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Feb 5, 2019 - Temporal and Spatial Trends of Polycyclic Aromatic Compounds in Air across the Athabasca Oil Sands Region Reflect Inputs from Open Pit M...
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Temporal and spatial trends of polycyclic aromatic compounds in air across the Athabasca oil sands region reflect inputs from open pit mining and forest fires Jasmin Schuster, Tom Harner, Ky Su, Anita Eng, Andrzej Wnorowski, and Jean-Pierre Charland Environ. Sci. Technol. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.estlett.9b00010 • Publication Date (Web): 05 Feb 2019 Downloaded from http://pubs.acs.org on February 11, 2019

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Temporal and Spatial Trends of Polycyclic Aromatic Compounds in Air across the Athabasca Oil Sands Region Reflect Inputs from Open Pit Mining and Forest Fires

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Authors: Jasmin K. Schuster*ᶧ, Tom Harner*, Ky Su*, Anita Eng*, Andrzej Wnorowski#, Jean-Pierre

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Charland#

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* Air Quality Research Division, Environment and Climate Change Canada, Toronto, ON, M3H 5T4, Canada

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#

Air Quality Research Division, Environment and Climate Change Canada, Ottawa, ON, K1V 1C7, Canada

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[email protected]

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Abstract

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Results are reported from a passive air monitoring study for polycyclic aromatic compounds (PACs) in the

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Athabasca oil sands region (AOSR) in Alberta, Canada. Polyurethane foam (PUF) disk passive air samplers

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were deployed for consecutive 2-month periods from November 2010 to January 2016 at 15 sites.

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Samples were analysed for polycyclic aromatic hydrocarbons (PAHs), alkylated PAHs (alkPAHs),

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dibenzothiophene and its alkylated derivatives (DBTs). Concentrations in air were in the range 0.3 – 43

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ng/m3, 0.15 – 460 ng/m3 and 0.04 – 130 ng/m3 for ΣPAHs, ΣalkPAHs and ΣDBTs, respectively. PACs at most

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sites exhibited a small but statistically insignificant increase in air over this 5-year period which is

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consistent with expectations as in situ bitumen extraction techniques gain predominance over open pit

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mining in the area. Significant PAC increases were observed at a site which is within a few kilometres of

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open pit mining that expanded over the study period. The 5-year regional trend for PACs in air provides a

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baseline against which planned future open pit mining projects (e.g. Teck Frontier) can be assessed for

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impact. Seasonal trends in concentrations in air were observed for more volatile PACs whereby

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concentrations in air were higher in winter than in summer. These trends were not observed for less

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volatile compounds. Two major forest fire episodes during April to July 2011 and June-July 2015 resulted

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in greatly elevated levels for PAH and a small subset of alkylated PAHs but not for the majority of alkPAHs

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and DBTs. Increases in regional PAH concentrations associated with forest fire periods were consistent

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with estimates based on published emission factors for PACs for wood combustion. Although forest fires

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are likely to be an important source of PAH concentrations in air across the AOSR, alkPAHs and DBTs

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appear to be primarily associated with emissions from oil sands mining operations. Air quality guidelines

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for alkPAHs and DBTs are still lacking.

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Introduction

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The AOSR in northern Alberta, Canada is the third largest bitumen deposit in the world, covering a

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mineable area about 4,800 km2 which has grown exponentially since 1967 with a current annual

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production volume of ~2.8 million barrels raw bitumen 1, 2.

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The publications by Kelly et al 2009 3 on the impacts of AOSR mining activities on the Athabasca River

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basin led to the launch of a regional environmental monitoring plan helmed by Environment and Climate

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Change Canada and Alberta Environment and Parks. Under the Joint Canada/Alberta Implementation Plan

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on Oil Sands Monitoring different studies focussed on monitoring different pollutants connected to the

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mining industry, fluxes between different environmental compartments, source attribution, and impacts

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on wildlife and community health2, 4-9.

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One of the first initiatives under the integrated monitoring plan was the establishment of a regional

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passive air sampling monitoring network to measure polycyclic aromatic compound (PAC) concentrations

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in air. Passive air sampling is a convenient and cost-effective method to monitor air concentrations of

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PACs on a large spatial scale and in remote areas 9, 10.

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The unsubstituted polycyclic aromatic hydrocarbons (PAHs) are the most widely monitored class of PACs.

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PAHs are listed under the United Nations Economic Commission for Europe Convention on Long Range

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Transboundary Air Pollution and on the Toxic Substances List under the Canadian Environmental

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Protection Act in 1999. They are released into the environment from combustion processes (e.g. forest

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fire, heating, waste incineration, traffic) and from unburned fossil fuels2. Certain PAH species are known

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for their toxicity and potential to cause cancer and mutations11. The alkylated PAHs are thought to be

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even more toxic than unsubstituted PAHs

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alkylated derivatives, DBTs) are enriched in bitumen relative to the parent PAHs and therefore act as

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markers for emissions from petrogenic sources 14. While PAHs are often the focus of national emissions

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inventories and long term monitoring networks, they usually reflect mostly pyrogenic emissions sources

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as listed above. Alkylated PAHs are seldom targeted for monitoring.

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The following study presents the PAC concentrations in air at 15 sampling locations in the AOSR for the

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period November 2010 to January 2016. These long term data are compared to results from conventional

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high volume active sampling at a subset of 3 sites in the same region and interpreted in the context of

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impact from mining operations. The data provide a 5-year baseline trend against which future mining

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Alkylated PAHs and dibenzothiophenes (including their

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expansions can be assessed. The role of forest fires is also explored including the impact on measured

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concentrations for the different categories of PACs.

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Method

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Details for sampling media preparation, deployment and analysis are described elsewhere9, 10, 15. In short,

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PUF disks were pre-cleaned with accelerated solvent extraction using acetone, petroleum ether (PE) and

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acetonitrile and dried under nitrogen prior to their deployment in double-domed sampling chambers for

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two months sampling periods. The 15 sampling locations discussed here are differentiated as enhanced

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deposition (ED, n=5), community (CO, n=3) and forest health (FH, n=7) sites and based on an existing

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network of air sampling sites operated by the Wood Buffalo Environmental Association (WBEA) (Figure

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S1) 16. Two field blanks and duplicate samples were deployed during each period.

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Prior to the extraction with ASE, the PUF disks were fortified for recovery monitoring using an isotopically

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labeled surrogate standard mixture. Samples were extracted using PE/acetone (75/25, v/v; 2 cycles).

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Samples were fractionated on a column (I.D. 0.9 cm, 4 g activated silica, 2.5 g activated alumina and 2 g

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sodium sulfate) with 20 mL PE (Fraction 1: non-polar, aliphatic compounds), with 25 mL PE/acetone

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(50/50, v/v) (Fraction 2: aromatic PACs) and with 20 mL methanol (Fraction 3: oxygenated and nitrated

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PACs). Samples were fortified with internal standard (13C-phenanthrene) prior to analysis.

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Sample extracts were analyzed using an Agilent 6890 series gas chromatography (GC) system with a 5975

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Mass Selective (MS) detector on a DB-XLB column (30 m, I.D. of 250 μm, 0.25 μm film thickness). Samples

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were injected at pulsed splitless mode and injector, source, and quadrupole were set to 290 ˚C, 230 ˚C,

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and 150 ˚C respectively. The oven temperature program started at 60 ˚C, where it was held for 1 minute

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and then increased to 320 ˚C at 5 ˚C/min where it was held for 10 minutes. PAHs, alkPAHs and DBTs were

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detected by Electron Ionization in selected ion monitoring mode. The target compounds were the parent

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PAHs acenaphthylene (AL), acenaphthene (AE), fluorene (FL), phenanthrene (PHE), anthracene (AN),

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fluoranthene (FLT), pyrene (PY), benzo(a)anthracene (BaAN), chrysene (CHR), benzo(b)fluoranthene

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(BbFLT), benzo(k)fluoranthene (BbFLT), benzo(a)pyrene (BaPY), perylene (PER), indeno(1,2,3-c,d)pyrene

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(IP), dibenzo(a,h)anthracene (DahAN), benzo(g,h,i)perylene (BghiPER), the alkylated PAH retene (RET), the

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C1-, C2-, C3- and C4-alkylated PAH groups for naphthalene (NAP), FL, PHE/AN, FLT/PYR,

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BaAN/CHR/triphenylenes and DBTs.

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All data were recovery and blank corrected. Average surrogate recoveries estimated from the labeled

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compounds

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benzo(b)fluoranthrene, d12-benzo(e)pyrene, d12-indeno(123-cd)pyrene, d14-dibenza(ah)anthracene, d13-

(d10-acenaphthene,

d10-anthracene,

d12-benz(a)anthracene,

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d12-chrysene,

d12-

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d10-benzo(b)naphtho(2,1-d)-thiophene,

d12-2,6-dimethylnaphthalene,

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5-methylchrysene,

d12-

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triphenylene) spiked prior to extraction were 90 ± 40 %. The method detection limit (MDL) was estimated

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as the average of the field blanks plus three times the standard deviation (SD). Field blank data was

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winsorized to the 10th percentile. Instrumental detection limits (IDL) and MDLs are reported in the SI

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data file. For the purpose of data analysis, values below MDL were replaced by 2/3 × MDL.

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Data outliers were determined as values higher than the site average + 3 × SD over the 5-year period,

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excluding data directly impacted by forest fire events.

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Effective air sample volumes for deriving air concentrations were estimated and adjusted for temperature

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following the process described by Shoeib and Harner17, while using a sampling rate of 5 m3/day as

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determined in the initial calibration period of this study by Harner et al. 10

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Results and Discussion

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Comparison of PAC levels from PAS and AAS methods

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PAS and a high-volume active air samplers (AAS) have been co-deployed at three of the ED sites at the

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centre of the oil sands operations (AMS05, AMS11, AMS13). Time series of PAC concentrations in air are

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available from both measurement methods for 2011-2015, though there are significant differences in

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both sampling frequency and sampling interval. PAS are deployed for consecutive 2 months periods (100

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% of the time) whereas AAS are collected in 6 day intervals for a 24h sampling period (i.e. approximately

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17 % of the time). Both sampling methods have been shown to provide comparable results for both gas-

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phase and particle-phase PACs 10, 18.

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When comparing the PAS and AAS derived data sets for PAC concentration in the AOSR air, we are

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focussing on overall concentrations of individual and grouped PACs (paired t-test) and temporal trends

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(increasing or decreasing slopes for concentration in air vs sampling date). To allow a direct comparison

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between the data sets the AAS data were transformed to averages corresponding with the PAS 2-month

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sampling periods (Figure 1).

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Overall, the majority of the concentrations (PAS vs AAS) were within a factor of two, despite the fact that

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AAS represent only ~17 % of the time sampled by PAS. A paired t-test for the individual compounds

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indicates that the PAS and AAS data are different, but this statistical method does not take into account

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the uncertainty associated with either the integrated PAS data points or the average AAS data points.

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The concentrations reported by PAS were higher with averages of 9.1 ± 5.3 ng/m3, 84 ± 48 ng/m3 and 18

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± 14 ng/m3 for PAHs, alkPAHs and DBTs respectively, while average concentrations reported by AAS are

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6.6 ± 6.8 ng/m3, 72 ± 61 ng/m3 and 7.5 ± 11 ng/m3. These averages exclude sampling periods with reported

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forest fire events close to the AOSR (April- July 2011 and June-July 2015). The environmental fate of PACs

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varied depending on their physical chemical properties which dictates if they are in a volatile (V), semi-

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volatile (SV) or particle-phase (PP) state. To account for that the compound groups were further split into

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PAHV, PAHSV, PAHPP and alkPAHV, alkPAHSV, alkPAHPP for a better comparison with results reported in Table

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S1.

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Temporal trends and seasonality over 5 years of measurements

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The passive air sampling in the AOSR is an ongoing campaign and data is now available for the period of

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Nov 2010 – Jan 2016. First results for the sampling campaign were reported for the period of Nov 2010 –

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Jul 2012 9. The results from 5 years of measurements allow us insights to the baseline concentrations of

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PACs in the AOSR and deeper understanding of the temporal trends, seasonality and special events such

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as forest fires (FF).

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The data from FF periods was removed for the analysis of the background concentrations and temporal

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trends. Over the 5 year period, the PACs show consistent levels in air at CO, ED and FH sites. Temporal

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trends at the individual sites were estimated by the slopes for the natural logarithm (ln) of the PAC

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concentrations in air against sampling dates and showed small increases and decreases over time which

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were not statistically significant trends (p > 0.1) (Table S2, Figure S2). Furthermore, increasing and

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decreasing trends were not consistent within PAC groups and sites. The annual change in PAC

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concentrations was calculated from the slopes and compared to average PAC values. The estimated

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annual trends were well within the sampling uncertainty and ranged from 1 – 30% of the margin estimated

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from the average PAC values ± the standard deviation over the 5 measurement years. It has to be noted

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that the daily production volume of raw bitumen increased from 1.7 to 2.5 million barrels between 2011

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and 2015 (Figure S3) 2. The annual growth rate for in-situ bitumen production was double the growth rate

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of bitumen production through open pit mining (60% vs 32% respectively). Fugitive dust suspended from

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mining and oil sands transportation processes has been shown to be a major contributor for PAHs and

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other pollutants to the atmosphere 7, 19. If we consider open pit mining a greater source for fugitive dust

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and hence PACs than in-situ operations, it would explain why the increase in total bitumen extraction is

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not strongly reflected in the overall trends of the PAC concentration in air. Overall, there was no

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considerable increase in the PAC concentration in air in the AOSR between 2011 and 2015 with the

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exception of two of the enhanced deposition sites, AMS11 and AMS13.

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The sites AMS11 and AMS13 reported higher concentrations of alkPAHSV after 2013 (t-test assuming

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unequal variances: p-values of 0.008 and 2×10-5 respectively) whereas PAHs concentrations were

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consistent over the measurement period. While AMS11 is close to the center of mining operations and

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changes in the surrounding area are difficult to determine, AMS13 is more isolated on the periphery and

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located between the two major mining areas (Figure S1, S4). A comparison of detailed historical satellite

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images of AMS13 and its surroundings shows a substantial increase in open mining development and

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activity within a distance of ~ 5km of the sampling site after 2013 20.

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As the results of this study cover 5 years with approximately 6 samples per site per year from PAS, we

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were able to determine the seasonal impact on PAC levels in the air. Figure S4 shows the average ratios

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between winter (Oct – Mar) and summer (Apr – Sep) over all sites for the individual PACs (with the

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exception of AMS14). We observe higher concentrations for PAHV and PAHSV during the winter months,

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which is a general tendency for PAHs that has been reported by others studies 21. This phenomenon is

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usually explained by reduced removal processes due to colder temperature and decreased photolysis

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reactions in the atmosphere and lower mixing heights during winter months. Other factors include higher

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emissions from domestic combustion processes during winter (i.e. heating) and less efficient combustion

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of fossil fuels at cold temperatures 21.

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Compared to PAHs the concentrations of alkPAHs were more consistent between the seasons with the

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exception of the very volatile C1-NAP, which was slightly elevated during the summer months. Fugitive

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dust has recently been recognized as a major source /carrier for alkPAHs in the AOSR 7. Snow cover in

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winter months could reduce the blow-off and transport of fugitive dust and its alkPAH load and offset any

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increases in primary emissions to air.

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The changes in DBT and alkDBT concentrations in air between summer and winter reflects their physical-

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chemical properties. More volatile DBT/alkDBTs exhibit higher concentrations during winter, while the

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particle bound alkDBT levels are higher during summer (Figure S5).

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Forest Fire impact on PAC concentrations

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The sampling period from 2011-2015 was marked by two major FF events in close proximity to the AOSR

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and the regional PAS network. The FF events occurred during the sampling periods for Apr – July 2011

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and June/July 2015. Figure S6 shows the extent of the FF episodes. The contribution of the FFs to elevated

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PAC levels during the PAS periods were estimated by subtracting average baseline levels during non-FF

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summer sampling periods. The results of this analysis are summarized in Figure S6, while Figure 2 shows

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the increase in PAHs attributed to FF for each of the 15 sites (grey portion of the stacked bars).

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Furthermore, the relative contribution of C4-alkylated phenanthrene congener i.e. Retene (RET), which is

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a known marker for wood burning 22, was also assessed and shown to be elevated during FF events (Figure

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S8). The average concentrations of PAC levels during FF events [PAC]FF and non-FF summer sampling

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periods [PAC]S were compared. The average ratio for [PAC]FF/[PAC]S over the 5 y period for all sites is 21.8

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± 11.2 and much higher compared to other PACs (Figure S7). For instance, for PAHV (AL, FL, PHE, AN) and

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PAHSV (FLT, PY) [PAC]FF/[PAC]S ratios were lower and ranged from 2.2 to 4.7.; whereas the ratio for PAHPP

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and alkPAHs are variable and exhibit no discernable difference between FF and non-FF periods. These

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results are in agreement with the analysis of PAH concentrations in air during a forest fire event May –

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July 2016 reported by Wentworth et al 23. In the case of the more volatile alkPAHV and alkPAHSV, the ratio

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was elevated during FF events but less than what was observed for PAHV. It is noteworthy that the main

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group of alkPAHs that show elevated levels during the FF events are the group of alkylated PHE/AN to

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which RET belongs. The exact synthesis pathway of RET during wood combustion has barely been

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discussed in the literature. A theory put forward is that it is a pyrolysis product of dehydroabietic acid, a

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terpene found in wood 22, which is shown in Figure S9. DBT/aDBTs show no increased levels in air during

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FF events, which suggests that the mining activity in the AOSR is the main contributor.

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The observed increase in PAHs during the FF event (note, excluding alkPAHs and DBTs) was assessed

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against expected emissions of PAHs to air24, 25 for the area burned by FFs

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∑alkPAHs and ∑DBTs are not available. To perform this comparison we selected a control volume having

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a radius of 250 km and a height of 1 km (boundary layer height32, 33) see Figure 2. The selection of 250 km

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radius reflects the size of the oil sands region and encompasses the areas that were affected by FFs. It is

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also consistent with characteristic travel distance of PAHs (i.e. distance at which 63% of the chemical is

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removed from air) which are on the order of about 100 km 34. The quantity of PAHs emitted from the FFs

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was converted into a comparable or ‘effective’ average air concentration representing the 2-month

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deployment period of the passive sampler. This was done by dividing the ∑PAH mass emitted from FFs by

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the volume of air estimated to pass through the control volume assuming an average wind speed of 9

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km/h 35. In addition to combustion emissions for wood, we also considered revolatilization of PAHs from

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trees as suggested by Rauert et al. 2017 36. This revolatilization accounts for PAHs that have accumulated

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in trees through air-wood transfer and then are released back to air when the wood is heated to a high

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temperature during FFs that favours partitioning to air vs wood. Details of the calculations discussed

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above are presented in Text S1.

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Based on these calculations, the ∑PAH mass released to air during the various FFs were: 150 tonnes, 170

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tonnes and 600 tonnes for the FF events of Apr/May 2011, June/July 2011 and June/July 2015 respectively.

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When we distribute this mass evenly through the control volume and account for the total volume of air

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passing through during the passive sampling period, the estimated and corresponding ∑PAH

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. Emissions factors for

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concentrations in air are 24 ng/m3, 28 ng/m3 and 100 ng/m3, respectively. This is shown in Figure 2 for the

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April/May 2011 FF event. These “effective air concentrations” for the ∑PAH (i.e. orange bar) are

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remarkably similar to the portion of measured air concentration of PAHs attributed to FF inputs as shown

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in Figure 2 (i.e. grey bars). This indicates that emissions calculations for PAHs from burning wood are

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consistent with the estimates derived from regional PAS network measurements. Although the relative

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contribution of revolatilized PAHs represents a minor contribution (~9%), it is not insignificant.

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In conclusion, we found that PAC concentrations in air over the 5 year sampling were relatively constant

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even though overall bitumen production in the Athabasca oil sands region increased substantially from

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2011 to 2015. We believe that this increase is not reflected in the regional PAC concentrations in air

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because the increase in production is mostly attributed to increases in-situ production over this period,

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whereas open-pit mining is more important as a source of PACs to air. We also saw a significant impact

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on the PAH concentrations in air during regional forest fire events consistent with estimates from emission

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factors. Forest fire events and their impact on air quality, including on PAC levels in air, are seasonal.

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Natural Resources Canada has been extending the length of the forest fire season in Canada 37 based on

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historical data and climate change predictions 38, 39.

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Acknowledgements

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This project was jointly supported by the Climate Change and Air Pollution (CCAP) and the Joint Oil Sands

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Monitoring (JOSM) programs. The Wood Buffalo Environmental Association (WBEA) is acknowledged for

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their support in passive air sample collection.

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Supplementary information

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Supplementary information is available for this publication. The SI contains individual PAC data observed

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in this study, information on the sampling sites, methods and additional figures.

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. Figure 1. The 5 year sampling period shows good agreement in derived air concentrations and temporal trends for selected PACs from co-located passive (PAS) and high volume (AAS) samplers for both gas-phase and particle-phase PACs. The PAC concentrations in air reported from PAS are compared to averages of the sampling points reported by AAS method corresponding to the PAS sampling periods (2 months). The data in this figure covers the period December 2010 to January 2016 and represents average levels of the three enhanced deposition (ED) sites AMS05, AMS11 and AMS13 for three target compounds, representing different gas-particle partitioning behavior: phenanthrene (PHE) and the groups of C2- and C4-alkylated phenanthrenes and anthracenes (C2-PHE/AN, C4-PHE/AN). The background shading in grey shows the estimated percentages in the gas and particle phase during each sampling period 40-42.

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Figure 2. The forest fire (FF) event during the sampling period of April/May 2011 caused a significant increase in ∑16PAH concentrations in air at the majority of the sampling sites (gray portion of bars) compared to expected average concentrations derived from other years (black portion of bars). The yellow polygon outlines the area within a 250 km radius of the passive sampling sites. The yellow shaded area on the map identifies FF activity during the sampling period 26, 28-31. The ∑16PAH mass released from the April/May 2011 FF event was estimated from the burned area and emission factors 24, 25 (orange portion of bar on far right). We also considered release of PAHs that have accumulated in tree wood as suggested by Rauert et al. (2017) 36 (green portion of bar). Emitted PAHs were converted to a theoretical or ‘effective average air concentration’ for a 60-day period (based on a 1 km boundary layer mixing height and an average wind speed of 9 km/h) so that they can be compared to concentrations derived from passive samplers deployed over 60 days. See text S1 for details.

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References 1. AER ST98-2014: Alberta's Energy Reserves 2013 and Supply/Demand Outlook 2014-2023. https://www.aer.ca/documents/sts/ST98/ST98-2014.pdf (2017/09/01), 2. Harner, T.; Rauert, C.; Muir, D.; Schuster, J.; Hsu, Y.-M.; Zhang, L.; Marson, G.; Watson, J.; Ahad, J.; Cho, S.; Jariyasopit, N.; Kirk, J.; Korosi, J.; Landis, M.; Martin, J.; Zhang, Y.; Fernie, K.; Wentworth, G.; Wnorowski, A.; Dabek, E.; Charland, J.-P.; Pauli, B.; Wania, F.; Galarneau, E.; Cheng, I.; Makar, P.; Whaley, C.; Chow, J.; Wang, X., Air Synthesis Review: Polycyclic Aromatic Compounds in the Oil Sands Region. Environmental Reviews 2018, (ja). 3. Kelly, E. N.; Short, J. W.; Schindler, D. W.; Hodson, P. V.; Ma, M.; Kwan, A. K.; Fortin, B. L., Oil sands development contributes polycyclic aromatic compounds to the Athabasca River and its tributaries. Proceedings of the National Academy of Sciences 2009, 106, (52), 22346-22351. 4. Fernie, K. J.; Marteinson, S. C.; Soos, C.; Smits, J. E. G.; Harner, T.; Chen, D.; Peters, L.; Palace, V., Altered thyroid function in nestling tree swallows in relation to their exposure and uptake of PAHs in the Athabasca Oil Sands Region. in preparation 2018. 5. Galarneau, E.; Hollebone, B. P.; Yang, Z.; Schuster, J., Preliminary measurement-based estimates of PAH emissions from oil sands tailings ponds. Atmospheric Environment 2014, 97, 332-335. 6. Kirk, J. L.; Muir, D. C.; Gleason, A.; Wang, X.; Lawson, G.; Frank, R. A.; Lehnherr, I.; Wrona, F., Atmospheric deposition of mercury and methylmercury to landscapes and waterbodies of the Athabasca oil sands region. Environmental Science & Technology 2014, 48, (13), 7374-7383. 7. Landis, M. S.; Pancras, J. P.; Graney, J. R.; White, E. M.; Edgerton, E. S.; Legge, A.; Percy, K. E., Source apportionment of ambient fine and coarse particulate matter at the Fort McKay community site, in the Athabasca Oil Sands Region, Alberta, Canada. Science of the Total Environment 2017, 584, 105-117. 8. Muir, D. C. G.; Kirk, J. L.; Wang, L.; Wiklund, J. A.; Evans, M. S.; Keating, J.; Kurek, J.; Smol, J. P., Spatial and temporal trends of deposition of polycyclic aromatic compounds in the Athabasca oil sands region recorded by lake ecosystems. in preparation 2018. 9. Schuster, J. K.; Harner, T.; Su, K.; Mihele, C.; Eng, A., First results from the oil sands passive air monitoring network for polycyclic aromatic compounds. Environmental Science & Technology 2015, 49, (5), 2991-2998. 10. Harner, T.; Su, K.; Genualdi, S.; Karpowicz, J.; Ahrens, L.; Mihele, C.; Schuster, J.; Charland, J.-P.; Narayan, J., Calibration and application of PUF disk passive air samplers for tracking polycyclic aromatic compounds (PACs). Atmospheric Environment 2013, 75, 123-128. 11. Organization, W. H., WHO guidelines for indoor air quality: selected pollutants. 2010. 12. Kang, H.-J.; Lee, S.-Y.; Kwon, J.-H., Physico-chemical properties and toxicity of alkylated polycyclic aromatic hydrocarbons. Journal of Hazardous Materials 2016, 312, 200-207. 13. Ott, F.; Harris, R.; O'hara, S., Acute and sublethal toxicity of naphthalene and three methylated derivatives to the estuarine copepod, Eurytemora affinis. Marine Environmental Research 1978, 1, (1), 4958. 14. Yang, C.; Wang, Z.; Yang, Z.; Hollebone, B.; Brown, C. E.; Landriault, M.; Fieldhouse, B., Chemical fingerprints of Alberta oil sands and related petroleum products. Environmental Forensics 2011, 12, (2), 173-188. 15. Eng, A.; Harner, T.; Pozo, K., A prototype passive air sampler for measuring dry deposition of polycyclic aromatic hydrocarbons. Environmental Science & Technology Letters 2013, 1, (1), 77-81. 16. Wood Buffalo Environmental Assosciation WBEA Monitoring Stations. http://wbea.org/networkand-data/monitoring-stations/ 17. Shoeib, M.; Harner, T., Characterization and comparison of three passive air samplers for persistent organic pollutants. Environmental Science & Technology 2002, 36, (19), 4142-4151.

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18. Markovic, M. Z.; Prokop, S.; Staebler, R. M.; Liggio, J.; Harner, T., Evaluation of the particle infiltration efficiency of three passive samplers and the PS-1 active air sampler. Atmospheric Environment 2015, 112, 289-293. 19. Graney, J. R.; Landis, M. S.; Puckett, K. J.; Studabaker, W. B.; Edgerton, E. S.; Legge, A. H.; Percy, K. E., Differential accumulation of PAHs, elements, and Pb isotopes by five lichen species from the Athabasca Oil Sands Region in Alberta, Canada. Chemosphere 2017, 184, 700-710. 20. Alberta Environment and Parks Oil Sands Information Portal. http://osip.alberta.ca/map/ 21. Prevedouros, K.; Brorström-Lundén, E.; Halsall, C. J.; Jones, K. C.; Lee, R. G.; Sweetman, A. J., Seasonal and long-term trends in atmospheric PAH concentrations: evidence and implications. Environmental Pollution 2004, 128, (1-2), 17-27. 22. Ramdahl, T., Retene—a molecular marker of wood combustion in ambient air. Nature 1983, 306, (5943), 580. 23. Wentworth, G. R.; Aklilu, Y.-A.; Landis, M. S.; Hsu, Y.-M., Impacts of a large boreal wildfire on ground level atmospheric concentrations of PAHs, VOCs and ozone. Atmospheric Environment 2018. 24. Amiro, B.; Todd, J.; Wotton, B.; Logan, K.; Flannigan, M.; Stocks, B.; Mason, J.; Martell, D.; Hirsch, K. G., Direct carbon emissions from Canadian forest fires, 1959-1999. Canadian Journal of Forest Research 2001, 31, (3), 512-525. 25. Aurell, J.; Gullett, B. K.; Tabor, D., Emissions from southeastern US Grasslands and pine savannas: Comparison of aerial and ground field measurements with laboratory burns. Atmospheric Environment 2015, 111, 170-178. 26. USDA Forest Service - Remote Sensing Applications Center, MODIS Active Fire Detections for the Canada (2010). In Active Fire Mapping Program: Salt Lake City, 2010. 27. USDA Forest Service - Remote Sensing Applications Center, MODIS Active Fire Detections for the Canada (2011). In Active Fire Mapping Program: Salt Lake City, 2011. 28. USDA Forest Service - Remote Sensing Applications Center, MODIS Active Fire Detections for the Canada (2012). In Active Fire Mapping Program: Salt Lake City, 2012. 29. USDA Forest Service - Remote Sensing Applications Center, MODIS Active Fire Detections for the Canada (2013). In Active Fire Mapping Program: Salt Lake City, 2013. 30. USDA Forest Service - Remote Sensing Applications Center, MODIS Active Fire Detections for the Canada (2014). In Active Fire Mapping Program: Salt Lake City, 2014. 31. USDA Forest Service - Remote Sensing Applications Center, MODIS Active Fire Detections for the Canada (2015). In Active Fire Mapping Program: Salt Lake City, 2015. 32. Gordon, M.; Makar, P.; M. Staebler, R.; Zhang, J.; Akingunola, A.; Gong, W.; Li, S.-M., A comparison of plume rise algorithms to stack plume measurements in the Athabasca oil sands. 2018; Vol. 18, p 1469514714. 33. Aggarwal, M.; Whiteway, J.; Seabrook, J.; Gray, L.; Strawbridge, K.; Liu, P.; O'Brien, J.; Li, S.-M.; McLaren, R., Airborne Lidar Measurements of Aerosol and Ozone Above the Canadian Oil Sands Region. 2017; p 1-49. 34. Thuens, S.; Blodau, C.; Wania, F.; Radke, M., Comparison of atmospheric travel distances of several PAHs calculated by two fate and transport models (the tool and ELPOS) with experimental values derived from a peat bog transect. Atmosphere 2014, 5, (1), 324-341. 35. Wood Buffalo Environmental Assosciation WBEA Historical Monitoring Data. http://wbea.org/historical-monitoring-data/ 36. Rauert, C.; Kananathalingam, A.; Harner, T., Characterization and Modeling of Polycyclic Aromatic Compound Uptake into Spruce Tree Wood. Environmental Science & Technology 2017, 51, (9), 5287-5295. 37. Natural Resources Canada Fire weather. https://www.nrcan.gc.ca/forests/climatechange/forest-change/17776 (October 2018),

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38. Flannigan, M.; Cantin, A. S.; De Groot, W. J.; Wotton, M.; Newbery, A.; Gowman, L. M., Global wildland fire season severity in the 21st century. Forest Ecology and Management 2013, 294, 54-61. 39. Flannigan, M. D.; Amiro, B. D.; Logan, K. A.; Stocks, B. J.; Wotton, B. M., Forest fires and climate change in the 21 st century. Mitigation and Adaptation Strategies for Global Change 2006, 11, (4), 847859. 40. Odabasi, M.; Cetin, E.; Sofuoglu, A., Determination of octanol–air partition coefficients and supercooled liquid vapor pressures of PAHs as a function of temperature: application to gas–particle partitioning in an urban atmosphere. Atmospheric Environment 2006, 40, (34), 6615-6625. 41. Harner, T.; Bidleman, T. F., Octanol− air partition coefficient for describing particle/gas partitioning of aromatic compounds in urban air. Environmental Science & Technology 1998, 32, (10), 1494-1502. 42. Finizio, A.; Mackay, D.; Bidleman, T.; Harner, T., Octanol-air partition coefficient as a predictor of partitioning of semi-volatile organic chemicals to aerosols. Atmospheric Environment 1997, 31, (15), 22892296.

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