Near-Road Air Pollution Pilot Study Final Report
Southern Ontario Centre for Atmospheric Aerosol Research University of Toronto 2019 Executive Summary
Context
Vehicles emit a complex mixture of air pollutants that can reach wide areas around busy roads. Near-road monitoring of air pollution is needed in order to assess the extent and potential health impacts of the resulting exposure. One-third of Canadians live near major roads and are thus potentially exposed to traffic emissions. This report documents a pilot study conducted between 2015 and 2017 involving six monitoring stations in the cities of Vancouver, British Columbia and Toronto, Ontario. These stations were established beside major roads in order to directly measure traffic-related air pollution and well away from busy roads as comparator urban background sites.
Findings
Local traffic dominates pollutant concentrations beside major roads
Air pollutants associated with vehicles, diesel vehicles in particular, were higher beside roads. For example, over 80% of nitrogen monoxide and 60% of black carbon was found to be coming from local traffic at the near-road sites. Local traffic also contributed up to half of the PM2.5 near roads, with the fraction varying over time. For example, during weekday morning rush hour, almost half of the overall PM2.5 beside Highway 401 in Toronto was due to traffic.
Near-road concentrations can vary widely
Concentrations of traffic-related air pollutants were typically highest during weekday morning rush hour and decreased in the afternoon and on weekends. Concentrations also decreased by up to a factor of four with increasing wind speed from 1 to 10 m/s and were six times higher when the monitoring station was directly downwind of the road. In general, dilution of vehicle exhaust depended on proximity to the road (i.e., traffic in the closest lanes typically dominated roadside concentrations). However, under stagnant wind conditions, little to no dilution occurred and concentrations were similar across a wide region spanning from the roadside to 150 m away.
Near-Road Pollution Study 2019 1 Large trucks contribute disproportionally to emissions
Heavy truck traffic produced pollutant concentrations beside a major road that were similar to those beside the busiest stretch of highway in North America, despite being lower in car traffic by a factor of ten. Emission factors were calculated based on measurements over approximately 400 days, consolidating the emissions from 200 million vehicles. These emission factors allowed direct comparisons across the three near-road sites in terms of the amount of pollutant emitted per kilogram of fuel used by vehicles. It was found that variability at and across the three sites depended more on the proportion of large trucks in the fleet than the total traffic volume. Emission factors for over 100,000 individual vehicle plumes also showed that a small portion of the trucks and cars were responsible for the majority of emissions. Policies and programs implemented to remove this small fraction of highest emitting vehicles from populated areas could yield large benefits.
Black carbon indicates elevated exposure to diesel exhaust
Black carbon can be used as a proxy to estimate exposure to the complex mixture of chemicals in diesel exhaust. The study found roadside levels of black carbon ≈ 1 μg/m3. These levels are in the range that has been associated with an elevated lifetime risk of lung cancer and are 400 times higher than the one in 100,000 risk-factor often used to establish exposure standards for the public. Another common pollutant associated with diesel emissions, nitrogen dioxide, exceeded Canada’s 2020 annual ambient air quality standard of 17 ppb (based on an annual average of hourly measurements) both near the major roads and as far away as 150 m.
Canada’s seasons can increase emissions
Colder winter temperatures increased near-road concentrations of nitrogen oxides and ultrafine particles. This finding suggests that the emission treatment systems on diesel vehicles may not function effectively under cold winter temperatures. In the case of ultrafine particles, the increase was due to less particle evaporation under colder temperatures.
Black carbon was found to be higher in Toronto in summer than winter, suggesting seasonal changes in Ontario diesel fuel formulation may be affecting tailpipe emissions. Notably, this potential influence of fuel composition may offer an effective intervention for reducing black carbon emissions from vehicles.
Near-Road Pollution Study 2019 2 As tailpipe emissions drop, non-tailpipe emissions are emerging
Improvements to vehicle technologies have led to an overall reduction in the tailpipe emissions of many pollutants. However, this study has revealed that non-tailpipe emissions (brake wear, tire wear and resuspension of road dust) are rising. For example, non-tailpipe emissions are now contributing more PM2.5 than primary tailpipe emissions in downtown Toronto. Moreover, non- tailpipe emissions are changing the composition of PM2.5 by increasing the concentrations of metals such as Ba, Cu, Fe, and Ca beside roads.
Near-road sites reflect pollutant concentrations across the city
Pollutant levels measured through sampling across Toronto with a mobile lab (on highways, arterial roads, and quiet streets) were similar to those measured at the near-road monitoring sites. Thus, the pollutant concentrations at the near-road sites appear to be representative of other near-road locations across the city with equivalent vehicle fleet characteristics. Thus near-road monitoring can capture the wide range in exposure to traffic pollutants that is being experienced by residents across a city.
Recommendations
Creating a national network
A national near-road monitoring network is needed and should include long-term near-road monitoring stations established in Canada’s largest cities.
A national near-road monitoring network should support development of policies or guidelines (e.g. siting facilities for vulnerable populations) and assessing the effectiveness of potential air quality or climate-related interventions. Relevant stakeholders should be engaged from the outset in order to consider in advance how the data will be used and by whom.
A national network for near-road monitoring should promote outreach and public involvement, to proactively engage Canadians, encourage behaviour change and build stronger societal support for new policies or regulations.
Near-Road Pollution Study 2019 3 A national network for near-road monitoring should publicly share data from near-road stations through web sites, phone apps, and public displays (e.g., electronic signs on highways) when and where impacts are potentially arising.
A national network for near-road monitoring should have a three-tiered design with: i) permanent stations, ii) easily deployable enclosures with selected instruments for shorter-term monitoring, and iii) widespread networks of inexpensive sensor technologies implemented as part of smart city initiatives.
The scale of a national network for near-road monitoring should strategically balance the coverage achieved with the resources required. This process should be informed by considering the risks related to only monitoring at selected locations and the potential severity of impacts that might thereby go undetected.
A national network for near-road monitoring should establish metrics to assess the effectiveness and adequacy of the program. These metrics should guide decisions on creating, relocating, or terminating monitoring stations or instruments. Suggested metrics include data completeness and reliability of instruments; the degree of variability in observations over time or across sites; and the focus and level of use of resulting data by researchers, policy developers, and the public.
Station design and placement
Site selection for stations should provide geographic coverage across Canada and take into consideration population and traffic density.
The near-road stations should be designed to be flexible and adaptable. Capacity and space within stations should be adequate so that instruments can be rotated across stations and evolve as new vehicle technologies and pollutants emerge.
Urban background stations are not essential to enabling near-road monitoring. Therefore, it is recommended that additional background stations not be included as a core component of the near-road monitoring network. However, urban background stations can provide useful data for a range of other research applications and it is thus recommended that the continuing operation of existing background stations be considered on a case-by-case basis.
Near-Road Pollution Study 2019 4 Measurement methods
Near-road stations should all be equipped with high-quality instruments to measure NOx, CO, CO2,
PM2.5, UFP, BC, traffic volume, vehicle types, and meteorological data in order to create a backbone for traffic-related air pollution monitoring across the country. Additional pollutants such as SO2 and
O3 may also be included depending on the pre-existing availability of other nearby measurements.
PM2.5 speciation and organic gases should be measured some of the time or at some of the stations. This partial coverage might be achieved by rotating higher time-resolution instruments annually across stations or, if using twenty-four-hour integrated sample collection, rotating samplers or only making measurements every six days.
Policy implications
Policies should be implemented to identify and remove the highest emitting vehicles from the road. Priority should be given to repairing, retrofitting, relocating or removing older heavy-duty diesel vehicles from the road. Better regulation of heavy-duty vehicles represents a substantial opportunity to improve air quality and reduce exposure to traffic pollution.
Emission interventions involving both incentives and penalties should be considered. For example, large gains in air quality could be achieved through incentives that reward proactive companies seeking to strengthen their social licence by operating clean diesel vehicle fleets. Strong corporate citizens might be rewarded through public recognition on their vehicles, priority lanes to expedite freight delivery, and/or restricted access to more populated regions within cities, such as near schools during daytime hours.
Exposure to traffic-related air pollutants should be reduced where people live, work and play. Strategies should be taken to shape communities so that residents’ exposure to traffic-related air pollution is reduced. These strategies can contribute to existing plans for vibrant and compact communities. For example, a mix of land uses (e.g., commercial, retail, etc.) can be promoted within higher exposure areas; pedestrian and cycling infrastructure can be moved away from high exposure areas; and walkability, transit service quality and access, and parking management can be improved. Indoor exposure can be reduced by improving building design and operation, including ventilation and filtration systems.
Near-Road Pollution Study 2019 5 Next steps
Better methods should be developed to estimate long-term exposure at sites of interest (e.g., a proposed daycare or school beside a major road) to guide siting decisions and the development of supporting policy. Specifically, more research is needed to develop reliable methods to extrapolate increased annual average exposure to traffic-based pollution based on very short-term measurements (e.g., a few weeks at a site of interest or repeated visits through sampling with a mobile laboratory), taking into account variability in traffic composition, seasonality, temperature, and wind speed and direction. More research is also needed to improve extrapolation of monitoring data to generate maps illustrating the variation of traffic-related air pollution concentrations across cities.
Data should continue to be used to generate real-world emission factors, especially as measurement of real-world emissions factors revealed significant differences from the laboratory- based values that are often used to satisfy emission standards. Comparison of emission factors from sites across Canada is recommended given the influences of seasonality, fleet composition, and geographic location identified in this study.
Much more traffic data should be collected and disseminated for major roadways in large urban areas. Traffic counts, fleet composition, and other traffic-related information are important determinants of population exposure to traffic-related air pollution. Traffic data are typically collected by municipalities in Canada but are difficult to obtain in standardized and systematic formats for large geographic areas. A standardized approach to collecting traffic data—truck data in particular—would provide valuable information that can be used to extrapolate results from near- road measurement sites across areas impacted by traffic and support traffic models.
More research is needed to evaluate how the composition and toxicity of PM2.5 is evolving. This study found that non-tailpipe emissions of PM2.5 are rising in downtown Toronto. These non-tailpipe sources include particles created through the abrasion of tires, brakes, and the road surface and the resuspension of road dust. This has changed the composition of traffic-related PM2.5. More research is needed to determine whether these emissions are growing in other Canadian cities, to understand factors governing this apparent rise, and to pilot possible intervention strategies.
Near-Road Pollution Study 2019 6 Integrated metrics to describe TRAP mixtures need to be developed rather than only developing standards based on individual pollutants. Near-road monitoring of air quality involves measurements of individual pollutants that serve as proxies for the complex mixture of chemicals present in vehicle emissions; however, many chemicals within this mixture may contribute to any given health outcome. Integrated metrics would be better suited to assessing the cumulative long- term impact of exposure to traffic emissions.
Related Peer Reviewed Publications
Most of the findings and data included in this report has been reviewed and published in the journal papers listed here.
Dabek-Zlotorzynska, E., Celo, V., Ding, L., Herod, D., Jeong, C., Evans, G.J., Hilker, N. 2019.
Characteristics and sources of PM2.5 and reactive gases near roadways in two metropolitan areas in Canada. Atmos. Environ. doi:10.1016/j.atmosenv.2019.116980 Healy, R., Sofowote, U., Su, Y., Debosz, J., Noble, M., Jeong, C-H., Wang, J.M., Hilker, N., Evans, G.J., Doerksen, G., Jones, K., Munoz, A. 2017. Ambient measurements and source apportionment of fossil fuel and biomass burning black carbon in Ontario. Atmos. Environ. 161: 34–47. Healy, R., Wang, J., Jeong, C-H., Lee, A., Willis, M., Jaroudi, E., Zimmerman, N., Hilker, N., Murphy, M., Eckhardt, S., Stohl, A., Abbatt, J., Wenger, J., Evans, G. 2015. Light absorbing properties of ambient black carbon and brown carbon from fossil fuel and biomass burning sources. J. Geophys. Res. 120.13: 6619–33. Healy, R.M., Wang, J.M., Sofowote, U., Su, Y., Debosz, J., Noble, M., Munoz, A., Jeong, C.-H., Hilker, N., Evans, G.J., Doerksen, G. 2019. Black carbon in the Lower Fraser Valley, British Columbia: Impact of 2017 wildfires on local air quality and aerosol optical properties. Atmos. Environ. doi: 10.1016/j.atmosenv.2019.116976 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. Disc. doi:10.5194/amt-2019-112 Jeong, C-H., Wang, J.M., Evans, G.J. 2016. Apportionment of urban particulate matter using hourly resolved trace metals, organics, and inorganic aerosol components. Atm. Chem and Phys. Discuss. doi:10.5194/acp-2016-189 Jeong, C-H., Wang, J.M., Hilker, N., Debosz, J., Sofowote, U., Su, Y., Noble, M., Healy, R.M., Munoz, A., Dabek-Zlotorzynska, E., Celo, V., White, L., Audette, C., Herod, D., Evans, G.J. 2019.
Temporal and spatial variability of traffic-related PM2.5 sources: Comparison of exhaust and non-exhaust emissions. Atmos. Environ. 198: 55–69.
Near-Road Pollution Study 2019 7 Sofowote, U.M., Healy, R.M., Su, Y., Debosz, J., Noble, M., Munoz, A., Jeong, C.-H., Wang, J.M.,
Hilker, N., Evans, G.J., Hopke, P.K. 2018. Understanding the PM2.5 imbalance between a far and near-road location: Results of high temporal frequency source apportionment and parameterization of black carbon. Atmos. Environ. 173: 277–88. Wang, J., Jeong, C-H., Hilker, N., Healy, R.M., Sofowote, U., Debosz, J., Su, Y., McGaughey, M., Doerksen, G., Munoz, A., White, L., Herod, D., Evans, G.J. 2018. Near-road air pollutant measurements: Accounting for inter-site variability using emission factors. Environ. Sci. Technol. 52: 9495–504. Wang, J., Jeong, C-H., Zimmerman, N., Healy, R.M., Hilker, N., Evans G.J. 2017. Real-world emission of particles from vehicles: Volatility and the effects of ambient temperature. Environ. Sci. Technol. 51: 4081–90. Wang, J., Jeong, C-H., Zimmerman, N., Healy, R.M., Wang, D.K.W., Ke, F., Evans, G.J. 2015. Plume- based analysis of vehicle fleet air pollutant emissions and the contribution from heavy emitters. Atmos. Meas. Tech. 8, 3263–275. Xu, J., Hilker, N., Turchet, M., Al-Rijleh, M-K., Tu, R., Wang, A., Masoud, F-S., Evans, G.J., 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. D–Trans. Environ. 62: 90–101. doi:10.1016/j.trd.2018.02.010
Papers submitted
Jeong, C-H., Traub, A., Huang, A., Hilker, N., Wang, J., Herod, D., Dabek-Zlotorzynska, E., Celo, V.
Evans, G.J. 2019. Long-term analysis of PM2.5 in Toronto during 2004–2017: Composition, sources, and oxidative potential. Submitted to Sci. Total Environ., August 2019.
Near-Road Pollution Study 2019 8 List of Abbreviations and Acronyms
AADT Annual average daily traffic AAQS Analysis and air quality section ACSM Aerosol chemical speciation monitor ARM Air resources manager BC Black carbon BrC Brown carbon BTEX Benzene, toluene, ethylbenzene, and xylene CAAQS Canadian ambient air quality standards CBD Central business district CE Collection efficiency CEPA Canadian Environmental Protection Act COD Coefficients of divergence CPF Conditional probability function EB Eastbound EC Elemental carbon ECCC Environment and Climate Change Canada EDT Eastern daylight savings time EF Emission factors EST Eastern standard time EUSAAR European Supersites for Atmospheric Aerosol Research FEP Fluorinated ethylene propylene FF Fossil fuel FMPS Fast mobility particle sizer FTP File transfer protocol GC Gas chromatography GIS Geographic information system HDDV Heavy-duty diesel vehicles IC Ion chromatography IR Infrared LDV Light-duty gasoline vehicles LST Local standard time MAC Mass absorption cross-section MDL Minimum detection limit MECP Ministry of the Environment, Conservation and Parks (Ontario) MTO Ministry of Transportation Ontario NAPS National Air Pollution Surveillance NR Near-road OA Organic aerosol OC Organic carbon OM Organic matter PAH Polycyclic aromatic hydrocarbons
Near-Road Pollution Study 2019 9 PAS Photoelectric aerosol sensor PASS Photoacoustic soot spectrometer PBW Particle-bound water PC Pyrolized carbon PM Particulate matter PMF Positive matrix factorization PN Particle number PNC Particle number concentration POC Primary organic carbon RDP Remote desktop protocol RH Relative humidity RIE Relative ionization efficiency RMSE Root-mean-square error SASS Speciation air sampling system SIA Secondary inorganic aerosol SMPS Scanning mobility particle sizer SOA Secondary organic aerosols SOCAAR Southern Ontario Centre for Atmospheric Aerosol Research SQL Structured query language TC Total carbon TEO Trace element oxide TOC Total organic carbon TOR Thermal–optical reflectance TOT Thermal–optical transmittance TRAP Traffic-related air pollutants UFP Ultrafine particles VOC Volatile organic compounds WB Westbound WIOC Water-insoluble OC WSOC Water-soluble OC XRF X-ray fluorescence
Near-Road Pollution Study 2019 10 List of Chemical Formulas
CH4 methane NH4NO3 ammonium nitrate
CO carbon monoxide (NH4)2SO4 ammonium sulphate
CO2 carbon dioxide NO nitrogen monoxide
C6H6 benzene NO2 nitrogen dioxide
HNO3 nitric acid NOx nitrogen oxides (NO + NO2)
HONO nitrous acid O2 oxygen
N2 nitrogen O3 ozone
NH3 ammonia SO2 sulphur dioxide
List of Chemical Elements by Abbreviation
Ag silver As arsenic Ba barium Br bromine Ca calcium Cd cadmium Co cobalt Cr chromium Cu copper Fe iron He helium Hg mercury K potassium Mn manganese Ni nickel Pb lead Pd palladium Rb rubidium S sulphur Se selenium Si silicon Sn tin Sr strontium Ti titanium V vanadium Zn zinc
Near-Road Pollution Study 2019 11 Acknowledgements
Prof. Greg. J. Evans. P. Eng., FCAE, FAAAS
This report describes the collective contributions of a dedicated team of researchers from four organizations:
• University of Toronto: Greg J. Evans (Lead Author), Nathan Hilker, Cheol-Heon Jeong, and Jon Wang
• The Ontario Ministry of the Environment Conservation and Parks (MECP): Jerzy Debosz, Robert Healy, Anthony Munoz, Michael Noble, Uwayemi Sofowote, and Yushan Su
• Metro Vancouver: Geoff Doerksen, Michyo McGaughey, and Ken Reid
• Environment and Climate Change Canada (ECCC): Celine Audette, Kaitlin Badali, Valbona Celo, Ewa Dabek-Zlotorszynka, Luyi Ding, Dave Henderson, Dennis Herod, David Johnson, Keith Jones, Corrine Schiller, and Luc White
Funding to the University of Toronto for the Near-Road Air Pollution Pilot Study was provided by ECCC, with some support for students from the Natural Sciences and Engineering Research Council (NSERC). Instruments and infrastructure used by the University of Toronto were provided through funding from the Canada Foundation for Innovation. Additional instruments and support were provided by ECCC and the Ontario MECP. The policy implications referenced in the report were proposed by SOCAAR and based on the key science findings resulting from the pilot study. We also want to acknowledge the editing support provided by Stephanie Halldorson.
The photo on the cover of this report was provided by Dr. Cheol-Heon Jeong.
This research was conducted on the traditional territories of the Huron-Wendat, Seneca, Mississaugas of the Credit River, Musqueam, Squamish, and Tsleil-Waututh.
Please cite as: Evans G.J., C. Audette, K. Badali, V. Celo, E. Dabek-Zlotorszynka, J. Debosz, L. Ding, G.N. Doerksen, R.M. Healy, D. Henderson, D. Herod, N. Hilker, C-H. Jeong, D. Johnson, K. Jones, A. Munoz, M. Noble, K. Reid, C. Schiller, U. Sofowote, Y. Su, J. Wang, L. White. “Near-Road Air Pollution Pilot Study Final Report,” Southern Ontario Centre for Atmospheric Aerosol Research, University of Toronto, 2019.
Near-Road Pollution Study 2019 12 Table of Contents
Executive Summary ...... 1 Related Peer Reviewed Publications ...... 7 List of Abbreviations and Acronyms ...... 9 List of Chemical Formulas ...... 11 List of Chemical Elements by Abbreviation ...... 11 Acknowledgements ...... 12 List of Figures ...... 15 List of Tables ...... 17 1 Introduction ...... 18 1.1 Background on Near-Road Air Pollution ...... 18 1.1.1 Identifying Traffic-Related Pollutants ...... 18 1.1.2 Defining Near-Road Concerns ...... 18 1.1.3 The Changing Environment of Near-Road Pollutants ...... 19 1.2 Canadian Background and Context ...... 20 1.2.1 Implications for Government ...... 20 1.3 Project Objectives ...... 22 1.3.1 Design Risks ...... 23 1.4 Chapter Outline of Report ...... 23 2 Site Setup, Equipment, and Database ...... 24 2.1 Site Setup ...... 24 2.1.1 Metro Vancouver Sites...... 24 2.1.2 Toronto Sites ...... 26 2.2 Instruments and Performance ...... 30 2.2.1 Recommended Instruments ...... 32 2.3 Database...... 32 2.3.1 Recommendations ...... 33 3 Results ...... 35 3.1 Spatial Differences Across Sites ...... 35 3.1.1 Hourly Averaged Pollutant Concentrations ...... 35 3.1.2 Twenty-four-Hour Integrated Pollutant Concentrations ...... 40 3.1.3 Summary ...... 53
Near-Road Pollution Study 2019 13
3.2 Temporal Patterns in Traffic-Related Pollutants ...... 54 3.2.1 Traffic Count ...... 54 3.2.2 Carbon Monoxide ...... 57 3.2.3 Nitrogen Monoxide ...... 59 3.2.4 Nitrogen Dioxide ...... 60 3.2.5 Black Carbon...... 61 3.2.6 Ultrafine Particles ...... 63 3.2.7 Ozone ...... 64 3.2.8 Fine Particulate Matter ...... 65 3.3 Vehicle Contribution to Urban Air Pollution ...... 66 3.3.1 Black Carbon Analysis ...... 66 3.3.2 Emission Factor Analysis ...... 68 3.3.3 Contribution of Traffic to PM2.5 ...... 80 3.3.4 Summary: Contribution of Vehicles to Urban Air Pollution ...... 90 3.4 Range of Exposure Across a City...... 91 3.4.1 Spatial Gradient Across Background to Near-Road Sites ...... 91 3.4.2 On-Road Mobile Sampling ...... 94 3.4.3 Near Highway Gradient Sub-Study ...... 96 3.4.4 Summary ...... 103 3.5 Parameters Governing Pollutant Concentrations ...... 104 3.5.1 Estimating the Contribution of Traffic to Near-Road TRAP Concentrations ...... 104 3.5.2 Associating Local TRAP Concentrations with Physical Parameters ...... 114 3.5.3 Summary ...... 118 4 Conclusions, Recommendations, and Broader Perspectives ...... 120 4.1 Conclusions ...... 120 4.1.1 Excess Air Pollution Near Major Roads ...... 120 4.1.2 Parameters Governing the Concentrations of TRAPs ...... 121 4.1.3 Traffic Pollution as a Result of Changes in Technology ...... 122 4.1.4 Estimating Exposure of Canadians to Traffic-Related Air Pollution ...... 122 4.2 Recommendations ...... 124 4.3 Broader Perspectives ...... 126 4.3.1 Risk Informed ...... 126 4.3.2 Valued by Canadians ...... 127 4.3.3 Adaptable ...... 127 4.3.4 Integrated and Cumulative ...... 128 4.3.5 Effective and Performance Measured ...... 129 5 References ...... 130
Near-Road Pollution Study 2019 14 List of Figures
Figure 1.1. Percentage of provincial or national populations living near a major arterial road ...... 21 Figure 1.2. Percentage of municipal population living near a major arterial road ...... 22 Figure 2.1. Location of near-road monitoring station (NR-VAN) on Clark Drive ...... 24 Figure 2.2. Plan view and street view of the Clark Drive near-road monitoring station (NR-VAN) ...... 25 Figure 2.3. Sunny Hill background station and meteorological tower (BG-VAN) ...... 26 Figure 2.4. Toronto near-road (red triangles) and background (blue squares) stations ...... 26 Figure 2.5. Side view of the NR-TOR-1 station (left) and aerial view of Highway 401 segment (right) ...... 27 Figure 2.6. Aerial (left) and side (right) views of the BG-TOR-N station...... 28 Figure 2.7. Location of sampling sites for distance decay gradient study...... 28 Figure 2.8. Plan view (left) of the Toronto near-road monitoring station (NR-TOR-2) and street view ...... 29 Figure 2.9. Aerial (left) and side view (right) of the BG-TOR-S site ...... 30 Figure 2.10. Participating agencies and location in the Near-Road study...... 33 Figure 3.1. Comparison of nitrogen oxides concentrations ...... 35 Figure 3.2. Comparison of mean NO2 (blue) and NO (orange) concentrations ...... 36 Figure 3.3. Comparison of NO2 (blue) and NO (orange) concentrations ...... 36 Figure 3.4. Comparison of ozone (ppb) and odd oxygen (NO2 + O3) ...... 37 Figure 3.5. Comparison of carbon monoxide (ppb) and carbon dioxide (ppm) ...... 38 Figure 3.6. Comparison of particulate matter less than 2.5 µm (PM2.5) and BC ...... 38 Figure 3.7. Comparison of ultrafine concentrations ...... 39 Figure 3.8. Mean concentrations of PM2.5 and PM2.5–10 in weekday and weekend ...... 40 Figure 3.9. Mean concentrations of levoglucosan ...... 41 Figure 3.10. Reconstructed PM2.5 mass by major component and site ...... 42 Figure 3.11. Mean concentrations of EC and OC in PM2.5 samples ...... 43 Figure 3.12. Mean concentrations of EC in weekday and weekend PM2.5 ...... 44 Figure 3.13. Ratio of summer to winter (S/W) EC2 and OC2 fractions in PM2.5 ...... 45 Figure 3.14. Seasonal distribution of EC, WIOC, and WSOC (WSOCbb + WSOCnb) ...... 46 Figure 3.15. Spatial distribution of crustal elements PM2.5 and PM2.5–10 ...... 47 Figure 3.16. Spatial distribution of trace elements in PM2.5 samples ...... 48 Figure 3.17. Spatial distribution of metals indicative of brake wear ...... 49 Figure 3.18. Weekday and weekend distribution of trace elements in PM2.5 samples ...... 50 Figure 3.19. Mean concentrations of NH3 ...... 51 Figure 3.20. Mean concentrations of HNO3 ...... 52 Figure 3.21. Mean concentrations of HONO ...... 53 Figure 3.22. Diurnal traffic patterns at NR-VAN...... 55 Figure 3.23. Diurnal traffic patterns at NR-TOR-1 ...... 56 Figure 3.24. Diurnal traffic patterns at NR-TOR-2 ...... 57 Figure 3.25. Diurnal patterns for CO with a comparison of weekday/weekend and seasonal patterns...... 58 Figure 3.26. Diurnal patterns of NO with a comparison of weekday/weekend and seasonal patterns...... 59 Figure 3.27. Diurnal patterns of NO2 with comparison of weekday/weekend and seasonal patterns ...... 61 Figure 3.28. Diurnal patterns for BC with comparison of weekday/weekend and seasonal patterns ...... 62 Figure 3.29. Diurnal patterns of the particle number concentration (PNC) of ultrafine particles ...... 63 Figure 3.30. Diurnal patterns of O3 ...... 64 Figure 3.31. Diurnal patterns for PM2.5 ...... 65 Figure 3.32. Mean mass concentrations of BC for the Ontario and Vancouver sites ...... 66 Figure 3.33. Mean weekday and weekend mass concentrations of BC for all sites ...... 67 Figure 3.34. Seasonal mean weekday and weekend mass concentrations of BC for all sites ...... 68
Near-Road Pollution Study 2019 15 Figure 3.35. Time series of the plume capture method ...... 70 Figure 3.36. Contributions of the top 5%, 10%, and 25% heaviest emitters ...... 70 Figure 3.37. Product distribution histograms of NOx and CO EFs ...... 71 Figure 3.38. Distribution of fraction of C4 vehicles and EFs at each site ...... 73 Figure 3.39. Mean WD/WE ratios for C4 vehicles and mean pollutant EFs ...... 74 Figure 3.40. NOx, BC, CO, and PN emission factor distributions vs. the percentage of large trucks ...... 75 Figure 3.41. Winter/summer ratios for pollutant factors ...... 76 Figure 3.42. Diurnal trends for NR-TOR-2 of fraction of C4 vehicles and pollutant medians. EFs ...... 77 Figure 3.43. Plot of predicted versus observed EFs ...... 79 Figure 3.44. Comparison of PM2.5 chemical speciation ...... 81 Figure 3.45. Diurnal patterns of organic aerosol (OA) and sulphate ...... 82 Figure 3.46. Diurnal patterns of Ba, Cu, and Zn ...... 83 Figure 3.47. Source profiles of PMF-resolved nine factors ...... 85 Figure 3.48. Source concentrations of PMF-resolved factors ...... 86 Figure 3.49. Diurnal variations of PMF-resolved factors ...... 86 Figure 3.50. Conditional probability function (CPF) plots for regional sources ...... 87 Figure 3.51. Enrichment ratios in Traffic_NT I and Traffic_NT II ...... 87 Figure 3.52. Diurnal variations of the count of total vehicles ...... 88 Figure 3.53. Diurnal variations of the contribution of PM2.5 sources ...... 89 Figure 3.54. Comparisons of NOx, CO, UFP, and BC...... 92 Figure 3.55. Comparison of the coefficient of divergence (COD) and correlation coefficients ...... 92 Figure 3.56. Seasonal variations of the coefficient of divergence (COD) of NOx, CO, UFP, BC, and PM2.5 ...... 93 Figure 3.57. Sampling route of the mobile measurements in the Greater Toronto Area ...... 94 Figure 3.58. Comparison of hourly averaged NOx, CO, UFP, and BC ...... 95 Figure 3.59. Comparison of NO2 ...... 96 Figure 3.60. Location of the sampling sites for the distance decay gradient study ...... 97 Figure 3.61. Downwind and upwind gradients of TRAPs at NR-TOR-1 ...... 97 Figure 3.62. Overall spatial gradients of NOx (NO+NO2), UFP, and BC ...... 98 Figure 3.63. Influence of ambient temperature on the concentrations of NOx, CO, UFP, and BC ...... 99 Figure 3.64. Correlation between NOx and ambient temperature ...... 100 Figure 3.65. Decay gradients of traffic-related air pollutants during air stagnation ...... 101 Figure 3.66. The ratio of TRAP concentrations at 10 m to those at 150 m ...... 101 Figure 3.67. Downwind and upwind decay gradients of traffic-related PM2.5 sources ...... 102 Figure 3.68. Decay gradients of traffic-related PM2.5 sources during downwind ...... 102 Figure 3.69. (a) Satellite image of the NR-VAN site...... 107 Figure 3.70. (a) Satellite image of the NR-TOR-1 site ...... 108 Figure 3.71. (a) Satellite image of the NR-TOR-2 site ...... 109 Figure 3.72. An example time period of CO2 concentrations recorded at NR-TOR-2 ...... 111 Figure 3.73. Average, dimensionless quantities of CL(WD)/CL ...... 115 Figure 3.74. Lines of best fit for normalized local pollutant quantities CL(WS)/CL...... 117
Near-Road Pollution Study 2019 16 List of Tables
Table 2.1. Integrated Particulate Matter (PM) Sampling Instruments and Target Group Analytes ...... 30 Table 2.2. Summary of Continuous Monitoring Instrumentation...... 31 Table 2.3. Base Set of Pollutants ...... 32 Table 3.1. Spatial Differences in BC Mass Concentrations and Source Contributions ...... 69 Table 3.2. Emission Contributions to Concentrations at Near-Road Sites ...... 72 Table 3.3. Fleet Weighted Averages for LDV and HDDV ...... 74 Table 3.4. Multiple Linear Regression for Each Pollutant EF ...... 78 Table 3.5. Contribution of the PMF-Resolved PM2.5 Sources at NR-TOR-1 and NR-TOR-2 ...... 84 Table 3.6. Average Pollutant Concentrations between March 2015 and April 2017 ...... 105 Table 3.7. Differences in Hourly Pollutant Concentrations ...... 106 Table 3.8. Average, Aggregated, and Comparative Pollutant Concentrations at NR-VAN ...... 107 Table 3.9. Average, Aggregated, and Comparative Pollutant Concentrations at NR-TOR-1 ...... 109 Table 3.10. Average, Aggregated, and Comparative Pollutant Concentrations at NR-TOR-2 ...... 110 Table 3.11. Average TRAP Associated with Local Influences ...... 112 Table 3.12. Local TRAP Concentrations between March 2015 and April 2017...... 113 Table 3.13. Coefficient of Determination Values for Linear Models ...... 116 Table 3.14. Regression Parameters of Best Fit Fitted to CL (WS)/CL ...... 117 Table 3.15. Coefficients of Determination for Models Applied to CL (WS)/CL ...... 118
Near-Road Pollution Study 2019 17 1 Introduction
1.1 Background on Near-Road Air Pollution
Exposure to traffic emissions has been associated with a wide range of adverse health outcomes (Brauer et al., 2013; Charman et al., 2017; HEI, 2010, 2013), including increased risk of cardiovascular and respiratory mortality, cancer (Hamra et al., 2015), adverse birth and developmental outcomes (Heck et al., 2013), respiratory diseases (Burr et al., 2004) such as asthma (Achakulwisut et al. 2019, Guarnieri and Balmes, 2014), and premature mortality (Anenberg et al.
2019, Ostro et al., 2015), and some pollutants in traffic exhaust (e.g., benzene [C6H6] and polycyclic aromatic hydrocarbons) can elicit toxicity on their own. Given the wide range of pollutants present in vehicle emissions and the variability of this mixture for different types and ages of vehicles (Zielinska et al., 2004), it is unlikely that all these adverse health outcomes are due to any single pollutant. It is more conceivable that different parts of the pollutant mixture are associated with different outcomes. 1.1.1 Identifying Traffic-Related Pollutants
Some traffic-related pollutants are more prevalent and easily measured, making them better metrics of the overall exposure. The most common are listed below:
• Elemental carbon (EC) has traditionally been used as the proxy to assess occupational exposure to diesel exhaust (a Class 1 carcinogen) (Demers et al., 2017). However, it is more likely that the integrated impact of diesel exhaust, rather than EC itself, is eliciting detrimental health outcomes.
• Metals, such as Ba and Cu, are of interest due to their relevance to non-tailpipe emissions.
• Nitrogen oxides (NOx) have been used as metrics of vehicle exhaust exposure, as these can easily be measured using passive methods.
• Ultrafine particles (UFP) are used as an indicator of traffic-related air pollution, as their concentrations can be measured at high time-resolution using easily portable instruments.
1.1.2 Defining Near-Road Concerns
While concentrations of traffic pollutants are known to decrease with distance from roadways, they can still be detected at distances notable to urban and agricultural planning. A review of near-road air pollutant monitoring studies showed that elevated concentrations of traffic-related pollutants, such as UFP, black carbon (BC), nitrogen oxides (NOx), and carbon monoxide (CO), generally occurred within 50 m of a road but levels above background continued to be measured up to 500 m
Near-Road Pollution Study 2019 18 from the road (Evans et al., 2011). This potentially impacts the approximately 10 million Canadians who live within 250 m of a major road. Notably, Canada is by no means unique in having a population this close to major roads. In the United States, for example, 19% of the population live within 500 m of a major roadway, with values up to 40% in more urbanized states such as California (Rowangould, 2013). For many pollutants, reductions found away from roads are primarily due to dilution and dispersion. However, reductions can be affected by other physical and/or chemical processes. For example, concentrations of NOx and UFP decrease with distance from roadways more steeply than other traffic pollutants due to chemical conversion (e.g., NOx) and evaporation (e.g., UFP). Generally, spatial variability in roadside air pollutants is affected mainly by distance from roadway (and modified by urban topography and meteorology). However, on a larger spatial scale, vehicle emissions are contributors to secondary pollutants such as ozone (O3) and atmospheric particulate matter with a diameter of less than 2.5 micrometres (PM2.5), thereby increasing citywide and regional levels of pollution. 1.1.3 The Changing Environment of Near-Road Pollutants
Significant progress has been made in reducing emissions from on-road vehicles over the past several decades. Compared to 1970 models, new vehicles are roughly 99 percent cleaner for common pollutants. Fuels are now much cleaner: lead has been eliminated, and sulphur levels are more than 90 percent lower than they were prior to regulation (EPA, 2018). However, the number of vehicles and fleet gasoline consumption has grown. According to Statistics Canada, the size of the Canadian vehicle fleet increased 17 percent between 2007 and 2016. With the spread of urban regions, more Canadians are commuting, and their commutes are longer. From 1996 to 2016, the number of car commuters increased by 28 percent and the median one-way distance driven increased from 7.8 km to 8.7 km (StatCan, 2016, StatCan, 2017). The types of vehicles are changing as well, as Canadians are buying trucks at a much higher rate than passenger cars. The percentage of new vehicle sales categorized as trucks rose 20 percent over the last decade (CIPMA, 2018). This growth in driving distances, as well as consumer preference-driven changes to the vehicle fleet, are producing an increase in Canadian consumption of gasoline at 0.3 percent annually. Adoption of vehicle technologies such as catalytic converters has also done much to reduce concentrations of secondary pollutants (e.g., O3 and PM2.5) in urban centres, but vehicle technologies and fuel mixtures continue to change, and monitoring must adapt to these changes in order to identify the possibility of new pollutants. Modern diesel vehicles, for example, are being equipped with selective catalytic reduction systems and particulate filters so as to satisfy Tier 3 and 4 emissions regulations (ECCC, 2017). Gasoline direct-injection engines, which improve vehicle mileage and thereby reduce carbon dioxide emissions are becoming increasingly common in Canada. While these vehicles may or may not provide climate benefits, they may also be emitting increased levels of other pollutants. Similarly, the move toward electric vehicles will drastically reduce tailpipe emissions but may potentially increase the emission of selected metals.
Near-Road Pollution Study 2019 19 1.2 Canadian Background and Context
Direct measurement near major roads is needed to understand the impacts of vehicle emissions on overall air quality and to track changes due to the rapid evolution in vehicle technologies. This report documents a pilot study that was conducted between 2015 and 2017 involving six monitoring stations in the cities of Vancouver, British Columbia and Toronto, Ontario. Three of these stations were established beside major roads to directly measure traffic-related air pollution in Canada. An additional three stations were established well away from major roads to measure background pollution in urban centres in Canada. This study arose out of a recommendation in the Government of Canada’s Smog Science Assessment (Environment Canada, 2012) to study near-road air pollution. In 2010, Environment and Climate Change Canada (ECCC)1 commissioned the Southern Ontario Centre for Atmospheric Aerosol Research (SOCAAR) at the University of Toronto to assess existing information on near-road monitoring and develop recommendations on the design of a near-road network for Canada. In 2012, ECCC and the University of Toronto launched a pilot study in collaboration with Metro Vancouver2 and the Ontario MECP.3 Three years of planning and development led to the creation of the six near-road and background stations in Vancouver and Toronto described in this report. 1.2.1 Implications for Government
Traffic-related air pollution has long been recognized as potentially impacting health, climate, and the environment. Near-road monitoring of air quality in Canada will have implications that directly relate to a wide range of federal and provincial programs, policies, and regulations, including the following:
• Canadian ambient air quality standards
• Vehicle emissions standards
• Vehicle fuel efficiency standards
• Emissions inventories
• Vehicle emissions monitoring programs
• Fuel composition regulations
1 Environment Canada was officially renamed Environment and Climate Change Canada in November 2015. Its current name and acronym (ECCC) will be used throughout this report. 2 Metro Vancouver Regional District (Metro Vancouver) is a regional government comprised of 21 municipalities, one Electoral Area, and one Treaty First Nation in British Columbia that plans for and delivers regional scale services, including air quality. 3 The Ontario Ministry of the Environment was renamed on June 24, 2014 to the Ministry of the Environment and Climate Change. On June 29, 2018, it was changed to the Ministry of the Environment, Conservation and Parks. The acronym MECP will be used throughout this report.
Near-Road Pollution Study 2019 20 Traffic-related air pollution also impacts municipal planning, interventions, programs, and initiatives, including the following:
• Siting of facilities for vulnerable populations (e.g., daycares, schools, parks, sports fields, hospitals, long-term care)
• Development of public transit and related interventions (e.g., the King Street pilot project in downtown Toronto)
• Urban infrastructure and form to enable traffic flow
• Urban freight strategies
• Initiatives to reduce urban greenhouse gas emissions
Direct knowledge of traffic-related concentrations of pollutants and how they may vary over space and time is needed to support a wide range of policy-related initiatives at all levels of government. Policies that arise out of the knowledge gained from this pilot study of near-road traffic pollution is of considerable importance in Canada, as approximately one-third of Canadians live within 250 m of a major road, with a range from 10% in Alberta to almost 45% in Ontario (Figure 1.1).
Figure 1.1. Percentage of provincial or national populations living near a major arterial road: within 50 m (purple), 100 m (gold) or 250 m (green).
In Canada’s major cities, the values exceed their provincial averages, with 49% of the Vancouver population living within 250 m of a major road and 56% in Toronto (Figure 1.2). Data on traffic-related air pollution is of considerable importance for future policy decisions across Canada. Increasing our knowledge of near-road pollutants in the changing traffic-related
Near-Road Pollution Study 2019 21 environment is necessary in order to support initiatives and policies to effectively reduce exposure. In the long term, reduction in exposure to these pollutants could yield a wide range of social, economic, health, and environmental benefits.
Figure 1.2. Percentage of municipal population living near a major arterial road for selected Canadian cities: within 50 m (blue), 100 m (red) or 250 m (green).
1.3 Project Objectives
The near-road pilot study by SOCAAR, ECCC, MECP, and Metro Vancouver set out to achieve a number of goals:
1. Evaluate air pollution near major roadways.
2. Understand parameters governing concentrations of traffic-related air pollutants.
3. Monitor the evolution of traffic pollution as a result of changes in vehicle technologies.
4. Support estimating exposure of Canadians to traffic-related air pollution and associated health outcomes.
The study was designed to investigate the feasibility of achieving these goals; as recommended in the Smog Science Assessment, the pilot study was designed to inform the creation of a future program of near-road monitoring stations.
Near-Road Pollution Study 2019 22 1.3.1 Design Risks
It would be impossible to monitor the exposure of all Canadians to traffic pollution everywhere all the time, so the design of the near-road pilot study was informed by an appreciation of risks arising from this inevitable limitation: What is the risk of not monitoring at some locations? What are the probabilities and potential severities of impacts that might go undetected, including social, economic, health, environmental, and climate-related risks? Operationalizing a risk-informed environmental monitoring design required informed judgements as to the relative elasticity of increased monitoring relative to the risks of potential environmental, economic, social, or health impacts that might thereby be avoided. These challenges were considered not only in the design of this pilot study, but also in the translation of the results into findings. Specifically, the risks associated with traffic-related air pollution were found to be sufficient to justify increased monitoring in additional cities across Canada.
1.4 Chapter Outline of Report
This report is divided into seven chapters, with this, Chapter One, serving as the introduction to the project with background on near-road pollution and project objectives. Chapter Two describes the six near-road stations in Vancouver and Toronto, the instruments used at each station, and the database that was created to compile the measurements. Chapter Three presents and discusses the main findings of the pilot study in five sections:
• Section 3.1 contrasts the spatial differences at near-road sites.
• Sections 3.2 illustrates temporal patterns at the near-road and background sites.
• Sections 3.3 explores the influence of vehicle emissions on concentrations of pollutants near roads.
• Sections 3.4 illustrates how near-road stations can help to evaluate exposure near roads.
• Sections 3.5 evaluates key parameters that influence the concentrations of pollutants near roads.
Chapter Four summarizes the findings and offers conclusions on the pilot study and recommendations for future work. In particular, the value of roadside monitoring is explored in terms of risk-based analysis and public engagement.
Near-Road Pollution Study 2019 23 2 Site Setup, Equipment, and Database
2.1 Site Setup
Six long-term monitoring sites were chosen as part of this study, including both near-road and background sites. One near-road and one background site were chosen in Vancouver and two near- road and two background sites were chosen in Toronto. In addition, temporary monitoring locations were established in Toronto for discrete studies within the larger near-road study, and data from a number of existing National Air Pollution Surveillance (NAPS) sites were integrated into the study for comparative purposes. A full description of permanent, temporary, and partnered sites used in the study is available in Appendix A. 2.1.1 Metro Vancouver Sites
In Vancouver, two monitoring stations equipped with instruments capable of measuring traffic- related pollutants were created in East Vancouver. The near-road monitoring station was located on Clark Drive (NR-VAN) and a background air quality station (BG-VAN) was established approximately three kilometres away at Sunny Hill Children’s Hospital (Figure 2.1). The near-road monitoring station was located on a busy roadway that transects a densely populated community. The background station was located away from traffic influences for comparative purposes.
Figure 2.1. Location of near-road monitoring station (NR-VAN) on Clark Drive and background monitoring station (BG-VAN) at Sunny Hill Children’s Hospital in East Vancouver.
Near-Road Pollution Study 2019 24 2.1.1.1 Clark Drive Near-Road Monitoring Station (NR-VAN)
The location for NR-VAN was chosen because it is on a key traffic corridor, on a truck route, and in a densely populated neighbourhood that was expected to be at the upper end of concentrations experienced. The site also met criteria that included access to power and sufficient space for an air monitoring shelter. The station was comprised of a 3 by 6 m shelter and a 12 m meteorological tower located on an empty lot located adjacent to Clark Drive. Given the constraints on the property, the station was placed relatively close to the curb lane (Figure 2.2).
Figure 2.2. Plan view and street view of the Clark Drive near-road monitoring station (NR-VAN). Considerable time was required to establish this station. The Real Estate and Facilities Management branch of the City of Vancouver and the study partner, Metro Vancouver, were in discussion for 18 months in order to reach an agreement for rental and use of the property. Numerous permits were required for the site, including a development permit, building permit, occupancy permit, and electrical permit. In addition to the permits, a geotechnical survey of the property was needed prior to installation and a traffic management plan was required during installation. Metro Vancouver relied on in-house expertise, including engineering, drafting, properties, and lawyers. Without these in-house resources considerable costs would have been added to the installation. 2.1.1.2 Sunny Hill Background Monitoring Station (BG-VAN)
The purpose of BG-VAN was to measure air quality within the urban environment of Vancouver away from local traffic influences, to allow comparison to the near-road measurements. The siting of the background station was within several kilometres of NR-VAN to minimize the confounding effects of other potential emission sources. The BG-VAN location was also void of major industrial emission sources (Figure 2.3).
Near-Road Pollution Study 2019 25 Figure 2.3. Sunny Hill background station and meteorological tower (BG-VAN). The station was established in a grassed tree clearing on the Sunny Hill Children’s Hospital property. Because the meteorological tower would have been very close to tall trees, the tower was placed on the roof of the Sunny Hill Children’s Hospital 100 m away from the 2.4 by 3.7 m portable trailer that held the other equipment. 2.1.2 Toronto Sites
Four permanent monitoring stations were established in Toronto. Two near-road monitoring stations (one beside Highway 401 and the second beside a major arterial road in downtown Toronto) and two background stations (Downsview in the north and Toronto Island in the south) (Figure 2.4). Two temporary locations near Highway 401 were also used for the Wintertime Gradient Study: a mobile lab (MAPLE) and a portable shelter (Airpointer).
Figure 2.4. Toronto near-road (red triangles) and background (blue squares) stations.
Near-Road Pollution Study 2019 26 2.1.2.1 Highway 401 Near-Road Monitoring Station (NR-TOR-1)
The NR-TOR-1 station was located 10 m south of the eastbound collector lanes of Highway 401, which is the busiest stretch of highway in North America with annual average daily traffic reported at 411,600 vehicles (Ontario Highway, 2016). The air monitoring station was 10 m and 119 m from the two edges of the highway, meeting its objective to monitor air pollution emitted directly from on-road vehicles along a major highway (Figure 2.5).
Figure 2.5. Side view of the NR-TOR-1 station (left) and aerial view of Highway 401 segment (right)
The Ministry of Transportation Ontario (MTO) approved a request for a variance allowing the Ministry of the Environment, Conservation and Parks (MECP) to establish the NR-TOR-1 station 4.4 m from the highway fence. Nav Canada (which owns and operates Canada’s civil air navigation service) needed to approve installation of the 10 m meteorological tower at the site due to its proximity to Toronto Pearson International Airport. Notably, for siting the shelter, a costly comprehensive geotechnical engineering survey was required and a gas line needed to be shortened, which added considerable start-up costs. As with the Vancouver station, siting was complex in terms of seeking of internal approval for spending, permits, and negotiations of a lease agreement. The time required to establish the NR- TOR-1 station from the conceptual design to operation was roughly three years at a total cost of approximately $300,000, not including costs for the station and instruments themselves. The station was a 3 by 6 m shelter and 10 m meteorological tower. To reduce costs and to ensure building security, the rooftop equipment could only be reached from within the shelter. 2.1.2.2 Toronto North Background Monitoring Station (BG-TOR-N)
The BG-TOR-N station was a 3 by 6 m shelter with a 10 m meteorological tower situated in Downsview in north Toronto (Figure 2.6). It was between two major arterial roads in order to monitor urban background levels of air pollution coming from the north of the city.
Near-Road Pollution Study 2019 27 Figure 2.6. Aerial (left) and side (right) views of the BG-TOR-N station and surrounding area.
Notably, three years were lost in negotiating failed partnerships with two different university sites, and it took approximately one year to commission the BG-TOR-N station once a site had been secured; this resulted in significant data loss to the study. 2.1.2.3 Temporary Installations
2.1.2.3.1 Wintertime Gradient Study A distance decay gradient study (Wintertime Gradient Study) was conducted near the NR-TOR-1 site from February 6 to 27, 2017. In addition to NR-TOR-1, two temporary stations were installed near this location: the Airpointer Shelter (130 m southeast) and the MAPLE Mobile Lab (150 m southeast) (Figure 2.7)
Figure 2.7. Location of sampling sites for distance decay gradient study (February 6 to 27, 2017).
Near-Road Pollution Study 2019 28 2.1.2.3.2 Mobile Unit On-road measurements of traffic-related pollutants using a mobile laboratory in a truck were conducted during August of 2015 in the Greater Toronto Area. The purpose was to map pollution levels across the city and provide on-road data for comparison with the near-road measurements. 2.1.2.4 College Street Near-Road Monitoring Station (NR-TOR-2)
The NR-TOR-2 station was established at the ground level and on the roof of the Southern Ontario Centre for Atmospheric Aerosol Research facilities (SOCAAR) on the University of Toronto St. George Campus. This site was selected because of its convenience to the researchers of the project and to leverage the many years of near-road monitoring data that had previously been collected at this site. The location was directly beside College Street, a 17 m four-lane typical arterial road in downtown Toronto and was surrounded by densely packed high-rise buildings to the east and commercial areas to the southwest (Figure 2.8). The continuous measurements were made at road level (3 m) while the integrated samples were collected on the roof (10 m) (se Appendix A.2)
Figure 2.8. Plan view (left) of the Toronto near-road monitoring station (NR-TOR-2) and street view (right) indicating location of the ground and rooftop level facilities. 2.1.2.5 Toronto South Background Monitoring Station (BG-TOR-S)
The Toronto South Background Monitoring Station (BG-TOR-S) was situated at Hanlan’s Point on the southernmost tip of the Toronto Islands. The station was a 2 by 3.7 m trailer with a 10 m mast above the trailer on a fenced site approximately 2.5 km from the downtown Toronto shoreline and 5.2 km from the NR-TOR-2 station. The site was accessible only by ferry and vehicle transport on the island is strictly limited to maintenance personnel (Figure 2.9). This station was ideally positioned to distinguish between urban background levels of air pollution transported from the city and background levels of pollutants transported into the city, as winds from the north transported air that contained a citywide vehicle exhaust signal to the station while winds from the south transported air that contained more regional emissions from the other side of the lake and further upwind. This station also served a second-fold purpose of enhancing air quality monitoring during the 2015 Pan American Games. Siting the station took over two years to complete, as there were no utilities or infrastructure nearby.
Near-Road Pollution Study 2019 29
Figure 2.9. Aerial (left) and side view (right) of the BG-TOR-S site on the southern point of Toronto Islands.
2.2 Instruments and Performance
A similar set of instruments was used for continuous and 24-hour integrated measurements at all six stations (Tables 2.1 and 2.2).
Table 2.1. Integrated Particulate Matter (PM) Sampling Instruments and Target Group Analytes Module Instrument Media (47 mm id) Analytes / Method Description
PM2.5 Mass Fine fraction Dichotomous Teflon filter 22 Elements / ED-XRF Sampler: (PM2.5) 25 Elements / ICP-MS -1 PM2.5: 15 L-min -1 Coarse fraction PM2.5-10 Mass PM2.5-10: 1.7 L-min Teflon filter (PM2.5–10) 22 Elements / ED-XRF
Cartridge A Quartz filter OC & EC / IMPROVE TOR
Cartridge B Teflon filter PM2.5 Mass / Archived (2 components) Quartz filter OC artifact / IMPROVE TOR
Carbonate-coated HNO3, HONO, SO2 / IC SUPER-SASS PLUS denuder Speciation Sampler: Citric acid-coated Ammonia (NH3) / IC -1 denuder 10 L-min Cartridge C (4 components) 20 inorganic anions and cations, and organic acids / IC Teflon filter 23 water-soluble metals / ICP-MS Biomass burning markers / IC
Nylon filter Volatile nitrate / IC
Near-Road Pollution Study 2019 30
Table 2.2. Summary of Continuous Monitoring Instrumentation Flow Time Parameter Instrument Type Manufacturer Model Rate Sensitivity Resolution (L/min) Inorganic Gases Chemiluminescence Thermo NO, NO2 42i 0.7 0.4 ppbv 1 s analyzer Scientific Filter correlation infrared Thermo CO 48i-TLE 0.5 40 ppbv 10 s gas analyzer Scientific
Non-dispersive infrared a CO2 LI-COR 840A 1 5 ppmv 1 s gas analyzer Volatile Organic Compounds (VOCs) IONICON VOCs PTR-TOF-MS 8000 0.1 0.2–1 ppbva 2 s Analytik Black Carbon (BC) / Elemental Carbon (EC) Magee BC Optical attenuation AE33 5 0.1 µg/m3 1 min Scientific Thermal–optical EC Sunset Lab OC–EC 8 0.4 µg/m3 1–2 hr transmittance Droplet Particle Photoacoustic soot Measurement PASS-3 1 8 Mm-1 a 2 s absorption spectrometere Techniques Ultrafine Particles (UFP) Particle Condensation particle Teledyne API 651b 3 100 # cm-3 2 s number counter
Particle Ultrafine particle c -3 e TSI 3031 5 2000 # cm 10 min number and monitor size Fast mobility particle TSI 3091d 10 500 # cm-3 1 s distribution sizere Particulate Matter (PM) and Chemical Speciation Organics, 0.5 µg/m3 Sulphate, Aerosol mass 0.1 µg/m3 Aerodyne ACSM 0.1 30 min Nitrate, spectrometere 0.04 µg/m3 Ammonium 0.4 µg/m3 Energy dispersive X-ray Cooper Trace metals Xact 625 16.7 variable 1 hr fluorescencee Environmental 3 Nephelometer / 0.5 µg/m Thermo SHARP PM2.5 mass radiometric mass 16.7 (1-hour 1 min Scientific 5030 monitor resolution) a Wang et al., 2015; b particles greater than 7 nm; the API 651 was equipped with a BGI SCC 0.732 impactor with 625 nm cut-off at 3 L/min; c particle size range 20–1000 nm; d particle size range of 6–856 nm; e used only at sites in Toronto.
Overall, most instruments performed well, indicating that reliable instruments are available to support the implementation of a larger network of near-road monitoring stations. Well- established instruments, such as the gas analyzers, performed well with just routine upkeep while more effort was required for newer instruments. For example, long-term measurement of ultrafine particles at multiple sites proved to be particularly challenging. The effort and expense required for instrument maintenance should be a major consideration in determining the level of
Near-Road Pollution Study 2019 31 instrumentation deployed at different locations. A full description and detailed evaluations of the instruments used in the study is available in Appendix B along with the small differences between stations. 2.2.1 Recommended Instruments
2.2.1.1 Base Set Parameters
Measurements that represent the minimum needed for near-road monitoring were identified (Table 2.3). This base set can be used at permanent stations and can also be housed in small, transportable enclosures for short-term (< 1 year) measurements.
Table 2.3. Base Set of Pollutants
Gases Particles Other Nitrogen oxides PM2.5 Traffic Carbon monoxide Ultrafine particles Meteorology4 Carbon dioxide Black carbon
2.2.1.2 Additional Instruments
Additional instruments will need to be used at selected sites or rotated across sites throughout the study. Enclosures at permanent sites should have adequate space to house these instruments in addition to the base set. For example, the rotation of instruments for time-resolved (e.g. hourly) measurement of PM composition (e.g. ACSM and Xact 625) across sites is recommended, as is their long-term or permanent deployment at a few “super-sites,” along with other more sophisticated and new emerging instruments. Data from this study has pointed to a need for monitoring of trace metals because of increased non-tailpipe emissions and the rapid growth of electric vehicles. Integrated 24-hour filter sampling could support the analysis of metals and also support analysis of OC–EC, which is needed at some sites to support calibration of BC measurements.
2.3 Database
This study’s partners were from different agencies located across Canada: federal (ECCC), provincial (MECP), regional (Metro Vancouver), and academic (SOCAAR/University of Toronto). A central, cloud-based database from DR DAS Ltd. was used to allow remote access to all partners wherever their location (Figure 2.10). An online project management platform was used to develop and organize the instrument and parameter settings for the collection of the data for the study, track the reporting and quality assurance of data, and to compile results for the final report.
4 The recommended meteorological parameters are wind speed and direction, air temperature, relative humidity, and precipitation.
Near-Road Pollution Study 2019 32 A detailed overview of the setup of the database, key equipment and software, and data input procedures is available in Appendix C.
Metro Vancouver DR DAS Cloud Server Vancouver Ohio, USA
University of Toronto/ SOCAAR Toronto
MECP ECCC Near-Road Toronto Database Archive Ottawa
Figure 2.10. Participating agencies and location in the Near-Road study.
2.3.1 Recommendations
Overall, the study underestimated the level of pre-planning needed for the data collection and storage and this caused inefficiencies throughout the study. Future studies should manage expectations and identify technical support contacts and availability for tasks before the study is activated. In addition, the study plan should identify all the instruments, channels, and metadata to accurately identify the work required by jurisdictions, partners, and vendors. 2.3.1.1 Pre-Planning
Although it is difficult to anticipate time, material, and human resource budgets to create and operate an effective database prior to initial set up, as much as possible should be in place prior to implementation. In this study, unanticipated complications, events, and restrictions could have been avoided with more intention to pre-plan and share expectations. Knowing security issues and firewalls across stations, for example, which can impact how data is stored and managed, should be a high priority. Smaller issues, such as anticipation of server space, can become high-priority issues once the study is underway. For example, working with Envista ARM on the cloud server required more resources than anticipated, and this required consensus among partners about whether to purchase another software licence or remove data within the database.
Near-Road Pollution Study 2019 33 2.3.1.2 Establishing In-Study Management Roles
Management of database administration was needed throughout the study, and roles and responsibilities should be established in the pre-planning stages as well. This should help to eliminate inconveniences experienced in this study, such as the Envista licence expiring mid-study and disrupting access to the cloud. Station administrators should be identified in the planning stages of the study and properly trained in contingencies to avoid data loss and effort. At the beginning of the study, for example, only the vendor had permissions to manually poll the stations, which was necessary during power outages, communication errors, or when instruments were offline. It was several months into the study before permissions were granted to researchers. In terms of human resources, skills and qualifications should be noted early in the process. In this study, for example, a researcher with programming knowledge was needed in order to produce the algorithms needed to collect certain data. 2.3.1.3 Consistency
Parameters and protocols should be established as early as possible and preferably before the study is online. These should be placed in an agile document that can be updated as necessary and published in a central area to be available to all researchers and partners at any point. In the current study, agencies did not discuss or follow the same setup protocols for instrumentation activation at the inception of the project and this led to a collection of unique setups and data summaries throughout the study, as making changes post-data collection could have resulted in data loss and inconsistent time series. Consistency of training procedures for researchers across all agencies would also have helped with data efficiency, keeping the data output consistent, and maintaining good data management, such as the consistent use of on-the-fly quality control rather than waiting for weekly reports.
Near-Road Pollution Study 2019 34 3 Results
3.1 Spatial Differences Across Sites
3.1.1 Hourly Averaged Pollutant Concentrations
Pollutant concentrations varied across the six sites, with the higher concentrations occurring at NR-
VAN and NR-TOR-1 (Figure 3.1). For example, the mean and median concentrations of NO2 at NR- VAN and NR-TOR-1 were above the 2020 Canadian Annual Ambient Air Quality Standard (CAAQS) of 17 ppb, while they were not at the other sites. Substantial emission reduction will be required if this 2020 CAAQS is to be achieved at these sites.
Figure 3.1. Comparison of nitrogen oxides concentrations (ppb) at the near-road and background sites based on hourly averaged data for June 2015 to March 2017. The dot indicates the mean value while the line indicates the median. The box indicates the 25th and 75th percentile and the whiskers indicate the 5th and 95th percentiles. The blue line illustrates the 2020 annual CAAQS for NO2 of 17 ppb.
Much of the NOx was still present in the form of NO, which can be converted to NO2 during transport away from the roads. Specifically, the hourly NO2 averaged over two years for NR-VAN and NR-TOR-1 were 21.5 and 19.3 ppb. An additional 24.6 and 36.9 ppb of NO were present, most of which would presumably become converted to NO2 as the exhaust was further dispersed and diluted (Figure 3.2). Dilution and production of NO2 during transport away from the road could yield concentrations that exceed the CAAQS as far as 150 m from the road. In fact, the annual average
NO2 at the Toronto West site (near NR-TOR-1 but 170 m from the highway) has been ≈ 17 ppb in recent years (2014–2016). Thus, a 150 to 200 m boundary region may be present alongside roads with heavy truck traffic like Highway 401 that will not meet the 2020 ambient NO2 standard.
Near-Road Pollution Study 2019 35 Figure 3.2. Comparison of mean NO2 (blue) and NO (orange) concentrations (ppb) at the near- road, background, and comparator sites based on hourly averaged data for June 2015 to March 2017. The grey line illustrates the 2020 CAAQS for NO2 of 17 ppb.
Figure 3.3. Comparison of NO2 (blue) and NO (orange) concentrations (ppb) at the near- road, background, and comparator sites based on 98th percentile of the daily-maximum one-hour values for June 2015 to March 2017. The grey line illustrates the 2020 CAAQS for NO2 of 60 ppb which should be based on three years of data
Near-Road Pollution Study 2019 36
Quantifying the width of this near-road boundary region requires location specific measurements as it can be greatly influenced by local topography and day-to-day changes in meteorology. However, this potential should be recognized from the outset when selecting truck routes or siting facilities such as schools alongside trucking routes. th The short-term 2020 hourly CAAQS for NO2 of 60 ppb—based on 98 percentile daily- maximum one-hour average values over three years—was not exceeded at the near-road or comparator sites based on the two years of data available (Figure 3.3). However, the equivalent
2025 CAAQS for NO2 of 42 ppb may still be exceeded at many sites if steps are not taken to further
reduce NOx emissions. Introducing programs that target the heaviest emitting and reward lower emitting trucks may be the most effective strategy. These programs should be guided by the “four Rs” of repair, retrofit, relocate, or retire heavy emitting trucks. Ozone concentrations were lower at the near-road sites due to reaction with nitrogen
oxides. The levels of odd oxygen species (NO2 + O3) were much more consistent across the sites for the same reason (Figure 3.4).
Figure 3.4. Comparison of ozone (ppb) and odd oxygen (NO2 + O3) concentrations (ppb) at the near-road and background sites based on hourly averaged data for June 2015 to March 2017. The dot indicates the mean value while the line indicates the median. The box indicates the 25th and 75th percentile and the whiskers indicate the 5th and 95th percentiles.
The concentrations of carbon monoxide were 40–50% higher at the near-road sites compared to the background sites (e.g., 350 ppb at NR-VAN vs 229 ppb at BG-VAN, see Figure 3.5), but were well under the Ontario and British Columbia maximum one-hour guideline values of 30 and 13 ppm, respectively. A very small portion (< 10%) of the vehicle fleet was responsible for this near-road increase in CO. Carbon dioxide concentrations were also higher near-road, which allowed
for the calculation of emission and dilution factors.
Near-Road Pollution Study 2019 37
Figure 3.5. Comparison of carbon monoxide (ppb) and carbon dioxide (ppm) concentrations at the near-road and background sites based on hourly averaged data for June 2015 to March 2017. The dot indicates the mean value while the line indicates the median. The box indicates the 25th and 75th percentile and the whiskers indicate the 5th and 95th percentiles.
In particular, vehicle exhaust was diluted on average 1.5 and 2.3 times less at NR-VAN than at NR-401 and NR-TOR-2 sites, respectively. The degree of dilution that occurred during transport of the plumes from the vehicle tailpipes to the measurement instrument was estimated by comparing
the concentration of local CO2 at the three sites (see Table 3.11). The CO2 concentrations at the background sites were closer to but still above the global average of ~400 ppm.
Figure 3.6. Comparison of particulate matter less than 2.5 µm (PM2.5) and BC (measured at 880 nm) concentrations (µg/m3) at the near-road and background sites based on hourly averaged data for June 2015 to March 2017. The dot indicates the mean value while the line indicates the median. The box indicates the 25th and 75th percentile and the whiskers indicate the 5th and 95th percentiles. The blue lines indicate a proposed occupational standard for EC of 1 µg/m3 3 and the 2020 CAAQS value of 8.8 µg/m for PM2.5.
The mean concentrations of PM2.5 were higher at the near-road sites but still below the 2020 annual CAAQS value of 8.8 µg/m3 except at NR-TOR-1, which had a value of 9.4 µg/m3 based on the SHARP measurements. As discussed in Section 3.3.3.4.1, both non-tailpipe and tailpipe
Near-Road Pollution Study 2019 38 emissions contributed to the higher PM2.5 near roads. The composition of PM2.5 is discussed in greater detail in Section 3.1.2.1. based on 24-hour integrated data. Logging uncompensated BC values at the Vancouver sites (see section 3.3.1) created excessive uncertainty in individual BC values. Averaging over several hundred hours (e.g., Figure 3.6) was found to improve the agreement to within 15%. The concentrations of BC were higher at the near-road sites and this rise varied seasonally (see Section 3.3.1). The mean levels at NR-VAN and NR-TOR-1 were above the value of 1 µg/m3 that has been proposed as a standard for occupational exposure to diesel exhaust (HCN 2019, Vermeulen 2014, Taxell and Santonen 2017). The near-road concentrations of BC at these two sites remained above 1 µg/m3 even after isolating contributions from vehicles by subtracting background contributions. Emissions of BC at these sites was strongly associated with the fraction of heavy-duty diesel vehicles (see Section 3.3.2.4) implying that significant exposure to diesel exhaust is occurring at these near-road sites. Ultrafine particles are useful markers of traffic emissions as is evident from the higher concentrations at the near-road sites. In particular, the upper 75th percentile values were up to three times higher at the near-road vs. background sites, as was the range of values (e.g., the box sizes in Figure 3.7). This suggests that ultrafine particle concentrations will generally be measurably
Figure 3.7. Comparison of ultrafine concentrations (number/cm3) at the near road and background sites based on hourly averaged data for June 2015 to March 2017. The dot indicates the mean value while the line indicates the median. The box indicates the 25th and 75th percentile and the whiskers indicate the 5th and 95th percentiles. higher if traffic emissions are influencing a site, and a low ultrafine particle concentration indicates that traffic is not influencing a site. However, ultrafine particles can be produced by other sources, such as plumes from industrial emissions and atmospheric nucleation events, and thus elevated ultrafine particle concentrations alone cannot be used to assess the influence of traffic.
Near-Road Pollution Study 2019 39 3.1.2 Twenty-four-Hour Integrated Pollutant Concentrations
This section characterizes the general pollution levels of PM2.5 and its components, as well as gas species measured for the period August 2015 to March 2017 at most sites. As detailed in Appendix D: Table D-1, results are provided either for a 17-month period (Study Period A: August 2015 to December 2016, noting that sampling stopped August 2016 for BG-VAN and BG-TOR-S) or for an overlapping but slightly longer 20-month period (Study Period B: August 2015 to March 2017). The 24-hour integrated samples were collected concurrently at near-road and urban background sites following the procedures used in the NAPS program. As summarized in Table 2.1, measured
chemical components of PM2.5 included inorganic and organic ions, total and water-soluble elements, carbonaceous compounds (OC, EC, biomass burning markers), and gas phase species
(HONO, HNO3, SO2, and NH3). The concentration of coarse PM fraction (2.5 < particles < 10 µm in
diameter; PM2.5–10) and its elemental composition were also measured. Concentrations of measured species are also compared with a selection of other urban NAPS locations, listed in Appendix D: Table D-1. 3.1.2.1 PM Mass and Major Chemical Components
Figure 3.8 shows that for both PM2.5 and PM2.5–10 the mean mass concentrations were generally higher at the near-road sites, especially at the NR-VAN and NR-TOR-1 sites, compared to reference background urban locations in Vancouver and Toronto. The weekly patterns at the near-road sites reveal that mass concentrations were influenced by anthropogenic emissions.
25.0 25.0 )
) Weekday
3 Weekday
3 - PM - PM10-2.5 2.5 Weekend 20.0 Weekend 20.0
15.0 15.0
10.0 10.0
5.0 5.0 Mean Mean mass concentration (µgm Mean Mean mass concentration (µgm 0.0 0.0
Figure 3.8. Mean concentrations of PM2.5 and PM2.5–10 in weekday and weekend samples for August 2015 to December 2016. Whiskers represent standard deviation.
3 For example, weekday/weekend mean enhancements of 2.1 µg/m for PM2.5 (representing 3 about 32% of PM2.5 concentrations) and 3.8 µg/m for PM2.5–10 (representing about 62% of PM2.5–10
concentrations) were found at NR-TOR-1. Smaller weekday/weekend enhancements for PM2.5 were found at the other sites, except BG-TOR-S (Figure 3.9). The weekend reduction at NR-TOR-1, suggested an association with the lower volume of truck traffic on weekends, given that there was little weekend/weekday difference in the total volume of vehicles at this site.
Near-Road Pollution Study 2019 40 The higher levels at BG-TOR-S of PM2.5 observed on weekends (Figure 3.9) and the warm season (Appendix D: Figure D-2) suggest substantial contributions from recreational wood burning (Healy at al., 2017). This is consistent with the weekday/weekend and seasonal (warm: April to September vs cold: October to March) season mass concentration difference observed for levoglucosan, a biomass burning tracer, at BG-TOR-S (Figure 3.9). BG-TOR-S is in a park where summertime campfires are popular. Forest fires were also detected on occasion during the summer at this site. Chemical mass reconstruction was used to determine the relative contributions of different
compound classes to PM2.5 mass. (See Appendix D: Table D-2 to see how the masses of these
chemical classes were calculated.) As seen in Figure 3.10, the composition of PM2.5 was primarily
composed of carbonaceous aerosol (OM = 1.6x [OC], EC), secondary inorganic aerosol (ASO4, ANO3), and mineral dust.
500 500 Weekday Warm Weekend Cold
400 400
)
)
3
3
m m
300 300
200 200
Levoglucosan (ng/ Levoglucosan (ng/ 100 100
0 0
Figure 3.9. Mean concentrations of levoglucosan in weekday and weekend and warm (April to September) vs cold (October to March) season for PM2.5 samples collected August 2015 to March 2017, noting that sampling stopped August 2016 for BK-VAN and BK-TOR-S. Whiskers represent standard deviation.
The relative contributions of these components varied by site, season, and weekdays and weekends (see Appendix D: Figure D-3). In particular, differences between the near-road and background sites were larger for EC than the other components (Figure 3.10). EC made its highest
contribution to PM2.5 at NR-VAN (21%) followed by NR-TOR-1 (~15%). The lowest contribution of EC
to the PM2.5 mass (~8%) was observed in NR-TOR-2, where the contribution was similar to the Toronto background sites.
Particulate secondary inorganic aerosol (SIA, e.g. ASO4, ANO3), the second most important
contributing component in PM2.5, was similar at the near-road and background sites but displayed
geographic differences between eastern and western Canada. ASO4 was a dominant component of
PM2.5 at all Toronto sites and other eastern sites, primarily due to the greater SO2 emissions in the
east (Dabek-Zlotorzynska et al., 2011). A typical seasonal pattern, in terms of higher ANO3 levels during the cold season, was seen at both the near-road and background locations (Appendix D: Figure D-3). This seasonality is explained by a greater partitioning of nitrates in the aerosol phase during colder periods. Weekday versus weekend enhancement in the nitrate and ammonium
Near-Road Pollution Study 2019 41 concentrations were more pronounced in Toronto, especially at the NR-TOR-1 and BG-TOR-S sites than in Vancouver (Appendix D: Figure D-4). This similarity in weekday and weekend patterns for ammonium nitrate (NH4NO3) at near-road and background sites across Toronto implied the formation process was citywide, perhaps due to traffic emission of NOx that became dispersed across the city before being converted to ammonium nitrate overnight.
10 ANO3 ASO4 OM EC Mineral Dust NaCl TEO Unidentified
8
) 3
6
4
2 Reconstructed Reconstructed Mass (mg/m 0
Figure 3.10. Reconstructed PM2.5 mass by major component and site August 2015 to December 2016.
In addition to carbonaceous and SIA components, mineral dust (representing wind-blown soil, non-tailpipe emissions, and re-suspended road dust) was also a major component of PM2.5, especially at the near-road sites (Figure 3.10). The average mineral dust concentrations at the near- road sites ranged from 0.62 to 0.90 µg/m3, compared with 0.37 to 0.47 µg/m3 for the background sites. As expected, mineral dust concentrations were larger during the warm season when dry conditions favour dust and soil resuspension. In general, the contribution of mineral dust was highest in spring, especially at NR-TOR-1, possibly due to less removal by rain and road sweeping during winter. In contrast, the NR-VAN station did not display this seasonality in mineral dust (Appendix D: Table D-4 and Appendix D: Figure D-3); instead, concentrations were consistent throughout the year. Similar to EC, the mineral dust concentrations showed significant differences between weekdays and weekends, highlighting the important influence of traffic non-tailpipe emissions and dust resuspension on weekdays (Appendix D: Figure D-3). It should be noted that road dust is a reservoir of particles deposited on the road surface, which are re-suspended by vehicular traffic; therefore, it may contain species found in high abundance in the urban aerosol mixture of components. Trace element oxide (TEO) concentrations varied from 0.03 to 0.05 µg/m3 and constituted only a minor fraction of PM2.5. However, the average TEO concentration was also
Near-Road Pollution Study 2019 42 highest on weekdays. More detailed information regarding trends of the selected PM chemical composition and gaseous pollutants is discussed below. 3.1.2.2 Carbonaceous Components
As indicated above, carbonaceous matter—with its two components of EC (similar to BC) and OC—
was the most abundant chemical component in PM2.5 at all roadside sites. These observations suggest that vehicular traffic represents an important source of roadside carbonaceous aerosols. EC is a primary pollutant, which is directly emitted by the incomplete combustion of fossil fuel, biofuel, and biomass. OC can originate as both primary PM (mostly from the combustion of biomass and fuels) and as secondary organic aerosols (SOA) through gas phase photochemical reactions of specific VOCs. Figure 3.11 shows the spatial variation of the carbonaceous species across the sites. These results show that the variability in EC concentrations between near-road sites and reference background and other urban sites is greater than that of OC. The highest mean concentrations of EC were observed at NR-VAN (1.4 µg/m3) and NR-TOR-1 (1.2 µg/m3). Particulate OC concentrations also tended to be higher at the near-road sites, especially at NR-VAN; however, the average difference between near-road and background OC concentrations was not as large as EC. EC represented on average ≈44% of the total carbon (TC = OC + EC) at NR-VAN and NR-TOR-1 as compared to ≈30% at the background sites (Appendix D: Table D-4).
2.5 3.5
3.0
) 3
2.0 )
3 m
m 2.5
g/ m 1.5 2.0
1.5 1.0
1.0
0.5 Organic Carbon (µg/ Elemental Elemental Carbon ( 0.5
0.0 0.0
Figure 3.11. Mean concentrations of EC and OC in PM2.5 samples collected August 2015 to March 2017, noting that sampling was from August 2015 to December 2016 for BG-VAN and BG-TOR-S. Whiskers represent standard deviation.
Significantly lower concentrations of EC (and also EC/TC ratios) were observed at the near- road sites on weekends compared to weekdays, with the lowest EC concentrations on Sundays (Figure 3.12, see also Appendix D: Table D-5 and Appendix D: Figure D-5). This points to the strong effect of heavy-duty trucks, as their number varied greatly between weekdays, Saturdays and Sundays whereas the total vehicle counts did not (see Section 3.2.1). The ratio of OC to EC concentrations has been used to study emission and transformation characteristics of carbonaceous aerosols. According to Watson et al. (2001), lower OC/EC ratios are observed from motor vehicle emissions (1.1) than from coal combustion (2.7) or biomass burning
Near-Road Pollution Study 2019 43 (9.0). Moreover, the OC/EC ratio for diesel vehicles has been observed to be much lower than that for gasoline vehicles (Brito et al., 2013). As shown in Appendix D: Table D-4, the OC/EC ratios at NR- VAN (1.3) and NR-TOR-(1.7), were much lower than the range of values of 2.5 to 3.3 at the reference and background sites. Moreover, the OC/EC ratio of 3.1 ± 1.3 for NR-TOR-2 was substantially higher indicating contributions from other sources of OC such as regional SOA and cooking (see Figure 3.47). The low OC/EC ratios at NR-VAN and NR-TOR-1 were consistent with the strong impact of
3.0 Weekday
2.5 Weekend
)
3
m g/
µ 2.0
1.5
1.0
Elemental Elemental Carbon ( 0.5
0.0
Figure 3.12. Mean concentrations of EC in weekday and weekend PM2.5 samples collected August 2015 to March 2017, noting that sampling was from August 2015 to December 2016 for BG-VAN and BG-TOR-S. Whiskers represent standard deviation (SD). traffic emissions. Further, the lower OC/EC ratios observed for weekdays (Appendix D: Table D-6) at NR-VAN (mean = 1.1 ± 0.5) and NR-TOR-1 (mean = 1.5 ± 1.1) than for weekends (mean = 1.9–2.1) were consistent with the higher proportion of diesel vehicles passing by NR-VAN and NR-TOR-1 on weekdays (see Section 3.2.1). In fact, 80% of the EC at these two sites was due to local traffic (Table 3.2). Receptor modelling using positive matrix factorization indicated, in contrast, that most of the OC at NR-TOR-1 was not from tailpipe emissions (Figure 3.47). Thus, while OC to EC ratios can be useful indicators of influence of traffic, more detailed analysis revealed contradictions and these ratios alone could not quantitatively resolve this influence. A stronger seasonal dependence for EC concentrations was observed for all sites in Toronto compared to the Vancouver area. The ratio of summer to winter EC concentrations at NR-TOR-1 was 1.7, whereas at NR-VAN it was 0.7. A similar unexpectedly high ratio of summer to winter concentrations (1.9) was reported by Healy et al. (2017) for BC concentration at NR-TOR-1 (see Section 3.3.1). Healy et al. (2017) noted that the seasonal changes in fuel composition in Ontario may play an important role to the observed seasonal EC (BC) concentrations; if true, a stronger summertime EC source would be observed only at the Toronto and not Vancouver near-road sites. Consistent with this, it is interesting to note that summer to winter ratios of EC2 and OC2 fractions (see Appendix B.1.3) were much higher in Toronto than in Vancouver and other sites (Figure 3.13). Similarly, OC displayed a weaker seasonal pattern at the Vancouver than the Toronto sites. In general, however, lower OC (OC3) concentrations were observed during the cold season, presumably due to a different fuel used (Appendix D: Table D-10). The lower OC yet higher OC/EC
Near-Road Pollution Study 2019 44 3.0 3.0 EC2 OC2 2.5 2.5
2.0 2.0
1.5 1.5
Ratio of S/W ConcentrationS/W of Ratio 1.0 1.0 Ratio of S/W Concentration S/W ofRatio 0.5 0.5
0.0 0.0
Figure 3.13. Ratio of summer to winter (S/W) EC2 and OC2 fractions in PM2.5 samples for August 2015 to March 2017, noting that sampling was from August 2015 to December 2016 for BG-VAN and BG-TOR-S. ratio during the cold season at NR-TOR-1 may have been due in part to greater seasonality in fuel composition in Ontario. In contrast, OC and EC did not change much over the seasons at NR-VAN. In addition, the higher OC/EC ratios during the cold season and on weekends (Appendix D: Figure D-6) suggested enhanced contribution from residential wood (biomass) burning. This is further supported by the increase in the concentrations of levoglucosan, commonly used as a tracer for biomass burning, clearly observed on weekends and during the cold period at all sites (Figure 3.14). EC2 and OC2 are often regarded as markers for diesel-fuelled vehicle emissions, while OC3 is greater than OC2 in emissions from gasoline-fuelled vehicles, especially at roadside sites (Cao et al. 2006). Significant weekday/weekends differences in EC2 and OC2 (Appendix D: Table D-11) provided further evidence that diesel vehicles made larger contributions on weekdays than weekends at these sites. However, OC3 was greater than OC2 at all three near-road sites during the warmer months, suggesting that gasoline vehicles contributed more than diesel vehicles to overall OC concentrations. The organic fractions were further divided into the water-soluble (WSOC) and water- insoluble OC (WIOC) fractions. The WIOC are typically fresh emissions originating from traffic or other local sources. Some WSOC components are emitted as primary particles, especially during biomass burning, and others as SOA, which is produced from chemical reactions of gaseous organic precursors in the atmosphere. Seasonal composite samples collected at each of three sites from 2015 to 2016 were analyzed for the total WSOC using a total organic carbon (TOC) analyzer (Figure 3.14). This showed that OC at both near-road sites was mostly water-insoluble, indicating the primary or less oxygenated nature of the OC. The contribution of WSOC to OC was higher in the NR- TOR-1 (33–41%) than NR-VAN samples (18–30%), even though both sites are highly impacted by traffic. The high slope and correlation of WSOC versus EC observed for NR-TOR-1 (WSOC = 0.50EC + 0.04; R2 = 0.94) suggested that the WSOC was from vehicle emissions. In contrast, the low slope and correlation at the BG-VAN (WSOC = 0.17EC + 0.23; R2 = 0.15) pointed to the WSOC originating from a different source. To better investigate the origins of the WSOC, it was further separated into biomass burning water-soluble organic carbon (WSOCbb) and non-biomass burning organic carbon (WSOCnb). To
Near-Road Pollution Study 2019 45 derive a “biomass smoke” contribution to OC, the measured concentration of levoglucosan was multiplied by the conversion factor, CF = 6.1 (Reche et al., 2012). Then, WSOCbb was estimated as
71% of calculated OC from wood smoke. WSOCnb was then determined by subtraction of WSOCbb from measured WSOC by the TOC method.
6 EC WIOC WSOCbb WSOCnb
5
4
3
2 Concentration (µg/m³) Concentration
1
0
Fall Fall Fall
Spring Spring Spring
Winter Winter Winter
Summer Summer Summer BG-VAN NR-VAN NR-TOR-1
Figure 3.14. Seasonal distribution of EC, WIOC, and WSOC (WSOCbb + WSOCnb) from samples collected from 2015 to 2016 at three sites. As seen in Figure 3.14, much of the WSOC collected at NR-VAN and BG-VAN was from biomass burning, particularly in fall and winter, while non-biomass burning related sources (e.g. traffic) dominated at NR-TOR-1. The WSOC beside these two major roads appears to be coming from different sources, and thus the WSOC data did not allow a more definitive apportionment of OC. These findings are preliminary, and analysis of more PM samples collected at other sites might allow better separation of sources. 3.1.2.3 Trace Metals
Traffic has been recognized as a significant contributor to elevated concentrations of metals (Garg et al., 2000; Guo et al., 2008; Harrison et al., 2003; Kleeman et al., 2000; Pant and Harrison, 2013; Pio et al., 2013). Elements associated with traffic can be emitted from various exhaust-related sources, including fuel and lubricant combustion (e.g., Ca, Cu, Zn, Ba), catalytic converters (Pd, Rh), or wear and tear of engines (Al, Fe). However, many of these can also arise from non-exhaust sources such as brake and tire wear (Al, Fe, Ba, Cu, Mn, Cd, Ni, Pb, and Zn) or re-suspended road dust (Al, Si, Ti, K,
Ca, and Fe). Non-exhaust emissions contribute mainly to the coarse mode (PM2.5–10) of PM while exhaust emissions contribute predominantly to PM2.5. The elemental composition of PM2.5 and
PM2.5–10 collected from August 2015 to December 2016 is summarized in Appendix D: Tables D-13, D-14, and D-15.
Near-Road Pollution Study 2019 46 900 1800 Al Si Ca Ti Fe Al Si Ca Ti Fe PM PM
800 2.5 1600 10-2.5
)
) 3
700 3 1400
600 1200
500 1000
400 800
300 600
200 400
100 200
Element Element concentration (ng/m Element Element concentration (ng/m 0 0
Figure 3.15. Spatial distribution of crustal elements PM2.5 and PM2.5–10 samples collected from August 2015 to December 2016.
Crustal elements (Ca, Al, Si, Ti, and Fe), which accounted for more than 85% of total elemental concentrations, reside mostly in the coarse fraction at all sites (Figure 3.15). Compared to the reference urban background sites, their concentrations were 1.5 to 2.5 times higher at the near- road sites, indicating an important contribution of traffic-induced road dust resuspension to anthropogenic elemental sources in both PM fractions. Among these elements, Fe showed the
highest enhancement at the NR sites, varying from an average of 2.2 to 2.5 times higher for PM2.5–10
and PM2.5, respectively. Notably, concentrations of Ca in both PM fractions collected at the near-road sites in Toronto (NR-TOR-1 and NR-TOR-2) were ~3 times higher than at the Vancouver sites (Figure 3.15; see also Appendix D: Tables D-13 and D-10). In addition, the enhancement of Ca concentrations in
PM2.5 observed at the near-road sites in Toronto compared to the background sites was larger than in Vancouver. Spearman rank correlations were calculated for Ca, Si, Fe, Ba, Cu, Zn, and Sb at each site (Appendix D: Table D-16). Preliminary evaluation of data shows that except for BG-TOR-S, Ca correlated highly with Si (R > 0.78) and Fe (R > 0.80) at all Toronto locations. This is clearly indicative of a common source of Ca, presumably due to the resuspension of road dust. The correlation coefficients of Ca with the metals indicative of brake and tire wear (Ba, Cu, Sb, Zn) at NR-TOR-1, NR- TOR-2, and BG-TOR-N are statistically significant (Appendix D: Table D-16) and this is consistent, as these metals are also present in road dust. At both Vancouver sites, Ca concentrations were lower, as were its correlations of Ca with Si (R = 0.72), Fe, Ba, Cu, Zn, and Sb (R < 0.6 for all), especially at the NR-VAN site; soil in Vancouver may contain less Ca than in Toronto given the differences in the prevailing geology. Similar seasonal and weekly variations of crustal elements for both PM fractions were observed at all sites (Appendix D: Figures D-9 and D-10). Concentrations of the crustal elements in coarse fractions measured during the warm season were almost double the values obtained during the cold season, mostly due to favourable conditions for resuspension of road dust and bare soil particles during the dry, warm months of the year. Increased concentrations of these elements in both fractions during the weekdays as compared to weekends demonstrates the significant effect of traffic on dust resuspension.
Near-Road Pollution Study 2019 47 The distribution of trace elements included in the CEPA toxic substances list (CEPA, 1999) and considered priority pollutants by USEPA (Zn, Cu, Pb, Sb, V, Ni, Cr, As Se, and Cd) is presented in Figure 3.16.
50 Zn Cu Pb Sb V Ni Cr As Se Cd 45
) 40 3
35
30
25
20
15
10
5 Element Element concentration (ng/m 0
Figure 3.16. Spatial distribution of trace elements in PM2.5 samples collected from August 2015 to December 2016.
The highest concentrations of toxic elements (many of them related to traffic emissions) were observed at NR-VAN and NR-TOR-1, clearly indicating a significant traffic source, which may have human health implications. For example, metals indicative of brake wear (Ba, Cu, Sb) were significantly enriched (~ 3 to 4x) near roadways, especially at NR-TOR-1 (Figure 3.17).
Similar levels of Cu and Sb were observed in PM2.5 samples collected at NR-VAN and NR- TOR-1 (Appendix D: Tables D-13 and D-14). However, the mean concentration of Ba at NR-TOR-1 was ~2 times higher than that of NR-VAN. This may be indicative of more braking at NR-TOR-1 due to frequent congestion on the highway or of a contribution of sources of Ba other than brake pads. For example, barium in the form of organometallic compounds is also used as an additive to diesel fuels to reduce smoke emissions (Pant and Harrison, 2013 and references therein). Consistent with this, receptor modelling estimated that about 70%, 25%, and 5% of the Ba at NR-TOR-1 was from brake wear, tailpipe emissions, and road dust, respectively. The concentration of zinc (Zn), which is also reported as a traffic-related pollutant, was higher at all sites in Toronto than in Vancouver (Figure 3.16; see also Appendix D: Tables D-11, D-12 and D-13). Notably, the Zn concentrations at NR-TOR-1 were only slightly higher than those at BG- TOR-N. Zn is reported to be nearly 1% by weight in rubber tires, (added as zinc oxide and organozinc compounds to facilitate the vulcanization process), and tire wear has been reported to be a
Near-Road Pollution Study 2019 48 35 Ba Cu Sb
30
) 3
25
20
15
10
5 Element Element concentration (ng/m
0
Figure 3.17. Spatial distribution of metals indicative of brake wear (Ba, Cu, Sb) in PM2.5 samples collected August 2015 to December 2016. significant source of Zn in high-traffic environments (Pant and Harrison, 2013 and references therein). However, Zn is also emitted from brake wear, motor oil, and other non-traffic-related sources and thus cannot be used as a marker that is unique to tire wear. This was the case in Toronto where ~70% of the zinc was found to originate from industry rather than traffic (see Figure 3.47). A pattern of higher concentrations of trace metals on weekdays compared to weekends supports the strong impact of anthropogenic sources of some metals in the near-road and other locations (Figure 3.18; see also Appendix D: Figure D-12). The morning rush-hour peak in the diurnal profiles for these metals provided more direct evidence of the influence of traffic emissions (see Figure 3.49). Some metals concentrations exhibited seasonality (Appendix D: Figure D-11) with slightly higher concentrations in summer in Toronto and slightly higher concentrations in winter in Vancouver. For example, at NR-TOR-1 and BG-TOR-S similar warm to cold concentration ratios of ~1.7 for Ba and ~1.4 for Cu were observed (Appendix D: Figure D-12). On the other hand, the mean concentrations of Zn did not show any major seasonal differences at the NR-TOR-1 site, whereas they were almost three times higher during the cold season at the BG-TOR-N site. The reasons for these similarities and differences are not known and need to be further explored. Some of the trace metals showed a slightly different spatial distribution pattern, demonstrating the presence of other important sources of metals in urban PM2.5 (Appendix D: Figure D-12). For example, at the Vancouver locations, concentrations of vanadium (V) and nickel (Ni), which are typical tracers of heavy oil combustion, were almost three times higher than in Toronto (Appendix D: Figure D-13), potentially due to marine transportation emissions in Vancouver. Higher concentrations of Ni were also observed at Burnaby, Edmonton, and Montreal,
Near-Road Pollution Study 2019 49 but with a different V to Ni ratio. Elevated concentrations of lanthanum (La) and La to Ce ratios > 2 were also found at the Vancouver sites, which points to the impact of marine transportation and/or the oil refining industry (Appendix D: Figure D-13). The higher concentrations of selenium (Se) and arsenic (As) observed in Toronto, Ottawa, and Montreal were presumably due to long-range transport of emissions from coal-fired plants in the United States (Appendix D: Figure D-13).
60 Zn Cu Pb Sb Ni V As Cr Se Cd
50
) 3 40
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Element Element concentration (ng/m 0
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Weekend Weekday BG- NR- NR- NR- BG- BG- VAN VAN TOR-1 TOR-2 TOR-N TOR-S
Figure 3.18. Weekday and weekend distribution of trace elements in PM2.5 samples collected from August 2015 to December 2016.
3.1.2.4 Gas Phase Species
Along with the particle components reported, additional measurements were made at 24-hour time resolutions to quantify gaseous ammonia, nitric acid, and nitrous acid collected on denuders. 3.1.2.4.1 Ammonia (NH3)
The average concentrations of NH3 measured at the near-road sites in Vancouver and Toronto were far higher than those at the background and other urban sites (Figure 3.19).
Several studies have identified vehicles as important sources of NH3 in urban areas worldwide (Livingston et al., 2009; Phan et al., 2013; Suarez-Bertoa et al., 2017) and NH3 is now the dominant reactive nitrogen species emitted by vehicles (Bishop and Stedman, 2015; Link et al., 2017; Suarez-Bertoa and Astorga, 2016). Three-way catalytic converters used in gasoline vehicles can produce NH3 during cold starts, accelerations, and when the catalyst is hot, or the fuel mix is too rich. Diesel-fuelled vehicles are also contributing increasing amounts of ammonia, due to the
Near-Road Pollution Study 2019 50 introduction of the selective catalytic reduction (SCR), a DeNOx catalytic system used in diesel technologies. Ammonia concentrations varied little between weekdays and weekends at the near- road sites, suggesting that gasoline rather than diesel vehicles were the major source. As reported by Suarez-Bertoa et al. (2017), higher on-road NH3 emission factors were obtained for gasoline vehicles than for diesel.
10
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Ammonia Ammonia (ppbv) 2
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Figure 3.19. Mean concentrations of NH3 (in ppbv) in denuder-collected samples from August 2015 to March 2017, noting that sampling was from August 2015 to December 2016 for NR-VAN and NR-TOR-S. Whiskers represent standard deviation.
There was a noticeable seasonal variation of the NH3 concentration in Toronto and other eastern sites (Appendix D: Figure D-15). This may have been due to agriculture or other biogenic sources, given that the differences between summer and winter were similar at the near-road and background sites. The western sites show much less seasonal variability. Additionally, biomass burning represents an important source of NH3, as observed, for example, during weekends and the warm season at BG-TOR-S (Appendix D: Figures D-14 and D-15). 3.1.2.4.2 Nitric Acid (HNO3)
Gaseous HNO3 is one of the most important acidic air pollutants, reaching mixing ratios of several parts per billion by volume (ppbv) in polluted air. NOx (the predominant emitted species from traffic sources) is converted into HNO3 through photochemical transformations. Nitric acid concentrations were low at all sites, ranging from 0.4 µg/m3 in Vancouver to 0.6 µg/m3 in Toronto (Appendix D: Figure D-14), and these levels were similar to those seen in major cities across Canada (Figure 3.20). Nitric acid displayed a weak seasonal pattern with higher values in the warm season than in the cold season (Appendix D: Figure D-15), presumably due to increased evaporation from NH4NO3 in summer and enhanced photochemical production due to the daytime reaction of NO2 with hydroxyl
Near-Road Pollution Study 2019 51 (OH) radicals. It is well known that HNO3 and NH3 levels are affected similarly by temperature and humidity. While NH3 concentrations steadily increased with rising temperatures, HNO3 levels were amplified only with temperatures > 20 °C. Periods with higher humidity led to low HNO3 concentrations, because HNO3 is highly water soluble and effectively deposited on surfaces (Ye et al., 2016).
1.2
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Figure 3.20. Mean concentrations of HNO3 in denuder-collected samples from August 2015 to March 2017, noting that sampling was from August 2015 to December 2016 for BG-VAN and BG-TOR-S. Whiskers represent standard deviation.
3.1.2.4.3 Nitrous Acid (HONO) HONO is an important precursor of the hydroxyl radical (OH) in the atmosphere that can increase the atmospheric oxidation capacity in street canyons (Yun et al., 2017). The reaction of OH radicals with nitrogen monoxide (NO) recycles HONO, and thus HONO acts as a radical reservoir. The hydroxyl radical plays a crucial role in the oxidation of VOCs, leading to the formation of ozone and secondary organic particulate matter (PM). HONO can be either formed through chemical reactions or emitted to the atmosphere from combustion processes such as direct emission from vehicles (Czader et al., 2015; Kurtenbach et al., 2001; Liang et al., 2017; Trinh et al., 2017). Major sources of HONO are gas phase formation from the reaction between OH and nitrogen monoxide (NO), the heterogeneous conversion of NO2 on humid surfaces, or photolysis of adsorbed nitric acid (HNO3; Ye et al., 2016). In this study, the highest denuder measured HONO-values were observed at NR-VAN. Relatively high HONO concentrations, ranging from 0.2 to 5.0 µg/m3 (mean = 1.07 µg/m3) were observed at BG-VAN (Figure 3.21). Compared to the NR-VAN, the average concentration of HONO was lower at NR-TOR-1, but still higher than at the other Toronto sites. The higher atmospheric HONO concentrations observed
Near-Road Pollution Study 2019 52 3.5
3.0 )
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Figure 3.21. Mean concentrations of HONO in denuder-collected samples from August 2015 to March 2017, noting that sampling was from August 2015 to December 2016 for NR-VAN and NR-TOR-S (see also Appendix D: Table D-1). Whiskers represent standard deviation (SD). in Vancouver indicate that a source may exist in addition to heterogeneous formation of HONO from NOx and direct emission from combustion processes. Alternatively, faster removal may be occurring in Toronto. The weekday and weekend pattern of HONO suggests some anthropogenic influence (Appendix D: Figure D-14). Maximum concentrations were measured in the cold season and minimum values in the warm season, which is consistent with faster photolytic removal in the summer (Appendix D: Figure D-15), as HONO is decomposed by photolysis to NO and OH. Thus, the highest HONO concentrations and HONO to NOx ratios were observed during cloudy periods. During rainy periods, wet deposition may have accelerated the loss of HONO (Xu et al., 2015). Further comparison of the obtained data with that of NOx and other pollutants measured at these sites would be valuable. 3.1.3 Summary
The variation across the sites revealed a strong influence of traffic emissions on pollutant concentrations near major roads. Pollutant concentrations were often highest at NR-VAN and NR-
TOR-1. Here the concentrations of NO2 exceeded the 2020 annual CAAQS of 17 ppb and the concentrations of BC were above the value of 1 µg/m3 that has been proposed as a standard for occupational exposure. Both NO2 and BC are strongly associated with exhaust from diesel vehicles. Vehicle emissions contributed a small but measurable amount to PM mass: fine PM mass was slightly higher at the near-road sites (more noticeably on weekdays) with a larger difference evident for coarse PM. BC and mineral dust were the major PM components contributed by the
Near-Road Pollution Study 2019 53 emissions. Higher concentrations of many metals were evident at the near-road sites due to resuspension of road dust and brake wear. Specifically, higher concentrations of Al, Si, Ca, and Fe were attributed to resuspension of road dust while Ba, Cu, and Sb were attributed to brake wear. Finally, ammonia concentrations were substantially higher beside roads, presumably due to emissions from gasoline vehicles.
3.2 Temporal Patterns in Traffic-Related Pollutants
Traffic exhibits regular diurnal, weekday/weekend and seasonal patterns that are reflected in near- road pollutant concentrations. Differences in these patterns between weekdays and weekends, or the near-road versus background sites helped reveal the influence of traffic emissions. Seasonal differences in the diurnal patterns also provided insight.5 This section presents the traffic patterns that were observed at each site and then describes the resulting diurnal patterns of pollutant concentrations. The traffic data were assessed and found to be reliable through comparisons with video-based and manual counting results (see Appendix B.3.4). 3.2.1 Traffic Count
3.2.1.1 NR-VAN
The diurnal trend of traffic volume at NR-VAN is shown in Figure 3.22 for summer (2016) and winter (2016–2017). The traffic volume increased rapidly in the early morning hours on weekdays in the northbound lanes where motor vehicles were travelling northward toward the central business district (CBD) and Port of Vancouver terminals. Clark Drive is a busy truck route with a trucking entry point to the port on Clark Drive about three kilometres away from the monitoring station. The increase in traffic volume northbound in the morning was more gradual on weekends. The southbound traffic volumes were greater in the afternoon when the flow of traffic is greater away from the CBD. Overall there was greater traffic volume southbound than northbound, which was likely the result of the southbound direction having an additional lane of traffic. Traffic volumes were also greater in the summer compared with the winter. The percentage of larger heavy-duty (C4: 15–36.5 m) vehicles changed throughout the day, with a higher percentage during the day than at night. Percentages were greater on weekdays than on weekends but there was little difference between summer and winter. The dominant class overnight was light-duty vehicles (C2: 1.0–7.6 m) with the total of C3 (7.6–15 m) plus C4 vehicles rising to be up to 25% of the vehicles around midday on weekdays.
5 The data displayed throughout this section is shown in local standard time (LST). Both cities, Vancouver and Toronto, observed daylight savings time which resulted in a one-hour shift forward in spring through the summer. Since the timing of sunrise and sunset, which plays a significant role in meteorology and thus diurnal air quality trends, is not altered by daylight savings time, the data was chosen to be displayed without adjustment of the time change but rather shown in LST. It is recognized that due to the time change vehicular traffic patterns are shifted by an hour during the shift and therefore care must be taken when interpreting the diurnal trends.
Near-Road Pollution Study 2019 54 Figure 3.22. Diurnal traffic patterns at NR-VAN. The rows show the northbound (top), southbound (middle) and total traffic (bottom) volumes. The first column shows the total vehicles, with the ratio of light-duty vehicles (C2; 1.0–7.6 m), smaller trucks (C3; 7.6–15 m) and larger heavy-duty vehicles (C4; 15–36.5 m) to total traffic in the second to fourth columns.
Near-Road Pollution Study 2019 55 3.2.1.2 NR-TOR-1
The diurnal trend of traffic volume at NR-TOR-1 is shown in Figure 3.23. Only data for the eastbound traffic was used, as the traffic sensor did not provide reliable data for the further westbound lanes (see Appendix B.3.4.2). Traffic on weekdays rose early in the morning and remained relatively constant until after 18:00. The percentage of C4 peaked at 3 a.m. overnight and decreased on weekdays during the morning and evening rush hour. On weekends, traffic increased to the same volume midday as weekdays but at a slower rate. The truck fraction was lower on Saturdays and even lower on Sundays, allowing greater separation of the influence of the vehicle types on pollutant levels (see Section 3.4).
Figure 3.23. Diurnal traffic patterns at NR-TOR-1 for eastbound traffic (Lanes 1 to 8) for weekdays (solid) and weekends (no fill) along with the percentage of C2, C3 and C4 vehicle types. The traffic sensor was not fully operational until after summer 2016; summer 2016 is therefore not shown.
3.2.1.3 NR-TOR-2
Traffic at NR-TOR-2 was higher on weekday mornings and evenings, consistent with commuters arriving and leaving downtown (Figure 3.24).6
6 Data after May 23, 2016 needed to be corrected due to repositioning of the Wavetronix sensor. Specifically, between May and September 2016 the sensor intermittently registered near zero vehicle counts for no reason that could be identified; these very low data were removed. This sensor was again repositioned in September 2016 and registered
Near-Road Pollution Study 2019 56 Figure 3.24. Diurnal traffic patterns at NR-TOR-2. Total traffic volume for all lanes and the fraction of vehicle sizes on weekdays and weekends. Traffic was lower and increased more gradually on weekends. The fraction of C3 trucks was higher overnight, peaking at ~4 to 5 a.m. and dropping down during the day. Further, the fraction of large trucks (C4) was lower on weekends. Overall, C4 was found to play a substantial role in determining traffic emissions. 3.2.2 Carbon Monoxide
Carbon monoxide (CO) emissions are associated with gasoline-fuelled vehicles with defective or no emission treatment systems. CO is a useful marker of traffic emissions, therefore, as it recognizes that only a small portion of the vehicle fleet contributes the majority of the emissions (see Section 3.3.2.1). The influence of traffic on CO was evident at the near-road site in Vancouver (NR-VAN) where the highest concentrations of CO occurred in winter on weekday mornings, with a smaller evening peak (Figure 3.25). In summer, concentrations were much lower, with only a morning
vehicle counts shifted to values that were 47% higher than in 2015–2016. These higher values were scaled down using a factor of 1.47. After applying these corrections, the data from 2015–2016 and 2016–2017 showed good agreement (see bottom row).
Near-Road Pollution Study 2019 57 weekday peak. Higher winter CO values throughout Vancouver have been attributed to lower mixing heights compared with the summer (Metro Vancouver 2018).
Figure 3.25. Diurnal patterns for CO with a comparison of weekday/weekend and seasonal patterns.
The diurnal traffic pattern at NR-VAN showed a rapid increase in volume in early morning on weekdays; however, while the traffic volume remained elevated (Figure 3.25) the CO concentrations decreased near noon. The decrease in CO and other pollutants in the late morning was consistent with faster removal through increased winds and/or mixing height. Similar patterns were observed for most traffic pollutants at the other near-road sites. The background site (BG-VAN) in Vancouver showed similar trends, with a morning weekday peak in winter that was considerably attenuated compared with that at NR-VAN. The diurnal patterns at this site, particularly on weekdays in winter, was presumably being governed by citywide traffic emissions even though it was well away from any major road. This is consistent with the wider spatial influence of traffic emissions in winter (see Section 3.4.3). The near-road site of NR-TOR-1 exhibited a similar diurnal pattern, with the highest CO concentrations in the mornings. In contrast to NR-VAN, this pattern was strongest in summer rather than winter. No reason was identified for high CO emissions on weekday mornings in the summer versus the winter. Notably, however, and more generally, midday CO values were slightly higher in winter than summer, presumably due to less mixing. Weekend mornings displayed a more gradual increase in concentrations with much less of a peak when compared with weekdays. This more gradual increase on weekends is consistent with the more gradual increases in traffic volume on weekends when compared to the onset of a highway filled to capacity within a few hours during the weekdays. The near-road site of NR-TOR-2 displayed a similar morning peak on weekdays during the summer, but with little diurnal variation on weekends. Afternoons in winter showed slightly higher CO concentrations on weekdays compared with weekends. In winter 2015–2016, the weekday and weekend diurnal patterns differed while in 2016–2017 they were much more similar.
Near-Road Pollution Study 2019 58 3.2.3 Nitrogen Monoxide
Nitrogen monoxide (NO) is primarily associated with emissions from diesel vehicles, the majority of which are medium-duty to heavy-duty trucks in Canada. In fact, 75% to 90% of the NO was found to be from nearby vehicles at the three near-road sites included in this study (see Table 3.2). The strong influence of vehicles was evident at the NR-VAN, which exhibited the highest concentrations of NO during weekday morning rush hour in winter (Figure 3.26). Following the large peak that occurred at 8 a.m., concentrations decreased until noon before increasing gradually throughout the remainder of the day into the evening. Notably, weekend mornings in winter displayed a
Figure 3.26. Diurnal patterns of NO with a comparison of weekday/weekend and seasonal patterns. considerably lower peak than weekdays. In summer, concentrations of NO at NR-VAN were much lower, but there was still a weekday morning peak. Higher winter values were consistent with lower mixing heights in winter than summer. In winter, the background site in Vancouver (BG-VAN) showed similarly timed peaks but with attenuated concentration compared with NR-VAN. In summer, there was little to no difference between weekdays and weekends. Other sites in the Vancouver region, such as Richmond Airport, Burnaby South, and Downtown Vancouver show a pattern similar to NR-VAN and BG-VAN, with a rapid increase in concentration on weekday mornings in winter and lower concentrations on weekends (Metro Vancouver, 2014). Concentrations of NO at NR-TOR-1 underwent a rapid increase on weekday mornings that was more pronounced in the summer than the winter. In summer, a maximum concentration occurred at 5 a.m. LST (6 a.m. EDT) followed by a rapid decrease. The summer peak occurred an hour earlier than winter because of the daylight savings time offset. The magnitude of the peak was lower in winter than summer although the concentration consistently remained elevated for several hours until decreasing in the late morning. Weekends followed a similar but attenuated trend compared to weekdays in both summer and winter.
Near-Road Pollution Study 2019 59 The near-road site of NR-TOR-2 exhibited similar patterns to NR-TOR-1 but with lower concentrations. In the summer, the peak decreased more rapidly compared with winter, occurring from 9 a.m. LST onward. On weekends the morning peak occurred later and was broader. BG-TOR-S exhibited the influence of traffic emissions, although the maximum concentration was an order of magnitude lower than at NR-TOR-1. For example, a larger morning peak was evident in the summer on weekdays compared with weekends. In the winter there was little to no difference between weekdays and weekends. The BG-TOR-N site also displayed a rapid increase in concentrations in the morning both in winter and summer on weekdays; this was similar to near-road sites but attenuated. Weekends showed a more gradual increase in concentrations. 3.2.4 Nitrogen Dioxide
Nitrogen dioxide (NO2) is a secondary pollutant often associated with vehicle emissions, given the rapid conversion of NO to NO2 that occurs near roads. NR-VAN exhibited the highest concentrations of NO2 during weekday morning rush hour in winter (Figure 3.27). Following the peak that occurred at 8 a.m., concentrations decreased until noon before increasing gradually to a smaller second peak at 5 p.m. This was consistent with evening rush hour when traffic volume was higher in the southbound lanes directly beside the station (see Figure 3.2.2). In winter, weekend mornings displayed considerably lower concentrations compared with weekdays. In summer, weekday concentrations were lower with an attenuated morning peak and a higher evening peak late in the day around 9 p.m. Higher values in winter than summer have been attributed to lower mixing heights in winter. BG-VAN exhibited similar diurnal trends to NR-VAN but with lower concentrations. Other
Vancouver region sites showed a similar trend to NR-VAN and BG-VAN where NO2 concentrations were higher on weekdays in winter, with the lowest concentrations on weekends in summer (Metro Vancouver, 2014). At NR-TOR-1, the highest concentrations also occurred in winter on weekdays with a morning peak and a smaller afternoon peak. In summer, the rush-hour peak was lower with the lowest concentrations occurring in the afternoon on weekends. In summer, weekend concentrations were, in general, lower than weekdays. As described in Section 3.3.2.2, the percentage of heavy- duty diesel vehicles was lower on weekends at NR-TOR-1, reducing the emissions of NOx.
In contrast to the other near-road sites in the study, NR-TOR-2 experienced the highest NO2 concentrations in the summer on weekday mornings. In both seasons, the weekend trend was similar with decreasing concentrations throughout the day and higher concentrations through the overnight period. Both background sites in Toronto, BG-TOR-S and BG-TOR-N, exhibited similar diurnal patterns to NR-TOR-2 on summer weekdays, with a morning peak and lower concentrations in the afternoon. The background site BG-TOR-S, which was located away from local emission sources, exhibited some of the lowest concentrations across the study sites, but still exhibited the weekday morning peak in its diurnal pattern. In winter, there was little difference between weekdays and weekends at this site. In summer, there was also little difference with the exception of the morning peak on weekdays.
Across all sites the lowest NO2 values occurred in the late afternoon in the summer, which corresponded to the highest ozone values.
Near-Road Pollution Study 2019 60
Figure 3.27. Diurnal patterns of NO2 with comparison of weekday/weekend and seasonal patterns. 3.2.5 Black Carbon
Black carbon, like NO, is associated with diesel emissions (ECCC, 2018). It is consistent then, that BC concentrations were generally higher on weekdays compared with weekends at all near-road sites. NR-VAN experienced greater BC concentrations in the winter (Figure 3.28). In the winter, BC concentrations increased rapidly on weekday mornings to a peak that occurred near 8 a.m. LST. Concentrations decreased until noon before increasing again in the afternoon. In the summer, weekday concentrations exhibited a peak in the morning and another in late evening. In both seasons, BC concentrations on weekends were considerably lower compared with weekdays. At BG-VAN, a similar diurnal pattern was experienced but with attenuated levels compared with NR-VAN. As with Toronto, this presumably reflects the citywide influence of traffic emissions, even well away from major roads. Other sites in the Greater Vancouver Area exhibited similar patterns to NR-VAN and BG-VAN: Richmond Airport experienced the greatest BC on winter weekdays, especially in the mornings and late afternoon while Burnaby South also showed higher BC on weekdays compared with weekends. NR-TOR-1 also exhibited a weekday morning peak characteristic of rush hour with a rapid rise followed by a slower decrease. This pattern was much more pronounced in summer than in winter. Similar to the findings of NO (Figure 3.26), the reduction in the weekday morning peak was more gradual in winter. The summer versus winter difference in this weekday morning peak was much larger for BC than NO, even though both are associated with diesel emissions. As described in Section 3.3.1, elevated summertime BC may be due to a change of fuel formulation in Ontario. NR-TOR-2 also exhibited a morning peak in concentrations on weekdays in summer while weekends showed little to no increase in mornings in summer, but concentrations were elevated overnight. As with NR-TOR-1 the weekday morning increase was small in winter with no discernable trend on weekends.
Near-Road Pollution Study 2019 61 BG-TOR-N exhibited a similar increase on weekdays in summer but with a smaller magnitude, suggesting an influence of traffic emissions even several hundred metres from any major road. The weekend pattern in summer was considerably attenuated compared with weekdays with a small peak in the morning before concentrations decreased over the day. On winter weekends, BC exhibited little to no diurnal pattern. At BG-TOR-S, summer showed a similar trend on weekdays to the near-road Toronto sites although concentrations were greatly attenuated. Presumably this reflects the citywide influence of traffic emissions, even well away from major roads. There was little to no diurnal trend in the winter. In summer on weekends, a different trend is apparent at BG-TOR-S where it is thought that biomass burning (i.e., beach bonfires) may have contributed to elevated BC in the overnight hours.
Figure 3.28. Diurnal patterns for BC with comparison of weekday/weekend and seasonal patterns.
Near-Road Pollution Study 2019 62 3.2.6 Ultrafine Particles
In winter, both NR-VAN and NR-TOR-1 exhibited a similar diurnal trend of a rapid increase in ultrafine particles (UFP) during weekday morning rush hour (Figure 3.29). The NR-VAN site had a pattern on weekdays of a large morning peak and smaller evening peak. The NR-TOR-1 site similarly experienced the highest concentration of UFP in the mornings with a declining trend until 4 p.m. before increasing again to an evening peak. Concentrations of UFP from vehicles can be higher in winter than summer due to reduced evaporation at colder temperatures (see Figure 3.41). Thus, the higher concentrations on winter weekdays at both NR-VAN and NR-TOR-1 were due to a combination of greater vehicle emissions and slower removal by mixing. On weekends, UFP concentrations were much lower than weekdays at both of these near-road sites, even though total traffic volume was only slightly lower on weekends. This suggested that trucks were making a disproportionate contribution. Also in winter, the timing of the peak on weekends was delayed compared with weekdays. This is consistent with findings for NO and NO2.
Figure 3.29. Diurnal patterns of the particle number concentration (PNC) of ultrafine particles (UFP) with comparison of weekday/weekend and seasonal patterns.
In summer, the NR-TOR-1 site experienced a weekday morning peak that was not apparent at the NR-VAN site. At the NR-VAN site there was an increase in the morning with a second hump near midday. On weekends, there was a similar but attenuated pattern, with a larger midday rise. At NR-TOR-2, the highest UFP count per volume occurred near midday in summer, as at the other two near-road sites. The three background sites, BG-VAN, BG-TOR-1, and BG-TOR-2, also followed this diurnal pattern in summer, with a weekday rush-hour rise in the morning followed by a midday peak. The midday peak at the background sites was consistent with the occurrence of midday nucleation events, indicating that traffic may or may not always be the dominant source of UFP near roads. The rise in the morning before 9 a.m. was presumably due to traffic while the subsequent rise toward
Near-Road Pollution Study 2019 63 noon was due to nucleation events. Regional nucleation events only occur on a minority of summer days, but the large number of particles produced is sufficient to dominate the seasonally averaged diurnal pattern. These events were presumably also impacting NR-VAN and NR-TOR-1 but were not evident in the diurnal patterns due to the overwhelming influence of traffic emissions. In summary, UFP are good indicators of traffic emissions in that a low concentration of UFP is indicative of a low influence of traffic. However, high concentrations may also arise due to nucleation events, particularly in summer at midday. 3.2.7 Ozone
Ozone (O3) is produced through secondary atmospheric chemistry rather than directly emitted by vehicles. However, vehicle emissions can influence O3 concentrations by promoting production across a region or decreasing concentrations beside busy roads through reactions with NOx. As expected, the highest O3 concentrations were experienced in the summer in the late afternoon at all sites (Figure 3.30).
Figure 3.30. Diurnal patterns of O3 with comparison of weekday/weekend and seasonal patterns.
In the summer at NR-VAN, there was slightly higher O3 in the morning hours on weekends compared with weekdays. This is likely due to the greater availability of NOx on weekdays. The background site, BG-VAN, also exhibited this trend. The diurnal patterns at NR-VAN were very similar with BG-VAN, exhibiting only slightly higher O3. Notably, in general, higher O3 was found in 2015 than 2016, consistent with the hotter temperatures in the summer of 2015 in Vancouver.
In winter, all sites experienced lower O3. This is consistent with lower photochemical production in this season. Winter weekdays were generally lower than weekends. Notably, another site operated downtown by Metro Vancouver had some of the lowest O3 concentrations across Canada due to the sea breeze that brings in relatively clean marine air from the Pacific in the summer. This air carries NOx east, and this is where O3 production tends to be greater.
Near-Road Pollution Study 2019 64 Sites across Toronto generally experienced similar O3 concentrations, consistent with O3 being a regional pollutant. However, in the summer of 2015, NR-TOR-1 and NR-TOR-2 both exhibited an apparent weekend effect with higher O3 on weekends compared with weekdays, presumably due to lower NOx emissions. In contrast, in the summer of 2016, there was much less difference between weekends and weekdays at the near-road sites. No reason for this difference between summer 2015 and summer 2016 was identified. In the winter, weekdays were generally lower than weekends with the exception of the background site BG-TOR-S where there was little difference between days. Overall, the Toronto background sites experienced higher O3 levels compared with the near-road sites. 3.2.8 Fine Particulate Matter
Fine particulate matter (PM2.5) exhibited very weak diurnal and seasonal patterns (Figure 3.31). In the summer of 2015, however, the Vancouver data were highly influenced by wildfire smoke events. The diurnal trends are therefore not thought to be representative of typical ambient conditions. Notably, two other Vancouver area sites, Richmond Airport and Burnaby South, showed similar diurnal trends as the NR-VAN and BG-VAN sites with the wildfire event the dominant feature in the summer of 2015.
Figure 3.31. Diurnal patterns for PM2.5 with comparison of weekday/weekend and seasonal patterns.
In the summer of 2016, no diurnal pattern was evident at either NR-VAN or BG-VAN apart from elevated PM2.5 near midnight on weekends. In winter, NR-VAN exhibited elevated PM2.5 in the evenings with weekdays having slightly higher concentrations than weekends. Concentrations were lower in 2015–2016, as the winter of 2016–2017 experienced prolonged cold periods. In winter, the
Richmond Airport showed elevated PM2.5 in the evening, which is similar to the trend experienced at NR-VAN.
Near-Road Pollution Study 2019 65 In summer at NR-TOR-1, an increase in PM2.5 during weekday morning rush hour was evident, suggesting an influence of traffic. However, this late morning weekday rise pattern was also evident in winter 2016–2017 at BG-TOR-N, indicating that it may not have been due to traffic alone.
On weekends in the summer of 2015 but not 2016, small increases in PM2.5 were observed overnight at all four Toronto sites; the cause is not known.
3.3 Vehicle Contribution to Urban Air Pollution
3.3.1 Black Carbon Analysis
Aethalometer data for the near-road sites and five MECP sites in southern Ontario were analyzed in detail for the period June 2015 to May 2016 to investigate spatial differences in BC mass concentrations. The highest BC mass concentrations were observed for the NR-VAN and NR-TOR-1 near-road sites (Figure 3.32; 1.91 and 1.74 μg/m3, respectively). This was expected given the high traffic volumes at these locations. Concentrations are higher at NR-VAN in part because the
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0.0
NR-VAN BG-VAN Etobicoke NR-TOR-1 Windsor W BG-TOR-N BG-TOR-S Scarborough NR-TOR-2 [g]NR-TOR-2 [r] Hamilton DTNWindsor DTN
Figure 3.32. Mean mass concentrations of BC for the Ontario and Vancouver sites. Whiskers represent uncertainty estimates. NR-TOR-2 sampling occurred at the ground [g] and roof [r] levels. sampling inlet is closer to the traffic for the former site (see Section 3.3.2). The next highest BC concentrations were observed at Etobicoke and at ground level at NR-TOR-2 (0.94 and 0.82 μg/m3, respectively). These sites were also near-road locations but with comparatively lower traffic volumes. At the remaining sites, lower but similar BC concentrations were observed (0.48–0.70 μg/m3). The lowest mean BC concentration was observed at BG-TOR-S (0.48 μg/m3). The uncertainty associated with reported BC mass concentrations is estimated to be 30% (Healy et al., 2017). Further incorrect AE33 channels (BCn2 vs BCn) were logged at the Vancouver sites. A six-week investigation of these raw vs. compensated data showed that averaging over a few hundred hours provided agreement to within 15%; the BC data in this report are typically averaged over two years (17,500h).
Near-Road Pollution Study 2019 66 3.3.1.1 Weekday/Weekend Behaviour of Black Carbon
Examination of weekday/weekend differences in BC mass concentrations demonstrates the importance of local traffic at many locations (Figure 3.33), with much higher concentrations observed on weekdays than weekends. This effect is most pronounced at the NR-TOR-1 and NR-VAN sites. Notably, a minimal effect is observed at BG-TOR-S due to the absence of local traffic on the Toronto Islands.
3.0
2.5
Weekday )
-3 Weekend
g m 2.0 m
1.5
1.0 Mass concentration ( 0.5
0.0
NR-VAN BG-VAN Etobicoke NR-TOR-1 Windsor W BG-TOR-N BG-TOR-S Scarborough NR-TOR-2 [g]NR-TOR-2 [r] Hamilton DTNWindsor DTN
Figure 3.33. Mean weekday and weekend mass concentrations of BC for all sites. Whiskers represent uncertainty estimates. Note: at NR-TOR-2, sampling occurred at both the ground [g] and roof [r] levels.
3.3.1.2 Seasonality of Black Carbon
The seasonality of BC concentrations was also investigated for all sites. In Ontario, a strong seasonal dependence was observed for BC mass concentrations, with mean summer/fall (June–November) concentrations higher than respective mean winter/spring (December–May) concentrations by almost a factor of two at NR-TOR-1 (Figure 3.34). This seasonal difference can be partly explained by increased transboundary transport of BC from the United States to Ontario in the summer months (Healy et al., 2017). However, the transboundary influence does not fully explain the large differences at NR-TOR-1 and Etobicoke, the Ontario sites most influenced by local traffic. It is possible that seasonal changes in fuel formulations may be impacting BC emissions in Ontario, but further evidence is required to confirm this hypothesis. In Vancouver, the trend is different, with higher BC concentrations observed in the fall and winter relative to the summer and spring. This may be associated with changes in meteorology and/or dispersion. A more detailed investigation of spatial and seasonal differences in BC mass concentrations in the Metro Vancouver area is currently underway and will be the subject of a future publication (Healy et al., 2019).
Near-Road Pollution Study 2019 67 3.0 Spring Summer
2.5 Fall )
-3 Winter
g m 2.0 m
1.5
1.0 Mass concentration ( 0.5
0.0
NR-VAN BG-VAN Etobicoke NR-TOR-1 Windsor W BG-TOR-N BG-TOR-S Scarborough NR-TOR-2 [g]NR-TOR-2 [r] Hamilton DTNWindsor DTN Figure 3.34. Seasonal mean weekday and weekend mass concentrations of BC for all sites. Whiskers represent uncertainty estimates. Note: at NR-TOR-2, sampling occurred at both the ground [g] and roof [r] levels.
3.3.1.3 Sources of Black Carbon
BC mass concentrations were also apportioned to BC from fossil fuel combustion (BCff) and BC from biomass burning (BCbb) using the method described in Healy et al. (2017) for the period June 2015 to May 2016. Table 3.1 shows data from each site, except for BG-TOR-N (which was not yet operational at that time), and includes annual mean mass concentrations of BC, BCff and BCbb. It is apparent from the data that BCff was the predominant source of BC at every site investigated, with BCbb having a minor (6–26%) annual mass contribution at all locations. Potential sources of BCff in Canada include on-road and off-road gasoline and diesel vehicles, rail and marine transport, petroleum industry upstream processes, and oil/coal combustion-based power generation. Sources of BCbb are predominantly associated with residential wood combustion and wildfires (ECCC, 2018). 3.3.2 Emission Factor Analysis
This section focuses on the application and results of emission factors calculated from measurements made at the near-road sites. Emission factors are a measure of the amount of pollutant emitted normally by the work done by a vehicle. They are useful in isolating contributions from vehicle emissions to local air pollutants, deconvoluting trends in vehicle emissions respective to other local sources, and accounting for effects from local meteorology and dilution/dispersion, and they can be used as inputs for emissions and dispersion models. Laboratory-based or individual vehicle measurement studies can record this work done by vehicles, usually reporting in units of brake horse power or distance driven. However, real-world studies cannot directly measure this work done, and instead commonly infer the work done by indirect measures, such as the fuel-based carbon balance method that uses the measurement of CO2 to estimate fuel burned.
Near-Road Pollution Study 2019 68 Table 3.1. Spatial Differences in BC Mass Concentrations and Source Contributions (June 2015–May 2016) 3 3 3 Station BC (µg/m ) BCff (µg/m ) BCbb (µg/m ) % BCbb NR-TOR-1 1.74 1.50 0.24 14 Etobicoke 0.95 0.83 0.12 13 NR-TOR-2 [g] 0.84 0.70 0.14 17 NR-TOR-2 [r] 0.59 0.47 0.12 20 Scarborough 0.69 0.61 0.08 12 Hamilton DTN 0.61 0.52 0.09 15 Windsor DTN 0.55 0.42 0.13 24 Windsor W 0.72 0.54 0.18 26 BG-TOR-S 0.51 0.39 0.12 25 NR-VAN 1.91 1.79 0.12 6 BG-VAN 0.69 0.59 0.10 14 Note: At NR-TOR-2, sampling occurred at both the ground [g] and roof [r] levels.
In the equation below, EFP is the fuel-based emission factor of pollutant P (e.g., in mass or number) per kilogram of fuel burned inferred by the background-subtracted integrated amount of -3 carbon combustion products ΔCO2 and ΔCO (in kg of C m ) within the plume, and wc is equal to 0.86, the carbon weight fraction for a gasoline dominated fleet (Wang et al., 2015).
∆[P] EFP = ( ) 푤C ∆[CO2] + ∆[CO]
This method also accounts for dilution that occurs between tailpipe and measurement. Two major assumptions are made using this method: 1) that dilution of the pollutant and CO2 + CO are similar within the time frame of emission to measurement, and 2) the signal after background subtraction contains only the local vehicle emissions signature. 3.3.2.1 Fleet Distribution of Emission Factor Values
Measurements from four month-long campaigns over four seasons, conducted in 2013 and 2014 at NR-TOR-2, were used to evaluate and apply the automatic plume capture and EF calculation, as described in Wang et al. (2015). This site was most suitable for this method due to the street canyon and lower traffic density, which, in addition to the high time-resolution measurements (~0.5–1 Hz), allowed for easily distinguishable vehicle plumes. An example of one of these vehicle plumes is shown in Figure 3.35. In total, 103,000 individual plumes were captured, which was 67% of the total dataset after quality assurance criteria were applied. Each plume can represent exhaust from one or multiple vehicles, typically lasting less than 180 seconds. With such high time-resolution of the EF capture, inter-fleet emission trends could be analyzed. It was found that the fleet distribution of EFs was lognormally distributed, with many plumes having little to undetectable levels of certain pollutants. This implies that only a small number of vehicles are heavy emitters, and emit disproportionally compared to the fleet, which is typical of modern urban fleet emissions in Canada and the United States (Barth et al., 1999; Bishop and Stedman, 1996; Dallmann et al., 2012; Hudda et al., 2013; Jimenez et al., 2000; Lawson et al., 1990; Schwartz, 2000). The percent contribution of emissions categorized from the top 5%, 10%, and 25% of emitters was calculated for each pollutant (Figure 3.36), where for CO, BC, and toluene, the top 5% of emitters contributed over 40% of fleet emissions.
Near-Road Pollution Study 2019 69 The emissions were most skewed for CO and BC, where this top 5% contributed over half of the fleet emissions. In contrast, NOx and benzene had lower contributions from the top 5% of emitters. Notably, however, more than 60% of NOx and benzene emissions came from the top 25% of emitters.
Figure 3.35. Time series of the plume capture method showing CO2, NOx, and particle number concentration (PNC) from the pictured vehicle (left panel) and associated slope of the CO2 signal and automated identification of the plume (right panel), with vertical lines indicating the beginning and end of the plume.
A rough conversion of the emissions limits for a mid-sized vehicle as defined by Ontario’s Drive Clean program using distance-based EF units to fuel-based EFs gives 6.5 and 44g per kg of fuel -1 (g kg-fuel ) for NOx and CO, respectively (MoE, 2010), and can be used as a reference point for evaluating the overall compliance of the fleet with the emissions testing program. It should be noted that this is by no means a perfect evaluation of the program effectiveness, and, rather, this analysis provides a worst-case estimate, as the captured plumes include contributions from heavy-duty diesel vehicles that are regulated differently by Drive Clean.
Figure 3.36. Contributions of the top 5%, 10%, and 25% heaviest emitters to the total fleet emissions.
Near-Road Pollution Study 2019 70 Assuming the same limits are set for all vehicles, only ~6% of the EF plumes exceeded the limits for both pollutants, meaning the majority of vehicles complied with these emission limits.
However, this small percentage of plumes contributed 26% of NOx and 54% of CO the total fleet emissions, respectively, based on EF product distributions (Figure 3.37). Additionally, because of the coincident pollutant measurements, it could also be quantified that the 6% of plumes that exceeded the NOx EF limit also contributed 20% of particle number (PN), 14% of BC, 7% of CO, and 8% of benzene, toluene, ethylbenzene and xylene (BTEX) fleet emissions. For CO, the percent contribution from these limit-exceeding EF plumes was lower except for BTEX, which was 12% of fleet emissions. This indicates that more stringent enforcing of CO limits may have a co-benefit in decreasing BTEX, whereas targeting high NOx emitting vehicles would decrease PN and BC more substantially.
Figure 3.37. Product distribution histograms of NOx and CO EFs with the estimated maximum allowable emissions defined by Ontario’s Drive Clean program indicated by the black line. The plumes that exceed this limit are in the shaded area with corresponding percent contributions of other pollutants from these plumes in the text box. Not shown are 28% and 67% of plumes with undetectable NOx and CO, respectively.
3.3.2.2 Daily-Integrated Emission Factors
To date, most real-world EF studies that have used the fuel-based carbon balance method have also used 1) individual plume capture methods in which the background signal can be subtracted from before or after the measured plume (as illustrated in the previous section) or 2) measurements made in highly controlled environments, such as inside road tunnels where measurements made outside or at the entrance of the tunnel can provide necessary data for background subtraction (Araizaga et al., 2013; Ho et al., 2009; Kirchstetter et al., 1999; Tkacik et al., 2014). Although these types of EF methods are invaluable in the analysis of inter-fleet and individual vehicle emissions, it is
Near-Road Pollution Study 2019 71 difficult to apply to high traffic density and open sites that exhibit more dynamic meteorology. For this reason, a daily-integrated EF method was explored using lower time-resolution data (hourly) for the three near-road sites (NR-VAN, NR-TOR-1, and NR-TOR-2). The daily-integrated EF method uses the same concept as the fuel-based carbon balance method with the exception that the entire contribution from vehicles over the day is treated as one large plume from the area. This method was evaluated against the plume capture method described in Section 3.3.2.1 at NR-TOR-2 with EFs averaged by day over the same measurement periods, as described in Wang et al. (2018). In short, the EFs from both methods compared well for NO, NOx, and PN, and reasonably well for BC. However, the EFs from the two methods did not compare well for CO due to lower contributions of CO from vehicles at NR-TOR-2. 3.3.2.2.1 Emission Measurements Site and sampling inlet placement are very important considerations when it comes to capturing vehicle emissions. This is especially true in the fine-spatial near-road gradient (0–15 m) where the sources are close and dilution and dispersion are large; when site parameters are not consistent within the study or monitoring network, these factors make it difficult to interpret the data from multiple sites. For example, the NR-VAN measurement inlet was only 6 m from the roadway, which is about half the distance of NR-TOR-1 (10 m) and NR-TOR-2 (14 m). The impact of this can be clearly observed where both the mean total and background-subtracted signals are higher at NR-VAN despite the traffic density being similar to NR-TOR-2 and being less than 10% of the traffic density at NR-TOR-1 (Table 3.2).
Table 3.2. Emission Contributions to Concentrations at Near-Road Sites Local Signal Mean Local % Total Signal Emission Units (Background-Subtracted) VAN TOR1 TOR2 VAN TOR1 TOR2 VAN TOR1 TOR2 CO2 9 6 4 ppm 444±0.9 437±0.5 430±0.4 39±0.8 25±0.4 17±0.3 CO 53 44 34 ppb 371±4 328±3 262±2 196±4 145±2 90±1 NO 90 88 75 ppb 37±0.8 25±0.5 4±0.1 33±0.7 22±0.5 3±0.1 NOx 69 75 69 ppb 59±0.9 44±0.6 16±0.2 45±0.8 33±0.6 11±0.2 103 #/ PNC 68 73 61 28±0.3 41±0.7 14±0.1 19±0.3 30±0.6 8.7±0.01 cm3 BC 80 73 64 ng/m3 1954±30 1706±30 840±10 1550±23 1252±24 539±10 Note: Mean Local %: portion of concentration due to local emissions calculated as local signal/total signal x 100%; Total Signal: measured concentration; Local Signal concentrations after background subtraction; and ± a 95% confidence interval for the concentrations.
The background subtraction allowed for the estimation of the average contribution of local emissions to the pollutant concentrations measured at the three near-road sites. The highest local contributions were at NR-TOR-1, which exhibited upwards of 90% and 80% local contributions for
NO and BC, respectively. NOx and PN local contributions were lower (between 68% and 69%), with the lowest being for CO at 53% and CO2 at only 9%. NR-VAN had similar local contributions as NR- TOR-1, whereas NR-TOR-2 had lower percent local contributions overall. When EFs were calculated for each site, the different levels of dilution due to the measurement distance from the roadway were in theory accounted for (Figure 3.35). Based on the local CO2 signals (Table 3.2), the air diluted on average 1.5x and 2.3x less at NR-VAN than at NR-TOR- 1 and NR-TOR-2, respectively. The EF comparison between the sites makes sense given the fleet
Near-Road Pollution Study 2019 72 make-up. Based on the Wavetronix traffic counter data, NR-VAN had 6.3x and NR-TOR-1 had 4.7x more large C4 (> 15 m) vehicles than NR-TOR-2, a site dominated by gasoline passenger vehicles.
NR-VAN had EF values for NO, NOx, and BC that were similar to NR-TOR-1, which had the
highest NO, NOx, BC, as well as PN values (Figure 3.38). It was not surprising that NR-TOR-2 had lower fleet-average EFs for most pollutants (except for CO, which was similar across all three sites), as it was mostly influenced by gasoline-fuelled vehicles. Notably, NR-VAN had mean PN EF values that were substantially lower than NR-TOR-1 (and much closer to NR-TOR-2), which was unexpected given the fraction of C4 vehicles observed at NR-VAN. It is unclear why NR-VAN exhibited lower PN emissions, but one theory is that warmer winter temperatures may have contributed to increased “within-plume” evaporation of volatile particles and hence lower PN EF values in Vancouver (Wang et al., 2017). Together, these EF values represent the fleet-average emissions from 200 million vehicles. The estimated annual daily traffic counts (in terms of number of vehicles) and number of sampling days (shown in brackets) at each site were 30,000 (445), 410,000 (441), and 17,000 (343) for NR- VAN, NR-TOR-1, and NR-TOR-2 respectively. Based on this information, these data represent emissions averaged over 13 (NR-VAN), 181 (NR-TOR-1), and ~6 (NR-TOR-2) million vehicles.
Figure 3.38. Distribution of fraction of C4 vehicles and EFs at each site. Box and whiskers represent 75th and 90th percentiles, respectively, and red squares show the mean value.
3.3.2.3 Temporal Patterns of Emissions
Temporal trends of the fleet EFs were investigated at two different timescales at all three sites using the daily-integrated EFs: weekly (weekday versus weekend) and seasonal (winter and summer). Diurnal patterns (morning rush hour, evening peak, and evening low) were also investigated but only at NR-TOR-2, as this was the only site where the required individual plume-capture-based method could be applied. 3.3.2.3.1 Weekday-to-Weekend Patterns On average, weekday EFs were higher at all three sites except for CO EFs, which tended to be similar or higher on the weekends (Figure 3.39). For the most part, weekday/weekend (WD/WE) ratios were highest at NR-TOR-1 relative to the other two sites, with BC and NO EF ratios being the highest
at ~1.9, followed by PN and NOx EF ratios of 1.6 and 1.5, respectively. The one exception was the WD/WE CO EF ratio, which was lowest at NR-TOR-1 with a value below 1, meaning that CO emission
Near-Road Pollution Study 2019 73 factors were higher on the weekend, consistent with the higher proportion of gasoline-fuelled cars on weekends.
Figure 3.39. Mean WD/WE ratios for C4 vehicles and mean pollutant EFs. Error bars indicate 95% confidence interval.
The C4 category exhibited the highest WD/WE differences relative to other meteorological and fleet factors (not shown) at all sites, with the highest ratio at NR-TOR-1 of 2.2 (compared to 1.7 at NR-VAN and 1.4 at NR-TOR-2). Heavy emitting heavy-duty diesel vehicles are commonly
associated with emissions of BC, NOx, and PN, and it is not surprising that C4 vehicles are a major influencing factor for WD/WE differences for these pollutants. These weekday-to-weekend patterns were further explored by separating the data for Saturdays and Sundays. It was found that the percentage of C4 vehicles was even lower on Sundays than on Saturdays at all three sites. Even though C4 vehicles represented a small portion of the fleet (< 10%), it played a dominant role in determining the fleet-average EF for many pollutants. A model was developed to estimate a weighted average EF for the fleets based on the percentage of trucks
normalized for fuel consumption (%VehC4-norm) using NOx, BC, PN, and CO emission factors for light- duty gasoline vehicles (cars; LDV) and heavy-duty diesel vehicles (trucks; HDDV) (Table 3.3).
Table 3.3. Fleet Weighted Averages for LDV and HDDV Emission Factors Pollutant LDV HDDV NOx (g/kg-fuel) 1.2 7.2 BC (mg/kg-fuel) 20.0 115.0 PN (# *1014/kg-fuel) 6.3 34.1 CO (g/kg-fuel) 11.0 11.0 Mileage (L/100km) 9.0 40.0
Near-Road Pollution Study 2019 74 Figure 3.40. NOx, BC, CO, and PN emission factor distributions vs. the percentage of large trucks (%VehC4) at NR-VAN, NR-TOR-1, and NR-TOR-2 with the median, and box and whiskers representing 75th and 90th percentiles. The red line represents the fleet- weighted average EFs that were calculated based on the percentage of C4 vehicles and the EF values for cars and trucks given in Table 3.3 assuming an FCHDDV/FCLDV of 4.5, with the upper and lower shaded areas representing the range of 3–6.
Because these EFs are based on CO2 from fuel burned and HDDVs have a much higher fuel consumption rate compared to LDVs, the relative CO2 contribution from the two vehicle types differs by more than simply their relative numbers. To account for this, the percentage of C4 vehicles (%VehC4) was normalized by a factor using the following equation:
FCHDDV (%VehC4 × ) FCLDV %VehC4−norm = FCHDDV (%VehLDV + %VehC4 × ) FCLDV
Near-Road Pollution Study 2019 75
where %VehC4-norm is the percentage of C4 vehicles normalized to the fuel consumption difference
between HDDVs and LDVs (FCHDDV/FCLDV = 4.5 [range: 3–6]; based on HDDV [NRCAN, 2016] and LDV
[NRCAN, 2018]), where %VehLDV is the percentage of C2 and C3 vehicles, and where FCHDDV/FCLDV is the difference in fuel consumption between the two vehicle types. This model provided a good estimate of the fleet-average EF across these three sites, and
thus may be applicable at other sites as well. The model worked best for NOx and BC EFs, as both pollutants are predominantly emitted at higher levels by HDDVs. PN EFs exhibited a smaller increase at lower percentage of C4 vehicles and did not fit as well. A constant CO EF was used for cars and trucks as neither vehicle type should typically be emitting CO. It is believed that the CO emissions were from a small percentage of heavy emitting cars and trucks. The curvature is evident in the
model for NOx, BC, and PN EFs (Figure 3.40) and points to the fleet-average EF becoming dominated by C4 vehicles at higher percentages of these vehicles (> 30%). These models were overlaid with the distribution of EFs calculated from measurements combined from the three sites (Figure 3.40), matching well with the EF versus percentage of C4 vehicles. 3.3.2.3.2 Seasonal Patterns
Winter EFs were higher than summer EFs for CO, NO, NOx, and PN (Figure 3.41). The highest of the winter/summer ratios was PN EFs at NR-TOR-1. BC was the only pollutant to have higher summer EFs relative to winter. With smaller and less consistent seasonal variations in the fraction of C4 vehicles and larger differences in ambient conditions across the sites, multiple factors likely affected seasonal trends of EFs (as discussed in Section 3.3.2.4).
Figure 3.41. Winter/summer ratios for pollutant factors along with the fraction of C4 vehicles and ambient temperature.
Near-Road Pollution Study 2019 76 3.3.2.3.3 Diurnal Patterns Diurnal trends of EFs were determined at NR-TOR-2 only. To identify these patterns, the fuel-based carbon balance method using individual plume captures was used, rather than the daily-integrated EF method that was used to examine the weekday/weekend and seasonal trends (Figure 3.42).
Figure 3.42. Diurnal trends for NR-TOR-2 of fraction of C4 vehicles and pollutant medians. EFs are shown with weekday median (coloured lines) and quartiles (shaded areas), respectively, and a black dashed line indicating weekend median. Corresponding bar graphs relate the ratio of median value at the hour of day versus the average overnight value (23:00–03:00). Average weekday traffic begins at about 4 a.m. and slowly increases into the morning rush hour (6 to 8 a.m.) when EFs for most of the pollutants increase. PN and BC EFs remained elevated after the morning rush hour, being most observable for PN EFs between 11 a.m. and noon. Notably, this increase in PN EFs does not coincide with the fraction of C4 vehicles; it may be due to other influencing factors that occur post-tailpipe, such as condensation or nucleation of small particles
(Rönkkö et al., 2017; Wang et al., 2018). Over weekday afternoons the EF of BC, PN and NOx decreased slightly, consistent with the small decline in the fraction of C4 vehicles. These EFs levelled off throughout the evening and were relatively constant for much of the night. At 3 to 5 a.m. there is an increase in BC, PN, and NOx EFs coinciding with an increased fraction of C4 vehicles, likely due to overnight garbage collection. Of note, the CO EFs were relatively consistent throughout the day,
Near-Road Pollution Study 2019 77 whereas toluene EFs showed some increase during the morning rush hour, implying a potential difference in morning rush-hour gasoline-fuelled fleet emissions versus the rest of the day. On weekends, the fraction of C4 vehicles was lower, yielding lower EF values for BC, PN and NOx. 3.3.2.4 Parameters Influencing Emission Factors
Parameters influencing fleet emissions were investigated by combining the EFs determined at all three sites for each respective pollutant and performing a multiple linear regression analysis. The three final parameters included were daily averaged ambient temperature, fraction of C4 vehicles, and average vehicle fleet speed; additional parameters explored did not yield significant associations. 3.3.2.4.1 Model Development Daily averaged ambient temperature, fraction of C4 vehicles, and average vehicle fleet speed were able to predict 28% of the variability of CO EFs and 46% to 75% of the variability of NO, NOx, PN, and
BC EFs (Table 3.4). NO, NOx, PN, and BC EFs all had positive and significant associations with the fraction of C4; EFs increased with increasing fraction of C4, which was observed to have a major effect in the comparison between weekday/weekend ratios (Figure 3.39). The opposite relationship with the fraction of C4 vehicles was observed for CO, consistent with CO being emitted mostly from cars not trucks. Increasing temperatures resulted in higher BC EFs, consistent with the higher summer vs winter time BC EFs at all three near-road sites (Figure 3.41). Fuel composition is hypothesized to be a reason for higher BC emissions, as fuel formulations contain lower volatility compounds such as heavier alkanes and aromatics in the summer (Trimm and Hunter, 2011), and this may affect PN and PM emissions from vehicles (Karavalakis et al., 2015; Takahashi et al., 2009; Ullman et al., 1995; Yuanwang et al., 2002).
Table 3.4. Multiple Linear Regression for Each Pollutant EF from Combined Near-Road Site Datasets Parameters Statistic CO NO NOx PN BC Overall model r2 0.28 0.75 0.72 0.46 0.46 Daily averaged β -0.36 -0.12 -0.26 -0.27 +0.37 ambient p-value 7.7x10-13 4.1x10-5 ~0.0 9.7x10-10 ~0.0 temperature Fraction of C4 β -0.16 +0.67 +0.62 +0.25 +0.72 vehicles p-value 4.8x10-3 ~0.0 ~0.0 3.4x10-7 ~0.0 Average vehicle fleet β +0.34 +0.18 +0.11 +0.30 +0.22* speed p-value 3.5x10-9 9.3x10-8 2.6x10-9 1.6x10-9 >0.05* * p-value ≥ 0.05 (insignificant) Note: Datasets use the overall coefficient of determination (adjusted r2) and the standardized coefficient (β) and its significance (p) for each parameter
PN EFs had negative relationships with ambient temperature. PN EFs post-tailpipe were affected seasonally with colder temperatures increasing condensation and/or nucleation of particles and warmer temperatures increasing evaporation of volatile particles. As Wang et al. (2017) have noted, these dynamic factors may be missed with in-lab EF studies.
Higher NOx winter EFs may have been due to the effect of ambient temperature on emission control technologies of certain diesel vehicles. The United Kingdom Department of Transportation
Near-Road Pollution Study 2019 78 reported that exhaust gas recirculation systems used to mitigate emissions in some diesel vehicles decreased in functionality or shut off completely under low ambient temperatures (U.K.DoT, 2016), and this would affect tailpipe emissions of NOx. Consistent with this, the concentration of NOx at NR- TOR-1 was found to be inversely associated with ambient temperature (Figure 3.64). The impact of cold Canadian winters on the performance of diesel emission treatment systems needs further investigation. Decreasing temperatures also increased CO EFs. No reason for this trend is known but, to speculate, cold temperatures may also be adversely impacting the performance of the three-way catalytic converters systems on cars. Increased local cold starts in the winter could contribute to increases in CO as has been found in past studies (Book et al., 2015; Chan et al., 2014; Nam et al., 2010; Singer et al., 1999). However, cold-start emissions were not expected to affect the area around these near-road sites, which are mostly influenced by warmed-up vehicles. Additionally, CO EFs did not show any diurnal pattern pointing to higher emissions in the morning (Figure 3.42). 3.3.2.4.2 Model Evaluation The multiple linear regression model performance was tested using a hold-out evaluation where a training set containing 70% of the data was randomly selected and used for a secondary multiple linear regression with the remaining 30% making up the test set. Ten-fold repetition was used to estimate the model uncertainty due to data selection (Figure 3.43). The slopes comparing predicted versus observed EFs were relatively close to one for all pollutant EFs, with mean values ranging between 0.77 and 0.97. The variability across repeated runs was also small. Finally, these three
Figure 3.43. Plot of predicted versus observed EFs over the ten runs with shaded areas representing 25th and 75th percentiles and the solid red line representing the median. Average (range) R2 and slope values are included in the text box (bottom-right panel).
Near-Road Pollution Study 2019 79 parameters were able to predict 28% of the variability of CO EFs and 45% to 74% of the variability of
NO, NOx, PN, and BC EFs at and across the three near-road sites. The remaining unexplained variability would in part be due to imprecision in the measurement of the EFs and in part due to other factors such as vehicle age and drive state. In particular, the EF values for PN, NO and NOx in the upper range, presumably from older high-emitting vehicles, were not well represented by the model but these represented a small portion of the overall vehicle fleet. Overall, this simple model was able to explain most of the variability in fleet averaged emission factors across multiple locations, days and pollutants.
3.3.3 Contribution of Traffic to PM2.5
3.3.3.1 Introduction
Much of the PM2.5 mass in urban areas is associated with regional secondary aerosol resulting in relatively homogeneous mass concentrations across cities. By contrast, metal-rich PM2.5 sources such as traffic, are often minor contributors to primary PM2.5 mass. However, their contributions can be spatially inhomogeneous, and may disproportionately contribute to PM2.5 toxicity due to the high oxidative potential of some transition metals. This spatial variability of traffic-related metals is of great interest, as these metals may disproportionately mediate health outcomes arising from PM2.5 exposures across metropolitan areas. The variations of PM2.5 chemical speciation data were examined to identify local pollutants with greater spatial variability. Specifically, hourly continuous chemical speciation data at NR-TOR-1 and NR-TOR-2 were analyzed using positive matrix factorization (PMF), and local and regional scale PM2.5 sources were thereby characterized at these two sites.7 3.3.3.2 Instrumentation and Application of Positive Matrix Factorization
Hourly trace metal concentrations were measured using Xact 625 continuous metals monitors at two near-road sites (NR-TOR-1 and NR-TOR-2) from May 10 to August 31, 2016. Coincident hourly measurements of OA, sulphate, nitrate, and ammonium were made using ACSMs along with measurement of BC and PM2.5 mass concentrations. Collocated measurements of the two ACSMs were conducted to estimate the correction factor for instrument variability prior to field deployment at the sites. Excellent correlations were observed between the ACSMs, with correlation coefficients (r2) of 0.88 to 0.96 across the four chemical species. The performance of the two Xact 625 metals monitors was verified using multi- metal reference samples. In addition to the highly time-resolved PM chemical speciation data, 24- hour integrated PM2.5 speciation measurements were conducted every three days using filter-based samplers at the near-road sites (see section 3.1.2). The ACSM and Xact 625 data at the two sites were also validated by comparison with these filter-based IC and XRF measurements obtained at each site. Hourly averaged aethalometer BC data were included in the analysis, as BC mass is related to fossil fuel combustion (BCff) and biomass burning (BCbb) through prior separation using the absorbance data collected at wavelengths of 370 nm and 880 nm (see Section 3.3.1). PMF is one of the most common receptor models used in the identification of PM sources in
7 Hourly particulate matter speciation data was not collected at NR-VAN, so this site was not included in the analysis.
Near-Road Pollution Study 2019 80 the atmosphere and their contributions. In this study, PMF analyses were performed on the hourly continuous chemical speciation data using EPA PMF 5.0, which incorporates the multilinear engine (ME-2) (Norris et al., 2014). Two steps of PMF analysis were investigated in the study. First, the application of PMF to ACSM organics mass spectra was conducted to identify organic aerosol (OA) factors at each site, and then the OA factors were used as variables in the second PMF analysis with inorganic species from the ACSM (i.e., sulphate, nitrate, and ammonium), trace metal elements from the Xact 625, and BCbb and BCff. In the combined PMF analysis, 12 trace elements (Ba, Ca, Cu, Fe, K, Mn, Pb, S, Se, Sr, Ti, and Zn) were chosen based on the data validation procedure with the collocated filter-based data. The robustness and rotational ambiguity of the PMF solution were evaluated by using the bootstrapping and displacement analysis of EPA PMF. The recommended evaluation procedures of PMF modelling is described in detail elsewhere (Brown et al., 2015). The temporal trends of PMF-resolved factors were examined for diurnal trends and weekday/weekend differences and compared with collocated supporting measurements (gaseous pollutants, meteorological parameters, and traffic counts). In order to identify the most probable direction of local sources, the conditional probability function (CPF) was evaluated for each source by estimating the probability that high source contributions of PMF-resolved sources are related to specific wind directions (Jeong et al., 2011). These analyses provided further insight into the nature of the PMF- resolved factors.
3.3.3.3 Spatial and Temporal Variations of PM2.5 Chemical Species
Figure 3.44 compares the concentrations of OA, sulphate, and trace elements between NR-TOR-1 and NR-TOR-2, contrasting the strong spatial variability between the highway and downtown sites for some trace elements vs the homogeneity of the major components such as OA and sulphate.
Figure 3.44. Comparison of PM2.5 chemical speciation of organic aerosol (OA), sulphate, and trace elements barium (Ba) and copper (Cu) measured at NR-TOR-2 and NR-TOR-1 from May 10 to August 31, 2016. Median (solid line) and mean (dotted line) concentrations are inside each box; the top and bottom of each box represent the 75th and 25th percentile values, and the top and bottom of each whisker and dot represent the 90th/10th and the 95th/5th percentile values, respectively. Despite the large difference in average daily traffic volume between the sites (NR-TOR-1: ~400,000 and NR-TOR-2: ~17,000 vehicles per day), no significant spatial differences between the sites were observed on average for sulphate and OA, which account for much of the PM2.5 mass.
Near-Road Pollution Study 2019 81 This homogeneity of major species across these two sites could mistakenly suggest that traffic volume does not influence PM2.5 near roads. As will be shown, however, traffic does contribute substantially to some components and can influence PM2.5 mass during morning rush hour, particularly when the highway is upwind. For example, the concentrations of Cu, Fe, Mn, Sr, and Ti at the highway site were higher than at the downtown site by factors of 4, 4, 2, 3, and 3, respectively. The strongest spatial variability was found for Ba, which was approximately ten times higher at the highway site as compared to the downtown site. The strong heterogeneity suggests that these trace elements can be considered as markers for local traffic emissions.
Diurnal variations of OA and sulphate, two large contributors to PM2.5 mass, are illustrated in Figure 3.45.
Figure 3.45. Diurnal patterns of organic aerosol (OA) and sulphate at downtown (NR-TOR-2) and highway (NR-TOR-1) sites. The error bars represent the 95% confidence intervals.
For sulphate, no strong differences between the two sites were evident in the diurnal patterns. The most prominent feature, an early afternoon peak, was seen at both sites. Thus, the influence of primary traffic emissions on this major PM2.5 species is negligible. On the other hand, OA exhibited different diurnal variations between the two sites, peaking at different times of the day. At NR-TOR-1, OA peaked in the morning (5 a.m. to 8 a.m.) on weekdays but not weekends, indicating some influence of rush-hour traffic. At NR-TOR-2, midday and evening OA peaks were prominent. The midday peak was only evident on weekdays, implying an anthropogenic influence, while the evening peak was higher on weekends than on weekdays. This evening peak in OA may have been due to cooking emissions from restaurants around the downtown site. Thus, OA did display spatial and temporal patterns, which may have in part been due to primary emissions from local traffic. The diurnal variations of Ba, Cu, and Zn at the two sites are depicted in Figure 3.46. Sharp diurnal patterns related to the morning rush hour were found for Ba and Cu at both sites (5 a.m. to 8 a.m. at NR-TOR-1 and 6 a.m. to 9 a.m. at NR-TOR-2). During morning rush hour, the concentrations of the trace elements were much higher at NR-TOR-1 than NR-TOR-2 (0.064 versus ~0.005 μg/m3 for Ba; 0.028 versus ~0.006 μg/m3 for Cu). For NR-TOR-2, Ca, Fe, Mn, Sr, and Ti were also clearly
Near-Road Pollution Study 2019 82 elevated during the morning rush hour on weekdays. Overnight (1–3 a.m.) on weekends saw higher Ba and Cu concentrations as compared to overnight on weekdays, which was probably due to higher overnight traffic volume (Figure 3.24) combined with more stagnant overnight atmospheric conditions.
Figure 3.46. Diurnal patterns of Ba, Cu, and Zn at downtown (NR-TOR-2) and highway (NR-TOR-1) sites. The error bars represent the 95% confidence intervals. Data between 00:00 and 01:00 a.m. were excluded due to high background noise. It should be noted that the temporal pattern of Zn was similar in some ways to that of other traffic-related trace metals, such as the high weekday versus weekend difference in the morning. However, the morning peak for Zn started much earlier. Moreover, no strong differences in Zn concentrations were observed between NR-TOR-1 (0.019 μg/m3) and NR-TOR-2 (0.016 μg/m3) and strong sporadic events were commonly observed in the early morning (i.e., 3 a.m. to 4 a.m.) at both sites with a Spearman r of 0.58 (P < 0.001). This suggests that Zn in Toronto is more closely related to local stationary source emissions, such as metals processing, than local traffic emissions. Zn has previously been used as an indicator of particles from tire wear, but this was not evident at either site in Toronto, despite the very high traffic volume at NR-TOR-1.
Near-Road Pollution Study 2019 83 3.3.3.4 Source Identification and Contribution
In order to identify OA factors, initial PMF analysis was applied to hourly organic mass spectra ranging from m/z 13 to m/z 120 (in total 92 m/z) measured by the ACSM. PMF evaluation tool software (PET, 3.04A; http://tinyurl.com/PMF-guide) (Ulbrich et al., 2009) was used to prepare the organic fragment data and error, execute PMF, and evaluate the results. In the PMF analysis for OA, the most reliable solution was explained by three factors (NR-TOR-1) and four factors (NR-TOR-2). The three OA factors at NR-TOR-1 included low-volatility oxygenated organic aerosol, semi-volatile oxygenated organic aerosol, and hydrocarbon-like organic aerosol. The same three factors were resolved at NR-TOR-2, as well as a cooking OA. These OA factors were combined in the second PMF dataset with three inorganic species from the ACSM, 12 trace metals from the Xact 625, and two BC sources from the Magee AE33. In the PMF analysis, five-factor to twelve-factor solutions were initially examined, and the most reliable solution was chosen based on the achievement of a constant and global minimum in the solution, the evaluation of rotational ambiguity and reproducibility in the solution, and the inclusion of physically meaningful factors. The robustness of the seven-factor solution in the PMF analysis was reinforced by the combination of the higher reproducibility and lower rotational ambiguity in the solution. There was no factor swap in the bootstrap displacement analysis of EPA PMF 5 with the solutions, indicating that the solution was well-defined and rotationally robust.
Table 3.5. Contribution of the PMF-Resolved PM2.5 Sources at NR-TOR-1 and NR-TOR-2 NR-TOR-1 NR-TOR-2 Sources Standard Standard Mean Mean Deviation Fraction Deviation Fraction (μg/m3) (μg/m3) (μg/m3) (μg/m3) Oxidized organics 3.2 2.2 34% 3.2 2.1 36% Secondary sulphate 1.9 2.3 21% 1.9 2.3 22% Secondary nitrate 0.5 1.0 6% 0.6 0.9 7% Tailpipe traffic emissions 1.7 1.7 19% 0.8 0.7 9% (Traffic_T) Non-tailpipe traffic emissions brake / tire wear 0.6 0.6 6% 0.2 0.2 2% (Traffic_NT I) Non-tailpipe traffic emissions road dust resuspension 0.3 0.3 4% 0.3 0.2 3% (Traffic_NT II) Industrial emissions 0.1 0.3 1% 0.1 0.1 1% Fireworks 0.1 0.3 1% 0.1 0.2 1% Secondary organic aerosol (OA II) 0.9 0.7 9% n/a n/a n/a Cooking emissions n/a n/a n/a 1.7 1.7 19%
From the PMF analysis, eight factors were identified for both NR-TOR-1 and NR-TOR-2: oxidized organics (Aged OA), secondary sulphate, secondary nitrate, tailpipe traffic emissions (Traffic_T), non-tailpipe traffic emissions brake/tire wear (Traffic_NT I), non-tailpipe traffic emissions road dust resuspension (Traffic_NT II), industrial emissions, and fireworks. Secondary organic
Near-Road Pollution Study 2019 84 aerosol (OA II) and cooking emissions were identified at NR-TOR-1 and NR-TOR-2, respectively (Table 3.5). Detailed discussion for the PMF analysis is given elsewhere (Jeong et al., 2019). The chemical profiles of NR-TOR-1’s nine factors and NR-TOR-2’s nine factors are shown in Figure 3.47.
Figure 3.47. Source profiles of PMF-resolved nine factors at NR-TOR-1 and NR-TOR-2 from May 10 to August 31, 2016. Black bars on the left y-axis represent the concentration of each species apportioned to the factor. Error bars represent the 95% confidence intervals of 100 bootstrap runs. Red dots on the right y-axis represent the percentage of each species apportioned to the factor. 3.3.3.4.1 Traffic-related Sources
Three traffic-related PM2.5 sources were resolved using PMF: Traffic_T associated with tailpipe emissions, Traffic_NT I associated with direct emissions from brakes, and Traffic_NT II associated with resuspension of already existing road dust. On average, these three traffic-related PM2.5 sources accounted for 28% (NR-TOR-1) and 15% (NR-TOR-2) of the total PM2.5 mass. The average concentrations of PM2.5 due to Traffic_T, Traffic_NT I, and Traffic_NT II factors were 2.1, 2.8, and 1.3 times higher at NR-TOR-1 than NR-TOR-2 (Figure 3.48). These differences between the sites are comparable to those found in the traffic-related portions of the concentrations of CO, CO2, and BC (see Table 3.2).
Near-Road Pollution Study 2019 85
Figure 3.48. Source concentrations of PMF-resolved factors identified at NR-TOR-1 and NR-TOR-2 sites. Box-whisker plot shows the concentrations of traffic-related factors (Traffic_T, Traffic_NT I, and Traffic_NT II). Median (solid line) and mean (dotted line) concentrations are inside each box; the top and bottom of each box represent the 75th and 25th percentile values, and the top and bottom of each whisker and dot represent the 90th/10th and the 95th/5th percentile values, respectively.
Traffic_T was the largest contributor among the traffic-related PM2.5 sources, accounting for
19% (NR-TOR-1) and ~9% (NR-TOR-2) of PM2.5. As shown in Figure 3.49, Traffic_T was distinguished by high loadings of hydrocarbon-like OA and BC. A clear weekday high pattern with a morning rush- hour peak, as well as the high correlations with NOx (Spearman r = 0.91 for NR-TOR-1) and ultrafine particles (r = 0.87 for NR-TOR-1) supported the association with primary emissions of internal combustion engine vehicles.
Figure 3.49. Diurnal variations of PMF-resolved factors at NR-TOR-1 and NR-TOR-2. Error bars represent the 95% confidence intervals.
Near-Road Pollution Study 2019 86
A prominent north–northwest directionality was observed in the CPF plot of the traffic
Figure 3.50. Conditional probability function (CPF) plots for regional sources (aged organics and secondary sulphate) and traffic-related sources (Traffic_T, Traffic_NT I, Traffic_NT II) at NR-TOR-1. The CPF threshold was set to the top 25th percentile. emissions (Figure 3.50), consistent with when NT-TOR-1 was downwind of Highway 401. The non-tailpipe emissions from brake wear, Traffic_NT I, was the major source of Ba, Cu,
Fe, and Sr and accounted for 6% (NR-TOR-1) and ~2% (NR-TOR-2) of PM2.5. Emissions of Ba, Cu, and Fe can arise from abrasion of brakes (Hagino et al., 2016). However, particles from brake emission metals can also contribute to elevated concentrations of these metals in road dust. The ratio of Ba to Ca in the source profile of Traffic_NT I (Figure 3.51) at NR-TOR-1 was 2.6 ± 2.2, far higher than that found in road dust or soil (~0.003 ± 0.003 for Traffic_NT II), pointing to direct releases from
Figure 3.51. Enrichment ratios in Traffic_NT I and Traffic_NT II at NR-TOR-1 and NR-TOR-2. Box plots are constructed with the 5th, 10th, 25th, 50th, 75th, 90th and 95th percentiles of 100 bootstrap runs. brakes rather than resuspension of road dust. The Traffic_NT II factor was characterized by mineral elements (Ca, Fe, Mn, and Ti), which is suggestive of soil, but it also contained some Ba and Cu. Road dust and soil samples were collected at 22 roadsides and urban parks across Toronto to characterize the chemical profiles of urban road dust and soil. The average ratio of Fe to Ca in the road dust and soil samples were 0.3 ± 0.1 (road dust) and 1.2 ± 0.0 (soil), which is comparable to the ratio (~0.5) found in the chemical profile of
Near-Road Pollution Study 2019 87 Traffic_NT II (Figure 3.44). This enrichment ratio supports that Traffic_NT II is closely related to road
dust. This factor accounted for 4% (NR-TOR-1) and 3% (NR-TOR-2) of PM2.5 (Table 3.5). The Traffic_NT II factor exhibited a clear diurnal trend and was notably higher on weekdays than weekends, indicating that this factor was associated with local anthropogenic activities, such as traffic, rather than being wind entrained soil particles (Figure 3.49). An inverse correlation of Traffic_NT II with relative humidity (r = -0.20 at NR-TOR-2; p < 0.01) suggested that its emissions were also promoted by favourable meteorological conditions, perhaps the drying of the pavement over the day. It is noted that Traffic_NT I was also inversely correlated with RH, while there was no inverse correlation between Traffic_T and RH. The diurnal trends of Traffic_NT I and II were similar to that of Traffic_T, with a strong morning rush-hour peak (Figure 3.49). The most distinct weekday/weekend difference of Traffic_NT I and II was found during the morning rush hour at both sites. It is interesting to note that despite the strong correlation between traffic-related sources
and traffic-related pollutants (i.e., NOx and ultrafine particles), very poor correlations were observed with total vehicle counts measured at NR-TOR-1. Higher correlations were observed between traffic- related factors and the number of C4 vehicles (length 15–37 m), suggesting the impact of HDDVs. As shown in Figure 3.52, no weekday/weekend differences in total traffic volume were observed at NR- TOR-1, whereas C4, accounting for 8% of the total vehicle count, exhibited a clear weekday and weekend difference and distinct morning, afternoon, and evening peaks. The highest correlation was observed between the C4 vehicle count and Traffic_NT II (r = 0.46, p < 0.01) followed by Traffic_T (r = 0.36, p < 0.01) and Traffic_NT I (r = 0.23, p < 0.01). This suggests that emission of both
tailpipe and non-tailpipe PM2.5 is affected by fleet composition rather than just the number of
Figure 3.52. Diurnal variations of the count of total vehicles at NR-TOR-2 and NR-TOR-1 and C4 vehicles (length 15–37 m) measured at NR-TOR-1. vehicles.
The diurnal contributions of the traffic-related PM2.5 sources are highlighted in Figure 3.53. During the morning rush hour, the traffic-related sources accounted for approximately 49% (33% for
Traffic_T, 11% for Traffic_NT I, and 5% for Traffic_NT II) of PM2.5 at NR-TOR-1 and 29% (20% for Traffic_T, 4% for Traffic_NT I, and 4% for Traffic_NT II) at NR-TOR-2. This indicates that traffic-
related sources can be a significant contributor of PM2.5, particularly during morning rush hour in urban environments. As the long-range transported contribution of regional scale industrial sources
has decreased, relative contributions of the tailpipe and non-tailpipe sources to PM2.5 have increased. It is also clear that the contribution of the non-tailpipe sources will increasingly become more significant as emissions control of the tailpipe source become stricter. Separate PMF analysis of 13 years (2004–2017) of data from NR-TOR-2 indicated that non-tailpipe emissions have increased by 21% to 27% per year from 2011 to 2016, so that non-tailpipe now exceeds tailpipe
Near-Road Pollution Study 2019 88 Figure 3.53. Diurnal variations of the contribution of PM2.5 sources identified on weekdays at NR-TOR-1 and NR-TOR-2. emission of PM2.5 at this site. 3.3.3.4.2 Non-traffic Sources
In summer, oxidized organics and secondary sulphate were the largest contributors to PM2.5 mass at both sites (34% and 21% for NR-TOR-1 and 36% and 22% for NR-TOR-2) and exhibited strong temporal correlations between two sites (r = 0.77 for organics, r = 0.91 for sulphate), implying that these factors represented long-range regional sources affecting PM2.5 levels equally across the urban area. CPF plots (Figure 3.50) pointed to long-range transport of emissions from the south-southwest and the high degree of oxidation observed in the sulphate and oxidized organics factors. The secondary nitrate factor accounted for approximately 6% (NR-TOR-1) and 7% (NR-TOR-
2) of summertime PM2.5. The temporal variability of the factor was also highly correlated between the sites (r = 0.73), indicating that the nitrate factor was homogenous across the city. For the organics and sulphate factors, no strong differences in the concentrations were observed between weekdays and weekends at both sites, consistent with a long-range origin (Figure 3.49). The contribution of the nitrate factor at NR-TOR-1 was found be higher during the weekday morning rush hour than on weekends. This evidence of a local traffic-related influence in conjunction with other evidence pointing to spatial homogeneity across the city indicated that both regional and local traffic emissions were contributing to the nitrate factor. It is not known if this local nitrate was due to emission of primary nitrate from emission treatment systems of diesel vehicles or enhanced formation of secondary nitrate beside the roadway, perhaps due to higher levels of ammonia (see Figure 3.19). As a minor source, the industrial factor characterized by high loadings of Zn was identified at both sites. The temporal variations of the factor were highly correlated between the two sites with sporadic peaks in the early morning at approximately 3 or 4 a.m. As shown in the diurnal plots, the strong weekday and weekend difference indicates the influence of an anthropogenic source, but there was no difference in the contribution of the source at NR-TOR-1 and NR-TOR-2 (< 1% of PM2.5 at both sites). These results suggest that the episodic events associated with this factor were due to emissions from an industrial metal processing operation, sufficiently upwind to impact both sites simultaneously yet close enough to create events at a similar time each day. This factor was responsible for 70% to 80% of the Zn observed at the sites and should be further investigated.
The cooking emissions factor accounted for 18% of total PM2.5 at NR-TOR-2 but was not
Near-Road Pollution Study 2019 89 evident at NR-TOR-1. The diurnal plot of the cooking source displayed unique peaks at noon and in the evening (6 p.m. to 9 p.m.) without a morning rush-hour peak. The evening peak was stronger, and the weekend peak was slightly higher, than the weekday evening peak, pointing to cooking emissions from restaurants around the downtown site. The fireworks factor was also a local source, influenced by holiday fireworks events near the downtown site (Victoria Day and Canada Day events at Toronto City Hall).
In summary, the overall concentration of trace metals in PM2.5 was higher at NR-TOR-1 than at NR-TOR-2 (maximum 13x higher Ba at NR-TOR-1). In contrast, regional scale PM2.5 sources
(sulphate and oxygenated organics) were major PM2.5 sources (> 50%) that were similar at both sites. Traffic-related PM2.5 sources were characterized by hydrocarbon fragments, high loading of trace metals (Ba, Cu, and Fe) and BC. On average, the traffic-related sources accounted for 28% (NR-
TOR-1) and 15% (NR-TOR-2) of the total PM2.5 mass. A strong spatial heterogeneity between the two sites (i.e., ~2-fold higher contribution at NR-TOR-1) and distinct diurnal patterns were observed for the traffic-related PM2.5 sources. During morning rush hour, the traffic-related factors were responsible for approximately half of the PM2.5 mass at the highway site. Nearly 40% of the traffic- related source contributions were contributed by non-tailpipe emissions from brake/tire wear and road dust resuspension in the urban environments. A longer multi-year PMF analysis indicates that non-tailpipe now exceeds tailpipe PM2.5 emissions at one of the sites. 3.3.4 Summary: Contribution of Vehicles to Urban Air Pollution
Vehicle emissions are the dominant source of many important air pollutants near roads. BC, for example, is a key pollutant both in relation to potential health impacts and climate forcing. The higher BC concentrations at the near-road sites along with the diurnal and weekday/weekend patterns points to vehicles as the major source of BC. While BC can also be produced by biomass burning, this analysis confirmed that at these sites fossil fuel combustion was the major source of BC. In Ontario, BC is higher in the summer than the winter, suggesting an impact from seasonal changes in fuel formulation affecting tailpipe emissions. This potential influence of fuel composition may offer an effective intervention for reducing BC emissions from vehicles. Other pollutants were also found to be emitted predominantly by traffic. Calculation of EFs based on ~100,000 vehicle plumes at NR-TOR-2 showed that a small portion of the vehicles in the fleet was responsible for the majority of these emissions. Daily-integrated EFs were calculated based on diurnal patterns from 200 million vehicles measured over 340–440 days and allowed direct comparisons across the three near-road sites. The inter-site and intra-site variability in the EFs were consistent with the differences in fleet composition and, in particular, the fraction of large trucks. It appears that the influence of vehicle emissions on air pollution near roads depends more on the proportion of large trucks in the fleet than the total traffic volume. Primary emissions from vehicles have traditionally not been considered a dominant source of PM2.5 in cities. However, as the contribution of regional PM2.5 sources has decreased, the relative contribution of primary vehicle emissions has risen. Further, non-tailpipe emissions are a growing concern due to the high concentrations of redox-active metals that can be present. Together, tailpipe and non-tailpipe vehicle emissions are producing, on average, 15% to 28% of the PM2.5 observed near roads. In fact, during morning rush hour, vehicles are contributing 29% to 49% of the overall PM2.5 at near-road sites in Toronto.
Near-Road Pollution Study 2019 90 3.4 Range of Exposure Across a City
Concentrations of traffic-related air pollutants (TRAPs) can be quite varied across cities. Dilution within the near-road region and into surrounding neighbourhoods can be influenced by urban topography and meteorology, and the meteorology influencing mixing and dispersion can vary diurnally and seasonally. The overall level of traffic emissions from surrounding areas also influences the pre-existing pollutant concentrations in the air diluting emissions on a roadway, and thus negligible to steep concentration gradients may exist near roadways depending on a wide range of parameters. This section presents three data interpretation strategies for assessing how much TRAP varies spatially across a city. Concentration differences across three sites are compared to illustrate the range of concentrations that may persist across a city. Sampling with a mobile lab was used to further assess this concentration range. Finally, the near-road spatial gradients at NR-TOR-1 are examined based on data from an intensive campaign. These strategies demonstrate that near-road monitoring stations can provide great insight into how much concentrations may vary both near roads and across cities, and thus help better estimate the resulting range in the exposure of urban populations. 3.4.1 Spatial Gradient Across Background to Near-Road Sites
A comparison of the gradient between the background to near-road sites in Toronto was used to characterize and compare the range of spatial and temporal differences in traffic-related pollutants.
Figure 3.54 displays box-and-whiskers plots of NOx, CO, UFP, and BC concentrations at BG-TOR-S, NR-TOR-1 and NR-TOR-2. The TRAP concentrations were highly varied across these three sites, consistent with the differences in the influence of traffic. The extent of the spatial variability also varied by pollutant. The highest difference was observed for NOx (NR-TOR-1/BG-TOR-S = 7) followed by UFP (5 times), BC (4 times), and CO (2 times). This difference in spatial variability across the four pollutants was in part due to their different chemical reactivity, and in part due to differences in the extent to which regional sources contributed to the concentrations (see Table 3.2). Notable in this regard, is that BG-TOR-S reflected regional background concentrations, thus these ratios gave an indication of how much traffic was increasing the pollutant levels at these sites. The size of the boxes indicates the temporal variability in hourly averaged concentrations at each of the sites. This temporal variability was generally largest at NR-TOR-1, presumably due to the large differences between periods when this site was upwind vs downwind of the highway. Also notable is that the spatial variation across the sites was similar in magnitude to the temporal variation at any given site (comparing the range across the site median values to the size of the box for a given site). The spatial distribution of TRAPs at multiple Toronto sites was also examined using correlation analysis and coefficients of divergence (COD). The concentrations of UFP, BC, NOx, CO, and PM2.5 measured at NR-TOR-1, NR-TOR-2, and BG-TOR-S from June 2015 to June 2016 were used in this analysis. The COD was calculated as p 1 2 CODjk = [(NNNNij − ik ) / ( ij + ik )] (1) p i=1
Near-Road Pollution Study 2019 91 th where Nij and Nik represent the hourly i concentration measured at sampling sites j and k, respectively, and p is the number of samples (Wongphatarakul et al., 1998).
Figure 3.54. Comparisons of NOx, CO, UFP, and BC at an urban background site (BG-TOR-S), a downtown Toronto site (NR-TOR2), and a Highway 401 site (NR- TOR1) using hourly averaged data from June 1, 2015 to June 30, 2016. Box- whisker plots demonstrate the median (solid line), mean (dashed), quartile (box), 10th/90th percentile (whiskers), 5th/95thpercentile (dots). The COD provided information about the relative inter-site uniformity, with a COD of zero indicating no inter-site variability, and a high COD value (approaching 1) indicating high variability. Figure 3.55 illustrates average COD and correlation coefficients (Pearson r) for TRAPs measured at
Figure 3.55. Comparison of the coefficient of divergence (COD) and correlation coefficients (Spearman r) of NOx, CO, UFP, BC, and PM2.5 measured at NR-TOR-1, NR-TOR-2, and BG-TOR-S. Error bars represent the 95% confidence intervals.
Near-Road Pollution Study 2019 92 the high-traffic near-road, downtown, and urban background sites (average values for three comparisons: NR-TOR-1 vs. NR-TOR-2, NR-TOR-1 vs. BG-TOR-S, and NR-TOR-2 vs. BG-TOR-S). In this analysis, high COD values (> 0.2) indicate heterogeneity in terms of the spatial distribution of the pollutants, mostly due to the impact of traffic sources and/or a short lifetime in the atmosphere. In contrast, high correlation coefficients across sites suggest the strong influence of regional and long- range transported sources across the sites. The highest COD was observed for NO, followed by UFP,
NO2, and BC, whereas CO and PM2.5 were characterized by the lowest COD. Notably, the lowest COD was found for CO concentrations, which indicates that CO is mostly influenced by background sources rather than local traffic emissions. This is consistent with the observation that only 30% to 50% of the CO at the near-road sites was due to local traffic (see Table 3.2). An inverse trend was observed between COD and r for most pollutants:
• PM2.5 exhibited the highest correlation with a low COD. This is consistent with PM2.5 in Toronto being composed mostly of regional scale components such as secondary organics, sulphate, and nitrate.
• The high COD and low r for UFP clearly support that UFP can be strongly affected by local sources including traffic emissions coupled with a short lifetime.
• A higher COD and lower r were found for NO as compared to NO2. The different trends in
COD and r between NO and NO2 are consistent with the rapid conversion of NO to NO2 during transport from vehicle tailpipes to background areas.
• The relatively high COD value for BC indicates a major contribution from local traffic. However, BC concentrations were well correlated across the near-road and background sites. The high correlation for BC is comparable with the result reported in Table 3.47 and the previous source apportionment study, which found that regional sources upwind in the United States also contribute substantially to BC in Southern Ontario (Jeong et al. 2013).
The seasonal variations of the average COD of the pollutants among the three sites are shown in Figure 3.56, illustrating weak seasonal trends in the spatial heterogeneity. A slightly higher spatial heterogeneity within a 20 km buffer (between NR-TOR-1 and BG-TOR-S) was found for UFP in
Figure 3.56. Seasonal variations of the coefficient of divergence (COD) of NOx, CO, UFP, BC, and PM2.5 measured at NR-TOR-1, NR-TOR-2, and BG-TOR-S.
Near-Road Pollution Study 2019 93 colder months compared to that in warmer months. The seasonality of the citywide heterogeneity might be due to the longer lifetime of UFPs in winter than summer. 3.4.2 On-Road Mobile Sampling
On-road measurements of traffic-related pollutants using a mobile laboratory were conducted in the daytime July 21 to Sept 3, 2015 outside of peak traffic time (10:00 a.m.–4:00 p.m.) across the
Greater Toronto Area. To further extend the analysis NOx concentrations simultaneously measured at conventional air quality monitoring stations operated by the MECP were included. The route of the mobile sampling in the Greater Toronto Area and locations of three near-road sites (NR-TOR-1, NR-TOR-2, and BG-TOR-S) and four conventional MECP air monitoring stations (Downtown, East, West, and North) are shown in Figure 3.57.
Figure 3.57. Sampling route of the mobile measurements in the Greater Toronto Area and the locations of near-road (NR-TOR-1, NR-TOR-2, and BG-TOR-S) and conventional MECP air monitoring stations (Downtown, East, West, and North) denoted by green stars. This study provided traffic-related pollutant concentrations measured while driving on major roadways and through residential areas in order to i) identify any differences on-road and near-road concentrations and ii) quantify the range of the concentrations that exist across a city. Comparison of the on-road and near-road measurements indicated that the range of concentrations across the near-road sites reflected the range observed on roads across the city. Concentrations of TRAPs observed during the mobile sampling period were compared in Figure 3.58. The mobile sampling data measured on the Highway 400 series (On-road HWY) were clearly higher than those on other arterial/local roads (On-road), However, the on-road and near- road concentrations were similar; for median NOx, UFP, and BC concentrations, On-road HWY ≈ NR- TOR-1 and On-road ≈ NR-TOR-2. Essentially, NR-TOR-1 located beside the highway reflected the median concentrations on the highway (but not the full range) while NR-TOR-2 located beside a downtown arterial road, reflected the median concentration, but not the range, on arterial roads and quiet residential streets (except perhaps for NOx). This finding was a bit surprising given that the
Near-Road Pollution Study 2019 94 NR-TOR-1 site was at times upwind of the highway and thus experiencing lower concentrations. The range of concentrations observed across the near-road and background sites was also similar in magnitude to that across the city, as measured through the mobile sampling although BG-TOR-S
Figure 3.58. Comparison of hourly averaged NOx, CO, UFP, and BC concentrations measures on-road (On-road HWY, On-road) with simultaneous near-road (NR-TOR-1, NR-TOR-2) and urban background (BG-TOR-S) measurements. Box-whisker plots demonstrate the median (solid line), mean (dashed), quartile (box), 10th/90th percentile (whiskers), 5th/95thpercentile (dots). captured some lower concentrations while On-road HWY captured some that were higher. These results imply that in general the near-road monitoring data can be used to approximate exposure to these pollutants on various urban road types, or conversely, that on-road mobile sampling can be used to estimate concentrations near roads. Concentrations of CO measured during the mobile campaign were higher than those measured at near-road sites. The higher influence of background CO levels discussed in the previous section could explain the discrepancy.
NO2 concentrations measured over a year at the four conventional MECP air monitoring stations located across Toronto were also compared to the mobile sampling and near-road measurements (Figure 3.59). NO2 ranged from 9 to 16 ppb across the four conventional sites, which was comparable to the median level at the NR-TOR-2 site. However, considerably higher NOx levels by a factor of ~5, were observed for On-road HWY and NR-TOR-1 as compared to the conventional sites. It should be noted that the short-term variability of NOx data from mobile sampling and air monitoring stations was comparable to the annual average variability across these sites. In summary, comparison across Toronto’s monitoring stations and mobile sampling both revealed considerable spatial heterogeneity across the city in the concentrations of UFP, BC, and
Near-Road Pollution Study 2019 95 Figure 3.59. Comparison of NO2 simultaneously measured at conventional air monitoring stations (Downtown, East, West, North) in Toronto with the on-road (On-road HWY, On-road), near-road (NR- TOR-1, NR-TOR-2), and urban background (BG-TOR-S) measurements. Box-whisker plots demonstrate the median (solid line), mean (dashed), quartile (box), 10th/90th percentile (whiskers), 5th/95thpercentile (dots).
NOx. The highest spatial heterogeneity was found in NOx followed by UFP and BC, while CO exhibited a low spatial variability. The concentrations observed at the near-road stations match on-road values measured on equivalent roads. 3.4.3 Near Highway Gradient Sub-Study
A sub-study was conducted at NR-TOR-1 from February 6 to 27, 2017 to evaluate how the concentrations of TRAP decrease with distance from highway during the winter. As Figure 3.60 shows, the NR-TOR-1 site was 10 m from the southern edge of Highway 401), a modified Airpointer was 130 m southeast of Highway 401, and the MAPLE mobile lab was 150 m southeast of Highway 401.
NO, UFP and BC were measured at all three locations. PM2.5, CO, organic aerosol (using ACSM), trace elements (using Xact) and meteorology data were obtained at both the NR-TOR-1 and MAPLE. Source apportionment was applied to identify the contribution of vehicle emissions to organic aerosol (OA) and trace metals, as well as to quantify their spatial gradient from the highway. All data, except for OA and trace element data, were averaged over one-minute intervals and time synchronization was performed for the comparison analysis; hourly OA and trace element values were used. Data inter-comparisons were made to ensure data quality and inter-comparability of data from the three sites, and corrections were applied to data having poorer agreements between instruments. Overall, a good correlation was found in the data from the three locations. In order to identify the impact of local traffic emissions on the highway, northerly wind (300 to 15°) with wind speeds higher than 1 m/s was defined as “downwind,” and southerly wind (130 to
Near-Road Pollution Study 2019 96 210°) with wind speeds higher than 1 m/s was defined as “upwind.” The prevailing wind was west (64%), and the downwind and upwind conditions accounted for approximately 14% and 7% of the entire measurement, respectively.
Figure 3.60. Location of the sampling sites for the distance decay gradient study from February 6 to 27, 2017. During the measurement period, the ambient temperature ranged from -11 to 8° C with a mean of 1° C; the average relative humidity was 66%. Overall wind speed was 2.6 ± 2.0 m/s (mean ±
Figure 3.61. Downwind and upwind gradients of TRAPs at NR-TOR-1 (10 m from the edge of Highway 401), the Airpointer shelter (130 m), and the MAPLE mobile lab (150 m) during the measurement period of February 6 to 27, 2017. Error bars represent the 95% confidence intervals.
Near-Road Pollution Study 2019 97 standard deviation): 2.1 m/s for downwind, 1.7 m/s for upwind. Air stagnation with low wind speeds (< 0.5 m/s) was observed during the winter period, accounting for 13% of the measurement period. Large differences in the downwind and upwind spatial gradients in the concentrations of NO, CO, UFP, and BC were found (Figure 3.61). Steep gradients were observed when the measurement sites were downwind vs negligible gradients when they were upwind. 3.4.3.1 Downwind Gradients
Downwind conditions produced steep gradients with large reductions in concentrations from 10 m to 150 m from the highway. With a wind speed of 2.1 m/s, this corresponded to approximately 5s (10 m) vs. 71s (150 m) of transit time from the highway. The downwind decay gradients varied by pollutant with NO exhibiting the strongest distance gradient followed by UFP, BC, and CO.
Downwind NO concentrations were higher at 10 m than 150 m by a factor of 4 (2.6 for NOx), while the NO/NOx ratio gradually decreased with increasing distance from the highway (i.e., 62% at 10 m vs. 40% at 150 m). A much weaker downwind difference between the two locations was observed for CO, consistent with CO being more influenced by background concentrations from non-traffic emissions. As shown in Table 3.2, only 44% of CO is associated with local traffic at this site as compared to > 75% for NOx, BC, and UFP. The strong background level of CO is also evident through the slightly higher upwind vs. downwind concentrations at 150 m.
The concentrations of NOx, UFP, and BC had similar gradients from the highway. The downwind decay gradient of TRAP, based on the average of these three pollutants expressed as an exponential curve, is shown in Figure 3.62. Further the downwind concentrations 150 m from the highway were still well above the upwind concentrations (Figure 3.61). In a previous study conducted near Highway 400 in summer, much stronger decay gradients were observed for UFP as compared to BC (Jeong et al., 2015). The small difference in the gradients of BC and UFP in winter
Figure 3.62. Overall spatial gradients of NOx (NO+NO2), UFP, and BC with distance downwind of Highway 401 in the winter month. The y- axis is normalized concentrations to the levels at 150 m from the highway.
Near-Road Pollution Study 2019 98 compared to the large difference in summer implies that evaporative losses of volatile UFP are much lower in winter than in summer, presumably due to the colder atmospheric temperatures. This also indicates that near-road personal exposure to UFP can be higher in winter than summer and that the distance around roads potentially impacted by some traffic pollutants can be wider in winter than in summer. 3.4.3.2 Ambient Temperatures
In order to examine the influence of ambient temperatures on TRAP, the concentrations during downwind conditions were classified into bins based on ambient temperature, -15 to -5° C; -5 to 5° C; and 5 to 15° C as shown in Figure 3.63. Strong temperature dependencies were found for ambient concentrations of NOx and UFP, particularly for the site closest to the highway. On the other hand, BC concentrations and their gradient did not vary with temperature, implying that the impact of colder temperatures did not change mixing and dilution during transport away from the highway.
This difference in NOx and UFP vs. BC suggests that the increased NOx and UFP concentrations at colder temperatures were more likely due to variability in traffic emissions rather than post-tailpipe dispersion. Most of the vehicles at this highway sampling location should have been warmed-up so this was not likely due to cold engines. In addition, BC had no temperature tendency and CO levels tended to decrease, rather than increase like NOx and UFP, as ambient temperature decreased; cold engines should cause higher not lower CO emissions.
Figure 3.63. Influence of ambient temperature on the concentrations of NOx, CO, UFP, and BC during downwind conditions.
The increased UFP at the colder temperatures is consistent with past studies that found a higher UFP emission factor during colder months; this observation was attributed to vehicle exhaust rapidly cooling at colder temperatures promoting nucleation and condensation of volatile precursors (Wang et al., 2017).
The temperature dependency of NOx concentrations in the winter months was postulated to be due to emission control devices performing poorly or malfunctioning in cold ambient temperatures. In order to generalize the ambient temperature dependency of NOx emissions, winter
Near-Road Pollution Study 2019 99 NOx data from NR-TOR-1 for two full winters (December 2015 to March 2016 and December 2016 to March 2017) were examined in relation to ambient temperature. Data from weekday morning rush hours (5 a.m. to 10 a.m.) when the station was downwind were used so that any differences due to atmospheric mixing height, vehicle fleet changes, or fuel additives would be negligible.
A distinct inverse correlation between NOx and ambient temperature on weekdays was found (Figure 3.64), which generalizes and further supports the hypothesis that near-road NOx concentrations are higher at lower ambient temperatures due to higher tailpipe emissions, likely due to poorer engine performance or lower NOx removal by emission treatment technologies. At
NR-TOR-1, large diesel trucks had a much stronger influence than cars on NOx emissions on weekdays than on weekends (See Section 3.3.2.3). Also, the higher NOx concentrations and clearer temperature dependency on weekdays suggests that NOx emissions from diesel trucks increase as ambient temperature decreases. The urea-based selective catalytic reduction technologies used on diesel vehicles have previously been reported to underperform in cold temperatures.
Figure 3.64. Correlation between NOx and ambient temperature measured in downwind condition from 5 a.m. to 10 a.m. on weekdays (left) and weekends (right) at NR-TOR-1 from December 2015 to March 2016 and December 2016 to March 2017. Error bars represent the 95% confidence intervals.
3.4.3.3 Air Stagnation
Air stagnation, defined as wind speed lower than 0.5 m/s, was frequently observed in the winter months. Figure 3.65 shows TRAP concentrations measured at 10 m and 150 m during stagnant air periods. Due to slow dispersion during the stagnation events, no strong differences in concentrations were observed between the two locations. Further the concentrations at 150 m were only a bit lower than those observed at 10 m (i.e. NR-TOR-1) under downwind conditions. These findings indicate that stagnant air may widen the roadway influenced area and produce concentrations at 150 m that are similar to near-road concentrations. For example, under windy conditions, the downwind pollutant concentrations were two to four times lower at 150 m than 10 m from the highway. For stagnant conditions the concentrations at 150 m were just as high as at 10 m, yielding a ratio close to one (Figure 3.66).
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Figure 3.65. Decay gradients of traffic-related air pollutants during air stagnation conditions (wind speed < 0.5 m/s) at NR-TOR-1 (10 m from the edge of Highway 401) and MAPLE mobile lab (150 m). Error bars represent the 95% confidence intervals.
People living in the influenced areas may experience increased health risks from exposure to the elevated levels of TRAPs during cold and stagnant winter weather. The TRAP levels at 150 m were clearly impacted by the highway when the air was stagnant or the site downwind. In order to estimate the exposure to TRAPs at 150 m, the percentage of the time the levels were well above typical concentrations for Toronto (upwind average + the 95% confidence interval) was calculated.
At 150 m from the highway, TRAP levels were above this threshold 65% (UFP), 64% (NOx), 51% (BC), and 48% (CO) of the time, indicating that people living within 150 m of the highway were being impacted by TRAP half of the time or more.
Figure 3.66. The ratio of TRAP concentrations at 10 m to those at 150 m during downwind condition (red) and stagnant air (brown).
3.4.3.4 Organic Aerosol and Trace Metals
Source apportionment of OA and trace metals measured at NR-TOR-1 and MAPLE was conducted using positive matrix factorization (PMF). Seven PM2.5 source factors were obtained through the
Near-Road Pollution Study 2019 101 PMF analysis from both locations: three secondary aerosol factors (secondary nitrate, secondary sulphate, oxygenated OA), two traffic-related factors (Tailpipe emissions, Traffic_T) and Non-tailpipe Emissions, Traffic_NT), biomass burning, and a local industrial factor. Traffic_T was characterized by hydrocarbon-like OA (HOA) and BC, which are species associated with fossil fuel combustions. Traffic_NT had high loadings of Ca, Ti, Fe, Cu, Sb, and Ba and was attributed to non-tailpipe emissions from brake and tire wear and vehicle induced re-entrainment of the mixed road dust. Clear diurnal variations related to traffic activities were observed for both factors, further supporting their traffic-related origins.
Figure 3.67 shows the reduction of PM2.5 concentrations from 10 m to 150 m under downwind and upwind conditions. A very sharp decay gradient was observed for Traffic_NT indicating approximately a seven-times higher exposure to related trace metals at 10 m than at 150 m from the highway. This sharper decay away from the highway for metals (than for gases or UFP) is presumably due to the metals being in larger particles that travel shorter distances.
Figure 3.67. Downwind and upwind decay gradients of traffic-related PM2.5 sources at NR-TOR-1 (10 m from the edge of Highway 401) and MAPLE mobile lab (150 m). Error bars represent the 95% confidence intervals.
During air stagnation events, concentrations of the Traffic_T factor at 150 m from the highway were almost the same as at 10 m during downwind conditions, indicating a wide spatial area extending beyond 150 m was strongly influenced by vehicle emissions (Figure 3.68).
Figure 3.68. Decay gradients of traffic-related PM2.5 sources during downwind (wind speed > 1 m/s) and air stagnation (wind speed < 0.5 m/s) conditions at NR-TOR-1 (10 m from the edge of highway 401) and MAPLE mobile lab (150 m).
During air stagnation events, concentrations of the Traffic_NT factor at 150 m from the highway were lower than at 10 m during downwind conditions, suggesting that wind also plays a
Near-Road Pollution Study 2019 102 substantial role in re-suspending road dust. Notably, average relative humidity during air stagnation was higher than under downwind conditions (81% vs. 62%), indicating the resuspension of road dust was also inhibited on wet road surfaces. These results indicate that the width of the geographic area around a road influenced by elevated traffic-related PM2.5 sources, and thus the extent of human exposure to related pollutants, can also vary with meteorological conditions. In summary, wintertime spatial distributions of TRAPs near a major roadway differed to different extents for different pollutants under different meteorological conditions (i.e., temperature, wind direction, atmospheric stability). However, a few patterns emerged:
• The magnitude of UFP and NOx emissions from motor vehicles and their subsequent transformation in near-road environments were strongly affected by ambient temperature.
• A very sharp decay gradient was observed for traffic-related road dust, yielding concentrations of related metals approximately seven times higher at 10 m than 150 m from the highway.
• The most significant near-road gradients were observed for NOx followed by UFP and BC.
In general, it was found that colder temperatures in winter may lead to elevated exposure to TRAPs (i.e., NOx and UFP) while stagnant air can widen the near-road influenced area. 3.4.4 Summary
People living within 150 m of a major road are often being exposed to traffic emissions. At the NR- TOR-1 site, traffic emissions increased the concentrations 150 m from the highway 50% to 65% of the time. Under higher wind speeds, the concentrations of tailpipe-related traffic pollutants decreased exponentially downwind of the highway, by a factor of 2.5 between 10 m and 150 m in winter. The gradient for non-tailpipe-related metals was steeper, dropping by a factor of seven over the same distance. While lower than beside the road, the concentrations due to tailpipe and non-tailpipe- related emissions 150 m downwind were still well above upwind concentrations, indicating that the area influenced by traffic emissions was wider than 150 m. Stagnant air conditions further widened this traffic-influenced area to the point where concentrations were similar 10 m and 150 m away from the road, suggesting that the influence of the traffic emissions extended far beyond 150 m. Colder temperatures in winter can further increase near-road exposure to some TRAPs (i.e.,
NOx, UFP). Emissions of NOx from vehicles are higher under colder temperatures, suggesting that the exhaust treatment systems on diesel trucks do not function as required under cold winter temperatures. Emissions of UFPs are also increased under cold temperatures. Traffic-related pollutants showed different degrees of inhomogeneity across the sites in Toronto. CO showed the smallest spatial variability, consistent with contribution from regional sources and its long atmospheric lifetime. NO showed the greatest variability, which is consistent with its rapid chemical transformation and minimal regional background contribution. Pollutant levels measured through on-road sampling with a mobile lab were similar to those measured at the near-road monitoring sites. Specifically, the concentrations measured at NR-TOR-1
Near-Road Pollution Study 2019 103 beside Highway 401 were comparable to those measured while driving on Highway 401 and other 400 series highways, which typically have six to eight lanes. The concentrations measured at NR- TOR-2 beside a downtown arterial road were comparable to those measured while driving on other arterial roads in Toronto. Taken together, the range of concentrations measured through the near-road + background sites encompassed that measured through the mobile sampling on highways, arterial roads, and quiet streets. This suggests that the near-road sites properly reflected the approximately one order of magnitude range in exposure of residents to traffic pollutants across the city.
3.5 Parameters Governing Pollutant Concentrations
3.5.1 Estimating the Contribution of Traffic to Near-Road TRAP Concentrations
Measurement of near-road TRAP concentrations is useful as it provides direct evidence of the concentrations that someone within the immediate vicinity of the road would be exposed to. The absolute concentrations are a combination of regional influences (e.g., chemistry or long-range transport), citywide or neighbourhood-wide influences (e.g. traffic or industry in other parts of the city), and local emissions (e.g., traffic with possible contributions from restaurants, construction, or grounds keeping). Being able to separate the local portion allows greater insight into the specific impact of on-road vehicle emissions on near-road air quality and what parameters may influence its severity. It is for this reason that urban background stations were deployed during this pilot study; these stations allowed for background air quality to be isolated from measurements made in the near-road regime. The remaining local concentrations can be conceptualized as the maximum reduction in concentration that could be achieved if activity on the given road were altered. TRAP levels in excess of background concentrations averaged over the entirety of the near- road pilot study (March 2015 to April 2017) were calculated for each near-road station using three different methods. These values provided a quantitative estimate of the amount of pollution directly attributable to nearby local sources (the major contributor to which is traffic). Further, the way in which these values varied with physical parameters such as wind direction and wind speed was analyzed. Lastly, recommendations for how to best isolate local and regional pollutant quantities from near-road measurements were developed. 3.5.1.1 Method 1: Average Near-Road and Background Concentrations
The siting of the monitoring stations was such that each station was impacted differently by traffic (NR-TOR-1 experienced greater nearby traffic intensities than NR-TOR-2, for example). Additionally, for each near-road station, a station located at a suitable background location collected measurements in tandem. Measurements taken at these urban background locations were intended to serve as a baseline for their respective near-road counterparts. In this section, pollutant concentrations directly attributable to local traffic were estimated by using differences between average concentrations measured at near-road stations and their closest urban background station. These pairs were chosen based on geographical proximity: NR-
Near-Road Pollution Study 2019 104 VAN and BG-VAN; NR-TOR-1 and BG-TOR-N; and NR-TOR-2 and BG-TOR-S. Average pollutant concentrations were calculated at each station and are summarized in Table 3.6.
Table 3.6. Average Pollutant Concentrations between March 2015 and April 2017
Pollutant NR-VAN BG-VAN NR-TOR-1 BG-TOR-N NR-TOR-2 BG-TOR-S
NO [ppb] 32.1 9.1 25 3.5 4.8 1.3
NO2 [ppb] 9.4 14.2 19 11 11 6.1 CO [ppb] 312.7 217 330 210 260 190
CO2 [ppm] 440 437 420 426 414
O3 [ppb] 12.1 16 19 25 25 28
3 PM2.5 [μg/m ] 7.73 5.47 9.4 7.9 7.2 7.0
UFP [cm-3] 24800 13100 40000 12000 15000 7400 BrCa (370nm) [μg/m3] 1.82 0.77 1.9 0.79 0.96 0.64 BC (880nm) [μg/m3] 1.85 0.67 1.7 0.58 0.82 0.48 a: optical absorbance measured at 370 nm and converted to mass concentration is referred to here as brown carbon (BrC) but this actually represents the contributions from both BrC and black carbon (BC)
Further, the average difference for a given pollutant “i” between near-road and urban background station pairs was calculated via 푁 1 훥퐶 = ⋅ ∑ 퐶 [푗] − 퐶 [푗] 푖 푁 푖,푁푅 푖,퐵퐺 푗=1 where N is the number of hours during which there was data for both the near-road (CNR) and background (CBG) stations. Note that these average differences, shown in Table 3.7, differ slightly from the differences in averages in Table 3.6 because of the exclusion of periods of time when only one station had data. Regarding the Toronto stations, the greatest contrast in the local TRAP concentrations, as calculated through the difference between the near-road and urban background sites, was between NR-TOR-1 and BG-TOR-N. This is an intuitive result, as the large volume of traffic on Highway 401 was expected to result in greater local pollutant concentrations. The most notable differences in local contributions between the two Toronto near-road stations are for UFP concentrations (~29,600 vs. ~7,400 cm-3), BC (1.03 vs. 0.34 μg/m3), and NO (21.5 vs. 3.5 ppb), which is likely a result of greater traffic—notably diesel vehicle traffic—on NR-TOR-1. Despite experiencing far less traffic than NR-TOR-1, the local TRAP concentrations at NR- VAN (Δ VAN, Table 3.7) were quite similar to those of NR-TOR-1 (Δ TOR-1). As noted in Section 3.3.2.1, vehicle emissions at NR-VAN were approximately 50% less diluted than at NR-TOR-1, due to the closer proximity of NR-VAN to the roadway, and this effect is part of the reason for the high local concentrations observed. As noted in Section 3.3.2 using emission factors to account for dilution yielded values for the two sites that were very similar. Note that because CO2 was not measured at BG-VAN, it is not possible to verify this using the method of station differences; however, lower exhaust dilution is apparent using other methods, which will be shown later in this section.
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Table 3.7. Differences in Hourly Pollutant Concentrations Between Near-Road and Background Station Pairs
Δ VAN Δ TOR-1 Δ TOR-2 Pollutant N µ ± 95% CI N µ ± 95% CI N µ ± 95% CI
NO [ppb] 10647 23.0 ± 0.5 14169 21.5 ± 0.4 13768 3.5 ± 0.1
NO2 [ppb] 10666 5.1 ± 0.1 13765 8.7 ± 0.1 11211 5.4 ± 0.1
CO [ppb] 9435 96 ± 2.3 6479 103 ± 2.7 13603 72 ± 1.5
CO2 [ppm] - - 7900 14.4 ± 0.6 10686 10.6 ± 0.4
O3 [ppb] 10535 -3.9 ± 0.1 13753 -5.9 ± 0.1 15109 -2.9 ± 0.1
3 PM2.5 [µg/m ] 10491 2.26 ± 0.07 14170 1.48 ± 0.06 15193 0.27 ± 0.05
UFP [cm-3] 9452 11600 ± 270 5212 29600 ± 784 7400 7400 ± 154
BC (370nm) [µg/m3] 10728 1.00 ± 0.02 8030 1.05 ± 0.03 14740 0.32 ± 0.01
BC (880nm) [µg/m3] 10728 1.18 ± 0.02 8036 1.03 ± 0.03 14740 0.34 ± 0.01
Note: Number of coincidental hours, N, and mean values with their respective 95% confidence intervals are reported.
3.5.1.2 Method 2: Downwind–Upwind Differences in TRAP Concentrations
One advantage of continuous high time-resolution instrumentation in conjunction with meteorological measurements at a near-road location was that measurements downwind of the roadway were decoupled from upwind measurements. Notably, the upwind measurements were essentially background concentrations, as there were no other nearby traffic sources upwind of the three near-road locations. Through this analysis, local TRAP concentrations were estimated without the need for coincidental measurements at an urban background location. 3.5.1.2.1 Downwind–Upwind Differences at NR-VAN The NR-VAN site was stationed on flat terrain (as was NR-TOR-1). However, upwind and downwind conditions were not as binary as NR-TOR-1, as NR-VAN was stationed near a busy intersection with a stoplight and two gas stations on the northwestern and northeastern sides. To the west of one station was a residential area, and so air masses originating from the west were less impacted by traffic than air from the east or south (Figure 3.69).
Average CO2 concentrations measured at the NR-VAN station indicated that the greatest contrast between wind direction-based sectors were as follows:
Downwind: 135° ≤ WD ≤ 195° Upwind: 235° ≤ WD ≤ 315°
These definitions (unlike those of the Toronto stations) were not orthogonal to the road and were unequal in their range; however, they appeared to provide the best representation of being most and least of influenced by traffic-related air pollution sources. Trucks had a dominant influence
Near-Road Pollution Study 2019 106 on emissions at NR-VAN and it is speculated that southbound trucks that stopped near or in front of the station for the red light at the intersection made a major contribution to emissions when they pulled away after the light turned green. As a result, the influence of local emissions was higher when the wind came from this direction.
Figure 3.69. (a) Satellite image of the NR-VAN site along with upwind (blue) and downwind (brown) sector definitions. (b) Average CO2 concentrations (an indicator of combustion sources) by wind direction with upwind concentrations highlighted in blue and downwind in brown. Error bars are 95% confidence intervals on the mean. Meteorological measurements were taken on top of a 10 m mast at the station location (labelled NR-VAN). Note that because of the proximity of the station to a major intersection, quadrant definitions are not orthogonal.
Based on these definitions for the least (upwind) and most (downwind) impacted wind sectors, pollutant concentrations were recorded on an hourly basis and classified according to quadrant, with these values averaged further to give downwind and upwind concentrations (Table 3.8).
Table 3.8. Average, Aggregated, and Comparative Pollutant Concentrations at NR-VAN
Downwind Downwind Upwind Upwind Δ (Downwind Pollutant N µ ± 95%CI N µ ± 95%CI – Upwind)
NO [ppb] 2472 57 ± 2.5 1887 9.7 ± 0.7 47
NO2 [ppb] 2475 21.9 ± 0.4 1890 11.5 ± 0.3 10.4 CO [ppb] 2222 414 ± 12.8 1615 210 ± 4.5 204
CO2 [ppm] 2338 462 ± 3.3 1829 416 ± 1.2 47
O3 [ppb] 2454 9.4 ± 0.4 1861 19.7 ± 0.5 -10.3 3 PM2.5 [μg/m ] 2460 8.8 ± 0.26 1742 5.6 ± 0.19 3.2 UFP [cm-3] 2314 30000 ± 776 1784 14100 ± 381 15900 BrC (370nm) [μg/m3] 2547 2.41 ± 0.06 1909 0.84 ± 0.04 1.57 BC (880nm) [μg/m3] 2547 2.48 ± 0.07 1909 0.84 ± 0.04 1.64
Near-Road Pollution Study 2019 107 3.5.1.2.2 Downwind–Upwind Differences at NR-TOR-1 NR-TOR-1 was ideal for downwind–upwind analysis, as it was located on flat terrain and upwind measurements were not significantly impacted by local sources (Figure 3.70). Here downwind and upwind measurements could be defined as pollutant concentrations measured for air masses originating from mirror 90° quadrants:
Downwind: WD ≥ 295° or WD ≤ 25° Upwind: 115° ≤ WD ≤ 205°
Stagnant periods (WS < 1.0 m/s) were omitted from these analyses as the wind direction was ambiguous.
Figure 3.70. (a) Satellite image of the NR-TOR-1 site along with upwind (blue) and downwind (brown)quadrant definitions (left). Average CO2 concentrations (an indicator of combustion sources) by wind direction with upwind concentrations highlighted in blue and downwind in brown. Error bars are 95% confidence intervals on the mean. Meteorological measurements were taken on top of a 10 m mast at the station location (labelled NR-TOR-1).
Based on these definitions of upwind and downwind measurements, pollutant concentrations were recorded on an hourly basis and classified according to quadrant, with these values averaged further to give downwind and upwind concentrations (Table 3.9). The Δ (downwind–upwind) values reported in Table 3.9 corresponded relatively well with the Δ TOR-1 values in Table 3.8 This is true for most pollutants, with NO, BC, BrC and UFP displaying
local concentrations that were higher than the upwind value. In contrast, O3 and PM2.5 (generally considered regional pollutants) exhibited higher upwind concentrations consistent with the higher regional concentrations typically observed when the wind is from the south. On average, values determined using this methodology were higher than in the previous section, as these upwind– downwind differences indicated the maximum impact of local traffic, when the site was directly downwind, while the first method yielded the average value for all possible wind directions (including when the monitoring site was upwind).
Near-Road Pollution Study 2019 108 Table 3.9. Average, Aggregated, and Comparative Pollutant Concentrations at NR-TOR-1 Δ Downwind Downwind Upwind Upwind Pollutant (Downwind N µ ± 95%CI N µ ± 95%CI – Upwind) NO [ppb] 2378 38 ± 1.1 1787 2.9 ± 0.3 34.9
NO2 [ppb] 2303 21.2 ± 0.4 1748 10.7 ± 0.4 10.5f CO [ppb] 2015 364 ± 5.4 1577 227 ± 3.2 137
CO2 [ppm] 2305 437 ± 1.0 1763 416 ± 1.1 21
O3 [ppb] 2313 15.3 ± 0.4 1771 33.2 ± 0.8 -17.9 3 PM2.5 [μg/m ] 2377 7.7 ± 0.21 1801 9.0 ± 0.27 -1.33 UFP [cm-3] 1839 57000 ± 1671 1313 15300 ± 513 41700 BrC (370nm) [μg/m3] 2337 2.32 ± 0.07 1772 0.91 ± 0.03 1.41 BC (880nm) [μg/m3] 2338 2.13 ± 0.06 1775 0.73 ± 0.03 1.40
3.5.1.2.3 Downwind–Upwind Differences at NR-TOR-2 Because the NR-TOR-2 station was located within a street canyon, defining upwind and downwind conditions was far more challenging than for the other two near-road stations, which were located on flat terrain. In fact, this methodology was found not to be applicable in urban canyons. At the NR-TOR-2 site, the height of the meteorological measurements was of paramount importance, as wind measurements made at a street-level receptor were heavily impacted by building wakes and roadside turbulence. Wind measurements at roof level were outside of the canyon and thus were not representative of ground-level conditions and were more indicative of the wind flows across larger spatial scales. Ground-level wind direction measurements taken near the face of the building added little value in inferring incoming air mass origin. In fact, because of the dynamics that govern wind flows within urban canyons (e.g., in-canyon vortex formation), the ground-level wind direction was generally considered to be reversed with respect to the roof (Dabberdt et al., 1973). Figure 3.71 shows the downwind–upwind quadrant definitions at NR-TOR-2,
Figure 3.71. (a) Satellite image of the NR-TOR-2 site along with upwind (blue) and downwind (brown) sector definitions. (b) Average CO2 concentrations (an indicator of combustion sources, measured at the ground-level receptor) by wind direction with upwind concentrations highlighted in blue and downwind in brown. Error bars are 95% confidence intervals on the mean. Meteorological measurements were recorded on the roof of the Wallberg Building (labelled NR-TOR-2).
Near-Road Pollution Study 2019 109 as defined by meteorological measurements recorded at roof level, along with wind-dependent CO2 concentrations, as measured at the ground-level site. Upwind and downwind quadrants at NR-TOR-2 were defined as follows:
Downwind: WD ≥ 300° or WD ≤ 30° Upwind: 120° ≤ WD ≤ 210°
As with the analysis for NR-TOR-1, the downwind and upwind quadrants at NR-TOR-2 were chosen to be orthogonal to the primary street axis; however, reliably distinguishing between downwind and upwind in this case was not possible. In general, although it is not quite as clear, CO2 concentrations originating from the previously defined upwind quadrant were lower on average than from the downwind quadrant. However, the magnitude of these differences was relatively small. Nevertheless, the same analysis as in the previous section was applied for illustrative purposes to generate the results in Table 3.10 below.
Table 3.10. Average, Aggregated, and Comparative Pollutant Concentrations at NR-TOR-2
Downwind Downwind Upwind Upwind Δ (Downwind – Pollutant N µ ± 95%CI N µ ± 95%CI Upwind)
NO [ppb] 1970 6.0 ± 0.2 5242 3.2 ± 0.1 2.8
NO2 [ppb] 1671 8.5 ± 0.2 4210 10.4 ± 0.2 -1.9 CO [ppb] 1990 248 ± 3.6 5165 247 ± 1.9 1.1
CO2 [ppm] 1938 423 ± 0.7 4994 421 ± 0.5 1.7
O3 [ppb] 2090 24.2 ± 0.3 5439 28.7 ± 0.3 -4.5 3 PM2.5 [μg/m ] 2036 3.8 ± 0.12 5435 9.0 ± 0.15 -5.2 UFP [cm-3] 1974 12900 ± 398 5087 16700 ± 220 -3800 BrC (370nm) [μg/m3] 2059 0.71 ± 0.02 5299 0.97 ± 0.02 -0.26 BC (880nm) [μg/m3] 2059 0.63 ± 0.02 5299 0.81 ± 0.02 -0.18
Clearly, from the negative downwind–upwind values, the analysis that proved successful for a site such as NR-TOR-1 was not useful for a site like NR-TOR-2, which was situated within complicated urban topography. Therefore, if no suitable urban background data are available, more robust methods should be used to separate local and background contributions from TRAP concentrations measured near roadways within urban canyons. When attempting to determine excess pollutant concentrations resulting from nearby on- road traffic, examining the difference in concentrations between downwind and upwind conditions appears to be a promising means of analysis, provided the station is positioned on flat terrain and local meteorology is not significantly impacted by nearby obstructions (e.g. the urban canyon effect seen with NR-TOR-2).
Downwind–upwind analysis at NR-VAN allowed a ΔCO2 value of 47.1 ppm to be calculated, which was previously not possible because no CO2 monitor was running at BG-VAN. This value is important as it gives some metric of vehicular plume dilution near the station; in other words, higher ΔCO2 values, 47 vs 21 ppm at NR-VAN and NR-TOR-1 respectively, indicate less diluted exhaust plumes. This value partially explains why most pollutant concentrations at NR-VAN were
Near-Road Pollution Study 2019 110 similar to those at NR-TOR-1 where traffic volumes are significantly higher but plumes are more diluted. This emphasizes the importance of distance from roadway when siting a near-road monitoring station. Viewed the other way around, this difference in CO2 also emphasizes how emissions in the traffic lane closest to the curb will have a greater impact on near-road concentrations than lanes further away, such that having seven versus sixteen lanes of traffic does not make a large difference. 3.5.1.3 Method 3: Estimating Local Concentrations
A less orthodox approach was used for estimating the influence traffic plays on roadside concentrations, using only the time series of TRAP concentrations without associated meteorology. With this methodology, the locality of pollutant sources is inferred based on the sharpness of spikes in the time-series concentrations (Sabaliauskas et al., 2014; Shairsingh et al., 2018). Sufficient time resolution is a mandatory constraint for this type of analysis, as it inherently relies on distinguishing vehicle-related responses (time scale of minutes) to regional responses (time scale of hours) in the data. The problem reduces itself to estimating two components of a time-series function from some pollutant concentration measured at a near-road station, CNR,
CNR(t) = CB(t) + CL(t)
where CL(t) is the rapidly varying local component, resulting from sources such as traffic, and CB is the slowly varying component resulting from meteorological influences, long-range transport, etc. The concept of analyzing the frequency components comprising an air quality time series has been applied successfully by others in the past (Dilmaghani, 2007; Klems, 2010; Tchepel, 2009).
The background component, CB(t), was determined by identifying the minimum value from
CNR within a rolling time window and interpolating across these minima, as shown in Figure 3.72. A
Figure 3.72. An example time period of CO2 concentrations recorded at NR- TOR-2 (solid black line) along with the output of a windowed minima interpolation algorithm (dashed black line) the concentrations of which are intended to be an estimate for regional CO2 concentrations inferred from a single near-road station.
Near-Road Pollution Study 2019 111 rolling time window of eight hours was used to identify the minimum values (Wang 2018, Hilker 2019). This eight-hour time window separated a background that was more regional in nature and contained less neighbourhood-wide influences (e.g., emissions from nearby roads), which remained within the local portion of the signal. The regional background thereby estimated were similar or slightly lower than the urban background measured under upwind conditions (Method 2) or at urban background stations (Method 1). A twelve-hour instead of eight-hour time window was used for the analysis in Section 3.3.2.2, producing a slightly lower background, and the thus estimates for the local signal in Table 3.2 are slightly higher than those in Table 3.11. Upon estimating the background contribution to roadside pollutant concentrations, the traffic-related portion, CL, was calculated from the difference:
퐶퐿(푡) = 퐶푁푅(푡) − 퐶퐵(푡) 퐶퐵 ≤ 퐶푁푅∀푡
Then, the average local component at each near-road station was calculated via
푁 1 퐶 = ∑ 퐶 [푗] 퐿 푁 퐿 푗=1
By applying this algorithm to the three near-road stations, NR-VAN, NR-TOR-1, and NR-TOR- 2, an estimate for the influence of traffic on roadside pollution levels was calculated without the need for meteorological data or measurements recorded at an urban background location. The average local pollutant concentrations are summarized at these three sites in Table 3.11 below.
Table 3.11. Average TRAP Associated with Local Influences Using Average Rolling Interpolated Minima N µ ± 95%CI N µ ± 95%CI N µ ± 95%CI Pollutant NR-VAN NR-VAN NR-TOR-1 NR-TOR-1 NR-TOR-2 NR-TOR-2 NO [ppb] 15134 27.6 ± 0.6 15524 18.3 ± 0.4 14937 3.8 ± 0.1
NO2 [ppb] 15148 9.7 ± 0.1 15087 9.2 ± 0.1 12359 5.3 ± 0.1 CO [ppb] 13935 153 ± 3.4 13008 114 ± 2.2 15152 69 ± 1.3
CO2 [ppm] 13503 39.0 ± 0.7 14812 19.6 ± 0.4 14626 13.3 ± 0.2 3 PM2.5 [μg/m ] 14879 4.0 ± 0.10 15484 4.30 ± 0.08 15730 2.92 ± 0.06 UFP [cm-3] 14463 15300 ± 251 12683 22800 ± 449 14931 7500 ± 108 BrC (370nm) [μg/m3] 15312 1.23 ± 0.02 15437 1.12 ± 0.02 15445 0.48 ± 0.01 BC (880nm) [μg/m3] 15312 1.26 ± 0.02 15443 1.01 ± 0.02 15451 0.41 ± 0.01
Note that this methodology was not applied to O3 concentrations, as O3 is strictly a secondary regional pollutant whose concentrations are often inversely related to levels of traffic due to NO’s titrating effect. Therefore, attributing O3 concentrations to local influences is not sensible. The average local concentrations reported in Table 3.11 are strikingly similar to the Δ values reported in Table 3.7 (Method 1), indicating that algorithms such as these could prove invaluable in teasing out information regarding background air quality (with the exception of secondary pollutants such as PM2.5 as O3) without necessarily the need for many background monitoring locations or even meteorological information.
Near-Road Pollution Study 2019 112 3.5.1.4 Comparison of Methods
The three methods for isolating the local excess concentration due to traffic are as follows:
• Method 1: Taking the difference in average TRAP concentrations measured at a near-road station and at a suitable urban background location.
• Method 2: Taking the difference in average TRAP concentrations measured during downwind and upwind conditions at a single near-road location.
• Method 3: Using time-series analysis to apportion TRAP data to regional and local concentrations.
The values obtained through Methods 1 and 3 agreed surprisingly well for most pollutants. Method 2 yielded higher values, as it compared upwind and downwind scenarios, whereas Methods 1 and 3 were averaged across all wind directions. Roadside concentrations were highest directly downwind, and this was the criterion used to select the downwind directionality (See Figures 3.69 and 3.67). Thus, the average downwind concentrations were larger than an average concentration calculated across all time periods, whereas average upwind concentrations were similar to average background concentrations. This effect, which yielded larger local TRAP concentrations for Method 2 than the other two methods, is examined further in the Supplementary Information (Section 3.7). In short, it was found that if Method 2 were redefined as
퐶퐿 ≔ 퐶푁푅 − 퐶푁푅|푈푊 as opposed to
퐶퐿 ≔ 퐶푁푅|퐷푊 − 퐶푁푅|푈푊 the local averages in Table 3.12 would agree better with Methods 1 and 3.
Table 3.12. Local TRAP Concentrations between March 2015 and April 2017 Using Methods 1 to 3
NR-VAN NR-TOR-1 NR-TOR-2
Pollutant 1 2 3 1 2 3 1 2c 3
NO [ppb] 23.0 46.8 27.6 21.5 34.9 18.3 3.5 3.8
NO2 [ppb] 5.1 10.4 9.7 8.7 10.5 9.2 5.4 5.3 CO [ppb] 96 204 153 103 138 115 72 69 a CO2 [ppm] 47 39 14 21 20 11 13 3 b PM2.5 [μg/m ] 2.3 3.2 4.0 1.5 4.3 0.3 2.9 UFP [cm-3] 11600 16000 15300 30000 42000 22800 7400 7500 BrC (370nm) [μg/m3] 1.05 1.57 1.23 1.05 1.41 1.12 0.32 0.48 BC (880nm) [μg/m3] 1.18 1.64 1.26 1.03 1.40 1.01 0.34 0.41 a: CO2 from the background site was not available for NR-VAN b: Value was not statistically different from zero c: The complex urban topography at NR-TOR-2 prevented meaningful application of Method 2.
Near-Road Pollution Study 2019 113 As it is defined currently, using only downwind values to obtain average local concentrations is overestimating in comparison to the other two methods, which are not constrained by wind direction. While utilizing only downwind data may be more relevant for characterizing vehicle emissions, it may overestimate average exposure levels. Method 1 is the costliest as it requires tandem measurements at two points in time (and therefore the cost associated with two monitoring stations and associated maintenance), and Method 3 is potentially the least costly, as it does not require meteorological data but does require relatively high time-resolution instrumentation (hourly or more frequent). Method 1, however, is the least complex and most generalizable to any near-road monitoring network, with Method 2 being perhaps the most complex as an astute analyst must account for micrometeorological effects. 3.5.2 Associating Local TRAP Concentrations with Physical Parameters
In real-world systems, it is difficult to attribute cause-and-effect to single variables, as the various physical processes responsible for measured concentrations are confounding. However, if estimates of local TRAP concentrations are known, inverse statistical modelling can allow for the evaluation of underlying processes. Specifically, the role of physical parameters, such as the local meteorological variables of wind speed and wind direction, can be regressed on local pollutant concentrations in order to quantify and constrain the relative importance of the processes responsible for elevated TRAP concentrations near roadways. The local TRAP estimates calculated from Section 3.5.1.4 were used to evaluate the relative influence of governing parameters. Specifically, a two-year time series of hourly values of local concentrations were generated for each of seven pollutants (NO, NO2, CO, CO2, UFP, BrC, and BC). To enable comparison across pollutants and near-road sites, local pollutant quantities inferred using
Method 3, CL(t), were normalized using their mean, C¯ L, calculated across the entirety of the time series. This dimensionless metric, expressing the deviation of each pollutant at any point in time relative to its mean concentration, is a very useful metric as it allowed generalization across all the pollutants. 3.5.2.1 Wind Direction
The important effect of wind direction on near-road concentrations was introduced in Section 3.5.1.2, wherein it was found that higher concentrations at each near-road site were experienced under downwind conditions. This analysis was extended so that changes in local pollutant concentrations were calculated for all wind directions and these hourly values were averaged over the two-year period (Figure 3.73). Using the dimensionless measure of relative concentration
(CL(WD)/CL) the data for each site was further averaged across seven pollutants. For both NR-VAN and NR-TOR-1, on average, the effect of wind direction was such that downwind measurements resulted in local pollutant enhancements by a factor of up to ~2 with respect to mean values. Upwind measurements were only ~1/4 of the mean values indicating that local traffic emissions had a small but non-zero influence on concentrations (likely due to variability in wind direction during a given hour). Notably, these trends were evident and similar for all the primary pollutants that were measured at either station (see also Appendix E: Figure E-1). The shaded area in Figure 3.73 indicates variability across pollutants, meaning these trends were generalizable for any TRAP within the near-road region.
Near-Road Pollution Study 2019 114 Figure 3.73. Average, dimensionless quantities of CL(WD)/CL aggregated for all primary air pollutants (NO, NO2, CO, CO2, UFP, BrC, and BC) measured at NR-VAN (blue, left) and NR-TOR-1 (brown, right). The shaded area indicates the range between individual pollutants (see Appendix E: Figure E-1). A horizontal dashed black line is drawn at unity. Wind directions are based on measurements recorded on a 10 m mast at either respective station.
As seen with the downwind–upwind analysis in Section 3.5.1.2.2, the NR-TOR-1 station experienced a clear wind-dependence, as it was both positioned on flat terrain and experienced effectively no impact from other nearby traffic sources aside from those from Highway 401. For NR- TOR-1, the association was close to sinusoidal in nature. The NR-VAN station, however, was located < 100 m away from a major intersection (Clark Drive and 12th Avenue), the effect of which is pronounced in Figure 3.73. The greatest enhancements to local pollutant quantities were evident when the wind direction was around 150°, directly downwind of this intersection. This was postulated to be due to high-emitting vehicles close to the NR-VAN site, accelerating southbound when the intersection light turned green. 3.5.2.1.1 Local TRAP Variability Explained by Wind Direction Regression analysis was used to estimate the amount of variability explained by parameters on an individual basis to assess the relative importance of each in describing and predicting local pollutant concentrations. In this section, wind direction is used as the only explanatory variable in the model
퐶 (푡) 퐿 ̂ ̂ = 훽0 + 훽1 ⋅ 푓(푊퐷) + 휀 퐶퐿̅ where β values are the linear regression parameter estimates and ε are residual values. The wind direction function, f(WD), was defined based on the data in Figure 3.73. For both NR-VAN and NR- TOR-1, a polynomial function whose degree was chosen based on the number of inflection points was fitted to the averaged CL(WD)/C L values. These polynomials are displayed graphically in Appendix E: Figure E-3. A higher degree polynomial was chosen for NR-VAN because of the presence of a major intersection. When winds were from ~150° at NR-VAN, the enhancement above the
Near-Road Pollution Study 2019 115 typical sinusoidal response would presumably not have been present if Clark Drive were the only roadway in the vicinity. This extra mode in CL(WD)/C L added two additional points of inflection and therefore two additional polynomial terms were used for NR-VAN.
In general, the polynomial function fitted to CL(WD)/C L at both sites was
푁 푖 푓(푊퐷) = ∑ 푘푖 ⋅ 푊퐷 푖=0 where N = 4 and 6 for NR-TOR-1 and NR-VAN, respectively. Appendix E: Table E-1 summarizes these polynomial parameters and Appendix E: Figure E-3 displays the resultant functions. Using these enhancement functions with respect to wind direction, the linear model was applied to each normalized local pollutant quantity at NR-VAN and NR-TOR-1, and the associated R2 values are reported in Table 3.13. Wind direction explained ~10% of the variability of local pollutant quantities at NR-VAN and ~20% at NR-TOR-1. The higher percentage at NR-TOR-1 reflects the larger contrast between downwind and upwind pollutant concentrations.
Table 3.13. Coefficient of Determination Values for Linear Models Applied to Normalized Local Pollutant Quantities as a Function of Wind Direction at NR-VAN and NR-TOR-1 NR-VAN NR-TOR-1 Pollutant R2 R2 NO 0.13 0.25
NO2 0.14 0.17 CO 0.10 0.28
CO2 0.09 0.13 UFP 0.12 0.23 BrC (370nm) 0.14 0.19 BC (880nm) 0.15 0.20 3.5.2.2 Wind Speed
The second variable examined in this section, local wind speed, is a parameter whose moderating effect on air pollution is relatively well known (Jones et al., 2010). Generally, stagnant conditions (very low wind speeds) allow pollutants to accumulate, exacerbating ground-level exposures, whereas high wind speeds allow greater dispersion of roadside pollution, resulting in lower concentrations. Here, the effect of wind speed on local roadside pollutant concentrations was determined at NR-VAN and NR-TOR-1. In relating wind speed to local pollutant concentrations, the following non-linear function was chosen for regression:
퐶퐿(푊푆) 푐1 = 퐶 퐶퐿̅ 푊푆 2 where c1 and c2 are regression coefficients representing maximum enhancements experienced during stagnant conditions (a threshold of 1 m/s is used) and the rate at which local pollutant concentrations decay with respect to wind speed, respectively. The resulting parameters are shown graphically in Figure 3.74 and are summarized in Table 3.14. Again, these plots show values
Near-Road Pollution Study 2019 116 averaged across all seven pollutants (NO, NO2, CO, CO2, UFP, BrC, and BC). The lines in Figure 3.74 indicate that the normalized pollutant concentrations varied based on wind speed by up to a factor of four at NR-VAN and three at NR-TOR-1.
Figure 3.74. Lines of best fit for normalized local pollutant quantities CL(WS)/CLbased on the regression function CL(WS)/CL = c1*WS**-c2 for seven pollutants measured at NR-VAN (red, left) and NR-TOR-1 (black, right). Solid lines represent a fit averaged across the seven pollutants, and the shaded region represents the range of fitted parameters for individual pollutants (see Appendix E: Figure E-2). On average, the parameter c2 is around 0.6, implying a decay factor of WS-0.6 may be generalizable for air pollutants near roadways on flat terrain. Further, the larger average c1 parameter at NR-VAN is consistent with a lower degree of dilution. Because NR-VAN was located closer to a roadway than both Toronto stations, exhaust plumes were more concentrated upon detection as they had less time to disperse, resulting in greater enhancement during stagnant conditions.
Table 3.14. Regression Parameters of Best Fit Fitted to CL (WS)/CL at NR-VAN and NR-TOR-1 NR-VAN NR-TOR-1 Pollutant c1 c2 c1 c2 NO 2.56 0.83 1.56 0.51
NO2 1.62 0.40 1.50 0.46 CO 2.53 0.81 1.54 0.50
CO2 2.36 0.76 2.05 0.88 UFP 1.58 0.37 1.01a 0.01a BrC (370nm) 1.72 0.45 1.71 0.63 BC (880nm) 1.76 0.47 1.62 0.56 Average Values 2.02 0.59 1.66a 0.59a a: Average values do not include coefficients from UFP at NR-TOR-1 which displayed no dependence on wind speed.
Near-Road Pollution Study 2019 117
3.5.2.2.1 Local TRAP Variability Explained by Wind Speed As in Section 3.5.2.1.1., the maximum variability in local pollutant concentrations associated with wind speed was assessed using a model with this as the only independent explanatory:
퐶 (푡) 푐̅ 퐿 ̂ ̂ ̂ ̂ 1 = 훽0 + 훽1 ⋅ 푓(푊푆) + 휀 = 훽0 + 훽1 ⋅ 푐̅ + 휀 퐶퐿̅ 푊푆 2
Here the two beta values are regression parameters of best fit, ε are residual values, and f(WS) is the site-dependent wind speed response, whose coefficients were averaged across all pollutants (solid lines in Figure 3.74; summarized also in Table 3.14). Using the above regression model, coefficients of determination were calculated for each primary air pollutant at NR-VAN and NR-TOR-1 and are shown in Table 3.15. In comparing Table 3.15 with Table 3.14, it is apparent that local pollutant quantities at NR-TOR-1 were much more strongly correlated with wind direction than with wind speed. At NR-VAN, however, wind speed and wind direction appear to explain roughly equivalent amounts of variability.
Table 3.15. Coefficients of Determination for Models Applied to CL (WS)/CL at NR-VAN and NR-TOR-1 NR-VAN NR-TOR-1 Pollutant R2 R2 NO 0.13 0.01
NO2 0.09 0.03 CO 0.14 0.02
CO2 0.16 0.02 UFP 0.05 0.01 BC (370nm) 0.07 0.01 BC (880nm) 0.08 0.01
3.5.3 Summary
In this section, local and regional pollutant quantities, as measured at three near-road monitoring locations, were separated using three methodologies of varying cost and complexity: differences between average concentrations measured near-road and at urban backgrounds, downwind– upwind differences, and methods of time-series analysis. The gold standard for reporting values in excess of regional concentrations was taken to be the first method, as there is little ambiguity regarding background air quality in this case (provided the siting of the stations is adequate). The other two methods, however, are appealing in that neither rely upon the need for background air quality monitoring. The downwind–upwind method produced results higher than the near-road and urban background differences at NR-VAN and NR-TOR-1, and its application failed when applied to NR-TOR-2, as micrometeorology was complicated at this site. Utilizing time-series analysis to extract information regarding background air quality from near-road data appears to be the most robust single-site method, as its results were consistent across all near-road sites.
Near-Road Pollution Study 2019 118 In using local pollutant concentrations as a function of time at the near-road monitoring locations NR-VAN and NR-TOR-1, values in excess of regional background were correlated with common meteorological parameters: wind speed and wind direction. Regarding the direction of wind, downwind conditions appear to enhance local concentrations by a factor of ~1.5 with respect to mean values; similarly, upwind conditions appear to suppress local concentrations by a factor of ~0.25 with respect to the mean. Wind speed appears to have an impact similar to wind direction: stagnant conditions enhanced mean local concentrations by a factor of 2.0 at NR-VAN and 1.7 at NR-TOR-1. At both locations, local concentrations exhibited a wind speed dependence of WS-0.6 (where wind speed, WS, is in m/s), implying dispersion mechanics were similar at both sites. The greater enhancement factor of 2.0 at NR-VAN is likely due to the fact that it is closer to the roadway than NR-TOR-1; although, because these values are relative to mean concentrations, other factors may be responsible.
Near-Road Pollution Study 2019 119 4 Conclusions, Recommendations, and Broader Perspectives
4.1 Conclusions
Vehicles emit a complex mixture of air pollutants that can reach wide areas around busy roads. As a result, a third of Canadians are potentially being exposed to vehicle emissions. Near-road monitoring of air pollution is needed in order to assess the extent and potential health impacts of this exposure. Specifically, monitoring is needed in major Canadian cities in order to
• Evaluate the excess air pollution near major roadways.
• Understand the local parameters governing the concentrations of TRAPs.
• Monitor the evolution of traffic pollution as a result of changes in vehicle and fuel technologies.
• Help estimate the exposure of Canadians to traffic-related air pollution and the associated health outcomes.
This study was undertaken to assess the feasibility of using near-road monitoring stations to achieve these goals. Conclusions pertaining to each of these goals are provided in this section. 4.1.1 Excess Air Pollution Near Major Roads
Traffic was found to contribute the overwhelming majority of some air pollutants beside major roads. For example, over 80% of NO and 60% of the BC was found to be originating from local traffic at the near-road sites. The traffic-related contribution for other pollutants such as CO and CO2 was lower due to their higher regional background concentrations (Section 3.3.2) Three methods for subtracting background concentrations were identified to isolate the excess air pollution due to traffic (Section 3.5.1):
• Method 1: using a separate background station;
• Method 2: comparing concentrations when the station was upwind versus downwind of the road; and
• Method 3: separating the background based on minima within time-series data.
All three methods provided reliable estimates that agreed well, although there were some caveats. Method 1 required the availability of a representative background station, which may often not be possible. Urban topography complicated and constrained the use of the Method 2, making it
Near-Road Pollution Study 2019 120 best suited for locations with wide open topography (e.g., beside a highway rather than downtown in cities). Method 2 also provided higher values describing the maximum excess due to traffic (i.e., when the station is directly downwind) rather than the excess averaged across all wind directions. Method 3 only required the availability of data with hourly or better time resolution and this was achieved with all the continuous instruments used in this study. Thus Method 3 should be routinely applied to data from future near‐road stations, with averaging over many days in order to account for variability due to wind speed and wind direction. This requirement for long‐term sampling will not be an issue at future near-road sites but it does constrain the ability to resolve excess air pollution based on intensive campaigns over a small number of days. Method 3 is not suited for use with twenty-four-hour integrated samples. Method 2 may be the only option unless background sites are available to support Method 1, as was the case in this study. Application of Method 1 to twenty-four-hour integrated filter samples from the near-road and background sites identified large excesses of many metals that were traffic related. Specifically, large excesses of metals such as Ba, Cu, Fe, and Ca pointed to contributions of primary PM2.5 from non-tail-pipe emissions. Receptor modelling of the one-hour Xact and twenty-four-hour filter-based metals data provided a method to resolve this excess into contributions from braking and resuspension of road dust. This receptor modelling revealed that tailpipe and non-tailpipe releases are now contributing a similar amount of PM2.5 in downtown Toronto (6% vs 10%); together, vehicles on average produce a quarter of the primary PM2.5 at the Toronto sites. Notably, during morning rush hour on Highway 401, vehicles are contributing almost half of the overall PM2.5. 4.1.2 Parameters Governing the Concentrations of TRAPs
Concentrations of traffic pollution are governed by a balance between the rates of emission, dilution, and subsequent removal through physical and chemical processes. Background concentrations from other sources also contribute to concentrations measured at near-road sites. Emission factors accounted for differences across sites in the extent of dilution and contribution of background concentrations. Daily-integrated emission factors were calculated based on background-subtracting diurnal patterns to estimate local pollutant concentrations. These emission factors were determined for ~400 days, thereby consolidating the emissions from 200 million vehicles and allowing direct comparisons across the three near-road sites. The inter-site and intra- site variability in the emission factors was consistent with differences in fleet composition and with the fraction of large trucks in particular. The influence of vehicle emissions on key air pollutants (i.e.
BC, NOx and UFP) at these sites depended more on the proportion of large trucks in the fleet than the total traffic volume. Emission factors were also calculated based on 100,000 individual vehicle plumes at NR-TOR-2. This analysis showed that a small portion of the vehicles in the fleet were responsible for the majority of the emissions. The degree of dilution of vehicle exhaust was governed by proximity to the road coupled with wind speed. The inlet at the NR-VAN site was only 6 m from the roadway, which is less than half the distance of NR-TOR-1 (10 m) and NR-TOR-2 (14 m). As a result, the vehicle plumes were diluted on average 1.5x and 2.3x less at NR-VAN than at NR-TOR-1 and NR-TOR-2, respectively. Proximity to a stop light may also have increased emissions from large trucks at NR-VAN, due to the high load associated with acceleration once the light turned green. Wind speeds of 1 to 10 m/s influenced dilution by a factor of three to four at NR-VAN and NR-TOR-1, further reducing concentrations as the emissions were transported away from the road. Under stagnant conditions
Near-Road Pollution Study 2019 121 there was little dilution and concentrations were similar near the road and 150 m away, suggesting that the air quality in a very wide region around the road was being influenced by the traffic. Wind direction altered the average excess concentration beside the road with downwind concentrations exceeding upwind by approximately a factor of six at the NR-VAN and NR-TOR-1 sites. The difference within the urban canyon at NR-TOR-2 was smaller and more complex. 4.1.3 Traffic Pollution as a Result of Changes in Technology
Measurement of real-world emissions factors revealed significant differences from laboratory-based values, which are often used to demonstrate compliance with government emission standards. These differences were in part related to the large seasonality of Canadian climates and its influence on vehicle emissions. Specifically, colder temperatures in winter increased near-road concentrations of NOx and UFP. The higher NOx emissions under colder temperatures (Figure 3.4.3.2) suggested that the urea-based selective catalytic reduction systems on diesel vehicles do not function properly under cold winter temperatures. Emission of UFP also increased under cold temperatures (Figure 3.3.2.3.2), presumably due to less particle evaporation under these conditions. Neither of these issues might be revealed through in-lab testing or on-road testing in warmer climates, highlighting the need for Canada to have its own near-road monitoring capacity. Higher BC concentrations at the near-road sites along with the weekday and weekend patterns pointed to diesel vehicles as the major source of BC. However, local traffic is not the only source, as 20% to 40% of the BC was found to have come from other sources, such as regional sources, biomass burning, and citywide background traffic emissions transported from other parts of the city. In Ontario, BC is higher in the summer than the winter, suggesting an impact from seasonal changes in fuel formulation affecting tailpipe emissions. This potential influence of fuel composition may offer an effective intervention for reducing BC emissions from vehicles. In related research at the NR-TOR-2 site, cars equipped with gasoline direct-injection engines (used in most new cars) were found to emit much higher amounts of BC and UFP. Near-road monitoring should be continued to establish the TRAP-related impacts of this major change in vehicle technology. 4.1.4 Estimating Exposure of Canadians to Traffic-Related Air Pollution
One-third of Canadians live near major roads and are thus potentially being exposed to traffic emissions. The level of exposure and portion of the population potentially being impacted depends on the roadside concentrations and how these levels decrease with distance from the road. Under higher wind conditions, the concentrations of tailpipe-related traffic pollutants in winter decreased from 10 m to 150 m downwind of NR-TOR-1 by a factor of 2.5. The gradient for non-tailpipe-related metals in PM2.5 was steeper, dropping by a factor of seven over the same distance. While lower than directly beside the highway, the concentrations due to tailpipe- and non- tailpipe-related emissions 150 m away were both still well above upwind concentrations, indicating that the area influenced by traffic emissions was wider than 150 m. Further, the concentrations 150 m from a highway were significantly above the upwind concentrations 50% to 65% of the time. The TRAPs measured in this project were selected as proxies to describe overall exposure to vehicle exhaust. It is likely that associated health impacts arise due to multiple chemicals in this complex mixture rather than any single pollutant. However, air quality standards specific to traffic exhaust mixtures (e.g., diesel exhaust) are not available, so this exposure can only be contextualized
Near-Road Pollution Study 2019 122 in terms of indicator pollutants. For example, occupational exposure to diesel exhaust, a recognized human carcinogen, is often monitored and quantified in terms of exposure to EC, which is a major and easily measured component in diesel exhaust particulate. In this near-road study, we measured BC, which is equivalent to but overestimated by up to 40% EC, as measured by the NIOSH 5040 method used for occupational exposure (Appendix B.3.2). However, despite this caveat, the data still clearly pointed to excessive exposure to diesel exhaust near roads: the annual average concentration of BC at the near-road sites ranged from 0.8 to 1.9 μg/m3 with the background- subtracted portion from local traffic ranging from 0.5 to 1.2 μg/m3 (Section 3.5.1). The majority of this traffic-related BC was found to be from diesel vehicle exhaust. The Health Council of the Netherlands has reported that lifetime occupational exposure to diesel exhaust in this range (i.e., at 1 μg/m3) corresponds to an excess risk of lung cancer, 400 times higher than the one per 100,000 often used to establish exposure standards for the general population (HCN 2019). Thus, long-term population exposure to the diesel exhaust particulate concentrations near 1 μg/m3 measured in this study is of concern and indicates the need for reductions (Section 3.2.5). Nitrogen dioxide is often used as another marker to assess exposure to TRAPs, with most
NOx coming from diesel vehicles. The hourly NO2 averaged over two years for NR-VAN and NR-TOR-1 were 21.5 and 19.3 ppb respectively, which exceed the 2020 annual CAAQS of 17 ppb (based on an annual average of hourly measurements). Further, an additional 24.6 and 36.9 ppb of NO were present, most of which would presumably become converted to NO2 as the exhaust was further diluted. Thus, the dilution of NO2 during transport by a factor of two to three may well be balanced by the production of additional NO2, resulting in concentrations that exceed the CAAQS as far as 150 m from the road. In fact, the annual average NO2 at the Toronto West MECP site at Resources Road, located 170 m from the highway has been ≈ 17ppb in recent years (2014–2016). Thus, there is likely an extended boundary region along roads with heavy truck traffic that will not meet the 2020 ambient NO2 standard. Stagnant air conditions can further widen this traffic-influenced area. For example, stagnant air during the February 2017 winter campaign yielded concentrations that were similar 10 m and 150 m away from NR-TOR-1, suggesting that the influence of the traffic emissions extended far beyond 150 m. Moving forward, introduction of newer trucks should start to reduce
NO2 concentrations by 2020. However, these emission reductions may or may not be enough to meet the 2020 annual CAAQS, and truck retirement or retrofit programs may also be needed. Retiring or relocating old trucks rather than adding new trucks may be the more effective strategy for achieving compliance with the 2020 CAAQS near trucking routes. Pollutant levels measured through sampling on roads across the city with a mobile lab were similar to those measured at the near-road monitoring sites. The range of concentrations measured together by the two near-road sites in Toronto covered the range measured through the mobile sampling on highways, arterial roads, and quiet streets, suggesting that the two near-road sites captured the factor of five range in exposure to traffic pollutants experienced by residents across the city. Specifically, the concentrations measured at NR-TOR-1 beside Highway 401 were comparable to those measured while driving on Highway 401 and other 400 series highways, which typically have six to eight lanes and a higher fraction of truck traffic. The concentrations measured at NR-TOR-2 beside a downtown arterial road were comparable to those measured while driving on other arterial roads in Toronto. Thus, the concentration at these near-road sites appear to be representative of other near-road locations across the city with equivalent vehicle fleet characteristics. At Vancouver’s NR-VAN site on Clark Drive, local traffic contributed significantly to
Near-Road Pollution Study 2019 123 PM2.5 and much of this elevation was due to BC from diesel emissions. Clark Drive is a heavily used trucking route, so it is not representative of most roads in the region. That said, data from NR-VAN may well represent the exposure of people on or near similarly busy trucking routes.
4.2 Recommendations
1. Long-term near-road monitoring stations should be established in Canada’s largest cities. Site selection for these stations should provide geographic coverage across Canada from east to west and take into consideration population and traffic density. These stations should use high-quality instruments with high accuracy and reliability to create a consistent standard for traffic-related air pollution monitoring across the country.
2. The near-road stations should all be equipped with a base set of instruments to measure
NOx, CO, CO2, PM2.5, UFP, BC, traffic volume and vehicle type, and representative 8 meteorology . Instruments to measure regional pollutants such as SO2 and ozone may also be included depending on their existing availability within the region.
3. PM2.5 speciation should be measured at least a portion of the time or at some of the stations to assess non-tailpipe emissions. Similarly, organic gases should be measured some of the time or at some of the stations. This partial coverage might be achieved by rotating higher time-resolution instruments annually across stations or, if using twenty-four-hour integrated sample collection, only making measurements once every six days.
4. Smaller more portable instruments can be used for shorter-term monitoring (e.g., < 1 year) of air quality near roads. A national strategy for monitoring traffic-related pollution should supplement the permanent monitoring stations with a secondary tier of smaller more easily deployable instruments for shorter-term monitoring. For example, good quality 3 instruments for measuring NOx, CO, CO2, UFP, and BC can all be integrated into a < 1 m enclosure (Section 3.4.3). These deployable enclosures could also support near-road monitoring in northern Canadian communities. Moreover, inexpensive sensor technologies implemented as part of smart city initiatives could provide a more localized yet complementary third tier of monitoring. However, more research is first needed to establish the reliability and limitations of using inexpensive monitoring technologies in this context.
5. Urban background stations are not essential to enabling near-road monitoring. Accounting for background concentrations is certainly critical to estimating excess pollution due to local traffic. However, this study identified alternate strategies to account for background concentrations that were found to be effective. It is recommended that additional background stations not be included as a core component of the near-road monitoring program. However, urban background stations can provide very useful data for a range of other research applications, and there certainly is value in continuing to operate existing background stations. This should be considered on a case-by-case basis.
8 Wind speed and direction, air temperature, relative humidity and precipitation.
Near-Road Pollution Study 2019 124 6. Methods need to be developed to extrapolate annual average exposure to traffic pollution based on very short-term measurements (e.g., a few weeks at a site of interest or repeated visits through sampling with a mobile laboratory). Variability in traffic composition, seasonality, temperature, and wind speed and direction would need to be accounted for in order to estimate spatial maps of traffic pollution across cities and annual values based on snapshot data collected. A foundation for this type of analysis has been provided in Section 3.5 but more research is required. It is recommended that better methods be developed to estimate long-term exposure at sites of interest (e.g., a proposed daycare beside a major road), to guide siting decisions and the development of supporting policy.
7. More research is needed on non-tailpipe emissions of PM2.5, including its evolution in
composition and toxicity. Non-tailpipe emissions of PM2.5 appear to be rising, and these sources now match primary tailpipe emissions in downtown Toronto. These non-tailpipe sources include particles created through abrasion of tires, the road surface or brakes, and
the resuspension of road dust. As a result, the composition of traffic-related PM2.5 is
changing, which is presumably influencing its toxicity. Existing PM2.5 composition data from additional Canadian cities should be analyzed using receptor modelling to see if non-tailpipe emissions are also growing in these cities. Factors governing the apparent rise in non- tailpipe emissions need be identified and understood (e.g., changes in vehicle fleet technologies? changes in the frequency of precipitation?), and potential intervention strategies should be piloted (e.g., improved traffic flow, increased road sweeping, and roadside barriers).
8. Policies should be implemented to identify and remove the highest emitting vehicles from the road. Priority should be given to retrofitting or removing older heavy-duty diesel vehicles from the road. Programs to remove the small fraction of remaining high-emitting cars could also provide large benefits. Overall, targeting the small portion of heavy emitting vehicles represents a substantial opportunity to improve air quality and thereby reduce the impact on the health of the one-third of Canadians who are potentially being exposed.
9. Data should continue to be used to generate real-world emission factors. Measurement of real-world emissions factors revealed significant differences from the laboratory-based values often used to satisfy emission standards. Comparison of emission factors from sites across Canada is recommended given the influences of seasonality, fleet composition, and geographic location identified in this study.
10. Strategies should be taken to shape communities so that exposure to traffic-related air pollutants (TRAPs) is reduced where people live, work and play. For example, a mix of land uses (e.g., commercial, retail, etc.) should be promoted within higher exposure areas, pedestrian and cycling infrastructure should be located away from high exposure areas, and walkability, transit service quality and access, and efficient parking management should be promoted. Indoor exposure should be reduced through building design and operation, including ventilation and filtration systems.
Near-Road Pollution Study 2019 125 4.3 Broader Perspectives
The near-road pilot study was undertaken to inform and support the creation of a national network of near-road monitoring stations. Beyond the technical findings discussed already, there are broader aspects that should also be considered in the design of the program underlying such a network. Specifically, the program should be risk informed, valued by Canadians, adaptable, support assessment of integrated and cumulative exposure, and be effective and performance measured. 4.3.1 Risk Informed
It would be impossible to monitor the exposure of all Canadians to traffic pollution everywhere all the time. Thus, the design of a national near-road monitoring network should be informed by an appreciation of the related risks: What is the risk of only monitoring at a small number of locations? What are the probabilities and potential severities of impacts that might go undetected? Broadly speaking, these impacts might be social, economic, health-related, environmental, and/or climate- related. More specifically, inadequate monitoring creates a risk of a delay or failure to identify impacts or benefits from
• A change in vehicle technology (e.g., the introduction of gasoline direct-injection engines)
• Poorly performing emission treatment technologies (e.g., widespread tampering or reduced
NOx emission treatment under cold temperatures)
• Regional or seasonal changes in fuel composition (e.g., the influence of aromatic content on BC formation)
• Differences between real-world and lab-based emission estimates (e.g., higher real-world emissions of UFP)
• Seasonality in emissions (e.g., higher ammonia releases in summer)
• Increased non-tailpipe emissions (e.g., possibly due to fleet change)
• Reduced CO2 due to introducing more fuel-efficient and electric vehicles
Operationalizing a risk-informed environmental monitoring design inherently requires judgements as to the relative elasticity of increased monitoring relative to the risks of potential environmental, economic, social, or health impacts. Considering related risks can and should thus guide siting of additional near-road stations. Sites might be selected because of high annual average daily traffic, or more importantly truck traffic, to allow assessment of a greater and ideally more representative portion of the vehicle fleet. It is essential that some of these sites include a significant fraction of heavy-duty diesel vehicles, as these appear to dominate the overall emissions of many key pollutants. It is also important that data from sites can easily be applied to other locations to infer impacts on potentially vulnerable communities. For example, data from near-road monitoring should support development policies or guidelines on siting of future schools, daycares, and long-term care facilities.
Near-Road Pollution Study 2019 126 Near-road monitoring data should also help to assess the expected and actual benefits of potential air quality or climate-related policies or interventions: Are regulations requiring increased fuel efficiency yielding a net reduction in the emissions of climate forcing pollutants? Does introducing a program to retrofit or retire heavy emitting vehicles yield measurable benefits to near- road air quality? Does introducing new requirements for fuel composition reduce the emissions of target pollutants? Does enhanced street sweeping reduce the airborne concentrations of key metals? 4.3.2 Valued by Canadians
A national strategy for near-road monitoring will need to proactively engage Canadians if it is to be valued by them. Data from near-road monitoring can provide early warnings of issues, support the development of policies and regulations, monitor the effectiveness of interventions, and help create new knowledge through research. However, if it is designed from the outset to promote outreach and public engagement, these data could also encourage individual behaviour change and build stronger societal support for new policies or regulations. Because air pollution in Canada is for the most part invisible, it is more difficult to engage the public in the important roles that they can play individually. Canadians might be better encouraged to reduce their emissions and exposure, and support related policies, if they could more directly see the benefits. For example, individuals and organizations might be more motivated to maintain their vehicles if they were rewarded when voluntarily participating in emission testing. Further, large gains in air quality could be achieved through programs that target the highest emitters, such as old trucks, and that rewards proactive companies seeking to strengthen their social licence to operate. Priority lanes or routes monitored by surveillance cameras could be made available as a highly visible reward to organizations operating clean diesel vehicle fleets, so that they could expedite freight delivery while reducing emissions and promoting their company’s contributions to sustainability. Air quality data is already readily available through websites and smartphone apps. Data from near-road stations should also be shared through these existing communication mechanisms. Data from near-road stations might also be proactively provided where impacts are potentially arising. For example, data might be displayed in accessible formats on nearby signs, such as on compass signs on the 400 series highways around Toronto. Open access to data from near-road monitoring stations could also provide the backbone to support more widespread community-based monitoring using emerging sensor technologies. This approach could support air quality monitoring networks throughout smart cities or allow for the extension of near-road monitoring to smaller communities, including urban indigenous communities, potentially impacted by higher traffic emissions. Specifically, the high-quality gold standard data from these stations provide a basis to evaluate the performance of these emerging technologies, and the wide range of concentrations experienced at near-road sites would make these stations well suited for this secondary purpose. 4.3.3 Adaptable
Creation of near-road monitoring stations requires a large commitment of time, effort and expense. Thus, it would likely not be feasible to design a flexible and adaptable network based on a small number of full monitoring stations that are relocated to alternate sites on a regular basis. However,
Near-Road Pollution Study 2019 127 the instrumentation and measurement methods used within stations can still be rotated and the instrument capabilities can evolve as new vehicle technologies and pollutants emerge. The stations might therefore be designed as adaptable, by allocating only a portion of their space to a base set of instruments (see Recommendation 2). The remainder should be used for instruments that might be rotated across sites so that not all pollutants are measured everywhere all the time (Recommendation 3). This rotation would also support cross-validation between the sites. Further new trial instruments could be introduced as opportunities and needs appear. Communities are often particularly interested in the impacts of specific local issues that arise relating to changes in traffic or the siting of new facilities. Smaller, more portable suites of instruments should be created for shorter-term (e.g., < 1 year) monitoring of air quality near roads.
Each suite should include instruments to measure NOx, CO, CO2, UFP, PM2.5 and BC all located within a shared enclosure. Ideally, these portable units would also enable common data acquisition and transmission. These systems could help address local issues, support the selection of new monitoring sites, assess candidate sites proposed for vulnerable communities (e.g., new schools or daycares), or allow temporary monitoring in smaller cities addressing traffic-related air pollution issues. Finally, inexpensive sensor technologies implemented as part of smart city initiatives could provide a more localized yet complementary third tier of monitoring. In summary, a national strategy to monitor traffic-related pollution should include three tiers: permanent stations, portable enclosures, and inexpensive sensor networks. 4.3.4 Integrated and Cumulative
Near-road monitoring of air quality involves measurements of individual pollutants that serve as proxies for the complex mixture of chemicals present in vehicle emissions. Specific pollutants may be more reflective of different vehicle types (e.g., diesel or gasoline) or sources (tailpipe or non- tailpipe) and resulting health outcomes may be more strongly associated with some types of pollutants than others. However, a broad range of adverse health outcomes are associated with TRAPs and many chemicals within the mixture may contribute to any given outcome. For this reason, integrated metrics describing TRAP mixtures rather than individual pollutants may provide better measures of exposure. Agents such as noise and light might also be integrated to create more holistic metrics of exposure to traffic that can be used to explore and understand associated health outcomes. Even more broadly, data from near-road monitoring can be integrated with other environmental monitoring data in order to understand the influences of traffic emissions on overall air quality, climate, water quality in streams or lakes, spring runoff, soil quality, or flora and fauna. Once roads are built, they usually become permanent even though the emission levels may change over time. Moreover, the density of surrounding roads and resulting traffic can increase over time as well. Thus, the cumulative impacts of exposure to traffic emissions over time and space need to be considered. Health researchers require estimates of exposure that can be extended temporally and spatially to identify and understand health outcomes due to long-term cumulative exposure. Urban planners need to be able to estimate the cumulative exposure that may result when siting proposed schools or daycares or the incremental exposure that will arise from expanding a road or changing its fleet composition (e.g., adding a truck or bus route). Near-road monitoring can guide the development of models and provide data for ongoing real-world validation.
Near-Road Pollution Study 2019 128 4.3.5 Effective and Performance Measured
Metrics need to be established in order to assess the effectiveness of a national program for near- road monitoring. These metrics may guide decisions on creating, relocating, or terminating monitoring stations. They may help guide decisions on adding or replacing instruments. The measured performance of instruments in terms of data completeness and reliability should be included within these criteria. The degree of variability in the observations over time or across sites can also guide decisions on the number of sites needed nationally and the balance between permanent and rotating instruments. More generally, the level of use of the resulting data by researchers, policy developers, and the public might guide creation of additional criteria.
Near-Road Pollution Study 2019 129 5 References
Achakulwisut, P., Brauer, M., Hystad, P., Anenberg, S.C. 2019. Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO₂ pollution: Estimates from global datasets. Lancet 3.4:e166–e178. Aiken, A.C., DeCarlo, P.F., Kroll, J.H., Worsnop, D.R., Huffman, J.A., Docherty, K.S., Ulbrich, I.M., Mohr, C., Kimmel, J.R., Sueper, D.T., Sun, Y., Zhang, Q., Trimborn, A., Northway, M.J., Ziemann, P.J., Canagaratna, M.R., Onasch, T.B., Alfarra, M.R., Prévôt, A.S., Dommen, J., Duplissy, J., Metzger, A., Baltensperger, U., Jimenez, J.L. 2008. O/C and OM/OC ratios of primary, secondary, and ambient organic aerosols with high-resolution time-of-flight aerosol mass spectrometry. Environ. Sci. Technol. 42.12:4478–85. Anenberg, S.C., Miller, J., Henze, D., Minjares, R., Achakulwisut, P. 2019. The global burden of transportation tailpipe emissions on air pollution-related mortality in 2010 and 2015. Environ. Res Lett. Accepted online. doi:10.1088/1748-9326/ab35fc Araizaga, A.E., Mancilla, Y., Mendoza, A. 2013. Volatile organic compound emissions from light-duty vehicles in Monterrey, Mexico: A tunnel study. Int. J. Environ. Res. 7:277–292. Aydin, M., Verhulst, K.R., Salzman, E.S., Battle, M.O., Montzka, S.A., Blake, D.R., Tang, Q., Prather, M.J. 2011. Recent decreases in fossil-fuel emissions of ethane and methane derived from firn air. Nature. 476:198–201. Barth, M., An, F., Younglove, T., Levine, C., Socora, G., Ross, M., Wenzel, T. 1999. Development of a Comprehensive Modal Emissions Model. National Research Council Transportation Research Board: Washington DC. Final Report, NCHRP Project 25–11. Bauer, J.J., Yu, X.-Y., Cary, R. Laulainen, N., Berkowitz, C. 2009. Characterization of the sunset semi- continuous carbon aerosol analyzer. J. Air Waste Manage. Assoc. 59:826–33. Birch, M.E. 2003. NIOSH diesel particulate matter (as elemental carbon). Method 5040, Issue 3. NIOSH Manual of Analytical Methods (NMAM). 4th ed. Available online at: www.cdc.gov/niosh/docs/2003-154/pdfs/5040.pdf Bishop, G., Stedman, D. 1996. Measuring the emissions of passing cars. Acc. Chem. Res. 29:489–95. Bishop, G.A., Stedman, D.H. 2015. Reactive nitrogen species emission trends in three light-/medium- duty United States fleets. Environ. Sci. Technol. 49.18:11234–40. Book, E.K., Snow, R., Long, T., Fang, T., Baldauf, R. 2015. Temperature effects on particulate emissions from DPF-equipped diesel trucks operating on conventional and biodiesel fuels. J. Air Waste Manage. 65:751–58. Brauer, M., Reynolds, C., Hystad, P. 2013. Traffic-related air pollution and health in Canada. Can. Med. Assoc. J. 185.18:1557–8. Brito, J., Rizzo, L., Herckes, P., Vasconcellos, P., Caumo, S., Fornaro, A., Ynoue, R., Artaxo, P., Andrade, M. 2013. Physical–chemical characterisation of the particulate matter inside two road tunnels in the São Paulo Metropolitan Area. Atmos. Chem. Phys. 13:12199–213. Brown, S.G., Eberly, S., Paatero, P., Norris, G.A. 2015. Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results. Sci. Total Environ. 518:626–35. Budisulistiorini, S.H., Canagaratna, M.R., Croteau, P.L., Baumann, K., Edgerton, E.S., Kollman, M.S., Ng, N. L., Verma, V., Shaw, S.L., Knipping, E.M., Worsnop, D.R., Jayne, J.T., Weber, R.J.,
Near-Road Pollution Study 2019 130 Surratt, J.D. 2014. Intercomparison of an aerosol chemical speciation monitor (ACSM) with ambient fine aerosol measurements in downtown Atlanta, Georgia. Atmos. Meas. Tech. 7:1929–41. doi:10.5194/amt-7-1929-2014 Burr, M.L. Karani, G., Davies, B., Holmes, B.A., Williams, K.L. 2004. Effects on respiratory health of a reduction in air pollution from vehicle exhaust emissions. Occup. Environ. Med. 61:212–18. doi:10.1136/oem.2002.003244 Cao, J.J., Lee, S.C., Ho, K.F., Fung, K., Chow, J.C., Watson, J.G. 2006. Characterization of roadside fine particulate carbon and its eight fractions in Hong Kong. Aerosol. Air Qual. Res. 6.2:106–22. Cavalli, F., Viana, M., Yttri, K.E., Genberg, J., Putaud, J.-P. 2010. Toward a standardised thermal– optical protocol for measuring atmospheric organic and elemental carbon: The EUSAAR protocol. Atmos. Meas. Tech. 3:79–89. Celo, V., Dabek‐Zlotorzynska, E., Mathieu, D., Okonskaia, I. 2010. Validation of simple microwave‐ assisted acid digestion method using microvessels for analysis of trace elements in
atmospheric PM2.5 in monitoring and fingerprinting studies. Open Chem. Biomed. Meth. J. 3:141–50. [CEPA] Canadian Environmental Protection Act, 1999. Toxic substances list. Available online at: www.canada.ca/en/environment-climate-change/services/canadian-environmental- protection-act-registry/substances-list/toxic.html Chan, T.W., Meloche, E., Kubsh, J., Brezny, R. 2014. Black carbon emissions in gasoline exhaust and a reduction alternative with a gasoline particulate filter. Environ. Sci. Technol. 48:6027–34. Charman, N., Edmonds, N., Egyed, M., Eng, L., Ling, B., Matz, C., Rouleau, M. 2017. Human Health Risk Assessment for Gasoline Exhaust. Ottawa: Health Canada. Chow, J.C., Watson, J.G., Chen, L.-W.A., Arnott, W.P. Moosmüller, H., Fung, K.K. 2004. Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environ. Sci. Technol. 38:4414–22. Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D.H., Merrifield, T.M. 2001. Comparison of IMPROVE and NIOSH carbon measurements. Aerosol Sci. Technol. 34:23–34. [CIPMA]. Canadian Independent Petroleum Marketers Association. 2018. Canadian gasoline consumption and vehicle trends: A comparison to the United States. Cipma.org. Available online at: www.cipma.org/2018/04/12/canadian-gasoline-consumption-and-vehicle-trends- a-comparison-to-the-united-states/ Czader, B.H., Choi, Y., Li, X., Alvarez, S., Lefer, B. 2015. Impact of updated traffic emissions on HONO mixing ratios simulated for urban site in Houston, Texas. Atmos. Chem. Phys. 15:1253–63. Dabberdt, W.F., Ludwig, F.L., Johnson Jr., W.B. 1973. Validation and applications of an urban diffusion model for vehicular pollutants. Atmos. Environ. 7:603–18. Dabek-Zlotorzynska, E., Dann, T.F., Martinelango, P.K., Celo, V., Brook, J.F., Mathieu, D., Ding, L.,
Austin, C.C. 2011. Canadian National Air Pollution Surveillance (NAPS) PM2.5 speciation
program: Methodology and PM2.5 chemical composition for the years 2003–2008. Atmos. Environ. 45:673–86. Dallmann, T.R., DeMartini, S.J., Kirchstetter, T.W., Herndon, S.C., Onasch, T.B., Wood, E.C., Harley, R.A. 2012. On-road measurement of gas and particle phase pollutant emission factors for individual heavy-duty diesel trucks. Environ. Sci. Technol. 46:8511–18. Demers, P., Pahwa, M., Rydz, E., Song, C., Harris, S., Young, S., Candido, E., Peter, A. 2017. Burden of Occupational Cancer in Ontario: Major Workplace Carcinogens and Prevention of Exposure.
Near-Road Pollution Study 2019 131 Toronto, ON: Cancer Care Ontario. Dilmaghani, S. 2007. Spectral Analysis of Air Quality Data. Ph.D. thesis. University of Southern California, United States of America. Drinovec, L., Močnik, G., Zotter, P., Prévôt, A.S.H., Ruckstuhl, C., Coz, E., Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., Hansen, A.D.A. 2015. The “dual-spot” Aethalometer: An improved measurement of aerosol black carbon with real-time loading compensation. Atmos. Meas. Tech. 8:1965–79. [ECCC] Environment and Climate Change Canada. 2017. Frequently asked questions: Regulations amending the on-road vehicle and engine emission regulations and other regulations made under the Canadian Environmental Protection Act, 1999. Canada.ca. Available online at: ec.gc.ca/lcpe-cepa/default.asp?lang=En&xml=9F40F55C-8046-4631-BD7E-DDCDBD0F7A0A [ECCC] Environment and Climate Change Canada. 2018. Canada’s Black Carbon Inventory, 2018 Edition. Canada.ca Available online at: www.canada.ca/content/dam/eccc/documents/pdf/climate-change/emissions-inventories- reporting/black-carbon-2018/black-carbon-2018-en.pdf Environment Canada. 2012. Canadian Smog Science Assessment: Highlights and Key Messages. Ottawa: Environment Canada. Available online at: http://publications.gc.ca/collections/collection_2012/ec/En88-5-2011-eng.pdf [EPA] United States Environmental Protection Agency. 2018. History of reducing air pollution from transportation in the United States. Epa.org. Available online at: www.epa.gov/transportation-air-pollution-and-climate-change/accomplishments-and- success-air-pollution-transportation Evans, G.J., Jeong, C.-H., Sabaliauskas, K., Jadidian, P., Aldersley, S., Larocque, H., Herod, D. 2011. Design of a near-road monitoring strategy for Canada. A final report to Environment Canada. Toronto: SOCAAR. Garg, B.D., Cadle, S.H., Mulawa, P.A., Groblicki, P.J., Laroo, C., Parr, G.A. 2000. Brake wear particulate matter emissions. Environ. Sci. Technol. 34:4463–69. Guarnieri, M., Balmes, J.R. 2014. Outdoor air pollution and asthma. Lancet. 383.9928:1581–92. doi:10.1016/S0140-6736(14)60617-6 Guo, G.H., Lei, M., Chen, T.B., Song, B., Li, X.Y. 2008. Effect of road traffic on heavy metals in road dusts and roadside soils. Acta Sci. Circumstantiae. 28.10:1937–45. Hagino, H., Oyama, M., Sasaki, S. 2016. Laboratory testing of airborne brake wear particle emissions using a dynamometer system under urban city driving cycles. Atmos. Environ. 131:269–78. Hamra, G.B., Laden, F., Cohen, A.J., Raaschou-Nielsen, O., Brauer, M., Loomis, D. 2015. Lung cancer and exposure to nitrogen dioxide and traffic: A systematic review and meta-analysis. Environ. Health Persp. 123.11:1107–12. Hansen A.D.A. 2007. Operating Manual for the AE31/22/42 Aethalometers. Berkeley, CA: Magee Scientific. Harrison, R.M., Tilling, R., Romero, M.S.C., Harrad, S., Jarvis, K. 2003. A study of trace metals and polycyclic aromatic hydrocarbons in the roadside environment. Atmos. Environ. 37:2391– 402. Hayes, P.L., Ortega, A.M., Cubison, M.J., Froyd, K.D., Zhao, Y., Cliff, S.S., Hu, W.W., Toohey, D.W., Flynn, J.H., Lefer, B.L., Grossberg, N., Alvarez, S., Rappenglück, B., Taylor, J.W., Allan, J.D., Holloway, J.S., Gilman, J.B., Kuster, W.C., de Gouw, J.A., Massoli, P., Zhang, X., Liu, J., Weber,
Near-Road Pollution Study 2019 132 R.J., Corrigan, A.L., Russell, L.M., Isaacman, G., Worton, D.R., Kreisberg, N.M., Goldstein, A.H., Thalman, R., Waxman, E.M., Volkamer, R., Lin, Y.H., Surratt, J.D., Kleindienst, T.E., Offenberg, J.H., Dusanter, S., Griffith, S., Stevens, P.S., Brioude, J., Angevine, W.M., Jimenez, J.L. 2013. Organic aerosol composition and sources in Pasadena, California, during the 2010 CalNex campaign. J. Geophys. Res. Atmos. 118:9233–57. doi:10.1002/jgrd.50530 [HC] Health Canada. 2017. Human Health Risk Assessment for Gasoline Exhaust. Publication no. 170279. Available online at: http://publications.gc.ca/collections/collection_2017/sc- hc/H144-52-2017-eng.pdf [HCN] Health Council of the Netherlands. 2019. Diesel Engine Exhaust: Health-based Recommendations for Occupational Exposure Limit. Publication no. 2019/02. Available online at: www.healthcouncil.nl/documents/advisory-reports/2019/03/13/diesel-engine- exhaust Healy, R.L., Sofowote, U., Su, Y., Debosz, J., Noble, M., Jeong, C.-H., Wang, J.M., Hilker, N., Evans, G.J., Doerksen, G., Jones, K., Munoz, A. 2017. Ambient measurements and source apportionment of fossil fuel and biomass burning black carbon in Ontario. Atmos. Environ. 161:34–47. Heck, J.E., Wu, J., Lombardi, C., Qiu, J., Meyers, T.J., Wilhelm, M., Cockburn, M., Ritz, B. 2013. Childhood cancer and traffic-related air pollution exposure in pregnancy and early life. Environ. Health Persp. 121.11–12:1385–91. doi:10.1289/ehp.1306761 [HEI] Health Effects Institute. 2010. Traffic-related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects. HealthEffects.org. Available online at: www.healtheffects.org/publication/traffic-related-air-pollution-critical-review-literature- emissions-exposure-and-health [HEI] Health Effects Institute. 2013. Understanding the Health Effects of Ambient Ultrafine Particles. HealthEffects.org. Available online at: www.healtheffects.org/publication/understanding- health-effects-ambient-ultrafine-particles Herich H., Hueglin, C., Buchmann, B. 2011. A 2.5 year’s source apportionment study of black carbon from wood burning and fossil fuel combustion at urban and rural sites in Switzerland. Atmos. Meas. Tech. 4:1409–20. Hicklin, W., Farrugia, P.S., Sinagra, E. 2018. Investigations of VOCs in and around buildings close to service stations. Atmos. Environ. 172:93–101. 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. Disc. doi:10.5194/amt-2019-112 Ho, K.F., Ho, S.S.H., Lee, S.C., Cheng, Y., Chow, J.C., Watson, J.G., Louie, P.K.K., Tian, L. 2009. Emissions of gas- and particle-phase polycyclic aromatic hydrocarbons (PAHs) in the Shing Mun Tunnel, Hong Kong. Atmos. Environ. 43:6343–51. Hudda, N., Fruin, S., Delfino, R.J., Sioutas, C. 2013. Efficient determination of vehicle emission factors by fuel use category using on-road measurements: Downward trends on Los Angeles freight corridor I-710. Atmos. Chem. Phys. 13:347–57. Jeong, C.-H., Evans, G.J. 2009. Inter-comparison of a Fast mobility particle sizer and a scanning mobility particle sizer incorporating an ultrafine water-based condensation particle counter. Aerosol Sci. Technol. 43:364–73.
Near-Road Pollution Study 2019 133 Jeong, C.-H., Herod, D., Dabek-Zlotorzynska, E., Ding, L., McGuire, M., Evans, G. 2013. Identification of the sources and geographic origins of black carbon using factor analysis at paired rural and urban sites. Environ. Sci. Technol. 47:8462–70. Jeong, C.-H., McGuire, M.L., Herod, D., Dann, T., Dabek-Zlotorzynska, E., Wang, D., Ding, L., Celo, V.,
Mathieu, D., Evans, G., 2011. Receptor model based identification of PM2.5 sources in Canadian cities. Atmos. Pollut. Res. 2:158–71. Jeong, C.-H., Wang, J.M., Evans, G.J. 2016. Source apportionment of urban particulate matter using hourly resolved trace metals, organics, and inorganic aerosol components. Atmos. Chem. Phys. Discuss. doi:10.5194/acp-2016-189 Jimenez, J.L., McManus, J.B., Shorter, J.H., Nelson, D.D., Zahniser, M.S., Koplow, M., McRae, G.J., Kolb, C.E., 2000. Cross road and mobile tunable infrared laser measurements of nitrous oxide emissions from motor vehicles. Chemosphere. 2:397–412. Jones, A.M., Harrison, R.M., Baker, J. 2010. The wind speed dependence on the concentrations of
airborne particulate matter and NOx. Atmos. Environ. 44:1682–90. Jordan, A., Haidacher, S., Hanel, G., Hartungen, E., Märk, L., Seehauser, H., Schottkowsky, R., Sulzer, P., Märk, T. D. 2009. A high-resolution and high sensitivity proton-transfer-reaction time-of- flight mass spectrometer (PTR-TOF-MS). Int. J. Mass Spectrom. 286:122–28. doi:10.1016/j.ijms.2009.07.005. Karavalakis, G., Short, D., Vu, D., Russell, R., Hajbabaei, M., Asa-Awuku, A., Durbin, T.D. 2015. Evaluating the effects of aromatics content in gasoline on gaseous and particulate matter emissions from SI-PFI and SIDI vehicles. Environ. Sci. Technol. 49:7021–31. Kirchstetter, T.W., Harley, R.A., Kreisberg, N.M., Stolzenburg, M.R., Hering, S.V. 1999. On-road measurement of fine particle and nitrogen oxide emissions from light- and heavy-duty motor vehicles. Atmos. Environ. 33:2955–68. Kleeman, M.J., Schauer, J.J., Cass, G.R. 2000. Size and composition distribution of fine particulate matter emitted from motor vehicles. Environ. Sci. Technol. 34:1132–42. Klems, J.P., Pennington, M.R., Zordan, C.A., Johnston, M.V. 2010. Ultrafine particles near a roadway intersection: Origin and apportionment of fast changes in concentration. Environ. Sci. Technol. 44:7903–07. Knox, A., Evans, G.J., Brook, J.R., Yao, X., Jeong, C.-H., Godri, K.J., Sabaliauskas, K., Slowik, J.G. 2009. Mass absorption cross-section of ambient black carbon aerosol in relation to chemical age. Aerosol Sci. Technol. 43:522–32. Kountouriotis, A., Aleiferis, P.G., Charalambides, A.G. 2014. Numerical investigation of VOC levels in the area of petrol stations. Sci. Total Environ. 470–471:125–224. Kurtenbach, R., Becker, K., Gomes, J., Kleffmann, J., Lörzer, J., Spittler, M., Wiesen, P., Ackermann, R., Geyer, A., Platt, U. 2001. Investigations of emissions and heterogeneous formation of HONO in a road traffic tunnel. Atmos. Environ. 35:3385–94. Lawson, D.R., Groblicki, P.J., Stedman, D.H., Bishop, G.A., Guenther, P.L. 1990. Emissions from lit-use motor vehicles in Los Angeles: A pilot study of remote sensing and the inspection and maintenance program. J. Air Waste Manage. 40:1096–105. Liang, Y., Zha, Q., Wang, W., Cui, L., Lui, K.H., Ho, K.F., Wang, Z., Lee, S.-C., Wang, T. 2017. Revisiting nitrous acid (HONO) emission from on-road vehicles: A tunnel study with a mixed fleet. J. Air Waste Manag. Assoc. 67.7:797–805. Link, M.F., Kim, J., Park, G., Lee, T., Park, T., Babar, Z.B., Sung, K., Kim, P., Kang, S., Kim, J.S., Choi, Y.,
Near-Road Pollution Study 2019 134 Son, Y., Lim, H-J., Farmer, D.K., 2017. Elevated production of NH4NO3 from the photochemical processing of vehicle exhaust: Implications for air quality in the Seoul Metropolitan Region. Atmos. Environ. 156:95–101. Livingston, C., Rieger, P., Winer, A. 2009. Ammonia emissions from a representative in-use fleet of light and medium-duty vehicles in the California South Coast Air Basin. Atmos. Environ. 43:3326–33. Metro Vancouver Air Quality and Climate Change Division. 2018. 2016 Lower Fraser Valley Air Quality Monitoring Report. MetroVancouver.org. Available online at: www.metrovancouver.org/services/air-quality/AirQualityPublications/2016- LFVAQMonitoringReport.pdf Middlebrook, A.M., Bahreini, R., Jimenez, J.L., Canagaratna, M.R. 2012. Evaluation of composition- dependent collection efficiencies for the Aerodyne aerosol mass spectrometer using field data. Aerosol Sci. Technol. 46:258–71. [MoE] Ministry of the Environment. 2010. Drive Clean Guide: Emissions Standards, Emission Test Methods, and Technical Information Relating to Ontario Regulation 361/98 As Amended. Toronto, ON: Ontario Ministry of the Environment. Nam, E., Kishan, S., Baldauf, R.W., Fulper, C.R., Sabisch, M., Warila, J. 2010. Temperature effects on particulate matter emissions from light-duty, gasoline-powered motor vehicles. Environ. Sci. Technol. 44:4672–77. Norris, G., Vedantham, R., Wade, K., Brown, S., Prouty, J., and Foley, C., 2014. EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide. Washington, DC: The US Environmental Protection Agency Office of Research and Development. [NRCAN] Natural Resources Canada. 2016. Fuel efficiency benchmarking in Canada’s trucking industry. Canada.ca. Available online at: www.nrcan.gc.ca/energy/efficiency/transportation/commercial-vehicles/reports/7607 [NRCAN] Natural Resources Canada. 2018. 2018 Fuel Consumption Guide. Available online at: www.nrcan.gc.ca/sites/www.nrcan.gc.ca/files/oee/pdf/transportation/tools/fuelratings/20 18%20Fuel%20Consumption%20Guide.pdf Ontario Highway Standards Branch, Traffic Office. 2016. Ontario Provincial Highways Traffic Volumes 2016. Toronto, ON: Ontario Ministry of Transportation. Available online at: www.raqsa.mto.gov.on.ca/techpubs/TrafficVolumes.nsf/fa027808647879788525708a004b 5df8/f51986ea499a13b08525745f006dd30b/$FILE/Provincial%20Highways%20Traffic%20V olumes%202016%20AADT%20Only.pdf Ontario Ministry of the Environment. 2012. Ontario’s Ambient Air Quality Criteria. Available online at: www.airqualityontario.com/downloads/AmbientAirQualityCriteria.pdf Ostro, B., Hu, J., Goldberg, D., Reynolds, P., Hertz, A., Bernstein, L., Kleeman, M.J. 2015. Associations of mortality with long-term exposures to fine and ultrafine particles, species and sources: Results from the California teachers study cohort. Environ. Health Persp. 123:549–56. Pant, P., Harrison, R.M. 2013. Estimation of contribution of road traffic emissions of particulate matter concentrations from field measurements: A review. Atmos. Environ. 77:78–97.
Peterson, M.R., Richards, M.H. n.d. Elemental, Carbonate, Total Carbon, and OCX2 in PM2.5 by the EPA/NIOSH Method – #83. RTI.org. Available online at: www.rti.org/sites/default/files/resources/OC-EC_Paper_83_3b.pdf Phan, N.-T., Kim, K.-H., Shon, Z.-H., Jeon, E.-C., Jung, K., Kim, N.-J. 2013. Analysis of ammonia
Near-Road Pollution Study 2019 135 variation in the urban atmosphere. Atmos. Environ. 65:177–85. Pio, C., Mirante, F., Oliveira, C., Matos, M., Caseiro, A., Oliveira, C., Querol, X., Alves, C., Martins, N., Cerqueira, M., Camoes, F. 2013. Size-segregated chemical composition of aerosol emissions in an urban road tunnel in Portugal. Atmos. Environ. 71:15–25.
Polidori, A. 2008. Organic PM2.5: Fractionation by polarity, FTIR spectroscopy, and OM/OC ratio for the Pittsburgh aerosol. Aerosol Sci. Technol. 42:233–46. doi:10.1080/02786820801958767 Reche, C., Viana, M., Amato, F., Alastuey, A., Moreno, T., Hillamo, R., Teinilä, K., Saarnio, K., Seco, R., Peñuelas, J. 2012. Biomass burning contributions to urban aerosols in a coastal Mediterranean City. Sci. Total Environ. 427/428:175–90. Rönkkö, T., Kuuluvainen, H., Karjalainen, P., Keskinen, J., Hillamo, R., Niemi, J.V., Pirjola, L., Timonen, H.J., Saarikoski, S., Saukko, E., Järvinen, A., Silvennoinen, H., Rostedt, A., Olin, M., Yli- Ojanperä, J., Nousiainen, P., Kousa, A., Dal Maso, M. 2017. Traffic is a major source of atmospheric nanocluster aerosol. P. Natl. Acad. Sci. USA. 114:7549–54. Rowangould, G.M. 2013. A census of the US near-roadway population: Public health and environmental justice considerations. Transport. Res. D–TR. E. 25:59–67. Sabaliauskas, K., Jeong, C-H., Yao, X., Evans, G.J. 2014. The application of wavelet decomposition to quantify the local and regional sources of ultrafine particles in cities. Atmos. Environ. 95:249–57. Schwartz, J. 2000. Smog Check II Evaluation. Part II: Overview of Vehicle Emissions. Sacramento, CA: California Inspection and Maintenance Review Committee. Shairsingh, K.K., Jeong, C-H., Wang, J.M., Evans, G.J. 2018. Characterizing the spatial variability of local and background concentration signals for air pollution at the neighbourhood scale. Atmos. Environ. 183:57–68. Singer, B.C., Kirchstetter, T.W., Harley, R.A., Kendall, G.R., Hesson, J.M. 1999. A fuel-based approach to estimating motor vehicle cold-start emissions. J. Air Waste Manage. 49:125–35. [StatCan] Statistics Canada. 2016. Municipalities in Canada with the largest and fastest-growing populations between 2011 and 2016. StatCan.gc.ca. Available online at: www12.statcan.gc.ca/census-recensement/2016/as-sa/98-200-x/2016001/98-200- x2016001-eng.cfm [StatCan] Statistics Canada. 2017. Journey to work: Key results from the 2016 Census. StatCan.gc.ca. Available online at: www150.statcan.gc.ca/n1/daily-quotidien/171129/dq171129c-eng.htm Suarez-Bertoa, R., Astorga, C. 2016. Unregulated emissions from light-duty hybrid electric vehicles. Atmos. Environ. 136:134–43. Suarez-Bertoa, R., Mendoza-Villafuerte, P., Riccobono, F., Vojtisek, M., Pechout, M., Perujo, A.,
Astorga, C. 2017. On-road measurement of NH3 emissions from gasoline and diesel passenger cars during real world driving conditions. Atmos. Environ. 166:488–97. Sun, Y.L., Zhang, Q., Schwab, J.J., Demerjian, K.L., Chen, W.N., Bae, M.S., Hung, H.M., Hogrefe, O., Frank, B., Rattigan, O.V., Lin, Y.C. 2011. Characterization of the sources and processes of organic and inorganic aerosols in New York City with a high-resolution time-of-flight aerosol mass spectrometer. Atmos. Chem. Phys. 11:1581–602. doi:10.5194/acp-11-1581-2011 Takahashi, K., Sakurai, Y., Furuse, T., Sakuraba, T., Imai, S., Kezuka, T., Aoki, R., Sekimoto, M., Watanabe, H. 2009. Effects of cetane number and chemical components on diesel emissions and vehicle performance. Conference Paper. SAE 2009 Powertrains Fuels and Lubricants Meeting, Florence, Italy.
Near-Road Pollution Study 2019 136 Taxell, P., Santoner T., 2017. Diesel engine exhaust: Basis for occupational exposure limit value. Toxicol. Sci. 158.2:243–51. Tchepel. O., Borrego, C. 2009. Frequency analysis of air quality time series for traffic-related pollutants. J. Environ. Monitor. 12:544–50. Tkacik, D.S., Lambe, A.T., Jathar, S., Li, X., Presto, A.A., Zhao, Y., Blake, D., Meinardi, S., Jayne, J.T., Croteau, P.L., Robinson, A.L. 2014. Secondary organic aerosol formation from in-use motor vehicle emissions using a potential aerosol mass reactor. Environ. Sci. Technol. 48:11235–42. Toronto Transportation Services. 2013. 2012 City of Toronto Road Classification of Streets List. Toronto.ca. Available online at: www.toronto.ca/wp-content/uploads/2017/11/9797- classification-city_streets.pdf Trimm, H.H., Hunter, W. 2011. Industrial Chemistry: New Applications, Processes and Systems. Oakville, ON: Apple Academic Press. Trinh, H.T., Imanishi, K., Morikawa, T., Hagino, H., Takenaka, N. 2017. Gaseous nitrous acid (HONO)
and nitrogen oxides (NOx) emission from gasoline and diesel vehicles under real-world driving test cycles. J. Air Waste Manag. Assoc. 67:412–20.
Turpin, B.J., Lim, H.J. 2001. Species contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass. Aerosol Sci. Technol. 35:602–10. doi:10.1080/02786820119445. [U.K.DoT.] United Kingdom Department of Transport. 2016. Vehicle Emissions Testing Programme: Moving Britain Ahead. London: U.K. Department of Transport. Ulbrich, I.M., Canagaratna, M.R., Zhang, Q., Worsnop, D.R., Jimenez, J.L. 2009. Interpretation of organic components from positive matrix factorization of aerosol mass spectrometric data. Atmos. Chem. Phys. 9:2891–918. Ullman, T.L., Spreen, K.B., Mason, R.L. 1995. Effects of cetane number on emissions from a prototype 1998 heavy-duty diesel engine. SAE Technical Paper 950251. SAE.org. Available online at: doi.org/10.4271/950251 Vermeulen R., Silverman D.T., Garshick E., Vlaanderen J., Portengen L., Steenland K. 2014. Exposure- response estimates for diesel engine exhaust and lung cancer mortality based on data from three occupational cohorts. Environ. Health Persp. 122:172–77. Wang, D., Fuentes, J.D., Travers, D., Dann, T., Connolly, T. 2005. Non-methane hydrocarbons and carbonyls in the Lower Fraser Valley during PACIFIC 2001. Atmos. Environ. 39:5261–72. Wang, J.M., Jeong, C.-H., Hilker N., Shairsingh, K.K., Healy, R.M., Sofowote, U., Debosz, J., Su, Y., McGaughey, M., Doerksen, G., Munoz, T., White, L., Herod, D., Evans, G.J. 2018. Near-road air pollutant measurements: Accounting for inter-site variability using emission factors. Environ. Sci. Technol. 52.16:9495–504. Wang, J.M., Jeong, C.-H., Zimmerman, N., Healy, R.M., Hilker, N., Evans, G.J. 2017. Real-world emission of particles from vehicles: Volatility and the effects of ambient temperature. Environ. Sci. Technol. 51:4081–90. 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. 8:3263–75.
Watson, J.G., Chow, J.C., Houck, J.E. 2001. PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in Northwestern Colorado during 1995. Chemosphere. 43:1141–51.
Near-Road Pollution Study 2019 137 Weimer, S., Drewnick, F., Hogrefe, O., Schwab, J.J., Rhoads, K., Orsini, D., Canagaratna, M., Worsnop, D.R., Demerjian, K.L. 2006. Size-selective non-refractory ambient aerosol measurements during the Particulate Matter Technology Assessment and Characterization Study—New York 2004 Winter Intensive in New York City. J. Geophys. Res. 111:D18305. doi:10.1029/2006JD007215.
Wongphatarakul, V., Friendlander, S.K., Pinto, J.P. 1998. A comparative study of PM2.5 ambient aerosol chemical databases. Environ. Sci. Technol. 32:3926–34. Xu, Z., Wang, T., Wu, J., Xue, L., Chan, J., Zha, Q., Zhou, S., Louie, P.K., Luk, C.W. 2015. Nitrous acid (HONO) in a polluted subtropical atmosphere: Seasonal variability, direct vehicle emissions and heterogeneous production at ground surface. Atmos. Environ. 106:100–9. Ye, C., Gao, H., Zhang, N., Zhou, X. 2016. Photolysis of nitric acid and nitrate on natural and artificial surfaces. Environ. Sci. Technol. 50:3530–36. Yuanwang, D., Meilin, Z., Dong, X., Xiaobei, C. 2002. An analysis for effect of cetane number on exhaust emissions from engine with the neural network. Fuel. 81:1963–70. Yun, H., Wang, Z., Zha, Q., Wang, W., Xue, L., Zhang, Li., Li, Q., Cui, L., Lee, S., Poon, S., Wang, T. 2017. Nitrous acid in a street canyon environment: Sources and contributions to local oxidation capacity. Atmos. Environ. 167:223–34. Zanatta, M., Gysel, M., Bukowiecki, N., Müller, T., Weingartner, E., Areskoug, H., Fiebig, M., Yttri, K.E., Mihalopoulos, N., Kouvarakis, G., Beddows, D.C., Cavalli, F., Putaud, J.P., Spindler, G., Wiedensohler, A., Alastuey, A., Pandolfi, M., Sellegri, K., Baltensperger, U. 2016. A European aerosol phenomenology-5: Climatology of black carbon optical properties at nine regional background sites across Europe. Atmos. Environ. 145:346–64. Zhang, Y., Tang, L., Croteau, P.L., Favez, O., Sun, Y., Canagaratna, M.R., Wang, Z., Couvidat, F., Albinet, A., Zhang, H., Sciare, J., Prévôt, A.S.H., Jayne, J.T., Worsnop, D.R. 2017. Field
characterization of the PM2.5 aerosol chemical speciation monitor: Insights into the composition, sources and processes of fine particles in Eastern China. Atmos. Chem. Phys. 17:14501–17. doi.org/10.5194/acp-17-14501-2017. Zielinska, B., Sagebiel, J., McDonald, J.D., Whitney, K., Lawson, D.R. 2004. Emission rates and comparative chemical composition from selected in-use diesel and gasoline-fueled vehicles. J. Air Waste Manage. Assoc. 54.9:1138–50. doi:10.1080/10473289.2004.10470973 Zimmerman, N., Jeong, C.-H., Wang, J.M., Ramos, M., Wallace, J.S., Evans, G.J. 2015. A source- independent empirical correction procedure for the fast mobility and engine exhaust particle sizers. Atmos. Environ. 100:178–84. Zimmerman, N., Wang, J.M., Jeong, C.-H., Ramos, M., Hilker, N., Healy, R.M., Sabaliauskas, K., Wallace, J.S., Evans, G.J. 2016. Field measurements of gasoline direct injection emission factors: Spatial and seasonal variability. Environ. Sci. Technol. 50:2035–43. Zotter, P., Herich, H., Gysel, M., El-Haddad, I., Zhang, Y., Močnik, G., Hüglin, C., Baltensperger, U., Szidat, S., Prévôt, A.S.H. 2016. Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol. Atmos. Chem. Phys. 17:4229–49. doi.org/10.5194/acp- 17-4229-2017
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