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Oil sands operations as a large source of secondary organic aerosols


Worldwide heavy oil and bitumen deposits amount to 9 trillion barrels of oil distributed in over 280 basins around the world1, with Canada home to oil sands deposits of 1.7 trillion barrels2. The global development of this resource and the increase in oil production from oil sands has caused environmental concerns over the presence of toxic compounds in nearby ecosystems3,4 and acid deposition5,6. The contribution of oil sands exploration to secondary organic aerosol formation, an important component of atmospheric particulate matter that affects air quality and climate7, remains poorly understood. Here we use data from airborne measurements over the Canadian oil sands, laboratory experiments and a box-model study to provide a quantitative assessment of the magnitude of secondary organic aerosol production from oil sands emissions. We find that the evaporation and atmospheric oxidation of low-volatility organic vapours from the mined oil sands material is directly responsible for the majority of the observed secondary organic aerosol mass. The resultant production rates of 45–84 tonnes per day make the oil sands one of the largest sources of anthropogenic secondary organic aerosols in North America. Heavy oil and bitumen account for over ten per cent of global oil production today8, and this figure continues to grow9. Our findings suggest that the production of the more viscous crude oils could be a large source of secondary organic aerosols in many production and refining regions worldwide, and that such production should be considered when assessing the environmental impacts of current and planned bitumen and heavy oil extraction projects globally.

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Figure 1: Relative increase in OA downwind of the OS.
Figure 2: OA mass screens during F1.
Figure 3: PMF analysis for F1.
Figure 4: Modelling SOA formation during F1.

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We thank the National Research Council of Canada flight crew of the Convair-580, the technical support staff of the Air Quality Research Division, S. Cober for the management of the study, and the community of Fort McKay for the support of the Oski ôtin ground site at Fort McKay. The project was supported by the Clean Air Regulatory Agenda and the Joint Oil Sands Monitoring program.

Author information

Authors and Affiliations



All authors contributed to the collection of observations in the field, in the laboratory or the development of the box model. J.L. and S.-M.L. wrote the paper with input from all co-authors. S.-M.L. designed and directed the flights. Y.M.T. and C.S. conducted the box modelling work with input from J.L. D.R.G., D.P., B.D.D. and P.L. provided bitumen volatility distributions.

Corresponding authors

Correspondence to John Liggio or Shao-Meng Li.

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Competing interests

The authors declare no competing financial interests.

Additional information

The data used are available on the Canada-Alberta Oil Sands Environmental Monitoring Information Portal (

Extended data figures and tables

Extended Data Figure 1 Flight tracks for the three transformation flights, F1, F2 and F3.

The approximate locations of the major OS plumes studied in this work are shown as the white shaded boxes. Map data: Google, image Landsat, 2015.

Source data

Extended Data Figure 2 Measured organic and sulfate aerosol concentration during F1.

Successive transects (labelled A, B, C and D) through the same major OS plumes at approximately 600 m altitude and 1 h apart in transit time. Inset pie plots show the mean relative mass fraction for organics (green), sulfate (red), nitrate (blue) and ammonium (orange) during the yellow highlighted section. Organics dominate the aerosol mass throughout the flight; note the change in magnitude between the OA scale on the left and SO4 scale on the right. Map data: Google, image Landsat, 2015.

Source data

Extended Data Figure 3 OA mass screens used to estimate SOA production.

a, b, OA mass screens for F2 (a) and F3 (b). The SOA production rate during these flights (~77 km and ~50 km between screens) is the sum of the differences in OA transfer rates between screens (that is,: 2.7 ± 1.0 t h−1 and 2.1 ± 0.9 t h−1). The overall formation rate from the OS source region (S) is the integrated OA transfer rate through screen B (5.3 ± 1.0 t h−1 and 4.3 ± 0.9 t h−1). Map data: Google, image Landsat, 2015.

Source data

Extended Data Figure 4 PMF analysis results and comparisons.

a, The OA AMS spectra from an α-pinene + OH radical smog chamber experiment as a function of photochemical ageing time in the chamber. b, PMF factor 1 from F1. A high degree of similarity is observed between these spectra after approximately 6 h of ageing in the chamber. c, Correlations between PMF factors 1 and 2 and the corresponding smog chamber data (terpene oxidation and bitumen vapour oxidation SOA).

Source data

Extended Data Figure 5 Bitumen volatility distributions.

The volatility distribution (mass fraction) based on carbon number are for OS that was thermally treated. Volatile hydrocarbons are trapped on polyurethane foam (PUF) tubes at 50–80 °C (red). The volatility of the remaining bitumen material is shown in green (50–80 °C) and that of bitumen which was solvent extracted from the sand without heating is shown in grey. Note the complete loss of hydrocarbons in the C12–C15 range upon heating (denoted in yellow). Data are stacked upon each other for clarity. Error bars represent the s.d. of n = 3 experiments.

Source data

Extended Data Figure 6 Bitumen-related IVOCs in ambient ground-based data.

a, Total ion chromatogram from ambient sampling in the OS when impacted by forest-influenced air (blue) and OS-operations air (red). The bitumen vapour headspace chromatogram is also shown (black), demonstrating that a large fraction of the gaseous mass in OS-impacted air has volatilities (C13–C16 range) critical for SOA formation. b, Associated volatility distribution for OS-impacted air scaled by SOA yield11. c, One-hour back trajectory for OS-impacted sample using the hybrid single particle Lagrangian integrated trajectory model (HYSPLIT). d, One-hour HYSPLIT back trajectory for forest-influenced sample.

Source data

Extended Data Figure 7 Background concentration time series.

a, b, The BC (a) and OA (b) time series for F1 with associated interpolated backgrounds. The background variability contributed little uncertainty to the overall analysis of ΔOA/ΔBC in Fig. 1.

Source data

Extended Data Figure 8 SOA production rate extrapolation.

a, Measured SOA for F1 extrapolated to one photochemical day. Total SOA production is the sum of scaled hourly SOA production rates (orange; see Supplementary Methods). The blue region represents the same scaling performed where only photolysis rate constants are varied in the model. Error bars represent a range of SOA estimates assuming ±20% on the initial OA estimates via the TERRA algorithm. b, Modelled dependence of OH radical concentration on the ozone photolysis frequency (JO1D). Further varying initial conditions for NOx, water vapour and isoprene in the model has a small effect on this relationship.

Source data

Extended Data Figure 9 Box-model performance evaluation.

a, b, Measured and modelled gas-phase species during plume intercepts of F1, where only the initial conditions (t = 0) of the species are constrained by measurements. Good agreement between model and observation is achieved. c, Sensitivity of predicted SOA for F1 to changes in the oxidation rate constant and yield (all other variables remain constant). Yield refers to the SOA mass yield during the oxidative ageing. Simulations using a single oxidative rate constant and yield represent upper and lower limits to SOA formation, while the base case simulation most closely resembles measurements. Error bars represent s.d. of the measured OA (n = 7).

Source data

Extended Data Figure 10 PMF factors from ground-based data in the OS.

PMF factors 1 (biogenic SOA (B-SOA)) and 2 (OS-SOA) from 1 year of ground-based data in the OS production region (monthly 25th to 90th percentiles shown, n = 22,280), indicating that factor 2 (using a collection efficiency of 1) is derived from the oxidation of OS emissions all year long, while factor 1 is from oxidation of biogenic emissions (that is, summer peak only).

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Discussion, Supplementary Tables 1-2 and Supplementary References. (PDF 637 kb)

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Liggio, J., Li, SM., Hayden, K. et al. Oil sands operations as a large source of secondary organic aerosols. Nature 534, 91–94 (2016).

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