Development and Comparison of Air Pollution Exposure Surfaces

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DEVELOPMENT AND COMPARISON OF AIR POLLUTION EXPOSURE SURFACES DERIVED FROM ON-ROAD MOBILE MONITORING AND SHORT-TERM STATIONARY SIDEWALK MEASUREMENTS Laura Minet, Rick Liu, Marie-France Valois, Junshi Xu, Scott Weichenthal, and Marianne Hatzopoulou Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05059 • Publication Date (Web): 23 Feb 2018 Downloaded from http://pubs.acs.org on February 26, 2018

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Environmental Science & Technology

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DEVELOPMENT AND COMPARISON OF AIR POLLUTION EXPOSURE SURFACES DERIVED FROM ON-ROAD MOBILE MONITORING AND SHORT-TERM STATIONARY SIDEWALK MEASUREMENTS

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Marianne Hatzopoulou1*

Laura Minet1, Rick Liu1, Marie-France Valois2, Junshi Xu1, Scott Weichenthal3,

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Department of Civil Engineering, University of Toronto, Toronto, Ontario, Canada

Division of Clinical Epidemiology, Faculty of Medicine, McGill University, Montreal,

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Quebec, Canada 3

Epidemiology, Biostatistics & Occupational Health, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

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*Corresponding author, Department of Civil Engineering, University of Toronto

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35 St George Street, Toronto, ON M5S 1A4

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

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ABSTRACT

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Land-use regression (LUR) models of air pollutants are frequently developed based on short-

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term stationary or mobile monitoring approaches, which raises the question of whether these two

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data collection protocols lead to similar exposure surfaces. In this study, we measured Ultrafine

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Particles (UFP) and Black Carbon (BC) concentrations in Toronto during summer 2016, using

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two short-term data collection approaches: mobile, involving 3,023 road segments sampled on

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bicycles, and stationary, involving 92 sidewalk locations. We developed four LUR models and

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exposure surfaces, for the two pollutants and measurement protocols. Coefficients of

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determination (R2) varied from 0.434 to 0.525. Various small-scale traffic variables were

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included in the mobile LUR. Pearson correlation coefficients between the mobile and stationary

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surfaces were 0.23 for UFP and 0.49 for BC. We also compared the two surfaces using personal

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exposures from a panel study in Toronto conducted during the same period. The personal

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exposures differed from the outdoor exposures derived from the combination of GPS information

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and exposure surfaces. For UFP, the median for personal outdoor exposure was 26,344 part/cm3,

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while the cycling and stationary surfaces predicted medians of 31,201 and 19,057 part/cm3.

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Similar trends were observed for BC, with median exposures of 1,764 (personal), 1,799 (cycling)

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and 1,469 ng/m3 (stationary).


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TOC ART

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1

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Understanding the small-scale variations of Ultrafine Particles (UFP) and Black Carbon (BC) in

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an urban area is crucial for accurate exposure assessment. However, the cost of sampling

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equipment often puts a curb to large monitoring campaigns with numerous simultaneous

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measurements. Land-use regression models (LUR) have been widely used to develop air

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pollution exposure surfaces for various traffic-related air pollutants, which were then used in the

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context of epidemiologic studies1–4. Early LUR models were developed for nitrogen dioxide

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(NO2), primarily because of the ease of measuring NO2 using passive samplers and its common

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use as a proxy for traffic-related air pollution. The recent availability of portable monitors for

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UFP and BC has motivated the development of LUR models. Yet, the nature of the instruments

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available to measure these two pollutants (e.g. high cost, power requirements, need for daily

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maintenance), have made it difficult to conduct long-term measurements at fixed locations thus

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giving rise to short-term monitoring campaigns. These campaigns have either taken place via

INTRODUCTION



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monitoring at fixed sites5–11, or via mobile monitoring, involving measurements conducted in

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motion9,12–18. In addition to allowing the investigation of a spatially larger area, the costs of

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sampling via mobile monitoring can be reduced5,19,20. However, the duration of sampling at each

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location (usually represented by a road segment) is shorter than the time typically spent at fixed

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locations, and it is difficult to quantify the number of repeated measures necessary in order to

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obtain a representative average for each road segment.

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LUR models and associated exposure surfaces are intrinsically dependent on the data

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collected, e.g. the sampling locations, repeated observations, and the pollutant concentrations

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associated with them. The influence of the data collection protocol on LUR models is therefore a

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topical question. With the emergence of mobile data collection approaches, the question of

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whether to promote the spatial extent at the expense of the duration of sampling becomes

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pertinent since the two approaches may lead to different models. Besides spatial extent and

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sampling duration, the two approaches differ in the location of the measurements: mobile

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sampling inherently occurs on the road while stationary sampling often occurs on the sidewalk.

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In the case of dissimilar results, knowing which approach leads to a more representative surface

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is crucial to guide future studies.

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In the context of UFP and BC, we explored the influence of the sampling approach on

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LUR models and associated exposure surfaces. We designed short-term stationary and mobile

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monitoring campaigns, occurring simultaneously during summer 2016 in Toronto, Canada. LUR

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models and exposure surfaces were derived for both pollutants and based on the two data

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collection approaches separately. The results of the exposure surfaces were compared with short-

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term outdoor personal exposures collected in a panel study that occurred during the same time

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period.


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2

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2.1

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Two simultaneous campaigns based on mobile and stationary protocols were conducted between

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May and August 2016 in Toronto with the aim of optimising spatial coverage and temporal

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coverage of a 7am to 7pm day.

METHODOLOGY Data collection

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The short-term stationary monitoring campaign was based on 92 near-road sampling

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locations (on the sidewalk, at the edge of the road), equally distributed between intersections and

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mid-blocks. Each visit to a fixed point consisted of a 20-minute sampling duration, whereby

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research assistants counted vehicles (i.e. passenger car, passenger truck, light commercial truck,

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short haul truck, long haul truck, coach bus, school bus, transit bus, and street cars) going

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through the intersection, or driving along the road. In contrast, the mobile data collection was

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performed using bikes. Ten routes of 24 to 31 km each were designed, passing through most of

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the fixed points, and representing a total of 270 km and 3,895 unique road segments (FIGURE

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S1).

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Each day was divided into three time blocks of 4 hours each (7am-11am, 11am-3pm, and

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3pm-7pm). Measurements along biking routes and fixed points were conducted at least once per

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time block for a minimum of five repetitions. We randomized the direction of the routes and the

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order of fixed points sampling, as well as the starting time. More detail on the campaign design

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is available in the supplemental information (SI).

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2.2

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Miniature Diffusion Size Classifiers (DiSCMini) and Microaethalometers (Microaeth model

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AE51) were used to collect UFP and BC concentrations with time resolutions of 1 and 30

Instrumentation



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seconds respectively. Both instruments were carried in backpacks with a tube connected to them

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and pointing outside of the bag.

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Seven DiSCMinis and six Microaeths were used during the campaign. As such, all

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instruments were collocated with a reference monitoring station (Particle Counter TSI 651 for

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UFP and Aethalometer AE 633 for BC) by concomitantly sampling air from the same outdoor

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source. We repeated this process before and after the campaign, over more than 48h each time.

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The purpose of this collocation was to select a reference DiSCMini and a reference Microaeth

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based on the Pearson correlation coefficients between the simultaneous measurements of all the

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instruments. The DiSCMini and the Microaeth with the highest average correlation were chosen

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as the reference, and the other instrument measurements were corrected based on simple linear

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regressions between the reference measurements and the measurements to correct. Records from

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the entire summer were then corrected based on the linear regressions determined.

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Garmin 800 GPS and the GPS mobile application Strava were used for the mobile

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monitoring campaign and they were set up to record GPS coordinates every second.

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2.3

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The BC measurements were corrected with the Optimized Noise-Reduction Algorithm (ONA)

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developed by the U.S. EPA to improve BC measurements using Aethalometers21.

Data processing

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We also attempted to adjust UFP and BC measurements for daily variations in air quality.

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A fixed station located at street level next to a downtown university building recorded hourly

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UFP and BC concentrations. We used these measurements to develop a correction factor similar

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to other studies5,18. This correction factor was specific to each day, as shown in equation (1). All

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measurements were multiplied by the correction factor (after correction by collocation, and ONA

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for BC). 6 ACS Paragon Plus Environment

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118 Median UFP ሺor BCሻ concentration over the campaign

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Correction Factor = Median UFP ሺor BCሻ concentration over the day of measurement

(1)

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The fixed points and cycling databases were then developed separately. For each visit to

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a fixed point, the 20 minutes of sampling were averaged. Meteorological variables (temperature,

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relative humidity, wind speed) corresponding to the measurement time and day were retrieved

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from the Toronto International Airport station. Traffic data were also stored. Subsequently, all

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visits to a same fixed point were averaged, providing average UFP and BC concentrations, and

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average meteorological conditions and traffic counts encountered at that point.

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For the cycling measurements, a few GPS records were sometimes missing due to signal

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losses. We therefore linearly interpolated the missing coordinates. The second step was to

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associate each GPS point with a road segment of the bikeways network provided by the City of

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Toronto on its Open Data portal (June 2016), which includes the road network of the city to

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which cycle tracks and trails not accessible by vehicles were added. For this step, we first drew

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the prescribed biking routes on ArcMap 10.4.1 before snapping the GPS points to the closest

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road segment of the route drawn to limit the potential mismatch of points with road segments.

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Then, UFP and BC values were associated with each GPS point based on the time stamp. BC

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concentrations were recorded every 30 seconds as an average of the 30 previous seconds. We

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therefore associated a same BC concentration with the 30 corresponding seconds of sampling.

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Hourly meteorological variables were also recorded. We first averaged UFP and BC

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concentrations, and meteorological variables by visit to a segment. Subsequently, we averaged

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all visits to a road segment in order to obtain unique UFP and BC values, and meteorological

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variables. Our final database consisted of 3,023 rows, equivalent to the number of road segments

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covered at least once during the campaign. 7 ACS Paragon Plus Environment


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2.4

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Built environment and land use variables were computed using ArcMap 10.4.1 for all the fixed

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points and road segments (TABLE S5).

Land use and built environment characteristics

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First, we calculated distance variables as the distance between the point or the segment

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and a specific feature: distance to the shore, to the Central Business District (CBD) and to the

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closest airport, rail line, highway, major road, as well as the nearest Nitrogen Oxides (NOx) and

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Particulate Matter (PM) emitting chimney. Other variables computed were within buffers of

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sizes 50m, 100m, 200m, 300m, 500m, 750m and 1000m created around each fixed point or road

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segment, and normalized by the area of the buffer. Areas of building footprint and of

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commercial, governmental, industrial, parks, residential, open area, and waterbody land uses

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were computed, as well as lengths of highway, major road, road and bus line. The number of

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intersections, bus stops, trees, NOx and PM emitting chimneys within the buffers were also

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calculated. The number of inhabitants within the 500m, 750m and 1000m buffers was

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determined. The average and the maximum building height were determined within buffers of

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25m, 50m and 100m. Finally, we computed the average hourly traffic volume within the various

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buffers.

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Land use, building footprint, road network, rail lines, and aerodrome shapefiles were

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extracted from the DMTI Spatial Inc. Database (2014). We retrieved the rail line, bus routes, bus

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stops, trees and building height shapefiles from the City of Toronto Open Data portal (June

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2016). We also downloaded on that portal the Neighbourhood shapefile and the Demographics

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data of 2011 that we combined to determine the number of inhabitants within each

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neighbourhood, a homogenous distribution of the inhabitants within each neighbourhood being

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hypothesized. The shapefiles locating the NOx and PM emitting chimneys were downloaded


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from the National Pollutant Release Inventory (NPRI) website of the Government of Canada.

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Finally, average traffic was generated between the hours of 6am and 7pm using EMME, a traffic

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assignment model for the Greater Toronto and Hamilton Area (GTHA). The Origin-Destination

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(OD) matrix of trips from the Transportation Tomorrow Survey (year 2011) was used and a

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static traffic assignment method was employed to generate a traffic volume on the roads of the

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GTHA.

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2.5

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We developed separate UFP and BC LUR models based on the fixed points and cycling data

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using the programming language R and its statistical package. The normal logarithms of the UFP

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and BC concentrations were computed.

Land use regression models

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Two procedures based on the literature were tested and applied to both the temporally

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and non-temporally adjusted UFP and BC concentrations. The first procedure, later called the

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forward model, was based on the methodology presented by Eeftens et al.22, which was also

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followed by Kerckhoffs et al.9 and Sabaliauskas et al.16. The steps of the algorithm were as

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follows: 1) The potential predictors were sorted based on the adjusted R2 of the simple linear

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regression between the predictor and ln(BC) or ln(UFP). 2) When considering non-temporally

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adjusted pollutant concentrations, the initial model consisted in the best combination of

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meteorological variables identified, i.e. the combination providing the highest adjusted R2. When

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considering temporally adjusted pollutant concentrations, no predictor was added to the initial

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model. 3) The variables were added one at a time to the initial model based on the order

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determined in step 1. Variables were kept only if including them increased the adjusted R2 by

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more than 1%, if the sign of the new variable in the model was meaningful, and if adding the

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new variable did not alter the signs of the variables already in the model. At this step, we 9 ACS Paragon Plus Environment


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checked whether the new variable was from the same category as a predictor already included in

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the model (i.e. same variable but different buffer). If it was the case, we tested which variable

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was more appropriate by keeping one of the variables and checking the one that led to the

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highest increase in the adjusted R2. Also, variables that were eliminated in a previous step were

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always tested again. 4) Once an intermediate model was reached, i.e. when all variables had been

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tested and adding any of them did not improve the adjusted R2, we removed one by one the

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variables that had a p-value higher than 0.1 starting with the variable with the highest p-value. 5)

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We checked the Variance Inflation Factor (VIF) of the variables and removed the variables with

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a VIF higher than 3, starting with the variable with the highest VIF.

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The second procedure, called the backward model, was based on the paper of

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Weichenthal et al.16. The steps followed are as follows: 1) The potential predictors were sorted

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based on the p-value of the simple linear regression between the predictor and ln(BC) or

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ln(UFP). 2) All the variables that were significant (i.e. p-value ≤ 0.05) were added to the initial

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model. If two variables were highly correlated (i.e. Spearman correlation coefficient ≥ 0.8), then

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the variable presenting the highest R2 in the univariate regression with the pollutant was kept. 3)

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Variables in the regression with a coefficient sign opposed to what was expected were removed.

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4) Variables that were not statistically significant were removed one at a time and only if doing

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so did not increase the Root Mean Square Error (RMSE) by more than 1%. 5) The non-

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significant variables remaining (i.e. p-value higher than 0.1) were removed starting with the

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variable with the highest p-value even if doing so increased the RMSE by more than 1%.

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In addition, we performed a 100-fold cross-validation. We randomly selected 90% of the

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points or road segments, and applied to them the forward and backward models developed

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previously to determine new coefficients. We then applied the new model to the 10% hold-out


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sample, and calculated the RMSE and Pearson correlation coefficient between the predicted and

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measured concentrations of this sample. We repeated this 100 times. We based our model choice

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on the adjusted R2 of the model, as well as on the median adjusted R2 of the validation models

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(i.e. models developed with 90% of the points) and on the median RMSE and Pearson

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correlation coefficients of the hold-out samples.

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After choosing our final fixed point model, we added the proportion of trucks (calculated

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as the number of short and long haul trucks divided by the total number of vehicles) as a

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predictor to see its impact on the adjusted R2 of the model, although it could not be used for

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predictive purposes since the traffic data we are using only includes a total traffic volume.

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Finally, we developed models based only on the road segments within 100m around the

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fixed points. The aim was to compare the predictors included in these models with the predictors

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included in the fixed point models.

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2.6

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To develop exposure surfaces, we divided the city of Toronto into grid cells of 100m by 100m

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and computed the predictors for the mid point of each cell. We calculated the predicted pollutant

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concentrations for each mid-point based on the final LUR models and associated each value with

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the corresponding cell to generate surfaces. We slightly modified the area of Toronto considered

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for the development of the surfaces (FIGURE S2). Indeed, the International airport of Toronto is

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located West of the city; however, since we did not conduct measurements close to the airport

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(due to the nature of our monitoring campaign being based on pedestrian and cycling

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measurements), we only predicted levels up to the extent of our sampling coverage.

Exposure surfaces and comparison with panel study

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The surfaces were not intended to predict short-term exposures; however, to explore their

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differences, they were compared with data from a panel study also conducted in Toronto during 11 ACS Paragon Plus Environment


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the same time period as the cycling and stationary campaigns (from May to August 2016).

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During this period, we collected data from a total of 62 visits by various participants, each visit

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consisting in one day extending from 9am to 4pm. Several visits could take place during the

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same day if more than one participant was recruited. The panel study was designed at the same

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time as the stationary and cycling campaigns. The participants were equipped with DiSCMinis,

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Microaeths and GPS during six hours each day and were asked to spend at least two hours

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outdoors. For the current study, only the outdoor personal exposures were considered. We

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determined the average outdoor UFP and BC exposure that each participant was exposed to

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during the study (personal exposure). A participant's outdoor trajectory was then intersected with

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the exposure surfaces to derive mobility-based exposures. The personal and mobility-based

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exposures were compared to identify the extent of bias in short-term exposure estimates

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generated by the various surfaces.

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3

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3.1

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Hourly UFP and BC concentrations recorded by the DiSCMinis and Microaeths exhibited very

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similar trends compared to the measurements of the reference station. However, the ranges could

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vary a lot for UFP; the highest difference between two DiSCMini measurements was 21,000

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particles/cm3 (noted part/cm3). The Pearson correlation coefficients between the DiSCMinis

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ranged from 0.36 to 0.98 with an average of 0.82. The chosen reference DiSCMini had

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correlation coefficients of 0.75 to 0.98 with the six other instruments. As for the Microaeths, the

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differences between two instruments were low, especially during the second colocation when the

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largest hourly averaged BC concentration difference between two instruments was less than 100

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ng/m3. The correlation coefficients between the instruments ranged from 0.71 to 0.99 for an

RESULTS Instrument performance


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average of 0.90. For these reasons, we did not correct the BC measurements. More detail is

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provided in the SI.

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3.2

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The average number of visits per fixed point was 5.1, i.e. an average of 102 minutes of sampling.

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Concerning the cycling campaign, the mean number of visits to each road segment was 6, the

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minimum and maximum were 2 and 15 respectively, corresponding to an average of 121 seconds

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per segment (0.8 sec/m).

Descriptive statistics

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The UFP and BC measurements at the fixed points were similar to the measurements by

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bike (FIGURE S5), and the distributions were all approximately lognormal. The median UFP

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concentrations were 20,867 and 19,756 part/cm3, and the median BC concentrations were 1,155

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and 1,223 ng/m3 for the fixed points and cycling campaigns, respectively. Interquartile ranges

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were comparable as well, but the extreme values were higher for the cycling records. The

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maximum UFP concentration for a fixed point was 61,255 part/cm3, while it was 241,547

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part/cm3 for a road segment (averages across all visits). Similarly, the maximum BC

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concentration for a fixed point was 7,268 ng/m3, and 39,152 ng/m3 for a road segment.

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We compared average UFP and BC concentrations at fixed points and on the portions of

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road segments within buffers of 100m around the fixed points (FIGURES S6 and S7). The

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median levels on road segments were in general higher than at fixed points. The median UFP

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concentrations at fixed points and on road segments were 23,556 and 28,304 part/cm3, while BC

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levels were 1,148 and 1,628 ng/m3. Pearson correlation coefficients between measurements at

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fixed points and on road segments within a 100m buffer around them (45 fixed points and an

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average of 212 m of road segments sampled around each point) were 0.07 and -0.11 for UFP and

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BC, indicating no correlation. 13 ACS Paragon Plus Environment


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3.3

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Using the meteorological variables to adjust for temporal variability proved to be more effective

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than using the data from the fixed monitoring station, with higher adjusted R2 and lower RMSE.

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We opted for the forward procedure for all models (more details in the SI).

LUR models

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TABLE 1 presents the LUR models. For UFP, the two models (fixed vs. mobile

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monitoring) present similar adjusted R2 (0.405 and 0.430 for the fixed point and mobile models).

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Including the proportion of trucks in the fixed point model increased the adjusted R2 from 0.405

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to 0.467. The adjusted R2 differed more distinctly between the BC fixed point and cycling

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models (0.53 and 0.432 respectively). However, the proportion of trucks did not have an

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important effect on the fixed point BC model as the adjusted R2 increased from 0.525 to 0.532.

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There are few common variables between the fixed point and cycling models for the two

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pollutants. Temperature and relative humidity are positively associated with UFP or BC, while

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wind speed is negatively associated. Several times, the buffer size employed is different between

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two models. For instance, traffic count is present in both BC models, but within a 750m buffer in

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the fixed point model and within a 100m buffer in the cycling model. This is also the case for the

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number of bus stops, the length of major roads, and the area of water. All coefficient signs are

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consistent among the four models, except for the area of water. More small-scale traffic variables

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(i.e. distances or predictors within buffers smaller than 500m) are included in the cycling models.

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The results of the cross validations are available in the SI. The cycling models developed

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with the road segments within 100m around fixed points were different from the fixed point

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models (TABLE S16). This is not surprising as the measurements at fixed points and on the

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surrounding road segments were not correlated.


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The models developed based on the fixed points were used to predict concentrations at

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the road segments sampled during the cycling campaign to investigate transferability of the

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variables. Inversely, the cycling models were also used to predict concentrations at the fixed

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points. The R2 of the linear regression between the predicted and the measured concentrations

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ranged between 0.0182 and 0.0888, indicating poor transferability of the variables and

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coefficients.

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TABLE 1 UFP and BC LUR Models (Standardized Coefficients) for the Fixed Points and Cycling Data ln(UFP)

ln(BC)

Fixed points

Cycling

Fixed points

Cycling

Adjusted R²

0.405

0.430

0.525

0.434

Temperature

2.96E-01***

1.76E-01*

7.64E-02***

2.26E-01**

Relative Humidity Wind Speed

-1.94E-01*

-2.60E-01***

7.11E-02**

Number of bus stops within 50m 2.21E-01*

Number of bus stops within 100m Number of bus stops within 300m

1.89E-01* 2.09E-01*

Length of bus line within 100m Distance to the CBD

1.00E-01*** -2.37E-01***

3.88E-01** -4.17E-01 ***

Distance to the closest airport

-1.29E-01***

Distance to the closest major road -1.44E-01***

Distance to the shore Length of highway within 1000m

2.77E-01*

Number of intersections within 750m

3.33E-01** 8.84E-02***

Length of major roads within 200m Length of major roads within 500m

2.51E-01*

Open area within 1000m

-2.14E-01*

Population within 500m

1.77E-01.

Area of water within 50m

1.75E-01.

2.79E-01*** -1.80E-01***

Area of water within 750m

-2.79E-01*** 1.96E-01*

Commercial area within 500m 1.49E-01***

Traffic within 100m

1.57E-01*** 4.47E-01***

Traffic within 750m 1.05E-01***

Number of trees within 750m

6.93E-02***

Building footprint area within 500m 8.72E-02***

Maximum building height within 50m

-1.19E-01***

Park area within 500m

1.32E-01***

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Length of roads within 500m Number of NPRI NOx chimneys within 200m Significance codes: 0 ‘***’, 0.001 ‘**’,0.01 ‘*’, 0.05 ‘.’

310

3.4

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FIGURE 1 presents the four surfaces derived from the LUR models. They all look dissimilar,

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which is confirmed by the weak correlations between them. The two BC surfaces have the

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highest correlation, which is 0.49, but the two UFP surfaces only have a correlation coefficient of

7.29E-02***

Exposure surfaces


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0.232. The two cycling surfaces (for BC and UFP) and the two fixed point surfaces are

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moderately correlated, with Pearson correlation coefficients of 0.63 and 0.49 respectively. Major

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roads are more visible in the two cycling surfaces, in light of the variables included in the cycling

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LUR models.

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FIGURE 2 compares the predictions of the surfaces to the panel study data. The

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participants spent on average 4 hours outdoors during each visit (TABLE S17), between 9am and

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4pm. The median UFP outdoor exposures derived from the combination of GPS information and

321

exposure surfaces were different from the personal exposures: 26,344 (personal), 31,201 (cycling

322

model) and 19,057 part/cm3 (fixed point model), and similar results were obtained for BC, with

323

median exposures of 1,764 (personal), 1,799 (cycling) and 1,469 ng/m3 (fixed point). For BC, the

324

cycling surface provides closer results while the mobility-based exposure derived from the fixed

325

point surface is lower. For UFP, the cycling surface tends to overestimate the exposure while the

326

fixed point surface tends to underestimate them. In general, both mobility-based methods seem

327

to correctly capture central tendencies of exposure. However, the maximum UFP and BC levels

328

recorded via personal outdoor monitoring are higher than those derived from the surfaces,

329

because personal outdoor monitoring captures very punctual events such as a bus or a truck

330

passing by, which can drastically affect the range of observed concentrations. Additional

331

comparisons suggesting low correlations are available in the SI (FIGURES S20 and S21).

332



Page 18 of 30

(a) UFP surface derived from the LUR model based on the fixed points campaign

(b) UFP surface derived from the LUR model based on the cycling campaign


Page 19 of 30


(c) BC surface derived from the LUR model based on the fixed points campaign

333

(d) BC surface derived from the LUR model based on the cycling campaign FIGURE 1 UFP and BC exposure surfaces obtained from the LUR models based on fixed points and cycling.



Page 20 of 30

80,000 70,000

UFP (part/cm³)

60,000 50,000 40,000 30,000 20,000 10,000 0 Personal measurements

Cycling surface

Fixed points surface

(a) UFP (45 personal measurements) 6,000

5,000

BC (ng/m³)

4,000

3,000

2,000

1,000

0 Personal measurements

334 335 336 337

Cycling surface

Fixed points surface

(b) BC (62 personal measurements) FIGURE 2 Comparison of the UFP and BC personal exposures with mobility-based exposures predicted by the fixed point and cycling surfaces (the lower and upper whiskers correspond to the minimum and maximum, the box is drawn between the first and the third quartile with the middle line corresponding to the median; axes are truncated).

338


Page 21 of 30


339

4

340

In this study, we developed LUR models based on short-term mobile and stationary monitoring

341

campaigns. The mobile campaign was conducted on bicycle while the stationary campaign was

342

conducted based on short-term sidewalk measurements. The predictors captured in the LUR

343

models based on each sampling campaign are different and exposure surfaces resulting from the

344

LUR predictions are dissimilar. The adjusted R2 of the UFP LUR were 0.405 and 0.430 for the

345

fixed point and cycling models, and 0.525 and 0.434 for the BC fixed point and cycling models,

346

respectively. These values fall within the range of R2 presented in the literature. TABLES S20

347

and S21 summarise the characteristics of various UFP and BC short-term monitoring campaigns

348

and LUR models published in the recent literature. For UFP, values for the R2 range between

349

0.23 and 0.85 for LUR models based on fixed-site monitoring, while varying between 0.13 and

350

0.72 for LUR models based on mobile monitoring. For BC, they range between 0.28 and 0.86 for

351

fixed-site monitoring and 0.12 and 0.68 for mobile monitoring. Based on the literature, it seems

352

that fixed monitoring generally leads to higher LUR R2 than mobile monitoring for BC (TABLE

353

S21).

DISCUSSION

354

A review of recent LUR models developed for various cities (TABLES S20 and S21)

355

illustrates that the number of fixed sites (between less than 206 and 16011 for a single city) and

356

the duration of sampling (between 15 minutes5 and 90 minutes8) vary significantly across

357

studies. Moreover, the aggregation of measurements in the case of mobile campaigns varies

358

significantly, as the unit of analysis ranges from a road segment, extending between two

359

intersections, to segments of equal length. The number of aggregation locations varies between

360

about 10012 and more than 4,00023, depending on the extent of the area covered. The average

361

time spent at each location has been reported to be as low as 18 seconds9 and reaching more than



Page 22 of 30

362

10 minutes18. During the cycling campaign, we spent on average 121 seconds on each road

363

segment, which is within the range of time reported in the literature. During our stationary

364

campaign, each fixed point was visited on average 102 minutes, comparable with the range of 15

365

to 90 minutes typical of most fixed monitoring campaigns.

366

In our study, extreme UFP and BC values measured on bicycles were more enhanced

367

than those measured at fixed points, which is related to the time spent at each location. At fixed

368

points, the average concentrations were almost always calculated based on 100 minutes of

369

sampling, whereas the averages of road segments sampled while cycling were computed based

370

on a very short duration, and were therefore highly influenced by the surrounding environment

371

(e.g. bus, construction work) when sampling. Comparing more specifically the concentrations on

372

the road segments surrounding the fixed points provides the same conclusion (FIGURES S6 and

373

S7). The mean concentrations are higher on road segments, and the maximum values are also

374

high.

375

Our findings are consistent with the results of Kerckhoffs et al.9 who reported cycling

376

measurements on average 1.7 and 2.1 times higher than stationary measurements for UFP and

377

BC respectively. However, the correlation between stationary and mobile measurements reported

378

by Kerckhoffs et al.9 (0.48) was higher than the one observed in this study. In the study of

379

Kerckhoffs et al.9, short-term stationary monitoring was performed in between mobile

380

measurements. Therefore, the comparison was done between measurements conducted very

381

close in time and in space. In light of the low correlation between stationary and mobile

382

measurements observed in our study, we can conclude that the data collection protocol in itself

383

(i.e. cycling on road vs standing on sidewalk) has a large influence on the data recorded, beyond


Page 23 of 30


384

the averaging time. Few stationary measurements are typically performed in cars on the road side

385

while standing on the curbside is more commonly observed6,11.

386

Few variables are common to the fixed point and cycling models for both pollutants,

387

suggesting that measurements on roads and at fixed points do not capture the same

388

characteristics. The signs associated with some variables are worth noting. Although temperature

389

is generally negatively associated with UFP and BC concentrations14,24, it presents a positive

390

coefficient in all our models. We can explain the positive association between temperature and

391

UFP concentrations by nucleation events occurring when temperature is high, which is supported

392

by the peaks in UFP concentrations generally recorded at the beginning of the afternoon.

393

Distances to the shore and to the CBD appear in the cycling and fixed point UFP models with

394

negative and positive signs respectively. Several highways run along the shore, and two cycling

395

routes were designed in particular to capture this effect. On the other hand, trucks, which were

396

positively associated with UFP levels in the fixed point model, do not usually drive downtown

397

but mainly on highways located north of the city or along the shore and far from downtown,

398

explaining the positive association between UFP levels and distance to the CBD in the fixed

399

point model. Another interesting variable is the water area, which has a positive coefficient in the

400

UFP fixed point model but a negative coefficient in the UFP and BC cycling models, a

401

difference probably due to the distinct buffer sizes included in these models. Finally, in the

402

cycling model, the number of trees is positively associated with UFP concentrations. Several

403

studies have showed that vegetation could be positively or negatively associated with air

404

pollutant concentrations25–27. Indeed, the characteristics of the street (i.e. building height, street

405

width) combined with the type of vegetation (e.g. canopy density, crown diameter, trunk height)



Page 24 of 30

406

can have a significant influence on the pollutants levels, by either increasing the turbulence or

407

decreasing the wind speed.

408

Weichenthal et al.15 and Sabaliauskas et al.12 developed LUR models for UFP in Toronto

409

based on measurements conducted in 2015. Many predictors included in the model from

410

Weichenthal et al.15 were related to small-scale traffic characteristics (i.e. distance to the

411

highway, distance to the major road, distance to the bus line, number of intersections, length of

412

bus line), while distance to the airport was also an important predictor. Both our UFP models,

413

and more specifically the cycling model, also include small-scale traffic variables, and distance

414

to the airport is a predictor in the cycling model. The predictors included in Sabaliauskas et al.12

415

model were related to the land use (e.g. population, industrial and residential areas), also

416

captured in our UFP LUR models via predictors such as open area, population, or water area.

417

All four surfaces are dissimilar, which raises concerns regarding their use for

418

epidemiologic studies. Major roads are highlighted in both cycling surfaces, because air pollutant

419

levels were collected directly on roads and were therefore affected by small-scale traffic

420

variables. In contrast, the fixed point surfaces are capturing hotspots at a larger spatial scale,

421

which is probably related to the nature of the monitoring approach, i.e. stationary locations being

422

more spaced. The models based on short-term stationary and mobile approaches developed by

423

Kerckhoffs et al.9 were similar in terms of predictors, and the concentrations they estimated

424

using their two models at 12,682 residential addresses were also similar. These observations are

425

different from the outcomes of our study, which highlights sensitivity of exposure surfaces to the

426

sampling protocol. In the study of Kerckhoffs et al.9, stationary measurements were carried out

427

in between mobile measurements at the same distance from traffic since a car was used for both

428

protocols. This is not the case for our study, since our study is not only capturing the difference


Page 25 of 30


429

between mobile and stationary measurements but also the location of where mobile and

430

stationary sampling would normally occur: on the road vs. on the sidewalk. Together, these two

431

studies begin to unveil the complexity in the development of LUR surfaces for traffic-related air

432

pollution, highlighting the importance of testing their sensitivity prior to use in epidemiological

433

studies.

434

Our comparison with data from outdoor personal exposures demonstrated that using two

435

different surfaces can lead to different values for daily exposure. Our results suggest that the

436

mobile monitoring campaign tends to overestimate short-term exposures to UFP, while using the

437

fixed point surface tends to underestimate the short-term exposures to both UFP and BC. For

438

BC, the cycling surface was able to capture the distribution of outdoor personal exposures. For

439

UFP, despite over-predicting the mean exposure, the cycling surface was able to better capture

440

the distribution. Mobile sampling enables the coverage of wide areas, and we took advantage of

441

this when designing our campaign as we sampled 270 km of unique roads. However, as the

442

measurements are done on a small time scale, they are more susceptible to be influenced by

443

short-term events. When comparing short-term exposures, capturing these short-term peaks is

444

important. It is worth noting that in our study, personal exposures were collected between 9am

445

and 4pm, which could have an impact on the comparison with exposures estimated based on the

446

surfaces as the monitoring campaigns took place between 7am and 7pm.

447

The development of exposure surfaces using data collected during short-term sampling

448

campaigns have gained momentum in recent years, primarily in light of advances in air pollution

449

monitoring technology. Small portable instruments have enabled the development of data

450

collection protocols that involve mobile sampling by foot, by bike, or by cars fitted with various

451

sensing devices. The development of inexpensive air pollution sensors will further enable these



Page 26 of 30

452

protocols and might even lead to ad-hoc data collection means through participatory air pollution

453

sensing. The question of robustness of LUR surfaces will become of utmost importance since the

454

streams of data collected are expected to become more diverse, including various locations in an

455

urban area: on-road, on the sidewalks, or at residential locations. There is a need to further

456

investigate the effect of the data collection protocol on exposure surfaces and evaluate the

457

sensitivity of LUR models.

458 459

ACKNOWLEDGMENTS

460

The authors thank Ryan Kulka, Xinli Tu, Marie-Sophie Wint, Kuruparam Satkunanathan,

461

Tszchun Chow, Tian Kang, James Mario and Teresa Li for their great help in collecting data.

462

SUPPORTING INFORMATION

463

The SI contains information about 1) the design of the campaigns, 2) the LUR predictors, 3) the

464

correction of the monitors, 4) the data collected, 5) the choice of the models, 6) the comparison

465

of the exposures, 7) the characteristics of various UFP and BC monitoring campaigns and LUR

466

models.

467

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Development and Comparison of Air Pollution Exposure Surfaces

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