Near-Road Air Pollutant Measurements: Accounting for Inter-Site

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Near-Road Air Pollutant Measurements: Accounting for Inter-Site Variability using Emission Factors Jonathan M Wang, Cheol-Heon Jeong, Nathan Hilker, Kerolyn K. Shairsingh, Robert Healy, Uwayemi Sofowote, Jerzy Debosz, Yushan Su, Michiyo McGaughey, Geoff Doerksen, Tony Munoz, Luc White, Dennis Herod, and Greg J. Evans Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01914 • Publication Date (Web): 19 Jul 2018 Downloaded from http://pubs.acs.org on July 22, 2018

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Environmental Science & Technology

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Near-Road Air Pollutant Measurements:

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Accounting for Inter-Site Variability using

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Emission Factors

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Jonathan M. Wang*,†,‡, Cheol-Heon Jeong†, Nathan Hilker†, Kerolyn K. Shairsingh†, Robert M.

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Healy‡, Uwayemi Sofowote‡, Jerzy Debosz‡, Yushan Su‡, Michiyo McGaughey⊥, Geoff

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Doerksen⊥, Tony Munoz‡, Luc White§, Dennis Herod§, Greg J. Evans†

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

†

Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S3E5 Canada ‡

Environmental Monitoring and Reporting Branch, Ontario Ministry of the Environment and Climate Change, Etobicoke, Ontario M9P3V6 Canada

§

Air Quality Research Division, Environment and Climate Change Canada, Ottawa, Ontario K1A0H3 Canada

⊥Air

Quality and Climate Change, Metro Vancouver, Burnaby, British Columbia V5H4G8 Canada

Corresponding author: Jonathan M. Wang Dept. Chemical Engineering and Applied Chemistry University of Toronto 200 College Street, Room 123, Toronto, Ontario, Canada M5S3E5 Tel. 416-978-5932 Fax. 416-978-8605 Email. [email protected]

ACS Paragon Plus Environment


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ABSTRACT

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A daily-integrated emission factor (EF) method was applied to data from three near-road

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monitoring sites to identify variables that impact traffic related pollutant concentrations in the

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near-road environment. The sites were operated for twenty months in 2015-2017, with each site

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differing in terms of design, local meteorology, and fleet compositions. Measurement distance

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from the roadway and local meteorology were found to affect pollutant concentrations

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irrespective of background subtraction. However, using emission factors mostly accounted for

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the effects of dilution and dispersion, allowing inter-site differences in emissions to be resolved.

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A multiple linear regression model that included predictor variables such as fraction of larger

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vehicles (>7.6 m in length; i.e., heavy-duty vehicles), vehicle speed, and ambient temperature

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accounted for inter-site variability of the fleet average NO, NOx, and particle number EFs

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(R2:0.50–0.75), with lower model performance for CO and black carbon (BC) EFs (R2:0.28-

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0.46). NOx and BC EFs were affected more than CO and particle number EFs, by the fraction of

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larger vehicles, which also resulted in measurable weekday/weekend differences. Pollutant EFs

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also varied with ambient temperature and because there was little seasonal changes in fleet

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composition, this was attributed to changes in fuel composition and/or post-tailpipe

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transformation of pollutants.

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ABSTRACT ART

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KEYWORDS

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Real-world, vehicle, emission factor, near road, air quality monitoring, urban, heavy-duty

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vehicles, ultrafine particles, black carbon

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INTRODUCTION

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Traffic is one of the main contributors to urban air pollution where both traffic and

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population density tend to be higher. Exposure to traffic emissions have been associated with

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numerous negative health effects1-3 including increased risk of cardiovascular and respiratory

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mortality, cancer,4, 5 adverse birth and developmental outcomes,6 respiratory diseases7 including

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asthma8 and premature mortality.9, 10 Thus traffic represents a public health concern because a

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large percentage of the population live near traffic sources and are potentially exposed to

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elevated levels of air pollution. For example in 2013, it was estimated that approximately 32% of

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the Canadian population live within 500 m of a highway and/or 100 m of a major roadway.2, 11 In

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the United States, 19% of the population live within 500 m of a major roadway, however, in

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more urbanized areas this percentage increases significantly for example 40% in California.12 A

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previous modelling study showed that emitted pollutants dilute by a factor of ~1000 from

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tailpipe-to-roadway within seconds and by a factor of 10 in the near road region in a matter of

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minutes.13 Additionally, downwind measurements of particle number concentration showed that

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at 27 m from a highway, levels were 11 times higher than upwind while at 280 m, concentrations

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were double those upwind.11 Furthermore, downwind pollutant concentrations may not only

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depend on vehicle emissions but also ambient meteorological conditions.14, 15 Thus, it is

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increasingly important to continuously measure pollutant levels and trends in the near-road

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environment, an area that is not typically captured by ambient air quality monitoring programs,16

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and has only recently been introduced in the United States.17

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Many factors affect near-road pollutant concentrations, which can complicate the

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interpretation of the measurement data. In addition to the already dynamic emissions from the

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vehicle fleet, the measurement distance (and height) from the roadway, obstructions, the


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presence of a street canyon, and meteorological conditions including wind direction and speed

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can all affect the concentrations measured.18 In order to account for some of the variance arising

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from dilution/dispersion, emission factors (EFs) can be used to isolate and normalize the local

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traffic contribution. Past real-world studies have used various methods in determining EFs19

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including integrated20, tunnel21, 22, remote sensing23, 24, upwind/downwind25, 26, and

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chasing/tailpipe27, 28 measurements; however, the inter-site variability inherent across a network

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of near-road stations, and unavoidable due to urban siting constraints, requires a more dynamic

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EF determination method. A plume-based EF method was initially explored, because this had

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been successfully used in an earlier study for evaluating vehicle and fleet emission

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characteristics on the individual plume level.29 The application of the plume-based method was

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more feasible in that case due to lower traffic density and a street canyon. However, at sites with

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higher traffic density such as highways, it was found that this method could not be easily applied.

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Thus, a broader, low-resolution method (daily timescale) that did not depend on the

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identification of individual plumes was used to determine daily fleet mean EFs that could be

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applied and compared at all sites investigated.

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In this study, the core goal is to develop, evaluate, and apply a daily-integrated EF method

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to 21 months of measurements from a near-road monitoring network in order to isolate vehicle

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fleet emissions and better account for differences in site setup parameters and local meteorology.

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The evaluation compares the daily-integrated EF method with a previously developed plume-

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based EF method. Additionally, the calculation of a background spline for subtraction was

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assessed through comparison with a local background site. Inter-site comparisons were made

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between pollutant concentrations and EFs, where the local contribution was determined. The

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value of this EF method and the resulting dataset is illustrated through the novel insights it



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provides into the parameters affecting weekday vs. weekend and seasonal trends of vehicle

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emissions. These parameters were combined into a multiple linear regression model that was

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used to explore the relative importance of the various factors that impact vehicle emissions.

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METHODS

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Measurement Sites

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Continuous measurements were made 2015-2017 at the near-road sites described in Table

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S1 (Supporting Information), where the near-road region is defined as being within 100 m of a

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major urban roadway (average daily traffic ≥15,000 vehicles) or highway.2, 30 Measurements

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were made at NR-Tor (43º39'32"N, 79º23'43"W) located in the Toronto downtown area (2015-

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07-01 to 2017-03-28), 15 m north of a four lane arterial roadway (17 m wide; ~15,000-26,000

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veh./day) within a street canyon where buildings on either side of the roadway range between

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three and five stories (~12-22 m); at NR-H401 (43°42'40"N, 79º32'36"W) located in a suburban

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Toronto area (2015-07-18 to 2017-03-28), 14 m south of a 17 lane highway (104 m wide;

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~30,000 veh./day) within an open terrain environment; and at NR-Van (49°15'37"N,

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123º4'40"W) located in a suburban Vancouver area (2015-07-11 to 2017-03-28), 6 m from a six

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lane major roadway (24 m wide; ~365,000-410,000 veh./day) within a low street canyon where

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buildings on either side are two stories high (~6 m). Additional information on the site and local

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meteorology is included in the Supporting Information (Figure S1 & Table S1).

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Mean (range) ambient temperature and relative humidity during this period were +11 °C (-

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25 to +35 °C) and 60% (11 to 93%) for Toronto, and +11 °C (-8 to +33 °C) and 73% (17 to 94%)

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for Vancouver. Instrumentation is described in Table S1 (Supporting Information) where regular

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calibrations were applied for the gas-phase instrumentation every 3-4 months at all sites. No


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substantial seasonal differences in the relationship between the AE33880nm BC (Black Carbon

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measured at 880 nm) and Sunset Elemental Carbon (EC).31 However, it was found that the

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AE33880nm BC was consistently a factor of ~1.5 higher than the Sunset EC. Thus, for better

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comparability to past studies, this factor was used to convert the AE33880nm BC measurements to

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EC mass concentration. Meteorological conditions (wind direction and speed, relative humidity,

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ambient temperature and pressure) were measured using a Vaisala WXT520 weather sensor at

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each site set on a tower 10 m above ground level (a.g.l.), with the exception of NR-Tor, where it

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was at roof level (~22 m a.g.l.) and for temperature/relative humidity at NR-Van (8 m a.g.l.). The

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Wavetronix SmartSensor 125 HD, a dual radar traffic detection system described in more detail

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in the Supporting Information, was utilized to count traffic based on detected vehicle lengths in

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three bins (C2: 1 to 7.6 m; C3: 7.6 – 15 m; C4: >15 m) and to measure the vehicle speed at the

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near-road sites averaged across all detectable lanes. Due to the limitation of the SmartSensor and

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width of the highway, only traffic counting measurements for the eight closest lanes out of

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seventeen at NR-H401 were useable. However, independent video analysis indicated that fleet

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characteristics were similar for the eight lanes used here and for all lanes.

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Daily-Integrated Emission Factors Measurement data were processed and analyzed using Igor Pro 6.34 and R. All pollutant

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data were averaged to hourly resolution. A background calculation method was tested by

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comparison with Bkg-Tor (South) site (43°36'44"N, 79º23'19"W) background measurements and

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is described in further detail in the Supporting Information. In short, a linear spline interpolation

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of minima determined in eight hour time windows of hourly averaged measurements was used to

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calculate the background (Figures S1 & S2) necessary for background subtraction. The local



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traffic contributions for each day, midnight-to-midnight, was then integrated. Daily-integrated

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fuel-based EFs were calculated using a carbon-balance method described in Eq.1 as follows:

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EF୔ = ቀ

∆ሾ୔ሿ

∆ሾେ୓మ ሿ

ቁ ‫ݓ‬େ

(1)

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where EFP is the emission factor of pollutant P (in mass or particle number) per kg of fuel burned

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assuming ambient conditions (i.e. 25 °C, 101.325 kPa) represented by the background-subtracted

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integrated amount of carbon combustion products ∆CO2 (in kg of C m-3), and the carbon mass

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fraction of a mixed-fuel fleet, wc = 0.86 kg C (kg-fuel-1).32-34

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The daily-integrated EF method described above uses a similar approach as the plume-

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based EF method described in Wang et al. (2015), with an important distinction that each capture

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extends over an entire day, as opposed to a single vehicle exhaust plume,29 and is assumed (after

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background subtraction) to represent predominantly the combined local traffic contribution. It is

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not expected that the two different techniques would necessarily result in the same EF values for

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two important reasons: 1) the daily-integrated EF method uses data of a much lower time

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resolution and 2) the integration is applied over the entire day, which may not only affect the

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baseline calculation between the two methods, but also potentially be affected by secondary

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formation and non-traffic related sources depending on the pollutant. The “capture scale” of the

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daily-integrated EF method may include a larger temporal period and hence geospatial area

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depending on meteorological conditions. However, they should agree if EFs averaged over these

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larger scales are similar to those in the single plumes. A comparison between the two methods is

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described in EF method evaluation section.

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Multiple Linear Regression Model Development Factors influencing fleet emission factors of different pollutants was investigated by

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combining the EFs determined at all three sites (NR-Tor: ~28%, NR-Van: ~28%, NR-H401:

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~44% of the dataset) for each respective pollutant and performing a multiple linear regression

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(MLR) in R. The predictor variables examined include the percentage of C3 (%VehiclesC3) and

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C4 (%VehiclesC4) vehicles, average vehicle fleet speed, ambient temperature, wind speed and

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direction, and relative humidity. Firstly, predictor variables were ranked based on their

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Spearman’s correlation coefficient with each pollutant EF. The highest-ranking variable was then

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added in a supervised stepwise linear regression, where only variables that increased the adjusted

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R2 by ≥1%, and reduced the residual error, were kept in the model. These criteria were adapted

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from a previous method.35 In the case of collinearity, the variable that contributed to a higher

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model R2 was kept. A final model was produced for each EF pollutant described in the MLR

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section. Model performance was further tested using cross-validation where a training set

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containing 70% of the data was randomly selected to create a new model. The remaining 30%

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made up a test set to evaluate the model made and was compared to the actual EFs. This was

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repeated 10 times with different randomizations.

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RESULTS AND DISCUSSION

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Evaluation of Emission Factor Methods

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Overall, mean values between the daily-integrated and plume-based EF methods29

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compared well. In Figure 1, mean PN and BC EFs compared the best, lying well within the

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daily-integrated EF distribution. Matching NO and NOx EFs is complicated by the divergence in

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the temporal scales of each method, leading to a bias towards fresher local emissions resulting in



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higher NO EFs using the plume-based EF method, and the opposite effect for the daily-

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integrated EF method resulting in higher NO2 EFs (not shown, NOx – NO), whereas NOx EFs

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were relatively close in value between the two methods (Figure 1). Mean CO EFs agreed in

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magnitude, however, the plume-based mean EF was only 63% of the value of the daily-

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integrated EF. The daily-integrated approach inherently assumes that the diurnal patterns are

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only due to vehicle emissions. This assumption is less valid for pollutants such as CO where

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traffic is not necessarily the only or even necessarily the dominant local source (i.e., cooking

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sources, non-vehicle incomplete combustion).

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Figure 1: Comparison of mean EF values between mean plume-based EFs and daily-integrated EFs (n = 85 – 112, depending on the pollutant) over coinciding measurement time periods in 2014-2015 from NR-Tor. Box-and-whiskers represent the distribution. EF units for CO, NO, and NOx are in g kg-fuel-1, BC in mg kg-fuel-1, and PN in 1014 kg-fuel-1.


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The two methods were further tested by correlating the day-to-day variation in the daily-

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integrated and plume-based EFs using the same dataset, so as to refine the data selection criteria,

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as described in more detail in the Supporting Information. It was found that the selection criteria

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that resulted in the best comparison between the two methods for all pollutants was a minimum

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daily integrated amount of CO2 of 300 ppm-day. Not only is CO2 used in the calculation of EFs,

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it acts as an indicator of the purity of the local traffic contribution because a very low CO2

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integrated amount indicates that local contributions were not measurably different from the

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regional air that was diluting it (i.e., the delta was not significantly different from zero). The

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resulting R2 and p-values for each pollutant are shown in Figure S5 (Supporting Information),

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with PN (R2: 0.61, p-value:

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1). Relative to the other meteorological conditions and fleet factors, %VehiclesC4 had the highest



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WD/WE differences with a ratio of 2.2 at NR-H401 corresponding to higher weekday BC, NO,

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NOx, and PN emissions expected from HDDVs (Figure 4a). At NR-H401, the largest mean

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WD/WE EF ratios were for NO and BC, which were 2.4 and 2.0, respectively, while WD/WE

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differences in PN and NOx EFs were also substantial with ratios of 1.8 and 1.6, respectively. At

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NR-Van, the WD/WE ratios were both ~1.8 for BC and NO, while NOx and PN EFs were 1.4

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and 1.6, respectively. Although WD/WE %VehiclesC4 ratio was only 1.7 at NR-Van, lower than

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at NR-H401, WD/WE EF ratios with the exception of CO were much closer to the WD/WE

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%VehiclesC4 ratio. Interestingly, PN and BC WD/WE EF ratios at NR-Van were close to those

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observed at NR-H401. This implies that the weekend change in %VehiclesC4 at NR-Van may

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have a larger relative impact than at NR-H401 or another factor is responsible for the higher

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weekday EFs. A similar trend was observed when using the percentage of larger vehicles (%LV:

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%VehiclesC3 + %VehiclesC4) and is discussed in the Supporting Information (Figure S7). At NR-

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Tor, WD/WE EF ratios for BC and NO were similar to the ratio for %VehiclesC4, all being

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around ~1.4 (Figure 4a), where there was a significant trend between %VehiclesC4 and NOx, PN,

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and BC EFs (p-value>0.05).

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Figure 4: Box-and-whisker plots and mean values (diamonds) for pollutant EFs and percentage of C4 Vehicles (%VehiclesC4 = C4 Vehicles/Total Vehicles x 100%) (a), and EF distributions for each pollutant binned by %VehiclesC4 at each site (b-e: bins of 0.18 for NR-Tor, 1.5 for NR-Van, 2.0 for NR-HWY401; based on the range of the parameter) where left-panel box and whiskers, and right-panel light and dark shaded areas represent 75th and 90th percentiles, respectively. %VehicleC4 ranges were different for each site. CO was the only pollutant to exhibit WD/WE EF ratios below one at NR-H401, and was

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approximately 1 for NR-Van and NR-Tor. CO emissions from the fleet between weekday and

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weekend, assumed to be mostly from gasoline fueled passenger vehicles at these sites, is not

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expected to change substantially. However, with fewer larger vehicles on the weekend, the

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contribution of CO relative to CO2 would be higher resulting in higher average CO EFs on the

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weekends. Interestingly, the PN EF WD/WE ratio at NR-Tor was also closer to 1 than the other

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sites, implying that particle emitters in and around the NR-Tor site were similar regardless of the

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day of week, which was not the case at NR-H401 and NR-Van. The influence of larger vehicles

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on some pollutants is further demonstrated in Figure 4b-e, where pollutant EFs were binned by

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%VehiclesC4. For all three sites, but more obviously at NR-H401 and NR-Van, NOx and BC EFs

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increased as the %VehiclesC4 increased (Figure 4c,e), whereas CO EFs had the opposite or small

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trends (Figure 4b). The comparison for PN EFs was more complicated, where both NR-Tor and



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NR-Van had smaller differences relative to %VehiclesC4 when compared to NR-H401, where

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only the 75th and 90th percentiles of the data showed increases in PN EFs with higher

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%VehiclesC4 (Figure 4d).

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Seasonal Trends in Emission Factors Seasonal EF trends were similar for each pollutant at all three sites and were found to

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coincide with changes in ambient temperature. When EFs are binned by temperature as shown in

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Figure 5c-f, EFs had a clear relationship with ambient temperature. CO, NOx, and PN EFs

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increased as ambient temperature decreased whereas BC EFs had the opposite trend increasing

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with warmer temperatures. Because fleet composition was the major factor influencing WD/WE

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differences, seasonal differences in the fleet were initially investigated to see whether differences

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in the fleet influenced seasonal EF trends. %VehiclesC4 showed no significant correlation with

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ambient temperature, except for NR-Tor (p-value: ~7x10-3). Additionally, the seasonal

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differences in %VehiclesC4 were small relative to those for most pollutant EFs with

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winter/summer ratios ranging from 0.9 at NR-H401 to 1.2 at NR-Van. The seasonal change in

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PN EFs has been discussed in more detail in Wang et al. (2017) for the NR-Tor site.57 In short,

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increased condensation and/or nucleation during colder temperatures, and increased evaporation

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on warmer days combine to create strong seasonal effects on PN EFs post-tailpipe. This is

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further supported by the small seasonal change (±10%) in %LV, whereas mean PN EFs were

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~30-80% higher in the winter depending on the site. All sites showed relatively comparable PN

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EFs above 0 °C, however, below 0 °C, PN EFs began to increase at the Toronto sites to much

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higher values at -10 °C (Figure 5e). This increase in PN EFs at low temperatures was not

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observed at NR-Van because few data were available below 0 °C. Considering that both sites are


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characterized by similar fleet composition, the effect of warmer temperatures may have been one

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of the contributing factors towards lower PN EFs at NR-Van.

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Figure 5: Box-and-whisker plots and mean values (diamonds) for ambient temperature (a) and %VehiclesC4 (b) (left), and pollutant EFs distributions in 5 °C temperature bins (c-f; right) where left-panel box and whiskers, and right-panel light and dark shaded areas represent 75th and 90th percentiles, respectively. EF trends in the extreme temperature bins at each site were limited by the number of data points (n

Near-Road Air Pollutant Measurements: Accounting for Inter-Site

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