Supplemental Information

Ambient volatile organic compounds in Canadian oil sands communities: Levels, sources and potential risk to public health

Md. Aynul Bari*, Warren B. Kindzierski

School of Public Health, University of Alberta, 3-57 South Academic Building, 11405-87 Avenue,

Edmonton, Alberta, T6G 1C9 Canada

37 pages, 15 figures, 12tables

*Corresponding author. Tel.:+1 780 492 0382; fax: +1 780 492 0364.

Email address: mdaynul@ualberta.ca (M.A. Bari).

S1

Fig. S1a. Oil sands deposits in Alberta (Government of Alberta, 2014).

S2

Fig. S1b. Locations of Fort McKay-AMS and Fort McMurray-Patricia McInnes-AMS (yellow stick pins) in the AOSR and industries in and surrounding the monitoring stations that report to NPRI during 2014 using Google Earth (Image © 2016 DigitalGlobe © 2016 Google).

S3

10 km

Fig. S2. Topographical map of the Athabasca Oil Sands Region (AOSR) (http://en-ca.topographic-map.com/places/Fort-McMurray-299326/)

S4

Fig. S3. Major industrial facilities (e.g., oil sands and heavy oil facilities, gas plants, generating plants) in Alberta that report to Environment Canada’s National Pollutant Release Inventory (NPRI) (© 2016 Google Imagery © 2016 TerraMetrics) (NPRI ID and corresponding emissions data are shown in Table S2).

S5

Fig. S4. Wind roses at Fort McKay and Fort McMurray-Patricia McInnes for Jan. 2010–Mar. 2015.

Fort McMurray-Patricia McInnesFort McKay

10.1%

5.1%3.4%

2.5%2.0%

9.4%

4.7%3.1%

2.4%1.9%

1-Jan

-12

1-Mar-

12

1-May

-12

1-Jul-

12

1-Sep

-12

1-Nov

-12

1-Jan

-13

1-Mar-

13

1-May

-13

1-Jul-

13

1-Sep

-13

1-Nov

-13

1-Jan

-14

1-Mar-

14

1-May

-14

1-Jul-

14

1-Sep

-14

1-Nov

-14

1-Jan

-15

1-Mar-

150

10

20

30

40

50

60

70

μg/m

3

01/Ja

n/12

01/M

ar/12

01/M

ay/12

01/Ju

l/12

01/S

ep/12

01/N

ov/12

01/Ja

n/13

01/M

ar/13

01/M

ay/13

01/Ju

l/13

01/S

ep/13

01/N

ov/13

01/Ja

n/14

01/M

ar/14

01/M

ay/14

01/Ju

l/14

01/S

ep/14

01/N

ov/14

01/Ja

n/15

01/M

ar/15

0

10

20

30

40

50

60

70

μg/m

3

Fig. S5. Temporal profile of acetaldehyde concentrations at oil sands communities for Jan. 2010–Mar. 2015.

S7

Fort McKay

Fort McMurray-Patricia McInnes

Fig. S6. Carcinogenic and non-carcinogenic risks of hazardous VOCs at oil sands communities for Jan. 2010–Mar. 2015. Boxes represent 25th (lower quartile) and 75th (upper quartile) percentile values, with median values as lines across the boxes, geometric and arithmetic mean values as cross and circles as well as 5th and 95th percentile concentrations as whiskers.

Benzene

Toluene

TVO

C

Ethylbenze

ne

m,p-X

ylene

o-Xylene

n-Butane

Isobutane

n-Pentane

Isopentane

2,3-Dim

ethylbutane

2-Methylpentane

3-Methylpentane

Methylyclopentane

n-Hexane 3-M

ethylhexane

Methylcyclohexane

n-Heptane

n-Octane

1-Butene

Isoprene

α-Pinene

Acetone

Methanol

Acetaldehyde

Fig. S7. Bootstrapping plots for PMF-derived source profiles (in percentage) at Fort McKay for study period Jan. 2010–Mar. 2015.

Fort McKay

Oil sands fugitives

Industrial solvent

Liquid/unburned fuel

Ethylbenzene/xylene-rich

Aged air mass/regional transport

Petroleum processing

Benzene

Toluene

TVO

C

m,p-X

ylene

o-Xylene

n-Butane

Isobutane

n-Pentane

Isopentane

2,3-Dim

ethylbutane

2-Methylpentane

3-Methylpentane

Methylyclopentane

n-Hexane 3-M

ethylhexane

Methylcyclohexane

n-Heptane

n-Octane

1-Butene

Isoprene

α-Pinene

Acetone

Methanol

AcetaldehydeE

thylbenzene

Fig. S8. Bootstrapping plots for PMF-derived source profiles (in percentage) at Fort McMurray for study period Jan. 2010–Mar. 2015.

S10

Fort McMurray

Oil sands fugitives

Liquid/unburned fuel

Ethylbenzene/xylene-rich

Aged air mass/regional transport

Toluene-rich

Mixed source

Fig. S9. Spatial distribution of summer and winter CWT values for liquid/unburned fuel at Fort McKay for Jan. 2010–Mar. 2015.

S11

Summer

Liquid/unburned fuel

CWT (μg/m3)

Winter

CWT (μg/m3)

Ethylbenzene/xylene-rich

Fig. S10. Spatial distribution of summer and winter CWT values for ethylbenzene/xylene-rich factor at Fort McKay for Jan. 2010–Mar. 2015.

S12

Summer

CWT (μg/m3)

Winter

CWT (μg/m3)

Petroleum processing

Fig. S11. Spatial distribution of summer and winter CWT values for petroleum processing source at Fort McKay for Jan. 2010–Mar. 2015.

S13

Summer

CWT (μg/m3)

Winter

CWT (μg/m3)

Fig. S12. Scatter plots of modeled versus measured total VOCs at Fort McKay and Fort McMurray for

January 2010– March 2015.

S14

Fig. S13. MODIS satellite image on forest fire days in May 16, 2011, 8 June 2011, July 11, 2012 and July 7, 2014 (NASA, 2011a,b; NASA, 2012, NASA, 2014).

S15

May 16, 2011 June 8, 2011

July 11, 2012 Bar = 100 km Bar = 50 km

Bar = 100 km

Fort McKay

Bar = 100 km

July 7, 2014

Fig. S14. Potential source regions of aged air mass/regional transport source on forest fire days (May 21, 2011; June 8, 2011; July 14, 2012; July 14, 2014).

S16

Aged air mass/regional transportMay 21, 2011

CWT (μg/m3)

Aged air mass/regional transportJuly 14, 2012

CWT (μg/m3)

Aged air mass/regional transportJune 14, 2011

CWT (μg/m3)

Aged air mass/regional transportJuly 16, 2014

CWT (μg/m3)

Fig. S15.Uncertainties in risk estimates of each source using bootstrapping technique. Boxes represent 25th (lower quartile) and 75th (upper quartile) percentile values, with median values as lines across the boxes, 5th and 95th percentile concentrations as whiskers. The filled circles represent risk estimates from base factors.

S17

Table S1. Estimated emissions for total VOCs releases to air (tonnes) for the year 2010 after CEMA (2012).

NPRI id Facility in Fort McMurray, Alberta Stacks

Plant fugitive

Mine fleet

Mine fleet face

Tailings areas

Total VOCs

2230 Suncor Energy Oil Sands Limited Partnership - Suncor Energy Inc. Oil Sands 116 6,198 1,173 1,084 3,376 11,95023275 CNRL Limited - Horizon Oil Sands Processing Plant and Mine 159 1,270 134 471 723 2,7572274 Syncrude Canada Ltd. - Mildred Lake Plant Site 202 3,486 332 715 3,259 7,9946572 Syncrude Canada Ltd. - Aurora North Mine Site 23 1,515 219 847 934 3,5386647 Shell Canada Energy - Muskeg River Mine and Jackpine Mine 10 829 653 537 1,438 3,467

19181 Suncor Energy Oil Sands Limited Partnership - Firebag 73 147 0 0 0 22017630 Suncor Energy Oil Sands Limited Partnership - MacKay River, In-Situ 41 51 0 0 0 92  Total for all facilities 624 13,496 2,511 3,654 9,730 30,015

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Table S2. Reported NPRI emission data for VOCs releases to air (tonnes) from major oil sands and heavy oil facilities in and surrounding the AOSR and other point sources (e.g., gas plants, generating plants) in Alberta.

        VOCs (tonnes)NPRI id Major industrial operations (e.g., oil and gas) in Alberta D1

(km)D2 (km) 2010 2011 2012 2013 2014 2015

6572 Syncrude Canada Ltd. - Aurora North Mine Site 14.7 60.91 5,182 4,702 4,693 8,26814,762 5,033

2274 Syncrude Canada Ltd. - Mildred Lake Plant Site 16.61 33.3 8,592 7,704 7,770 20,73215,282 17,593

23275 CNRL - Horizon Oil Sands Processing Plant and Mine 17.64 67.1 3,602 3,432 5,189 4,560 5,028 1,009

6647 Albian - Shell Canada Energy Muskeg River Mine and Jackpine Mine 19.39 66.81 1,460 2,050 2,259 2,614 2,670 2,2371763

0 Suncor Energy Oil Sands Limited Partnership - MacKay River, In-Situ 22.85 41.54 40 19 39 32 30 53

2230 Suncor Energy Oil Sands Limited Partnership - Suncor Energy Inc. Oil Sands 23.22 28.02 28,94012,649 16,087 9,529

12,271 17,515

6902 Enbridge Pipelines Inc. - Athabasca Terminal 25.18 25.58 400 493 541 276 58 –1918

1 Suncor Energy Oil Sands Limited Partnership - Firebag 45.01 62.67 158 193 194 272 333 3061921

9 TransCanada Energy Ltd. - MacKay River Power Plant 60.83 12.3621 19 17 19 18 20

22378 Nexen Energy ULC-Long Lake Project, Fort McMurray 96.49 50.15 534 481 473 606 365 353

22141 ConocoPhillips Canada Resources Corp., Surmont SAGD Commercial Battery 119 70.16 38 39 35 36 34 65

22750 Connacher Oil and Gas Limited, Pod One, Fort McMurray 120 74.17 12 62 51 45 80 63

6625 Cenovus FCCL Ltd, Christina Lake SAGD Bitumen Battery, Lac La Biche 185 135 15 25 58 105 102 2,4264136 CNRL-Wolf Lake and Primrose Plant, Cold Lake 277 227 212 152 229 237 199 244

442 Imperial Oil Resources Limited-Cold Lake 299 249 222 216 215 223 287 2852128 Peace River Complex 326 328 14 12 130 138 22 182249

3 Shell Canada Ltd-Orion Complex-Cold Lake 330 281 – – 15 15 32 106546 Shell Canada Ltd - Scotford Upgrader Cogeneration, Fort Saskatchewan 393 349 238 386 329 315 427 4171039 H.R. Milner Generating Station, Grand Cache 395 570 – – 14 13 9 –2286 TransAlta Keephills Generating Plant, Duffield 452 608 108 127 148 128 160 147

683 Semcams Gas Plant, Fox Creek 457 432 371 175 187 208 105 115267 Capital Power-Genesee Generating Station,Warburg 460 420 – 1 – – – –

2284 TransAlta Sundance Generating Plant, Duffield 489 410 264 186 176 214 266 2521033 Alberta Power-Battle River Generating Station, Forestburg 526 478 – – – – – –1036 Alberta Power-Sheerness Generating Station, Hanna 639 591 – – 16 13 15 13

D1: Distance from Fort McKay, D2: distance from Fort McMurray, –: not available.

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Table S3. Data status for all measured VOC species (n = 60) during the period of January 2010–March 2015 at Fort McKay and Fort McMurray-Patricia McInnes.

 MDL

Jan 2010–Mar 2015 (ppb)

MDL Apr 2015–Aug 2016

(ppb)

MDL Jan 2010–Mar 2015

(μg/m3)*

MDL Apr 2015–Aug 2016

(μg/m3)* >MDL (%) <MDL (%)   >MDL (%) <MDL (%)Benzene 0.03 0.01 0.10 0.03 82 18 84 16Toluene 0.03 0.01 0.11 0.04 49 51 84 16Ethylbenzene 0.03 0.01 0.13 0.04 36 64 27 73m,p-Xylene 0.03 0.03 0.13 0.13 59 41 57 43o-Xylene 0.03 0.01 0.13 0.04 37 63 33 67n-Butane 0.03 0.03 0.07 0.07 72 28 79 21Isobutane 0.03 0.02 0.07 0.05 68 32 69 312,2-dimethylbutane 0.03 0.01 0.11 0.04 32 68 7 932,3-Dimethylbutane 0.03 0.02 0.11 0.07 38 62 26 74n-Pentane 0.03 0.1 0.09 0.29 49 51 41 59Isopentane 0.03 0.03 0.09 0.09 81 19 81 192-Methylpentane 0.03 0.01 0.11 0.04 55 45 48 523-Methylpentane 0.03 0.01 0.11 0.04 53 47 45 55Methylcyclopentane 0.03 0.02 0.10 0.07 36 64 36 64n-Hexane 0.03 0.01 0.11 0.04 53 47 48 522-Methylhexane 0.03 0.01 0.12 0.04 27 73 18 823-Methylhexane 0.03 0.02 0.12 0.08 37 63 30 70Cyclohexane 0.03 0.02 0.10 0.07 35 65 16 84Methylcyclohexane 0.03 0.01 0.12 0.04 48 52 24 76n-Heptane 0.03 0.01 0.12 0.04 51 49 30 702-Methylheptane 0.03 0.01 0.14 0.05 28 72 14 86n-Octane 0.03 0.02 0.14 0.09 37 63 21 79n-Nonane 0.03 0.01 0.16 0.05 26 74 14 861-Butene 0.03 0.02 0.07 0.05 23 77 27 73Isoprene 0.03 0.01 0.08 0.03 27 73 23 77α-Pinene 0.03 0.3 0.17 1.67 52 48 26 74Acetone 0.2 0.4 0.47 0.95 80 20 84 16Methanola 2 3 0.04 0.39 40 60 53 47Acetaldehyde 0.2 3 0.36 5.40 37 63   35 652,3-dimethylpentane 0.03 0.02 0.11 0.07 17 83 23 772,4-dimethylpentane 0.03 0.01 0.12 0.04 1 99 4 962,2,4-trimethylpentane 0.03 0.01 0.15 0.05 4 96 23 77

S20

Fort McKay Fort McMurray

2,3,4-trimethylpentane 0.03 0.01 0.14 0.05 4 96 6 94Cyclopentane 0.03 0.02 0.09 0.06 21 79 11 89Cyclopentene 0.03 0.3 0.08 0.83 1 99 – –3-methylheptane 0.03 0.02 0.14 0.09 16 84 8 92n-Decane 0.03 0.06 0.17 0.35 22 78 14 86n-Undecane 0.03 0.5 0.19 3.19 11 89 12 88n-Dodecane 0.03 0.4 0.21 2.78 7 93 8 921,2,4-TMB 0.03 0.03 0.15 0.15 19.8 80 22 781,3,5-TMB 0.03 0.02 0.15 0.10 6 94 8 92n-Propylbenzene 0.03 0.05 0.15 0.25 4 96 4 96

Table S3. (continued).

 MDL

Jan 2010–Mar 2015(ppb)

MDLApr 2015–Aug 2016

(ppb)

MDL Jan 2010–Mar 2015

(μg/m3)*

MDLApr 2015–Aug 2016

(μg/m3)* >MDL (%) <MDL (%)

>MDL (%)

<MDL (%)Isopropylbenzene 0.03 0.01 0.15 0.05 3 97 3 972-Methyl-2-butene 0.03 0.3 0.09 0.86 1 99 4 963-Methyl-1-butene 0.03 0.3 0.10 1.03 0 100 0 1002-Methyl-1-pentene 0.03 0.3 0.10 1.03 3 97 2 984-Methyl-1-pentene 0.03 0.3 0.10 1.03 – – 0 1001-Pentene 0.03 0.01 0.09 0.03 1 99 0 100cis-2-Butene 0.03 0.02 0.07 0.05 2 98 0 100trans-2-Butene 0.03 0.01 0.07 0.02 2 98 0 100cis-2-Pentene 0.03 0.02 0.09 0.06 1 99 3 97trans-2-Pentene 0.03 0.02 0.09 0.06 0 100 2 98cis-2-Hexene 0.03 0.3 0.10 1.03 0 100 – –trans-2-Hexene 0.03 0.3 0.10 1.03 0 100 0 100Formaldehydeb 2 3 0.04 3.68 0 100 0 100β-Pinene 0.03 3 0.20 20.37 10 90 8 92Styrene 0.03 0.04 0.18 0.24 4 96 5 95Naphthalene 0.03 0.5 0.16 2.62 19 81 13 87MEK (Methyl ethyl ketone) 0.03 0.3 0.09 0.88 18 82 13 87MIBK (Methyl isobutyl ketone) 0.03 0.4 0.12 1.64 0 100 0 100

aMethanol MDL values were 2 ppb, 0.03 ppb and 3 ppb for Jan 2010–Apr 2014, May 2014–Mar 2015, and Apr 2015–Aug 2016, respectively; bFormaldehyde MDL values were 2 ppb, 0.03 ppb and 3 ppb for Jan 2010–Apr 2014, May 2014–Nov 2015, and Dec 2015–Aug 2016, respectively; *1 μg/m3 = 1 ppb x MW/24.45, where MW is the molecular weight of each VOC species and 24.45 is the molar gas volume (L) at standard temperature and pressure; –: not measured.

S21

Fort McKay Fort McMurray

Table S4. Dose-response assessment for assessing health risks associated with exposure to VOCs at Fort McKay for 2010–2015.             Chronic   Acute      Ambient concentrations Non-cancer Cancer MRL REL

VOCs

HAP

No.IARC WOE units

Arithmetic mean

Geometric mean Max

RfC µg/m3 Source

EPA WOE

IUR 1 in10-6

1/(µg/m3) Source

Benchmark 1 in10-5

(µg/m3) µg/m3 µg/m3

Benzene 15 1 µg/m3 0.80 0.65 5.58 30 IRIS CH 7.8E-06 IRIS 1.28 29 1300Toluene 152 3 µg/m3 3.29 1.11 86.9 5000 IRIS InI – – – 3800 37000Ethylbenzene 77 2B µg/m3 0.69 0.39 7.24 1000 IRIS D 2.5E-06 CAL 4.0 22000 –Xylenes (mixed) 169 3 µg/m3 1.89 0.96 27.9 100 IRIS InI – – – 8700 22000n-Hexane 95 – µg/m3 1.40 0.81 33.0 700 IRIS InI – – – – –Acetone – µg/m3 6.61 5.10 29.7 31000 ATSDR – – – – – –Methanol 103 – µg/m3 11.2 6.80 125 20000 IRIS – – – – – 28000Acetaldehyde 1 2B   µg/m3 8.53 6.49 28   9 IRIS B2 2.2E-06 IRIS 4.5   – 470

RfC: Reference concentrations, IUR: inhalation unit risk Sources: IRIS = Integrated Risk Information System; ATSDR = US Agency for Toxic Substances and Disease Registry; IARC WOE = weight-of-evidence for carcinogenicity in humans (1 - carcinogenic; 2A - probably carcinogenic; 2B - possibly carcinogenic; 3 - not classifiable; 4 - probably not carcinogenic).EPA WOE (2005 Guidelines) = weight of evidence for carcinogenicity under 2005 EPA cancer guidelines: CH - carcinogenic to humans).EPA WOE (1986 Guidelines) = weight-of-evidence for carcinogenicity under the 1986 EPA cancer guidelines: A - human carcinogen; B1 - probable carcinogen, limited human evidence; B2 - probable carcinogen, sufficient evidence in animals; C - possible human carcinogen; InI - inadequate information to assess carcinogenic potential; D - not classifiable. MRL = ATSDR minimum risk levels for no adverse effects for 1 to 14-day exposures. REL = California EPA reference exposure level for no adverse effects. Most, but not all, RELs are for 1-hour exposures; –, not available, *Cancer benchmark used in Alberta, British Columbia, and the Atlantic provinces except in Ontario and Quebec (Health Canada, 2004). 

Table S5. Dose-response assessment for assessing health risks associated with exposure to VOCs at Fort McMurray for 2010–2015.

            Chronic   Acute      Ambient concentrations Non-cancer Cancer MRL REL

VOCs

HAP

No.IARC WOE units

Arithmetic mean

Geometric mean Max

RfC µg/m3 Source

EPA WOE

IUR 1 in10-6

1/(µg/m3) Source

Benchmark 1 in10-5

(µg/m3) µg/m3 µg/m3

Benzene 15 1 µg/m3 0.87 0.69 7.72 30 IRIS CH 7.8E-06 IRIS 1.28 29 1300Toluene 152 3 µg/m3 3.34 1.06 129 5000 IRIS InI – – – 3800 37000Ethylbenzene 77 2B µg/m3 0.55 0.33 5.46 1000 IRIS D 2.5E-06 CAL 4.0 22000 –Xylenes (mixed) 169 3 µg/m3 1.31 0.69 17.8 100 IRIS InI – – – 8700 22000n-Hexane 95 – µg/m3 1.16 0.56 20.1 700 IRIS InI – – – – –Acetone – µg/m3 7.44 5.55 50.7 31000 ATSDR – – – – – –Methanol 103 – µg/m3 18.1 11.6 107 20000 IRIS – – – – – 28000Acetaldehyde 1 2B   µg/m3 8.59 6.57 60.8   9 IRIS B2 2.2E-06 IRIS 4.5 – 0

RfC: Reference concentrations, IUR: inhalation unit risk Sources: IRIS = Integrated Risk Information System; ATSDR = US Agency for Toxic Substances and Disease Registry; IARC WOE = weight-of-evidence for carcinogenicity in humans (1 - carcinogenic; 2A - probably carcinogenic; 2B - possibly carcinogenic; 3 - not classifiable; 4 - probably not carcinogenic).EPA WOE (2005 Guidelines) = weight of evidence for carcinogenicity under 2005 EPA cancer guidelines: CH - carcinogenic to humans).EPA WOE (1986 Guidelines) = weight-of-evidence for carcinogenicity under the 1986 EPA cancer guidelines: A - human carcinogen; B1 - probable carcinogen, limited human evidence; B2 - probable carcinogen, sufficient evidence in animals; C - possible human carcinogen; D - not classifiable. MRL = ATSDR minimum risk levels for no adverse effects for 1 to 14-day exposures. REL = California EPA reference exposure level for no adverse effects. Most, but not all, RELs are for 1-hour exposures; –, not available, *Cancer benchmark used in Alberta, British Columbia, and the Atlantic provinces except in Ontario and Quebec (Health Canada, 2004).

S22

Table S6. PMF input data statistics for Fort McKay for January 2010–March 2015.

Species Category S/N Min 25th Median 75th Max%

Modeled Samples

% Raw Samples

Benzene Strong 2.78 0.10 0.48 0.69 0.87 5.58 98% 100%Toluene Weak 3.07 0.11 0.59 0.98 1.54 62.09 98% 100%Ethylbenzene Strong 0.77 0.13 0.30 0.30 0.30 7.24 98% 100%m,p-Xylene Strong 1.86 0.13 0.56 0.65 0.78 19.77 98% 100%o-Xylene Strong 0.81 0.13 0.30 0.30 0.30 8.15 98% 100%n-Butane Strong 3.42 0.10 1.45 2.02 2.70 29.65 98% 100%Isobutane Strong 2.70 0.12 0.62 0.78 1.07 8.07 98% 100%n-Pentane Strong 2.23 0.24 1.90 1.90 1.90 18.29 98% 100%Isopentane Strong 3.50 0.09 0.88 1.33 2.54 14.14 98% 100%2,3-Dimethylbutane Weak 1.06 0.07 0.46 0.46 0.46 2.04 98% 100%2-Methylpentane Strong 1.92 0.11 0.73 0.84 0.92 6.58 98% 100%3-Methylpentane Strong 1.55 0.11 0.42 0.49 0.57 8.76 98% 100%Methylcyclopentane Weak 0.85 0.10 0.31 0.31 0.31 6.29 98% 100%n-Hexane Weak 1.83 0.14 0.70 0.74 0.77 32.96 98% 100%3-Methylhexane Strong 1.12 0.16 0.70 0.70 0.70 15.34 98% 100%Methylcyclohexane Strong 1.48 0.12 0.64 0.64 0.64 7.14 98% 100%n-Heptane Strong 1.82 0.12 0.94 1.02 1.02 44.58 98% 100%n-Octane Strong 1.34 0.19 1.05 1.05 1.05 21.03 98% 100%1-butene Strong 0.86 0.11 0.55 0.55 0.55 22.15 98% 100%Isoprene Weak 1.27 0.08 1.81 1.81 1.81 11.21 98% 100%α-Pinene Weak 1.48 0.17 0.78 0.83 0.89 6.51 98% 100%Acetone Strong 3.36 0.85 3.30 5.28 7.35 29.65 98% 100%Methanol Weak 1.27 0.26 6.35 6.35 6.35 51.83 98% 100%Acetaldehyde Weak 1.80 0.58 5.99 5.99 5.99 27.71 98% 100%

S/N = signal to noise ratioExcluded samples: 08/06/10, 07/26/12, 10/06/12, 11/06/13, 01/24/15

Table S7. PMF input data statistics for Fort McMurray for January 2010–March 2015.

Species Category S/N Min 25th Median 75th Max%

Modeled Samples

% Raw Samples

Benzene Strong 2.87 0.16 0.49 0.67 0.86 7.72 98% 100%Toluene Strong 2.95 0.19 0.50 0.87 1.39 128.69 98% 100%Ethylbenzene Strong 0.55 0.13 0.26 0.26 0.26 5.46 98% 100%m,p-Xylene Strong 1.66 0.13 0.56 0.56 0.68 12.31 98% 100%o-Xylene Strong 0.76 0.13 0.30 0.30 0.30 5.51 98% 100%n-Butane Strong 3.86 0.21 1.60 2.40 3.02 26.33 98% 100%Isobutane Strong 2.84 0.09 0.65 0.90 1.11 15.09 98% 100%n-Pentane Strong 0.48 0.11 0.21 0.21 0.21 1.58 98% 100%Isopentane Weak 1.77 0.15 1.34 1.34 1.34 30.86 98% 100%2,3-Dimethylbutane Strong 3.45 0.18 0.86 1.24 1.91 17.61 98% 100%2-Methylpentane Strong 1.39 0.11 0.49 0.49 0.49 9.50 98% 100%3-Methylpentane Weak 1.04 0.11 0.32 0.32 0.32 6.93 98% 100%Methylcyclopentane Weak 0.74 0.12 0.37 0.37 0.37 9.49 98% 100%n-Hexane Weak 0.39 0.10 0.27 0.27 0.27 3.37 98% 100%3-Methylhexane Weak 0.97 0.12 0.37 0.37 0.37 10.76 98% 100%Methylcyclohexane Weak 0.60 0.12 0.33 0.33 0.33 5.97 98% 100%n-Heptane Weak 0.62 0.14 0.37 0.37 0.37 7.37 98% 100%n-Octane Weak 0.15 0.11 0.20 0.20 0.20 3.41 98% 100%1-butene Weak 0.33 0.14 0.50 0.50 0.50 10.44 98% 100%Isoprene Weak 0.30 0.11 1.24 1.24 1.24 15.13 98% 100%α-Pinene Weak 0.45 0.17 0.44 0.44 0.44 6.23 98% 100%Acetone Strong 4.27 0.88 3.70 5.27 8.40 50.74 98% 100%Methanol Weak 2.78 1.99 10.05 11.28 11.60 84.29 98% 100%Acetaldehyde Weak 1.88 1.53 6.69 6.69 6.69 28.25 98% 100%

S/N = signal to noise ratioExcluded samples: 02/03/12, 07/08/12, 09/30/12, 07/10/14, 01/12/15

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S24

Table S8. Regression diagnostics of a 5-factor solution at Fort McKay and a 6-factor solution at Fort McMurray for Jan. 2010–Mar. 2015.

  Fort McKay   Fort McMurray-Patricia McInnes

  KS Test KS Test KS Test KS Test

Species Intercept Slope SE r2 Stat p-Value Intercept Slope SE r2 Stat p-Value

Benzene 0.22 0.79 0.33 0.757 0.112 0.004 0.29 0.56 0.27 0.564 0.123 0.001

Toluene 0.12 0.93 1.63 0.975 0.334 0.000 0.90 0.05 1.22 0.075 0.096 0.024

Ethylbenzene 0.08 0.69 0.19 0.780 0.162 0.000 0.15 0.53 0.32 0.590 0.228 0.000

m,p-Xylene 0.19 0.87 0.50 0.881 0.136 0.000 0.39 0.53 0.77 0.684 0.192 0.000

o-Xylene 0.02 0.94 0.24 0.817 0.187 0.000 0.16 0.54 0.30 0.667 0.208 0.000

n-Butane 0.09 1.01 1.03 0.867 0.120 0.002 0.19 0.88 1.46 0.727 0.129 0.001

Isobutane 0.43 0.56 0.67 0.541 0.136 0.000 0.28 0.66 0.58 0.613 0.152 0.000

n-Pentane 0.00 0.91 0.13 0.522 0.137 0.000 0.63 0.65 1.28 0.602 0.217 0.000

Isopentane 1.08 0.20 1.26 0.154 0.284 0.000 0.39 0.78 0.83 0.785 0.174 0.000

2,3-Dimethylbutane 0.25 0.81 0.87 0.786 0.174 0.000 0.04 0.80 0.28 0.346 0.214 0.000

2-Methylpentane 0.29 0.39 0.30 0.537 0.216 0.000 0.15 0.82 0.64 0.541 0.127 0.001

3-Methylpentane 0.19 0.34 0.23 0.395 0.274 0.000 0.29 0.49 0.41 0.427 0.158 0.000

Methylcyclopentane 0.27 0.22 0.29 0.225 0.299 0.000 0.20 0.27 0.19 0.292 0.270 0.000

n-Hexane 0.19 0.14 0.15 0.085 0.375 0.000 0.64 0.11 0.50 0.228 0.248 0.000

3-Methylhexane 0.30 0.18 0.29 0.214 0.281 0.000 0.12 0.68 0.34 0.816 0.228 0.000

Methylcyclohexane 0.19 0.21 0.21 0.160 0.329 0.000 0.06 0.82 0.46 0.743 0.195 0.000

n-Heptane 0.27 0.12 0.21 0.108 0.333 0.000 0.56 0.49 0.85 0.763 0.188 0.000

n-Octane 0.09 0.32 0.11 0.423 0.370 0.000 0.21 0.77 1.15 0.635 0.216 0.000

1-butene 0.38 0.35 0.35 0.386 0.318 0.000 0.27 0.72 0.50 0.820 0.234 0.000

Isoprene 1.17 0.08 0.98 0.014 0.350 0.000 0.39 0.14 0.57 0.100 0.394 0.000

α-Pinene 0.34 0.11 0.23 0.068 0.304 0.000 0.37 0.18 0.39 0.127 0.242 0.000

Acetone 0.56 0.96 2.91 0.802 0.160 0.000 0.91 0.78 2.98 0.578 0.173 0.000

Methanol 5.70 0.29 7.70 0.171 0.237 0.000 4.75 0.14 4.90 0.036 0.286 0.000

Acetaldehyde 3.24 0.41 4.86 0.073 0.286 0.000 3.27 0.37 3.82 0.128 0.296 0.000KS Test (Kolmogorov–Smirnov Test)

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Table S9. Bootstrapping (BS) mapping (%) for 5-factor solution at Fort McKay and 6-factor solution at Fort McMurray for Jan. 2010–Mar. 2015.

  Fort McKay   Fort McMurray

Sources Oil

sand

s fu

gitiv

es

Liqu

id/u

nbur

ned

fuel

Eth

ylbe

nzen

e/xy

lene

-ric

h

Age

s ai

r mas

s/re

gion

al tr

ansp

ort

Pet

role

um p

roce

ssin

g

Unm

appe

d

Oil

sand

s fu

gitiv

es

Liqu

id/u

nbur

ned

fuel

Eth

ylbe

nzen

e/xy

lene

-ric

h

Age

s ai

r mas

s/re

gion

al tr

ansp

ort

Tolu

ene-

rich

Mix

ed s

ourc

e

Unm

appe

d

Oil sands fugitives 100 0 0 0 0 0 90 4 0 0 0 0 6

Liquid/unburned fuel 0 99 0 0 0 1 0 89 0 0 0 0 11

Ethylbenzene/xylene-rich 0 0 90 0 0 10 0 1 96 0 0 0 3

Ages air mass/regional transport 0 0 0 97 0 3 0 0 0 95 0 0 5

Petroleum processing 2 2 2 0 58 36

Toluene-rich 0 0 0 0 96 0 4

Mixed source 0 0 0 0 1 88 11

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Table S10. Summary of PMF analysis and diagnostics of error estimation by run for Fort McKay and Fort McMurray data (Jan. 2010–Mar. 2015).

  Fort McKay   Fort McMurray-Patricia McInnesDiagnostic 5-factor 6-factor 7-factor 5-factor 6-factor 7-factorQexpected 2537 2026 1760 1095 1070 1533Qtrue 1614.43 1169.88 902.784 844.509 572.494 560.475Qrobust 1614.43 1169.89 902.797 844.514 572.492 560.479Qrobust/Qexpected 0.636 0.577 0.513 0.771 0.535 0.366Displacement (DISP) %dQ <0.1% <0.1% <0.1% <0.1% <0.1% <0.1%DISP swaps 0 0 0 0 0 0

Bootstrapping (BS) mapping Petroleum processing 58%

Petroleum processing 78%Solvents 78%

Aged air mass 68%Mixed source 57%

EB/X-rich 69%Mixed source 46%

Benzene/pentane-rich 51%Mixed source 60%

Bootstrapping with displacement (BS-DISP)

% of cases accepted 96% 90% 84% 84% 92% 76% # of decreases in Q 0 1 2 4 0 2 # of swaps in best fit 0 0 1 3 1 6 # of swaps in DISP 4 9 13 9 8 16

Computer run-time (hours) > 10 > 10 > 10 > 10 > 10 > 10

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Table S11a. Pearson correlation of PMF-derived sources with criteria air pollutants measured at Fort McKay for Jan. 2010–Mar. 2015.Fort McKay SO2 NO2 O3 THC TRS PM2.5

Oil sands fugitives 0.03 0.30** -0.17** 0.07 0.17* 0.11Liquid/unburned fuel -0.07 0.08 -0.10 -0.16* -0.1 -0.07Ethylbenzene/xylene-rich 0.02 0.14* -0.12 -0.06 0.13 0.02Aged air mass/regional transport 0.06 -0.26** 0.15* -0.24** 0.14* 0.52**Petroleum processing 0.16* -0.06 -0.02 -0.03 0.19** 0.23**

**Correlation is significant at the 0.01 level (2-tailed), *Correlation is significant at the 0.01 level (2-tailed).

Table S11b. Pearson correlation of PMF-derived sources with criteria air pollutants measured at Fort McMurray for Jan. 2010–Mar. 2015.Fort McMurray SO2 NO2 O3 THC TRS PM2.5 COOil sands fugitives -0.04 0.21** -0.15** 0.07 0.18** 0.01 0.13Liquid/unburned fuel 0.05 0.24** -0.18* 0.15* 0.1 0.05 0.20**Ethylbenzene/xylene-rich -0.05 0.09 -0.10 -0.01 0.02 -0.002 0.01Aged air mass/regional transport -0.03 -0.22** 0.28** -0.06 0.03 0.19** 0.21**Toluene-rich 0.12 -0.01 0.105 -0.09 0 -0.04 0.17**Mixed source 0.01 -0.07 -0.04 0.17** 0.11 0.41** 0.004

**Correlation is significant at the 0.01 level (2-tailed), *Correlation is significant at the 0.01 level (2-tailed).

Table S12a. Pearson correlation of PMF-derived sources with PM2.5 components measured at Fort McKay for Jan. 2010–Mar. 2015.Fort McKay SO4

2– NO3– NH4

+ Cl– K+ Ca2+ Al Ba Cr Fe Mn Mo Zn Ti Pb V As MissingMass1

Oil sands fugitives -0.21**0.21*

*0.22*

* -0 0.1 -0.1 0 0.1 0.13 -0.05 0.03 0.16* 0.04 -0.160.04 0.01 0.04

0.17*

Liquid/unburned fuel -0.01 -0.02 -0.09 -0.18* -0.07 -0.28** 0.1 -0 0.06 -0.09 0.07 0.20** 0.01 -0.16 -0 -0.19* -0.15 -0.07

Ethylbenzene/xylene-rich 0.13 -0.04 0.07 0.07 0.14 -0.08 -0 0.1 0.13 0.06 0.07 -0.03 0.01 -0.080.14 0.11 -0.01

0.001

Aged air mass/regional transport

0.20**

0.31**

0.33** 0.05 0.32*

*0.36*

*0.24*

*0.22*

* 0.04 0.19** 0.01 -0.11 -0.04 0.17* 0.1

1 0.12 0.23** 0.51**

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Petroleum processing 0.07 -0.03 0.02 -0.2 0.09 0.14 0.10.29*

* -0.020.37*

* 0.03 0.06 -0.02 0.17* -0 0.20* 0.070.23**

**Correlation is significant at the 0.01 level (2-tailed), *Correlation is significant at the 0.01 level (2-tailed).1particularly carbonaceous aerosol (Bari and Kindzierski, 2017)

Table S12b. Pearson correlation of PMF-derived sources with PM2.5 components measured at Fort McMurray for Jan. 2010–Mar. 2015.Fort McMurray SO4

2– NO3– NH4

+ Cl– K+ Ca2+ Al Ba Cr Fe Mn Mo Zn Ti Pb V As MissingMass1

Oil sands fugitives 0.06 0.01 0.01 -0.1 0.07 -0.03 -0 0 0.01 -0.04 -0.05 -0.04 -0.04 -0.050.04 0.02 -0.06

-0.05

Liquid/unburned fuel 0.11 0.04 0.07 -0.1 0.1 -0.14 -0.1 0 0.01 -0.03 -0.04 0.01 -0.01 -0.12 -0.1 0.02 -0.11 -0.05

Ethylbenzene/xylene-rich 0.09 -0.03 0.01 -0 -0.02 -0.06 0 -0.1 0.05 -0.01 -0.01 -0.09 -0.05 -0.010.05 0.16* 0.02

0.05

Aged air mass/regional transport -0.07 0.34*

* -0.06 -0.1 0.18* 0.19* 0.16* 0.30** 0.04 0.35** 0.02 -0.03 -0.08 0.26** 0.0

5 -0 0.10 0.28**

Toluene-rich -0.03 0.02 -0.04 0.05 -0.02 -0.12 -0 0 -0.01 -0.09 -0 0.12 -0.03 -0.07 -0 -0.1 -0.05 0.08

Mixed source0.21*

*0.29*

* 0.39**0.21*

*0.45*

*0.28*

*0.22*

* 0.20** 0.15* 0.17*0.32*

* -0.010.29*

* 0.020.01 -0.0 0.14

0.42**

**Correlation is significant at the 0.01 level (2-tailed), *Correlation is significant at the 0.01 level (2-tailed).1particularly carbonaceous aerosol (Bari and Kindzierski, 2017)

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Data analysis techniques

Source apportionment method: Positive matrix factorization (PMF)Source apportionment is a method consisting of various techniques or tools to identify sources of air pollutants and apportion the relative contribution of each source to measured concentrations. One important technique for source apportionment is receptor modeling that has been developed and used over the last 40 years to understand the nature of source-receptor relationships in the air on the basis of measured concentrations at the receptor sites. Receptor model is a mass balance approach and can be applied to identify emission sources and estimate contributions of all identified sources to measured pollutant concentrations in the time period of interest (Hopke, 1991). It is based on two major assumptions: that source profiles i.e., composition of the emission sources do not change significantly over the period of sampling at the receptors, and the air pollutants (or chemical species) do not react chemically or undergo phase partitioning during transport from source to receptor. The method can thus be used to detect the hidden source information from ambient measurement datasets.

The multivariate receptor model positive matrix factorization (PMF) was used to determine possible emission sources of measured VOCs concentrations at oil sands communities. For PMF analysis, the dataset was for the selected study period January 2010 to March 2015. The PMF input file consisted of 247 and 249 daily (24 h) samples for Fort McKay and Fort McMurray, respectively. Five daily samples from both Fort McKay and Fort McMurray dataset were excluded due to outliers and/or heavily influenced by wildfires events. Out of 60 measured VOC species, 24 species were selected for PMF analysis at both oil sands communities based on their higher frequency of detection (at least 20% of the data above the method detection limit) (Table S3), potential toxicity (e.g., methanol, acetaldehyde) and using source-specific tracers (e.g, biogenic tracer isoprene).

PMF is a multivariate technique based on a constrained, weighted least squares fit, where the weights are derived from analytical uncertainties (Paatero and Tapper, 1994; Paatero, 1997). In comparison to principal component analysis (PCA), PMF produces a better fit to the data and provides non-negative factors, error estimates and better data treatment including handling or adaptation for missing values and values below the detection limit (Paatero and Tapper, 1994). PMF has been widely used for receptor modeling of ambient fine particulate matter, volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs). The objective of a PMF model applied to an airborne PM data matrix, X of dimensions n by m (n is the number of samples and m is chemical species to be measured) is to resolve the number of source factors p, the species profile F (p x m) of each source, and the amount of mass G (n x p) contributed by each factor to each individual sample, which is based on the mass conservation principle as follows (Hopke, 1991):

x ij=∑k=1

pg ik f kj+eij

where:xij is the concentration at a receptor for the jth species measured in the ith samplegik is the contribution of the kth factor to the ith samplefkj is the mass fraction of the jth species from the kth factoreij is the residual for the jth species in the ith sample

It is assumed that the contributions and mass fractions are all non-negative, i.e., results are constrained to physically possible solutions (Eatough et al. 2008). Moreover, each data point can be weighted individually e.g., by adjusting the uncertainty of measured values below the detection limit so that they have less influence on the solution than measurements above the detection limit. Based upon the

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uncertainties of each observation, PMF provides a solution that minimizes an object function, Q(E), as follows (Polissar et al., 1998):

Q( E )=∑i=1

n

∑j=1

m

(( x ij−∑k=1

pg ik f kj )/uij)

2

where:uij is the overall uncertainty for the jth species measured in the ith sample.

In this study, the United States Environmental Protection Agency’s EPA PMF program version 5.0 (USEPA, 2014a) was used for VOCs source apportionment. EPA PMF operates in a robust mode, meaning that ‘outliers’ are not allowed to influence the fitting of the contributions and profiles.

Uncertainty estimation is an important step in PMF, where each of the data values is assigned an estimated uncertainty including both measurement uncertainty and source profile variability. In this study, the approaches of Polissar et al. (1998) and USEPA (2014a) were adopted to estimate concentration values and their associated uncertainties including missing data and below detection level values. In this study, analytical uncertainty values of each VOCs in each sampling day were not available. Therefore, uncertainty of each VOCs was assigned using historical records (2002-2003) of duplicate sampling for urban VOCs performed by Environment Canada and 15% uncertainty was used for each VOCs measured concentration. Therefore, The uncertainty of each species was calculated by estimating analytical uncertainty by multiplying the method detection limit (MDL) by 0.5 and adding sampling uncertainty for each species (set at 15% of the measured concentration) as follows:

Unc=√(Error Fraction ×concentration)2+(0.5 × MDL)2

The concentration values were used for the measured values and the sum of the analytical and sampling uncertainty was used as the overall uncertainty assigned to each measured value.

In PMF analysis, below detection level data values are generally replaced by ½ of the detection limit. However this censoring practice can prevent error estimation features of the EPA PMF version 5.0 model, introduce hard-to-estimate bias, and occasionally give rise to ghost factors (Brown et al., 2015). Therefore, in this study no censoring was done for data below detection limit and original measured values were used as measured concentrations of each component. Uncertainties of below detection level data values were set to 5/6 of the MDL. Missing data values were replaced by the median of all measured concentrations of the given species, and their associated uncertainties were set at four times the median concentration after USEPA (2014a). For selection of species categories, the signal to noise ratio (S/N) approach (Paatero and Hopke, 2003) was not adopted in this study. Instead, species were evaluated (strong or weak) based on residuals and observed predicted statistics after the initial base run.

The model was run 20 times and a seed value of 30 was taken in order to replicate the results. To consider possible temporal changes in source profiles and other sources of variability, 10% extra modeling uncertainty was applied. All runs were converged and a global minimum was found. The optimal number of factors was chosen after analyzing several model performance criteria after Lee et al. (1999) e.g., goodness-of-fit Q-values for the entire run, scaled residual matrices, scatter plots between species, agreement between predicted and measured mass, and physical meaningfulness of the source profiles and contributions.

The plausibility and interpretability of solutions with four to ten factors were checked and a 5-factor solution was chosen for Fort McKay and a 6-factor solution for Fort McMurray. The common identified sources at both communities were oil sands fugitives, liquid/unburned fuel, ethylbenzene/xylene-rich and

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aged air mass/regional transport. The other sources included petroleum processing at Fort McKay and tolune-rich and a mixed source at Fort McMurray. All runs converged and a global minimum was found. Qrobust equalled Qtrue, indicating no outliers impacting the results. Qexpected (number of non-weak data values in X) – (number of element in G and F, taken together) was calculated. In this study, the ratio of Q robust

and Qexpected for a 5-factor (6-factor) solution was 0.636 (0.535) at Fort McKay (Fort McMurray) indicating an overall acceptable fit of the model. Fpeak solutions for parameters between –0.5 and 1.5 were checked and the base run was preferred as an optimum solution and chosen for further analysis, as this is most likely to be physically meaningful (Paatero et al., 2002).

In this study, several approaches e.g., plausibility and interpretability of factors, PMF error estimation results were examined to obtain the optimum number of factor solutions. From PMF trials of four to ten factor solutions results indicated that increasing or decreasing the number of factors showed splitting or combining of sources, respectively and did not appear to be physically meaningful. For example, at Fort McKay a 6-factor solution yielded an additional solvents factor and 7-factor solution resolved 2 additional factors i.e., solvents and mixed source (characterized by benzene, isoprene and methanol). While at Fort McMurray, a 7-factor solution yielded a benzene/pentane-rich factor and for a 5-factor solution oil sands fugitives and ethylbenzene/xylene factors were combined to a single factor resulting in a physically meaningful source factor.

Error estimation is an important feature in new version of EPA PMF 5.0 model. Variability in the PMF solution was assessed via a bootstrapping (BS) analysis. A total of 100 bootstrap runs were performed with a minimum r2-value of 0.8. A solution is considered stable when all factors bootstrap over 80% of the time. Compared to 6- and 7-factor solutions, results were generally stable for a 5-factor solution at Fort McKay where all 5 factors mapped to a base factor in ~≥ 90% of runs at Fort McKay except for the petroleum processing factor (mapped on 58%) (Table S10). In a 6-factor solution at Fort McMurray there were 4 factors mapped in 90–96%, and the remaining two factors in ~90% of runs. To understand uncertainty of the PMF solution including effects of random errors and rotational ambiguity, two additional error estimation methods i.e., displacement (DISP) analysis and bootstrapping with displacement (BS-DISP) were implemented (Table S10). Details of these methods are described in the guidelines of the EPA PMF version 5.0 (USEPA, 2014a). For DISP and BS-DISP, the number of swaps at the lowest dQmax level and the percent change in Q (%dQ), where swaps occur if factors change so much that they exchange identities, indicates a ‘not-well-defined’ solution (Paatero et al., 2014).

In this study, 96% and 90% of cases were accepted at Fort McKay and there were zero swaps in best fit and 4 and 9 swaps in DISP for 5- and 6-factor solutions, respectively indicating stable solution with less uncertainty compared to a 7-factor solution (84% of cases accepted, 1 swap in best fit and 13 swaps in DISP). While at Fort McMurray error results were generally more stable at 6-factor-solution (92% cases accepted, 1 swap in best fit) compared to 5- and 7-factor solutions (84% and 76% cases were accepted and 3 and 6 swaps in best fit, respectively). Therefore, in this study a 5-factor solution was chosen at Fort McKay and a 6-factor solution was chosen at Fort McMurray based on based on BS/BS-DISP results and clear interpretability of factors as discussed previously.

Verification of source assignments from PMF ModelIn environmental applications the goal of receptor modeling is to estimate number and composition of sources (i.e., the factors that explains the data variability), but also to point out any trend and/or correlation among observations and identify potential markers for pollutant sources. To verify different local and regional emission sources of VOCs, a number of different statistical approaches were undertaken. These included investigating relation between sources using Pearson correlation analysis, conditional bivariate probability function (CBPF) analysis and backward trajectory analysis.

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Correlation analysisPearson correlation analysis was carried out to investigate relationships between identified VOC sources and criteria air pollutants (NO2, SO2, O3, THC, TRS, PM2.5, CO) and PM2.5 components (e.g., ions, metals) measured at Fort McKay and Fort McMurray-Patricia McInnes. This assisted in the interpretation of source profiles.

Conditional bivariate probability function (CBPF) analysisTo understand probable ‘local’ point source impacts, conditional probability function (CPF) is commonly used in source apportionment studies (e.g., Kim and Hopke, 2004; Jeong et al., 2011; Amato and Hopke, 2012; Bari et al., 2015). In this study, conditional bivariate probability function (CBPF) was used developed by Uria-Tellaetxe and Carslaw (2014), which coupled CPF with bivariate polar plots providing more information on the nature of emission sources and revealing hidden contributions from different source types depending on wind speed. The CBPF includes CPF with wind speed as a third variable and allocates the observed pollutant concentration to a cell defined by ranges of wind direction and wind speed and can be defined as:

CBPF∆ θ , ∆u=m∆ θ ,∆ u∨C ≥ x

n∆ θ ,∆ u

where m∆θ , ∆ uis the number of samples in the wind sector ∆ θ with wind speed interval ∆ u having concentration C greater than a threshold value x, and n∆θ ,∆ uis the total number of samples in that wind direction-speed interval. To estimate CBPF, the daily fractional mass contribution from each source relative to the total of all sources was used rather than using the absolute source contributions. The same daily fractional contribution was assigned to each hour of a given day to match to the hourly wind data. In this study, calm wind conditions with a wind speed less than 1 m/s (i.e. 3.6 km/h) were excluded from the calculation due to isotropic behavior of wind vanes under calm winds. The threshold criterion was set at the highest 25% of the concentrations to define the directionality of local sources.

An accepted assumption is that important local sources are likely to be located in the directions that have high conditional probability values. The analysis of CPF or CBPF is based on the assumption that the air masses carrying emissions have a straight travel path for the hour. A large degree of uncertainty may be associated with paths of trajectories for turbulent airflows arriving at a receptor site. This may cause some uncertainties in defining the direction of air masses coming from a particular source region.

Backward trajectory analysisTo better understand the influence from long-range source regions, backward trajectory analysis was conducted using the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2003). Source data was the Global Data Assimilation System (GDAS) model accessed through the HYSPLIT web archive (http://ready.arl.noaa.gov/archives.php). The geographic region covered by the air trajectories was divided into 8800 grid cells of 0.5° x 0.5° latitude and longitude. Forty eight-hour backward trajectories starting at noon local time at were calculated for every sample day using gridded meteorological data combined with the Geographic Information System (GIS) based software TrajStat (Wang et al., 2009). In general trajectories are calculated at an approximate height of the mixing layer e.g., 500 m above the ground level and this has been commonly used in most source apportionment studies (Zeng and Hopke, 1989; Jeong et al., 2011; Bari et al., 2015). This height is also beneficial for minimizing surface frictional effects and for representing winds in the lower boundary layer. Real air parcel dilution and flow may also differ according to actual mixing height at different seasons. Myrick et al. (1994) reported that in Alberta median mixing layer heights at solar noon in summer and winter were 659m and 162 m, respectively. Therefore, in this study backward trajectory analysis was performed using a mixing layer height of 700 m for summer and 200 m for winter.

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Concentration-weighted trajectory (CWT) methodPotential Source Contribution Function (PSCF) analysis is a common method to study the most probable upwind distant source regions and it is widely used to identify regional sources based on the HYSPLIT model (Hopke et al., 1995; Pekney et al., 2006; Jeong et al., 2011; Bari et al., 2015). PSCF values for the grid cells in the study domain are calculated by counting the trajectory segment endpoints that terminate within each cell (Ashbaugh et al., 1985). However, one of the limitations of PSCF is that the same PSCF values can be observed for grid cells if sample concentrations are slightly higher or extremely higher than the criterion concentrations, making it hard to differentiate large sources from moderate ones. To overcome this limitation of PSCF, we used the concentration-weighted trajectory (CWT) method in this study, where each grid cell was assigned a weighted concentration by averaging the sample concentrations that have associated trajectories crossing the grid cell as follows (Seibert et al., 1994; Hsu et al., 2003):

C ij=1

∑l=1

M

τ ijl

∑l=1

M

C l τ ijl

where C ij is the average weighted concentration in the ijth cell, l is the index of the trajectory, M is the total number of trajectories, C l is the concentration observed on arrival of trajectory l, and τ ijl is the time spent in the ijth cell by trajectory l.

For PMF-derived source contributions, CWT was calculated for every sample day using 48-hr backward trajectories starting at noon local time at a height of 700 m above the ground level during summer (200 m during winter). A high value for C ij implies that air parcels traveling over the ijth cell would be, on average, associated with high concentrations at the receptor. In this study, CWT is a function of PMF-derived source contributions of different factors and the residence time of a trajectory arriving at the oil sands community stations in each grid cell.

Polissar et al. (1999) stated that it is likely that small values of the number of trajectory segment endpoints that fall in the grid cell (nij) may produce high CWT values with high uncertainties. A small number of trajectory segment endpoints (e.g., < 2) can lead to the false identification of upwind and downwind source areas known as the tailing effect due to evenly distributed weight along the path of trajectories (Hsu et al., 2003). To reduce this effect, CWT values were multiplied by an arbitrary weight function W ij to better reflect uncertainty in the values for these cells. The weighting function reduced CWT values when the total number of the endpoints in a particular cell was less than about three times the average value of the end points per cell (PPC). In this case, W ij was defined as below:

W ij=0.7 if PPC<nij ≤ (3x PPC ) 0.42 if (0.5 x PPC )<nij ≤ PPC

0.2 if (0.5 x PPC ) ≥ nij

Other uncertainties may exist due to factors unable to be accounted for in the backward trajectory analysis, e.g., an increasing number of trajectory endpoints approaching the receptor location, distance travelled by single trajectories, turbulent airflows, potential deposition, chemical and physical processes that occur along the trajectory pathway (Stohl, 1998; Cheng et al., 2013, 2015).

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Human health risk assessment of VOCs species and sourcesA screening-level health risk assessment offers an initial assessment about whether a substance is likely to pose a risk to public health (USEPA, 2016, Health Canada, 2004a). For assessing public health risks associated with long-term (chronic) and short-term (acute) inhalation exposures, dose-response criteria of the United States regulatory agencies have been used in several studies to screen ambient concentrations of hazardous air pollutants (Macey et al., 2014; Bunch et al., 2014; Bari et al., 2016; Bari and Kindzierski, 2017). The USEPA’s risk assessment methods (USEPA, 1991, 2009) are generally used in studies to evaluate carcinogenic and non-carcinogenic risks of ambient trace metals in PM2.5, (e.g., Hieu and Lee, 2010; Li et al., 2016). However, due to the nature of multiple source signatures, pollutant-specific risk estimates may provide limited information for air quality management. Source-specific risk estimates may add valuable information for understanding potential control strategies for particular sources (Mukerjee and Biswas, 1992; Wu et al., 2009). Source-risk apportionment – which couples the risk assessment process and the source apportionment model – has been applied in several studies worldwide to determine relative source contributions to human health risks (Liao et al., 2015; Bari and Kindzierski, 2017).

For assessing public risks associated with long-term (chronic) and short-term (acute) inhalation exposures, dose-response criteria of generally recommended by USEPA's Office of Air Quality Planning and Standards (OAQPS) were used to screen ambient concentrations of VOCs at oil sands communities based on the dataset over 6 years. For chronic exposure, carcinogenic and non-carcinogenic risks were evaluated using EPA inhalation unit risk (IUR, (µg/m3)–1) and reference concentrations (RfCs, mg/m3) associated with the concentration for each trace element (USEPA, 2014b, 2015). For acute exposure, available United States Agency for Toxic Substances and Disease Registry (ATSDR) minimal risk levels (MRLs) and California EPA reference exposure levels (RELs) were used.

Health risk to PMF-derived VOC sources was also screened by summing carcinogenic and non-carcinogenic risks of all available risk-posing species in a particular source. We applied a ‘point estimate approach’ that has been used in several studies (Wu et al., 2009; Khan et al., 2016; Bari and Kindzierski, 2017). In this study, exposure concentration (EC) was calculated using the following equation according to USEPA’s Superfund program (USEPA, 1991, 2009):

ECij=C ij ×ET × EF × ED

ATwhere ECij and C ij are the exposure concentration and concentration of the jth species from the ith source (µg/m3), respectively, ET : air exposure time (24 hours/day), EF : exposure frequency (350 days/year, assuming 15 vacation days per year), ED: exposure duration (24 years for adults), AT : average time (for carcinogens, 70 yrs x 365 days/yr x 24 hours/day; for non-carcinogens, AT : ED x 365 days x 24 hrs/day). Cancer and non-cancer risk from exposure to the i th source was estimated as the sum of cancer and non-cancer risks of all available n risk-posing VOCs species in PMF-derived profiles.

Carcinogenic risk i=∑j=1

n

ECij × IUR j

Non−carcino genic risk i=∑j=1

n ECij

RfC j

where, IUR j and RfC j are the inhalation unit risk and the reference concentration for the j th species, respectively. The excess cancer risk range recommended by the USEPA (2009) for public health protection is a one in a million (1 x 10–6) acceptable risk level to one in a thousand (1 x 10–4) tolerable risk level. Provincial regulatory agencies across Canada have varying guidance of acceptable cancer risk, with Alberta, British Columbia, and the Atlantic provinces accepting an incremental lifetime cancer risk of 1 in 100,000 (1 x 10–5) (Health Canada, 2004b; Kindzierski et al., 2011). A non-cancer risk is represented by

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hazard index (HI). USEPA (2009) states that if HI < 1, there is no appreciable risk of adverse health effects, while HI > 1 indicates a chance of non-cancer effects occurring.

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