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Supplementary Information

Quantifying the air quality and health benefits of greening freight movements

Laura Minet1, Tufayel Chowdhury1, An Wang1, Yijun Gai1, Daniel Posen1, Matthew Roorda1, Marianne Hatzopoulou1*

1Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario, Canada

*Corresponding author, Department of Civil Engineering, University of Toronto35 St George Street, Toronto, ON M5S 1A4

Telephone: 416-978-0864. Email: [email protected]

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LIST OF ABBREVIATIONS

ACM2 Asymmetrical Convective Model version 2 NEI National Emission Inventory

AYLL Attributable Years of Life Lost NH3 Ammonia

BC Black Carbon NMVOC Non-Methane Volatile Organic Compound

CanCHEC Canadian Census Health and Environment Cohort NO Nitric oxide

CEMS Continuous Emissions Monitoring System NO2

Nitrogen dioxide

CEREACentre d'Enseignement et de Recherche en Environnement Atmosphérique

NOx

Nitrogen oxides

CMAQ Community Multiscale Air Quality model NPRI National Pollutant Release Inventory

CO Carbon monoxide NYC New York CityCRA Comparative Risk Assessment NYS New York stateCRF Concentration Response Function O3 OzoneCTM Chemical Transport Model OD Origin-DestinationDA Dissemination Area PC Primary organic CarbonDp Particle diameter PIF Potential Impact FractionEC Elemental Carbon PinG Plume-in-GridEF Emission Factor PM Particulate Matter

EMEP European Monitoring and Evaluation Programme POA Particulate Organic Aerosol

GLC Global Land Cover RMSE Root Mean Square ErrorGTHA Greater Toronto and Hamilton Area RR Risk RatioHONO Nitrous acid SIREAM SIze REsolved Aerosol Model

IESO Independent Electricity System Operator SNAP Selected Nomenclature for sources of Air

PollutionMD Mineral Dust SO2 Sulfur dioxide

MEGAN Model of Emissions of Gases and Aerosols from Nature SOAP Secondary Organic Aerosol Processor

MFB Mean Fractional Bias SUV Sport Utility VehicleMFE Mean Fractional Error VOC Volatile Organic CompoundMNBE Mean Normalized Bias Error VSL Value of Statistical YifeMNGE Mean Normalized Gross Error VSLY Value of Statistical Life YearMOVES MOtor Vehicle Emission Simulator WRF Weather and research forecasting

MOZART4 Model for OZone and Related chemical Tracers WTP Willingness To Pay

MTO Ontario Ministry of Transportation YLL Years of Life Lost

NAICS North American Industry Classification System

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TABLE OF CONTENTSList of abbreviations............................................................................................................21 Methodology.................................................................................................................4

1.1 Greater Toronto and Hamilton Area......................................................................41.2 Diagram of the methodology.................................................................................51.3 Combining DMTI and GLC2000 data...................................................................51.4 WRF model set up and validation against weather stations...................................6

1.4.1 Wind.................................................................................................................61.4.2 Temperature and daily rainfall.........................................................................8

1.5 Correction of MOZART4 boundary conditions.....................................................91.6 Classification of NPRI facilities under the SNAP...............................................111.7 Height of NPRI facilities......................................................................................141.8 Emissions of power plants...................................................................................141.9 Monthly and hourly factors for annual emissions attribution..............................161.10 Speciation factors for the different SNAP sectors............................................18

2 Results.........................................................................................................................202.1 Validation of the concentrations simulated against monitoring station

measurements............................................................................................................................202.2 Base case simulation............................................................................................222.3 Changes in emissions under the scenario without diesel emissions....................232.4 Exposure...............................................................................................................25

3 References...................................................................................................................27

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1 METHODOLOGY

1.1 Greater Toronto and Hamilton Area

The Greater Toronto and Hamilton Area (GTHA) is composed of two municipalities (Toronto and Hamilton) and four regional municipalities (Durham, Halton, Peel and York) (Figure S1). The international airport is located at the border between Peel region and Toronto. Highway 401, which is one the busiest highways in North America, crosses four regions: Durham, Toronto, Peel, and Halton.

Figure S1 Population of each region of the GTHA

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1.2 Diagram of the methodology

Figure S2 summarizes the input data used for setting-up Polair3D over the GTHA.

Figure S2 Diagram of the setup of Polair3D

1.3 Combining DMTI and GLC2000 data

The Global Land Cover 2000 (GLC2000) (1) is based on daily global data collected in the year 2000 by the VEGETATION instrument on board of the SPOT 4 satellite. The spatial resolution of the GLC2000 dataset is 1 km2 at the equator, and land cover types are divided into 23 categories. For each grid cell of our domains, the percentage of land covered by each category is determined.

To take into account the land use changes between 2000 and 2016, and particularly the extension of the urban area, we combined the GLC2000 dataset with DMTI data from 2013. In the DMTI dataset, which does not cover entirely our domains, the land use is divided into 7 categories: Commercial, Governmental and Institutional, Residential, Parks and Recreational, Open Area, Resource and Industrial, and Waterbody. In the grid cells where DMTI data are available, we converted the DMTI land use into the GLC2000 classification as follows:

- Commercial, Governmental and Institutional, Residential, Resource and Industrial, and Open Area were attributed to “Artificial Area”

- Waterbody was attributed to “Water Bodies”- Parks and recreational was attributed to “Shrub area, closed-open, deciduous”

Figure S3 compares the artificial area land cover under the two cases (with and without DMTI data) for the domain D3. The map with the combination of GLC2000 and DMTI data reflects the extension of the GTHA over the last 20 years and is more representative of the current state.

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(a) GLC2000 (b) GLC2000 and DMTI

Figure S3 Comparison of the percentage of artificial area in the grid cells of the domain D3 using GLC2000 solely, and using a combination of GLC2000 and DMTI data

1.4 WRF model set up and validation against weather stations

A total of 20 meteorological stations within the GTHA domain provide measurements of at least wind (speed and direction), temperature or rain fall over the summer 2016 (2).

1.4.1 Wind We developed wind roses for 7 stations based on the measurements and simulations from

the WRF model for August 2016 (Table S1). Except for the Toronto City Centre monitoring station, which is located downtown Toronto and is probably influenced by surrounding buildings, the distribution of wind directions for August is well preserved. In March, a few more stations (i.e., Mono Centre, Toronto INTL, and Uxbridge West) present discrepancies between measured and simulated winds.

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Table S1 Wind roses based on measurements and WRF simulations at 7 weather stations located within the GTHA for the week of August 14th to 20th, 2016

Meteorological Station Measurement Simulation

Burlington Piers

Guelph Turfgrass

Hamilton

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Kitchener Waterloo

Mono centre

Toronto City Centre

Toronto INTL

1.4.2 Temperature and daily rainfall

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The average temperatures recorded during the week of August 14th, 2016, for each station varied between 21.3 and 24.9 °C, while the simulated temperatures varied between 21.3 and 23 °C. The Pearson correlation coefficient between hourly measured and simulated temperature varied between 9 and 75%. The lowest value corresponds to Toronto City Centre, which is where wind simulations were less accurate. As for the daily rainfall in August, measurements between 3.1 and 14.3 mm were recorded, and the WRF model simulated rainfall between 1.8 and 15.2 mm.

For the week of March 20th, 2016, the average temperature recorded varied between -2.1 and 1.8°C, while the simulated temperature varied between -2.1 and 1.2 °C. Pearson correlation coefficient between hourly measured and simulated temperature varied between 68 and 100%. As for the daily rainfall in August, measurements between 0.35 and 17.3 mm were recorded, and the WRF model simulated rainfall between 2.5 and 9.0 mm.

1.5 Correction of MOZART4 boundary conditions

A comparison of the ozone (O3), nitrogen dioxide (NO2) and nitric oxide (NO) concentrations simulated in MOZART4 for the two weeks of simulation with measurements from 17 to 19 monitoring stations of the GTHA produced certain discrepancies: on average, the simulated O3 concentrations exceeded the measurements by 55 µg/m³ (about 100 % overestimation), while the NO2 and NO levels were underestimated by an average of 5.5 and 2.8 µg/m³ (about 40 and 90 % underestimations), respectively. Similar observations were made in the winter of 2016 for O3, and a larger discrepancy was observed for NO2 and NO concentrations (average underestimations of 15.5 and 5.3 µg/m³, i.e., 60 and 95 %, respectively). For this purpose, correction factors, expressed as the ratio of the measured to the simulated concentrations, were applied to correct the O3, NO2, and NO initial and boundary conditions. These were calculated as averaged ratios for the four time periods in the MOZART4 output (12-6am, 6am-12pm, 12-6pm, 6pm-12am) over each week considered (Tables S2 and S3).

MOZART4 model simulates average concentrations for time periods of 6 hours (12-6am, 6am-12pm, 12-6pm, 6pm-12am). We thus proceeded as follows:

1) We averaged the concentrations of O3, NO2, and NO at the 15 monitoring stations for each time period over the two weeks simulated

2) We determined the concentrations from MOZART4 at the monitoring stations for each time period over the same week.

3) We computed the average ratio of measured to simulated concentrations for each time period and each pollutant (Tables S2 and S3).

4) We multiplied the hourly boundary conditions of NO2, NO and O3 by the average ratio calculated for each time period.

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Table S2 NO2, NO and O3 average measured and simulated (MOZART4) concentrations at the monitoring stations for the week of August 14th to 20th, 2016

Pollutant Variable 12-6am 6am-12pm 12-6pm 6pm-12am

NO2Average Measured Concentration (µg/m3) 14.6 15.6 9.6 12.6Average Simulated Concentration (µg/m3) 6.2 12.6 9.8 1.6Average Ratio (Measured/Simulated) 2.5 1.4 1.1 9.9

O3Average Measured Concentration (µg/m3) 34.4 47.2 82.1 68.0Average Simulated Concentration (µg/m3) 133.3 99.4 88.1 130.7Average Ratio (Measured/Simulated) 0.3 0.5 0.9 0.5

NOAverage Measured Concentration (µg/m3) 3.1 5.4 1.7 1.0Average Simulated Concentration (µg/m3) 0.0 0.0 0.7 0.1Average Ratio (Measured/Simulated) 78.3 278.5 3.9 6.9

Table S3 NO2, NO and O3 average measured and simulated (MOZART4) concentrations at the monitoring stations for the week of March 20th to 26th, 2016

Pollutant Variable 12-6am 6am-12pm 12-6pm 6pm-12am

NO2Average Measured Concentration (µg/m3) 35.4 24.5 13.5 24.7Average Simulated Concentration (µg/m3) 7.8 13.0 10.6 4.8Average Ratio (Measured/Simulated) 5.6 1.9 1.3 8.9

O3Average Measured Concentration (µg/m3) 35.1 50.7 70.0 56.3Average Simulated Concentration (µg/m3) 106.6 85.9 81.8 106.6Average Ratio (Measured/Simulated) 0.3 0.6 0.9 0.5

NOAverage Measured Concentration (µg/m3) 4.3 1.7 1.1 5.3Average Simulated Concentration (µg/m3) 0.3 0.1 0.1 0.2Average Ratio (Measured/Simulated) 20.8 6.5 4.7 49.1

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1.6 Classification of NPRI facilities under the SNAP

The classification of the NPRI facilities under the SNAP was made by comparing the North American Industry Classification System (NAICS) code of each facility with the SNAP using the Emission Inventory Guidebook of the European Environment Agency (3). Table S4 summarizes the classification chosen.

Table S4 Classification of the NAICS codes under the SNAP

NAICS 4 SNAP Sector

1129 Other Animal Production 10 Agriculture

2111 Oil and Gas Extraction 5Extraction and distribution of fossil fuels and geothermal energy

2122 Metal Ore Mining 5Extraction and distribution of fossil fuels and geothermal energy

2123 Non-Metallic Mineral Mining and Quarrying 5Extraction and distribution of fossil fuels and geothermal energy

2213 Water, Sewage and Other Systems 9 Waste treatment and disposal2371 Utility System Construction 3 Combustion in manufacturing industry2373 Highway, Street and Bridge Construction 3 Combustion in manufacturing industry2389 Other Specialty Trade Contractors 3 Combustion in manufacturing industry3111 Animal Food Manufacturing 4 Production processes3112 Grain and Oilseed Milling 4 Production processes3113 Sugar and Confectionery Product Manufacturing 4 Production processes3114 Fruit and Vegetable Preserving and Specialty Food Manufacturing 4 Production processes3115 Dairy Product Manufacturing 4 Production processes3116 Meat Product Manufacturing 4 Production processes3118 Bakeries and Tortilla Manufacturing 4 Production processes3119 Other Food Manufacturing 4 Production processes3121 Beverage Manufacturing 4 Production processes3131 Fibre, Yarn and Thread Mills 4 Production processes3133 Textile and Fabric Finishing and Fabric Coating 4 Production processes3159 Clothing Accessories and Other Clothing Manufacturing 4 Production processes3211 Sawmills and Wood Preservation 3 Production in manufacturing industry3212 Veneer, Plywood and Engineered Wood Product Manufacturing 4 Production processes3219 Other Wood Product Manufacturing 4 Production processes3221 Pulp, Paper and Paperboard Mills 3 Production in manufacturing industry3222 Converted Paper Product Manufacturing 4 Production processes3231 Printing and Related Support Activities 6 Solvent and other product use3241 Petroleum and Coal Product Manufacturing 4 Production processes3251 Basic Chemical Manufacturing 4 Production processes3252 Resin, Synthetic Rubber, and Artificial and Synthetic Fibres and Filaments Manufacturing 4 Production processes3253 Pesticide, Fertilizer and Other Agricultural Chemical Manufacturing 4 Production processes3254 Pharmaceutical and Medicine Manufacturing 6 Solvent use

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3255 Paint, Coating and Adhesive Manufacturing 6 Solvent use3256 Soap, Cleaning Compound and Toilet Preparation Manufacturing 6 Solvent use3259 Other Chemical Product Manufacturing 4 Production processes3261 Plastic Product Manufacturing 4 Production processes3262 Rubber Product Manufacturing 6 Solvent use3271 Clay Product and Refractory Manufacturing 4 Production processes3272 Glass and Glass Product Manufacturing 4 Production processes3273 Cement and Concrete Product Manufacturing 4 Production processes3274 Lime and Gypsum Product Manufacturing 4 Production processes3279 Other Non-Metallic Mineral Product Manufacturing 4 Production processes3311 Iron and Steel Mills and Ferro-Alloy Manufacturing 4 Production processes3312 Steel Product Manufacturing from Purchased Steel 4 Production processes3313 Alumina and Aluminum Production and Processing 4 Production processes3314 Non-Ferrous Metal (except Aluminum) Production and Processing 4 Production processes3315 Foundries 4 Production processes3321 Forging and Stamping 4 Production processes3322 Cutlery and Hand Tool Manufacturing 4 Production processes3323 Architectural and Structural Metals Manufacturing 4 Production processes3324 Boiler, Tank and Shipping Container Manufacturing 4 Production processes3326 Spring and Wire Product Manufacturing 4 Production processes3327 Machine Shops, Turned Product, and Screw, Nut and Bolt Manufacturing 4 Production processes3328 Coating, Engraving, Heat Treating and Allied Activities 4 Production processes3329 Other Fabricated Metal Product Manufacturing 4 Production processes3331 Agricultural, Construction and Mining Machinery Manufacturing 4 Production processes3332 Industrial Machinery Manufacturing 4 Production processes3333 Commercial and Service Industry Machinery Manufacturing 4 Production processes3334 Ventilation, Heating, Air-Conditioning and Commercial Refrigeration Equipment Manufacturing 4 Production processes3335 Metalworking Machinery Manufacturing 4 Production processes3336 Engine, Turbine and Power Transmission Equipment Manufacturing 4 Production processes3339 Other General-Purpose Machinery Manufacturing 4 Production processes3341 Computer and Peripheral Equipment Manufacturing 4 Production processes3342 Communications Equipment Manufacturing 4 Production processes3344 Semiconductor and Other Electronic Component Manufacturing 4 Production processes3345 Navigational, Measuring, Medical and Control Instruments Manufacturing 4 Production processes3352 Household Appliance Manufacturing 4 Production processes3353 Electrical Equipment Manufacturing 4 Production processes3361 Motor Vehicle Manufacturing 4 Production processes3362 Motor Vehicle Body and Trailer Manufacturing 4 Production processes3363 Motor Vehicle Parts Manufacturing 4 Production processes3364 Aerospace Product and Parts Manufacturing 4 Production processes

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3365 Railroad Rolling Stock Manufacturing 4 Production processes3369 Other Transportation Equipment Manufacturing 4 Production processes3371 Household and Institutional Furniture and Kitchen Cabinet Manufacturing 4 Production processes3372 Office Furniture (including Fixtures) Manufacturing 4 Production processes3391 Medical Equipment and Supplies Manufacturing 4 Production processes3399 Other Miscellaneous Manufacturing 4 Production processes4111 Farm Product Wholesaler-Distributors 4 Production processes4121 Petroleum Product Wholesaler-Distributors 4 Production processes4162 Metal Service Centres 4 Production processes4179 Other Machinery, Equipment and Supplies Wholesaler-Distributors 4 Production processes4181 Recyclable Material Wholesaler-Distributors 4 Production processes4183 Agricultural Supplies Wholesaler-Distributors 4 Production processes4184 Chemical (except Agricultural) and Allied Product Wholesaler-Distributors 4 Production processes4851 Urban Transit Systems 8 Other mobile sources and machinery4861 Pipeline Transportation of Crude Oil 5 Extraction and distribution of fossil fuels4862 Pipeline Transportation of Natural Gas 5 Extraction and distribution of fossil fuels4869 Other Pipeline Transportation 5 Extraction and distribution of fossil fuels4931 Warehousing and Storage 4 Production processes5111 Newspaper, Periodical, Book and Directory Publishers 6 Solvent and other product use5311 Lessors of Real Estate 2 Non-industrial combustion plants5413 Architectural, Engineering and Related Services 2 Non-industrial combustion plants5417 Scientific Research and Development Services 2 Non-industrial combustion plants5419 Other Professional, Scientific and Technical Services 2 Non-industrial combustion plants5621 Waste Collection 9 Waste treatment and disposal5622 Waste Treatment and Disposal 9 Waste treatment and disposal5629 Remediation and Other Waste Management Services 9 Waste treatment and disposal6112 Community Colleges and C.E.G.E.P.s 2 Non-industrial combustion plants6113 Universities 2 Non-industrial combustion plants6221 General Medical and Surgical Hospitals 2 Non-industrial combustion plants8111 Automotive Repair and Maintenance 4 Production processes8113 Commercial and Industrial Machinery and Equipment (except Automotive and Electronic) Repair and Maintenance 4 Production processes8123 Dry Cleaning and Laundry Services 6 Solvent and other products Use9111 Defense Services 8 Other mobile sources and machinery9119 Other Federal Government Public Administration 8 Other mobile sources and machinery

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1.7 Height of NPRI facilities

Some facilities of the NPRI were missing information regarding the height of their stack, which influences the height at which pollutants are released. For each SNAP sector (excluding the “combustion in energy and transformation industries” sector, which corresponds to the power plants, which we dealt with separately, and the “road transportation” sector), we computed the average height of the facilities for which it was available (Table S5). We attributed the average height to the incomplete facilities.

Table S5 Height of the NPRI facilities in the GTHA

SNAP Number of facilities with height

Average of Height Min of Height Max of Height

2 6 65.3 54.9 893 6 63.5 50.6 76.24 69 57.9 5.5 1945 3 82.4 68 94.246 6 65.2 16.5 137.29 13 45.6 2 184.4

1.8 Emissions of power plants

As emissions of NH3, CO, NOx, PM10, PM2.5, SO2 and NMVOC from power plants provided by the NPRI database (4) for Ontario, and the U.S. EPA National Emission Inventory (NEI) (5) for New York State (NYS) were not comprehensive, we estimated those missing by calculating average emission factors based on data available. Emission factors (EFs) for each power plant were determined according to the equation (1).

EF= annual emissionsannual power generation

∗hourly generation (1)

Table S6 summarizes the EFs for the Ontarian power plants. Cells in red indicate EFs that were inferred from other power plants:

- For NH3, as only two power plants had data, and as their EFs were equal, we attributed the same EF to the other plants, regardless of their generation capacity;

- For NOx and NMVOCs, a linear regression developed based on the EFs and on the generation capacity of the other plants was used to obtain the EFs missing;

- For PM10, as only one EF was missing, it was estimated equal to the PM2.5 EF of the same plant, as PM2.5 and PM10 EFs from other plants were also equal;

- Finally, for SO2, data for only one plant was available. Comparison with SO2 EFs from US natural gas power plants suggested it was particularly high, and we therefore averaged the SO2 EF estimated for the three natural gas plants from NYS.

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Table S6 Emissions Factors (EFs) for the Ontarian power plants included in our domains. Cells in red highlight missing data that was completed.

Ontario Plant IESO Name NH3 CO NOx PM10 PM2.5 SO2 NMVOC Capacity

(MW)DESTEC 12.6 154.1 163.7 8.2 8.2 3.8 56.8 140

GTAA 12.6 1385.2 789.0 114.6 104.5 3.8 56.1 128HALTONHILLS 12.6 52.3 177.6 1.9 1.9 3.8 90.9 757

LENNOX 12.6 369.9 1537.9 3.9 3.9 2824.0 166.8 2130PORTLANDS 12.6 269.7 251.6 6.0 6.0 3.8 84.4 639

SITHE.GOREWAY 12.6 281.0 75.8 2.1 2.1 3.8 101.1 942

TADOUGLAS 12.6 143.8 601.5 6.7 6.7 3.8 55.8 122THOROLDCGS 12.6 290.9 233.1 24.1 24.1 3.8 66.7 320WHITBYCGS 12.6 277.3 547.5 9.4 9.4 3.8 52.1 56

YORKCGS 12.6 1482.8 411.4 21.4 21.4 3.8 74.7 464

Table S7 summarizes the EFs for the NYS power plants, which are divided between coal and petroleum (COAL&PET), natural gas and petroleum (GAS&PET) and natural gas (GAS) power plants. Emissions were provided as hourly emissions, but then summed up to estimate annual emissions to be consistent with emissions in Ontario.

- As there were only two COAL&PET power plants in the domains, the EFs missing were equalled to the EFs from the other power plant when available;

- For GAS&PET power plants, emissions of CO and PM were based as the average EFs over the other GAS&PET plants;

- For GAS plants, the PM2.5 and PM10 EFs were equalled to the corresponding EFs of the only GAS&PET plant providing this data. NMVOC EFs from Ontarian power plants were used;

- The NH3 EFs were taken from the Ontarian power plants.

Table S7 Emissions Factors (EFs) for the Ontarian power plants included in our domains. Red cells highlight missing data that was completed.

New York State Plants_ORIS Code

Fuel Type NH3 CO NOx PM10 PM2.5 SO2 NMVOC Capacity (MW)

2549COAL&PE

T 12.6 145.8 529.5 51.8 18.02790.

4 17.5 1336.5

6082COAL&PE

T 12.6 185.5 844.7 51.8 18.02435.

7 10.6 1840.650449 GAS&PET 12.6 140.6 421.8 111.5 111.5 2.8 61.8 184.150451 GAS&PET 12.6 81.2 701.7 111.5 111.5 3.3 61.8 19.054041 GAS 12.6 757.0 1237.0 111.5 111.5 4.1 61.8 135.054131 GAS 12.6 395.0 472.2 111.5 111.5 3.5 61.8 40.054593 GAS&PET 12.6 110.9 545.2 111.5 111.5 2.8 61.8 38.0

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1.9 Monthly and hourly factors for annual emissions attribution

Tables S8 and S9 show the hourly and monthly factors used to differentiate the emissions from EDGAR by month and time of the day. The SNAP (Selected Nomenclature for Air Pollution) sectors are defined as follows:

- Combustion in Energy and Transformation Industries (1)- Non-Industrial Combustion Plants (2)- Combustion in Manufacturing Industry (3)- Production Processes (4) - Extraction and Distribution of Fossil Fuels and Geothermal Energy (5)- Solvent and Other Product Use (6)- Road Transport (7)- Other Mobile Sources and Machinery (8)- Waste Treatment and Disposal (9)- Agriculture (10)

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Table S8 Hourly factors used for each SNAP sector

SNAP Sector 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 12 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 0.8 0.8 0.8 0.8 0.83 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 0.8 0.8 0.8 0.8 0.84 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 16 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 0.5 0.5 0.5 0.5 0.57 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 0.5 0.5 0.5 0.5 0.58 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 0.8 0.8 0.8 0.8 0.89 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 110 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 111 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Table S9 Monthly factors used for each SNAP sector

SNAP sector JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1 1.252 1.225 1.06 1.007 0.886 0.832 0.826 0.85 0.926 0.993 1.047 1.1122 1.831 2.03 1.884 1.426 0.56 0.432 0.181 0.172 0.208 0.832 1.112 1.4053 1.042 1.056 1.089 1.04 0.957 0.946 0.897 0.846 0.93 1.055 1.078 1.0684 0.952 0.939 1.008 1 0.978 0.984 0.982 0.931 0.993 1.094 1.095 1.0415 0.993 1.001 1.001 0.999 1 0.999 1.001 1.001 1.001 1.001 1.001 1.0016 0.95 0.931 0.998 1.006 0.977 0.996 0.976 0.931 0.986 1.098 1.107 1.047 0.834 0.878 0.973 1.01 1.04 1.053 1.075 1.076 1.05 1.067 1.01 0.9278 0.95 0.931 0.998 1.006 0.977 0.996 0.976 0.931 0.986 1.098 1.107 1.049 1 1 1 1 1 1 1 1 1 1 1 110 0.7 0.85 1 1 1.15 1.3 1.3 1.15 1 1 0.85 0.711 1 1 1 1 1 1 1 1 1 1 1 1

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1.10 Speciation factors for the different SNAP sectors

Table S10 summarizes the speciation factors for CO, NH3, NOx, PM2.5, PMcoarse and SO2

exhaust emissions provided by EMEP/EDGAR. Table S11 summarizes the speciation factors for PM2.5 and PMcoarse tire wear and brake emissions. The subscripts 0 to 4 correspond to the bins defined in the paper: 0.01, 0.0398, 0.1585, 0.6310, 2.5119 and 10 µm.

For traffic-related PM emissions, the speciation factors were modified to account for the specificities of cars and fuels of Canada. In the study of Smyth et al. (6), where the CTM AURAMS was applied in Canada, the proportions of the fine fraction of mobile exhaust emissions attributed to Elemental Carbon (EC) and Primary Organic Carbon (PC) were 0.556 and 0.342, respectively. The remaining fraction was distributed between sulphate, nitrate, ammonium, crustal material, sea-salt, and particle-bound water. We considered this distribution as a base and speciated the PM2.5 traffic exhaust emissions as 56% POA, 34% BC, and 10% MD. The PMcoarse traffic exhaust emissions were entirely considered as MD. The speciation of tire wear (4.2% MD, 67.2% POA and 28.6% BC) and brake emissions (81.5% MD, 14.9% POA and 3.6% BC) was based on the study of Simons (7).

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Table S10 Speciation of the exhaust emissions of CO, NH3, NOx, PM2.5, PMcoarse and SO2 for the different SNAP sectors. The factors represent the percentage of emissions of each pollutant attributed to each sub-

pollutant. PPOA, PBC and PMD refer to Particulate Primary Organic Aerosol (POA), Black Carbon (BC) and Mineral Dust (MD).

Sources From EMEP/EDGAR From Smyth et al. (6)SNAP sector 1 2 3 4 5 6 7 8 9 10 7

CO emissions

CO 100 100 100 100 10

0 100 100 100 100 100

NH3 emissions

NH3100 100 100 100 10

0 100 100 100 100 100

NOx emissions

NO 90 90 90 90 90 90 90 90 90 90NO2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2HONO 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8

PM2.5 and PMcoarse emissions

PPOA_0 1 2.475 3.75 2.47

5 0 3 2.55 3 0.75

2.55

PPOA_1 1 8.085

12.25

8.085 0 9.8 8.33 9.8 2.4

58.33

PPOA_2 1 11.22 17 11.2

2 0 13.6 11.56

13.6 3.4 11.56

PPOA_3 1 11.22 17 11.2

2 0 13.6 11.56

13.6 3.4 11.56

PPOA_4 1 0 0 0 0 0 0 0 0 0

PBC_0 1 2.475 1.5 2.47

5 1.5 3.75 4.2 1.5 4.5 4.2

PBC_1 1 8.085 4.9 8.08

5 4.9 12.25

13.72 4.9 14.

713.72

PBC_2 1 11.22 6.8 11.2

2 6.8 17 19.04 6.8 20.

419.04

PBC_3 1 11.22 6.8 11.2

2 6.8 17 19.04 6.8 20.

419.04

PBC_4 1 0 0 0 0 0 0 0 0 0

PMD_0 1 0 0 0 0 0 0 0 0 0

PMD_1 1 6.6 6 6.6 16 2 2 8 6 2

PMD_2 1 13.2 12 13.2 32 4 4 16 12 4

PMD_3 1 13.2 12 13.2 32 4 4 16 12 4

PMD_4 1 0 0 0 0 0 0 0 0 0SO2 emissions

SO2100 100 100 100 10

0 100 100 100 100 100

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Table S11 Speciation of the tire wear and brake emissions of PM2.5 and PMcoarse. The factors represent the percentage of emissions of each pollutant attributed to each sub-pollutant. PPOA, PBC and PMD refer to

Particulate Primary Organic Aerosol (POA), Black Carbon (BC) and Mineral Dust (MD).

Tire wear BrakePPOA_0 6.8 3.9PPOA_1 13.6 7.8PPOA_2 13.6 2.8PPOA_3 33.2 2.8PPOA_4 0 0PBC_0 2.9 1.0PBC_1 5.8 1.3PBC_2 5.8 0.7PBC_3 14.1 0.7PBC_4 0 0PMD_0 0 0PMD_1 0.6 16.3PMD_2 1.3 32.6PMD_3 2.3 32.6PMD_4 0 0

2 RESULTS

2.1 Validation of the concentrations simulated against monitoring station measurements

Figure S4 displays the variations of NO2, O3 and PM2.5 concentrations recorded and simulated at the monitoring stations over the two weeks.

21

March 2016 August 2016

(a) NO2

(b) O3

(c) PM2.5

Figure S4 Boxplots of the simulated and measured concentrations of NO2, O3 and PM2.5

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2.2 Base case simulation

Table S12 provides descriptive statistics of the summer and the winter simulations. Figure S5 describes the hourly variations of the simulated concentrations.

Table S12 Comparison of the simulated NO2, O3, PM2.5 and BC concentrations (in µg/m3) in the winter and in the summer over the GTHA

Concentrations in µg/m3 across Time and Location NO2 O3 PM2.5 BC

Mean Winter 10.8 60.1 12.0 0.22Summer 6.4 94.1 8.6 0.17

Median Winter 11.0 60.5 12.0 0.21Summer 6.0 93.4 8.6 0.16

Min Winter 6.7 31.2 9.8 0.18Summer 2.8 61.8 5.8 0.11

Max Winter 42.9 64.4 23.3 2.55Summer 44.0 109.6 21.0 2.62

(a) NO2 (b) BC

(c) O3 (d) PM2.5

Figure S5 Hourly variations of the average concentrations of (a) NO2, (b) BC, (c) O3 and (d) PM2.5 over the GTHA. The lower and the upper whiskers correspond to 5th and the 95th percentile, respectively; the box is

drawn between the first and the third quartile, and the middle line corresponds to the median exposure. The minimum and maximum values are not displayed.

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2.3 Changes in emissions under the scenario without diesel emissions

The maps on Figure S6 display the BC and PM2.5 surface emissions under the base case, and the percent change in emissions under the case without diesel emissions. The boxplots on Figure S7 show the changes in emissions by region.

(a) B

ase

case

surf

ace

emis

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s

(b)

Perc

ent c

hang

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the

surf

ace

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ithou

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Figure S6 Comparison of the surface emissions of BC and PM2.5 under the base case and the case without diesel commercial vehicle emissions

24

Figure S7 Percent change in NO2, PM2.5, and BC surface emissions in the scenario without diesel emissions. The lower and the upper whiskers correspond to 1.5 times the interquartile range; the box is drawn between

the first and the third quartile, and the middle line corresponds to the median exposure.

25

2.4 Exposure

Table S13 provides descriptive statistics of the exposure of the GTHA population to NO2, O3, PM2.5 and BC. Figure S8 displays boxplots of NO2 and BC exposure for the population living within 500 m of the Highway 401.

Table S13 Comparison of the NO2, O3, PM2.5 and BC exposure (in µg/m3) of the population of each region of the GTHA under the base case

NO2 O3 BC PM2.5

Hamilton

WinterMean 15.8 57.3 0.264 11.5Minimum 11.2 31.2 0.189 10.1Maximum 42.9 61.2 2.554 23.3

SummerMean 8.7 95.9 0.197 9.1Minimum 5.4 61.8 0.146 8.0Maximum 44.0 100.7 2.617 21.0

Halton



Peel



Toronto



York



DurhamWinter

Mean 6.6 65.5 0.159 8.8Minimum 3.7 35.2 0.115 7.0Maximum 41.0 69.0 1.606 12.4


26

(a) NO2 (b) BC

Figure S8 Percent changes in exposure to NO2 and BC for the population living within 500 m of Highway 401, under the case study with lower freeway-sourced diesel emissions. The lower and the upper whiskers correspond to 5th and the 95th percentile, respectively; the box is drawn between the first and the third

quartile, and the middle line corresponds to the median exposure.

27

3 REFERENCES

1. Bartholome, E., and A. S. Belward. GLC2000: A New Approach to Global Land Cover Mapping from Earth Observation Data. International Journal of Remote Sensing, Vol. 26, No. 9, 2005, pp. 1959–1977.

2. Environment and Climate Change Canada. Historical Data - Climate. http://climate.weather.gc.ca/historical_data/search_historic_data_e.html. Accessed May 1, 2019.

3. European Monitoring and Evaluation Programme (EMEP). EMEP. http://www.emep.int/. Accessed Feb. 11, 2019.

4. Environment and Climate Change Canada. National Pollutant Release Inventory. http://www.ec.gc.ca/inrp-npri/. Accessed Aug. 9, 2017.

5. U.S. Environmental Protection Agency. Air Markets Program Data | Clean Air Markets.6. Smyth, S. C., W. Jiang, H. Roth, M. D. Moran, P. A. Makar, F. Yang, V. S. Bouchet, and

H. Landry. A Comparative Performance Evaluation of the AURAMS and CMAQ Air-Quality Modelling Systems. Atmospheric Environment, Vol. 43, No. 5, 2009, pp. 1059–1070. https://doi.org/10.1016/j.atmosenv.2008.11.027.

7. Simons, A. Road Transport: New Life Cycle Inventories for Fossil-Fuelled Passenger Cars and Non-Exhaust Emissions in Ecoinvent V3. International Journal of Life Cycle Assessment, Vol. 21, No. 9, 2016, pp. 1299–1313. https://doi.org/10.1007/s11367-013-0642-9.

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