SUPPLEMENTARY INFORMATION

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1 doi: /nature17646 Supplementary Methods Aircraft Aerosol Measurements. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, (Aerodyne Research Inc.) was deployed to measure real-time nonrefractory particulate matter (NR-PM, i.e., ammonium, nitrate, sulfate and organics). The ambient air was drawn through a forward facing, shrouded isokinetic particle inlet (Droplet Measurement Technologies, Boulder, CO, USA) from which the HR-ToF-AMS sub-sampled. The total residence time in the inlet and associated tubing was approximately 1 second. The working principle of the HR- ToF-AMS has been reported in detail previously 30. In brief, particles (~50nm 1µm) are aerodynamically focused and impact a tungsten heater (~600 o C) to vaporize the NR-PM. The resultant gas-phase species are ionized by electron impact ionization and then detected by a high-resolution time-of-flight mass spectrometer. The HR-ToF-AMS was operated in V-mode only (10 seconds, mass resolving power ~2000), with background measurements (filtered ambient air) performed for approximately 3-5 minutes, 4-5 times per flight. Ionization efficiency (IE) calibrations were performed using monodisperse ammonium nitrate particles prior to a select number of fights, but did not change significantly during this study (± 9%). The AMS collection efficiency was derived by comparing the total AMS derived mass with the mass derived from the size distribution measurements, assuming a density commensurate with the chemical composition (from the AMS). The collection efficiency ranged from 0.5 to 1. The HR-ToF-AMS data were processed using AMS data analysis software (Squirrel, version 1.51H for unit mass resolution (UMR) data and Pika, version 1.10H for high resolution peak fitting, with the updated fragmentation table 30. Elemental analysis (oxygen- and hydrogen-to-carbon ratios, O:C and H:C) is based upon the improved method proposed recently 50. Positive matrix factorization (PMF, PMF Evaluation Tool (PET) version 2.06) was used for the deconvolution of the AMS organic mass into the forms of organic aerosol contributing to the organic mass measurement in this study as described by Ulbrich et al PMF is a multivariate factor analysis tool that decomposes a matrix of sample data into two matrices, namely, factor contributions and factor profiles. The ability of PMF to separate the signals of a multi-component aerosol matrix has been well established and is widely used for identifying different types of OA based on the observed 1

2 ion fragments in field measurements PMF analyses were done in the robust mode. The default convergence criteria were not modified. The Q values as a function of FPEAK from -1 to +1 were examined. For the variables with signal-to-noise ratio (SNR) less than 0.2 ( bad variables) and downweight variables with SNR between 0.2 and 2 ( weak variables), their error estimates were increased by a factor of 10 and 3, respectively, as recommended by Paatero and Hopke 54. In this study, the SNR of all m/z fragments are larger than 0.2. The error values for m/z 44, 18, 17 and 16 were multiplied by sqrt(4). Refractory black carbon (rbc, referred to as BC herein and in the main text) particle measurements were made with a Single Particle Soot Photometer (SP2; Droplet Measurement Technologies, Boulder, CO, USA.), which is based upon the principle of laser-induced incandescence and is capable of sub-second time resolution. The SP2 sub-sampled from the same isokinetic particle inlet described above, with a total residence time of 1 second. The principle of operation of the SP2 and its use on board aircraft have been described extensively 31,32. Briefly, individual BC particles sampled by the SP2 were irradiated with an intra-cavity pump laser (1064 nm), heating the BC particles to >4000 o C and resulting in BC incandescence which was monitored in the visible band (λ= nm). The BC mass for each particle was estimated from the incandescence peak intensity which was calibrated with a mono-disperse aerosol stream (selected with a scanning mobility particle sizer; SMPS, TSI inc.) of regal black particles. Mass calibration of these particles was determined prior to the field study using an Aerosol Particle Mass (APM) analyzer (Kanomax Inc., Japan) coupled to an SMPS. An experimentally determined transmission efficiency correction was applied to the data to account for non-unity transmission of particles between 70 and 90 nm. The single particle events were binned into discrete 1 second intervals to compute a mass concentration (µg m -3 ). Particle size distribution measurements spanning the range of 0.06 µm to 20 µm in diameter were also made through the same isokinetic particle inlet described above. These measurements were performed with an Ultra High Sensitivity Aerosol spectrometer (UHSAS; µm; DMT Inc.) and a Forward Scattering Spectrometer Probe (FSSP) Model 300 ( µm; Particle Measuring Systems Inc.). Aircraft Gaseous Measurements. A subset of volatile organic compounds (VOCs) was measured with a high-resolution proton transfer time-of-flight mass spectrometer (PTR-ToF-MS, Ionicon Analytik GmbH). The operation of the PTR-MS has been presented in great detail 33. Briefly the 2

3 PTR-MS is a soft ionization technique that allows for detection of VOCs that have a greater proton affinity than water. This includes species such as unsaturated hydrocarbons (eg: isoprene), aromatics (eg: benzene), and a variety of oxygenated compounds. Ambient air was drawn through a rear facing, 3 meter-long, 6.35 mm (¼ inch) diameter Perfluoroalkoxy (PFA) sampling line by a diaphragm pump at a rate of 6 slpm. A portion of this ambient air (270 sccm) was drawn into the PTR-ToF-MS through a heated PFA tube (60 0 C), resulting in an overall delay time for the instrument of 2 seconds. Background measurements were performed for 3-5 minutes, by passing the ambient air through a platinum wool catalyst heated to 350 o C, approximately 3-5 times per flight. The frequency of the measurement was 0.5 Hz. The raw PTR-ToF-MS data were acquired with the TofDaq software and post processed with ToFWare (Tofwerk AG, Switzerland). A more extensive set of hydrocarbons was measured via on board canister sampling, followed by analysis by gas chromatography mass spectrometry (GC-MS). Integrated air samples were collected during each flight into pre-cleaned and passivated 3L stainless steel canisters housed in a manifold containing 6 canisters which could be sampled sequentially. The integration period of each sample was approximately 20 seconds. For each flight, integrated samples were collected covering both in and out of plume conditions. Samples were analyzed for a total of 156 species, including C1-C8 alkanes, branched alkanes, alkenes, aromatics and halogenated hydrocarbons. NO and NO 2 (NO x = NO + NO 2 ) were measured separately with two chemiluminescence-based NO/NO x instruments (TECO 42i; Thermo Electron Corporation, Waltham, MA, USA). A photolytic converter (Air Quality Design Inc.) was operated inside one instrument to selectively convert NO 2 to NO followed by detection of NO. Ambient air was drawn through a 6.35 mm (¼ inch) diameter PFA sampling line resulting in an overall delay time of approximately 6 seconds. Data was recorded with a 1 second time resolution. Instrument zeros were performed 3-5 times per flight for a duration of ~5 minutes by passing ambient air through an in-line Koby air purifier cartridge. Calibrations were carried out multiple times pre and post flight throughout the study with NIST traceable standards (Scott Marrin Inc). The conversion efficiency of the photolytic converter was determined to be ~60%, which was applied to the subsequent data. Bitumen Vapor Oxidation Laboratory Experiments. Laboratory experiments were performed with authentic samples of bitumen recovered in the Athabasca OS region. The experiments were 3

4 performed to provide representative HR-ToF-AMS organic spectra of bitumen derived SOA (via OH radical oxidation), which could be compared to those acquired during ambient sampling in the OS plumes. Bitumen vapour was produced by passing a stream of zero air (~ 120 sccm) through a ½ O.D. PFA tube heated to ~ 50C which contained ~100g of bitumen. These bitumen vapours were introduced into a 2 m 3 Teflon smog chamber for SOA experiments. No discernable difference in the mass spectra of the SOA formed from vapors evolved at higher temperatures was observed. This experimental approach for forming SOA from other precursor gases has been outlined previously 27. Briefly, mono-disperse acidic sulfate particles (molar NH 4 /SO 4 1.2) were generated via atomization (model 3706, TSI), dried through a diffusion drier, and size-selected with a differential mobility analyzer (DMA) (model, 3081, TSI) to have a mode mobility diameter (D m ) of ~150 nm. A high concentration of seed particles (~5000 particle cm -3 ) was added into the chamber to suppress new particle formation from the added VOC precursor and oxidant. OH radical was produced by photolysis of H 2 O 55 2, which was added by bubbling zero air through a 30% H 2 O 2 solution (Sigma Aldrich). A 10 ppm cylinder of NO (Scott Marrin) was used to achieve a 10 ppbv NO concentration in the chamber. The chamber was filled with particles, precursors and H 2 O 2 for 1 hour at which point the UV lights in the chamber were turned on for the duration of the experiment. Previous experiments under these conditions 27 indicated that OH radical concentrations of approximately 2x10 6 to 4x10 6 molecules cm -3 were generated in the chamber, estimated by monitoring the loss of a precursor hydrocarbon. The VOC concentrations in the chamber were measured online with a PTR-ToF-MS as described in the Supplementary Methods. The concentration and composition of SOA coated onto the seed particles were measured with a HR-ToF-AMS operated in V-mode, with data analyzed as described the Supplementary Methods. Bitumen Volatility distribution measurements. Volatility distribution (VD) measurements of the sand containing bitumen and of the vapors evolved from the bitumen at various temperatures were conducted with two complimentary and related approaches: (1) direct thermal treatment of oil sand material followed by cryogenic trapping and flash vaporization-gas-chromatography mass spectrometry and (2) thermal treatment followed by solvent extraction and gas chromatography with flame ionization detection (GC-FID). Both approaches yielded similar results with respect to the derived volatility distributions and are described below. 4

5 For method 1 above, approximately 1g of the oil sand sample was crushed and placed in a desorption tube (Gerstel inc.) and held at constant temperatures ranging from 20 o C 100 o C for approximately 10 min (Gerstel Thermal Desorption System, TDS3). Evolved bitumen vapors were cryogenically trapped at -120 o C (Gerstel Cooled Injection System, CIS4) throughout the desorption period. The trapped sample was then ballistically heated to 320 o C and injected onto a GC (Agilent 6890N) column (HP-5MS) for a total run length of 62min. A Time-of-Flight Mass Spectrometer (LECO Pegasus IV) was used in selected ion mode (SIM) to quantify the relative response of m/z=57 to the hydrocarbon standards. The same sample was used for all temperatures studied. This approach was used to derive the volatility distribution of the evolved bitumen vapors only (as opposed to the entire bulk bitumen sample, ie: method 2) utilizing an approach described in detail previously 56. Briefly, the volatility of the trapped vapors is determined via calibration of the GC retention time with hydrocarbon standards of known Effective Saturation Concentration (C*). With this approach, the entire chromatogram including the unresolved complex mixture (UCM) is binned into discrete bins of C*. The relative fraction within each bin can be derived assuming that the retention time is proportional to the volatility, and applying the average relative response factors for hydrocarbons within a specific volatility bin. A representative list of calibration standards is provided elsewhere 56. The 2 nd method was similar in that it thermally treated the oil sand sample prior to analysis. For these thermal treatments, approximately 1 g of fresh pulverized oil sand was evenly spread out within a precombusted fused quartz crucible (Technical Glass Products). The crucible was placed within an openended quartz tube inside a temperature programmable furnace (Lindberg Blue M) and heated for 8 hours. Experimental temperatures included 50, 60, 70, 80, 90, and 100 o C. One blank experiment was also performed at 50 o C without a sand sample. Connected upstream of the tube furnace was a mass flow controller programmed to allow an ultra-high purity helium carrier gas flow of 230 sccm resulting in a 60 second residence time through the tube furnace. Rather than cryogenically trapping evolved hydrocarbons, a stainless steel pipe housing two precleaned 22mm x 100mm polyurethane foam (PUF) tubes (SKC Inc.) was connected immediately downstream of the tube furnace for collection of the desorbed hydrocarbons at each given temperature. These PUF samples were then solvent extracted. Prior to each experiment, two PUF filters were cleaned by three separate accelerated solvent extractions (ASE) with 90:10 (% by Volume) dichloromethane:methanol at 1500 psi and 100 o C for 5 minutes. PUF filters were then 5

6 allowed to dry overnight in a vacuum oven at 50 o C in a 5 mm Hg vacuum. Immediately following each experiment, the PUF filters were extracted under the same conditions used for cleaning and extracts were concentrated down to approximately 1 ml via rotary evaporation and stored in amber glass vials in a freezer for analysis. A pre-cleaned PUF filter was spiked with a recovery standard (1- chlorooctadecane and p-terphenyl, Restek # and 31095) and extracted under the same conditions to measure ASE extraction efficiency. The bulk oil sand material, after the 8-hour thermal treatment was also solvent extracted, as was a raw sample without thermal treatment, under the same ASE parameters. The extracts were rotary evaporated to a final volume between 5 and 15 ml and stored in 40-mL amber vials in a freezer. Concentrated sample extracts were analyzed via GC-FID. Triplicate 1-uL injections were made on a 30m x 0.23mm x 0.25 µm HP-5 column (5% phenyl methylpolysiloxane). The GC oven was programmed to ramp from 40 o C to 300 o C at 10 o C min -1 and held at 300 o C for 10 minutes. The inlet was set to splitless mode at 250 o C with 2 ml min -1 helium carrier gas and the FID was set to 250 o C with an acquisition rate of 30 Hz. An n-alkane external calibration standard (DRO, Restek # 31064) was used to quantify peaks for a mass diesel range organic compounds (DRO) per mass oil sand basis (mass DRO/mass oil sand). The volatility distribution is similarly derived assuming that the value of C* for an evolved hydrocarbon is the same as that for an n-alkane with the same carbon number. Ambient volatility distribution measurements. Ambient gas-phase VOC measurements in the OS region were obtained from August 17-September 6, 2013, at a ground site located amongst multiple OS facilities. Measurements were made with A Griffin 450 gas chromatograph coupled to a cylindrical ion-trap mass spectrometer (GC-CIT-MS) with electron impact ionization. The instrument sampled from a 3.6 m long cm outer diameter stainless steel inlet from a height of 3.5 m above ground. Air samples were pre-concentrated for 10 minutes at a flow rate of 2100 ml min -1 on a dual sorbent trap containing Tenax TA and Carboxen 1017 held at 40 C and were desorbed at a temperature of C for 5 minutes onto a 30m 0.25 mm (inner diameter) 0.25 µm (film thickness) DB-5MS column. In addition to speciated measurements of VOCs, an unresolved complex mixture was consistently observed when the sampled air was directly impacted by OS operations according to Hysplit back trajectories (Extended data Figure 6). Conversely, this UCM was not observed when the sampled air was influenced by the background air only. The chromatographic UCM from the headspace of a sample of oil sand material is qualitatively similar to the OS influenced 6

7 ambient sample (Extended data Figure 6A), particularly in the carbon number range associated with significant SOA formation potential. A volatility distribution of the UCM in OS influenced ambient air (Extended data Figure 6B) was derived in a manner similar to that described in section S1.9, by sampling a 50 component VOC mixture containing C10 C16 n-alkanes (Supelco U). The VD was then scaled by estimated n-alkane SOA yields 11 to account for the fact that compounds of lower C* have higher SOA yields. The scaled VD of ambient OS impacted air indicates that gas-phase IVOCs likely from bitumen vapors exist in the atmosphere downwind of the OS, and will hence be important contributors to SOA formation. Box modelling Aging Scheme. In order mimic aerosol aging, the gas phase components in both the V-SOA (VOC SOA) and SI-SOA (Semi- and intermediate volatility SOA) were aged by two different oxidation schemes, with differences in the SOA mass formed and the amount moved between volatility bins. The traditional SOA (V-SOA here) are formed from smaller aromatic and olefin compounds and the SI-SOA precursors are longer chain alkanes. Given that these SOA groups represent different precursor species, resulting in differences in product distributions, molecular size and functional groups, differences in ageing rates are expected. The dual oxidation scheme used here was selected based on prior model studies that have also used differing aging rates for traditional SOA and SI-SOA. For example, Murphy et al., 48 used different oxidation rates for biogenic precursors and S/IVOC precursors for a lagrangian model with receptor site. They also found that over forested areas in the US they needed to assume a slower or no continuous aging for biogenic species after the first precursor oxidation step, while for urban areas they needed to assume continuous aging for SVOC/IVOCs 49,57,58. Using the GEOS-CHEM model Pye et al., 59 found that using different oxidation aging rates varying from 1x10-11 to 4x10-11 cm 3 molec -1 sec -1 for SVOC/IVOCs and no continuous oxidation aging for biogenic precursors best matched observations. Koo et al., 60 using the PMCAMx and CMAQ models, also did not continuously age biogenic precursors while using two different prescribed aging rates for anthropogenic traditional VOCs (2x10-11 cm 3 molec -1 sec -1 ) and SVOC/IVOCs (4x10-11 cm 3 molec -1 sec -1 ). Recently, Zhang et al., 61 developed an organic aerosol scheme in the regional model CHIMERE where two ageing mechanisms for SOA formation were used. With respect to the SOA formed in that model from traditional VOCs, they applied chemical aging in the VBS framework adopting different SOA yields for high- and low-no x environments, while another applied a single-step oxidation scheme without chemical aging. The model aging scheme used in the current study is in line with the generally accepted modelling approaches 7

8 described above. Rather than using a zero aging scheme for biogenic VOCs, an oxidation rate lower than SVOC/IVOC, as suggested by Hayes et al. 24 was employed. There is also a theoretical basis for selecting two different oxidation rates based upon well-known structure-reactivity relationships for OH reactions 62 resulting in a parameterization for the rate coefficient for aging as a function of increases in carbon number. Given the difference in average carbon number between primary traditional VOCs and primary SVOC/IVOCs, it is expected that the first-generation SVOC/IVOC products should also have a higher carbon number than that of the firstgeneration VOC oxidation products. Thus, the faster aging rate for SVOC/IVOCs here is consistent with this structure-reactivity relationship. In addition, the rate coefficient for aging SVOC/IVOC (4x10-11 cm 3 molec -1 sec -1 ) is similar than that expected for the oxidation of a long chain alkane 62. The sensitivity of the SOA formation formed from IVOCs to the oxidation scheme used was further examined with additional box model simulations as shown in Extended Data Figure 9C. These further lagrangian box model simulations were performed by varying aging rates (1x10-11 cm 3 molec -1 sec -1 to 4x10-11 cm 3 molec -1 sec -1 ) and stoichiometric gas-phase yields (7.5% per decade decrease in volatility to 40% per 2 decade decrease in volatility). All other inputs to the model remained constant. The two simulations with the slowest (blue dashed line) and fastest (grey dashed line) oxidation rates (1x10-11 and 4x10-11 cm 3 molec -1 sec -1 ) provide an upper and lower limit to the uncertainties associated with chemical aging rates, with the black curve representing the base case simulation. The purple curve is similar in shape to the base case but predicts slightly higher SOA formation because the rate for traditional SOA formation was double the base case (2x10-11 cm 3 molec -1 sec -1 ). The red curve has a similar maximum SOA to the base case but a slower rate of decrease. This simulation used a constant aging rate of 2x10-11 cm 3 molec -1 sec -1 and a constant yield of 40%. The green curve under-predicts observations and used a constant aging rate of 2x10-11 cm 3 molec -1 sec -1 and the same gas yields as the base case. Clearly, the choice of oxidation scheme will have an important effect on the SOA predicted. However it is expected the base case simulation represents the best estimate of the SOA formation rate, as it lies near the middle between the upper and lower limits, and is consistent with the schemes chosen in numerous regional air quality models that reasonably reproduce ambient forested and urban observations around the world 24,48,49,

9 Supplementary Discussion. Relative formation rates of SOA ( OA/ BC; Fig. 1). The evolution of OA formed during transformation flights in the OS region is examined through the use of an inert tracer to normalize the OA mass and factor out variability associated with emissions, transport and dilution within plumes (Figure 1). Typically in this approach CO is used as a normalizing tracer, whereby an enhancement ratio is defined as OA/ CO and represents the increase above background levels. Many observations have demonstrated that this enhancement ratio increases with photo-chemical processing in the atmosphere, proportional to the photochemical age (PA) of an air mass 19,20,23. When downwind of urban areas, with no additional OA inputs, a OA/ CO ratio increase can only be due to formation of SOA, and hence a qualitative measure of the relative increase in SOA as function of time (ie: PA) can be obtained. In large scale OS emission plumes CO levels are generally low and difficult to reliably differentiate from background concentrations. As an alternative we use black carbon (BC; OA/ BC) as a conserved tracer since background values of BC are near zero and hence BC is more reliably determined. A variable background for OA was derived by box-car smoothing the background pollutant data across the entire time series of a flight, where the background level was defined based upon the BC data, and was linearly interpolated within the OS plume. The background for BC was extremely low as there are no other BC sources in the region other than the OS, providing a means of identifying background from plume data. The OA background did not vary substantially. The OA background derived in this manner was highly consistent in time (and space) with that of BC. Deriving the backgrounds in this manner resulted in an OA background averaged across the time series of 4.5±0.4 µgm -3 and a BC background of 0.4±0.01 µgm -3. An example of the resultant backgrounds is provided in Extended Data Figure 7. In Figure 1, PA is defined as Log 10 (NO x /NO y ) where a PA value of zero is for fresh emissions and 1 for the case where 90% of emitted NO x was converted to products. Since during the daytime NO x is primarily NO 2 and the most important reaction for NO 2 is the reaction with the OH radical, the rate equation 19 [ NOx ] PA = k[ OH ] dt = 2.303Log10 (1) [ NOy ] 9

10 can be used to assign a time scale (ie: dt) for PA if the concentration of OH and the rate constant for NO 2 + OH (k) is known 19. While the OH levels were not measured, the Lagrangian nature of F2, F2, and F3 (Supplementary Table 1) dictates that the time scale associated with the range of PA observed here must be approximately 4 hours (e.g., the transport time from the OS sources to the last flight screen (D) in F1). This time scale corresponds to an average OH level of 1x10 7 molecules cm -3 assumed in Equation (1) and shown in Figure 1; a level consistent with the OH derived from plume box modelling here, and that derived using the ratio of toluene to benzene. The increases in OA/ BC with increasing PA for these flights (Figure 1) show that large amounts of SOA were formed in these plumes in a short time (6-fold increase over 4 hours relative to the initial OA/ BC). In the event that the OH levels during previous studies downwind of major urban centers (Figure 1) were less than 1x10 7 molecules cm -3, then the SOA evolution observed in OS plumes would be comparatively even faster. Supplementary Table 1. Meteorological and other parameters during the three measurement flights of this study. Parameter F1 F2 F3 Wind speed (m/s) * 9.5± ± ±1.0 Wind Dir (Deg) 218±16 281±11 256±11.7 A-B Distance (km) A-B time w (min) A-B time A (min) B-C Distance (km) B-C time w (min) B-C time A (min) C-D Distance (km) 29.0 C-D time w (min) 45.6 C-D time A (min) 50 *average meteorological conditions (wind speed and direction) shown for one altitude only, when the same plume was intercepted. Distances between flight screens (labelled A, B, C and D in Extended Data Figure 1). Time between screens based on the measured wind speed between screens (W). Time between screens based on the aircraft flying time (A). 10

11 Comparison to SOA estimates downwind of cities (Fig 2). To put the OS SOA production rates from these flights in context they are compared with the daily estimated SOA formation rate downwind of large urban centers in North America (Figure 2). The SOA formed within about one photochemical day of urban areas was estimated using reported OA/ CO and annual CO emissions scaled down to one day. Values of OA/ CO downwind of urban areas have been measured around the world and are remarkably similar (~50 90 µgm -3 ppm -1 ) despite differences in emissions, transport and transformation 23. Hence, assuming that the urban CO is co-emitted with SOA precursor gases, the OA/ CO ratio can be used to coarsely derive a total urban SOA formation rate. This approach has been used previously to estimate the total North American and global urban source of anthropogenic SOA 23,24. For these estimates, OA/ CO enhancements for those regions, which range from µgm -3 ppm -1 were used 19,63,64. The CO emissions for the Mexico City metropolitan area (MCMA) were obtained from Stremme et al., Greater Toronto area (GTA) CO emissions are derived from the 2010 Canadian APEI (Air Pollutant Emission Inventory) and include on-road, off-road and industrial point and area sources. The Houston area CO emissions are similarly obtained from the US EPA 2011 NEIv2 (National Emissions Inventory version 2). While oil Sands activities occur 24 hours per day, simply scaling up the hourly SOA estimates here with 24 hour multiplier may add significant uncertainty. Alternatively, to qualitatively compare the SOA production from the above cities to that of the OS region, the determined SOA production rates (tonnes hr -1 ) are extrapolated to a photochemical day as defined by the time integrated OH radical concentration. The instantaneous SOA rate of production can be reasonably assumed to be proportional to the OH radical concentration, d( SOA) k[ OH ][ precursors] (2) dt Equation (2) indicates that the instantaneous rate of SOA production is proportional to OH radical if the precursor concentrations do not significantly change over the course of SOA production. The daily (ie; per photochemical day) SOA production rate from these flights are derived by scaling the determined production rates with the cumulative OH radical concentration over 24 hours. The hourly SOA production rate (for any given hour) is scaled by the time integrated OH radical concentration as 11

12 shown in Extended Data Figure 8A for flight 1, from which the daily SOA production rate is then the sum of the hourly production rates. The same type of extrapolation is performed for flights 2 and 3. The OH radical concentrations used for this extrapolation were estimated from the measurement constrained box model described above and used to derive Figure 4 (See Methods). The box model was run 24 times for a full diurnal cycle, where each run was for a 3-hr duration, with the start hour incrementing by 1-hr, until the full diurnal cycle was generated. The initial box model concentrations were generated using a method combining observations (see Methods) with results from the regional air quality model, GEM-MACH, at 2.5-km grid spacing 66, run for the same study time period. Critical gases with respect to OH production and loss were extracted (e.g. isoprene, oxides of nitrogen, ozone, water vapor) from the 600-m altitude level of GEM-MACH for the hourly location of the center of the plume for the simulation of September 4, 2013 (Flight 1). Twenty four concentration ratios for each GEM-MACH gas-phase species were then calculated as the concentration at each hour of the day relative to the mid-afternoon GEM-MACH concentration. This yielded the relative diurnal profile for each model species which were then applied to the box model concentrations (i.e. observed values from mid-afternoon times) to derive the initial box model concentrations over the entire diurnal cycle. The modelled OH at the final flight screen as a function of J(O1D) is shown in Extended Data Figure 8B and illustrates an elliptical dependence of OH on J(O1D), with lower OH mixing ratios in the morning relative to the late afternoon. Extended Data Figure 8B also demonstrates that the effect of variable NOx and hydrocarbons on the OH is relatively small, as the same model runs performed by only varying associated j values (not NOx and hydrocarbons) resulted in a small difference in this relationship. Note that the OH radical concentration at the peak of photochemical activity is in reasonable agreement with the OH estimated using a photochemical clock (ie: 1x10 7 molecules cm -3 ; Figure 1). Extrapolating by modelled OH (using both modelling scenarios of Extended Figure 9B) results in SOA productions rate ranges of 49-74, and tonnes day -1, for flights 1,2 and 3 respectively. For this estimation, an upwards correction to the SOA production rate to account for depositional loss of SOA is not applied in order to be comparable with urban estimates which are net of deposition. However, applying this correction based upon the maximum ~7% per hour deposition loss rate described above (ie: 28% mass loss over 4 hours) results in estimated daily SOA production rates of tonnes day -1, when scaled by the integrated OH radical. 12

13 Obviously there will be a high degree of uncertainty associated with estimating SOA from urban areas, driven by uncertainties in CO emission inventories and variability in OA/ CO 23. Although the anthropogenic SOA production in the OS is extrapolated to a photochemical day (above), this scaling assumes that the final flight screens capture all of the formed SOA from the source region. However, these flights only quantified the SOA production rate at distances up to approximately 150 km from the source region. Additional SOA production beyond the last flight screens is not included in this estimation, but will occur. Hence, the daily SOA production rate downwind of the OS will be larger than estimated here. The SOA estimated downwind of urban areas assumes a OA/ CO value approximately a photochemical day away from urban sources, which is likely greater than the 4-5 hours away from OS sources studied here. In addition, the effect of nighttime oxidation chemistry (ie: via O 3 and NO 3 radical) on daily (24 hr) SOA formation rates in OS production plumes has not been included, but is expected to have a non-negligible contribution. Despite clearly underestimating the daily SOA production rates in OS plumes the estimates here and those of urban anthropogenic derived SOA remain of similar magnitude. Potential Contribution of biogenic SOA in OS plumes. Recent studies have suggested that anthropogenic emissions may at times enhance the formation of biogenic SOA in urban areas 67,68. Given the harsh fragmentation associated with the AMS and the large forested regions surrounding the OS, factor 2 could coincidentally appear as similar to bitumen vapor SOA, yet derived via biogenic VOC oxidation enhanced by OS processing emissions (ie: NO x ). However, observational evidence suggests that a biogenic SOA enhancement as a source of factor 2 is not significant in the OS region as indicated by the PMF analysis of long term, ground based organic aerosol data (also by aerosol mass spectrometry) within the OS region. From this ground based data identical PMF factors (1 and 2 above) were also resolved in addition to others including biomass burning and HOA factors. The monthly 25 th to 90 th percentiles of these two SOA factors for 2013 is shown in Extended Data Figure 10. The factor 1 monthly data sharply increases in July coincident with the peak of biogenic activity, but also sharply decreases to levels approaching zero during the winter months, in the absence of biogenic emissions. This further supports the assignment of factor 1 during transformation flights to biogenic SOA. In contrast, the equivalent factor 2 concentrations maximize in June but do not decrease to near zero during the winter months as would be expected for a biogenic derived SOA. Decreases in the fall and winter for factor 2 are likely attributed to a complex combination of 13

14 decreased photo-chemistry and/or decreased OS emissions during this time. Regardless, this supports the assertion of factor 2 as an OS derived SOA rather than a biogenic SOA enhanced by OS emissions. Furthermore, box modelling results (Fig 4C) indicate that the combined isoprene, terpene and sesquiterpene derived SOA accounts for less than 9% of the SOA formed in these OS plumes. Note that Extended Data Figure 10 represents the average aerosol types/fractions that impact the ground based site every month and are not expected to be reflective of the PMF fractions from the inplume aircraft data of Figure 3 (which is always impacted by the OS plume). However, there is good consistency between the box model results of flight 1 in Figure 4 and the relative PMF fractions shown in figure 2; both of which demonstrate that approximately 10% of the mass in the plume is biogenic SOA. An overall assessment of the influence of biogenic vs anthropogenic SOA in the entire Athabasca region is beyond the scope of this study. IVOC emission ratios for the OS. In order to incorporate IVOC emissions from this type of oil extraction source into regional models, IVOC emission factors and/or SOA formation intensity inputs are ideally required. However, IVOCs were not measured directly in this study, but inferred from box modelling and hence direct emission estimates are not possible. However, useful information can be obtained in the ratio of total inferred IVOC to total known VOCs (or individual VOCs) from the point at which the box model is initiated in Figure 4. Several of these ratios are provided in Table 2. Table 2. Estimates of IVOC to VOC emission ratios and SOA scaled to production Parameter Value IVOC * :ARO1 (ppbv/ppbv) IVOC:ARO2 (ppbv/ppbv) IVOC:Benzene (ppbv/ppbv) IVOC:Total VOC (ppbv/ppbv) 5 SOA intensity (kg SOA m -3 Bitumen) * IVOC is inferred from the box model as ppb (Figure 4) resulting in the range of ratio values.. ARO1 is a lumped group of measured aromatics for use in the box model (k OH < 2x10 4 ppm -1 min -1 ).. ARO2 is also a lumped group of measured aromatics with higher reactivity than ARO1 (k OH >2x10 4 ppm -1 min -1 ).. Total VOC is derived from a regional model (emission inventory).. SOA intensity derived from reported monthly statistics for bitumen production

15 In Table 2 the ratio of IVOC to ARO1, ARO2 and benzene represent the values at the first modelled point of Figure 4, with IVOCs from the base case modelling scenario in the range of ppbv. However, this remains up to 1 hour removed from the emission source. Hence, the accuracy of such ratios will depend upon the relative differences in OH reactivity between the IVOCs and VOCs. The IVOC:Total VOC in Table 2 was derived using a regional air quality model (GEM-MACH), where the reported total VOC emissions from the facilities (from emissions inventories) is scaled by a factor of 5 (ie: IVOC:Total VOC). This scaling resulted in a predicted IVOC concentration in the range inferred at the first model point in Figure 4 (~3 ppbv). However, since emissions inventories likely underestimate VOC emissions, there may be significant uncertainty in this value. Finally, an approximate SOA formation intensity (per m 3 of extracted bitumen) is also shown in Table 2, based upon the reported mined bitumen for the month of August, 2013 when the study occurred (scaled to one day). While there will be significant uncertainty in the values of Table 2, and thus should be used with caution, they represent a possible starting point for future model development. 50 Canagaratna, M. R. et al. Elemental ratio measurements of organic compounds using aerosol mass spectrometry: Characterization, improved calibration, and implications. Atmos. Chem. Phys. 15, , doi: /acp (2015). 51 Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R. & Jimenez, J. L. Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data. Atmos. Chem. Phys. 9, , doi: /acp (2009). 52 Slowik, J. G. et al. Photochemical processing of organic aerosol at nearby continental sites: Contrast between urban plumes and regional aerosol. Atmos. Chem. Phys. 11, , doi: /acp (2011). 53 Ng, N. L. et al. Organic aerosol components observed in Northern Hemispheric datasets from Aerosol Mass Spectrometry. Atmos. Chem. Phys. 10, , doi: /acp (2010). 54 Paatero, P. & Hopke, P. K. Discarding or downweighting high-noise variables in factor analytic models. Analytica Chimica Acta 490, , doi: /s (02) (2003). 55 Wang, K., Ge, M. & Wang, W. Kinetics of the gas-phase reactions of 5-hexen-2-one with OH and NO3 radicals and O3. Chem. Phys. Lett. 490, 29-33, doi: /j.cplett (2010). 56 Presto, A. A., Hennigan, C. J., Nguyen, N. T. & Robinson, A. L. Determination of volatility distributions of primary organic aerosol emissions from internal combustion engines using thermal desorption gas chromatography mass spectrometry. Aerosol Sci. Technol. 46, , doi: / (2012). 57 Lane, T. E., Donahue, N. M. & Pandis, S. N. Simulating secondary organic aerosol formation using the volatility basis-set approach in a chemical transport model. Atmos. Environ. 42, , doi: /j.atmosenv (2008). 58 Murphy, B. N. & Pandis, S. N. Exploring summertime organic aerosol formation in the eastern United States using a regional-scale budget approach and ambient measurements. Journal of Geophysical Research Atmospheres 115, doi: /2010jd (2010). 15

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