higher than the EFs (0.03-0.39g/km) on weekend. Besides, the range of EFs for CO on weekend is within the range of its EFs on weekdays.

We observed large differences in EFs between modelled and plume-based methods, for CO and

NOx. The modelled EFs for CO are approximately two times as high as the plume-based CO EFs. In fact, our results (1.42-3.84g/km) are within the ranges of the CO EFs found in other real- world studies conducted in Beijing and Sao Paulo. A study in Beijing using a portable emission measurement system to estimate EFs, found that CO EFs had a range of 1.3-12.7g/km (A. Wang et al., 2011). In the Sao Paulo study, the EFs of CO for LDVs were observed to be 5.8±3.8g/ km based on measurements in two tunnels (Pérez-Martínez et al., 2014). A previous study in Canada indicated that the plume-based method tended to underestimate CO EFs due to the fact that a large portion of the fleet did not have detectable CO emissions (Wang et al., 2015).

In addition, the range of the NOx EFs estimated using the plume-based method was roughly 40% of the modelled EFs for the weekday and weekend respectively. This result is consistent with a previous study which found that modelled NOx tend to be up to three times higher than plume- based results (Reyna et al., 2015). Another study found that fleet averaged NOx EFs derived from plume-based methods were closer for LDVs, while they were considerably lower than those reported for HDVs (Wang et al., 2015).

Figure 4.5 Box plots of hourly EFs derived from emission model and plumed-based method

for weekday and weekend: (a) Distribution of hourly Nitrogen Oxides (NOx) emission factors, and (b) Distribution of hourly Carbon Monoxide (CO) emission factors. Boxes represent the inter-quartile range (25th to 75th percentile), and whiskers indicate the minimum and maximum values. Dots indicate outliers, and crosses refer to mean values.

Given that the plume-based analysis could only generate EFs for fleet average, we employed the emission model MOVES in the following analysis to estimate the emissions of various pollutants from different vehicle classes, highlighting high-emitters.

4.3.4 Contribution of trucks, transit and other vehicles to total emissions

The emissions generated from the emission model for different vehicle classes were reconstructed to estimate their corresponding contributions. Figure 4.6 presents total emissions for each pollutant and the relative contributions of trucks, transit buses, and other vehicles to total emissions on Thursday and Sunday for the networks with transit bus and streetcar service, respectively. The streetcar network refers to the network with streetcar stops in the central lanes.

Figure 4.6 Box plots of hourly segment emissions (left) and stacked bar charts of average proportions of transit, truck and other vehicles (right) for transit bus and streetcar network: (a) Distribution of hourly Carbon Monoxide (CO) emissions (left) and contribution of vehicle classes (right), (b) Distribution of hourly Nitrogen Oxides (NOx) emissions (left) and contribution of vehicle classes (right), and (c) Distribution of hourly Elemental Carbon (EC) emissions (left) and contribution of vehicle classes (right). The transit bus and streetcar in the parentheses refer to transit bus network and streetcar

71 network, respectively. Boxes represent the inter-quartile range (25th to 75th percentile), and whiskers indicate the minimum and maximum values. Dots indicate outliers, and crosses refer to mean values.

For both networks, traffic emissions were generally higher on Thursday than Sunday. Besides, hourly emissions of all pollutants exhibited a higher variability on Thursday. We further investigated the proportions of emissions from different vehicle classes among the different pollutants for the two networks. For the transit bus network, passenger cars and light-duty trucks as well as other vehicles, excluding transit buses and trucks, accounted for a large share of the total CO emissions (approximately 93%). In contrast, transit buses contributed to a large proportion of the total emissions of NOx and EC (approximately 60% and 70% respectively). Moreover, less than 5% of the CO was generated from trucks, and they contributed roughly 10% of the total emissions of NOx and EC.

The important contributions of diesel vehicles including transit buses and trucks could potentially explain the large differences in EFs estimated based on modelled and plume-based methods. In particular, the differences would be smaller on days/times with high proportions of diesel vehicles, while the differences would be larger when there were fewer diesel vehicles since plume-based measurements did not capture all the plumes from the light-duty vehicles.

For the streetcar network, segment emissions include all vehicle classes except for the streetcar as it is electrically fuelled (emissions from electricity generation are ignored). Vehicle classes excluding trucks account for more than 95% of the total CO emissions, and roughly 85% and

70% of the total emissions of NOx and EC respectively.

4.3.5 Comparison of the networks with transit bus and streetcar service

The calibration results for the transit bus network demonstrate that changing from the model default range of 48-58km/h to a higher range of 50-65km/h could improve simulated speeds compared to radar speeds. In contrast, the desired speed distribution was calibrated to 28-48km/h for the network with a streetcar, based on observed speeds. This means that the presence of a streetcar in the central lanes decreases the traffic average speed.

On the other hand, segment emissions for all pollutants were higher in the network with transit bus service compared to the network with streetcar service. Specifically, hourly emissions of CO

was a little lower in the streetcar network while the emissions of NOx and EC were approximately twice in transit bus network. This is predominately due to the emissions of the transit bus itself.

The network with transit bus has higher EF values for CO and NOx than the network with streetcar. Besides, the EC EFs for all vehicles excluding transit in these two networks are not significantly different (P<0.05). The EF comparison results for each pollutant are different as the EF for each pollutant has a different association with traffic speed (Bokare and Maurya, 2013).

Overall, these findings are consistent with previous studies, which have demonstrated that the majority of the NOx and EC come from diesel-fuelled vehicles, while gasoline vehicles emit the largest proportion of CO (Dallmann et al., 2013).

4.3.6 Contribution of truck, transit and other vehicles to near-road concentrations

The concentrations generated from different vehicle classes were estimated using the street- canyon air quality model based on the abovementioned emission results for the transit bus network. Figure 4.7 shows the relative contributions of trucks, transit buses, and other vehicles on the study segment to total CO, NOx and EC.

Figure 4.7 Stacked bar charts of relative contribution of vehicle classes to air quality: (a) Relative contribution of vehicle classes to CO concentrations, (b) Relative contribution of vehicle classes to NOx concentrations, and (c) Relative contribution of vehicle classes to EC concentrations.

In Figure 4.7, we observe that across one week, the highest proportion (approximately 91%) of total CO concentrations comes from light-duty vehicles. Meanwhile, transit buses and trucks contribute about 6% and 3% of total CO concentrations, respectively. In contrast, transit buses makes up the majority of total NOx and EC concentrations with 59% and 61%, respectively.

Approximately 37% and 30% of total NOx and EC concentrations are estimated to come from other vehicles. In general, trucks were found to generate a relatively small proportion of NOx or EC concentrations on our study segment with percentages of 4% and 9%.

In addition, the diurnal trends of all pollutants across weekdays were also examined. The diurnal trends of concentrations from different vehicle classes and total concentrations were hourly averaged across the weekdays. The results indicate that the highest concentrations occur during the afternoon peaks when there were high traffic volumes on the network. It is important to note that there is another peak concentration period observed between 7pm to 9pm, as the wind direction was almost orthogonal to the receptor and the wind brought pollutants towards the receptor.

Other vehicles were found to dominate the changes of CO concentrations across the study period on the segment. Besides, there are more fluctuations in NOx and EC concentrations since both transit and other vehicles influence these two pollutants. For instance, although there were less light-duty vehicles running on the study segment around midnight, the concentrations of NOx and EC concentrations were still relatively high due to the operation of night buses.

4.3.7 Effect of transit buses on total emissions

In light of the large contribution of the transit buses to total emissions and near-road concentrations of NOx and EC, the average EF of transit buses and the fleet average EF were calculated by dividing total emissions by the corresponding total number of vehicles and total travelled distance for these vehicles. The fleet average considers all vehicle classes except for transit buses, and its EF is a weighed value according to the proportion of each vehicle type. Per passenger emissions for transit buses and private vehicles were also calculated by averaging total emissions by the number of people in the corresponding vehicles. Ridership data for the College St. transit bus were obtained from the Toronto Transit Commission (“Toronto Transit Commission Ridership and cost statistics for bus and streetcar routes , 2012 Ridership and cost statistics for bus and streetcar routes , 2012,” 2012), while the average passenger number on

75 private vehicles (passenger cars and passenger trucks) was assumed as 1.6 according to the Canadian Vehicle Survey (Natural Resources Canada, 2009).

In Table 4-1, we observe that each transit bus emits much more than the fleet average as the ratios of EFs between transit and fleet average are always larger than 1 for different pollutants. This is expected since EFs are normalized on a per-vehicle basis. This comparison however, illustrates the magnitude of transit bus emissions compared to the fleet average. In particular, each transit bus is estimated to produce NOx and EC up to 70 times and 126 times the fleet average. This finding supports our previous observation that the largest contribution of total NOx and EC emissions comes from transit buses (Section 3.3).

Furthermore, per passenger emission results reveal that generally a transit bus emitted CO roughly 80% less than a private vehicle. Moreover, our results indicate that on a gram per passenger kilometer travelled (PKT), a transit bus emitted NOx and EC up to 8 times and 22 times as much as a private vehicle. Lastly, we found that both ratios are always larger on Sunday than Thursday, which indicates that transit buses are relatively more efficient on Thursday, when ridership is higher.

Ratios of EFs (in g/vkt) between transit and Ratios of per passenger emissions (in g/pkt) fleet average between transit and private vehicles

Thursday Sunday Thursday Sunday 2.2, 2.1 2.2, 2.1 0.19, 0.16 0.19, 0.14 CO (1.1 - 3.5) (1.5 - 3.6) (0.06 - 0.43) (0.03 - 0.68) 68.0, 64.5 69.8, 67.0 7.3, 6.0 7.5, 6.0 NOx (32.6 - 126.6) (49.1 - 118.7) (2.3 - 17.2) (1.1 - 26.5) 89.7, 73.9 125.7, 115.4 15.1, 13.1 21.5, 13.3 EC (27.1 - 235.7) (25.1 - 232.9) (5.7 - 31.4) (2.5 - 84.7)

4.3.8 Comparison of near-road air quality results

The concentrations of NOx and CO were simulated based on traffic emissions calculated using modelled and plume-based EFs. In both cases, we employed estimated traffic emissions as the inputs and concentrations measured at the rooftop level as urban background concentrations.

Figure 4.8 presents the hourly distributions of simulated concentrations for the two pollutants based on modelled emissions as well as plume-based emissions, and measured concentrations at street level. Note that the dispersion model was implemented for the period extending from June 20 to June 24, 2016.

Figure 4.8 Box plots of hourly concentrations derived from emission model and plumed- based method, as well as measured concentrations at street level for two pollutants: (a)

Distribution of hourly Nitrogen Oxides (NOx) concentrations and (b) Distribution of hourly Carbon Monoxide (CO) concentrations. Boxes represent the inter-quartile range (25th to 75th percentile), and whiskers indicate the minimum and maximum values. Dots indicate outliers, and crosses refer to mean values.

For both pollutants, simulated concentrations based on modelled emissions are higher than those based on the plume-based method. This is expected since hourly EFs calculated using the emission model for both pollutants were higher than those derived from the plume-based approach (Section 4.3.6).

In addition, the mean and median values of simulated NOx concentrations derived from modelled emissions were 8% and 10% lower than the respective concentrations measured at street level, which are closer to the concentrations derived from the plume-based method with mean and median values 18% and 20% lower than the measured concentrations. Observed NOx concentrations have higher variability than simulated results.

Moreover, we observe that the mean simulated CO concentrations based on plume-based emissions is slightly closer (2% closer) to measured CO concentrations compared to simulated concentrations based on modelled emissions. On the other hand, the difference in median values between simulated results derived from modelled emissions and measured concentrations is 2%, which is smaller than the difference (11%) between simulated results based on the plume-based method and measured concentrations.

Overall, we found that simulated concentrations based on modelled emissions are generally closer to measured concentrations in terms of mean and median values as compared to concentrations derived from the plume-based method. A previous study developed an integrated modelling approach to estimate urban air pollution for a traffic network in downtown Toronto, and estimated 100% of the AERMOD modelled CO concentrations and 97.5% of the QUIC modelled NOx concentrations within a factor of two of the corresponding observed concentrations (Misra et al., 2013).

4.4 Conclusions and Recommendations for Future Studies

This study aims at comparing estimates of EFs generated from MOVES and plume-based measurements along a busy urban corridor in downtown Toronto. In addition, the contributions of vehicle classes to various pollutants were evaluated using MOVES to highlight the high- emitters and explore the potential explanations for the differences in EFs estimates based on MOVES and plume-based methods. Finally, a street-canyon dispersion model was employed to determine the effects of differences in EFs on the resulting estimates of near-road air pollution.

We compared the fleet averaged EFs of NOx and CO derived from the emission model with the EFs calculated using real-world measurements. The EFs derived from the model for all pollutants on weekends were generally within the range of their EFs on weekdays. In contrast, the EFs estimated using the plume-based method for NOx were much higher on weekdays than on weekends, and the range of CO EFs on weekends was within the range of the EFs on weekdays.

Moreover, we observe large differences in EFs for CO and NOx between modelled and plume- based methods. The modelled EFs for CO and NOx are about two times as high as plume-based EFs. This is consistent with previous studies indicating that the plume-based method tends to underestimate CO and NOx EFs due to many factors, such as instrument sensitivity, differences of dilution rates between pollutants and CO2, and local meteorology (Wang et al., 2015). More importantly, EFs derived from the plume-based method in this study represent vehicles passing the mid-block on College Street, which do not capture vehicle acceleration, deceleration and idling when they are close to the intersection.

In order to investigate the influence of vehicle classes to different pollutants on the study segment, the total emissions and proportions of emissions from truck, transit and other vehicles were estimated for the same network with transit bus service vs. streetcars. The results of the transit bus network reveal that the largest proportion of CO emissions comes from light duty vehicles, while transit emissions account for approximately 60% and 70% of the total NOx and EC respectively. Meanwhile, trucks contribute a relatively small portion of the total emissions for all pollutants. For the streetcar network, all vehicle classes excluding trucks contribute more than 95% of the total CO emissions, as well as 85% and 70% of the total emissions of NOx and EC respectively.

The concentrations generated from different vehicle classes were estimated using a street-canyon dispersion model based on emission results for the transit bus network. The results indicate that most CO concentrations come from light duty vehicles, while NOx and EC concentrations are influenced by light duty vehicles and transit buses. In addition to the influence of traffic volume, we observe that ambient concentrations are also significantly influenced by wind direction.

The comparison of EFs between transit bus and fleet average demonstrate that each transit bus emits much larger amounts of NOx and EC. In addition, we found that transit buses were more

efficient for CO while less efficient for NOx and EC when compared with private vehicles on a per passenger level. This finding is of crucial importance to the development of new public transit systems, which need to consider the efficiency of public transportation in terms of fuel consumption and air pollution. Besides, it also stresses the need to collect local traffic characteristics such as vehicle type and count for a better understanding of on-road emissions.

In addition, the hourly distributions of simulated concentrations for CO and NOx based on modelled and plume-based emissions as well as concentrations measured at street level show that generally simulated concentrations based on modelled emissions were closer to measured concentrations at street level as compared to simulated concentrations derived from the plume- based method. The difference in estimated concentrations derived from the two methods is not as large as the difference in estimated emissions due to the influence of meteorology and of the urban background. It is important to note that background concentrations contribute considerably to street-level concentrations since our study area is located in downtown Toronto and concentrations generated from vehicles on other links could affect our study area.

Our discussion is strongly tied to the findings of our study and highlights what we learned based on our analysis and how it informs future work. Regarding the estimates of emissions derived from the plume-based method, our study shows that one of the main limitations of this approach is the single-point measurement location at mid-block. We recommend that additional data collection points should be located along the study segment, especially closer to the intersection in order to capture the impact of stop-and-go traffic on real-world emissions in urban environments. In addition, we observed that the radar falls short of identifying all pertinent vehicles classes, which is a crucial factor affecting road emissions. Image-processing techniques and automatic vehicle classification would ensure more reliable fleet composition data. Moreover, licence plate recognition and cross-checking against vehicle registry information would be ideal to improve our understanding of the contributions from different vehicles. Finally, improving instrument sensitivity will result in more accurate emission estimates especially for pollutants such as CO that are characterized by concentrations close to background levels (Franco et al., 2013).

We also stress the importance of accurate speed data especially that our corridor is on an arterial road. For example, we noted that drivers along College Street are more aggressive in reality

81 compared to the output of the traffic simulation model and therefore, we calibrated the speed distribution in the model. The reliability of emission estimates based on the emission model was improved by calibrating the speed profiles derived from the traffic simulation model. Ideally, GPS data would also be used to ascertain that the acceleration-deceleration profiles generated by the traffic simulation model are representative of local conditions. Furthermore, we noted various limitations to the radar performance and we suggest that radar systems should be positioned on both sides of the road to provide better quality control given that radar performance decreased with the increase in the distance from the traffic lane. Lastly, measurement campaigns in different seasons could enable us to conduct temporal and seasonal analysis of EFs based on these two methods.

Chapter 5 Embedding Local Driving Behaviour in Regional Emission Models to Increase the Robustness of On-Road Emission Inventories Chapter overview

This study presents the development of operating mode (opmode) distributions derived from local drive cycle construction methods developed based on real-world GPS data collection, and their impacts on average-speed emission factors (EFs). A data collection campaign was conducted between March and July 2018 whereby 82 research participants were recruited to record daily driving behaviors in the Greater Toronto and Hamilton Area (GTHA) for a period of one week. A drive cycle construction methodology was employed to build representative drive cycles based on micro-trips. The constructed drive cycles were compared with the interpolated drive cycles derived from the default database of the USEPA MOVES model. The results indicate that the MOVES default opmode distributions lead to higher average-speed EFs than the ones derived from local data. The difference between two drive cycle construction methods was also evaluated by comparing the variability in opmode distributions and the resulting average speed EFs. We observed that EFs were similar within each speed category, and the variation in cumulative opmode distributions was highest for an average speed of 40mph. Moreover, a Monte Carlo Simulation was conducted to generate EF distributions based on local opmodes, further illustrating that local drive cycles generated considerably lower emission estimates than those based on the default database of MOVES. Finally, the minimum number of GPS data points required to develop a local opmode database with adequate variability was determined, illustrating that about 4,400 to 19,300 seconds were needed to generate robust distributions for different speed categories and road types.

5.1 Introduction

Transportation represents one of the largest sources of greenhouse gas (GHG) emissions and contributes to poor air quality (Handford, 2014). National and local transportation agencies are required to generate accurate emission inventories in order to enable the development of effective policies and regulations. However, current transportation emission inventories lack accuracy due to their inability to capture various traffic conditions. Generally, they employ Vehicle Kilometres Travelled (VKT) models to estimate regional and national emissions based

83 on average speed and few modal variables accounting for traffic conditions. However, VKT models cannot sufficiently capture the dynamic nature of on-road traffic and driving behavior.

Three widely used vehicle emission models are the USEPA’s MOVES model (national and county levels), the California Air Resources Board EMFAC model, and the European COPERT model (Handford, 2014). MOVES2014a is the latest version of the USEPA MOtor Vehicle Emissions Simulator (MOVES). It is based on a disaggregate approach to estimate emission rates at different analysis scales (US EPA, 2015). The particularity of the MOVES model lies in its structural flexibility that enables users to model different drive cycles. Unfortunately, MOVES2014a only incorporates US national average driving schedules. For this reason, users must provide local drive cycles and inputs to fully benefit from the MOVES features (André et al., 2006). Lin et al. (2011) compared MOVES default drive schedules with detailed operating mode (opmode) distributions derived from DynusT, a dynamic traffic assignment model, and found that MOVES underestimated emissions in congested conditions, particularly for heavy- duty vehicles.

There are three different methods for describing travel activity in MOVES. The average speed approach, the drive cycle approach (speed by second), and the opmode distribution approach (fraction of activity by op mode) (US EPA, 2015). The latter provides users with the ability to account for the significant impact on emissions of variations in vehicle speed, acceleration, as well as road characteristics such as grade (Frey et al., 2008; Sentoff et al., 2015). Emission rates are calculated using a binning approach based on vehicle specific power (VSP) that allows sensitivity to average speed and congestion (Liu and Barth, 2012). The choice of appropriate opmode distributions for emissions is based on second-by-second vehicle speed trajectory, also known as the drive cycles. Drive cycles allow a more accurate estimation of energy consumption by representing driving behavior and acceleration rates (Carslaw et al., 2013). Fujita et al. (2012) compared different emission modeling software with field measurements and found that the estimation of emissions in MOVES was highly sensitive to opmode selection. This emphasizes the importance of basing transportation emission modeling on drive cycle data derived from real- world local driving behaviour.

The incorporation of local second-by-second drive cycles into MOVES allows total emissions to be estimated more accurately at a macroscopic level. Few studies have developed local drive

84 schedules for MOVES (Alam and Hatzopoulou, 2016b). Perugu (2018) developed a “Modified Indian Driving Cycle” specific for application in Hyderabad (India) based on second-by-second driving data collected by an Android-based mobile application “Speed Tracker”. The developed drive cycles resulted in higher emission rates as vehicles in Hyderabad were found to have a higher idling proportion than was represented in MOVES’s default drive cycles. Farzaneh et al. (2014) conducted another study based on the development of local drive cycles in MOVES for different regions of Texas. The authors developed local drive cycles for different classes of vehicles by road type based on GPS data from 240 vehicles in five metropolitan areas in Texas. The largest differences between MOVES default emission rates and those based on the study’s results were for low and high speeds.

Several methods exist in the construction of drive cycles depending on the type of driving activity being used (Dai et al., 2008). Micro-trip and segment based methods are the most prevalent. Figure 5.1 presents an example illustrating different drive cycles are derived based on micro-trip and segment based methods. With the micro-trip method, a drive cycle is constructed by chaining representative micro-trips and matching them as closely as possible to the recorded data. The major limitation of this method is that the beginnings and ends of micro-trips are based on specific speed, acceleration, and duration constraints. They therefore cannot be classified by Level of Service (LOS) or road type. The micro-trip method has been mostly used to develop drive schedules representative of a single trip or schedules replicating region-wide driving behaviours. On the other hand, segment based cycle construction consists in the development of drive cycles affected by changes in roadway type or LOS, in addition to stops. The segment based method was used in the development of the default national drive cycles by roadway type in MOVES (Dai et al., 2008).

Figure 5.1 An example illustrating different drive cycles derived based on micro-trip and segment based methods

Although recent drive cycles have been developed for the Toronto waterfront area based on simulated data (Amirjamshidi, 2015), no real-world drive cycles have been developed for the Greater Toronto and Hamilton Area (GTHA) region. Another study constructed drive cycles for Toronto using a calibrated vehicle motion model CALMOB6 to produce speed traces (Raykin et al., 2012).

This study collects real-world second-by-second travel data in the GTHA to develop local drive cycles based on both the segment and the micro-trip methods. It aims to achieve the following three tasks: a) construct representative drive cycles and compare them with default drive cycles in the MOVES model; b) compare average-speed EFs and resulting emission estimates based on the micro-trip and segment methods for different speed categories; c) determine the required sample size needed to develop an EF database with adequate variability.

5.2 Materials and methods

5.2.1 Study area and data collection

The GTHA is the most populous metropolitan area in Canada. It consists of the city of Toronto and four municipalities surrounding it (Government of Canada, 2016). In this study, we

86 randomly invited 82 participants from 69 households in the region to help us collect their typical daily driving data. The study took place between March 2018 and July 2018.

The eligibility of participants depended on their age and accessibility to a personal vehicle. Eligible participants had to be over 18 years of age and hold a valid driving licence. Additionally, their household had to own at least one vehicle that the participant was entitled to use. The recruitment of participants was conducted across the University of Toronto St George campus and through online social media (Twitter) and advertisement websites such as Kijiji. Advertisements were targeted in specific data-poor regions to cover as much of the GTHA as possible.

Each invited participant was asked to complete a basic questionnaire. Participant information including gender, age, and driving experience as well as vehicle characteristics such as vehicle type and model were collected. This study was approved by the Research Ethics Board at the University of Toronto.

Participants were subsequently handed a GPS unit to be stored in their vehicle to record driving activity for an entire week. The Qstarz BT-1000X GPS device unit was selected. It records instantaneous speed and position data (including longitude, latitude, and altitude) at a second-by- second interval and has a memory capacity of approximately 64 hours of 1-Hz data. In order for the unit to last the entire week, a vibration detector was used so that it can conserve battery power by turning off the recorder if no motion was detected for 10 minutes.

The dynamic accuracy of this GPS device has been assessed in a real-world setting, demonstrating that 88.4% of data points in an urban canyon fell within a 10m buffer. This is only slightly lower than the 89.5% of the data points in an open area which fell within that same buffer size. The median error for car trips in the urban canyon was 1.5m (Schipperijn et al., 2014). This study also conducted a prior test to evaluate the quality of altitude data collected by the GPS device against the Google Elevation API. We drove a vehicle on a predefined route while carrying all of the GPS units. The total distance was 4.2 km, and the altitude difference was 59 m.

The GTHA road network was defined based on four road types in concordance with the classification established in the MOVES model (U.S. Environmental Protection Agency, 2015b),

87 namely rural restricted (RR), rural unrestricted (RU), urban restricted (UR), and urban unrestricted (UU). Restricted roads are distinguished from unrestricted ones, in that their access generally requires the use of ramps. They include freeways, expressways, and highways.

5.2.2 Data Cleaning and Processing

GPS data quality was checked between each two consecutive observations, and a time check variable was created to flag the discontinuous timestamps. Two main factors were identified in the MOVES model as limiting the acceleration and deceleration rates of a vehicle, namely the obtainable power from the engine, and the friction coefficient between the pavement and vehicle tires (U.S. Environmental Protection Agency, 2015a). As such, the threshold values of the maximum acceleration/deceleration rates were defined as 14 mph/s and -10 mph/s, respectively (U.S. Environmental Protection Agency, 2015a).

Furthermore, the upper limit of the available acceleration decreases as the vehicle speed increases. On the other hand, the deceleration rate is mostly influenced by the effective friction coefficient between vehicle tires and the pavement (Eboli et al., 2016). Two threshold values of the Vehicle Specific Power (VSP), which represents the tractive power exerted by a vehicle to move itself and its cargo or passenger, were adopted based on the EPA guidelines (U.S. Environmental Protection Agency, 2015b): a maximum value for positive VSP of 62.5 kW/Mg, and a maximum value for negative VSP of -47.5 kW/Mg. Additionally, a 3-point moving average method was employed. The majority of the data was valid and less than 1 % of the raw data were processed using this method.

The location information of second-by-second data recorded by GPS units was then assigned to the corresponding road types through the ArcMap 10.1.3 software package. Road type information (rural and urban arterial and freeway/highways) was derived from CanMap RoutesLogistics Ontario (v2014.2) and Land Information Ontario Data Description 2017 (DMIT Spatial Inc., 2014; Ministry of Natural Resources, 2017).

The VSP (in kW/Mg) of each observation was calculated based on instantaneous speed, acceleration, road grade, and road load coefficient using (5.1), where A (in kW-s/m), B (in kW- 푠2/푚2), and C (in kW-푠3/푚3) are the road load coefficients, u (in m/s) is the instantaneous speed of the vehicle, a (in m/푠2) is the instantaneous acceleration of the vehicle, g (in 9.8 m/푠2) is the

88 acceleration due to gravity, ∆H (in m) refers to elevation difference, ∆L (in m) refers to distance difference, m (in Mg) is the source mass. According to the MOVES guidelines, the light-duty road load coefficients are assumed to remain constant in the model as the impact of their changes have been directly incorporated into the emission and energy rates (U.S. Environmental Protection Agency, 2015a).

∆퐻 퐴푣+ 퐵푣2+퐶푣3+푚푣(푎+푔∗ ) 푃 = ∆퐿 (5.1) 푣,푡 푚

The MOVES emission estimation process involves grouping second-by-second vehicle activity into 23 operating modes (opmodes) delimited by ranges of average speed and VSP values. Running emissions are calculated by multiplying the associated emission rates of each opmode identification number (opmode ID) and the corresponding time duration. In this study, the emission estimation was conducted externally using the generated opmode distributions and corresponding emission rates matrix derived from the MOVES model (Tu et al., 2018).

5.2.3 Drive Cycle Methods

Two drive cycle methods, namely the segment method and micro-trip method were employed to establish region-specific EFs and opmode distributions. They were subsequently compared with default opmode distributions derived from the MOVES model. Additionally, micro-trip cycles derived from the micro-trip method were used to develop local representative drive cycles that can replace the existing MOVES default drive cycles and enable modellers to perform more accurate average-speed emission modelling at a regional level (Farzaneh et al., 2014).

5.2.3.1 Micro-trip based method

We extracted micro-trips from each individual trip collected from participants’ daily driving activities based on the four road types mentioned earlier and the different speed bins defined in Table 5-1. The criteria and processes used were similar to the ones the Eastern Research Group employed to generate drive cycles for MOVES (Epa et al., 2003). A previous study conducted by Farzaneh et al. demonstrated that this method could be used to develop Texas-specific drive cycles of light-duty vehicles (Farzaneh et al., 2014). Table 5-2 presents the procedure for extracting micro-trips.

Table 5-1 Speed bin definitions for grouping drive cycles Average Speed Bin (mph) Speed Bin Definition (mph) 5 0 ≤ & < 7.5 10 7.5 ≤ & < 12.5 15 12.5 ≤ & < 17.5 20 17.5 ≤ & < 22.5 25 22.5 ≤ & < 27.5 30 27.5 ≤ & < 35 40 35 ≤ & < 45 50 45 ≤ & < 55 60 55 ≤ & < 65 70 ≥ 65

Table 5-2 Procedure for extracting micro-trips and corresponding justification N Procedure Justification Micro-trips were first separated by the 1 Divide the trip into micro-trips based on time check variable start and end time of each trip Micro-trips end if there are more than 30 seconds of To avoid extended idling/ parking 2 consecutive zero records periods at the end of the micro-trips If there is an idling period with a duration of more than 35 To avoid extended idling/ parking 3 seconds, micro-trips begin 5 seconds before the non-zero periods at the beginning of the micro- speed trips To limit the trip length and obtain 4 The micro-trips end if the length exceeds 2 miles homogenous micro-trips 5 The micro-trips with duration of less than 20 seconds or with Idling micro-trips were not included in an average speed less than 1 mph are deleted this study

5.2.3.2 Development of representative drive cycles and comparison with default drive cycles

Following the extraction of micro-trips, a representative drive cycle was developed for each road type and speed category. A drive cycle is constructed by chaining micro-trips with the ultimate aim that the developed cycle can match the observed data closely. Several variables including average speed, average acceleration, and VSP distribution (Kamble et al., 2009; Lai et al., 2013)

90 have been employed in previous studies to assess the representativeness of each micro-trip. This study uses the opmode distribution as the assessment measure due to its consistency with the emission estimation methodology in MOVES. Moreover, opmode distribution better represents the variability of emission estimates.

First, the opmode distribution of the entire database in each speed bin was calculated and was identified as target distribution T. The first micro-trip within each speed bin was selected based on the root-mean-square-error (RMSE) using (5.2) to obtain the minimum distance from the target distribution. The RMSE method can construct a representative drive cycle that has an opmode distribution close to the opmode distribution derived from the entire database. The first micro-trip was added to the constructed drive cycle C = M1. The following micro-trip was progressively selected from the remaining micro-trips to construct the drive cycle C = M1 + M2, while ensuring that the new opmode distribution of C has the lowest distance from the target distribution T based on the RMSE value.

1 RMSE = √ ∑푛 (푦 − 푦̂)2 (5.2) 푛 푖=1 푖 푖 Where,

푦푖 = opmode percentage of the drive cycle at Opmode ID i,

푦̂푖 = opmode percentage of the target opmode distribution at Opmode ID i, i = opmode ID, n = number of total opmode IDs.

Most of the legislative drive cycles have durations ranging between 10 and 30 min (Hung, 2007). A 20-min cycle duration was selected in this study for constructing the representative local drive cycles. The above process of selecting and adding micro-trips was repeated until the developed drive schedules reached 20 min with the objective of obtaining RMSE values that were as small as possible. As each micro-trip starts and ends at different speeds, a value of 2 mph for the maximum gap was employed as a constraint on speed and acceleration between two consecutive micro-trips (Farzaneh et al., 2015).

For each road type and speed category, the constructed drive cycles were subsequently compared in terms of their opmode distributions with their corresponding target opmode distributions, as well as the interpolated drive cycles derived from default drive cycles in MOVES.

5.2.4 Segment-based method

In the development of this method, a street ID was also assigned to each second of the data based on the document Land Information Ontario Data Description 2017 (Ministry of Natural Resources, 2017). The trips were divided based on the street IDs, which can better represent activity for specific roadway types and traffic conditions. The segment-based cycles were also grouped into four road types and different speed categories.

When vehicles crossed a route with signalized intersections, some GPS points were allocated to adjacent roads. The following algorithms were developed to adjust these shifting points: a) If the number of points on a specific link was less than the estimated minimum travel time, which was calculated by dividing the link length by the maximum feasible speed, the GPS points were assigned to the previous street ID; 2) If the number of points met the minimum seconds needed to cross this link but the street ID of the next link repeated the street ID of the previous link, we considered that the vehicle was at the intersection. The street ID GPS points allocated would then be changed to the previous street ID. A visual examination of these changes was carefully conducted.

5.2.4.1 Comparison of opmode distributions derived from the segment and micro-trip methods

A comparison of opmode distributions derived from each method was conducted based on the root-mean-square-error (RMSE) between the median cumulative opmode distribution and the other observations for each speed bin for the different road types (5.2). The median cumulative opmode distribution was obtained in each speed bin by selecting the cumulative opmode distribution that has the lowest distance to the other cumulative opmode distributions. This analysis was conducted in parallel with the evaluation of the number of observations available for each speed bin. A high RMSE value implies that there is a higher probability of large speed variations while maintaining the same micro-trip or segment level average speed. On the other hand, a low RMSE value implies that for a specific speed bin, the opmode distribution of the collected data is consistent with the median cumulative opmode distributions, meaning that drivers are generating similar behavior. The inclusion of the number of observations available for each method helps evaluate the possible correlation between RMSE values and the number of observations for each speed bin.

5.2.4.2 Comparison of Emission Estimates

The drive cycles derived by the micro-trip and segment methods were grouped for different speed categories and for each road type. EFs (g/km) were calculated based on opmode distributions and the associated emission rates for all drive cycles. As such, we established a database of average-speed EFs for different road types and speed categories. A Monte Carlo Simulation (MCS) was then used to randomly select EFs from the distribution of each EF database and associate them with links according to their road type and average speed. The total emissions represented the sum of emissions from all links. This process was repeated 1000 times so that a distribution of the total emissions could be generated.

When the MOVES model is provided with an average speed and the corresponding road characteristics, it assigns a single drive cycle and EF associated with each average speed. In other words, the MOVES average speed method generates a “deterministic” estimate of total emissions.

The distributions of GHG emissions were calculated using the EF database generated by the micro-trip and segment methods and were compared with the estimate calculated from the average speed method, using MOVES default drive-cycles.

In addition, an emission estimate for the City of Toronto was generated using average speed and traffic volume information. For this purpose, a travel demand model developed at the University of Toronto, GTAModel V4.0, was employed to generate an origin-destination (OD) matrix based on trip data collected from the 2011 Transportation Tomorrow Survey (TTS). The TTS is a comprehensive travel survey conducted every five years in the GTHA to collect household data, demographics, and trip data (e.g., the method of transportation, origin, and destination) for all members in a household on a typical weekday (Data Management Group, 2018). Traffic assignment was conducted based on the EMME4 platform, which used the origin-destination (OD) matrix and applied a user equilibrium traffic assignment to generate average travel speeds and traffic volume for each road link.

Figure 5.2 illustrates the methodology adopted for comparing the emission estimates derived based on the different methods.

Figure 5.2 Methodology for developing distributions of GHG emissions based on the micro- trip method and segment method, as well as calculating total emissions based on MOVES average speed method

5.2.5 Testing the effect of sample size

An MCS process was employed to determine the minimum number of seconds (or GPS points) needed to develop an EF database with adequate variability. The key question was to test whether it was possible to capture the range of drive-cycles with the smallest possible number of instantaneous speeds.

The focus of this analysis was on high EFs since low emissions associated with idling are produced on all types of roads. The 95th percentile of the EFs derived from the drive cycles in

th each speed category was used to assess variability. Thus, 푆95 was defined as the 95 percentile value of the EFs for a given speed category with a sample size of M. A subsample of m EFs were randomly selected from M (m < M). The subsample of m EFs is deemed to provide adequate th variability if the maximum EF in the subset 푠푚푎푥 is greater than the 95 percentile of EFs in the sample M. An MCS was repeated 200 times for each value of m and the subsample m was increased from 1% of the sample M, until adequate variability was met. If the frequency of s푚푎푥

> 푆95 was greater than 0.95 (i.e., 190 times) for the 200 simulations, then the sample size m was deemed to generate sufficient variability in EFs. The average duration of the m subsamples would then be considered as a sufficient sample size. Figure 5.3 below illustrates the process.

Figure 5.3 Methodology for testing the effect of sample size

5.3 Results and discussion

5.3.1 Descriptive analysis

In this study, 82 participants from 69 households were recruited to record their one-week normal daily driving patterns in the GTHA from March 2018 to July 2018. The coverage of their trips is shown as black dots in Figure 5.4. Participants recorded 1,113 valid individual trips, of which most were conducted in an urban setting. Data coverage amounted to a total travelled distance of about 22,000 km, and total travel time of 945 hours.

Figure 5.4 Collected GPS data points in the GTHA region

The proportions of collected samples in each road category (RR, RU, UR, and UU) were 1.7%, 3.7%, 9.4%, and 85.2%. The corresponding proportions of each road type in the GTHA are 1.0%, 11.7%, 3.0%, and 84.3%. It is important to note that there was a slightly higher proportion of urban restricted and slightly lower rural unrestricted roads in the collected sample compared to the corresponding proportions in the GTHA. This is expected since most drivers in rural areas

96 would choose restricted roads. As relatively few trips were collected in rural areas, the analysis in this paper mainly focuses on drive cycles collected on UR and UU roads.

The accuracy of altitude data estimated from the GPS devices was evaluated against data derived from Google Map Elevation API. One GPS unit was selected as a reference to provide a list of latitude-longitude pairs to request the corresponding latitude data from Google API. The mean and standard deviation of Pearson correlations between data derived from Google and all GPS units were 0.96 and 0.018 respectively. The precision of the road grade was influenced by random errors in absolute values, but only relative changes in altitude data were of interest in our study.

The grades were calculated by dividing altitude differences by travelled distance for both GPS and Google data. The mean and median values of grade differences between GPS and Google data were 0.007 and 0.000, respectively, indicating that the differences of relative changes in altitude data have a symmetric distribution around zero. A previous study has demonstrated that the net effect of grades along a route for overall route average fuel use would be small due to the opposite effects of positive grade and negative grade on fuel use (Boroujeni and Frey, 2014).

5.3.2 Development of drive cycles and comparison with MOVES

Representative drive cycles were developed based on micro-trips derived from the collected trips. MOVES includes 12 default drive cycles for urban restricted roads and 10 default drive cycles for urban unrestricted roads. When MOVES calculates emissions for a specific average speed, it generates a new opmode distribution by interpolating the opmode distributions of the nearest two drive cycles (U.S. Environmental Protection Agency, 2015a). Two speed bins were selected in Figure 5.5 as examples to compare against the Toronto specific cycles, the interpolated drive schedules from the MOVES model, and the target opmode distributions calculated based on the entire database.

Figure 5.5 a) and b) both indicate that the opmode distributions of drive cycles developed from local GPS data match well with the opmode distributions derived from all the data for the corresponding speed bins. The average speed of the Toronto-specific drive cycle in the average speed bin of 70 mph for urban restricted roads was 71.4 mph, so the MOVES opmode distributions were calculated based on default drive cycle IDs 1009 and 1017 with their average

97 speed values of 73.8 mph and 66.4 mph respectively. Similarly, the average speed of the Toronto-specific drive cycle in the average speed bin of 30 mph for urban unrestricted roads was 30.45 mph, so the interpolated opmode distributions were calculated based on default drive cycle IDs 1029 and 1030 with their average speed values of 31.02 mph and 25.38 mph respectively.

Since GHG emission rates (g/s) increase with the increase in opmode ID, Figure 5.5a) indicates that default drive cycles consist of more aggressive and higher power demand driving behavior compared to Toronto-specific drive cycles based on real-world data, hence leading to higher emissions. Additionally, a small proportion of opmode ID1 was observed in Toronto-specific drive cycles while it was non-existent for default drive cycles. Default drive cycles therefore do not include deceleration or braking (decelerate at -2 mph/s or harder, or continuously decelerate at -1 mph/s for 3 seconds) of typical drivers at an average speed of around 70 mph on urban restricted roads.

In Figure 5.5 b), similarly to urban restricted roads, the Toronto-specific drive cycles have higher proportions of opmode IDs with relatively low emission rates (such as opmode IDs 21, 33, and 35) in each speed range compared to MOVES. However, higher proportions of idling were observed in drive cycles derived from the real-world data.

a) Opmode distributions for speed bin 70 mph on urban restricted road

b) Opmode distributions for speed bin 30 mph on urban unrestricted road

Figure 5.5 Comparison of the target opmode distribution, the Toronto-specific cycle opmode distribution, and the default MOVES opmode distribution for two road types and speed categories: a) urban restricted (UR) road with average speed bin of 70 mph; and b) urban unrestricted (UU) road with average speed bin of 30 mph

5.3.3 Comparison of drive cycles derived from micro-trip method and segment method

The mean and standard deviation of the lengths of drive cycles on urban restricted and urban unrestricted roads derived from the segment method were 6.1± 7.5 km, and 1.2 ± 2.1 km, respectively. The lengths of drive cycles on urban restricted and urban unrestricted roads derived from the micro-trip method were 3.1 ± 0.7 km, and 2.2 ± 1.4 km, respectively. Average-speed EF and opmode distributions were estimated for drive cycles derived from trips based on the micro-trip and segment methods.

Figure 5.6 a) and b) present a comparison of distributions of EFs derived from both methods for urban unrestricted and urban restricted roads. Both methods generated similar EF distributions. For the urban unrestricted road, average EF values derived from the segment method were generally higher than those derived from the micro-trip method with a mean percentage difference of 3.8%. However, the average EF value derived from the micro-trip method was 7.4% higher than that of the segment method in the highest 70 mph speed bin.

For the urban restricted road, the average EF value derived from the segment method was noticeably lower than that derived from the micro-trip method for the 5-mph average speed bin. On the other hand, the average EFs based on segment method were still generally higher in other speed bins compared to the micro-trip method. This could be explained by the fact that the segment method includes higher proportions of idling compared to the micro-trip method as it accounts for all the idling periods when vehicles wait at intersections.

a) EF distributions based on two methods for urban unrestricted roads

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b) EF distributions based on two methods for urban restricted roads

Figure 5.6 Comparison of EF distributions derived from segment method and micro-trip method for: a) urban unrestricted road and b) urban restricted road

The cumulative opmode distributions for all drive cycles were derived from both methods for urban unrestricted roads and urban restricted roads for different average speed bins. The median cumulative opmode distribution was obtained in each speed bin by selecting the cumulative opmode distribution that has the lowest distance to the other cumulative opmode distributions.

Figure 5.7 presents the average RMSE calculated based on the average distances between the median cumulative opmode distribution and the other observations for each speed category. The number of observations used in the development of the opmode distributions for each speed bin is also presented to evaluate any possible association with the RMSE values. Figure 5.7 a) and b) both show similar trends in RMSE values. In urban unrestricted roads (Figure 5.7 a), the average RMSE values initially increase with the increase in average speed bin while the number of observations available decreases for both methods. They then reach a peak at an average speed bin of 40 mph, indicating that private vehicles travelling at 40 mph experience a higher probability of large speed variations while maintaining the same micro-trip or segment level average speed.

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The increase in RMSE values in parallel with a decrease in available observations between the low speed bins and the 40-mph speed bin observed in both methods indicate that higher RMSE values are linked with a low number of available observations due to insufficient data. However, the RMSE values subsequently decrease between the 50-mph and 70-mph speeds bins while the number of observations for each category is still low. This observation indicates that the high RMSE values at the 40-mph speed bin come from the high variability in driver behavior at this speed resulting in a more diverse set of opmode distributions.

Vehicles have less variation in driving behavior in congested and free flow conditions (low and high speed bins). However, at an average speed of 40mph, drivers are continuously adapting to their surrounding environment based on driver-specific behavior resulting in a more diverse set of opmode distributions, especially on urban unrestricted roads. In urban restricted roads (Figure 5.7 b), the trend of the RMSE curves for each method is similar to the one in urban unrestricted roads with a peak at the 40-mph speed bin. However, the curves representing the number of observations gradually increase with the increase in speed bins. This observation confirms the hypothesis that high RMSE values are encountered at an average speed of 40 mph due to variability in driver behavior.

a) Urban unrestricted road

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b) Urban restricted road

Figure 5.7 Average RMSE between the median cumulative opmode distributions and the other collected observations, as well as the number of observations associated with each speed bin based on two methods (micro-trip and segment level): a) urban unrestricted road; b) urban restricted road

5.3.4 Comparison of emission estimates for the city of Toronto

GHG emissions were estimated for the City of Toronto using the database of average-speed EFs extracted from MOVES but adopting the local opmode distributions developed based on both the micro-trip and segment methods. In addition, average-speed EFs based on MOVES default opmode distributions were extracted in order to compare emissions based on local drive cycles vs. defaults. Emissions from private vehicles were estimated on a link level as the product of link length, link volume, and EF. An MCS was employed to randomly select EFs from the distribution of EFs and associate them with links according to their road type and average speed. The MCS was repeated 1000 times.

Figure 5.8 presents the distributions of daily GHG emissions estimated based on each method. The daily GHG emissions conducted using average-speed EFs derived from MOVES with default opmode distributions resulted in 8,363 tons of GHGs in the City of Toronto. This estimate was much higher than the distribution of emissions estimated based on both the micro- trip and segment methods. The micro-trip and segment methods resulted in mean and standard deviation values of 5,556 tons (std. dev. 18.7) and 5,667 tons (std. dev. 19.4), respectively. Given

103 the calculated standard deviations of the emission results and Z value of 1.96 for a 95% confidence level, the number of iterations needed for the MCS were estimated as about 54 and 58 respectively for the micro-trip and segment methods so the mean of the estimate was accurate within ±5 units for a 95% confidence level. It is also worth noting that the total city emissions estimated based on the segment method were 2% higher than the estimate based on the micro- trip method. This result was expected since the segment method includes higher proportions of idling compared to the micro-trip method because it accounts for all the idling periods when vehicles wait at intersections.

To put our study in context, the average EFs for the City of Toronto were calculated as 256.1 g/km, 173.6 g/km, and 170.2 g/km based on the deterministic method, segment method, and micro-trip method, respectively. The estimated EFs based on segment and micro-trip methods were lower than the modelled EFs derived from simulated freeway drive cycles for the Toronto area in a previous study, which ranged from 211 g/km to 250 g/km (Amirjamshidi, 2015). A previous study in China collected vehicle activity data and employed the International Vehicle Emission (IVE) model to establish vehicle emission inventories. The authors estimated higher fleet-average EFs for Beijing (390.2 g/km) and Shanghai (413.1 g/km) (Liu et al., 2011, 2007). Besides, the regional GHG emission inventory reports for the City of Dallas and the City of Chicago in the U.S. have indicated their fleet-average EFs as 318.7 g/km and 347.2 g/km (ICF International, 2012; North Central Texas Council of Governments, 2015). The differences in mean EFs can be explained by differences in vehicle technology as well as the differences in driving behaviors and fleet composition.

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Figure 5.8 Comparison of daily GHG emissions for the City of Toronto based on two methods (segment method and micro-trip method)

5.3.5 Testing effect of sample size

Figure 5.9 presents the average duration of the m subsamples deemed to generate sufficient variability for the EFs in each speed bin (95 % of subsamples with s푚푎푥 > 푆95 in 200 MCS).

For the micro-trip method, the minimum duration ranges from approximately 1.2 hours to 5.1 hours. A smaller sample size is needed for urban restricted roads compared to urban unrestricted roads. Also, the minimum duration needed to generate EFs with sufficient variability generally decreases in higher speed bins for both road types.

In the segment method, the minimum duration ranges from approximately 1.7 hours to 5.4 hours. A relatively longer duration is needed for UR roads. This is due to the fact that the length of drive cycles derived from the segment method on UR roads is 6.1± 7.5 km, which is much longer than the other cases. For this reason, selected subsamples also include longer drive cycles for consistent comparison with other roads.

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Figure 5.9 Minimum duration (in seconds) of the sample size needed to generate drive cycles based on two methods (segment method and micro-trip method) UR refers to urban restricted; UU refers to urban unrestricted

5.4 Conclusion

This study presents a comparison of EF databases derived from the micro-trip and segment drive cycle construction methods based on real-world GPS data collection in the GTHA. The analysis was developed for different speed categories and four road types (RR, RU, UR, and UU) to allow for a comparison with the MOVES default drives cycles that were developed for these categories.

The Toronto-specific drive cycles were developed for each speed bin based on the micro-trip method. The proposed methodology consisted in chaining micro-trips to construct representative drive cycles with an opmode distribution that was as close as possible to the target opmode distribution corresponding to the entire database. We observed that the interpolated drive cycles representative of MOVES’ default drive cycles have a higher proportion of high emitting opmode IDs than those developed locally. This result is consistent with the findings of a previous study conducted in Texas (Farzaneh et al., 2015).

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Moreover, a comparison of EFs and opmode distributions derived from the micro-trip and segment methods was conducted. The variability of EFs derived from both methods are similar, with the segment method having slightly higher EFs. The RMSE results of the average distances between the median cumulative opmode distribution and the other observations for each speed category indicate high RMSE values for an average speed of 40 mph independently of the number of observations available. At an average speed of 40 mph, drivers continuously adapt to their surrounding environment depending on their driver specific behavior therefore resulting in a more diverse set of opmode distributions. On the hand, driver behavior has less of an effect on opmode distribution in congested or free flow conditions.

Daily GHG emissions for the City of Toronto were also estimated based on the distribution of each EF derived from the micro-trip method, the segment method, and the EFs derived directly from the MOVES model. An MCS was conducted 1,000 times to generate the distributions of regional emissions based on the micro-trip and segment methods. Results indicate that GHG estimates are similar for the two methods but are much lower than the results of the estimation using MOVES default opmode distributions. This study would be valuable for transportation planners to consider uncertainties in emission estimation and employ appropriate methods to improve the estimation of on-road emission inventories.

Finally, the minimum duration of GPS data required to develop an EF database with adequate variability was determined. This analysis was conducted for each speed category by road type. The results demonstrate that approximately 4,400 to 19,300 seconds were needed to develop EF distributions for each speed bin based on either method (micro-trip or segment). Generally, we also observed that the higher the speed bin, the lower the number of seconds needed to capture enough variability in driving cycles.

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Chapter 6 A Machine Learning Approach Capturing the Effects of Driving Behaviour and Driver Characteristics on Trip-Level Emissions Chapter overview

This study investigates the effects of different variables including meteorology, trip characteristics (such as time of day), driving characteristics (such as the frequency of extended idling), and driver characteristics (such as driving experience) on trip-level emission factors (EFs). Drivers in the Greater Toronto and Hamilton Area (GTHA) were recruited to collect in- vehicle GPS data over a one-week study period from March to July 2018. Data from 1,113 driving trips were collected, including characteristics of the trips and the drivers (51 independent variables). Trip emissions were estimated in addition to an eco-score indicator (on a hundred point scale) based on log-transformed emission intensities of greenhouse gases (GHG) in CO2eq and fine particulate matter (PM2.5). A machine learning approach, the Extreme Gradient Boosting

(XGBoost), was used to develop prediction models for CO2eq and PM2.5 emissions at a trip level. The coefficient of determination (R2) and root-mean-square-error (RMSE) of eco-score models were respectively 0.84 (std. dev. 0.05), and 10.26 (std. dev. 1.24) for CO2eq, and 0.85 (std. dev.

0.03), and 10.64 (std. dev. 0.79) for PM2.5. The novel Shapley additive explanation (SHAP) measures were employed to reveal the importance of various features affecting trip emissions.

For CO2eq, driving behavior such as the frequency of extended idling was found to have the most significant impact on the trip emission intensity. Additionally, driving experience was the most significant discrete feature affecting the eco-score. For PM2.5, the most significant feature was driver age, which was highly correlated with vehicle model year. Finally, commuter drivers were found to have lower CO2eq and PM2.5 emission intensities, owing to their familiarity with route and traffic conditions.

6.1 Introduction

Emission performance of Internal Combustion Engine Vehicles (ICEV) is influenced by the size of an engine, the type of fuel it uses, and the use of exhaust after-treatment systems (Ben-Chaim et al., 2013). Over the past decade, advanced engine control technologies have contributed the most to reductions in vehicle emissions. However, the benefits derived from improved vehicle fuel efficiency and more stringent emission standards can be offset by the continuous growth in

108 travel demand and congestion (Krecl et al., 2018). The concept of eco-driving has been gaining momentum in parallel with rising air quality concerns in urban settings (Zhou et al., 2016). Furthermore, recent developments in automated vehicle technology have emphasized the importance of optimizing driving behavior for eco-friendliness (Zhao et al., 2018)

Several studies have evaluated the impact of different factors on vehicle emissions (Frey et al., 2008; Sentoff et al., 2015; Zhou et al., 2016). These studies are usually conducted by comparing multiple drivers along a single route or by gathering real-world second by second data for analysis. Zhou et al. (2016) conducted a review of fuel consumption models with respect to their underlying calculation transparency. The study included an evaluation of the primary factors that affect fuel consumption classified into six categories namely, travel-related, weather-related, vehicle-related, roadway-related, traffic-related, and driver-related factors. The authors observed that roadway-related (grade, curvature), driver-related (speed, acceleration), and traffic-related factors have the most significant effects on fuel consumption. Sentoff et al. (2015) evaluated the impact of road grade and driving style based on real-world vehicle operating mode (opmode) data collected from 82 volunteers in Vermont. The study indicated that differences in person-to- person driving style have a large effect on the level of emissions. Frey et al. (2008) quantified the variability in emissions of light-duty vehicles by route, time of day, road grade, and vehicle. The study was conducted using a portable emissions measurement system (PEMS) on two origin- destination pairs with three alternatives routes. Driver behavior, time of day (congestion level), and road grade had the most impact on emissions. P. Belz (2011) used field data collected with in-vehicle instrumentation on a predefined route to capture variations in driving behavior in unconstrained speed and acceleration at intersections, areas of grade change, and horizontal curvature. The study found the speeds of younger drivers were 4.8 km/h greater than those of older drivers; the variations were also influenced by geometric roadway characteristics, which highlighted the importance of accounting for driver style in emission modeling. Aggressive driving and variations in road grade are generally associated with increased emissions. Although studies have attempted to develop empirical models representing relationships between emissions, road grade and driving style (Song and Yu, 2009), the uncertainty in these factors across different regions and demographics is not yet well defined (Sentoff et al., 2015). Chen et al. (2018) used principal component analysis (PCA) and multiple linear regression to establish eco-driving behavior based on vehicle operating and fuel consumption data. The study focused

109 on the relationship between speed, acceleration, driving duration, and fuel consumption. Nine typical eco-driving recommendations were identified and defined in the study. Zheng et al. (2017) also evaluated emissions related to driver characteristics by using a driver behavior questionnaire and observed acceleration and deceleration behavior. The authors found large differences in emissions and fuel consumption among different drivers. Lastly, a recent study investigated the impact of driving style on fuel consumption based on a large dataset from a fleet of buses for three years using a linear classifier Naïve Bayes’ algorithm (Ferreira et al., 2015). Its findings indicate that introducing simple practices such as optimal clutch and avoiding engine running in idle, can reduce fuel consumption from 3 to 5L/100 km. Real-time eco-driving guidance to drivers along different traffic conditions has been shown to reduce fuel consumption and its associated emissions by 10 to 20% at an individual vehicle level (Barth and Boriboonsomsin, 2009). Meanwhile, recent investigations have shown that the increased level of eco-driving can potentially increase carbon dioxide (CO2) emissions at the road network level in the presence of heavy traffic (Alam and McNabola, 2018, 2014).

Despite the breadth in the literature on eco-driving, additional work is needed to evaluate how driver behavior affects real-world emissions and the implications of variability in driver behavior on vehicle emissions. The estimation of total emissions at a given time and location should not only consider the composition of the fleet but also the characteristics of drivers. This study aims to quantitatively analyse the effects of driver and trip characteristics on emission intensities at a trip level, by incorporating two general categories of effects, namely road congestion which is external to the driver, and driving behaviour. The relative importance and impacts of driving behavior parameters, driver characteristics, and trip characteristics were evaluated by a machine learning approach; the Extreme Gradient Boosting (XGBoost). The novel Shapley additive explanation (SHAP) measures were employed to investigate the impacts of features on trip eco- score for each individual trip. The analysis is conducted based on regional second by second driving data and a baseline questionnaire containing information collected in the Greater Toronto and Hamilton Area (GTHA).

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6.2 Materials and methods

6.2.1 Study area and data collection

In this study, driving trips conducted by volunteer drivers in the GTHA were collected from March to July 2018. The recruitment of participants was conducted through various forms of advertisement. These included posters across the university campus, announcements through the University of Toronto Transportation Research Institute (UTTRI) email server, postings on social media (Facebook and Twitter), and advertisements on websites such as Kijiji.

The eligibility of participants for this research was dependent on the following three conditions: 1) Participants must be over 18 years of age; 2) Participants must hold a valid driving licence; 3) The household of the participant should own at least one vehicle that the participant is entitled to use. Drivers were excluded from the study if their private vehicles were hybrid or electric since the focus of this study was on vehicles with internal combustion engines.

Each invited participant was asked to fill a baseline questionnaire at the beginning of the study. The data collected included basic information such as age, occupation type and gender, as well as driving information such as driving experience, the frequency of driving, and the purpose of driving trips on weekdays and weekends. Additionally, vehicle information including vehicle make, model, displacement and fuel type was collected.

Participants were asked to install a GPS device (Qstarz BT-Q1000X Travel Recorder) in their vehicles for a period of one week. If more than one participant in the household used the vehicle, they were asked if they were interested in participating in the study. In that case, participants were required to continuously record the driver for each trip, so we could distinguish the trips by participant. In case other drivers entitled to use a vehicle weren’t interested in participating in the study, we would permanently delete their trip records when the GPS unit was returned. This study has received a certificate of compliance from the Research Ethics Board at the University of Toronto.

The GPS unit was placed horizontally in the area between the driver seat and the front passenger seat. It can record data for approximately one week once fully charged as it incorporates an internal vibration sensor that can save battery power by independently switching off when movements were not detected for 10 consecutive minutes. Nonetheless, participants were

111 reminded by the researchers halfway through the study to charge the device once during the one- week period.

The GPS device recorded spatial location including altitude, latitude, and longitude, as well as timestamp, speed, distance, vehicle direction, and quality assurance parameters. All data were recorded at a frequency of 1 Hz. The database was password-protected for protection and a numeric code that is non-retraceable was assigned to each participant to ensure confidentiality of the information collected.

Hourly historical meteorology data including temperature, humidity, wind speed, and precipitation (such as fog, freezing rain, thunderstorm) were collected from a fixed station operated by Environment Canada (Toronto City Center Station), located on the Toronto Islands, around 3 km south of the city.

6.2.2 Data processing

The quality of data was first examined based on quality assurance parameters recorded by the device. Approximately less than 0.02% of data were recorded as “abnormal” due to the malfunctions of the device such as signal loss. This study also examined the altitude data collected from the GPS by comparing it with data derived from Google Map Elevation API.

The range of acceptable vehicle acceleration/deceleration values was defined between 16.1km/h/s and 22.5km/h/s based on the limitation of the obtainable power from the engine and the friction coefficient between the pavement and vehicle tires (U.S. Environmental Protection Agency, 2015b). Furthermore, two threshold values for vehicle specific power (VSP), which is the tractive power required by a vehicle to achieve a desired speed and acceleration with its cargo and passenger, were defined between 47.5kW/Mg and 62.5kW/Mg according to guidelines by the USEPA (U.S. Environmental Protection Agency, 2015b). A three-point moving average method was used to process data with acceleration/deceleration and VSP values outside of the corresponding ranges.

The road network layer (DMTI Spatial Inc.) was used for assigning road type information to second by second GPS records using ArcMap 10.1.3. The road type for each link was defined according to the road classification criteria adopted by the USEPA model MOVES (2014b). These include rural restricted (RR), rural unrestricted (RU), urban restricted (UR), and urban

112 unrestricted (UU), and were categorized using information from CanMap RoutesLogistics Ontario (v2014.2) and Land Information Ontario Data Description 2017 (DMIT Spatial Inc., 2014; Ministry of Natural Resources, 2017).

Second by second VSP values for each trip were calculated based on instantaneous speed, acceleration, and road grade using the VSP equation (6.1) adapted from the USEPA guidelines (US EPA, 2015). Road grade was calculated based on the travelled distance and the corresponding altitude changes recorded in the GPS unit. We used the default road load coefficients as the light-duty road load coefficients were assumed to be constant in the MOVES guideline. Vehicle mass was obtained from the vehicle manufacturers’ website based on the vehicle make, model, and model year provided by each volunteer.

In this study, 69 vehicles were used by the participants. The ages of the vehicles in the sample range from 0 to 18. The average vehicle age is 6 years. Based on 2016 data from the Ontario Ministry of Transportation (MTO), the average model year for light-duty vehicles was 2010, which corresponds to an average age of 6 years (Ontario’s Ministry of Transportation, 2016). The average mass of the vehicles in this study is around 1.82 metric tons (std. dev. 0.38 tons).

퐴푣+ 퐵푣2+퐶푣3+푚푣(푎+푔∗sinθ) 푃 = (6.1) 푣,푡 푚 Where, 퐴 = coefficient of rolling resistance (kW-s/m) 퐵 = coefficient of rotational resistance (kW-s2/m2) 퐶 = coefficient of aerodynamic drag (kW-s3/m3) 푣 = instantaneous speed (m/s) 푎 = instantaneous acceleration (m/s2) 푔 = the acceleration due to gravity (9.8 m/s2) m = vehicle mass (tons) θ = road grade

Based on MOVES opmode definition (U.S. Environmental Protection Agency, 2015b), instantaneous speeds and the corresponding acceleration values were employed to allocate one of 23 “running exhaust” opmode IDs to the second by second data. Each opmode ID was assigned an emission rate. In this study, the CO2eq and PM2.5 exhaust emissions were calculated based on

113 the opmode distributions and their corresponding emission rates matrix derived from the MOVES model (Tu et al., 2018; Xu et al., 2016).

The PM2.5 emissions also include brake wear and tire wear emissions. Brake wear particulates are positively correlated with the vehicle’s mass and also dependent on the geometry of the brakes, wheels and rims (U.S. Environmental Protection Agency, 2014). Besides, brake wear emissions are correlated with driving behavior, where more aggressive stop and go driving would generate higher wear and emissions. There are five opmode bins (opmode ID 0, 1, 11, 21, 33) associated with brake wear emissions for light-duty vehicles (Bai et al., 2015). Brake wear emissions were estimated using the corresponding opmodes within the MOVES model.

As for tire wear emissions, the MOVES model has simplified the calculation by associating the tire wear emission rate with the average trip speed. Generally, a higher average trip speed results in lower tire wear emissions. The quantitative relationship between tire wear and the average trip speed for different model years and vehicle types was derived from the MOVES database.

Finally, emission factors (EFs) of CO2eq and PM2.5 (including exhaust, tire and brake wear) were calculated for each trip by dividing the trip emissions by the total travelled distance for the trip.

6.2.3 Database development

A database was created including trip emissions in addition to different types of variables encompassing meteorology, trip characteristics, driving characteristics, and driver characteristics.

The EFs of CO2eq and PM2.5 emissions were calculated for each trip. The EF distribution generated by all trips was then log-transformed as it was lognormal.

As for hourly meteorology data (4 variables), three continuous variables namely temperature, humidity, and wind speed were averaged for each trip based on timestamp. In addition, a dummy variable was created to represent extreme weather conditions. A weather status of any of the following conditions during the trip: fog, rain, freezing rain, thunderstorm, or snowy, was assigned a value of 1, otherwise, it was assigned a value of 0.

Regarding trip characteristics (9 variables), a dummy variable was created to indicate whether the trip was conducted on a weekday or weekend. Another dummy variable indicated whether or not the start of the trip was within traffic peak hour periods (6-9 am, and 4-7 pm). Duration and distance of the trips were derived directly from the GPS unit and the proportions of time driving

114 on rural or urban restricted road types were calculated. The proportions of distance driving on these two road types were also estimated per trip. Finally, a coefficient of variation for the altitude was calculated by dividing the standard deviation by the mean value of the altitude. This variable is a measure of relative variability and was normalized by the travelled distance to represent the variability of altitude per kilometer.

Based on the driving data recorded by the GPS units, we generated 11 variables representative of driving characteristics. The first variable included the coefficient of variation (standard deviation divided by the mean) of the average speed normalized by the travelled distance. Acceleration and deceleration were represented through four variables including average acceleration/deceleration and their corresponding normalized coefficients of variation. Additionally, six relevant driving events were defined based on values published in the literature namely sharp acceleration, sharp deceleration, running at low speed, extended idling, frequent stop, and extended acceleration (C. Chen et al., 2018). Sharp acceleration and deceleration were defined as the acceleration/deceleration rates that are larger than the 95th percentile of all the collected acceleration/deceleration rates. These values were found to be 6.40km/h/s and -7.15km/h/s respectively. Running at low speed was defined as running at less than the 25th percentile speed of all collected speeds for more than 60s. The threshold values for low speed were found to be 94.1km/h, 33.8km/h, 35.1km/h, and 13.4km/h for RR, RU, UR, and UU roads respectively. In this study, extended idling was defined as passenger vehicles stopping for over 60s. Frequent stops were calculated as the cumulative count of events where speed dropped to zero in each trip. Finally, extended acceleration indicates the continuous acceleration of a vehicle at an acceleration rate of 4.5km/h/s or higher for more than 5s (Wang et al., 2004). All six events were also normalized by the trip distance in order to determine the occurrence of each event per kilometer travelled.

Driver characteristics (25 variables) were extracted from the basic personal and driving experience information collected by the participants through the baseline questionnaire. Age was the only variable treated as a continuous variable. Dummy variables were created to include gender, and home location (within or outside the City of Toronto). Categorical variables such as occupation type or the reasons why the participant drives on a typical weekday and weekend were converted into a form that could be input into the machine learning algorithm using a one- hot encoding method. Finally, three ordinal variables were also included to account for how long

115 participants have been driving, how often they used their vehicles, and how many kilometers they drove over the last year.

Vehicle characteristics (2 variables) were defined as one continuous variable representing engine displacement and one dummy variable indicating whether the vehicle had a turbo engine.

Overall, we generated 51 variables including 4 meteorology variables, 9 trip related variables, 11 driving variables, 25 driver specific variables, and 2 vehicle variables. Table 6-1 presents a basic description of each variable.

Table 6-1 Description of 51 independent variables potentially associated with trip emissions Independent NO. Type Description Variable 1 Temperature continuous Average value over the trip 2 Humidity continuous Average value over the trip 3 Meteorology Wind speed continuous Average value over the trip Indicates if weather was poor or normal (0 - normal; 4 Weather bad dummy 1 - poor) Indicates whether trip was during weekend (0 - no; 5 Weekend dummy 1 - yes) Indicates whether trip was during traffic peak hour 6 Peak dummy (0 - no; 1 - yes) Time 7 continuous Total trip time duration 8 Distance continuous Total trip distance Trip RR time 9 continuous Proportion of time spent on RR during a trip Characteristics proportion RR distance 10 continuous Proportion of distance of RR during a trip proportion UR time 11 continuous Proportion of time spent on UR during a trip proportion UR distance 12 continuous Proportion of distance of UR during a trip proportion 13 Altitude CV continuous Mean divided by std. dev. of altitude per km 14 Driving Speed CV continuous Mean divided by std. dev. of speed per km

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Characteristics Average 15 continuous Average value over the trip acceleration Acceleration 16 continuous Mean divided by std. dev. of acceleration per km CV Average 17 continuous Average value over the trip deceleration Deceleration 18 continuous Mean divided by std. dev. of deceleration per km CV Sharp 19 continuous acceleration Sharp 20 continuous deceleration Running low 21 continuous speed Frequency of the event per km Extended 22 continuous idling Frequent 23 continuous stop Extended 24 continuous acceleration 25 Age continuous Years of age 26 Gender dummy Indicates participant’s gender (0 - male; 1 - female) Live in City Indicates whether the participant lives in City of 27 dummy of Toronto Toronto (0 - no; 1 - yes) 28 Student dummy 29 Full time dummy 30 Part time dummy Driver 31 Retired dummy Indicates whether the participant had chosen this Characteristics Not occupation type (0 - no; 1 - yes) 32 dummy employed Not 33 dummy answered Driving Indicates driving licence type (0 - not full licence; 1 34 dummy licence - full licence) 35 Driving time ordinal Indicates number of years of driving, larger means

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longer time Indicates frequency of driving, smaller means more 36 Often drive ordinal frequently Kilometer Indicates number of kilometers driven in the past 37 ordinal driven year, larger means longer distance Commute Indicates whether the trip purpose on a typical 38 dummy weekday weekday includes commuting (0 - no; 1 - yes) Business Indicates whether the trip purpose on a typical 39 dummy weekday weekday includes business (0 - no; 1 - yes) Shop Indicates whether the trip purpose on a typical 40 dummy weekday weekday includes shopping (0 - no; 1 - yes) Social Indicates whether the trip purpose on a typical 41 dummy weekday weekday includes social (0 - no; 1 - yes) Recreational Indicates whether the trip purpose on a typical 42 dummy weekday weekday includes recreational (0 - no; 1 - yes) Indicates whether the trip purpose on a typical Other 43 dummy weekday includes other reason such as dropping off weekday children (0 - no; 1 - yes) Commute Indicates whether the trip purpose on a typical 44 dummy weekend weekend includes commuting (0 - no; 1 - yes) Business Indicates whether the trip purpose on a typical 45 dummy weekend weekend includes business (0 - no; 1 - yes) Shop Indicates whether the trip purpose on a typical 46 dummy weekend weekend includes shopping (0 - no; 1 - yes) Social Indicates whether the trip purpose on a typical 47 dummy weekend weekend includes social (0 - no; 1 - yes) Recreational Indicates whether the trip purpose on a typical 48 dummy weekend weekend includes recreational (0 - no; 1 - yes) Indicates whether the trip purpose on a typical Other 49 dummy weekend includes other reason such as volunteer weekend work (0 - no; 1 - yes) Displaceme 50 continuous Engine size Vehicle nt Characteristics Indicates whether the vehicle had a turbo engine (0 51 Turbo dummy - no; 1 - yes)

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6.2.4 Eco-score evaluation model

An eco-score evaluation system was developed for each trip to represent its relative emission intensities compared to all other trips based on a hundred score system. The method involves rescaling the log-transformed values of CO2eq (g/km) and PM2.5 (mg/km) EFs using a scaling method presented in (6.2). We used the 95th percentile and 5th percentile of log-transformed values as the range to reduce the skew in the distribution of the score system.

(95th percentile (x) −푥 ) Eco-score =100* 푖 (6.2) 95th percentile (x) – 5th percentile(x)

Where x and 푥푖 indicate the log-transformed EF for CO2eq or PM2.5 from the entire database and the log-transformed value for the trip 푖.

The EFs corresponding to the scores of 0 and 100 were 620.7g/km and 157.9g/km for CO2eq,

7.87mg/km and 1.45mg/km, for PM2.5. High scores indicated environmentally friendly trips that had low emission intensities while low scores indicated high levels of emissions per kilometer travelled. Furthermore, if the emission levels were higher than the 95th percentile, a score of 0 was assigned. In contrast, if emission levels were lower than the 5th percentile, a score of 100 was assigned.

For the CO2eq eco-score model, the mean and median values of the score system were estimated as 60.09 and 64.6, and their corresponding CO2eq emission levels were 272.8g/km, and

256.4g/km respectively. For the PM2.5 eco-score model, the mean and median values of the eco- score system were estimated as 57.95 and 62.10, and their corresponding PM2.5 emission levels were 2.95mg/km and 2.75mg/km. On average, the tire wear and brake wear emissions accounted for 3.6% (std. dev. 2.4%) and 17.9% (std. dev. 7.9%) of PM2.5 emissions for each trip. The

Pearson correlation between total PM2.5 emissions and PM2.5 exhaust emissions is 0.985.

6.2.5 Machine learning model and feature attribution method

Given the complex relationships among vehicle emissions and the various types of features in this study, a machine learning algorithm was employed to explore the hidden influences of the features on the emission levels and the eco-score of the trips in the GTHA.

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Gradient boosting, in the form of an ensemble of prediction models, is a machine learning method for regression and classification (Bühlmann and Hothorn, 2007). It establishes the model in a stage-wise fashion by allowing optimization of an arbitrary differentiable loss function (Svetnik et al., 2005). XGBoost is an efficient implementation of the gradient tree boosting concept, and it is a regularized model formalized to control over-fitting (Chen and He, 2015).

We employed the XGBoost model to capture non-linear relationships in vehicle emission intensities and various features. As a tree-based model, it can naturally deal with both continuous and categorical data and high-order interactions between features (Chen and He, 2015). When fitting a decision tree to a training dataset, the top few nodes on which the tree is split on are relatively more important features, so the feature selection is processed automatically (Yan-yan Song and Ying Lu, 2015). This model has been demonstrated to outperform other machine learning methods noticeably and consistently in terms of accuracy for structured and tabular data (Robinson et al., 2017; Wang, 2011).

The XGBoost model implementation in this study was based on the scikit-learn and XGBoost Python libraries (Pedregosa et al., 2011). We started with the default parameter settings (such as learning rate = 0.3, minimum sum of instance weight needed in a child = 1, and maximum depth of a tree = 6) in the model and used a grid search method to tune these parameters to minimize the loss function that would penalize inaccurate results. We employed root-mean-square-error

∑푇 (푦̂−푦 )2 (RMSE) = √ 푡=1 푡 푡 as the loss function, where T is the sample size, 푦̂ is predicted value, 푇 푡 and 푦푡 is observed value. In this context, an observed value was estimated based on VSP and emission rates, while a predicted value was modelled by XGBoost.

The performance of the model was evaluated using 10-fold cross-validation. The dataset was randomly divided into 10 subsets, and the model was trained based on 9 sets, and further evaluated based on the performance of the single remaining testing set. This process was repeated 10 times so that each of the 10 sets is used as the testing set once. The evaluation metrics were calculated based on the average performance over the 10th iteration (Robinson et al., 2017). The value of 10 allows the proportions of the training set and testing set in the total dataset to be 90% and 10% respectively. We used both RMSE and R2 values to present the final

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2 2 ∑푡(푦푡−푦̂푡) performance of the model. R (푦̂푡, 푦푡) = 1 − 2 , where 푦̂푡 is the predicted score, 푦푡 is the ∑푡(푦푡−푦̅) observed score, and 푦̅ is the mean score of 푦푡.

Once the model was deemed fit, we demonstrated the order of feature importance and the impact of features on the model output using the SHAP (Shapley additive explanation) method. SHAP values calculate the importance of a feature by comparing the performance of the model with and without the feature (Lundberg et al., 2018). In typical feature attribution methods, the order in which variables are added to evaluate their importance affects the relative importance of the variables proposed in the results. Moreover, going through all combinations would be computationally intensive.

For this purpose, Lundberg and Lee (2017) recently developed fast exact tree solutions for SHAP values, which exponentially reduce the processing time and enable a new richer visualization of the individualized features for the entire dataset. Their Tree SHAP method consists of averaging differences in predictions over all possible orderings of the features, rather than using the order specified by their position in the tree. This method has been demonstrated to provide better identification of influential features compared to traditional feature attribution methods such as gain, split count, and permutation (Lundberg et al., 2018). We fit all the data in the model and employed this method to present the order of feature importance and their relative impact on the model output.

The SHAP Python library was used to provide individualized feature attributions and results were presented using SHAP summary plots. Traditional feature attribution methods provide a ranking of the relative importance of features in the training dataset but do not relate the specific value of each feature to the output. SHAP summary plots provide a visual representation of the range and distribution of impacts a specific feature has on the model’s output in addition to the relation between the feature value and its impact (Lundberg et al., 2018). In this representation:  Features are sorted by their global impact on the y-axis  Dots representing individual SHAP values are plotted horizontally and stacked vertically when several trips have a similar impact on the model’s output  Each dot is colored by the value of that feature from low (blue) to high (red), with a smooth gradual change in color.

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6.3 Results and discussion 6.3.1 Altitude data quality

The dynamic accuracy of GPS BT-Q1000XT in terms of longitude and latitude values have been assessed in a recent study, indicating that the median error of the GPS receiver is 0.5 m for vehicles in different real-world settings (Schipperijn et al., 2014). In this study, we examined the quality of altitude data collected from the GPS in two steps: First, the variability of altitude data collected from different GPS devices was investigated by driving a vehicle with all GPS devices on a designed route. Then, latitude and longitude data from one GPS device were used to derive altitude data from Google Map Elevation API, which uses a range of digital elevation model data sources to generate the terrain layer (Goudarzi and Landry, 2017).

The altitude data collected from different GPS units were very close to each other and had consistent trends. The distributions of second-by-second grade differences based on GPS device and Google Map API were calculated by dividing the altitude difference by corresponding travelled distance. The results indicate that the distribution of grade difference is almost symmetric around zero and the 25th to 75th percentiles of the grade differences were within -2% to + 2%. A previous study conducted by Frey et al., (2008) indicates that on a mesoscale, there is a compensation between the increase in fuel use and emissions associated with positive grades and the decrease associated with negative grades. For a one-way trip, the average changes in fuel use and emissions range from -0.2% to 3.2% when grades are considered versus when they are not (Frey et al., 2008). This indicates that this systematic error would not influence the investigation of the impacts of other factors on vehicle emissions.

6.3.2 Descriptive analysis

This study included 82 participants from 69 households in the GTHA. A total of 1,113 individual trips were collected based on a one-week data collection period for each participant. Data collection took place between March and July 2018. The road network in the GTHA consists of 1.0% RR, 11.7% RU, 3.0% UR, and 84.3% UU roads. The distribution of roads covered in this study includes 1.7% RR, 3.7% RU, 9.4% UR, and 85.2% UU roads.

Figure 6.1 presents the collected trip activity across the GTHA. The top three roads with the highest trip frequency in the sampled data are Don Valley Parkway, Highway 401, and Highway

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404; these include the major highways crossing the city. A summary of the characteristics of the dependent variables and 51 independent variables are presented in Table 6-2.

Figure 6.1 Number of trips that took place on the road network across the GTHA

Table 6-2 Descriptive statistics for dependent variables and independent features across all trips Category Name Unit Mean Std.* Min 25%** 50%*** 75%**** Max 1696. CO2eq g/km 303.6 166.1 112.4 207.5 256.4 342.0 4 Dependent ln(CO2eq) - 5.62 0.42 4.72 5.34 5.55 5.83 7.44 Variable CO2eq score - 60.1 27.4 0 43.5 64.6 80.0 100

PM2.5 mg/km 2.77 2.12 0.15 1.58 2.10 3.17 25.32

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Name Unit Mean Std.* Min 25%** 50%*** 75%**** Max

^3 ln(PM2.5*10 ) - 7.75 0.55 5.02 7.36 7.65 8.06 10.14

PM2.5 score - 58.9 27.5 0 41.0 63.8 79.8 100 Temperature ℃ 17.6 6.6 -6.3 15.5 18.5 21.8 30.7 Humidity % 67.9 16.0 19.7 57.9 68.3 79.0 98.2 Meteorology Wind speed km/h 15.5 9.2 0.0 9.0 14.3 21.0 71.9 Weather bad - 0.09 0.28 0 0 0 0 1 Weekend - 0.30 0.46 0 0 0 1 1 Peak - 0.46 0.50 0 0 0 1 1 3843 Time duration second 2779 3132 117 1248 2019 3180 3 2489 Distance m 19356 23278 1017 5340 12804 24958 97 RR time Trip % 0.3 1.6 0.0 0.0 0.0 0.0 19.5 proportion Characteristics RR distance % 0.6 3.3 0.0 0.0 0.0 0.0 31.1 proportion UR time % 14.0 19.9 0.0 0.0 3.2 22.4 99.9 proportion UR distance % 22.2 26.8 0.0 0.0 4.1 45.7 99.0 proportion Altitude CV /km 0.021 0.057 0.001 0.006 0.011 0.020 1.540 Speed CV /km 0.261 0.425 0.003 0.041 0.098 0.260 3.793 Average km/h/s 2.84 0.40 1.27 2.57 2.85 3.09 4.54 acceleration Acceleration /km 0.100 0.118 0.003 0.026 0.051 0.122 0.751 CV Average km/h/s -3.02 0.48 -6.81 -3.30 -2.98 -2.72 -1.49 deceleration Deceleration - /km -0.10 0.12 -0.72 -0.12 -0.052 -0.027 CV 0.003 Driving Sharp count/k Characteristics 1.5 1.3 0.0 0.6 1.2 2.0 8.7 acceleration m Sharp count/k 1.5 1.4 0.0 0.5 1.1 2.0 11.2 deceleration m Running low count/k 0.4 0.4 0.0 0.1 0.3 0.4 3.2 speed m count/k Extended idling 0.3 0.4 0.0 0.0 0.1 0.3 6.4 m count/k Frequent stop 3.4 3.9 0.0 1.2 2.2 4.0 38.3 m

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Name Unit Mean Std.* Min 25%** 50%*** 75%**** Max Extended count/k 0.2 0.2 0.0 0.0 0.1 0.3 1.9 acceleration m Age years 39.8 12.5 19 30 40 49 93 Gender - 0.4 0.5 0 0 0 1 1 Live in City of - 0.5 0.5 0 0 1 1 1 Toronto Student - 0.1 0.3 0 0 0 0 1 Full time - 0.6 0.5 0 0 1 1 1 Part time - 0.1 0.3 0 0 0 0 1 Retired - 0.0 0.2 0 0 0 0 1 Not employed - 0.1 0.3 0 0 0 0 1 Not answered - 0.0 0.1 0 0 0 0 1 Driving licence - 0.8 0.4 0 1 1 1 1 Driving time - 3.4 1.0 0 3 4 4 4 Often drive - 0.1 0.2 0 0 0 0 1 Kilometer - 2.6 1.5 0 2 2 3 6 driven Commute Driver - 0.7 0.5 0 0 1 1 1 weekday Characteristics Business - 0.4 0.5 0 0 0 1 1 weekday Shop weekday - 0.5 0.5 0 0 0 1 1 Social weekday - 0.4 0.5 0 0 0 1 1 Recreational - 0.3 0.5 0 0 0 1 1 weekday Other weekday - 0.1 0.2 0 0 0 0 1 Commute - 0.1 0.3 0 0 0 0 1 weekend Business - 0.1 0.4 0 0 0 0 1 weekend Shop weekend - 0.7 0.5 0 0 1 1 1 Social weekend - 0.7 0.5 0 0 1 1 1 Recreational - 0.6 0.5 0 0 1 1 1 weekend Other weekend - 0.0 0.2 0 0 0 0 1 Vehicle Displacement L 2.5 0.8 1.4 2 2.4 2.7 6.2 Characteristics Turbo - 0.1 0.3 0 0 0 0 1 * Standard deviation ** 25 percentile *** 50 percentile **** 75 percentile

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About 46% of the trips were conducted during the morning and afternoon peak periods. The average trip duration and distance were 2,779 seconds and 19.4 km, respectively. Additionally, the average proportion of distance on UR roads was 22.2% for each trip. The average age of participants was 40, and 60% of participants were male. Furthermore, 50% of participants reside in the City of Toronto, a proportion that is close to the result of the latest travel diary survey (the Transportation Tomorrow Survey) which estimates that 48% of the region’s residents live in the City of Toronto (Ashby, 2018).

Regarding occupation and job status, 10% of participants were students, and 70% of participants had full-time or part-time jobs. Furthermore, 80% of participants have completed all stages for a full driving licence in Ontario. Most participants drove every day, and 61% of drivers drove around 10,000 km to 30,000 km over the past year. The majority of the participants drove on weekdays for commuting. Conversely, most participants drove on weekends predominantly for shopping, social, and recreational purposes.

6.3.3 Model results

With the goal of exploring the importance of individual variables, the first part of the analysis involved including all independent variables in the eco-score evaluation model with the exception of variables typically used in emission estimation models namely, vehicle model year, vehicle weight, road grade, instantaneous speed, and instantaneous acceleration, or variables directly related to emission estimation such as average speed, travelled distance, and trip duration. The second part of the analysis developed models with all discrete variables to better understand the impacts of these predetermined trip features.

6.3.3.1 Eco-score and EF prediction models

The trip eco-scores and EFs were used as dependent features in the XGBoost model. The mean 2 and standard deviation values of R and RMSE of the 10 tests for the CO2eq eco-score evaluation system were estimated as R2=0.84 (std. dev. 0.05), and RMSE=10.26 (std. dev. 1.24). 2 2 Additionally, R and RMSE for the PM2.5 eco-score evaluation system were R =0.85 (std. dev. 0.03), and RMSE=10.64 (std. dev. 0.79).

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Regarding the EFs, the mean and standard deviation of R2 and RMSE values of the 10 models were calculated as R2=0.81 (std. dev. 0.04), and RMSE=72.69 g/km (std. dev. 14.67 g/km) for 2 CO2eq and R =0.83 (std. dev. 0.05) and RMSE=1.06 mg/km (std. dev. 0.34 mg/km) for PM2.5.

A set of predicted and observed values of the eco-score evaluation and EF prediction models were randomly selected from 10 tests to be presented in Figure 6.2. The model performance was similar for both evaluation metrics. They both have relatively lower accuracy when the score is lower, or when the EF of the trip is higher since the number of trips in this category is small, which makes it harder to train the model.

Generally, the eco-score evaluation models for both CO2eq and PM2.5 had better performance than EF prediction models. This is expected since dependent features in the former had less dispersion after the transformation using (6.2). Therefore, the eco-score evaluation models were employed in the rest of the analysis.

a) Predicted vs. observed CO2eq eco-scores b) Predicted vs. observed PM2.5 eco-scores

c) Predicted vs. observed CO2eq EFs d) Predicted vs. observed PM2.5 EFs

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Figure 6.2 Scatter plot of predicted and observed eco-scores and EFs: a) CO2eq eco-scores;

b) PM2.5 eco-scores; c) CO2eq EFs; d) PM2.5 EFs.

Figure 6.3 a) and b) presents a SHAP summary plot representing the global impact of each feature on the eco-score evaluation model for CO2eq and PM2.5 (Lundberg et al., 2018). All the features are sorted by their global impact based on calculated SHAP values. The dots represent the SHAP values and are plotted horizontally. The color of each dot represents the value of that feature, from low (blue) to high (red). When the variation of a feature’s impact (x-axis) and value (y-axis) gradually changes the model’s output, a smooth transition in color can be observed across SHAP values horizontally. For example, a high value of extended idling is associated with an increase in CO2eq EFs and therefore a low SHAP value (eco-score decreases). However, the lower the value of extended idling, the higher the SHAP values. This association is illustrated by a gradual change in color across the feature horizontally.

Figu

a) SHAP summary plot for CO2eq eco-score model

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b) SHAP summary plot for PM2.5 eco-score model

Figure 6.3 SHAP summary plot of top 20 features ranked by global feature importance in the XGBoost eco-score model: a) CO2eq eco-score model, and b) PM2.5 eco-score model. The higher the SHAP value for a feature, the more ecofriendly the trip. Every trip in the dataset is run through the model and a dot is created for each feature attribution value, so one trip gets one dot on each feature’s line. Dots are colored by the feature’s value for the trip and pile up vertically to show density.

Based on Figure 6.3 a), the three most important features affecting the eco-score model are associated with the frequency per kilometer of idling lasting more than 60s, frequent stops, and vehicles running at low speed for more than 60s. The change in colors (from red to blue) of the three features indicates a decrease in the value of the feature, which results in a smooth increase in CO2eq eco-score. However, the general trend of longer tails reaching to the left, but not to the right, means that extreme values of these features can significantly reduce the score or increase the EF but cannot significantly improve the score or lower the EF. This reveals non-linear relationships among the features and the score.

It is important to note that higher SHAP value densities are observed for positive SHAP values (increase in eco-score) indicating that low SHAP values of the features were less common in the

129 dataset. The large impact of the three most significant features evaluated can be justified by a minority of trips in the dataset occurring in highly congested conditions; hence leading to low SHAP values (lower score) as the high occurrence of these features are inevitable in congested conditions. However, even in congested conditions, the decision to take sharp decelerations is driver dependent. Sharp deceleration therefore only has a large impact on a minority of trips that include voluntary sharp decelerations, marking driver specific variations.

Finally, it appears that the age of the driver has a large negative impact on the eco-score for a minority of trips with older drivers. This result seems counter-intuitive as older drivers tend to be more experienced and drive more smoothly thus reducing emissions. However, this result can be explained by the fact that driver age and vehicle model year have a Pearson correlation of -0.33. Older drivers tend to have older vehicles with higher emissions, offsetting their more ecofriendly driving compared to younger drivers.

For the PM2.5 eco-score model in Figure 6.3 b), the most significant feature is driver age, which has a negative impact on the eco-score. This is due to the fact that the improvement in emission rates of PM2.5 is roughly three to five times higher compared to improvements in GHG emissions

(in CO2eq). Therefore, the effect of driver age, which is associated with vehicle model year, has the largest impact on PM2.5 eco-score.

Similar to the CO2eq model, the occurrences of extended idling, frequent stop, running low speed, and sharp deceleration per kilometer have significant influences on the PM2.5 eco-score. Weekday trips that have a social purpose were found to have a negative impact on the eco-score.

Driving behavior features, namely average deceleration and sharp acceleration are negatively associated with PM2.5 eco-score. The proportion of travel distance on UR over a trip is negatively associated with PM2.5 eco-score. This can be explained by the fact that PM2.5 emission rates (g/s) are much higher (approximately 4 -10 times) when the vehicle speed is over 40 km/h versus under 40 km/h (U.S. Environmental Protection Agency, 2011).

6.3.3.2 Eco-score models excluding driving characteristics

The previous prediction model was built based on all continuous and discrete features. The performance of the model was dominated by continuous features related to driving characteristics. In order to better understand the relative impact of predetermined trip variables,

130 we further developed a model only based on discrete variables including dummy, categorical and ordinal variables representing driver characteristics, meteorology data, vehicle characteristics, and trip characteristics as shown in Table 6-1.

This model contains all discrete features and aims to explore the hidden impacts of features not revealed by the previous prediction model. The mean and standard deviation values of R2 and 2 RMSE from the 10 tests for the CO2eq eco-score model are R =0.19 (std. dev. 0.04), and RMSE=25.03 (std. dev. 2.21). The mean and standard deviation values of R2 and RMSE from 2 the 10 tests for the PM2.5 model are R =0.43 (std. dev. 0.07), and RMSE=21.20 (std. dev. 1.43). Figure 6.4 a) and b) present the SHAP summary plots of the top 20 discrete features ranked by global feature importance for CO2eq and PM2.5.

a) SHAP summary plot for CO2eq eco-score model with top 20 discrete features

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b) SHAP summary plot for PM2.5 eco-score model with top 20 discrete features

Figure 6.4 SHAP summary plot of top 20 features ranked by global feature importance in

the XGBoost eco-score model with discrete features: a) CO2eq eco-score model, and b)

PM2.5 eco-score model. The higher the SHAP value for a feature, the more ecofriendly the trip. Every trip in the dataset is run through the model and a dot is created for each feature attribution value, so one trip gets one dot on each feature’s line. Dots are colored by the feature’s value for the trip and pile up vertically to show density.

Based on Figure 6.4 a), two ordinal features indicating the number of kilometers a participant has driven over the past year and the number of years a participant has been driving since obtaining their permit have the most significant global impact on their eco-score. The distribution and density of the SHAP values for the kilometers driven consolidates the hypothesis of driver specific behaviour. In fact, kilometers driven has a large impact for a minority of trips (low density) with participants that have driven a low number of kilometers over the past year, indicating less driving experience and higher emissions (tail reaches to the left). Besides, this feature was positively associated with the trip distance collected in this study, so the participants

132 who drove longer distance could experience good traffic conditions on the UR roads and tended to have lower trip-level CO2eq EFs.

Regarding the driving time, it appears that driving time of drivers has a large positive impact on the CO2eq eco-score for a minority of trips with drivers that haven’t driven much since obtaining their permit. This result seems counter-intuitive as drivers that have driven for a longer period tend to be more experienced and drive more smoothly thus reducing emissions. However, this result can be explained by the fact that driving time and driver age have a Pearson correlation factor of 0.6, which was already found to be negatively linked with vehicle model year. These results indicate that driving experience was better explained by the number of kilometers driven in the last year rather than the time a participant has been driving regularly. The occurrence of participants that haven’t driven for a long period since obtaining their licence is highly correlated with age, which is itself correlated with vehicle model year. This indicates that older participants tend to drive older vehicles.

Besides, weekend trips that have a business or recreational purpose were found to have significant impacts on the CO2eq eco-score. The results show that if a participant drove for business during the weekend, this could significantly improve the score, meaning that their EF would decrease. On the other hand, if the participant chose to conduct recreational activities during the weekend their impact on EF would be relatively small. This could be explained by the fact that driving trips for business usually occur during working hours so if these trips were switched to weekend, they could significantly lower the EF as traffic congestion is generally lower. As for recreational trips, they usually occur during non-peak hours, so the impact on EF of having recreational trips during the weekend is limited.

Variables indicating whether typical weekday trips include a commuting purpose and if the participant has a full-time job were found to be associated with driver experience. The density of SHAP values indicates that the trips of drivers having these characteristics tend to slightly increase or not affect the CO2eq eco-score. On the other hand, a minority of trips with drivers that didn’t have these characteristics experience a relatively large decrease in eco-score. This result was expected as drivers that commute on a daily basis are familiar with their route and can subsequently drive more smoothly thus reducing emissions. Finally, trips conducted in peak traffic conditions score lower than trips conducted at other times of the day.

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As for the PM2.5 eco-score model, Figure 6.4 b) reveals that the most significant feature is whether a trip purpose on a typical weekday includes commuting. It demonstrates that drivers that commute on a daily basis would obtain higher scores as they are familiar with the roads in terms of traffic conditions.

The kilometers a participant had driven over the past year have a significant impact on the PM2.5 eco-score. Generally, drivers with less driving in the previous year would have a lower PM2.5 eco-score. However, some experienced drivers tended to drive at higher speeds and accelerations thus worsening their eco-scores. As mentioned in Section 3.2.1, when the vehicle specific power is high, its PM2.5 exhaust emission rates would be much (approximately 4 -10 times) higher than emissions rates at low speed (less than 40km/h). Besides, additional accelerations could cause higher brake and tire wear emissions.

The number of years a participant has been driving has a negative impact on the PM2.5 eco-score as this feature is related to the vehicle model year. Emissions of PM2.5 exhaust, brake and tire wear emissions are generally higher with older vehicles.

Weekend trips that include a shopping purpose have a negative impact on the eco-score since shopping trips usually occur in a congested traffic condition during the weekend. Weekday trips that have a business purpose and weekend trips that have a business purpose have opposite impacts on the PM2.5 eco-score; weekend business trips have lower PM2.5 EFs. Moreover, trips occurring during peak periods tended to have a lower eco-score and hence higher emissions.

6.3.3.3 Factor effect analysis on eco-score models for individual trips

We further explored the model results in Section 3.3.2 by showing the contribution of features in pushing the individual eco-score from the base value, calculated as the average eco-score over the dataset (Lundberg et al., 2018). The red color in Figure 6.5 a) and b) indicates that the feature increased the eco-score, while blue color indicates that the feature decreased the eco-score.

We chose two trips with calculated CO2eq and PM2.5 eco-score for illustration. For the trip in

Figure 6.5 a), its CO2eq eco-score was much higher than the base value mainly because the trip has the following characteristics: the participant had a full-time job and had driven over 40,000 km in the past year. Additionally, the trip was conducted on a weekend and during a non-peak period. For the trip in Figure 6.5 b), its PM2.5 eco-score was high as the trip has the following

134 characteristics: the participant’s trip purposes included business during the weekend, and this participant had driven over 50,001 km in the previous year. Besides, this trip was conducted during the weekend and in a non-peak period.

If all the results were rotated and stacked horizontally, we could see the explanation for the entire database. The goal is to predict whether a trip is likely to have a high/low eco-score based on groupings of the discrete variables. In fact, by stacking all trips against each other, a visual representation illustrates the eco-score as their sum. Figure 6.6 presents the same information as Figure 6.5 but considering all the trips stacked horizontally forming a hierarchical clustering of the SHAP values and identifying groups that share common factors related to the eco-score of their trips.

Based on Figure 6.6 a), the results of the earlier analysis for CO2eq are consolidated as high eco- score trips are associated with trips having full-time experienced drivers that are familiar with their daily commute route. On the other hand, low eco-score trips (higher EFs) are associated with clusters including younger less experienced drivers or drivers with inconsistent travel behavior (Not employed, retired).

Figure 6.6 b) shows that participants whose trip purposes during the weekday include business tend to have lower PM2.5 eco-scores especially when the trips are conducted during peak hours. Besides, trips conducted by participants who have driven low distances in the previous year are more likely to have low eco-score trips. Finally, we found commuter trips are generally associated with high PM2.5 eco-scores.

a) SHAP feature attributions explains the output of the CO2eq model for a specific trip

b) SHAP feature attributions explains the output of the PM2.5 model for a specific trip

Figure 6.5 The XGBoost model was used to predict the score. Each prediction was explained using SHAP values. Red feature attributions increase the score, while blue feature attributions decrease the score. SHAP feature attributions explained the output of

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the model as a sum of the effects of each feature being introduced into a conditional

expectation for a specific trip: a) CO2eq score model; and b) PM2.5 score model.

0 a) SHAP feature attributions for all database in CO2eq model as in a) but rotated 90 and stacked. Trips were grouped based on SHAP values that share similar reasons for higher scores or lower scores

0 b) SHAP feature attributions for all database PM2.5 model as in a) but rotated 90 and stacked. Trips were grouped based on SHAP values that share similar reasons for higher scores or lower scores.

Figure 6.6 The XGBoost model was used to predict the score. Each prediction was explained using SHAP values. Red feature attributions increase the score, while blue feature attributions decrease the score. SHAP feature attributions for all database were rotated by 900 and stacked, which were then used to identify distinct subgroups that share similar reasons for higher scores or lower scores (Lundberg et al., 2018). A few of the

noticeable subgroups were annotated with the features that define them: a) CO2eq score

model; and b) PM2.5 score model.

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6.4 Discussion and conclusions

This study employed a machine learning approach to capture the relationships between CO2eq and PM2.5 EFs and various categories of variables including meteorology, trip characteristics, driving characteristics, driver characteristics, and vehicle characteristics based on real-world GPS data from driving trips. The novel tree SHAP method provided a concise visual representation of the relations between variables and trip eco-score. Driver specific variables that decrease the eco-score of trips were identified.

An eco-score evaluation model was developed to estimate the relative emission intensity level of trips conducted in the GTHA. The mean and standard deviation values of the R2 and RMSE for 2 the CO2eq eco-score were R =0.84 (std. dev. 0.05), and RMSE=10.26 (std. dev. 1.24), while they 2 were R =0.85 (std. dev. 0.03) and RMSE=10.64 (std. dev. 0.79) for the PM2.5 eco-score.

Based on the eco-score evaluation model, a SHAP value method was employed to interpret the global importance of each feature. The first part of the analysis involved all independent variables in the eco-score evaluation model except variables typically used in emission estimation models or variables directly related to emission estimation. In the CO2eq eco-score analysis, continuous features associated with driving behavior were found to have the most significant impact on the trip eco-score. While the most important features affecting emissions are directly related to congestion, the interpretation of the sharp acceleration and deceleration features based on SHAP summary plots show that the eco-score of trips is also dependent on specific driver behavior (Aggressive/Smooth). Our results are consistent with the previous findings that driver-related (driver behavior and aggressiveness) and traffic-related (traffic flow) factors have the most significant effects on fuel consumption (Zhou et al., 2016). As for the

PM2.5 eco-score model, driver age was found to have the most significant influence on the eco- score since it was associated with the vehicle model year which itself was associated with PM2.5 emission rate (g/s). This stresses the significance of older vehicles on emissions in an urban area and highlights the importance of setting progressively more stringent emission standards for on- road vehicles in order to reduce PM2.5 emissions. We also observed that the proportion of travel distance on arterial roads with traffic lights has a negative impact on PM2.5 EFs due to more frequent acceleration and deceleration events.

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The second part of the analysis entailed the development of a model based only on discrete variables including dummy, categorical and ordinal variables representing driver characteristics, meteorological data, vehicle characteristics, and trip characteristics. The interpretation of the SHAP summary plot of this model showed that the number of kilometers a participant has driven over the past year was the most significant feature of the CO2eq eco-score. This finding suggests that this feature could be a better indicator of an individual’s driving experience than the number of years a participant has been driving regularly, since the latter variable does not capture the actual amount of driving. Furthermore, we observed that full-time drivers that conduct a daily commute have lower CO2eq and PM2.5 intensities as they are more familiar with their route, compared with drivers that do not drive the same route on a daily basis. In addition, avoiding traffic peak hour or congested traffic conditions could also improve eco-scores and lower emission intensities.

The study results could be helpful for individuals to better plan their trips. It could also be useful for individuals to understand the impact of speed profiles on emissions and improve their driving behaviour to achieve lower trip-level emission intensities and fuel consumption. In addition, this study could assist policymakers and city planners in evaluating policies and strategies for moderating on-road traffic emissions. For instance, more efforts should be put on motivating people to avoid congested traffic conditions (e.g. congestion pricing and alternative mode choice), since we found the most significant features affecting emissions are directly related to congestion.

While this study provides a basis for understanding the various features affecting on-road emissions, further research is needed to improve the clustering of continuous and discrete characteristics so that the aggregated effect of features in different aspects including driver characteristics, driving behaviors, and trip characteristics can be quantified. We noted a high proportion of trips collected on UU roads. Although it would not influence the analysis of the effects of other features on the emission level, additional data should be collected on other roads to better capture the effect of road types on trip emissions. Besides, more detailed traffic information (e.g. traffic volume and average speed for all other vehicles on each road) could enable us to better capture the effects of driving and driver specific characteristics on trip-level emissions. This information could be obtained from real-time traffic data collected by the City of Toronto or mined from tools like Google Maps.

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Chapter 7 A Gradient Boost Approach for Predicting Near-Road Ultrafine Particle Concentrations using Detailed Traffic Characterization Chapter overview

This study investigates the influences of meteorology, land use, built environment, and traffic characteristics on near-road ultrafine particle (UFP) concentrations. To achieve this objective, minute-level UFP concentrations were measured at various locations along a major arterial road in the Greater Toronto Area (GTA) from February to May 2019. Each location was sampled five times, at least once in the morning, mid-day, and afternoon. Each visit lasted for 30 minutes, resulting in 2.5 hours of minute-level data collected at each location. Local traffic information, including vehicle class and turning movement, were processed using computer vision techniques and organized in minute intervals. The number of fast-food restaurants, cafes, trees, traffic signals, and building footprint, were found to have positive impacts on the mean UFP, while the distance to the closest major road was negatively related to the UFP. We employed the Extreme Gradient Boosting (XGBoost) method to develop prediction models for UFP concentrations. The Shapley additive explanation (SHAP) measures were used to present the influence of each feature on model output. The model results demonstrated that minute-level counts of local traffic from different directions had significant impacts on near-road UFP concentrations. Besides, model performance was robust under a random cross validation as coefficients of determination (R2) ranged from 0.63 to 0.69, but it revealed weaknesses when data at specific locations were eliminated from the training dataset. This result indicates that proper cross-validation techniques need to be developed to better evaluate machine learning models for air quality predictions.

7.1 Introduction

Ultrafine particles (UFP), defined as particles with diameter <100 nm, have been associated with various health effects, including cardiovascular and respiratory outcomes (Martins et al., 2010; Weichenthal, 2012). In urban environments, near-road UFP is emitted in large amounts from motor vehicles and have short atmospheric lifetimes ranging from seconds to hours (Sabaliauskas et al., 2012). In addition, some studies have suggested that high UFP concentrations occur close to bars, cafes, and restaurants in the local street environment due to cooking fumes (Gower et al., 2014). Besides direct emissions, UFP can form through a series of

139 secondary chemical reactions by gas-to-particle conversion (Choi and Paulson, 2016). This localized nature of UFP results in its high temporal and spatial variability around roadways and across urban areas.

Several studies have characterized the influences of meteorological characteristics, land use, and traffic volume on UFP levels in urban environments (Abernethy et al., 2013; Hatzopoulou et al., 2013; Hoek et al., 2011a). These studies have employed either short-term fixed monitoring with portable sensors or mobile monitoring (Hankey and Marshall, 2015b; Hoek et al., 2011b). Paulson et al. (Paulson et al., 2017) conducted mobile and stationary measurements at five sites and evaluated the effects of urban built-environment and vehicular emissions on near-road UFP levels. After controlling for traffic, UFP concentrations were generally higher in the morning than in the afternoon due to a relatively stable surface layer (Paulson et al., 2017). Weichenthal et al. (Weichenthal et al., 2014) collected UFP and black carbon (BC) concentrations at 73 sites in Montreal, Canada. They employed random-effect models to examine the relationships between the near-road UFP and traffic and built environment predictors. The study indicated that interquartile increase in road width, building height, and truck ratio were the most significant predictors of mean UFP concentrations; road width and industrial zoning were the strongest predictors of maximum UFP concentrations. Minet et al. (2018) compared the performance of land-use regression (LUR) models developed for UFP and BC concentrations based on mobile monitoring and short-term stationary sidewalk measurements. The result showed that fixed and mobile monitoring LUR models presented similar performance with coefficients of determination (R2) varying from 0.434 to 0.525 (Minet et al., 2018).

Recently, a number of studies investigated the factors influencing the UFP model performance, the sampling representativeness, sampling locations and durations, and regression algorithms (Kerckhoffs et al., 2016; Messier et al., 2018). Patton et al. (2015) tested the transferability and generalizability of LUR models of hourly UFP in urban neighborhoods in the Boston Area. Four neighborhood-specific regression models and one Boston-area model were developed, and the transferability and generalizability of models were tested with and without neighborhood- specific calibration (Patton et al., 2015). The authors found that significant UFP predictors included wind speed and direction, temperature, highway traffic volume, and distance from the highway edge (Patton et al., 2015). The transferability of neighborhood-specific LUR models producing hourly UFP was limited, while the general model performed acceptably (adjusted-R2

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= 0.26, range: 0.13-0.30) when calibrated with local data (Patton et al., 2015). Weichenthal et al. (2016) conducted a comparison of linear regression and a machine learning approach for UFP concentrations based on mobile monitoring data. The study indicated that the differences in predictive performance were not statistically significant when evaluated using the cross- validation procedure. Rivera et al. (2012) evaluated the spatial distribution of UFP in urban settings by measuring UFP for 15 min on the sidewalks of 644 participants’ homes during non- rush hours. The LUR model explained 36% of UFP variation, and results showed that the best predictors of UFP were traffic intensity, distance to a nearest major crossroad, and area of high- density residential land. Adding sampling date and hour of the day to the model increased the R2 to 51% without changing the regression slopes (Rivera et al., 2012). Saha et al. (2019) systematically evaluated the performance of urban UFP exposure models as a function of sampling strategy and concluded that 1 to 3 h of sampling per day for 10 - 15 days per site could provide 15 min-interval mean UFP estimates comparable to models derived from an entire dataset.

Models developed based on short-term monitoring campaigns generally have poor performance, however they are attractive as they can achieve higher spatial coverage than long-term monitoring. This raises the question of whether detailed traffic characteristics per site can improve model performance, and if different cross-validation strategies would influence the evaluation of model performance. This study aims to estimate minute-level UFP concentrations measured at multiple sites along a major arterial road using a machine learning approach; the Extreme Gradient Boosting (XGBoost). The Shapley additive explanation (SHAP) measures were employed to quantitatively evaluate the influences of meteorology, land use, built environment, and detailed traffic characteristics on UFP concentrations. Various approaches for cross validation were employed to test model performance.

7.2 Materials and methods

7.2.1 Study area and data collection

In this study, near-road UFP were measured at 24 locations, including 18 intersections and 6 mid-block locations, along Hurontario Street in the Greater Toronto Area (GTA) between February and May 2019. Hurontario Street is a major arterial road crossing the City of Mississauga from the south to the north, and the southern part of the City of Brampton. It goes

141 through various land-uses such as commercial/restaurant establishments, residential buildings, open areas, and industrial areas. It spans from two major intersections: Hurontario Street and Mineola Road in the south and Hurontario Street and Steeles Avenue in the north, as shown in Figure 7.1. All measurements were performed during the daytime hours (7:00 am - 7:00 pm) on different weekdays. To avoid measurement bias, visits to the 24 locations were randomly scheduled, keeping the constraint that each site was measured five times, at least once in the morning, mid-day and afternoon period. Each visit lasted for 30 minutes. In total, 2.5 hours of data were collected for each location.

Figure 7.1 Sampling intersections and mid-block locations along Hurontario Street displayed on Google Maps. The mid-block locations are named using the following convention: ‘intersection on the south_intersection on the north’

UFP concentrations were measured at one-minute intervals using a portable nanoparticle sizing and counting instrument (NanoScan SMPS TSI 3910). On account of its operating temperature ranging from 10 to 30deg C, the NanoScan was surrounded with hot-water bags in an insulated

142 bag to maintain the equipment’s working temperature within the required range. The insulated bag was placed on an adjustable-height work table so that the intake sampling tube provided by the NanoScan manufacturer was at approximately 1.5 m above street level. All measurements were conducted on sidewalks as close as possible to the roadway to minimize the variability of the UFP concentrations in terms of the distance from curbside. During measurements, traffic information was captured using two action cameras with a 140deg wide-angle view. The camera was affixed on a 3.0 m tripod stand. At intersections, two cameras were placed on the opposite corners with one of them near the UFP monitoring sensor. At mid-block locations, a single camera was placed close to the instrument. The cameras recorded high-resolution (1080p) videos at 30 frames per second. All instruments’ clocks were synchronized daily, and their operation status was examined every 30 mins.

Besides, meteorological data were extracted from a closest fixed station operated by Environment Canada at Toronto Pearson Airport, approximately 5.0 km from the Hurontario corridor. It provides an hourly record of wind direction, wind speed, temperature, and relative humidity. Land use variables, including road network and building footprint, were derived from the database provided by DMTI Spatial Inc. Transit lines and bus stops were obtained from the City of Brampton and the City of Mississauga Open Data portals. Statistics Canada online database provided the locations of port and shore. Restaurant types and locations, trees, and traffic stop signs were derived from OpenStreetMap Data. Moreover, the locations of nitrogen oxides (NOx) and particulate matter (PM) emitting chimneys were derived from the National Pollutant Release Inventory (NPRI) website of the Government of Canada.

7.2.2 Data processing

During each visit, monitoring occurred for 30 mins at each location. Traffic data were recorded continuously and then processed in one-minute intervals. Hourly meteorological data were assigned to each sampling period. The quality of UFP was examined based on the status recorded by the device. Approximately 3% of data had a status marked ‘error’, such as tilt error and inlet flow error, and were removed from the database. We employed the interquartile range (IQR) method to identify UFP data outliers. The IQR range was calculated as the UFP difference between the third quartile Q3 and first quartile Q1. Any UFP values that are more than 1.5 IQR below Q1 or more than 1.5 IQR above Q3 were considered outliers and removed.

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7.2.2.1 Land use and built environment variables

Land use and built environment variables were processed for all locations using ArcMap 10.4.1. First, distance variables were calculated, the closest distance between the sampling location and a specific variable such as shore, port, rail line, highway, major road, as well as NOx and PM emitting chimneys. Besides, various land-use variables were computed within different buffers of 50, 100, 200, 300, 500, 750, and 1000 m around each location. These variables include building footprint area, and areas of commercial, governmental, institutional, recreational, residential, industrial, parks, waterbody, and open space land use. The total lengths of highways, major roads, local roads, transit routes, and the number of bus stops, intersections, trees, fast food, cafes, pubs, and restaurants were also processed within the above buffer sizes.

Pearson correlations were first used to evaluate potential associations between mean UFP level at each location and its corresponding land use and built environment variables. If the correlation coefficients between the two variables were higher than 0.5, the variable with a stronger correlation to the mean UFP was retained. We then employed the K-means clustering method to cluster all sampling locations into different groups. This clustering process allows for the classification of sites based on the similar influence of environmental and land use information on UFP concentrations.

7.2.2.2 Meteorological data

Wind direction recorded by the weather station represents the angle difference (in 10deg) with respect to the true north and ranges from 1 to 36. The relative location of the UFP measurement to the intersection (corner) or mid-block location (side) was noted. To calculate the angle between the road and the wind direction measured by the weather station, the orientation of Hurontario Street was measured with respect to the true north on Google Maps. Since the orientation of Hurontario Street, as well as the direction of the wind, were all measured with respect to the true north, a cosine value was calculated based on the difference between the relative location of the instrument to the sampling location and wind direction. A value larger than 0.8 would represent that the UFP instrument is at a downwind location while a value smaller than -0.8 would represent that it is at an upwind location, with respect to Hurontario Street. Figure 7.2 illustrates the method for defining upwind and downwind locations.

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Figure 7.2 Upwind and downwind conditions at intersections and mid-block locations

7.2.2.3 Traffic data

During UFP measurements, cameras were affixed on the 3.0 m high tripod stand for recording traffic entering and leaving the intersection or passing at the mid-block location. All video footage of traffic information, including vehicle type, vehicle counts, and their corresponding route decisions, were processed using computer vision techniques.

In this study, we first generated bounding boxes of each object using the real-time object detection method YOLOv3. This method regards object detection as a regression problem and can output the coordinates of bounding boxes of detected objects with associated class probabilities in each video frame (Redmon et al., 2016). The default models pretrained on the COCO benchmark were used to classify vehicle types, including car, truck, and bus (Redmon and Farhadi, 2018). The minimum confidence level was set as 0.6 to detect vehicles. Then, we employed Deep SORT method to track multiple detected vehicles. This method combines the Kalman filtering method and the Hungarian algorithm, making it a state-of-the-art online tracking algorithm (Redmon and Farhadi, 2018). Previous studies have applied these two methods for solving multi-object detection and tracking problems, such as passenger counting in buses and freeway traffic incident detection (Munich et al., 2018; Nakashima et al., 2019).

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Since the camera was installed at the corners of the intersection or one side of the mid-block location, homography transformation method was employed to transform the intersections and vehicle lanes into the corresponding area in the aerial view image from Google Maps as shown in Figure 7.3. Four corresponding points were needed in both video frame and aerial view image to calculate the transformation matrix, which enabled us to derive the location of any point in the video footage on the aerial view image. As such, the vehicle trajectories captured in the video footages can be transformed and shown on the aerial view image. We then drew straight lines at each intersection leg and counted vehicle turning movement when the vehicle trajectories passed the lines. Finally, the vehicle classes (car, truck, and bus) and their turning movements were organized in northbound (NB), southbound (SB), westbound (WB), and eastbound (EB) directions in the one-minute interval. Buses and trucks were assumed to run on diesel, which is accurate since the local transit operator owns a very small percentage of low emission buses or electric buses (Natural Resources Canada, 2017).

In order to evaluate the performance of computer vision method, we randomly selected 30 one- minute video footages from different videos recorded at intersections and mid-block locations. Altogether, 60-min video footage was manually processed, and the validation was conducted by comparing the total counts of car, bus, and truck derived from computer vision method and manual counting.

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Figure 7.3 Video processing system for traffic detection, tracking, and counting

7.2.2.4 Cluster analysis

Given near-road UFP were only measured at 24 locations with limited spatial variability, we employed the K-means clustering method to cluster all sampling locations into different groups based on the gap statistic method. This clustering process allows for the classification of sites based on the similar influence of land use and built environment variables on UFP concentrations. Pearson correlations coefficients were computed to evaluate potential linear associations between the mean UFP level at each location and land use and built environment characteristics. If the correlation coefficients between two variables were higher than 0.5, the variable with a stronger correlation to the mean UFP was retained for the cluster analysis. We then built statistical models within each cluster to capture the temporal variability of minute- level UFP concentrations influenced by the varying meteorological data and traffic characteristics.

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7.2.3 Machine learning model

Since our measured data did not have sufficient spatial variability, we employed the XGBoost model to capture non-linear relationships in UFP and various features. This tree-based learning algorithm can naturally deal with both continuous and categorical data and high-order interactions between features (Chen and He, 2015). When fitting a decision tree to a training dataset, the top few nodes on which the tree is split on are relatively more important features, so the feature selection is completed automatically (Yan-yan Song and Ying Lu, 2015). XGBoost model also includes L1 and L2 regularization terms in the loss function, which can control overfitting (Chen and He, 2015). The machine learning method was implemented based on the scikit- learn and XGBoost Python libraries (Chen and He, 2015).

For hyperparameter optimization, we first employed random search to find the ranges of potential hyperparameter values that have relatively large impacts on the model performance evaluated based on test dataset; then we used exhaustive grid search to discover the best combination of hyperparameter values within these ranges. A set of hyperparameters have been examined in this study including the learning rate, the maximum depth of the tree, the number of trees to grow, the minimum number of samples on a leaf, and the minimum loss reduction. Root-

∑푇 (푦̂−푦 )2 mean-square-error (RMSE) = √ 푡=1 푡 푡 was used as the loss function, where T is the sample 푇 size, 푦̂푡 is the predicted value, and 푦푡 is the measured value. In this context, the observed value was measured UFP concentration, while a predicted value was estimated by XGBoost.

7.2.4 Cross-validation

We employed 10-fold cross-validation to evaluate model performance. The dataset was randomly divided into ten subsets, and the model was trained based on data from nine subsets. Then, the performance of the model was evaluated based on the remaining subset as the test dataset. This process was repeated ten times so that each of the ten sets would be used as a test dataset once. The overall performance of the model was evaluated based on the averaged performance over ten iterations. To present model performance, we employed RMSE and R2

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2 ∑푡(푦푡−푦̂푡) (푦̂푡, 푦푡) = 1 − 2 , where 푦̂푡 is the predicted value, 푦푡 is the measured value, and 푦̅ is the ∑푡(푦푡−푦̅) mean value of 푦푡.

Given that each sampling location was measured 30 mins per visit and we conducted five visits, ignoring the dependence structure in the data could introduce an overfitting issue. Blocking in space and time might also induce extrapolations by restricting the ranges or combinations of predictor features available for model training, thus overestimating interpolation errors (Roberts et al., 2017). Therefore, we employed different strategies for dividing the dataset into a training dataset and test dataset; then we evaluated the model performance in terms of UFP prediction:

(1) Traditional random cross-validation: All measured UFP data at all locations over sampling periods were pooled together and randomly split into ten subsets. We treated each one-minute UFP data as an independent record and ignored the potential dependence structure, which could underestimate predictive errors.

(2) Minute data chunked cross-validation: To reduce the influence dependence in UFP data, the consecutive one-minute records were grouped into three-minute, five-minute, and ten-minute chunks at each location. The random cross-validation was then processed for chunked UFP data so that the training dataset would not include UFP data a couple of minutes before or after the UFP data in the test dataset, which could mitigate the impact of data dependence.

(3) Location blocked cross-validation: We treated all the measurements at each location as a group, and randomly divided groups into the training dataset and the test dataset, so that the test dataset included measured data at 2 or 3 locations that were not presented in training dataset for the model development. We used this method to test the transferability of the model. To a certain extent, it can capture the influence of the diversity of land use and built environment variables on near-road UFP data. We hypothesize that this method would provide the most reasonable evaluation in terms of model performance, yet the estimate would be lower and have higher variability.

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7.2.5 Feature attribution method

Once the model with the best predictors was selected, we fit all the data in the model and employed the SHAP (Shapley additive explanation) method to present the order of feature importance and their relative impact on the model output. This method generates SHAP values by averaging differences in predictions over all possible orderings of the features based on fast and exact tree solutions (Lundberg and Lee, 2017).

We employed the SHAP Python library to provide individualized feature attributions, and results were shown using SHAP summary plot (Lundberg and Lee, 2017). It can provide a visual representation of the range and distribution of impacts of a specific feature on the model’s output (Lundberg et al., 2018). In this representation, features are sorted by their global impact on the y- axis. Each dot represents a SHAP value, i.e., the impact of a feature on the model output for the individual record, and the dots would be stacked vertically when this feature has a similar impact on the model’s output for several records. The dot is colored by the value of the feature from low (blue) to high (red), with a smooth gradual change in color (Lundberg et al., 2018).

7.3 Results

7.3.1 Traffic data validation

We randomly selected 30 one-minute video footages from different videos captured at intersections and mid-block locations, respectively. In total, 60-min video footage was counted manually for the comparison. The validation results show that the computer vision method generally provided better performance at mid-block locations than at intersections. Specifically, the Pearson correlations of total counts derived from the computer vision method and manual counting were 0.91, 0.84 and 0.88 for car, truck, and bus at the intersections, and 0.98, 0.83, 0.92 for car, truck and bus at mid-block locations. Besides, total counts derived from the computer vision method on average were 5% lower than those derived by the manual counting, which was due to factors such as dark shadows induced by intense sunlight and vehicle occlusion in the congested traffic.

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7.3.2 Descriptive analysis

We measured one-minute level near-road UFP concentrations at multiple locations along a major arterial road in the GTA. Each location was scheduled to be randomly visited five times, and each visit lasted for 30 mins between February and May 2019.

Figure 7.4 presents the boxplot of UFP concentrations at each location. The locations were ranked based on the mean UFP levels. This figure reveals that the variability of mean UFP concentrations was high across sampling locations, although they were located along the same corridor. The highest mean UFP level at the Dundas and Hurontario Street intersection was approximately eight times higher than the lowest mean UFP level at the Bristol Road and Hurontario Street intersection. Meanwhile, the median UFP levels across different locations exhibit a relatively smaller difference, with the highest median UFP about four times higher than the lowest median UFP. Besides, the mean and median UFP levels at intersections were around 1.21 times and 1.34 times higher than those at mid-block locations, respectively.

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Figure 7.4 Boxplot of one-minute UFP concentrations at each location. The mid-block location is named as the ‘intersection on the south_ intersection on the north’. Boxes represent the inter-quartile range (25–75th percentile), and whiskers indicate the minimum and maximum values. Crosses refer to mean values.

We investigated the distribution of UFP data under different levels of temperature, humidity, and wind speeds as well as under different wind direction conditions (figures are presented in the supplement). The UFP data were grouped into three categories based on three levels of wind speed, temperature, and humidity, respectively: 0-25 percentile, 25-75 percentile, and 75-100 percentile. The variability of UFP data was also analyzed based on wind direction condition: upwind or downwind. Figure 7.5 presents the distribution of UFP data under different levels of temperature, humidity, and wind speeds as well as under different wind direction conditions. UFP data in the lowest percentile range (0-25) of temperature and wind speed exhibited higher variability and mean values were 3.8 times and 2.8 times higher than those in the rest of the percentile range (25-100). In contrast, relative humidity was positively associated with UFP number concentrations, which is consistent with previous studies (Jian et al., 2012). The mean

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UFP level in the percentile range 75-100 was 3.1 times higher than that in the 0-75 percentile range. The Pearson correlation between temperature and relative humidity is -0.40. Besides, the mean value of UFP measured in the downwind direction was slightly higher (8.2%) and showed much higher variability than the mean value of UFP measured in the upwind direction. This result could be explained by the placement of the device as close as possible to the curbside. There were generally high traffic volumes passing the device, so the vehicles’ exhaust was measured directly by the device and less influenced by the wind direction.

a) UFP data at different temperature levels b) UFP data at different humidity levels

c) UFP data at different wind speed levels d) UFP data at different wind directions

Figure 7.5 Boxplot of one-minute UFP concentrations in different levels of temperature, humidity and wind speed as well as different wind directions: a) temperature level, b) humidity level, c) wind speed level, and d) wind directions. Boxes represent the inter- quartile range (25–75th percentile), and whiskers indicate the minimum and maximum values. Crosses refer to mean values.

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In addition, we generated 164 land use and built environment variables within various buffer sizes and the distance to the closest road network variables. Table 7-1 shows the correlation matrix of mean UFP levels and the land use and built environment variables selected based on the method in Section 2.2.1. The number of fast-food restaurants within 50m buffer was found to have the highest correlation with the mean UFP level. The number of cafes within 200m buffer also showed a positive correlation with UFP concentrations. These results are consistent with findings in previous studies as the cooking fumes and coffee roasting can significantly increase the UFP level (Gower et al., 2014). The number of trees in the 100m buffer had a positive correlation with the mean UFP level as the presence of roadside vegetation could diminish the dispersion of air pollutants (Brantley et al., 2014).

Similarly, building area in 500m buffer could limit the dispersion of near-road UFP and result in relatively higher UFP levels. We also found that the number of traffic signals had a positive correlation with UFP concentrations, indicating that the UFP level would be higher where there were more events of idling, acceleration, and deceleration. Lastly, distance to the closest major road had negative correlations with all other variables. This variable was negatively correlated with UFP number concentrations, which was expected since near-road UFP levels were significantly influenced by the traffic in urban environments.

Table 7-1 Correlation matrix of the mean UFP as well as land use and built environment variables

Building Distance Number of Number of Number of Number of fast- area in 500m to the trees in 100m traffic signals cafes in food restaurants buffer closest buffer in 500m 200m buffer in 50m buffer major road buffer Mean UFP 0.43 -0.32 0.41 0.42 0.35 0.69

Building area in -0.18 0.25 0.22 0.25 0.35 500m buffer

Distance to the -0.24 -0.09 -0.03 -0.04 closest major road

Number of trees 0.39 0.13 0.17 in 100m buffer

Number of 0.28 0.17

154 traffic signals in 500m buffer

Number of 0.31 cafes in 200m buffer

7.3.3 Model results

In order to estimate the impacts of meteorology, land use, built environment, as well as traffic characteristics on near-road UFP number concentrations, we first employed k-means clustering method to group locations into different clusters based on land use and built environment variables. A machine learning model was built within each cluster to predict the impacts of meteorology and traffic on the temporal variability of UFP levels. We also employed different cross-validation methods to generate training and test subsets to evaluate model performance.

7.3.3.1 Cluster analysis

We clustered the 24 sampling locations into two groups. Cluster 1 included all the locations with a relatively long distance to the closest major road while having a low number of traffic signals, trees, building area, and restaurants; cluster 2 included all other locations with opposite attributes compared to cluster 1, i.e., a closer distance to the closest major road, more traffic signals, more building area, more trees, or more restaurants. Table 7-2 Descriptive statistic for cluster centroids presents the descriptive statistics for the cluster centroids (cluster 1 and cluster 2). Cluster 1 includes 13 locations, and cluster 2 includes 11 locations. The meteorological variables in these two clusters were similar. The mean values of temperature, relative humidity, and wind speed were 6.0℃, 58.1%, and 21.3km/h in cluster 1 and 8.5℃, 56.1%, and 22.4km/h in cluster 2. Regarding UFP number concentrations, mean and median values were 17480.0/cm3 and 9598.4/cm3 in cluster 1, and 27301.1/cm3 and 12324.2/cm3 in cluster 2. The coefficient of variation (CV), defined as the ratio of the standard deviation to the mean, of UFP levels in cluster 2 was 1.26 times higher than that in cluster 1.

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Table 7-2 Descriptive statistic for cluster centroids

Variable Name Unit Cluster 1 Cluster 2

Building area in 500m buffer m2 42438.2 89157.6

Distance to the closest major road m 12.7 11.4

Number of trees in 100m buffer count 3.2 9.1

Number of traffic signals in 500m buffer count 11.2 13.4

Number of cafes in 200m buffer count 0.5 0.8

Number of Fast Food 50m count 0 0.1

7.3.3.2 Predictive models in cluster

We used the one-minute UFP concentration as a dependent feature and meteorological data and traffic characteristics as independent features to establish the XGBoost model using traditional random 10-fold cross-validation for the two clusters. Given UFP data were only sampled at 24 locations with limited spatial variation, one separate model was developed for each cluster that had similar spatial characteristics, so that we could better capture the influence of traffic and meteorological data on the temporal variability of minute-level UFP concentrations for each cluster.

The mean and standard deviation values of R2 and RMSE of the ten tests for the UFP levels in cluster 1 were estimated as R2=0.74 (std. dev. 0.08), and RMSE=4132.0/cm3 (std. dev. 578.6/cm3). Besides, R2 and RMSE for the UFP levels in cluster 2 were R2=0.55 (std. dev. 0.09), and RMSE=4912.7/cm3 (std. dev. 385.9/cm3). The performance of the model in cluster 2 was lower than that in cluster 1 since the UFP level in cluster 2 was influenced by many more local factors like restaurants.

Figure 7.6 presents SHAP summary plots of SHAP values, representing the impact of each feature on the UFP models for cluster 1 and cluster 2. All features were sorted based on their global impact on calculated SHAP values. If the change in a feature gradually changes the model’s output, a smooth transition in color can be observed across SHAP values horizontally (Lundberg et al., 2018). In Figure 7.6 a), a high value of relative humidity (red) had a positive

156 impact on model output in cluster 1 and therefore, a high SHAP value (UFP concentration increases). However, the changes in colors (from red to blue) of the wind speed reveals a decrease in this feature, which leads to an increase in UFP levels. In Figure 7.6 b), a long tail of the dots of temperature reaching to the right, but not to the left, indicates that extreme low- temperature values could significantly increase the UFP levels, but high temperature values cannot significantly decrease the UFP levels. Specifically, the magnitude of low temperature impact on UFP levels (around 9000/cm3) could be approximately twice as that of the highest temperature impact on UFP levels (around -4000/cm3). This reveals non-linear relationships among the features and UFP levels.

When comparing the ranking of features in Figure 7.6 a) and b), we observe that the variations of UFP levels in cluster 1 were mainly influenced by meteorological data including relative humidity, wind speed, and temperature. UFP levels in cluster 2 were mainly influenced by traffic characteristics such as the total car counts in the WB and EB as well as the car counts in the NB and SB directions, which ranked higher than wind speed and humidity. It is important to note that the feature time of day had opposite impacts on UFP concentrations in the two models. In cluster 1, the variable time of day had a significant positive impact on the UFP level. Therefore, the UFP level was high in the early morning, probably due to a stable surface layer (Paulson et al., 2017). In contrast, the variable time of day had a positive impact on UFP concentrations in the second model. This could be explained by the nearby fast-food restaurants and cafes, as well as higher traffic levels at cluster 2 locations. As a result, UFP levels were higher in the afternoon when fast-food restaurants and cafes were busier and traffic volumes were higher.

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a) SHAP summary plot for UFP model for cluster 1

b) SHAP summary plot for UFP model for cluster 2

Figure 7.6 SHAP summary plot of features ranked by global feature importance in the XGBoost UFP model: a) UFP model for cluster 1, and b) UFP model for cluster 2. The higher the SHAP value for a feature, the higher the UFP concentration. Every UFP record in the dataset is run through the model, and a dot is created for each feature attribution value, so one UFP value gets one dot on each feature’s line. Dots are colored by the feature’s value and pile up vertically to show density.

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7.3.3.3 Model results based on different cross-validation methods

Using the entire data (both clusters), the influence of three different cross-validation methods on model performance was further explored using meteorological data and traffic characteristics. We first employed traditional cross-validation to split the UFP dataset into ten subsets randomly and then trained and evaluated the XGBoost model. The mean and standard deviation values of R2 and RMSE of the ten tests for the UFP levels were estimated as R2=0.69 (std. dev. 0.05), and RMSE=4641.3/cm3 (std. dev. 466.7/cm3). This method could underestimate the predictive error as the potential dependence structure of minute-by-minute UFP level was ignored.

Further, we grouped the UFP data into chunks to reduce the dependence of UFP concentrations as the test dataset would not include the UFP records immediately before or after the ones in the training dataset. Three-minute, five-minute and ten-minute chunks were each used to group UFP data, and then UFP in chunks were split into ten subsets for the cross-validation. The mean and standard deviation values of R2 of the models trained on three-minute, five-minute and ten- minute chunk data were estimated as R2=0.67 (std. dev. 0.07), R2=0.64 (std. dev. 0.11), and R2=0.63 (std. dev. 0.11), respectively.

Lastly, we employed block cross-validation to randomly split UFP data into ten subsets based on locations. Therefore, UFP data measured at 2 or 3 sampling locations would be grouped as one subset. The model was trained based on the UFP data measured at locations in the nine subsets, and the performance was tested on UFP levels measured at blocked locations. Figure 7.7 presents the scatterplot of measured and modelled UFP data for 10 test sets. Overall, the mean and standard deviation values of R2 were estimated as R2=0.39 (std. dev. 0.23). The performance of the model exhibited a considerable variability, with the value of R2 ranging from 0.14 to 0.81 for different test sets. This was because some locations had very high variability in UFP data, while our models were only trained on a limited number of locations, with land use, built environment features, and local traffic characteristics specific to them. Thus, models generally provided predictions with less variability compared to the measured UFP levels.

The traditional cross-validation method had the highest R2. Besides, the chunked data cross- validation approach did not significantly deteriorate the model performance. However, the block cross-validation results highlighted the importance of developing appropriate cross-validation

159 strategies for machine learning models to explain the variation in temporal and spatial distribution characteristics of near-road air pollutants.

a) Scatter plots of measured and modelled UFP for test sets with R2 lower than 0.4

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b) Scatter plots of measured and modelled UFP for test sets with R2 higher than 0.4

Figure 7.7 Scatter plots of measured and modelled UFP for different test sets: a) test sets with R2 lower than 0.4, and b) R2 higher than 0.4. The confidence interval is 95%. Redline indicates 45deg line.

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7.4 Discussion and conclusions

This study employed a machine learning approach to investigate the impacts of local traffic characteristics, meteorology, land use and built environment variables on near-road UFP concentrations, measured at various locations along a major arterial road in the GTA. We employed a k-means clustering method to group sites and established a machine learning model within each cluster. The SHAP summary plot was used to visually and quantitatively present the influence of each feature on UFP levels. Lastly, different cross-validation methods were explored.

In total, 164 land use and built environment variables were generated. A correlation matrix with mean UFP level and land use and built environment variables at each location was also established. The results indicated that the number of fast-food restaurants within 50m buffer had the highest correlation with the mean UFP level. Similarly, the number of cafes within 200m buffer had a positive correlation with the UFP concentrations. The building area and the number of trees in 500m buffer were both positively correlated with the UFP levels, as they might reduce the dispersion of the near-road UFP. Two traffic-related features, distance to the closest major road and the number of traffic signals, had negative and positive correlations with UFP levels, revealing that UFP concentrations would be higher when there was more traffic.

We further used k-means to group locations into two clusters based on above-mentioned land use and built environment variables and developed a model within each cluster. Cluster 1 included all the locations with a relatively long distance to the closest major road while having a low number of traffic signals, trees, building area, and restaurants; cluster 2 included all other locations with opposite attributes compared to cluster 1. When comparing the ranking of features in two models, the variations of UFP levels in cluster 1 were mainly captured by meteorological data. In contrast, UFP levels in cluster 2 were mainly influenced by local traffic such as the total car counts in different directions, which ranked higher than wind speed and humidity. Besides, the feature time of day had opposite impacts on UFP levels in the two clusters. In cluster 1, the UFP level was high in the early morning due to a stable surface layer. In cluster 2, UFP levels were higher in the afternoon when fast-food restaurants and cafes were busier, and traffic volumes were higher. This result indicates we need to collect more local variables to better explain the variation in the near-road UFP levels at cluster 2 locations.

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The last part of the study evaluated model performance using different cross-validation methods. Traditional cross-validation method resulted in the highest R2 values, although it could induce underestimation of prediction errors. The R2 values decreased from 0.67 to 0.63 when we converted minute-level UFP data into 3min, 5min, and 10min chunks. Lastly, we split the dataset based on the locations and tested model performance for estimating UFP levels measured at 2 or 3 locations in the test dataset. The performance of the model exhibited a large variability, with R2 values ranging from 0.14 to 0.81, indicating that some extreme UFP values could not be predicted by the model.

Although this research sheds light on the impacts of meteorology, land use, built environment and local traffic on near-road UFP levels, further research is needed to explain extremely high UFP levels. In addition, the impact of traffic on local UFP could be better investigated if we could subtract the background concentrations from measured concentrations. Regarding the video processing system, a transfer learning method would improve the training of the models for more precise vehicle classification, for example into different truck types.

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Chapter 8 Data-Driven Approach to Capture the Association Between Local Truck Movements and Near-Road Black Carbon Concentrations Using Mobile Measurements Chapter overview

This study captures the influence of land-use variables and local traffic characteristics on near- road black carbon (BC) concentrations using mobile measurements. A data collection campaign was designed along 19 corridors in the City of Toronto and implemented during the period extending from March to June 2019. Each corridor was randomly scheduled to be driven on, back and forth, four times on weekdays from morning to noon, resulting in eight measurements of BC concentrations along each corridor. Simultaneously, traffic information was recorded by a camera placed on the dashboard. We employed a computer vision method to derive vehicle counts which were used to estimate traffic flow, density, and average speed. Truck counts were estimated along each corridor. A Pearson correlation of 0.75 was observed between truck counts and total traffic. We employed a machine learning model to evaluate the influence of features on BC mean and maximum concentration. Average speed and truck counts ranked as the top two features in the BC maximum concentration model. The BC mean concentration model did not include local vehicle counts and achieved a mean R2 of 0.61.

8.1 Introduction

Ambient Black Carbon (BC) concentrations are an environmental issue with negative implications for both human exposure and the climate (Brewer, 2019). Inhalation of BC has been shown to cause adverse health effects, including respiratory and cardiovascular disease (Hvidtfeldt et al., 2019). BC concentrations also induce changes in the patterns of rain and clouds (Kucbel et al., 2017). BC originates mainly in urban areas from the burning of fossil fuel, particularly from diesel engines. The contribution of trucks on near-road BC concentrations is highest since the BC emission factors (EF) of heavy-duty diesel vehicles (HDVs) are around 4 - 8 times those from light-duty gasoline vehicles (de Miranda et al., 2019; Xu et al., 2018). This highlights the importance of capturing the impact of high-emitters on local BC concentrations in urban areas.

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Both fixed and mobile sampling land-use regression (LUR) modelling studies generally quantified the contribution of traffic to BC concentrations in urban areas through the distance to major roads and traffic intensity variables around sampling stations (Kerckhoffs et al., 2017; Liu et al., 2019). These included variables such as the traffic intensity on the nearest road to the measurement station or length of major roads in predetermined buffer areas around the monitoring stations (Kerckhoffs et al., 2017). Recent mobile sampling LUR studies included annual average daily traffic (AADT) as an additional variable to account for average traffic volumes on sampled routes (Hankey and Marshall, 2015b). However, these methods failed to capture at a high spatial resolution the real-time contribution of vehicle classes on measurements of the sampled road network. Steve and Hankey (2015a) estimated the share of cyclist exposure attributable to traffic emissions using multiple linear regression models (MLR) of video-derived traffic counts. Based on partial video-data of the sampled routes, they randomly selected on- street locations and conducted manual traffic counts by classifying vehicles into passenger vehicles, trucks, and buses. The derived models showed a high correlation between trucks and high BC concentration events (Hankey and Marshall, 2015a). Another study measured BC concentrations in an urban area based on a mobile measurement, and the hourly total traffic rates were extracted from 24 speed traps (Krecl et al., 2019). In this study, two statistical approaches including MLR model and random forest (RF) model, were used to predict the BC concentrations, and results showed that the MLR and RF model explained 24% and 54% of the variability, respectively (Krecl et al., 2019). The authors found that the most impactful predictor for both models was the traffic rate of HDVs (Krecl et al., 2019).

The moving observer method is a procedure used to estimate traffic flows on road links based on data collected from the observer vehicle (Christopher, 1973). The method makes it possible to obtain both speed and traffic flow data from a single experiment and takes advantage of the fundamental equation of traffic linking flow as a product of density and space mean speed. The method involves deriving traffic flow from three recorded variables during sampling namely, the number of opposing vehicles met, the number of vehicles overtaking the observer vehicle while travelling, and the number of vehicles the observer vehicle overtook. The moving observer method has been validated in various studies based on manual traffic counts conducted on roadways (Alhomaidat and Ardekani, 2015; Christopher, 1973). While the moving observer method provides a relatively inexpensive method to capture traffic flows at a high spatial

165 resolution, the manual counts required make it a time-consuming approach. However, recent advances in computer vision have made it possible to reliably conduct real-time vehicle detection and classification (Li et al., 2018).

Given the temporal and spatial distribution characteristics of trucks in urban areas and their disproportionally high impact on near-road air pollutants, this study aims to evaluate the impacts of land-use variables and diesel vehicles to local BC concentrations based on mobile measurements conducted in the City of Toronto. The truck counts on sampled corridors were derived based on the moving observer method using video-recorded data. We further employed a machine learning approach Extreme Gradient Boosting (XGBoost) and the Shapley additive explanation (SHAP) method to quantitatively examine the impacts of land-use variables and local traffic characteristics on near-road BC concentrations.

8.2 Materials and methods

8.2.1 Study area and data collection

Ambient BC and traffic data were collected during a mobile monitoring campaign conducted in the City of Toronto, Canada, during the period extending from March to June 2019. The BC data were collected in 10-sec resolution with a flow rate of 100 ml/min using microaethalometers (MicroAeth model AE51) mounted on the passenger-side front door of a vehicle with the sampling tube provided by the manufacturer stretching outside the window.

We selected 19 corridors including 12 north-south corridors and 7 east-west corridors as shown in Figure 8.1. The mean and standard deviation values of the length of corridors are 3.3 km (std. dev. 0.86 km). A few corridors are considered two streets geographically, College St. continues to the east as Carlton St., and St George St. continues to the south as Beverly St. In this study, we treated them as the same corridor. Each corridor was randomly scheduled to be measured four times on different days. Each time, we first drove in one direction and turned around at the end of the corridor, then we drove on this corridor again in the opposite direction. In general, driving in one direction took less than a half-hour, so that we could collect traffic data in two directions within one hour and measured BC concentrations twice each time. In total, BC data were measured eight times for each corridor. All data collections were conducted from 7:00 am until noon on weekdays.

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Figure 8.1 Distribution of 19 corridors in the City of Toronto

At the same time, traffic data were recorded using a wide-angle action camera placed on the dashboard. One research assistant sat in the front passenger seat and counted parked vehicles on the right side. The parked vehicle information was summarized at the segment level. A segment was artificially defined as a stretch of the corridor with two major intersections on two ends. For instance, a segment can be defined as extending from the College St and Bay St intersection to the Dundas St and Bay St intersection. Generally, each corridor includes four or five segments. The mean and standard deviation of segment lengths are 712.0 m and 384.0 m, respectively. Idling vehicles were not counted as parked vehicles. Three types of vehicles, including car, truck, and bus, were recorded, respectively.

GPS devices (Qstarz BT-Q1000X Travel Recorder) were installed in the vehicles, recording instantaneous speed and position data (including longitude, latitude, and altitude) at a second-by- second interval. The accuracy of this GPS device has been investigated in a previous study, and

167 we observed that the median error for car trips in the urban canyon was 1.5 m (Schipperijn et al., 2014). All instrument clocks were synchronized daily.

Road network and land use information were collected at the natural segment level from the Open Data portal provided by the City of Toronto. Each artificially defined segment (or corridor) consists of seven to eight natural segments. The mean and standard deviation values of the natural segment lengths are 103.1 m and 67.9 m.

8.2.2 Data processing

8.2.2.1 Black carbon data

The quality of BC measurements was examined at the end of each day. Air quality data were assigned to GPS points based on their timestamp. We employed the Optimized Noise-Reduction Algorithm (ONA) developed by the U.S. EPA to reduce the occurrence of negative values (approximately 3%) to virtually zero while preserving the significant dynamic trends in time series (Hagler et al., 2011). Since the ONA method would estimate averaged values based on a delta-attenuation value, the mean value of ONA-processed BC data and the maximum value of original BC data were used in the model development to investigate the impact of land-use variables and local diesel vehicles on near-road BC concentrations.

8.2.2.2 Moving observer method

The moving observer method was originally proposed by Wardrop and Charlesworth (1954). It is a method for measuring fundamental stream characteristics, including traffic speeds (v), density (k), and flows (q) by observations made from a moving observer vehicle. This method needs

‘observers’ in the vehicle to count vehicles overtaking (mover) and overtaken (movertaken) by the observer vehicle when it is with the traffic flow, as well as the vehicles met (ma) when the observer vehicle is running in the opposite direction. Besides, the travelling time of the observer vehicle with the traffic flow (tw) and against the traffic flow (ta) were recorded.

When the observer vehicle moves with the traffic stream on a segment of length (l), two cases can be considered: the first case considers the traffic stream to be moving and the observer to be stationary; the second case considers the traffic stream to be stationary and the observer to be

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moving (Solanki et al., 2016). In the first case, if mover is the number of vehicles overtaking the observer during the period tw, and q is the traffic flow then:

mover = q * tw (8.1)

In the second case, if movertaken is the number of vehicles overtaken by the observer over a segment of the segment and k is the density then:

movertaken = k * l (8.2)

When considering the case when the observer is moving within the stream, the number of vehicles in the traffic flow mw can be calculated as:

mw = mover - movertaken = q * tw - k * l (8.3)

Since there are two unknown variables q and k in (8.3), for generating a second equation, the observer vehicle was driven in the opposite direction against the traffic stream during the period ta. In this case, the observer vehicle can be considered as moving in the stream with a negative speed, which means all the vehicles should be counted as overtaking the observer vehicle. If the number of vehicles in the traffic stream encountered by the observer is denoted as ma, we can get the following equation:

ma = q * ta + k * l (8.4)

Therefore, we can derive the traffic flow (q), average speed (v), and traffic flow density (k) based on the following equations:

푚 + (푚 − 푚 ) q = 푎 표푣푒푟 표푣푒푟푡푎푘푒푛 (8.5) 푡푤+ 푡푎

푙 v = 푚 − 푚 (8.6) 푡 − 표푣푒푟 표푣푒푟푡푎푘푒푛 푤 푞

푞 k = (8.7) 푣

The feasibility of this method has been investigated in previous studies and results indicated that it was efficient and practical to estimate traffic information including traffic flow, average speed, and density on a road network (Mulligan and Nicholson, 2002; Solanki et al., 2016).

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8.2.2.3 Local traffic data characterization

During mobile sampling, a wide-angle action camera was placed on the dashboard for recording traffic in the front and sides of our observer vehicle. We employed computer vision techniques to process the videos to derive counts of vehicles overtaking and overtaken by the observer vehicle, as well as counts of the vehicles in the opposite direction.

We first detected the vehicles using a real-time object detection method YOLOv3 primarily due to its fast performance with reasonable accuracy (Redmon and Farhadi, 2018). This algorithm generates coordinates of bounding boxes of detected objects with corresponding class probabilities in each frame (Redmon et al., 2016). We used the default models pretrained on the COCO benchmark to classify vehicle types including car, truck, and bus in the video footage. Then, a multiple-object tracking framework Deep SORT was employed to track vehicles in the past and current video frames. This method integrates appearance information so that it can better handle longer periods of occlusions and effectively reduces the number of identity switches (Wojke et al., 2018). By combing the object detection method and the multiple-object tracking framework, we can derive trajectories of all the vehicles.

During the measurement, we kept driving in the leftmost lane to mitigate the vehicle occlusion issue in the opposite direction. We created two vertical reference lines on two sides of our observer vehicle in all frames. When the vehicle trajectory passes the vertical line on the left, it would be counted as a vehicle in the opposite direction. When the vehicle trajectory passes the vertical line on the right, there are two cases: if the vehicle trajectory direction is towards the observer vehicle, this vehicle is counted as overtaken by the observer vehicle; if the vehicle trajectory direction is away from observer vehicle, this vehicle is counted as overtaking the observer vehicle. A timestamp was created for each vehicle record so that we could generate disaggregated vehicle data at the segment level or natural segment level.

It is worth mentioning that this automatic count method would include parked vehicles. Detecting and counting parked vehicles at curbside has been a challenging task. A previous study developed a system to estimate each parked car’s approximate location by fusing information from the camera, GPS, and inertial sensors (Grassi et al., 2017). In our study, we chose to have one research assistant count parked vehicles on the right side at the segment level.

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At corridor level and segment level, we can derive corrected counts of vehicles overtaking and overtaken by the observer vehicles, as well as in the opposite direction by subtracting parked vehicles from automatic vehicle counts. Then we calculated traffic flow, average speed, and density based on (8.5), (8.6), and (8.7). The total traffic on the corridor or segment was calculated as the density multiplied by the corresponding distance. The truck and bus ratios were calculated by dividing the corrected truck and bus counts by corrected total counts when the observer vehicle was moving. Therefore, we could calculate the total number of trucks and buses by multiplying the total traffic with the corresponding ratios at the corridor level and segment level.

We did not have detailed parked vehicle information at the natural segment level, so we assumed the traffic flow q was constant across the natural segments if they made up the same segment where we had parked vehicle counts and were able to calculate traffic flow. Since the vehicle speed recorded by the GPS device at each natural segment was different, we calculated the density for each natural segment using (8.7). Then, we calculated the total traffic on each natural segment by multiplying the density with the corresponding length. Finally, the truck and bus counts were identified by multiplying the truck and bus ratios calculated at the segment level with the total traffic estimated at the natural segment level. The traffic video processing method is summarized in Figure 8.2.

To represent the traffic information at different levels, the local traffic flow q and vehicle counts in two directions were summed, and the average traffic flow speeds in the two directions were averaged for each measurement. In total, traffic data were measured at least four times on each corridor.

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Figure 8.2 Video processing system for traffic detection, tracking, and counting

8.2.2.4 Land use variables

For each GPS point, land-use variables were processed using ArcMap 10.4.1. We calculated the residential area, commercial establishments, governmental area, open area, park area, industrial area and water body area within different buffers of 25m, 50m, 100m, 200m, 500m, and 1,000m. The total length of major road, highway, bus routes, rail lines, and roads were also estimated within the above buffer sizes. Lastly, the distance to the closest major road, highway, bus, rail line, and the shore were calculated. There were 76 land-use variables created at the natural segment level.

8.2.3 Machine learning model and feature attribution method

We employed a machine learning model to evaluate the influence of land-use variables and local traffic on near-road BC concentrations at the natural segment level. Independent features include traffic flow, average speed, total traffic, truck counts, and bus counts while dependent features

172 include the maximum BC concentrations and mean BC concentrations, all were averaged across different measurements for each natural segment.

In this study, we used XGBoost model to capture complex relationships in near-road BC concentrations and various features. It is a tree-based gradient boosting framework which can naturally deal with sparse features, continuous features, and categorical features as well as high- order interactions between features (Chen and He, 2015). Besides, it can overcome the overfitting issue by penalizing more complex models through both LASSO (L1) and Ridge (L2) regularizations. Most importantly, there are feature importance measures that could be employed for tree-based models to qualitatively and quantitatively investigate the impact of each feature on the model output. In this study, we used the scikit-learn and XGBoost Python libraries (Pedregosa et al., 2011).

A set of hyperparameters including learning rate, maximum depth of the tree, number of trees to grow, the minimum number of samples on a leaf, and minimum loss reduction were tuned using a combination of random and grid search. We employed root-mean-square-error as the loss function, where T is the sample size, 푦̂푡 is the predicted value, and 푦푡 is the measured value:

∑푇 (푦̂−푦 )2 (RMSE) = √ 푡=1 푡 푡 (8.8) 푇

In this context, the observed value was measured BC concentration, while the predicted value was estimated by XGBoost. The performance of the model was evaluated using 10-fold cross- validation. The evaluation metrics were calculated based on the average performance of ten iterations. We used NRMSE and coefficient of determination R2 to evaluate the final performance of the model.

푅푀푆퐸 NRMSE = , (8.9) 푦푚푎푥−푦푚푖푛

푦푚푎푥 and 푦푚푖푛 are the maximum and minimum value of the observations.

2 2 ∑푡(푦푡−푦̂푡) R (푦̂푡, 푦푡) = 1 − 2 (8.10) ∑푡(푦푡−푦̅) where 푦̂푡 is the predicted score, 푦푡 is the observed score, and 푦̅ is the mean score of 푦푡.

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Once the best model was selected, we employed SHAP method to demonstrate the order of feature importance and the impact of features on the model output. This method evaluates the importance of a feature by comparing the performance of the model with and without the feature (Lundberg and Lee, 2017). Lundberg and Lee (Lundberg et al., 2018) developed fast and exact tree solutions for SHAP values by averaging differences in predictions over all possible orderings of the features.

We employed the SHAP Python library to provide individualized feature attributions, and results were shown using SHAP summary plot (Lundberg et al., 2018). It sorts the features by their global impact on the y-axis. Each dot in the figure represents a SHAP value, showing the impact of the feature on the model output for the individual record. In this context, the observed value was measured BC concentration. The dot is colored based on the value of the feature low (blue) to high (red), with a smooth change in color (Lundberg et al., 2018). The dots will be stacked vertically if this feature has a similar impact on the model’s output for different observations (Lundberg et al., 2018).

8.3 Results

8.3.1 Traffic data validation

We evaluated the performance of vehicle detection and tracking system by comparing total counts of car, truck, and bus derived by the computer vision method and manual counting method, respectively. In total, we randomly selected 60 one-minute video footage from our video database of approximately 27 hours of videos recorded on different corridors. The validation results indicate that the computer vision method generally provided better performance for vehicle classes with higher volume. Specifically, Pearson correlations of total counts derived from two methods were 0.96, 0.83, and 0.77 for car, truck, and bus, respectively.

8.3.2 Descriptive analysis

In this study, we measured BC concentrations and collected traffic information on 19 corridors. Each corridor was randomly scheduled to be measured four times in each direction. The traffic data were processed for each direction and were then aggregated for each corridor based on methods described in Section 2.2.3. In total, we measured BC concentrations eight times and derived traffic data four times for each corridor.

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8.3.2.1 BC concentrations at the corridor level

Figure 8.3 a) and b) present the distribution of near-road BC concentrations ranked by mean values and maximum values calculated based on all the measurements at each corridor. The triangle indicates the coefficient of variation (CV), which was calculated as the ratio of the standard deviation to the mean, as a standardized measure of the dispersion of a frequency distribution. The CV values were calculated based on the mean and maximum BC values of each measurement on the corridor.

Figure 8.3 a) shows that the highest BC mean concentration was about 2900 ng/m3 occurring at Wellesley St., which was almost three times higher than that measured at Queens Quay, located along the shoreline. In terms of CV values, we found some corridors, such as Parliament St. and Sherbourne St., had a considerable variability of BC mean values across different measurements. In contrast, Bathurst St. had a high BC mean concentration with a relatively low CV, which indicated that mean BC concentrations were generally higher on Bathurst St. during the measurement period. Queen St. also had a very low CV, meaning the mean BC concentrations measured at different times had similar levels, at around 1400 ng/m3.

Figure 8.3 b) presents the corridors ranked by the highest BC concentrations, and the CV values were also calculated correspondingly. The highest BC concentrations could be higher than the maximum value in the boxplot, which was calculated as 1.5 times of the interquartile range (IQR) over the third quartile Q3. The highest value of BC maximum concentrations was about 58,800 ng/m3 occurring at Parliament St., which was about 24 times higher than that measured at Queens Quay. This result highlighted the importance of capturing the distribution of BC maximum concentration as its difference could be considerable across different corridors.

Besides, we find that BC maximum concentrations at Parliament St. had much large variability indicating that the BC concentration was extremely high, likely due to high-emitters during some measurements. Other variables might also influence the measurement of extreme high BC levels, such as the distance between the high-emitters and the observer vehicle. More measurements might be needed on Parliament St. to better capture the characteristics of the high BC concentrations. On the other hand, Bathurst St. had a very low CV for high BC concentrations on different days, which could imply that there were likely some high emitters consistently appearing on this street during our sampling period.

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a) Distribution of BC concentrations along each corridor ranked by mean values

b) Distribution of BC concentrations along each corridor ranked by highest values

Figure 8.3 Boxplot of BC concentrations along each corridor ranked by mean and highest values: a) corridor locations ranked by BC mean values and b) corridor locations ranked by BC highest values. Boxes represent the inter-quartile range (25–75th percentile), and whiskers indicate the minimum and maximum values. Crosses refer to mean values. Triangles refer to CV values.

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8.3.2.2 Truck distributions at the corridor level

We derived truck ratios and truck counts from traffic flow for each corridor based on the method in Section 2.2.3. The mean and standard deviation values of the truck ratios were 6.3% and 3.0% respectively. The truck ratios did not show a strong correlation with total traffic, while truck counts and total traffic had a Pearson correlation of 0.75. The truck counts were averaged across different measurements. Figure 8.4 presents the distribution of mean truck counts along each corridor.

We observe that Bathurst St. had the highest mean truck count of 28 per driving time with a minimal CV value of 0.08. This result was consistent with the assumption derived from Figure 8.3 b) that Bathurst St. could have a consistent and high number of high-emitters during our sampling period, leading to high BC concentrations with small variability. However, this assumption needs to be validated by taking other variables such as meteorological data and background concentrations into consideration using an empirical model. Besides, Parliament St. had a high truck count of 17 with a more considerable CV value of 0.41 and BC concentrations on this street also showed substantial variability in Figure 8.3 a) and b).

Three east-west corridors including Bloor St., College St. & Carlton St., and Dundas St., all had high mean truck counts of 21, 20, and 20 per driving time respectively. The truck counts on Dundas St. had a CV of 0.30, showing a higher variability than Bloor St. and College St. with CV values of 0.11 and 0.08 respectively. This result could be explained by the presence of commercial buildings such as supermarkets, restaurants, and stores on both sides of Dundas St., meaning delivery trucks occasionally appeared on this street and were consequently counted during each measurement. This leads to higher variability of truck counts on Dundas St. compared to the other two streets.

In addition, Bayview Ave. and Broadview Ave. both had low average truck counts of 4, and their CV values were 0.29 and 0.23, respectively. Queens Quay is located along the shoreline also had a relatively low truck count of 10 with a high CV value of 0.47.

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Figure 8.4 Distribution of mean truck along each corridor

8.3.3 Model results

In order to examine the importance of individual variables, including land use and local traffic features, we first established a machine learning model for evaluating the impacts of local high- emitters on the near-road BC maximum concentration. The mean and standard deviation values of the averaged BC maximum concentrations at the natural segment level were 3651.4 ng/m3 and 3217.4 ng/m3 respectively. Then, we developed a model for the BC mean concentration based on the same features to explore the spatial distribution of BC mean concentrations in urban environments. The mean and standard deviation values of the BC mean concentrations at the natural segment level were 1819.8 ng/m3 and 922.0 ng/m3, respectively. In total, 749 natural segments were employed in model development.

8.3.3.1 BC maximum concentration prediction model

The BC maximum concentration was used as a dependent feature in the XGBoost model. The mean and standard deviation values of R2 and RMSE of the ten tests for the BC maximum

178 concentration prediction model were estimated as R2=0.47 (std. dev. 0.08), and NRMSE=0.10 (std. dev. 0.01). Figure 8.5 presents a SHAP summary plot of SHAP values representing the impact of each feature on the BC max concentration. All the features are ranked by their global impact based on estimated SHAP values. The dots indicate the SHAP value and are plotted horizontally. The color of each dot shows the value of the feature, from low (blue) to high (red) (Lundberg and Lee, 2017).

The most significant feature is average traffic speed, which has a negative impact on the BC maximum concentration. A longer tail of the dots reaching to the right, but not to the left, reveals that extreme low average speed would significantly increase BC max concentrations, but high average speed could not significantly mitigate the local BC maximum levels. Specifically, the magnitude of the impact of low speed on the maximum concentration (2,500 ng/m3) could be around 2.5 times the impact of high speed on maximum concentrations (-1,000 ng/m3).

We observe that the feature with the second-largest impact on the BC maximum level was truck counts, which were estimated based on the mobile measurements. This result is expected since trucks, including construction trucks, delivery trucks, refuse trucks, and short-haul trucks typically run on diesel in Ontario (Natural Resources Canada, 2017). The BC emission rates of these high emitters are much higher than gasoline vehicles, this indicates that diesel trucks could induce high BC concentrations in urban environments (Xu et al., 2018).

In addition, the length of major roads in 200m buffer, and distance to the nearest shore both have a positive impact on the BC maximum concentration. Park area in 1000m buffer also has a positive impact. This can be explained by the location of park area in northeast Toronto, generally near highways, so more trucks would contribute to the BC max concentration when they were entering and leaving the highways.

The feature distance to the nearest bus route showed a negative effect on maximum BC levels since bus routes are usually planned on major arterial roads in urban areas, so the BC concentrations would be influenced by less traffic if the natural segments were located further away from the nearest bus route. Distance to the nearest highway showed a positive impact since the nearest highway, for the majority of the natural segments collected in this study, was the Gardiner expressway, which was built along the shoreline in south Toronto. The further a

179 segment was from this highway, the closer it was to the downtown area where there was more traffic and congestion.

Residential area in a 500m buffer showed a negative impact on BC concentrations since the traffic volume usually would be less in a residential area. On the contrary, government area in 200m buffer exhibited a positive impact since government buildings are mainly located in the downtown area. Lastly, we find bus counts also had a positive impact on BC max concentrations, although its importance was relatively low compared to other features.

Figure 8.5 SHAP summary plot of features ranked by global feature importance in the XGBoost model for BC maximum concentration. The higher the SHAP value for a feature, the higher the BC max concentration. Every observation in the dataset is run through the model, and a dot is created for each feature attribution value, so one observation gets one dot on each feature’s line. Dots are colored by the feature’s value for the observation and pile up vertically to show density.

8.3.3.2 BC mean concentration prediction model

The BC mean concentration was used as a dependent feature in the XGBoost model. The mean and standard deviation values of R2 and RMSE of the ten tests for the BC mean concentration

180 prediction model were estimated as R2=0.61 (std. dev. 0.06), and NRMSE=0.09 (std. dev. 0.01). Figure 8.6 presents a SHAP summary plot of SHAP value representing the impact of each feature on the BC mean concentration.

We find that length of major roads in 800m buffer has the most significant impact on BC mean concentration. Traffic flow exhibits a complicated relationship with the BC level, which could be explained by traffic flow having a non-linear relationship with average speed. Lower traffic flow could indicate that the traffic density was very high, and speeds were very low, which results in high BC concentrations. On the other hand, high traffic flow could indicate the traffic was at some critical combination of speed and density.

Similar to results in Section 3.3.1, distance to the nearest highway and distance to the nearest shore both had a positive impact on the BC mean concentrations; distance to the nearest bus route had a negative impact on the BC levels. Besides, the length of roads in a 400m buffer positively affected BC concentrations. Average traffic speed revealed a negative impact on the model output.

Compared to the BC maximum concentration model, the BC mean concentration model had a higher R2 , which was expected since land-use variables should be employed to better explain general trends of the spatial distribution of near-road mean BC levels. Meanwhile, it is essential to note that the former model was able to capture the local contribution from diesel trucks to BC maximum concentrations.

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Figure 8.6 SHAP summary plot of features ranked by global feature importance in the XGBoost model for BC mean concentration. The higher the SHAP value for a feature, the higher the BC mean concentration. Every observation in the dataset is run through the model, and a dot is created for each feature attribution value, so one observation gets one dot on each feature’s line. Dots are colored by the feature’s value for the observation and pile up vertically to show density.

8.4 Discussion and conclusions

This study captured the association between local truck movements and near-road BC concentrations using mobile measurements. We measured BC concentrations and used a camera to record traffic data on 19 corridors in the City of Toronto. The traffic information was processed using the computer vision method. Besides, land-use variables were derived for different natural segments. We employed a machine learning approach to investigate the impacts of local traffic and land-use variables on BC concentrations. The SHAP summary plot was used to quantitatively present the importance of each feature on BC concentrations.

The BC concentrations were first analysed at corridor level, and the corridors were ranked based on BC mean concentrations and BC maximum concentrations respectively. The results revealed that the highest BC mean concentration was about three times higher than the lowest BC mean concentration. In contrast, the highest BC maximum concentration was about 24 times higher than the lowest average value of BC maxima. This result emphasises the significance of

182 capturing the distribution of BC maximum concentration in urban areas. Besides, we employed CV as a standardized measure of the dispersion of the BC distribution. The CV values of BC mean concentrations at different corridors ranged from around 200 ng/ m3 to 5800 ng/m3. Sherbourne St had very high CV values of both BC mean concentrations and BC max concentrations, which indicated that more measurements might be needed to better capture the characteristics of BC concentrations on this street. In contrast, Bathurst street ranked high in terms of mean and maximum BC concentrations and had low CV values, which might imply there were high-emitters during the sampling periods.

As for traffic data, we employed the moving observer method to derive fundamental traffic information, including traffic flow, traffic density, and average traffic speed. This method needs total counts of vehicles overtaking and overtaken by the observer as well as the vehicles counted by the observer in the opposite direction. Additionally, the travel time of the observer in the two directions was required. We employed a computer vision method to detect, track, and count different vehicle types, including car, bus, and truck. Truck ratios and truck counts were derived from traffic flow for each corridor and segment. The results showed that the mean and standard deviation values of the truck ratios were 6.3% and 3.0% respectively. Besides, truck ratios did not exhibit a strong correlation with total traffic. The Pearson correlation between truck counts and total traffic was 0.75. This result suggested that we could further develop a prediction model for the truck counts in the urban areas based on total traffic and other predictors including land use and temporal variables, although more data collection would be needed.

In addition, we found that three east-west corridors Bloor St, College St, and Dundas St had similar high truck counts while Dundas St had a relatively higher CV value. This result could be explained by the presence of supermarkets, restaurants, and stores on both sides of Dundas St, so delivery trucks occasionally appeared on this street and were counted during each measurement, resulting in higher variability of truck counts. This indicated that we should improve the truck classification in the future analysis by using a transfer learning method so that we could better present truck distributions and facilitate the analysis of their impacts on near-road pollutants in urban environments.

We further developed two models using a machine learning approach for the BC maximum concentrations and BC mean concentrations at the natural segment level. The SHAP summary

183 plot of the BC maximum concentration model indicated that average traffic speed had the most significant impact and was negatively associated with BC maximum concentrations. Besides, a longer tail of the dots of this feature reaching to the right, but not to the left, indicated that the magnitude of the impact of low speed on maximum concentrations was around 2.5 times as that of the impact of high speed on BC max concentrations.

In addition, we found that truck counts had the second-largest impact on BC maximum concentrations. This result was expected since diesel trucks generally emit much higher BC concentrations than gasoline vehicles. Besides, the feature bus counts also had a positive impact on BC maximum concentrations, although it ranked much lower than truck counts in terms of feature importance. This result could be explained by lower EFs for transit buses than diesel trucks in the City of Toronto.

As for the BC mean concentration model, it had a better performance with fewer predictors compared to BC maximum concentration model and did not include diesel vehicle counts. The feature that had the most significant impact on the model output was the length of major roads in an 800 m buffer. This feature could be regarded as an indicator of the traffic volume, and BC mean concentrations tended to be higher when there was more traffic. The feature traffic flow, derived from the moving observer method, exhibited a complicated relationship with BC concentrations because traffic flow had a non-linear correlation with the traffic speed.

This study captured the distribution of trucks in local environments, casting light on the impacts of land-use variables and local traffic on near-road BC concentrations. However, further research is needed to help us better understand the impacts of traffic characteristics on high BC concentrations. For instance, capturing vehicles surrounding the observer vehicle using a 360- degree camera and measuring the distance between the observer vehicle and high emitters based on active methods such as LiDAR instruments, would be valuable. Lastly, more data collection should be conducted for developing a machine learning model to capture the spatial and temporal distribution of trucks in urban areas.

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Chapter 9 Summary and Conclusion Chapter overview 9.1 Summary of Chapters

This dissertation addressed the gaps in the current literature on local traffic emissions and near- road air quality. We first introduced the adverse health effects of air pollutants and the contribution of vehicle emissions to near-road air quality. Then, by presenting the complex association between near-road air pollution and different factors such as built environment, traffic characteristics and meteorological conditions, we introduced various methods to estimate spatiotemporal variations in air quality in urban areas and identified the need to overcome the current challenges in local traffic emissions estimation and near-road air quality modelling. The subsequent chapters have addressed the following research questions: 1) Do project-level emission estimates generated by a traffic emission model vary with different aggregation levels for vehicle activity data? To what extent will these differences influence air quality estimation in hot-spot analysis? 2) What are the differences between default emission intensities and drive cycles in an emission model and those collected based on real-world measurements? How do we improve the robustness of on-road emission inventory estimation using locally collected data? 3) How can we quantitatively evaluate the effects of different variables including driver and trip characteristics on emission intensities at a trip level? 4) How do we refine near-road air quality models with a data-driven approach using local traffic characteristics to capture the association between different factors such as built environment, meteorological conditions, traffic and near-road air pollution concentrations?

Chapter 3 investigated the influence of traffic volume and speed data on the simulation of vehicle emissions and near-road air quality in an effort to inform the development of project- level analysis. We calibrated a traffic microsimulation model based on the range of speeds extracted from radar records at a fixed point and observed that the simulated drive cycles generated were mostly different from the radar records, thus leading to significant differences in the resulting emission estimates. In addition, the use of the hourly average speed from radar forced MOVES to employ default drive cycles, which could not correctly represent drive cycles

185 close to a signalized intersection, and the emission results were approximately two times lower than the results estimated based on the vehicle trajectory method. We further examined the effect of data inputs on air quality estimates, and the results revealed that PM concentrations estimated based on simulated speed profiles were similar, while they were approximately two or three times higher than the results derived from radar data.

Chapter 4 presented a comparison of fleet averaged EFs for NOx and CO derived from a traffic emission model with EFs estimated using plume-based measurements. We observed that the modelled EFs for NOx and CO were about two times as high as plume-based EFs. Besides, we found that the EFs from the model for all pollutants on weekends were generally within the range of their EFs on weekdays. In contrast, the EFs estimated using the plume-base method for NOx were much higher on weekdays than on weekends. We further investigated the contributions of vehicle classes to traffic emissions and various pollutants. The results suggested that transit buses accounted for approximately 60% and 70% of the total NOx and EC in the transit bus network. Meanwhile, for the streetcar network, vehicle classes excluding trucks accounted for more than 95% of the total CO emissions. The large contributions of diesel vehicles, including transit buses and trucks, could potentially explain the substantial differences in EFs estimated based on modelled and plume-based methods. Moreover, we demonstrated that simulated concentrations based on modelled emissions were closer to measured concentrations at street-level as compared with simulated concentrations derived from the plume-based method. The difference in estimated concentrations derived from the two methods was not as large as that difference in estimated emissions attributable to the influence of meteorology and of the urban background.

Chapter 5 presented the development of opmode distributions derived from local drive cycle construction methods developed based on real-world GPS data collection. We developed Toronto-specific drive cycles for each speed bin based on the micro-trip method and observed that the interpolated drive cycles derived from MOVES default drive cycles had a more substantial proportion of high emitting opmode bins than those developed locally. In addition, we found that the variability of EFs derived from the micro-trip and segment methods were similar, with the segment methods having slightly higher EFs. The RMSE results of the average distance between the median cumulative opmode distribution and the other observations for each speed category revealed that at an average speed of 40 mph, drivers would keep adapting to their surrounding environment, therefore, leading to a more diverse set of opmode distributions.

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Furthermore, we estimated daily GHG emissions for the City of Toronto based on the distribution of each EF derived from the micro-trip method, the segment method, and the EFs derived from the MOVES model. Results indicated that GHG emissions were similar for the two methods but were much lower than the results of the estimation using MOVES default opmode distributions. We also estimated the minimum duration of GPS data required to develop an EF database with adequate variability.

Chapter 6 explored the relationships between CO2eq and PM2.5 EFs and various variables, including meteorology, trip characteristics, driving characteristics, and driver characteristics using a machine learning approach and a novel SHAP method based on real-world GPS data from driving trips. In the CO2eq eco-score model, we found continuous features associated with driving behaviour, namely extended idling event, frequent stop event, running low-speed event, and sharp deceleration event, to have the most significant impact on the trip eco-score, indicating the most important features affecting emissions were directly related to congestion and specific driver behaviours (aggressive/smooth). Regarding the PM2.5 eco-score model, driver age was found to have the most significant impact on the eco-score, since it was associated with the vehicle model year, which itself was correlated to PM2.5 emission rate (g/s). We also developed a model based on discrete variables including dummy, categorical and ordinal variables. The results showed the number of kilometres a participant has driven over the past year was the most significant feature of the CO2eq eco-score, and we found it was a better indicator of an individual’s driving experience than the number of years a participant has been driving regularly.

In addition, we found that full-time drivers that conducted a daily commute had lower CO2eq and

PM2.5 intensities as they were more familiar with their routes, compared with drivers that did not drive the same route on a daily basis. Besides, avoiding traffic peak hours or congested traffic conditions could improve eco-scores.

Chapter 7 investigated the influences of meteorology, land use, built environment, and traffic characteristics on near-road UFP concentrations at various locations along a major arterial road in the GTA. The correlation matrix results indicated that the number of fast-food restaurants within 50m buffer and the number of cafes within 200m buffer had positive correlations with the mean UFP level. Besides, two traffic-related features, distance to the closest major road and the number of traffic signals, were found to have negative and positive correlations with UFP levels. We further used k-means to group locations into two clusters and developed a machine learning

187 model within each cluster. Cluster 1 included all the locations with a relatively long distance to the closest major road while having a low number of traffic signals, trees, buildings, and restaurants; cluster 2 included all other locations with opposite attributes compared to cluster 1. The results indicated that the variations in UFP levels were mainly influenced by meteorological data in cluster 1; the variations in UFP levels were mainly affected by local traffic in cluster 2. Besides, the feature time of day had opposite impacts on UFP levels in the two clusters. In addition, we evaluated model performance using different cross-validation methods. The model performance was robust under random cross-validation as R2 ranged from 0.63 to 0.69, but it exhibited a large variability with R2 values ranging from 0.14 to 0.81, when data at specific locations were eliminated from the training dataset.

Chapter 8 evaluated the impacts of land-use variables and local traffic characteristics on near- road BC concentrations based on mobile measurements conducted in the City of Toronto. The results revealed that the highest BC mean concentration was approximately three times higher than the lowest BC mean concentration at the corridor level. In contrast, the highest BC maximum concentration was about 24 times higher than the lowest average value of BC maxima. This result emphasizes the significance of capturing the distribution of BC maximum concentration in urban areas. We employed the moving observer method to derive fundamental traffic information, including traffic flow, traffic density, and average traffic speed. Truck counts were also estimated along each corridor. Truck ratios did not exhibit a strong correlation with total traffic, and a Pearson correlation of 0.75 was observed between truck counts and total traffic. We further developed two models using a machine learning approach for the BC maximum concentrations and BC mean concentrations at the natural segment level. The SHAP summary plot of the BC maximum concentration model demonstrated that average traffic speed and truck counts ranked as the top two features that had negative and positive impacts on BC maximum concentrations, respectively. As for the BC mean concentration model, it had a better performance (a mean R2 of 0.61) with fewer predictors compared to BC maximum concentration model and did not include diesel vehicle counts.

9.2 Research Contributions

This dissertation aimed to address crucial gaps in the current literature on local traffic emission estimation and near-road air quality modelling.

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First, in Chapter 3, we quantified the variability in emissions and concentrations obtained using different sources of traffic data and identified the most critical inputs for project-level analysis of traffic emissions and near-road air quality. Traffic microsimulation is generally not available as a tool for government agencies. As cities invest in infrastructure for traffic counting, our study investigated the effect of using traffic counts from radars to derive estimates of emissions along a road segment using a rough estimate for vehicle speed. This approach was compared with an emission estimate generated using a full analysis of individual vehicle drive-cycles derived from a calibrated traffic simulation model. Our study is significant since it provides recommendations for project-level analysis and PM hotspot analysis. Government agencies should be careful when using raw radar data for emission modelling purposes.

Second, in Chapter 4, we compared fleet averaged EFs derived from MOVES with those estimated using a plume-based approach. In addition, we presented the disproportional contribution of high emitters to traffic emissions. This finding could be useful for a better understanding of the substantial differences in EFs estimated based on modelled and real-world measurements. It also stresses the need to collect local traffic characteristics such as vehicle type and count for a better understanding of on-road emissions. In Chapter 5, we validated default drive cycles in the MOVES model against representative drive cycles developed based on real- world GPS data collection. We also introduced a method to increase the robustness of on-road emission inventories by embedding representative driving behaviour in regional emission models. These two studies demonstrated the limitations of the current MOVES2014 model and presented the extent to which emission estimates could be different when using the default database in MOVES compared to using real-world data, including emission intensities and local drive cycles. The validation results would be valuable for transportation planners to consider uncertainties in emission estimation and employ appropriate methods to improve the estimation of on-road emission inventories.

Third, in Chapter 6, we developed eco-score models and quantitatively evaluated the effects of various factors such as driver and trip characteristics on emission intensities at a trip level. The study results could be helpful for individuals to better plan their trips. It could also be useful for individuals to understand the impact of speed profiles on emissions and improve their driving behaviour to achieve lower trip-level emission intensities and fuel consumption. In addition, this study could assist policymakers and city planners in evaluating policies and strategies for

189 moderating on-road traffic emissions. For instance, more efforts should be put on motivating people to avoid congested traffic conditions (e.g. congestion pricing and alternative mode choice), since we found the most significant features affecting emissions are directly related to congestion.

Fourth, in Chapter 7, we explored the effects of various factors on minute-level UFP concentrations based on short-term monitoring campaigns. We presented a method to develop a video processing system for traffic detection, tracking, and counting at signalized intersections and roadside. This study also stressed the significance of using detailed traffic characteristics per site to improve near-road air quality predictions. It also highlighted the need to use appropriate cross-validation techniques to better evaluate machine learning models for near-road air pollution predictions. In Chapter 8, we captured the distribution of truck activities in urban environments, casting light on the impacts of land-use variables and detailed traffic information on near-road BC concentrations based on a mobile monitoring. We demonstrated a method to use computer vision techniques to derive vehicle counts, which were used to estimate traffic flow, density, and average speed. This approach could be used by city planners to better understand truck activity in urban areas with high spatial resolution. These two studies presented different methods of deriving real-time traffic information from videos and highlighted the significance of using local traffic characteristics to explain the spatiotemporal variation in near- road air pollution concentrations.

9.3 Recommendations for Future Research

The following are recommendations that could become potential study areas of future research: 1) This dissertation validated fleet average emission intensities derived from the MOVES model against those derived from plume-based measurements. Other validation methods based on real-world measurements, such as using portable emissions measurement systems (PEMS), could be employed to capture the emission intensities of individual vehicles, which could lead to a better understanding of the uncertainties in vehicle emissions in the Canadian context. Empirical models could be developed to capture the associations between instantaneous emission rates, vehicle characteristics, location information and ambient conditions. 2) This research investigated the influence of different factors such as built environment and

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local traffic on measured UFP and BC concentrations. Although these two air pollutants have significant negative impacts on health, they are not regularly monitored, so we could not directly obtain their background levels. It is important to develop background concentration subtraction methods specifically for UPF and BC concentrations measured in urban environments, which could improve our understanding of local traffic on near-road UFP and BC concentrations. 3) This dissertation employed both air quality dispersion models and empirical models for estimating near-road air quality in different studies. It could be useful for developing a hybrid modelling framework, including a dispersion model and an empirical model, so that the estimation results could benefit from both models. Specifically, dispersion models generally can provide better regional air quality prediction as background levels, and empirical models can better capture the local effects of factors such as traffic on near-road air pollution levels. An integration of two types of models could better capture the spatiotemporal variations in air quality. 4) This research introduced two methods to derive local traffic characteristics from videos based on computer vision techniques. Given the disproportional contribution of high-emitters on near-road air pollution, the vehicle classification model could be improved by using a transfer learning method to obtain more detailed vehicle types, such as light commercial trucks and delivery trucks. Besides, more information could be derived from videos in future studies such as vehicle speeds as well as counts of pedestrians and cyclists. It could facilitate policymakers to propose specific strategies for reducing population exposures in urban environments. 5) This dissertation highlighted the significance of using real-world traffic data on both traffic emission estimation and air quality modelling. It could be also beneficial to investigate the effects of other data sources, including the built environment and meteorological conditions, based on different sensors and instruments. For instance, using the LiDAR to get detailed built environment characteristics. Detailed local data could facilitate models to better estimate high-resolution near-road air pollution.

References

Abernethy, R.C., Allen, R.W., Mckendry, I.G., Brauer, M., 2013. A Land Use Regression Model for Ultra fine Particles in Vancouver, Canada. Environ. Sci. Technol. 47, 5217–5225. https://doi.org/10.1021/es304495s

Abo-Qudais, S., Qdais, H.A., 2005. Performance evaluation of vehicles emissions prediction models. Clean Technol. Environ. Policy 7, 279–284. https://doi.org/10.1007/s10098-005- 0279-x

Abou-Senna, H., Radwan, E., 2013. VISSIM/MOVES integration to investigate the effect of major key parameters on CO2 emissions. Transp. Res. Part D Transp. Environ. 21, 39–46. https://doi.org/10.1016/j.trd.2013.02.003

Adams, M.D., Kanaroglou, P.S., 2016. Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models. J. Environ. Manage. 168, 133–141. https://doi.org/10.1016/j.jenvman.2015.12.012

Ait-Helal, W., Beeldens, A., Boonen, E., Borbon, A., Boréave, A., Cazaunau, M., Chen, H., Daële, V., Dupart, Y., Gaimoz, C., Gallus, M., George, C., Grand, N., Grosselin, B., Herrmann, H., Ifang, S., Kurtenbach, R., Maille, M., Marjanovic, I., Mellouki, A., Miet, K., Mothes, F., Poulain, L., Rabe, R., Zapf, P., Kleffmann, J., Doussin, J.F., 2015. On-road measurements of NMVOCs and NOx: Determination of light-duty vehicles emission factors from tunnel studies in Brussels city center. Atmos. Environ. 122, 799–807. https://doi.org/10.1016/j.atmosenv.2015.09.066

Alam, A., Diab, E., El-Geneidy, A.M., Hatzopoulou, M., 2014. A simulation of transit bus emissions along an urban corridor: Evaluating changes under various service improvement strategies. Transp. Res. Part D Transp. Environ. 31, 189–198. https://doi.org/10.1016/j.trd.2014.06.010

Alam, A., Hatzopoulou, M., 2016a. Deriving local operating distributions to estimate transit bus emissions across an urban network. Transp. Res. Rec. 2570, 57–68. https://doi.org/10.3141/2570-07

Alam, A., Hatzopoulou, M., 2016b. Deriving Local Operating Distributions to Estimate Transit Bus Emissions Across an Urban Network. Transp. Res. Rec. J. Transp. Res. Board 2570, 57–68. https://doi.org/10.3141/2570-07

Alam, A., Hatzopoulou, M., 2014. Investigating the isolated and combined effects of congestion , roadway grade , passenger load , and alternative fuels on transit bus emissions. Transp. Res. PART D 29, 12–21. https://doi.org/10.1016/j.trd.2014.03.005

Alam, M.S., McNabola, A., 2018. Network-wide traffic and environmental impacts of acceleration and deceleration among Eco-Driving Vehicles in different road configurations. Transp. Plan. Technol. 41, 244–264. https://doi.org/10.1080/03081060.2018.1435436

191 192

Alam, M.S., McNabola, A., 2014. A critical review and assessment of Eco-Driving policy & technology: Benefits & limitations. Transp. Policy 35, 42–49. https://doi.org/10.1016/j.tranpol.2014.05.016

Alhomaidat, F., Ardekani, S., 2015. A Statistical Comparison of Traffic Measurements from the Moving versus Stationary Observer Methods. J. Transp. Technol. 05, 204–213. https://doi.org/10.4236/jtts.2015.54019

Alimissis, A., Philippopoulos, K., Tzanis, C.G., Deligiorgi, D., 2018. Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos. Environ. 191, 205– 213. https://doi.org/10.1016/j.atmosenv.2018.07.058

Amirjamshidi, G., 2015. Assessment of Commercial Vehicle Emissions and Vehicle Routing of Fleets using Simulated Driving Cycles. University of Toronto.

André, M., Joumard, R., Vidon, R., Tassel, P., Perret, P., 2006. Real-world European driving cycles, for measuring pollutant emissions from high- and low-powered cars. Atmos. Environ. 40, 5944–5953. https://doi.org/10.1016/j.atmosenv.2005.12.057

Apte, J.S., Messier, K.P., Gani, S., Brauer, M., Kirchstetter, T.W., Lunden, M.M., Marshall, J.D., Portier, C.J., Vermeulen, R.C.H., Hamburg, S.P., 2017. High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. Environ. Sci. Technol. 51, 6999–7008. https://doi.org/10.1021/acs.est.7b00891

Ashby, B., 2018. TTS 2016 DATA GUIDE [WWW Document]. URL http://dmg.utoronto.ca/pdf/tts/2016/2016TTS_DataGuide.pdf

Bai, S., Du, Y., Reid, S., 2015. Contributions of Tire Wear and Brake Wear to Particulate Matter Emissions Inventories for On-Road Mobile Sources. 2015 Int. Emiss. Invent. Conf.

Bai, S., Technology, S., Blvd, N.M., Suite, D., 2016. Mobile Source NO x Emissions in the National Emissions Inventory.

Bai, Y., Li, Y., Wang, X., Xie, J., Li, C., 2016. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos. Pollut. Res. 7, 557–566. https://doi.org/10.1016/j.apr.2016.01.004

Bard, D., Kihal, W., Schillinger, C., Fermanian, C., S??gala, C., Glorion, S., Arveiler, D., Weber, C., 2014. Traffic-related air pollution and the onset of myocardial infarction: Disclosing benzene as a trigger? A small-area case-crossover study. PLoS One 9. https://doi.org/10.1371/journal.pone.0100307

Barth, M., Boriboonsomsin, K., 2009. Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transp. Res. Part D Transp. Environ. 14, 400–410. https://doi.org/10.1016/j.trd.2009.01.004

Bates, J.T., Pennington, A.F., Zhai, X., Friberg, M.D., Metcalf, F., Darrow, L., Strickland, M., Mulholland, J., Russell, A., 2018a. Application and evaluation of two model fusion approaches to obtain ambient air pollutant concentrations at a fine spatial resolution (250m)

193

in Atlanta. Environ. Model. Softw. 109, 182–190. https://doi.org/10.1016/j.envsoft.2018.06.008

Bates, J.T., Pennington, A.F., Zhai, X., Friberg, M.D., Metcalf, F., Darrow, L., Strickland, M., Mulholland, J., Russell, A., 2018b. Application and evaluation of two model fusion approaches to obtain ambient air pollutant concentrations at a fine spatial resolution (250m) in Atlanta. Environ. Model. Softw. 109, 182–190. https://doi.org/10.1016/j.envsoft.2018.06.008

Belz, N., Aultman-Hall, L., 2011. Analyzing the Effect of Driver Age on Operating Speed and Acceleration Noise. Transp. Res. Rec. J. Transp. Res. Board 2265, 184–191. https://doi.org/10.3141/2265-21

Ben-Chaim, M., Shmerling, E., Kuperman, A., 2013. Analytic modeling of vehicle fuel consumption. Energies 6, 117–127. https://doi.org/10.3390/en6010117

Bernard, Y., Tietge, U., German, J., Muncrief, R., 2018. Determination of real-world emissions from passenger vehicles using remote sensing data.

Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G., Di Carlo, P., 2017. Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmos. Pollut. Res. 8, 652–659. https://doi.org/10.1016/j.apr.2016.12.014

Bing, X., Jiang, Y., Zhang, C., Zhang, Y., Zhang, J., 2014. Effects of intersection lane configuration on traffic emissions. Adv. Transp. Stud. 23–36. https://doi.org/10.4399/978885486697212

Bishop, J., Molden, N., Boies, A., 2017. Real-world environmental impacts from modern passenger vehicles operating in urban setting. Int. J. Transp. Dev. Integr. 1, 203–211. https://doi.org/10.2495/TDI-V1-N1-203-211

Bokare, P.S., Maurya, A.K., 2013. Study of Effect of Speed , Acceleration and Deceleration of Small Petrol Car on Its Tail Pipe. Int. J. Traffic Transp. Eng. 3, 465–478. https://doi.org/10.7708/ijtte.2013.3(4).09

Borge, R., de Miguel, I., de la Paz, D., Lumbreras, J., Pérez, J., Rodríguez, E., 2012. Comparison of road traffic emission models in Madrid (Spain). Atmos. Environ. 62, 461–471. https://doi.org/10.1016/j.atmosenv.2012.08.073

Borken-Kleefeld, J., Chen, Y., 2015. New emission deterioration rates for gasoline cars - Results from long-term measurements. Atmos. Environ. 101, 58–64. https://doi.org/10.1016/j.atmosenv.2014.11.013

Boroujeni, B.Y., Frey, H.C., 2014. Road grade quanti fi cation based on global positioning system data obtained from real-world vehicle fuel use and emissions measurements. Atmos. Environ. 85, 179–186. https://doi.org/10.1016/j.atmosenv.2013.12.025

Borrego, C., Costa, A.M., Ginja, J., Amorim, M., Coutinho, M., Karatzas, K., Sioumis, T.,

194

Katsifarakis, N., Konstantinidis, K., De Vito, S., Esposito, E., Smith, P., André, N., Gérard, P., Francis, L.A., Castell, N., Schneider, P., Viana, M., Minguillón, M.C., Reimringer, W., Otjes, R.P., von Sicard, O., Pohle, R., Elen, B., Suriano, D., Pfister, V., Prato, M., Dipinto, S., Penza, M., 2016. Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise. Atmos. Environ. 147, 246–263. https://doi.org/10.1016/j.atmosenv.2016.09.050

Brady, J.M., Stokes, M.D., Bonnardel, J., Bertram, T.H., 2016. Characterization of a Quadrotor Unmanned Aircraft System for Aerosol-Particle-Concentration Measurements. Environ. Sci. Technol. 50, 1376–1383. https://doi.org/10.1021/acs.est.5b05320

Brantley, H.L., Hagler, G.S.W., J. Deshmukh, P., Baldauf, R.W., 2014. Field assessment of the effects of roadside vegetation on near-road black carbon and particulate matter. Sci. Total Environ. 468–469, 120–129. https://doi.org/10.1016/j.scitotenv.2013.08.001

Brauer, M., Reynolds, C., Hystad, P., 2013. Traffic-related air pollution and health in Canada. Can. Med. Assoc. J. 185, 1557–1558. https://doi.org/10.1503/cmaj.121568

Brewer, T.L., 2019. Black carbon emissions and regulatory policies in transportation. Energy Policy 129, 1047–1055. https://doi.org/10.1016/j.enpol.2019.02.073

Brimblecombe, P., Townsend, T., Lau, C.F., Rakowska, A., Chan, T.L., Močnik, G., Ning, Z., 2015. Through-tunnel estimates of vehicle fleet emission factors. Atmos. Environ. 123, 180–189. https://doi.org/10.1016/j.atmosenv.2015.10.086

Brook, J.R., Setton, E.M., Seed, E., Shooshtari, M., Doiron, D., Awadalla, P., Brauer, M., Hu, H., McGrail, K., Stieb, D., Subarrao, P., Demers, P., Manuel, D., McLaughlin, J., Carlsten, C., Azad, M., Atkinson, S., Burnett, R., Lou, W., Rainham, D., Evans, G., Copes, R., Pantelimon, O., Smargiassi, A., Davies, H., Villeneuve, P., Van Den Bosch, M., Chaumont, D., Feddema, J., Takaro, T., Hakami, A., Johnson, M., Hatzopoulou, M., Habib, A., Fuller, D., Widener, M., 2018. The Canadian Urban Environmental Health Research Consortium - A protocol for building a national environmental exposure data platform for integrated analyses of urban form and health. BMC Public Health 18, 1–15. https://doi.org/10.1186/s12889-017-5001-5

Bühlmann, P., Hothorn, T., 2007. Boosting Algorithms: Regularization, Prediction and Model Fitting. Stat. Sci. 22, 477–505. https://doi.org/10.1214/07-STS242

Cabaneros, S.M., Calautit, J.K., Hughes, B.R., 2019. A review of artificial neural network models for ambient air pollution prediction. Environ. Model. Softw. 119, 285–304. https://doi.org/10.1016/j.envsoft.2019.06.014

Carslaw, D.C., Beevers, S.D., Tate, J.E., Westmoreland, E.J., Williams, M.L., 2011. Recent evidence concerning higher NOx emissions from passenger cars and light duty vehicles. Atmos. Environ. 45, 7053–7063. https://doi.org/10.1016/j.atmosenv.2011.09.063

Carslaw, D.C., Rhys-Tyler, G., 2013. New insights from comprehensive on-road measurements of NOx, NO2 and NH3 from vehicle emission remote sensing in London, UK. Atmos. Environ. 81, 339–347. https://doi.org/10.1016/j.atmosenv.2013.09.026

195

Carslaw, D.C., Williams, M.L., Tate, J.E., Beevers, S.D., 2013. The importance of high vehicle power for passenger car emissions. Atmos. Environ. 68, 8–16. https://doi.org/10.1016/j.atmosenv.2012.11.033

Chaloulakou, A., Mavroidis, I., Gavriil, I., 2008. Compliance with the annual NO2 air quality standard in Athens. Required NOx levels and expected health implications. Atmos. Environ. 42, 454–465. https://doi.org/10.1016/j.atmosenv.2007.09.067

Chang, S.Y., Vizuete, W., Valencia, A., Naess, B., Isakov, V., Palma, T., Breen, M., Arunachalam, S., 2015. A modeling framework for characterizing near-road air pollutant concentration at community scales. Sci. Total Environ. 538, 905–921. https://doi.org/10.1016/j.scitotenv.2015.06.139

Chen, C., Zhao, X., Yao, Y., Zhang, Y., Rong, J., 2018. Driver’s Eco-driving Behavior Evaluation Modeling based on Driving Events. J. Adv. Transp. 2018, 1–19. https://doi.org/https://doi.org/10.1155/2018/9530470

Chen, G., Knibbs, L.D., Zhang, W., Li, S., Cao, W., Guo, J., Ren, H., Wang, B., Wang, H., Williams, G., Hamm, N.A.S., Guo, Y., 2018. Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information. Environ. Pollut. 233, 1086–1094. https://doi.org/10.1016/j.envpol.2017.10.011

Chen, H., Bai, S., Eisinger, D., Niemeier, D., Claggett, M., 2008. Modeling Uncertainties and Near-Road Pm 2 . 5 : a Comparison of Caline4 , Cal3Qhc and Aermod. Quality 5.

Chen, T., He, T., 2015. xgboost : eXtreme Gradient Boosting, R package version 0.4-2.

Cheng, Y., Lee, S.C., Ho, K.F., Chow, J.C., Watson, J.G., Louie, P.K.K., Cao, J.J., Hai, X., 2010. Chemically-speciated on-road PM2.5 motor vehicle emission factors in Hong Kong. Sci. Total Environ. 408, 1621–1627. https://doi.org/10.1016/j.scitotenv.2009.11.061

Choi, W., Paulson, S.E., 2016. Closing the ultrafine particle number concentration budget at road-to-ambient scale: Implications for particle dynamics. Aerosol Sci. Technol. 50, 448– 461. https://doi.org/10.1080/02786826.2016.1155104

Christopher, W., 1973. A theoretical analysis of the moving observer method. Transp. Res. 7, 293–311.

Cieplak, T., Rymarczyk, T., Tomaszewski, R., 2019. A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers. MATEC Web Conf. 252, 03009. https://doi.org/10.1051/matecconf/201925203009

Dai, Z., Niemeier, D., Eisinger, D., 2008. Driving cycles: a new cycle-building method that better represents real-world emissions. U.C. Davis-Caltrans Air Qual. Proj. 66, 37.

Dallmann, T.R., Kirchstetter, T.W., Demartini, S.J., Harley, R.A., 2013. Quantifying on-road emissions from gasoline-powered motor vehicles: Accounting for the presence of medium- and heavy-duty diesel trucks. Environ. Sci. Technol. 47, 13873–13881. https://doi.org/10.1021/es402875u

196

Data Management Group, 2018. Transportation tomorrow survey. de Miranda, R.M., Perez-Martinez, P.J., de Fatima Andrade, M., Ribeiro, F.N.D., 2019. Relationship between black carbon (BC) and heavy traffic in São Paulo, Brazil. Transp. Res. Part D Transp. Environ. 68, 84–98. https://doi.org/10.1016/j.trd.2017.09.002

Delavarrafiee, M., Frey, H.C., 2018. Real-world fuel use and gaseous emission rates for flex fuel vehicles operated on E85 versus gasoline. J. Air Waste Manag. Assoc. 68, 235–254. https://doi.org/10.1080/10962247.2017.1405097

Dewulf, B., Neutens, T., Lefebvre, W., Seynaeve, G., Vanpoucke, C., Beckx, C., Van de Weghe, N., 2016. Dynamic assessment of exposure to air pollution using mobile phone data. Int. J. Health Geogr. 15, 1–14. https://doi.org/10.1186/s12942-016-0042-z

Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M.B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y., Mickley, L.J., Schwartz, J., 2019. An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environ. Int. 130, 104909. https://doi.org/10.1016/j.envint.2019.104909

Dias, D., Amorim, J.H., Sá, E., Borrego, C., Fontes, T., Fernandes, P., Pereira, S.R., Bandeira, J., Coelho, M.C., Tchepel, O., 2018. Assessing the importance of transportation activity data for urban emission inventories. Transp. Res. Part D Transp. Environ. 62, 27–35. https://doi.org/10.1016/j.trd.2018.01.027

Dias, D., Tchepel, O., 2018. Spatial and temporal dynamics in air pollution exposure assessment. Int. J. Environ. Res. Public Health 15. https://doi.org/10.3390/ijerph15030558

Dixit, P., Miller, J.W., Cocker, D.R., Oshinuga, A., Jiang, Y., Durbin, T.D., Johnson, K.C., 2017. Differences between emissions measured in urban driving and certification testing of heavy- duty diesel engines. Atmos. Environ. 166, 276–285. https://doi.org/10.1016/j.atmosenv.2017.06.037

DMIT Spatial Inc., 2014. CanMap ® RouteLogistics.

Eboli, L., Mazzulla, G., Pungillo, G., 2016. Combining speed and acceleration to define car users’ safe or unsafe driving behaviour. Transp. Res. Part C Emerg. Technol. 68, 113–125. https://doi.org/10.1016/j.trc.2016.04.002

Environment and Climate Change Canada, 2019. Canadian Environmental Sustainability Indicators: Air pollutant emissions. https://doi.org/10.1016/b978-0-12-401733-7.00029-3

Epa, U.S., Transportation, O., Quality, A., Division, S., 2003. Roadway-Specific Driving Schedules for Heavy-Duty Vehicles.

Fallah-shorshani, M., Shekarrizfard, M., Hatzopoulou, M., 2017. Integrating a street-canyon model with a regional Gaussian dispersion model for improved characterisation of near- road air pollution. Atmos. Environ. 153, 21–31. https://doi.org/10.1016/j.atmosenv.2017.01.006

197

Fallah-Shorshani, M., Shekarrizfard, M., Hatzopoulou, M., 2017. Integrating a street-canyon model with a regional Gaussian dispersion model for improved characterisation of near- road air pollution. Atmos. Environ. 153, 21–31. https://doi.org/10.1016/j.atmosenv.2017.01.006

Farzaneh, M., Zietsman, J., Lee, D., Johnson, J., Wood, N., Ramani, T., Gu, C., 2014. Texas- Specific Drive Cycles and Idle Emissions Rates for Using with EPA’s MOVES Model- Final Report 7.

Farzaneh, R., Gu, C., Wood, N., Zietsman, J., 2015. Developing Texas-Specific Drive Cycles for Use with the MOVES Model 1–18.

Ferm, M., Sjoberg, K., 2015. Concentrations and emission factors for PM2.5 and PM10 from road traffic in Sweden. Atmos. Environ. 119, 211–219. https://doi.org/10.1016/j.atmosenv.2015.08.037

Ferreira, J.C., Almeida, J., Rodrigues, A., 2015. The Impact of Driving Styles on Fuel Consumption : A Data Warehouse and Data Mining based Discovery Process. Ieee 16, 1– 10.

Forehead, H., Huynh, N., 2018. Review of modelling air pollution from traffic at street-level - The state of the science. Environ. Pollut. 241, 775–786. https://doi.org/10.1016/j.envpol.2018.06.019

Franco, V., Fontaras, G., Dilara, P., 2012. Towards Improved Vehicle Emissions Estimation in Europe. Procedia - Soc. Behav. Sci. 48, 1304–1313. https://doi.org/10.1016/j.sbspro.2012.06.1106

Franco, V., Kousoulidou, M., Muntean, M., Ntziachristos, L., Hausberger, S., Dilara, P., 2013. Road vehicle emission factors development: A review. Atmos. Environ. 70, 84–97. https://doi.org/10.1016/j.atmosenv.2013.01.006

Frey, H.C., Zhang, K., Rouphail, N.M., 2008. Fuel use and emissions comparisons for alternative routes, time of day, road grade, and vehicles based on in-use measurements. Environ. Sci. Technol. 42, 2483–2489. https://doi.org/10.1021/es702493v

Fuertes, E., Standl, M., Cyrys, J., Berdel, D., Berg, A. Von, Bauer, C., Kr, U., Sugiri, D., 2013. A longitudinal analysis of associations between tra ffi c-related air pollution with asthma , allergies and sensitization in the GINIplus and LISAplus birth cohorts 1–20. https://doi.org/10.7717/peerj.193

Fujita, E.M., Campbell, D.E., Zielinska, B., Chow, J.C., Lindhjem, C.E., DenBleyker, A., Bishop, G.A., Schuchmann, B.G., Stedman, D.H., Lawson, D.R., 2012. Comparison of the MOVES2010a, MOBILE6.2, and EMFAC2007 mobile source emission models with on- road traffic tunnel and remote sensing measurements. J. Air Waste Manag. Assoc. 62, 1134–1149. https://doi.org/10.1080/10962247.2012.699016

Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Denier Van Der Gon, H., Facchini, M.C., Fowler, D., Koren, I., Langford, B., Lohmann, U., Nemitz, E., Pandis, S., Riipinen, I.,

198

Rudich, Y., Schaap, M., Slowik, J.G., Spracklen, D. V., Vignati, E., Wild, M., Williams, M., Gilardoni, S., 2015. Particulate matter, air quality and climate: Lessons learned and future needs. Atmos. Chem. Phys. 15, 8217–8299. https://doi.org/10.5194/acp-15-8217- 2015

Gao, Y., Wang, Z., Liu, C., Peng, Z.R., 2019. Assessing neighborhood air pollution exposure and its relationship with the urban form. Build. Environ. 155, 15–24. https://doi.org/10.1016/j.buildenv.2018.12.044

Geiser, M., Casaulta, M., Kupferschmid, B., Schulz, H., Semmler-Behnke, M., Kreyling, W., 2008. The role of macrophages in the clearance of inhaled ultrafine titanium dioxide particles. Am. J. Respir. Cell Mol. Biol. 38, 371–376. https://doi.org/10.1165/rcmb.2007- 0138OC

Geng, G., Murray, N.L., Chang, H.H., Liu, Y., 2018. The sensitivity of satellite-based PM 2.5 estimates to its inputs: Implications to model development in data-poor regions. Environ. Int. 121, 550–560. https://doi.org/10.1016/j.envint.2018.09.051

Ghafghazi, G., Hatzopoulou, M., 2015. Simulating the air quality impacts of traffic calming schemes in a dense urban neighborhood. Transp. Res. Part D Transp. Environ. 35, 11–22. https://doi.org/10.1016/j.trd.2014.11.014

Ghassoun, Y., Löwner, M.O., Weber, S., 2019. Wind direction related parameters improve the performance of a land use regression model for ultrafine particles. Atmos. Pollut. Res. 10, 1180–1189. https://doi.org/10.1016/j.apr.2019.02.001

Ghosh, A., Pramanik, P., Banerjee, K. Das, Roy, A., Nandi, S., Saha, S., 2019. Analyzing correlation between air and noise pollution with influence on air quality prediction. IEEE Int. Conf. Data Min. Work. ICDMW 2018-Novem, 913–918. https://doi.org/10.1109/ICDMW.2018.00133

Goel, A., Kumar, P., 2014. A review of fundamental drivers governing the emissions, dispersion and exposure to vehicle-emitted nanoparticles at signalised traffic intersections. Atmos. Environ. 97, 316–331. https://doi.org/10.1016/j.atmosenv.2014.08.037

Gokhale, S., Raokhande, N., 2008. Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at urban traffic intersection during winter period. Sci. Total Environ. 394, 9–24. https://doi.org/10.1016/j.scitotenv.2008.01.020

Gorjestani, A., Menon, A., Cheng, P.-M., Shankwitz, C., Donath, M., 2008. The Design of an Optimal Surveillance System for a Cooperative Collision Avoidance System – Stop Sign Assist: CICAS-SSA Report #2.

Goudarzi, M.A., Landry, R.J., 2017. ASSESSING HORIZONTAL POSITIONAL ACCURACY OF GOOGLE EARTH IMAGERY IN THE CITY OF MONTREAL , CANADA 43, 56– 65.

Government of Canada, 2016. Release and concepts overview 2016 Census of Population : Population and dwelling counts release.

199

Gower, S., Macfarlane, R., Belmond, M., Bassil, K., Campbell, M., 2014. Path To Healthier Air: Toronto Air Pollution Burden of Illness Update, Toronto Public Health.

Grassi, G., Jamieson, K., Bahl, P., Pau, G., 2017. Parkmaster. Proc. Second ACM/IEEE Symp. Edge Comput. - SEC ’17 1–14. https://doi.org/10.1145/3132211.3134452

Guanochanga, B., Cachipuendo, R., Fuertes, W., Benitez, D.S., Toulkeridis, T., Torres, J., Villacis, C., Tapia, F., Meneses, F., 2018. Towards a real-time air pollution monitoring systems implemented using wireless sensor networks: Preliminary results. 2018 IEEE Colomb. Conf. Commun. Comput. COLCOM 2018 - Proc. https://doi.org/10.1109/ColComCon.2018.8466721

Gulia, S., Goyal, P., Goyal, S.K., Kumar, R., 2019. Re-suspension of road dust: contribution, assessment and control through dust suppressants—a review. Int. J. Environ. Sci. Technol. 16, 1717–1728. https://doi.org/10.1007/s13762-018-2001-7

Gulia, S., Shiva Nagendra, S.M., Khare, M., Khanna, I., 2015. Urban air quality management-A review. Atmos. Pollut. Res. 6, 286–304. https://doi.org/10.5094/APR.2015.033

Guo, J., Xia, F., Zhang, Y., Liu, H., Li, J., Lou, M., He, J., Yan, Y., Wang, F., Min, M., Zhai, P., 2017. Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: Implications for PM2.5 remote sensing. Environ. Pollut. 221, 94–104. https://doi.org/10.1016/j.envpol.2016.11.043

Hagler, G.S.W., Thoma, E.D., Baldauf, R.W., 2010. High-resolution mobile monitoring of carbon monoxide and ultrafine particle concentrations in a near-road environment. J. Air Waste Manag. Assoc. 60, 328–336. https://doi.org/10.3155/1047-3289.60.3.328

Hagler, G.S.W., Yelverton, T.L.B., Vedantham, R., Hansen, A.D.A., Turner, J.R., 2011. Post- processing method to reduce noise while preserving high time resolution in aethalometer real-time black carbon data. Aerosol Air Qual. Res. 11, 539–546. https://doi.org/10.4209/aaqr.2011.05.0055

Handford, D., 2014. Development and Applications of an Emissions Micro-Simulation Tool for Transportation Infrastructure Design. Univ. Alberta 53, 1689–1699. https://doi.org/10.1017/CBO9781107415324.004

Hankey, S., Marshall, J.D., 2015a. On-bicycle exposure to particulate air pollution: Particle number, black carbon, PM2.5, and particle size. Atmos. Environ. 122, 65–73. https://doi.org/10.1016/j.atmosenv.2015.09.025

Hankey, S., Marshall, J.D., 2015b. Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring. Environ. Sci. Technol. 49. https://doi.org/10.1021/acs.est.5b01209

Hankey, S., Sforza, P., Pierson, M., 2019. Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community. Environ. Sci. Technol. 53, 4305–4315. https://doi.org/10.1021/acs.est.8b05249

200

Hatzopoulou, M., Weichenthal, S., Dugum, H., Pickett, G., Miranda-Moreno, L., Kulka, R., Andersen, R., Goldberg, M., 2013. The impact of traffic volume, composition, and road geometry on personal air pollution exposures among cyclists in Montreal, Canada. J. Expo. Sci. Environ. Epidemiol. 23, 46–51. https://doi.org/10.1038/jes.2012.85

Health Canada, 2017. Health Impacts of Air Pollution in Canada An estimate of premature mortalities.

Heidari, B., Marr, L.C., 2015. Real-time emissions from construction equipment compared with model predictions. J. Air Waste Manage. Assoc. 65, 115–125. https://doi.org/10.1080/10962247.2014.978485

Hilker, N., Wang, J.M., Jeong, C.-H., Healy, R.M., Sofowote, U., Debosz, J., Su, Y., Noble, M., Munoz, A., Doerksen, G., White, L., Audette, C., Herod, D., Brook, J.R., Evans, G.J., 2019. Traffic-related air pollution near roadways: discerning local impacts from background. Atmos. Meas. Tech. Discuss. 1–32. https://doi.org/10.5194/amt-2019-112

Hoek, G., Beelen, R., de Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., Briggs, D., 2008. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos. Environ. 42, 7561–7578. https://doi.org/10.1016/j.atmosenv.2008.05.057

Hoek, G., Beelen, R., Kos, G., Dijkema, M., van der Zee, S.C., Fischer, P.H., Brunekreef, B., 2011a. Land use regression model for ultrafine particles in Amsterdam. Environ. Sci. Technol. 45, 622–8. https://doi.org/10.1021/es1023042

Hoek, G., Beelen, R., Kos, G., Dijkema, M., Zee, S.C.V. Der, Fischer, P.H., Brunekreef, B., 2011b. Land use regression model for ultrafine particles in Amsterdam. Environ. Sci. Technol. 45, 622–628. https://doi.org/10.1021/es1023042

Hoek, G., Krishnan, R.M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., Kaufman, J.D., 2013. Long-term air pollution exposure and cardio-respiratory mortality: A review. Environ. Heal. A Glob. Access Sci. Source 12. https://doi.org/10.1186/1476-069X-12-43

Hong, K.Y., Pinheiro, P.O., Minet, L., Hatzopoulou, M., Weichenthal, S., 2019. Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks. Environ. Res. 176, 108513. https://doi.org/10.1016/j.envres.2019.05.044

Hood, C., MacKenzie, I., Stocker, J., Johnson, K., Carruthers, D., Vieno, M., Doherty, R., 2018. Air quality simulations for London using a coupled regional-to-local modelling system. Atmos. Chem. Phys. 18, 11221–11245. https://doi.org/10.5194/acp-18-11221-2018

Huang, C., Tao, S., Lou, S., Hu, Q., Wang, Hongli, Wang, Q., Li, L., Wang, Hongyu, Liu, J., Quan, Y., Zhou, L., 2017. Evaluation of emission factors for light-duty gasoline vehicles based on chassis dynamometer and tunnel studies in Shanghai, China. Atmos. Environ. 169, 193–203. https://doi.org/10.1016/j.atmosenv.2017.09.020

Huang, Y., Organ, B., Zhou, J.L., Surawski, N.C., Hong, G., Chan, E.F.C., Yam, Y.S., 2018. Remote sensing of on-road vehicle emissions: Mechanism, applications and a case study

201

from Hong Kong. Atmos. Environ. 182, 58–74. https://doi.org/10.1016/j.atmosenv.2018.03.035

Hülsmann, F., 2014. Integrated agent-based transport simulation and air pollution modelling in urban areas - the example of Munich 112.

Hung, W.T., 2007. Development of a practical driving cycle construction methodology : A case study in Hong Kong 12, 115–128. https://doi.org/10.1016/j.trd.2007.01.002

Hvidtfeldt, U.A., Sørensen, M., Geels, C., Ketzel, M., Khan, J., Tjønneland, A., Overvad, K., Brandt, J., Raaschou-Nielsen, O., 2019. Long-term residential exposure to PM 2.5 , PM 10 , black carbon, NO 2 , and ozone and mortality in a Danish cohort. Environ. Int. 123, 265– 272. https://doi.org/10.1016/j.envint.2018.12.010

ICF International, 2012. Chicago 2010 Regional Greenhouse Gas Emissions Inventory.

Jaikumar, R., Shiva Nagendra, S.M., Sivanandan, R., 2017. Modeling of real time exhaust emissions of passenger cars under heterogeneous traffic conditions. Atmos. Pollut. Res. 8, 80–88. https://doi.org/10.1016/j.apr.2016.07.011

Jeong, C.H., Traub, A., Evans, G.J., 2017. Exposure to ultrafine particles and black carbon in diesel-powered commuter trains. Atmos. Environ. 155, 46–52. https://doi.org/10.1016/j.atmosenv.2017.02.015

Ježek, I., Blond, N., Skupinski, G., Močnik, G., 2018. The traffic emission-dispersion model for a Central-European city agrees with measured black carbon apportioned to traffic. Atmos. Environ. 184, 177–190. https://doi.org/10.1016/j.atmosenv.2018.04.028

Ježek, I., Katrasnik, T., Westerdahl, D., Mocnik, G., 2015. Black carbon, particle number concentration and nitrogen oxide emission factors of random in-use vehicles measured with the on-road chasing method. Atmos. Chem. Phys. 15, 11011–11026. https://doi.org/10.5194/acp-15-11011-2015

Jian, L., Zhao, Y., Zhu, Y.P., Zhang, M.B., Bertolatti, D., 2012. An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Sci. Total Environ. 426, 336–345. https://doi.org/10.1016/j.scitotenv.2012.03.025

Jiang, Y.Q., Ma, P.J., Zhou, S.G., 2015. Macroscopic modeling approach to estimate traffic- related emissions in urban areas. Transp. Res. Part D Transp. Environ. https://doi.org/10.1016/j.trd.2015.10.022

Johnson, T., 2013. Vehicular Emissions in Review. SAE Int. 2013-01-0538 6, 699–715. https://doi.org/10.4271/2013-01-0538

Just, A.C., Wright, R.O., Schwartz, J., Coull, B.A., Baccarelli, A.A., Tellez-Rojo, M.M., Moody, E., Wang, Y., Lyapustin, A., Kloog, I., 2015. Using High-Resolution Satellite Aerosol Optical Depth to Estimate Daily PM2.5 Geographical Distribution in Mexico City. Environ. Sci. Technol. 49, 8576–8584. https://doi.org/10.1021/acs.est.5b00859

202

Kamble, S.H., Mathew, T. V, Sharma, G.K., 2009. Development of real-world driving cycle : Case study of Pune , India. Transp. Res. Part D 14, 132–140. https://doi.org/10.1016/j.trd.2008.11.008

Kamińska, J.A., 2018. The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: A case study in Wrocław. J. Environ. Manage. 217, 164–174. https://doi.org/10.1016/j.jenvman.2018.03.094

Kerckhoffs, J., Hoek, G., Messier, K.P., Brunekreef, B., Meliefste, K., Klompmaker, J.O., Vermeulen, R., 2016. Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model. Environ. Sci. Technol. 50, 12894–12902. https://doi.org/10.1021/acs.est.6b03476

Kerckhoffs, J., Hoek, G., Vlaanderen, J., van Nunen, E., Messier, K., Brunekreef, B., Gulliver, J., Vermeulen, R., 2017. Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring. Environ. Res. 159, 500–508. https://doi.org/10.1016/j.envres.2017.08.040

Kim, Y., 2011. Quantification of Vehicle-induced Turbulence on Roadways Using Computational Fluid Dynamics Simulation by Quantification of Vehicle-induced Turbulence on Roadways Using Computational Fluid Dynamics Simulation 93.

Kingsy Grace, R., Manju, S., 2019. A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems. Wirel. Pers. Commun. https://doi.org/10.1007/s11277-019-06535-3

Klompmaker, J.O., Montagne, D.R., Meliefste, K., Hoek, G., Brunekreef, B., 2015. Spatial variation of ultrafine particles and black carbon in two cities: Results from a short-term measurement campaign. Sci. Total Environ. 508, 266–275. https://doi.org/10.1016/j.scitotenv.2014.11.088

Kloog, I., Nordio, F., Coull, B.A., Schwartz, J., 2012. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the mid-atlantic states. Environ. Sci. Technol. 46, 11913–11921. https://doi.org/10.1021/es302673e

Korek, M., Johansson, C., Svensson, N., Lind, T., Beelen, R., Hoek, G., Pershagen, G., Bellander, T., 2017. Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden. J. Expo. Sci. Environ. Epidemiol. 27, 575–581. https://doi.org/10.1038/jes.2016.40

Kousoulidou, M., 2011. Experimental and theoretical investigation of European road transport emissions evolution with the use of conventional fuels and biofuels.

Kousoulidou, M., Fontaras, G., Ntziachristos, L., Bonnel, P., Samaras, Z., Dilara, P., 2013. Use of portable emissions measurement system (PEMS) for the development and validation of passenger car emission factors. Atmos. Environ. 64, 329–338. https://doi.org/10.1016/j.atmosenv.2012.09.062

203

Krecl, P., Cipoli, Y.A., Targino, A.C., Toloto, M. de O., Segersson, D., Parra, Á., Polezer, G., Godoi, R.H.M., Gidhagen, L., 2019. Modelling urban cyclists’ exposure to black carbon particles using high spatiotemporal data: A statistical approach. Sci. Total Environ. 679, 115–125. https://doi.org/10.1016/j.scitotenv.2019.05.043

Krecl, P., Targino, A.C., Landi, T.P., Ketzel, M., 2018. Determination of black carbon, PM2.5, particle number and NOx emission factors from roadside measurements and their implications for emission inventory development. Atmos. Environ. 186, 229–240. https://doi.org/10.1016/j.atmosenv.2018.05.042

Kucbel, M., Corsaro, A., Švédová, B., Raclavská, H., Raclavský, K., Juchelková, D., 2017. Temporal and seasonal variations of black carbon in a highly polluted European city: Apportionment of potential sources and the effect of meteorological conditions. J. Environ. Manage. 203, 1178–1189. https://doi.org/10.1016/j.jenvman.2017.05.038

Kumar, P., Morawska, L., Birmili, W., Paasonen, P., Hu, M., Kulmala, M., Harrison, R.M., Norford, L., Britter, R., 2014. Ultrafine particles in cities. Environ. Int. 66, 1–10. https://doi.org/10.1016/j.envint.2014.01.013

Kuuluvainen, H., Poikkimäki, M., Järvinen, A., Kuula, J., Irjala, M., Dal Maso, M., Keskinen, J., Timonen, H., Niemi, J. V., Rönkkö, T., 2018. Vertical profiles of lung deposited surface area concentration of particulate matter measured with a drone in a street canyon. Environ. Pollut. 241, 96–105. https://doi.org/10.1016/j.envpol.2018.04.100

Lai, H.C., Ma, H.W., Chen, C.R., Hsiao, M.C., Pan, B.H., 2019a. Design and application of a hybrid assessment of air quality models for the source apportionment of PM2.5. Atmos. Environ. 212, 116–127. https://doi.org/10.1016/j.atmosenv.2019.05.038

Lai, H.C., Ma, H.W., Chen, C.R., Hsiao, M.C., Pan, B.H., 2019b. Design and application of a hybrid assessment of air quality models for the source apportionment of PM2.5. Atmos. Environ. 212, 116–127. https://doi.org/10.1016/j.atmosenv.2019.05.038

Lai, J., Yu, L., Song, G., Guo, P., Chen, X., 2013. Development of City-Specific Driving Cycles for Transit Buses Based on VSP Distributions: Case of Beijing. J. Transp. Eng. 139, 749– 757. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000547

Lau, C.F., Rakowska, A., Townsend, T., Brimblecombe, P., Chan, T.L., Yam, Y.S., Mo??nik, G., Ning, Z., 2015. Evaluation of diesel fleet emissions and control policies from plume chasing measurements of on-road vehicles. Atmos. Environ. 122, 171–182. https://doi.org/10.1016/j.atmosenv.2015.09.048

Lejri, D., Can, A., Schiper, N., Leclercq, L., 2018. Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale. Transp. Res. Part D Transp. Environ. 63, 588–603. https://doi.org/10.1016/j.trd.2018.06.023

Li, S., Dragicevic, S., Castro, F.A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., Cheng, T., 2016. Geospatial big data handling theory and methods: A review and research challenges. ISPRS J. Photogramm. Remote Sens. 115, 119–133. https://doi.org/10.1016/j.isprsjprs.2015.10.012

204

Li, S., Lin, J., Li, G., Bai, T., Wang, H., Pang, Y., 2018. Vehicle type detection based on deep learning in traffic scene. Procedia Comput. Sci. 131, 564–572. https://doi.org/10.1016/j.procs.2018.04.281

Lin, J., Chiu, Y.C., Vallamsundar, S., Bai, S., 2011. Integration of MOVES and dynamic traffic assignment models for fine-grained transportation and air quality analyses. 2011 IEEE Forum Integr. Sustain. Transp. Syst. FISTS 2011 176–181. https://doi.org/10.1109/FISTS.2011.5973657

Lin, M.Y., Guo, Y.X., Chen, Y.C., Chen, W.T., Young, L.H., Lee, K.J., Wu, Z.Y., Tsai, P.J., 2018. An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements. Sci. Total Environ. 636, 1139–1148. https://doi.org/10.1016/j.scitotenv.2018.04.248

Liu, B., Christopher, F., 2012. Development of a simplified version of MOVES and application to iterative case studies. Proc. Air Waste Manag. Assoc. Annu. Conf. Exhib. AWMA 1, 530–537.

Liu, B., Frey, H.C., 2015. Variability in Light-Duty Gasoline Vehicle Emission Factors from Trip-Based Real-World Measurements. Environ. Sci. Technol. 49, 12525–12534. https://doi.org/10.1021/acs.est.5b00553

Liu, H., Barth, M., 2012. Identifying the effect of vehicle operating history on vehicle running emissions. Atmos. Environ. 59, 22–29. https://doi.org/10.1016/j.atmosenv.2012.05.045

Liu, H., He, K., Barth, M., 2011. Traffic and emission simulation in China based on statistical methodology. Atmos. Environ. 45, 1154–1161. https://doi.org/10.1016/j.atmosenv.2010.10.051

Liu, H., He, K., Wang, Q., Huo, H., Lents, J., Davis, N., Nikkila, N., Chen, C., Osses, M., He, C., 2007. Comparison of vehicle activity and emission inventory between Beijing and Shanghai. J. Air Waste Manag. Assoc. 57, 1172–1177. https://doi.org/10.3155/1047- 3289.57.10.1172

Liu, J., 2015. Driving Volatility in Instantaneous Driving Behaviors: Studies Using Large-Scale Trajectory Data. University of Tennessee - Knoxville.

Liu, M., Peng, X., Meng, Z., Zhou, T., Long, L., She, Q., 2019. Spatial characteristics and determinants of in-traffic black carbon in Shanghai, China: Combination of mobile monitoring and land use regression model. Sci. Total Environ. 658, 51–61. https://doi.org/10.1016/j.scitotenv.2018.12.135

Lu, H., Song, G., Zhao, Q., Wang, J., He, W., Yu, L., 2018. An investigation of the uncertainty of handbook of emission factors for road transport (HBEFA) for estimating greenhouse gas emissions: A case study in Beijing. Transp. Res. Rec. 2672, 79–88. https://doi.org/10.1177/0361198118796710

Lu, S.-J., Wang, D., Wang, Z., Li, B., Peng, Z.-R., Li, X.-B., Gao, Y., 2019. Investigating the Role of Meteorological Factors in the Vertical Variation in PM2.5 by Unmanned Aerial

205

Vehicle Measurement. Aerosol Air Qual. Res. 19, 1493–1507. https://doi.org/10.4209/aaqr.2018.07.0266

Lundberg, S., Lee, S.-I., 2017. A Unified Approach to Interpreting Model Predictions 1–10.

Lundberg, S.M., Erion, G.G., Lee, S.-I., 2018. Consistent Individualized Feature Attribution for Tree Ensembles.

Martins, L.D., Martins, J.A., Freitas, E.D., Mazzoli, C.R., Gonçalves, F.L.T., Ynoue, R.Y., Hallak, R., Albuquerque, T.T.A., de Andrade, M.F., 2010. Potential health impact of ultrafine particles under clean and polluted urban atmospheric conditions: A model-based study. Air Qual. Atmos. Heal. 3, 29–39. https://doi.org/10.1007/s11869-009-0048-9

Melorose, J., Perroy, R., Careas, S., 2015. A Summary of the First Three Years of LightDuty Vehicle Data, Statewide Agricultural Land Use Baseline 2015. https://doi.org/10.1017/CBO9781107415324.004

Messier, K.P., Chambliss, S.E., Gani, S., Alvarez, R., Brauer, M., Choi, J.J., Hamburg, S.P., Kerckhoffs, J., Lafranchi, B., Lunden, M.M., Marshall, J.D., Portier, C.J., Roy, A., Szpiro, A.A., Vermeulen, R.C.H., Apte, J.S., 2018. Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environ. Sci. Technol. 52, 12563–12572. https://doi.org/10.1021/acs.est.8b03395

Michanowicz, D.R., Shmool, J.L.C., Cambal, L., Tunno, B.J., Gillooly, S., Olson Hunt, M.J., Tripathy, S., Naumoff Shields, K., Clougherty, J.E., 2016a. A hybrid land use regression/line-source dispersion model for predicting intra-urban NO2. Transp. Res. Part D Transp. Environ. 43, 181–191. https://doi.org/10.1016/j.trd.2015.12.007

Michanowicz, D.R., Shmool, J.L.C., Tunno, B.J., Tripathy, S., Gillooly, S., Kinnee, E., Clougherty, J.E., 2016b. A hybrid land use regression/AERMOD model for predicting intra- urban variation in PM 2.5. Atmos. Environ. 131, 307–315. https://doi.org/10.1016/j.atmosenv.2016.01.045

Minet, L., Liu, R., Valois, M.-F., Xu, J., Weichenthal, S., Hatzopoulou, M., 2018. Development and Comparison of Air Pollution Exposure Surfaces Derived from On-Road Mobile Monitoring and Short-Term Stationary Sidewalk Measurements. Environ. Sci. Technol. 52. https://doi.org/10.1021/acs.est.7b05059

Ministry of Natural Resources, 2017. Land Information Ontario Data Description ORN Road Net Element [WWW Document]. URL https://www.sse.gov.on.ca/sites/MNR- PublicDocs/EN/CMID/ORN Road Net Element - Data Description.pdf

Misra, A., Roorda, M.J., MacLean, H.L., 2013. An integrated modelling approach to estimate urban traffic emissions. Atmos. Environ. 73, 81–91. https://doi.org/10.1016/j.atmosenv.2013.03.013

Monks, P., Carruthers, D., Carslaw, D., Dore, C., Harrison, R., Heal, M., Jenkin, M., L., A.C., Stedman, J., Tomlin, A., Williams, M., 2015. Linking Emission Inventories and Ambient Measurements.

206

Morawska, L., Thai, P.K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G.S.W., Jayaratne, R., Kumar, P., Lau, A.K.H., Louie, P.K.K., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., Rahman, M.M., Ristovski, Z., Shafiei, M., Tjondronegoro, D., Westerdahl, D., Williams, R., 2018. Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environ. Int. 116, 286–299. https://doi.org/10.1016/j.envint.2018.04.018

Mulligan, A.-, Nicholson, A., 2002. Uncertainty in Traffic Flow Estimation Using the Moving- Observer Method. Inst. Prof. Eng. New Zeal. Transp. Group. Tech. Conf. Pap. 2002 12p.

Munich, C.R., Part, V., Zhu, P., Wen, L., Du, D., Bian, X., Ling, H., 2018. VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results: Munich, Germany, September 8-14, 2018, Proceedings, Part V 11206, 1–23. https://doi.org/10.1007/978-3-030-01216-8

Nakashima, H., Arai, I., Fujikawa, K., 2019. Passenger Counter Based on Random Forest Regressor Using Drive Recorder and Sensors in Buses. 2019 IEEE Int. Conf. Pervasive Comput. Commun. Work. PerCom Work. 2019 561–566. https://doi.org/10.1109/PERCOMW.2019.8730761

Natural Resources Canada, 2017. 2017 Fuel Consumption Guide 43.

Natural Resources Canada, 2009. Canadian Vehicle Survey.

Nesamani, K.S., Saphores, J.D., McNally, M.G., Jayakrishnan, R., 2017. Estimating impacts of emission specific characteristics on vehicle operation for quantifying air pollutant emissions and energy use. J. Traffic Transp. Eng. (English Ed. 4, 215–229. https://doi.org/10.1016/j.jtte.2017.05.007

Noland, R.B., Quddus, M.A., 2006. Flow improvements and vehicle emissions: Effects of trip generation and emission control technology. Transp. Res. Part D Transp. Environ. 11, 1–14. https://doi.org/10.1016/j.trd.2005.06.003

North Central Texas Council of Governments, 2015. Development of Dallas-Fort Worth On- Road Mobile 2014 Air Emissions Reporting Requirements and Hazardous Air Pollutant Inventory for the 12-County Metropolitan Statistical Area.

Nyhan, M.M., Kloog, I., Britter, R., Ratti, C., Koutrakis, P., 2019. Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data. J. Expo. Sci. Environ. Epidemiol. 29, 238–247. https://doi.org/10.1038/s41370-018- 0038-9

Ontario’s Ministry of Transportation, 2016. Ontario statistics and Management Reporting Summary of Vehicle Population by Vehicle Class 2016.

Pang, Y., Fuentes, M., Rieger, P., 2014. Trends in the emissions of Volatile Organic Compounds (VOCs) from light-duty gasoline vehicles tested on chassis dynamometers in Southern California. Atmos. Environ. 83, 127–135. https://doi.org/10.1016/j.atmosenv.2013.11.002

207

Park, S.S., Kozawa, K., Fruin, S., Mara, S., Hsu, Y.-K., Jakober, C., Winer, A., Herner, J., 2011. Emission Factors for High-Emitting Vehicles Based on On-Road Measurements of Individual Vehicle Exhaust with a Mobile Measurement Platform. J. Air Waste Manage. Assoc. 61, 1046–1056. https://doi.org/10.1080/10473289.2011.595981

Parvez, F., Wagstrom, K., 2019. A hybrid modeling framework to estimate pollutant concentrations and exposures in near road environments. Sci. Total Environ. 663, 144–153. https://doi.org/10.1016/j.scitotenv.2019.01.218

Patton, A.P., Zamore, W., Naumova, E.N., Levy, J.I., Brugge, D., Durant, J.L., 2015. Transferability and generalizability of regression models of ultrafine particles in urban neighborhoods in the boston area. Environ. Sci. Technol. 49, 6051–6060. https://doi.org/10.1021/es5061676

Paulson, S.E.., DeShazo, J.R.., Winer, A.M.., Venkatram, A.., Choi, W.., Ranasinghe, D.., Schulte, N.., Wu, L.., Bunavage, 2017. Identifying Urban Designs and Traffic Management Strategies for Southern California that Reduce Air Pollution Exposure.

Pedregosa, F., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Dubourg, V., Pedregosa, F., Gramfort, A., Michel, V., Thirion, B., Pedregosa, F., Weiss, R., 2011. Scikit-learn : Machine Learning in Python To cite this version : Scikit-learn : Machine Learning in Python. J. Mach. Learn. Res. 12. https://doi.org/https://dl.acm.org/citation.cfm?id=2078195

Peitzmeier, C., Loschke, C., Wiedenhaus, H., Klemm, O., 2017. Real-world vehicle emissions as measured by in situ analysis of exhaust plumes. Environ. Sci. Pollut. Res. 24, 23279–23289. https://doi.org/10.1007/s11356-017-9941-1

Pepe, N., Pirovano, G., Lonati, G., Balzarini, A., Toppetti, A., Riva, G.M., Bedogni, M., 2016a. Development and application of a high resolution hybrid modelling system for the evaluation of urban air quality. Atmos. Environ. 141, 297–311. https://doi.org/10.1016/j.atmosenv.2016.06.071

Pepe, N., Pirovano, G., Lonati, G., Balzarini, A., Toppetti, A., Riva, G.M., Bedogni, M., 2016b. Development and application of a high resolution hybrid modelling system for the evaluation of urban air quality. Atmos. Environ. 141, 297–311. https://doi.org/10.1016/j.atmosenv.2016.06.071

Pérez-Martínez, P.J., Miranda, R.M., Nogueira, T., Guardani, M.L., Fornaro, A., Ynoue, R., Andrade, M.F., 2014. Emission factors of air pollutants from vehicles measured inside road tunnels in S??o Paulo: case study comparison. Int. J. Environ. Sci. Technol. 11, 2155–2168. https://doi.org/10.1007/s13762-014-0562-7

Perugu, H., 2019. Emission modelling of light-duty vehicles in India using the revamped VSP- based MOVES model: The case study of Hyderabad. Transp. Res. Part D Transp. Environ. 68, 150–163. https://doi.org/10.1016/j.trd.2018.01.031

Perugu, H., 2018. Emission modelling of light-duty vehicles in India using the revamped VSP- based MOVES model: The case study of Hyderabad. Transp. Res. Part D Transp. Environ.

208

1, 0–1. https://doi.org/10.1016/j.trd.2018.01.031

Picornell, M., Ruiz, T., Borge, R., García-Albertos, P., de la Paz, D., Lumbreras, J., 2019. Population dynamics based on mobile phone data to improve air pollution exposure assessments. J. Expo. Sci. Environ. Epidemiol. 29, 278–291. https://doi.org/10.1038/s41370-018-0058-5

Quaassdorff, C., Kwak, K.H., Borge, R., Lee, S.B., 2017. Traffic emission simulation and validation with measured data in South Korea. HARMO 2017 - 18th Int. Conf. Harmon. within Atmos. Dispers. Model. Regul. Purp. Proc. 2017-Octob, 65–69.

Rahimi, A., 2017. Short-term prediction of NO2 and NOx concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran. Ecol. Process. 6. https://doi.org/10.1186/s13717-016-0069-x

Raykin, L., Roorda, M.J., MacLean, H.L., 2012. Impacts of driving patterns on tank-to-wheel energy use of plug-in hybrid electric vehicles. Transp. Res. Part D Transp. Environ. 17, 243–250. https://doi.org/10.1016/j.trd.2011.12.002

Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You Only Look Once: Unified, Real- Time Object Detection. CVPR.

Redmon, J., Farhadi, A., 2018. YOLO v.3. Tech Rep. 1–6.

Reis, S., Seto, E., Northcross, A., Quinn, N.W.T., Convertino, M., Jones, R.L., Maier, H.R., Schlink, U., Steinle, S., Vieno, M., Wimberly, M.C., 2015. Integrating modelling and smart sensors for environmental and human health. Environ. Model. Softw. 74, 238–246. https://doi.org/10.1016/j.envsoft.2015.06.003

Reyna, J.L., Chester, M. V, Ahn, S., Fraser, A.M., 2015. Improving the Accuracy of Vehicle Emissions Pro fi les for Urban Transportation Greenhouse Gas and Air Pollution Inventories.

Rivera, M., Basagaña, X., Aguilera, I., Agis, D., Bouso, L., Foraster, M., Medina-Ramón, M., Pey, J., Künzli, N., Hoek, G., 2012. Spatial distribution of ultrafine particles in urban settings: A land use regression model. Atmos. Environ. 54, 657–666. https://doi.org/10.1016/j.atmosenv.2012.01.058

Roberts, D.R., Bahn, V., Ciuti, S., Boyce, M.S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J.J., Schröder, B., Thuiller, W., Warton, D.I., Wintle, B.A., Hartig, F., Dormann, C.F., 2017. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography (Cop.). 40, 913–929. https://doi.org/10.1111/ecog.02881

Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M.A., Pendyala, R.M., 2017. Machine learning approaches for estimating commercial building energy consumption. Appl. Energy 208, 889–904. https://doi.org/10.1016/j.apenergy.2017.09.060

Ryu, B.Y., Jung, H.J., Bae, S.H., 2015. Development of a corrected average speed model for

209

calculating carbon dioxide emissions per link unit on urban roads. Transp. Res. Part D Transp. Environ. 34, 245–254. https://doi.org/10.1016/j.trd.2014.10.012

Sabaliauskas, K., Jeong, C.H., Yao, X., Jun, Y.S., Jadidian, P., Evans, G.J., 2012. Five-year roadside measurements of ultrafine particles in a major Canadian city. Atmos. Environ. 49, 245–256. https://doi.org/10.1016/j.atmosenv.2011.11.052

Saha, P.K., Li, H.Z., Apte, J.S., Robinson, A.L., Presto, A.A., 2019. Urban Ultrafine Particle Exposure Assessment with Land-Use Regression: Influence of Sampling Strategy. Environ. Sci. Technol. 53, 7326–7336. https://doi.org/10.1021/acs.est.9b02086

Samaras, C., Tsokolis, D., Toffolo, S., Magra, G., Ntziachristos, L., Samaras, Z., 2018. Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model. Transp. Res. Part D Transp. Environ. 65, 772–783. https://doi.org/10.1016/j.trd.2017.10.016

Sarafian, R., Kloog, I., Just, A.C., Rosenblatt, J.D., 2019. Gaussian Markov Random Fields versus Linear Mixed Models for satellite-based PM 2.5 assessment: Evidence from the Northeastern USA. Atmos. Environ. 205, 30–35. https://doi.org/10.1016/j.atmosenv.2019.02.025

Sayegh, A., Tate, J.E., Ropkins, K., 2016. Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density, and meteorological conditions using Boosted Regression Trees. Atmos. Environ. 127, 163–175. https://doi.org/10.1016/j.atmosenv.2015.12.024

Schipperijn, J., Kerr, J., Duncan, S., Madsen, T., Klinker, C.D., Troelsen, J., 2014. Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings. Front. Public Heal. 2, 1–8. https://doi.org/10.3389/fpubh.2014.00021

Sentoff, K.M., Aultman-Hall, L., Holmén, B.A., 2015. Implications of driving style and road grade for accurate vehicle activity data and emissions estimates. Transp. Res. Part D Transp. Environ. 35, 175–188. https://doi.org/10.1016/j.trd.2014.11.021

Servin, O., Boriboonsomsin, K., Barth, M., 2006. An Energy and Emissions Impact Evaluation of Intelligent Speed Adaptation. IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC 1257– 1262. https://doi.org/10.1109/ITSC.2006.1707395

Sider, T., Goulet-Langlois, G., Eluru, N., Hatzopoulou, M., 2016. Quantifying the effects of input aggregation and model randomness on regional transportation emission inventories. Transportation (Amst). 43, 315–335. https://doi.org/10.1007/s11116-015-9577-2

Sider, T.M.N., Alam, A., Farrell, W., Hatzopoulou, M., Eluru, N., 2014. Evaluating vehicular emissions with an integrated mesoscopic and microscopic traffic simulation. Can. J. Civ. Eng. 41, 856–868. https://doi.org/10.1139/cjce-2013-0536

Smit, R., Bluett, J., 2011. A new method to compare vehicle emissions measured by remote sensing and laboratory testing: High-emitters and potential implications for emission

210

inventories. Sci. Total Environ. 409, 2626–2634. https://doi.org/10.1016/j.scitotenv.2011.03.026

Smit, R., Kingston, P., Wainwright, D.H., Tooker, R., 2017. A tunnel study to validate motor vehicle emission prediction software in Australia. Atmos. Environ. 151, 188–199. https://doi.org/10.1016/j.atmosenv.2016.12.014

Smit, R., Ntziachristos, L., Boulter, P., 2010. Validation of road vehicle and traffic emission models - A review and meta-analysis. Atmos. Environ. 44, 2943–2953. https://doi.org/10.1016/j.atmosenv.2010.05.022

Smit, R., Somervell, E., 2015. The use of remote sensing to enhance motor vehicle emission modelling in New Zealand.

Solanki, M.J., Umrigar, F.S., Zala, L.B., Amin, A.A., 2016. Application of Moving Car Observer Method for Measuring Travel Time , Delay & Vehicle Flow under Heterogeneous Traffic Condition of C . B . D . Area : Case Study of Surat-Rajmarg ( Chowk to Delhi Gate ). Int. J. Curr. Eng. Technol. 6, 799–803.

Song, G., Yu, L., 2009. Estimation of Fuel Efficiency of Road Traffic by Characterization of Vehicle-Specific Power and Speed Based on Floating Car Data. Transp. Res. Rec. J. Transp. Res. Board 5005, 11–20. https://doi.org/10.3141/2139-02

Song, G., Yu, L., Tu, Z., 2012a. Distribution Characteristics of Vehicle-Specific Power on Urban Restricted-Access Roadways. J. Transp. Eng. 138, 202–209. https://doi.org/10.1061/(ASCE)TE .1943-5436.0000706

Song, G., Yu, L., Zhang, Y., 2012b. Applicability of Traffic Microsimulation Models in Vehicle Emissions Estimates. Transp. Res. Rec. J. Transp. Res. Board 2270, 132–141. https://doi.org/10.3141/2270-16

Soulhac, L., Salizzoni, P., Cierco, F., Perkins, R., 2011. The model SIRANE for atmospheric urban pollutant dispersion ; part I , presentation of the model. Atmos. Environ. 45, 7379– 7395. https://doi.org/10.1016/j.atmosenv.2011.07.008

Stedman, J.R., 2004. The predicted number of air pollution related deaths in the UK during the August 2003 heatwave 38, 1087–1090. https://doi.org/10.1016/j.atmosenv.2003.11.011

Sun, Z., Hao, P., Ban, X. (Jeff), Yang, D., 2015. Trajectory-based vehicle energy/emissions estimation for signalized arterials using mobile sensing data. Transp. Res. Part D Transp. Environ. 34, 27–40. https://doi.org/10.1016/j.trd.2014.10.005

Svetnik, V., Wang, T., Tong, C., Liaw, A., Sheridan, R.P., Song, Q., 2005. Boosting: An ensemble learning tool for compound classification and QSAR modeling. J. Chem. Inf. Model. 45, 786–799. https://doi.org/10.1021/ci0500379

Tao, Z., Kokas, A., Zhang, R., Cohan, D.S., Wallach, D., 2016. Inferring atmospheric particulate matter concentrations from Chinese social media data. PLoS One 11, 1–15. https://doi.org/10.1371/journal.pone.0161389

211

Thunis, P., Miranda, A., Baldasano, J.M., Blond, N., Douros, J., Graff, A., Janssen, S., Juda- Rezler, K., Karvosenoja, N., Maffeis, G., Martilli, A., Rasoloharimahefa, M., Real, E., Viaene, P., Volta, M., White, L., 2016. Overview of current regional and local scale air quality modelling practices: Assessment and planning tools in the EU. Environ. Sci. Policy 65, 13–21. https://doi.org/10.1016/j.envsci.2016.03.013

Tobías, A., Rivas, I., Reche, C., Alastuey, A., Rodríguez, S., Fernández-Camacho, R., Sánchez de la Campa, A.M., de la Rosa, J., Sunyer, J., Querol, X., 2018. Short-term effects of ultrafine particles on daily mortality by primary vehicle exhaust versus secondary origin in three Spanish cities. Environ. Int. 111, 144–151. https://doi.org/10.1016/j.envint.2017.11.015

Toronto Transit Commission Ridership and cost statistics for bus and streetcar routes , 2012 Ridership and cost statistics for bus and streetcar routes , 2012, 2012. 1–4.

Tripathy, S., Tunno, B.J., Michanowicz, D.R., Kinnee, E., Shmool, J.L.C., Gillooly, S., Clougherty, J.E., 2019. Hybrid land use regression modeling for estimating spatio-temporal exposures to PM 2.5 , BC, and metal components across a metropolitan area of complex terrain and industrial sources. Sci. Total Environ. 673, 54–63. https://doi.org/10.1016/j.scitotenv.2019.03.453

Tu, R., Kamel, I., Wang, A., Abdulhai, B., Hatzopoulou, M., 2018. Development of a hybrid modelling approach for the generation of an urban on-road transportation emission inventory. Transp. Res. Part D Transp. Environ. 62, 604–618. https://doi.org/10.1016/j.trd.2018.04.011

Turkensteen, M., 2017. The accuracy of carbon emission and fuel consumption computations in green vehicle routing. Eur. J. Oper. Res. 262, 647–659. https://doi.org/10.1016/j.ejor.2017.04.005

U.S. Environmental Protection Agency, 2015a. Population and Activity of On - road Vehicles in MOVES2014 Draft Report Population and Activity of On - road Vehicles in MOVES2014 Draft Report.

U.S. Environmental Protection Agency, 2015b. Exhaust Emission Rates for Light - Duty On - road Vehicles in MOVES2014 Final Report Exhaust Emission Rates for Light - Duty On - road Vehicles in MOVES2014 Final Report.

U.S. Environmental Protection Agency, 2014. Brake and tire wear emissions from on-road vehicles in MOVES2014. Epa-420-R-14-013.

U.S. Environmental Protection Agency, 2011. Development of Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions Simulator ( MOVES2010 ) Final Report Development of Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions Simulator ( MOVES2010 ) Final Report.

U.S. Environmental Protection Agency, 1995. Traffic Air Dispersion Model (1995) User’s Guide to CAL3QHC Version 2.0: A Modeling Methodology for Predicting Pollutant Concentrations Near Roadway Intersections 006.

212

U.S Enviromental Protection Agency, 2015. Exhaust Emission Rates for Heavy - Duty On - road Vehicles in MOVES2014 Exhaust Emission Rates for Heavy - Duty On - road Vehicles in MOVES2014.

US EPA, 2015. MOVES2014a User Guide [WWW Document]. URL https://www.epa.gov/moves/moves2014a-latest-version-motor-vehicle-emission-simulator- moves

Van den Bossche, J., De Baets, B., Verwaeren, J., Botteldooren, D., Theunis, J., 2018. Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment. Environ. Model. Softw. 99, 58–69. https://doi.org/10.1016/j.envsoft.2017.09.019

Van den Bossche, J., Peters, J., Verwaeren, J., Botteldooren, D., Theunis, J., De Baets, B., 2015. Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset. Atmos. Environ. 105, 148–161. https://doi.org/10.1016/j.atmosenv.2015.01.017

Wang, A., Ge, Y., Tan, J., Fu, M., Shah, A.N., Ding, Y., Zhao, H., Liang, B., 2011. On-road pollutant emission and fuel consumption characteristics of buses in Beijing. J. Environ. Sci. 23, 419–426. https://doi.org/10.1016/S1001-0742(10)60426-3

Wang, Ge, Y., Tan, J., Fu, M., Shah, A.N., Ding, Y., Zhao, H., Liang, B., 2011a. On-road pollutant emission and fuel consumption characteristics of buses in Beijing. J. Environ. Sci. 23, 419–426. https://doi.org/10.1016/S1001-0742(10)60426-3

Wang, J., Dixon, K., Li, H., Ogle, J., 2004. Normal Acceleration Behavior of Passenger Vehicles Starting from Rest at All-Way Stop-Controlled Intersections. Transp. Res. Rec. 1883, 158– 166. https://doi.org/10.3141/1883-18

Wang, J., Rakha, H.A., Fadhloun, K., 2017. Validation of the Rakha-Pasumarthy-Adjerid car- following model for vehicle fuel consumption and emission estimation applications. Transp. Res. Part D Transp. Environ. 55, 246–261. https://doi.org/10.1016/j.trd.2017.06.030

Wang, J.M., Jeong, C.H., Zimmerman, N., Healy, R.M., Wang, D.K., Ke, F., Evans, G.J., 2015. Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters. Atmos. Meas. Tech. 8, 3263–3275. https://doi.org/10.5194/amt-8-3263-2015

Wang, S., 2011. Web-Age Information Management. https://doi.org/10.1007/978-3-642-23535-1

Wang, Westerdahl, D., Wu, Y., Pan, X., Zhang, K.M., 2011b. On-road emission factor distributions of individual diesel vehicles in and around Beijing, China. Atmos. Environ. 45, 503–513. https://doi.org/10.1016/j.atmosenv.2010.09.014

Wardop, J.G., Charlesworth, G., 1954. a Method of Estimating Speed and Flow of Traffic From a Moving Vehicle. Proc. Inst. Civ. Eng. 3, 158–171. https://doi.org/10.1680/ipeds.1954.11628

Weichenthal, S., 2012. Selected physiological effects of ultrafine particles in acute

213

cardiovascular morbidity. Environ. Res. 115, 26–36. https://doi.org/10.1016/j.envres.2012.03.001

Weichenthal, S., Farrell, W., Goldberg, M., Joseph, L., Hatzopoulou, M., 2014. Characterizing the impact of traffic and the built environment on near-road ultrafine particle and black carbon concentrations. Environ. Res. 132, 305–310. https://doi.org/10.1016/j.envres.2014.04.007

Weichenthal, S., Ryswyk, K. Van, Goldstein, A., Bagg, S., Shekkarizfard, M., Hatzopoulou, M., 2016. A land use regression model for ambient ultra fi ne particles in Montreal , Canada : A comparison of linear regression and a machine learning approach. Environ. Res. 146, 65– 72. https://doi.org/10.1016/j.envres.2015.12.016

Weichenthal, S., Van Ryswyk, K., Goldstein, A., Shekarrizfard, M., Hatzopoulou, M., 2015. Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model. Environ. Pollut. 1–8. https://doi.org/10.1016/j.envpol.2015.04.011

Weinmayr, G., Hennig, F., Fuks, K., Nonnemacher, M., Jakobs, H., Möhlenkamp, S., Erbel, R., Jöckel, K.-H., Hoffmann, B., Moebus, S., 2016. Erratum to: Long-term exposure to fine particulate matter and incidence of type 2 diabetes mellitus in a cohort study: effects of total and traffic-specific air pollution. Environ. Heal. 15, 45. https://doi.org/10.1186/s12940-016- 0128-x

Weiss, M., Bonnel, P., Hummel, R., Manfredi, U., 2011. Analyzing on-road emissions of light- duty vehicles with Portable Emission Measurement Systems (PEMS), JRC Scientific and. https://doi.org/10.2788/23820

WHO, 2017. WHO | WHO Global Urban Ambient Air Pollution Database (update 2016) [WWW Document]. WHO. URL https://www.who.int/phe/health_topics/outdoorair/databases/cities/en/ (accessed 9.29.19).

Wojke, N., Bewley, A., Paulus, D., 2018. Simple online and realtime tracking with a deep association metric. Proc. - Int. Conf. Image Process. ICIP 2017-Septe, 3645–3649. https://doi.org/10.1109/ICIP.2017.8296962

Wu, C. Da, Zeng, Y.T., Lung, S.C.C., 2018. A hybrid kriging/land-use regression model to assess PM2.5 spatial-temporal variability. Sci. Total Environ. 645, 1456–1464. https://doi.org/10.1016/j.scitotenv.2018.07.073

Wu, X., Freese, D., Cabrera, A., Kitch, W.A., Weiss, M., Bonnel, P., Kühlwein, J., Provenza, A., Lambrecht, U., Alessandrini, S., Carriero, M., Colombo, R., Forni, F., Lanappe, G., Le Lijour, P., Manfredi, U., Montigny, F., Sculati, M., Olia, A., Abdelgawad, H., Abdulhai, B., Razavi, S.N., Baklanov, A., Molina, L.T., Gauss, M., Ma, X., Jia, H., Kimbrough, S., Baldauf, R.W., Hagler, G.S.W., Shores, R.C., Mitchell, W., Whitaker, D.A., Croghan, C.W., Vallero, D.A., Palash, S.M., Kalam, M.A., Masjuki, H.H., Masum, B.M., Rizwanul Fattah, I.M., Mofijur, M., Pernigotti, D., Georgieva, E., Thunis, P., Bessagnet, B., Fujita, E.M., Campbell, D.E., Zielinska, B., Chow, J.C., Lindhjem, C.E., DenBleyker, A., Bishop, G.A., Schuchmann, B.G., Stedman, D.H., Lawson, D.R., Gurjar, B.R., Ravindra, K.,

214

Nagpure, A.S., 2016. Particulate matter and gaseous pollutions in three megacities over China: Situation and implication. Atmos. Environ. 62, 235–249. https://doi.org/10.1016/j.rser.2013.03.003

Xiao, L., Lang, Y., Christakos, G., 2018. High-resolution spatiotemporal mapping of PM2.5 concentrations at Mainland China using a combined BME-GWR technique. Atmos. Environ. 173, 295–305. https://doi.org/10.1016/j.atmosenv.2017.10.062

Xie, X., Semanjski, I., Gautama, S., Tsiligianni, E., Deligiannis, N., Rajan, R.T., Pasveer, F., Philips, W., 2017. A review of urban air pollution monitoring and exposure assessment methods. ISPRS Int. J. Geo-Information 6, 1–21. https://doi.org/10.3390/ijgi6120389

Xie, Y., Chowdhury, M., Bhavsar, P., 2011. An Integrated Tool for Modeling the Impact of Alternative Fueled Vehicles on Traffic Emissions: A Case Study of Greenville, South Carolina. Transp. Res. Board 90th Annu. Meet.

Xu, H., Bechle, M.J., Wang, M., Szpiro, A.A., Vedal, S., Bai, Y., Marshall, J.D., 2019a. National PM 2.5 and NO 2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Sci. Total Environ. 655, 423–433. https://doi.org/10.1016/j.scitotenv.2018.11.125

Xu, H., Bechle, M.J., Wang, M., Szpiro, A.A., Vedal, S., Bai, Y., Marshall, J.D., 2019b. National PM 2.5 and NO 2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Sci. Total Environ. 655, 423–433. https://doi.org/10.1016/j.scitotenv.2018.11.125

Xu, J., Wang, J., Hilker, N., Fallah-Shorshani, M., Saleh, M., Tu, R., Wang, A., Minet, L., Stogios, C., Evans, G., Hatzopoulou, M., 2018. Comparing emission rates derived from a model with those estimated using a plume-based approach and quantifying the contribution of vehicle classes to on-road emissions and air quality. J. Air Waste Manag. Assoc. 68, 1159–1174. https://doi.org/10.1080/10962247.2018.1484395

Xu, X., 2018. Regional Emission Analysis using Travel Demand Models and MOVES-Matrix (# 18-05363 ). Transp. Reserch Rec. 695.

Xu, X., Liu, H., Anderson, J.M., Xu, Y., Hunter, M.P., Rodgers, M.O., 2016. Estimating Project- Level Vehicle Emissions with VISSIM and MOVES-Matrix. 95th Annu. Meet. Transp. Res. Board. https://doi.org/10.3141/2570-12

Yan-yan Song, Ying Lu, 2015. Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiatry 27, 130–135. https://doi.org/10.11919/j.issn.1002- 0829.215044

Yang, X., Zheng, Y., Geng, G., Liu, H., Man, H., Lv, Z., He, K., de Hoogh, K., 2017. Development of PM2.5 and NO2 models in a LUR framework incorporating satellite remote sensing and air quality model data in Pearl River Delta region, China. Environ. Pollut. 226, 143–153. https://doi.org/10.1016/j.envpol.2017.03.079

Yasmin, F., Morency, C., Roorda, M.J., 2017. Macro-, meso-, and micro-level validation of an

215

activity-based travel demand model. Transp. A Transp. Sci. 13, 222–249. https://doi.org/10.1080/23249935.2016.1249437

Yu, B., Ma, Y., Xue, M., Tang, B., Wang, B., Yan, J., Wei, Y.M., 2017. Environmental benefits from ridesharing: A case of Beijing. Appl. Energy 191, 141–152. https://doi.org/10.1016/j.apenergy.2017.01.052

Yu, X., Prevedouros, P., Sulijoadikusumo, G., 2010. Evaluation of Autoscope, SmartSensor HD, and Infra-Red Traffic Logger for Vehicle Classification. Transp. Res. Rec. J. Transp. Res. Board 2160, 77–86. https://doi.org/10.3141/2160-09

Zhai, Z., Song, G., Liu, Y., Cheng, Y., He, W., Yu, L., 2019. Characteristics of operating mode distributions of light duty vehicles by road type, average speed, and driver type for estimating on-road emissions: Case study of Beijing. J. Intell. Transp. Syst. Technol. Planning, Oper. 23, 191–202. https://doi.org/10.1080/15472450.2018.1528447

Zhang, K., Batterman, S., 2013. Air pollution and health risks due to vehicle traffic. Sci. Total Environ. 450–451, 307–316. https://doi.org/10.1016/j.scitotenv.2013.01.074

Zhang, M., Song, Y., Cai, X., Zhou, J., 2008. Economic assessment of the health effects related to particulate matter pollution in 111 Chinese cities by using economic burden of disease analysis 88, 947–954. https://doi.org/10.1016/j.jenvman.2007.04.019

Zhang, Q., Fan, J., Yang, W., Chen, B., Zhang, L., Liu, J., Wang, J., Zhou, C., Chen, X., 2017. The effects of deterioration and technological levels on pollutant emission factors for gasoline light-duty trucks. J. Air Waste Manag. Assoc. 67, 814–823. https://doi.org/10.1080/10962247.2017.1301275

Zhang, Q., Wu, L., Fang, X., Liu, M., Zhang, J., Shao, M., Lu, S., Mao, H., 2018. Emission factors of volatile organic compounds (VOCs) based on the detailed vehicle classification in a tunnel study. Sci. Total Environ. 624, 878–886. https://doi.org/10.1016/j.scitotenv.2017.12.171

Zhao, H., Stone, R., Zhou, L., 2010. Analysis of the particulate emissions and combustion performance of a direct injection spark ignition engine using hydrogen and gasoline mixtures. Int. J. Hydrogen Energy 35, 4676–4686. https://doi.org/10.1016/j.ijhydene.2010.02.087

Zhao, W., Ngoduy, D., Liu, R., 2018. A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection. https://doi.org/10.1016/j.trc.2018.05.025

Zheng, F., Li, J., Van Zuylen, H., Lu, C., 2017. Influence of driver characteristics on emissions and fuel consumption. Transp. Res. Procedia 27, 624–631. https://doi.org/10.1016/j.trpro.2017.12.142

Zheng, Y., Yi, X., Ming, L., Ruiyuan, L., Zhangqing, S., Eric, C., Tianrui, L., 2015. Forcasting Fine-Grained Air Quuality Based on Big Data. Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 2267–2276.

216

Zhou, M., Jin, H., Wang, W., 2016. A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing. Transp. Res. Part D Transp. Environ. 49, 203–218. https://doi.org/10.1016/j.trd.2016.09.008

Zimmerman, N., Wang, J.M., Jeong, C., Wallace, J.S., Evans, G.J., 2016. Assessing the Climate Trade-Offs of Gasoline Direct Injection Engines. https://doi.org/10.1021/acs.est.6b01800

Appendices

APPENDIX A: VALIDATION RESULTS OF SIMULATED TRAFFIC VOLUMES AGAINST MANUAL COUNTS

We calculated the hourly number of vehicles passing the radar based on the turning movement at the intersection and then compared it with the number of vehicles detected at the data collection point set-up in VISSIM for Thursday and Sunday. The results are shown in FIGURE A-1 and illustrate minimal differences between simulated and observed counts. In general, these differences are within the range of what other studies have documented in terms of traffic simulation model performance (Hourdakis, 2003).

FIGURE A-1 Comparison of hourly number of vehicles between VISSIM and street-level counts

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APPENDIX B: CALIBRATION OF TRAFFIC SIMULATION MODEL

The traffic simulation model VISSIM includes default values for each variable, but it also allows users to modify the parameters within their corresponding reasonable ranges based on field- measured conditions. In terms of traffic speeds, comparisons between simulated speeds and radar data were used to calibrate the model. In total, 18 parameters in the traffic simulation model and their calibration ranges were selected based on a literature review of car-following and lane- changing behaviours (TABLE B-1) (Park and Schneeberger, 2003; Cunto and Saccomanno, 2008; Habtemichael and Santos, 2012). We employed a one-at-a-time (OAT) approach to vary the parameters within acceptable ranges (based on the literature review) in order to calibrate the traffic speeds and match the distribution of the radar records.” TABLE B-1 List of parameters with their default values and ranges for calibration

Parameters Unit Default Calibration Range

Desired Speed Distribution km/h 48-58 45-60, 50-65

Average Standstill Safety Distance m 2 1, 3

Preceding Vehicles - 4 1, 3

Look Ahead Distance m 250 200, 300

Waiting Time before Diffusion s 60 20, 40

Minimum Headway m 0.5 0.2, 0.8

Maximum Deceleration (Own) m/s2 -4 -2, -6

Maximum Deceleration (Trailing) m/s2 -3 -1, -5

-1m/s2 per Distance (Own) m 100 50, 150

-1m/s2 per Distance (Trailing) m 100 50, 150

Accepted Deceleration (Own, Trailing) m/s2 -1.0 -1.5, -0.5

Safety Distance Reduction Factor - 0.6 0.2, 0.8

Maxi. Deceleration for Cooperative Braking m/s2 -3 -5, -1

Additive part of Desired Safety Distance m 2 0, 4

Multiplicative Part of Desired Safety Distance m 3 1, 5

Emergency Stop m 5 7

Lane Change m 200 150, 300

Random Seed - 42 25, 67

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