Within-City Spatial Variations in Multiple Measures of PM2.5 Oxidative

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Ecotoxicology and Human Environmental Health

Within-City Spatial Variations in Multiple Measures of PM2.5 Oxidative Potential in Toronto, Canada Scott Weichenthal, Maryam Shekarrizfard, Alison Traub, Ryan Kulka, Kenan Al-Rijleh, Sabreena Anowar, Greg J. Evans, and Marianne Hatzopoulou Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05543 • Publication Date (Web): 08 Feb 2019 Downloaded from http://pubs.acs.org on February 10, 2019

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Environmental Science & Technology

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Within-City Spatial Variations in Multiple Measures of PM2.5 Oxidative

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Potential in Toronto, Canada

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Scott Weichenthal*1,2, Maryam Shekarrizfard3, Alison Traub4, Ryan Kulka2, Kenan Al-

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Rijleh3, Sabreena Anowar3, Greg Evans4, Marianne Hatzopoulou3

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1McGill

University, Department of Epidemiology, Biostatistics and Occupational Health,

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Montreal, Quebec, H3A 1A2, Canada

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2 Air

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3University

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Canada

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4University

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Toronto, Ontario, M5S 3E5, Canada

Health Science Division, Health Canada, Ottawa, Ontario, K1A 0K9, Canada of Toronto, Department of Civil Engineering, Toronto, Ontario, M5S 1A4, of Toronto, Department of Chemical Engineering and Applied Chemistry,

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*Corresponding Author

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Scott Weichenthal

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Faculty of Medicine

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Department of Epidemiology, Biostatistics, and Occupational Health

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McGill University

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1020 Pins Ave. West

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Montreal, QC H3A 1A2, Canada

ACS Paragon Plus Environment

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Email: [email protected]

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Tel: (514) 398-1584

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Abstract

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Few studies have characterized within-city spatial variations in the oxidative

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potential of fine particulate air pollution (PM2.5). In this study, we evaluated multiple

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measures of PM2.5 oxidative potential across Toronto, Canada (2016-2017) including

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glutathione/ascorbate-related oxidative potential (OPGSH and OPAA) and dithiothreitol

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depletion (OPDTT). Integrated 2-week samples were collected from 67 sites in summer

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and 42 sites in winter. Multivariable linear models were developed to predict OP based

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on various land use/traffic factors and PM2.5 metals and black carbon were also

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examined. All three measures of PM2.5 oxidative potential varied substantially across


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Toronto. OPAA and OPDTT were primarily associated with traffic-related components of

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PM2.5 (i.e. Fe, Cu, and black carbon) whereas OPGSH was not a strong marker for traffic

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during either season. During summer, multivariable models performed best for OPAA

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(R2CV=0.48) followed by OPDTT (R2CV=0.32) and OPGSH (R2CV=0.22). During winter,

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model performance was best for OPDTT (R2CV=0.55) followed by OPGSH (R2CV=0.50) and

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OPAA (R2CV=0.23). Model parameters varied between seasons and between-season

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differences in PM2.5 mass concentrations were weakly/moderately correlated with

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seasonal differences in OP. Our findings highlight substantial within-city variations in

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PM2.5 oxidative potential. More detailed information is needed on local sources of air

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pollution to improve model performance.

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1.

Introduction


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Outdoor fine particulate air pollution (< 2.5 um, PM2.5) is an important contributor

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to the overall global burden of disease and is responsible for millions of deaths around

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the world each year.1 Traditionally, PM2.5 exposures have been measured as particle

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mass concentrations (i.e. ug/m3); however, this metric is limited in that it treats all

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particles as equally toxic and does not consider spatial or temporal differences in

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particle composition or “biological activity”. As oxidative stress and the production of

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reactive oxygen species are thought to play an important role in explaining air pollution

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health effects,2-5 more recent studies have also examined particle oxidative potential

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(OP) as a complementary measure to traditional mass-based exposure measurements.

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Numerous assays are available to estimate particle oxidative potential which

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provides an integrated measure of the capacity of PM to oxidize target molecules.3,6 In

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general, acellular assays are most common and include those based on dithiothreitol

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consumption (OPDTT), electron spin resonance (OPESR), or the depletion of the

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antioxidants ascorbate (OPAA) and glutathione (OPGSH) in a synthetic respiratory tract

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lining fluid.7-14 A recent study examined temporal variations in particle OP over a 1-year


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period in France and noted higher OP levels during the winter months for the OPDTT,

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OPAA, and OPGSH assays with little seasonal difference reported for OPESR.15 Moreover,

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OPESR was predominantly associated with transition metal components whereas the

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other assays were correlated with both organic and inorganic species reflecting sources

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including biomass burning, brake/tire wear, and traffic/fossil fuel combustion.15 Likewise,

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other studies of particle oxidative potential and chemical constituents suggest that

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organic and inorganic particle components both contribute to the oxidative potential of

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airborne particles. 9-10,12,16-17

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In addition to temporal variations, spatial variations in particle oxidative potential

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have also been reported.7, 12,14,17-20 In Canada, between-city differences in OPGSH have

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been shown to modify associations between long-term PM2.5 exposures and lung cancer

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mortality21 as well as short-term associations between PM2.5 and emergency room visits

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for myocardial infarction22 and respiratory outcomes.14 To date, land use regression

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models for within-city spatial contrasts in particle oxidative potential have been limited to

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European cities. In particular, these studies suggest that traffic proximity/road length,

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harbor proximity, population density, green space, emissions from brake and tire wear,


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and industrial land use may be important predictors of within-city spatial contrasts in

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particle oxidative potential.12,17-20

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In this study, we examined within-city spatial variations in three measures of

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PM2.5 oxidative potential in Toronto, Canada including OPAA, OPGSH, and OPDTT. We

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also collected detailed information on PM2.5 metals and black carbon content. To our

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knowledge, this is the first study to attempt to model within-city spatial variations in

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PM2.5 oxidative potential in North America.

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2.0

Methods

2.1

Spatial Monitoring Study

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The details of our spatial monitoring campaign have been described previously.23

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Briefly, two PM2.5 monitoring campaigns were conducted in Toronto, Canada during

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summer 2016 and winter 2017. In total, 67 were monitored in summer and 42 sites were

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monitored in winter; 29 sites were monitored during both seasons. Daily mean

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temperatures in summer ranged from 18.3oC to 27.2oC whereas winter temperatures

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ranged from -8.8oC to 7.5oC. Total precipitation during the summer (38.2 mm) and

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winter (39.7 mm) sampling periods were similar.

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Integrated 2-week samples were collected at each site using Teflon filters with

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cascade impactors at a flow-rate of 5 liters/minute. During each season, all PM2.5

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samples were collected simultaneously using pre-set pump timers and separate

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sampling kits at each monitoring site. This eliminated the need to adjust for temporal

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differences in PM2.5 mass/components between sites as all sites were monitored over

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exactly the same time-period. PM2.5 mass concentrations were determined

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gravimetrically prior to metals analysis by X-ray fluorescence (XRF) according to EPA

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Method IO-3.3 in Compendium of Methods for the Determination of Metals in Ambient

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Particulate Matter (EPA 625/R-96/010a). All PM2.5 samples were also analyzed using a


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SootScan Model OT21 Transmissometer at 880 nm (BC880nm) and 370 nm (BC370nm).

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Measurements at 880 nm are interpreted as black carbon whereas measurements at

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370 nm respond more strongly to biomass PM from sources such as

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residential/commercial wood burning. These two measures were highly correlated in our

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data (r > 0.93) and thus we simply refer to black carbon.

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2.2

PM2.5 Oxidative Potential Analyses PM2.5 was extracted from filters into HPLC grade methanol (Caledon) through

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vortexing (1,800 rpm) followed by sonication, for 10 minutes each, and with rinse with

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1.0 mL of methanol. The extraction efficiency, which was typically above 75%, was

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determined from the pre and post extraction filter weight. The methanol was evaporated

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at 37oC using a 34-position nitrogen evaporator (N-EVAP, Organomation). PM2.5 was

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then resuspended in ultrapure water (Caledon) containing 5% HPLC methanol to a

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concentration of 75 μg/mL. The re-suspended PM2.5 samples were analyzed in triplicate

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using three acellular in vitro assays: the ascorbate (AA) assay, the glutathione (GSH)


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assay, and the dithiothreitol (DTT) assay. Ascorbate and glutathione oxidative potential

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(OPAA and OPGSH) were assessed using a methodology similar to that described

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previously.14 Briefly, PM2.5 samples were incubated at a concentration of 75 μg/mL for 4

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hours at 37oC with synthetic respiratory tract lining fluid (RTLF) containing equimolar

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concentrations of AA, GSH, and uric acid in an ultraviolet-visible plate reader (Molecular

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Devices, Spectra Max 190) alongside positive controls (1.0 μM Cu(NO3)2, 0.02% H2O2)

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and blanks. Ascorbate depletion was calculated as the percent difference between the

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initial and final absorbance values measured at 260 nm and blank-corrected. After the

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4-hour incubation period, GSH depletion was measured using the glutathione-reductase

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enzyme recycling assay.24 Percent depletion was calculated by comparing GSH

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concentrations measured in samples and positive controls to those measured in blanks.

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Depletion was typically under 40% so a linear instead of first order measure of depletion

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was adequate.

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Dithiothreitol oxidative potential (OPDTT) was assessed using a methodology

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similar to that described in Cho et al.25 Briefly, re-suspended PM2.5 samples were

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incubated with 100 μM dithiothreitol (DTT) in a 96 well plate alongside positive controls


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(5.0 μM Cu(NO3)2), blanks, and DTT standards (containing 0 – 100μM DTT) for 35

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minutes at 37oC, with constant shaking. After 5, 15, 25, and 35 minutes, remaining DTT

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was measured by adding 1.0 mM 5,5’-dithiobis(2-nitrobenzoic acid) (DTNB) to each well

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and measuring absorbance at 412 nm. Samples were initially analyzed at a

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concentration of 75 μg/mL; however, if DTT depletion exceeded 25% after 35 minutes,

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the sample was re-analyzed at a lower concentration (37.5 μM or 50 μM) as depletion

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rates can become non-linear beyond 25% depletion.

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OPAA, OPGSH, and OPDTT were first normalized by PM2.5 mass based on the

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quantity used in each assay (i.e. % depletion/µg for OPAA and OPGSH and pmol/min/µg

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for OPDTT); these values were then converted to units of % depletion/m3 (for OPAA and

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OPGSH) or pmol/min/m3 (for OPDTT) based on PM2.5 mass concentrations at each

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location in units of µg/m3. To facilitate comparisons with other studies, descriptive data

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for OPAA and OPGSH are also presented in units of pmol/min/m3 by multiplying

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%Depletion/m3 values by 9.3 (i.e. 1% depletion/m3 = 9.3 pmol/min/m3 based on the 4-

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hour incubation period, 200 µM of AA (or GSH) initially present, and the 0.18 ml of PM

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solution added to the 0.2 ml well). Negative values were observed for OPGSH in 13


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summer samples and 5 winter samples: these values were replaced with half the

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analytical detection limit (0.077%/g or 0.72 pmol/min/ug).

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2.3

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Parameters for land use regression models Various land-use and built environment characteristics were derived for each

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monitoring site using ArcMap 10.4.1 (ESRI, Redlands, CA). Land use composition was

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computed within circular buffers of 100m, 200m, 300m, 500m, 700m and 1000m, and

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included specific categories for residential, commercial, governmental/institutional,

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resource/industrial, parks, open area, water, as well as building footprints (DMTI Spatial

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Inc. Database 2014). Moreover, the length of bus routes, highways, major roads, and

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rail lines were derived in each buffer (City of Toronto Open Data portal 2016). Other

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variables computed included population density (Toronto Neighbourhood and

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Demographics data of 2013), mean and maximum building height, number of bus stops,

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number of road intersections, and number of trees (City of Toronto Open Data portal

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2016). Average hourly traffic volume between 6am and 7pm was estimated using a

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traffic assignment model and an origin-destination (OD) matrix of trips extracted from

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the Transportation Tomorrow Survey (TTS) (year 2011) for the Greater Toronto and


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Hamilton Area (GTHA). Distances between each sampling location and the closest rail

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line, major road, highway, Pearson airport, lakeshore, and city center were also

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estimated. Industrial emissions of CO, NOx, total VOCs, total metals, and PM were

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evaluated as surrogate measures of local industrial combustion activities. Emissions

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values were assigned to each location using the nearest facility with emissions

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(tonnes/year) weighted by 1/distance to the closest facility. Emissions data were

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obtained from the National Pollutant Release Inventory. Finally, the locations of

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restaurants and gas stations in Toronto were extracted using the Google Places

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function in R and counts of these were assigned to each monitoring site with the circular

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buffers described above.

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2.4

Statistical Analyses

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Models were developed to predict within-city spatial variations in OPAA (%

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depletion/m3), OPGSH (% depletion/m3), and OPDTT (pmol/min/m3); separate models were

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developed for the summer and winter seasons. Annual models are not presented as

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only 29 sites were monitored during both the summer and winter campaigns.


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Alternatively, data collected at sites during both the summer and winter months were

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used to evaluate between-season differences in OP across Toronto.

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Our model building procedure followed several steps: 1) First, we examined

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single variable linear regression models and associated scatter plots with linear and

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non-linear lines of fit to identify potential non-linear relationships (quadratic terms were

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evaluated for non-linear relationships); 2) Variables that were associated with the

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outcome (i.e. 95% CI excluded the null) were retained for potential inclusion in the final

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model; 3) Spearman’s correlations were determined for the candidate predictors

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identified in step 2 and highly correlated variables (r > 0.7) were removed (retaining the

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best predictor of each correlated pair); 4) All parameters retained after step 3 were

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eligible for inclusion in the final model; 5) “Non-significant” parameters were only

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removed from multivariable models if doing so improved model fit (i.e. decreased the

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root mean square error); 6) Finally, interaction terms were evaluated between model

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covariates and were retained if doing so improved model fit. As the primary purpose of

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statistical modeling was prediction, we decided to include interaction terms to account

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for the fact that a given land use parameter may be more or less strongly associated


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with PM2.5 oxidative potential given the presence of a second land use characteristic.

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For example, road length and industrial land use may each be related to PM2.5 oxidative

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potential but road length may be more strongly associated with OP in areas with higher

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industrial land use because of the types of vehicles that tend to travel in industrial areas

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(this would be indicated by a positive coefficient for the interaction term between these

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variables). A leave-one-out cross validation procedure was used to evaluate final model

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performance using the loocv procedure in Stata version 15 (Statacorp, College Station,

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Texas, USA). As the primary purpose of modelling was for prediction, we did not impose

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pre-conceived rules on the direction of effects for parameters included in the model. All

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OPAA, OPGSH, and OPDTT values were log transformed to normalize distributions. All

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covariates were standardized (by subtracting the mean and dividing by 2 standard

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deviations) prior to model development.

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2.5

Mapping the Land Use Regression Models

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Maps of exposure surfaces were generated by first dividing the city of Toronto

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into grid cells of 100m by 100m using ArcMap (ArcMap 10.4.1; ESRI, Redlands, CA).


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Next, the final set of predictors for land use regression models were computed for the

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mid-point of each grid cell. Finally, the predicted values for each mid-point were

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calculated and associated with the corresponding grid cell for mapping.

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3.

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Results In total, PM2.5 samples were collected from 67 sites in summer and 42 sites in

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winter; 29 sites were monitored during both seasons. Descriptive data for PM2.5

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oxidative potential and black carbon are shown in Table 1 and correlations between

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oxidative potential metrics, PM2.5 metals, and black carbon are shown in Figure 1. In

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general, correlations between OPAA, OPGSH, and OPDTT were low to moderate during

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summer (0.27 < r < 0.77) and winter (0.34 < r < 0.58). During summer, OPAA and OPDTT

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were moderately correlated with traffic-related pollutants including black carbon (OPAA

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(r=0.63); OPDTT (r=0.51)) and PM2.5 metals including Fe (OPAA (r=0.75); OPDTT (r=0.62)),

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Cu (OPAA (r=0.66); OPDTT (r=0.63)), and Ba (OPAA (r=0.64); OPDTT (r=0.65)) which are

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markers of non-tailpipe emissions such as brake/tire wear. OPGSH was weakly

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correlated with these traffic-related components (0.12 < r < 0.24) during summer and

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was moderately correlated with Mn and Zn; however, correlations for Mn and Zn were


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influenced by a single outlying value and these correlations decreased (r < 0.2) when

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this point was removed. Correlations between OPDTT and traffic related components (i.e.

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Fe, Cu, Ba, and BC) decreased during winter (0.18 < r < 0.26) but OPAA remained

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moderately correlated with these components (0.39 < r < 0.51). OPGSH was weakly

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correlated with traffic-related components during winter (0.21 < r < 0.30).

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On average, between-season differences in PM2.5 oxidative potential were small

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in magnitude for sites monitored during both the summer and winter campaigns (Figure

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2). Specifically, OPDTT (mean difference: 10.5 pmol/min/m3, 95% CI: 3.70, 17.3) and

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OPAA (mean difference: 0.227 % Depletion/m3, 95% CI: -0.0728, 0.526) tended to be

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greater during summer whereas little difference was observed for OPGSH (mean

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difference: 0.0737 % Depletion/m3, 95% CI: -0.765, 0.912). However, overall averages

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masked larger between-season differences for individual sites as illustrated in Figure 3.

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In particular, the magnitudes of between-season differences at individual sites were

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greater for OPGSH than for OPAA (panels A and B) and only 4 sites monitored during both

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seasons had higher OPDTT during winter (panel C). Moreover, patterns of seasonal

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differences for OP were often different than the pattern for PM2.5 mass concentrations;


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this was particularly true for OPGSH (Figure 3). On average, PM2.5 concentrations were

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0.992 g/m3 (95% CI: 0.627, 1.36) higher during summer, with only two sites having

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higher mean concentrations winter (Figure 3). In simple linear models, between-season

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differences in PM2.5 mass concentrations were not correlated with seasonal changes in

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OPGSH (R2=0.03) or OPDTT (R2=0.07) but were moderately correlated with between-

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season differences in OPAA (R2=0.37). Of the specific elements examined, between-

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season differences in Se (a marker for fossil fuel combustion)26 explained the largest

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proportion of between-season variability in OPAA (R2=0.26) whereas between-season

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differences in traffic-related components including Fe, Cu, Ba, and black carbon

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explained less of this variation (0.10 < R2 < 0.17). Seasonal differences in vanadium

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(R2=0.13) and black carbon (R2=0.14) explained a small proportion of between-season

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variability in OPGSH (R2=0.13) whereas, Cu, and Ba explained a similar proportion of

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between-season variations in OPDTT (0.10 < R2 < 0.12).

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To explore between-season differences in OP further (i.e. summer – winter), we

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examined multivariable linear regression models for relationships between the various

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land use/traffic parameters described above and seasonal differences in OP at each


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site (Supplemental Table S1). For OPGSH, bus lines and trees had positive coefficients

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in these models whereas coefficients for higher traffic counts were negative. For OPAA,

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park space had a positive coefficient whereas coefficients for rail and street cars had

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negative coefficients in models for seasonal differences. Finally, models for seasonal

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differences in OPDTT had positive coefficients for traffic counts and open spaces and a

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negative coefficient for rail.

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OPGSH, OPAA, and OPDTT each varied substantially between monitoring sites

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during both the summer and winter campaigns as illustrated in Figure 4. During

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summer, multivariable linear models performed best for OPAA followed by OPDTT and

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OPGSH (Figure 5). Models for OPAA and OPDTT during summer included parameters for

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traffic, commercial/industrial/residential land use, and industrial emissions. Specifically,

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length of highways (700m) (R2=0.10), major roads (300m) (R2=0.063), CO emissions

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(R2=0.084), and government land use (1000m) (R2=0.067) were the strongest predictors

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of summer OPAA whereas traffic (200m) (R2=0.18), CO emissions (R2=0.12), park space

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(500m) (R2=0.090), residential land use (1000m) (R2=0.082), and industrial land use

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(1000m) (R2=0.05) were the strongest predictors for OPDTT. Few important predictors


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were identified for OPGSH during summer (i.e. CO emissions (R2=0.17), distance to

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highways (R2=0.05), and restaurants within 1000m (R2=0.09) and this model performed

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poorly.

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During winter the multivariable model for OPGSH explained the majority of the

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spatial variability for this parameter across Toronto and included terms for length of

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major roads, park space, open space, and interactions between these variables (Figure

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6). Of these, the inverse association with park space (500m) (R2=0.18) explained the

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largest proportion of spatial variability in OPGSH during winter followed by major roads

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(100m) (R2=0.11) and open space (200m) (R2=0.090). The winter model for OPDTT also

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explained the majority of spatial variability for this parameter and included terms for

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distance to shore, open space, road junctions, industrial emissions for metals and CO,

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and interaction terms between these factors. The strongest predictor of spatial

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variations in OPDTT during winter was open space (300m) (R2=0.18) followed by

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industrial emissions for metals (R2=0.14), CO emissions (R2=0.11), distance to shore

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(R2=0.11), and road junctions (100m) (R2=0.041). The winter OPAA model included

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terms for traffic counts, distance to the airport, commercial land use, distance to rail,


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buildings, and interaction terms between distance to rail, buildings, and commercial land

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use but explained little spatial variability in this parameter. The strongest predictors of

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spatial variations in OPAA during winter was distance to the airport (R2=0.17), followed

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by commercial land use (1000m) (R2=0.14), traffic (500m) (R2=0.12), length of rail

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(500m) (R2=0.12), and buildings (500m) (R2=0.040). Predicted spatial variations in

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summer OPAA and OPDTT across Toronto are shown in Figures 7. These surfaces

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highlight potential hot-spots for PM2.5 oxidative potential across Toronto, particularly the

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very high traffic region around the airport.

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Finally, one additional set of analyses were conducted for OPAA (summer and

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winter) and OPDTT (summer) to examine predictors of within-city spatial variations not

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related to transition metals from brake/tire wear. Specifically, we extracted residuals

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from linear regression models between OPAA/OPDTT and Fe (a marker for brake/tire

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wear and highly correlated (r > 0.86) with both Cu and Ba) and evaluated predictors of

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spatial variations in these residuals. The number of trees within 100m (= 0.140, 95%

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CI: 0.0603, 0.220; R2=0.16) and distance to highways (= -0.0841, 95% CI: -0.162, -

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0.00577, 0.220; R2=0.068) were the only variables associated with spatial variations in


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residuals for OPAA during summer whereas park space within 500m was associated

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with spatial variations in residuals for OPAA during winter (= -0.276, 95% CI: -0.433, -

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0.120; R2=0.25). Park space within 500m was the only predictor of spatial variation in

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residuals for OPDTT during summer (= 0.118, 95% CI: 0.00786, 0.229; R2=0.068).

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4.

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Discussion We conducted large-scale monitoring campaigns for multiple measures of PM2.5

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oxidative potential during the summer and winter months in Toronto, Canada and noted

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several interesting findings. First, OPAA and OPDTT were each correlated with markers of

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traffic-related air pollution during summer whereas weaker correlations were observed

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during winter, particularly for OPDTT. OPGSH was not a strong marker of traffic-related air

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pollution in either season. Interestingly, the summer and winter models for all three OP

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measures generally contained different predictors. Specifically, no parameters

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overlapped between seasons for OPGSH, one parameter overlapped for OPDTT (CO

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emissions), and three parameters overlapped for OPAA (commercial land use, traffic

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counts, distance to rail). As land use/traffic data are only surrogate measures of the

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underlying sources/components of interest, it appears that the extent to which these


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parameters reflect sources/components related to PM2.5 oxidative potential may differ

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between seasons.

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In general, the predictive performance of multivariable models for OPAA, OPDTT,

365

and OPGSH was modest and model performance varied between seasons. Indeed, it

366

seems that traditional GIS parameters may not perform as well in predicting spatial

367

variations in PM2.5 oxidative potential as they do for other air pollutants such as NO2.

368

Interestingly, when we examined predictors of spatial variations in OPAA and OPDTT not

369

attributable to transition metals associated with brake/tire wear (i.e. the residual models

370

explained above) few important predictors were identified. This suggests that many of

371

the land use/traffic/built environment parameters included in final models for OPAA and

372

OPDTT were capturing spatial variations in transition metals associated with brake/tire

373

wear. For OPAA, the inverse association with distance to highways in the residual model

374

suggests that other factors (e.g. organic compounds) emitted near highways also

375

contribute to particle oxidative potential independent of transition metals. Moreover, the

376

positive association between OPAA and the number of trees within 100m in the summer

377

residual model is also interesting. One explanation is that trees are a marker for some


22

Page 23 of 50


378

unmeasured source of PM2.5 with elevated oxidative potential; however, the fact that we

379

did not observe this relationship in the winter OPAA residual model argues against this

380

hypothesis. A second possibility is that biogenic volatile organic compounds released by

381

trees during the summer months undergo photochemical reactions that produce

382

localized increases in secondary organic aerosols that contribute to particle oxidative

383

potential.27 While we cannot reliably confirm or refute either of these hypotheses in the

384

present study, future studies should explore this possibility further. More generally,

385

additional effort is needed to identify and geocode local sources of air pollution

386

(primarily non-traffic sources) that may contribute to PM2.5 oxidative potential,

387

particularly for organic compounds as these also play an important role in predicting OP

388

levels.15 Specifically, detailed inventories of air pollution sources related to biomass

389

burning are not currently available and these sources are particularly relevant as they

390

tend to be local in nature and vary seasonally (i.e. residential heating). Moreover,

391

existing evidence suggests that biomass particles contribute to particle oxidative

392

potential and that OPAA depletion by wood smoke particles is not inhibited by co-

393

incubation with a metal chelator.31,32 Therefore, biomass burning may play an important


23


394

role in determining within-city spatial variations in PM2.5 oxidative potential and should

395

be explored more thoroughly in future investigations.

Page 24 of 50

396

To date, only a small number of studies have examined within-city spatial

397

variations in OPGSH, OPAA, and OPDTT and none of these studies have been conducted

398

in North America. Yanosky et al.18 examined spatial variations in OPGSH using data from

399

34 PM10 monitoring sites across London, UK and reported a positive association with

400

brake/tire wear emissions and an inverse association with NOx emissions from heavy-

401

goods vehicles. Gulliver et al.20 developed land use regression models for OPAA and

402

OPGSH in five European cities and also measured PM2.5 metals and black carbon (PM2.5

403

absorbance). As in this study, they reported stronger correlations between OPAA and

404

traffic-related components (i.e. Fe, Cu, and PM2.5 absorbance) than for OPGSH. Cross-

405

validation R2 values ranged from 0.31-0.82 for OPAA and 0.05-0.38 for OPGSH with all

406

models containing a small number of predictors related to traffic, green/natural space,

407

and industrial land use. Other studies of within-city spatial variations in OPAA and OPGSH

408

were not identified but three additional studies have examined spatial variations in

409

OPDTT. Specifically, Yang et al.12 reported moderate correlations between OPDTT and


24

Page 25 of 50


410

traffic-related components of PM2.5 (i.e. Fe, Cu, and PM2.5 absorbance) across 40 sites

411

in the Netherlands/Belgium and a model including terms for traffic, natural space, and

412

regional OPDTT explained the majority of spatial variations across the study area

413

(R2=0.60). Jedynska et al.19 also reported land use regression models for OPDTT across

414

ten European cities but model performance was poor (median R2=0.33) and the authors

415

reported a high number of samples below the limit of detection for OPDTT. Recently

416

,Bates et al.28 described large-scale variations in OPDTT in the United States and found

417

that emissions from vehicles and biomass burning were important predictors of OPDTT.

418

Our findings related to traffic components of PM2.5 and OPDTT in Toronto are consistent

419

with these results. In an earlier study, these authors also reported higher OPDTT in

420

Atlanta during winter32 whereas we generally observed higher values during summer.

421

Reasons for this difference are not entirely clear but could be explained in part by lower

422

rates of particle resuspension in Toronto during the winter months owing to snow.

423

Alternatively, seasonal results between the two studies may not be directly comparable

424

given the much colder winter temperatures in Toronto and different sampling media

425

used between the two studies. Nevertheless, we did note a small number of sites in


25


426

Toronto with higher OPDTT during winter and thus local sources may play an important

427

role in determining seasonal patterns. As only five sites were monitored in the Atlanta

428

study,32 it is not clear if this seasonal pattern holds across Atlanta area or is limited to

429

the specific monitoring locations.

430

Page 26 of 50

Collectively, existing studies of spatial variations in OPAA, OPGSH, and OPDTT

431

highlight the difficulty of modeling these parameters and provide little consistent

432

evidence related to specific sources of interest beyond fossil fuel/biomass combustion

433

and transition metals associated with brake and tire wear. As noted here and

434

elsewhere20, future studies need to consider sources not currently captured in traditional

435

GIS databases if we are to improve these models for use in population-based studies.

436

This may also help to explain seasonal differences in OP on a local scale as our

437

observation of different model parameters between the summer and winter models

438

suggests that current land use parameters are imperfect measures of local sources that

439

ultimately predict OP. While the models presented above may be useful in future

440

epidemiological studies, a more conservative approach would be to simply use the

441

measurement data collected at each location and apply a buffer around each site to


26

Page 27 of 50


442

define the study population (e.g. 1km). Indeed, as none of the OP parameters examined

443

had strong models for both seasons, their application in population-based studies may

444

underestimate the true association between particle oxidative potential and chronic

445

health outcomes owing to imprecision in model estimates.

446

This study had a number of important advantages including simultaneous

447

monitoring at all monitoring sites, detailed information on multiple measures of PM2.5

448

oxidative potential, and information on particle components including PM2.5-metals and

449

black carbon. However, it is important to note several limitations. First, only 29 sites had

450

information for both the summer and winter campaigns and thus we could not develop

451

models for spatial variations in annual average PM2.5 oxidative potential across Toronto.

452

It is possible that annual average models may perform better than season-specific

453

models and we will explore this possibility in future studies. As noted above, we

454

observed substantial heterogeneity in model performance between seasons and

455

different predictors were often selected for the summer and winter models for all three

456

measures of oxidative potential. This suggests that the current land use/traffic

457

parameters provide imperfect estimates of spatial variations in air pollution sources


27


Page 28 of 50

458

related to OP and further work is needed to document and geocode local sources of air

459

pollution that may contribute to within-city spatial variations in particle oxidative

460

potential.

461

In summary, we conducted a large-scale spatial monitoring study in Toronto,

462

Canada to evaluate within-city variations in multiple measures of PM2.5 oxidative

463

potential. Our findings suggest that PM2.5 oxidative potential varies substantially within

464

cities and that systematic seasonal differences may exist for PM2.5 oxidative potential on

465

a local scale. Moreover, our results highlight the fact that seasonal trends in PM2.5 mass

466

concentrations may not correspond to seasonal changes in particle oxidative potential.

467

Additional work is needed to identify the particle components/sources that explain this

468

discrepancy as well as the geographic parameters than can be used to predict

469

exposures for large population-based studies.

470

Supporting Information

471

Multivariable linear models for between-season differences in OPGSH (%Depletion/m3),

472

OPAA (%Depletion/m3), and OPDTT (pmol/min/m3) in Toronto, Canada (Table S1);

473

Multivariable models for OPAA (%Depletion/m3) in Toronto, Canada (Table S2);


28

Page 29 of 50


474

Multivariable linear models for spatial variations in OPGSH (%Depletion/m3) in Toronto,

475

Canada (Table S3); Multivariable linear models for spatial variations in OPDTT

476

(pmol/min/m3) in Toronto, Canada (Table S4).

477 478 479 480 481 482 483 484 485 486 487 488 489


29


490 491

Page 30 of 50

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38

Page 39 of 50


Table 1. Descriptive Statistics for PM2.5, black carbon, and PM2.5 oxidative potential in Toronto, Canada Pollutant

n

Mean

SD

Min

5th

25th

50th

75th

95th

Max

PM2.5 (g/m3)

67

6.41

0.78

4.85

5.16

5.90

6.32

6.84

7.82

8.53

OPAA (%Depletion/m3)

66

2.00

0.54

1.03

1.30

1.71

1.96

2.10

2.83

4.72

OPAA (pmol/min/m3)

66

18.6

5.02

9.58

12.1

15.9

18.2

19.5

26.3

43.9

OPGSH (%Depletion/m3)

66

2.29

2.12

0.0840

0.441

0.698

1.74

3.08

5.99

13.8

OPGSH (pmol/min/m3)

66

21.3

19.7

0.781

4.10

6.49

16.2

28.6

55.7

128

OPDTT (pmol/min/m3)

66

36.7

11.9

20.5

23.4

29.5

35.0

40.1

62.3

88.9

BC880nm (ng/m3)

67

1965

534

653

1194

1652

1933

2168

2753

4187

BC370nm (ng/m3)

67

1079

306

289

585

918

1060

1223

1545

2227

PM2.5 (g/m3)

42

5.32

0.88

4.01

4.34

4.80

5.13

5.86

6.38

8.80


40

1.71

0.532

0.844

0.972

1.34

1.60

1.99

2.70

3.26

OPAA (pmol/min/m3)

40

15.9

4.95

7.85

9.04

12.5

14.9

18.5

25.1

30.3


40

2.66

2.46

0.336

0.376

0.759

2.14

3.58

8.01

11.7

OPGSH (pmol/min/m3)

40

24.7

22.9

3.12

3.50

7.06

19.9

33.3

74.5

109

OPDTT (pmol/min/m3)

40

27.2

13.2

7.59

10.0

18.8

26.0

31.7

51.5

80.8

BC880nm (ng/m3)

42

1197

319

519

766

1069

1157

1290

1593

2638

BC370nm (ng/m3)

42

1024

218

477

709

882

1035

1105

1339

1778

29

5.77

0.76

4.73

4.73

5.33

5.65

6.20

7.49

8.06

Summer

Winter

Annual* PM2.5 (g/m3)


39


Page 40 of 50


26

1.81

0.459

1.32

1.33

1.56

1.75

1.93

2.40

3.50

OPAA (pmol/min/m3)

26

16.8

4.27

12.3

12.4

14.5

16.3

17.9

22.3

32.5


26

2.08

1.03

0.497

0.733

1.30

1.87

2.54

4.08

4.23

OPGSH (pmol/min/m3)

26

19.3

9.58

4.62

6.82

12.1

17.4

23.6

37.9

39.3

OPDTT (pmol/min/m3)

26

30.6

10.7

16.9

17.7

25.7

28.5

34.1

46.3

70.1

BC880nm (ng/m3)

29

1497

450

940

961

1233

1470

1609

1919

3412

BC370nm (ng/m3)

29

1000

247

695

700

837

993

1081

1250

2002

*Annual data reflects those sites with both summer and winter data


40

Page 41 of 50


Figure 1. Spatial correlations between PM2.5 oxidative potential (OPGSH, OPAA, OPDTT), PM2.5 metals, BC880nm, and BC370nm during summer (A) and winter (B) A

B


41

Page 42 of 50

As Rb Pb Zn Sb Cr Co Ni Cs Zr Fe Ba Cu Cl Na Mn Sr Ti Br BC.880 BC.370 K PM2.5 S P Mg Ca Al Si Se V OP.DTT OP.GSH OP.AA


As Rb Pb Zn Sb Cr Co Ni Cs Zr Fe Ba Cu Cl Na Mn Sr Ti Br BC.880 BC.370 K PM2.5 S P Mg Ca Al Si Se V OP.DTT OP.GSH OP.AA

0.09 −0.11 0.17 −0.28 0.21

1 0.09

1

0.1

0.15

0.1

−0.12 −0.12 −0.15 −0.19 −0.16 −0.07 0.15 −0.08 −0.14 −0.17 −0.27 −0.19 −0.15 0.17 −0.29 −0.35 −0.16 −0.22 −0.13 −0.15 −0.11 −0.21 −0.28 −0.09 −0.14 −0.19

0.56

0.04 −0.03 0.15

0.01

0.08

0.1

0.16

0.21

0.1

0.09

0.37

0.19

0.18

0.17

0.11

0.2

0.36

0.19

0.25

0.26 −0.01 −0.21 −0.2 −0.13 −0.02

0.69

0.05

0.05

0.14

0.21

0.4

0.19

0.1

0.08

0.13

0.08

0.07

0.01

0.03 −0.12 0.05

0.15

0.08

0.1

0.03

−0.11

0.1

1

0.7

0.17

0.28

0.7

1

0

0

1

−0.28 −0.19 −0.11

−0.11 −0.01 0.33

0.15 −0.01 0.04 −0.22

0.21

0.04

0.4

0.02

0.07

0.16

0.11

0.07 −0.07 −0.01 −0.04

−0.22 −0.01 −0.15 0.02 −0.08 −0.11 −0.04 −0.06 0.19 1

0

0

0.07

−0.1 −0.17 −0.32 −0.18 −0.1

0.33

0.63

0.61

0.6

0.64

0.62

0.42

0.58

0.51

0.55

0.55

0.05

0.43

0.34

0.38

0.2

0.31

0.27

0.39

0.36

0.31

0.27

0.15

0.08

0.08

0.33

0.25 0.32

1

0.69

0.64

0.55

0.73

0.69

0.72

0.62

0.61

0.69

0.68

0.68

0.32

0.63

0.54

0.24

0.39

0.33

0.22

0.6

0.61

0.61

0.53

0.06

0.13 −0.04 0.18

0.69

1

0.43

0.48

0.61

0.49

0.57

0.54

0.64

0.62

0.57

0.57

0.38

0.55

0.46

0.3

0.29

0.29

0.35

0.53

0.53

0.56

0.5

0.01

0.15 −0.09 −0.04 0.12

0.05

0.63

0.64

0.43

1

0.85

0.83

0.92

0.89

0.75

0.7

0.71

0.79

0.88

0.39

0.73

0.61

0.22

0.43

0.38

0.32

0.55

0.52

0.45

0.4

0.14

0.08

0.06 −0.12 −0.03 0.02 −0.08 0.61

0.55

0.48

0.85

1

0.91

0.96

0.93

0.69

0.64

0.78

0.86

0.89

0.28

0.75

0.59

0.23

0.4

0.36

0.4

0.6

0.54

0.5

0.46

0.25

0.07 −0.15 0.15

0.16 −0.11

0.6

0.73

0.61

0.83

0.91

1

0.95

0.97

0.83

0.74

0.93

0.94

0.95

0.44

0.84

0.68

0.4

0.55

0.48

0.5

0.81

0.76

0.75

0.7

0.2

0.11 −0.19 0.01

0.05 −0.04 0.64

0.69

0.49

0.92

0.96

0.95

1

0.98

0.77

0.7

0.83

0.89

0.93

0.35

0.8

0.64

0.27

0.48

0.43

0.4

0.66

0.62

0.57

0.52

0.1

0.14

0.56

0.15 −0.12 0.04

0.4

−0.01 0.49

0.02

0.15

0.36

0.55

0.28

0.3

0.27

0.46

0.36

0.21

0.22

0.5

0.23

0.29

0.24

0.3

0.51 0.49

0.07 −0.16 0.08

0.14 −0.06 0.62

0.72

0.57

0.89

0.93

0.97

0.98

1

0.82

0.76

0.87

0.92

0.96

0.34

0.81

0.66

0.29

0.46

0.4

0.42

0.7

0.67

0.65

0.58

0.16

0.29

0.18

0.21

−0.07 −0.07

0.1

0.21

0.19

0.42

0.62

0.54

0.75

0.69

0.83

0.77

0.82

1

0.89

0.8

0.83

0.84

0.37

0.76

0.63

0.41

0.49

0.28

0.5

0.66

0.78

0.74

0.66

0.02

0.25

0.07

0.08

0.4

−0.01 0.15

0.16

0.4

0.12

0.58

0.61

0.64

0.7

0.64

0.74

0.7

0.76

0.89

1

0.74

0.73

0.74

0.25

0.63

0.53

0.35

0.31

0.13

0.43

0.48

0.62

0.57

0.47

0.04

0.13 −0.06 0.01

0.24

0.19 −0.11 0.51

0.69

0.62

0.71

0.78

0.93

0.83

0.87

0.8

0.74

1

0.89

0.89

0.47

0.81

0.66

0.5

0.63

0.46

0.58

0.83

0.84

0.82

0.8

0.21

0.37

0.17

0.16

0.5

0.1

−0.04 −0.08 0.21

0.55

0.68

0.57

0.79

0.86

0.94

0.89

0.92

0.83

0.73

0.89

1

0.94

0.44

0.86

0.73

0.42

0.51

0.45

0.57

0.8

0.78

0.77

0.72

0.19

0.31

0.27

0.24

0.52

0.01 −0.17 0.09

0.08 −0.04 0.55

0.68

0.57

0.88

0.89

0.95

0.93

0.96

0.84

0.74

0.89

0.94

1

0.37

0.84

0.7

0.38

0.53

0.44

0.5

0.74

0.78

0.72

0.69

0.1

0.26

0.22

0.21

0.55

−0.07 −0.27 0.37

0.13

0.05

0.32

0.38

0.39

0.28

0.44

0.35

0.34

0.37

0.25

0.47

0.44

0.37

1

0.57

0.55

0.31

0.4

0.53

0.41

0.53

0.37

0.49

0.47

0.19

0.21

0.02

0.24

0.21

−0.03 −0.19 0.19

0.08 −0.01 0.43

0.63

0.55

0.73

0.75

0.84

0.8

0.81

0.76

0.63

0.81

0.86

0.84

0.57

1

0.95

0.51

0.51

0.39

0.5

0.74

0.71

0.71

0.69

0.27

0.36

0.26

0.24

0.39

−0.01 −0.15 0.18

0.07

0.34

0.54

0.46

0.61

0.59

0.68

0.64

0.66

0.63

0.53

0.66

0.73

0.7

0.55

0.95

1

0.45

0.38

0.29

0.42

0.6

0.57

0.6

0.58

0.26

0.3

0.26

0.28

0.34

0.01 −0.14 0.38

0.24

0.3

0.22

0.23

0.4

0.27

0.29

0.41

0.35

0.5

0.42

0.38

0.31

0.51

0.45

1

0.39

0.45

0.43

0.56

0.62

0.57

0.69 −0.07 0.14

0.03

0.06

0.06

0.03

0

−0.14

0.1

0.01

0.8

0.12 −0.11 0.01 −0.04 0.07 −0.01 0.04 −0.14 0.07 −0.16 −0.04 −0.21 −0.06 −0.12 −0.16 0.03 −0.16 0.07 −0.04 −0.05

0.49

0.69 −0.15 0.33

0.23 −0.03 0.33

1

0.14

0.14

0.06

0.01 −0.07 −0.03 −0.01

−0.07 0.15 −0.13 −0.05 −0.11 −0.17 −0.17 −0.43 −0.13 −0.13 0.34

0.23

0.28 −0.19 0.15 −0.03

0.6

0.4

0.2

0

0

0.17

0.17

0.07

0.04

0.29

0.43

0.4

0.55

0.48

0.46

0.49

0.31

0.63

0.51

0.53

0.4

0.51

0.38

0.39

1

0.45

0.48

0.57

0.61

0.58

0.59

0.25

0.35

0.27

0.21

0.56

0.33

0.29

0.38

0.36

0.48

0.43

0.4

0.28

0.13

0.46

0.45

0.44

0.53

0.39

0.29

0.45

0.45

1

0.48

0.57

0.5

0.49

0.53

0

0.23

0.07

0.44

0.31

0.2

0.05 −0.04 0.27

0.22

0.35

0.32

0.4

0.5

0.4

0.42

0.5

0.43

0.58

0.57

0.5

0.41

0.5

0.42

0.43

0.48

0.48

1

0.6

0.66

0.61

0.64

0.08

0.35

0.05

0.19

0.33

−0.05 −0.22 0.36

0.15 −0.21 0.39

0.6

0.53

0.55

0.6

0.81

0.66

0.7

0.66

0.48

0.83

0.8

0.74

0.53

0.74

0.6

0.56

0.57

0.57

0.6

1

0.86

0.84

0.86

0.19

0.3

0.17

0.2

0.48

−0.11 −0.13 0.19

0.08 −0.06 0.36

0.61

0.53

0.52

0.54

0.76

0.62

0.67

0.78

0.62

0.84

0.78

0.78

0.37

0.71

0.57

0.62

0.61

0.5

0.66

0.86

1

0.92

0.94

0.07

0.34

0.09

0.06

0.38

−0.17 −0.15 0.25

0.1

−0.12 0.31

0.61

0.56

0.45

0.5

0.75

0.57

0.65

0.74

0.57

0.82

0.77

0.72

0.49

0.71

0.6

0.57

0.58

0.49

0.61

0.84

0.92

1

0.96

0.14

0.45

0.14

0.1

0.4

−0.17 −0.11 0.26

0.03 −0.16 0.27

0.53

0.5

0.4

0.46

0.7

0.52

0.58

0.66

0.47

0.8

0.72

0.69

0.47

0.69

0.58

0.69

0.59

0.53

0.64

0.86

0.94

0.96

1

0.08

0.41

0.16

0.06

0.36

0.15

0.06

0.01

0.14

0.25

0.2

0.23

0.16

0.02

0.04

0.21

0.19

0.1

0.19

0.27

0.26 −0.07 0.25

0

0.08

0.19

0.07

0.14

0.08

1

0.38

0.32

0.04

0.16

−0.13 −0.28 −0.21 −0.17 −0.16 0.08

0.13

0.15

0.08

0.28

0.36

0.29

0.29

0.25

0.13

0.37

0.31

0.26

0.21

0.36

0.3

0.14

0.35

0.23

0.35

0.3

0.34

0.45

0.41

0.38

1

0.08 −0.04 −0.09 0.15

0.3

0.21

0.24

0.18

0.07 −0.06 0.17

0.27

0.22

0.02

0.26

0.26

0.03

0.27

0.07

0.05

0.17

0.09

0.14

0.16

0.32

0.26

−0.07 −0.29 0.11

0.2

0.39

0.15 −0.35 0.07 −0.12 −0.16 0.31 −0.13 −0.16

−0.43 −0.21 −0.01 −0.1

0.07

0.03

−0.13 −0.09 −0.2 −0.32 0.07

0.26 −0.02 0.15 1

0.34 −0.14 −0.13 −0.18 −0.04 0.33

0.18 −0.04 0.36

0.27

0.22

0.3

0.21

0.08

0.01

0.16

0.24

0.21

0.24

0.24

0.28

0.06

0.21

0.44

0.19

0.2

0.06

0.1

0.06

0.04 −0.02 0.34

0.14 −0.19 −0.02 −0.1 −0.05 0.25

0.32

0.46

0.5

0.51

0.49

0.4

0.24

0.5

0.52

0.55

0.21

0.39

0.34

0.06

0.56

0.31

0.33

0.48

0.38

0.4

0.36

0.16

0.12

0.55

0.15

0.57

0.34

0.57

1

0.58

0.58

1

−0.2

−0.4

−0.6

−0.8

−1

Figure 2. Distributions of

OPDTT (panel

A) and

OPGSH

and

OPAA

(panel B) in Toronto,

Canada for sites with summer and winter data (n=26) A

OP−DTT: Winter

OP−DTT: Summer

OP−DTT: Annual 10

20

30

40

50

60

70

80

3 pmol min m

B


42

Page 43 of 50



43


Page 44 of 50

Figure 3. Between-season differences in OPGSH (panel A), OPAA (panel B), OPDTT (panel C), and PM2.5 (panel D) by site. Sites with positive values had higher concentrations during summer; sites with negative values had higher concentrations during winter.


44

Page 45 of 50


Figure 4. Between-site variations in OPGSH, OPAA, and OPDTT during summer (left) and winter (right) in Toronto, Canada. Green bars indicate sites above the mean value (Z=0) and red bars indicate sites below the mean.


45


Summer

Page 46 of 50

Winter


46

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Figure 5. Multivariable models for OPAA (% Depletion/m3), OPGSH (% Depletion/m3), and OPDTT (pmol/min/m3) during summer


47


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Figure 6. Multivariable models for OPAA (% Depletion/m3), OPGSH (% Depletion/m3), and OPDTT (pmol/min/m3) during winter


48

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Figure 7. Predicted spatial distributions of OPAA (%Depletion/m3) and OPDTT (pmol/min/m3) during summer in Toronto, Canada (ArcMap 10.4.1; ESRI, Redlands, CA) Legend

A)

Summer OPAA

B)

Summer OPDTT

TorontoGrid_OPnew ln(OPAA ln(OPAA Summer) < 0.04 Summer) 0.04 - 0.11 0.12 - 0.19 0.20 - 0.26 0.27 - 0.34 0.35 - 0.42 0.43 - 0.50 0.51 - 0.57 0.58 - 0.65 0.66 - 0.73 0.74 - 0.81 0.82 - 0.89 0.90 - 0.96 0.97 - 1.04 > 1.04

ln(OPDTT Legend


Tor_OPnew summer Summer) ln_op_dtt_per_min_per_m3_S < 3.16 3.16 - 3.23 3.24 - 3.30 3.31 - 3.38 3.39 - 3.45 3.46 - 3.53 3.54 - 3.61 3.61 - 3.68 49 3.69 - 3.75 3.76 - 3.83 3.84 - 3.91


Page 50 of 50

TOC ART

OP−DTT: Winter

OP−DTT: Summer

OP−DTT: Annual 10

20

30

40

50

60

70

80

pmol min m3


50

Within-City Spatial Variations in Multiple Measures of PM2.5 Oxidative

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