We sought to use a dynamic multimedia environmental fate model to reconcile the emissions and residue levels reported for three representative PAHs in the AOSR. Data describing emissions to air compiled from two official sources result in simulated concentrations in air, soil, water, and foliage that tend to fall close to or below the minimum measured concentrations of phenanthrene [PHE], pyrene [PYR], and benzo(a)pyrene [BaP] in the environment.

Accounting for evaporative emissions (e.g., from tailings pond disposal) provides a more realistic representation of PAH distribution in the AOSR. Such indirect emissions to air were found to be a greater contributor of PAHs to the AOSR atmosphere relative to reported direct emissions to air. The indirect pathway transporting uncontrolled releases of PAHs to aquatic systems via the atmosphere may be as significant a contributor of PAHs to aquatic systems as other supply pathways. Emission density estimates for the three PAHs that account for tailings pond disposal are much closer to estimated global averages than estimates based on the available emissions datasets, which fall close to the global minima.

—Parajulee and Wania

Comparison of log PHE, PYR, and BaP emission densities in t/km2·y based on: per country emissions estimates from Shen et al. (black), AOSR data from the NPRI and EIA (blue), and estimated emissions from inverse modeling for the AOSR (red) in this study. Average emissions densities from Shen et al. are represented by the dashed black line. Source: Parajulee and Wania. Click to enlarge.

PAHs—many of which are highly carcinogenic—are produced during the process of extracting petroleum from the oil sands. Previous models have assessed only the PAHs that are released directly into the atmosphere during extraction. These numbers tend to fall within acceptable regulatory levels.

Parajulee’s model takes into account other indirect pathways for the release of PAHs that haven’t been assessed before. Parajulee and Wania used the non-steady-state multimedia fate model CoZMo-POP (Coastal Zone Model for Persistent Organic Pollutants—developed by Frank Wania and Donald Mackay at Trent University) in all model simulations.

CoZMo-POP is a non-steady state mass balance model for POPs in the coastal zone based on the fugacity approach. Fugacity is a thermodynamic quantity related to chemical potential or activity that characterizes the escaping tendency from a phase. (Mackay 1982) In other words, fugacity describes mathematically the rates at which chemicals diffuse, or are transported between phases.

First, Parajulee and Wania parameterized the model better to represent the AOSR environment and the contaminants being modeled. They then ran model simulations for PHE, PYR, and BaP with two sets of officially reported emissions scenarios: one which included only emissions to air (AIR), and another which described emissions into a tailings pond compartment (TP). The emissions scenarios were first evaluated by comparing the model predicted concentrations with those measured in the environment.

During a last set of model simulations for all contaminants, they adjusted emissions scenarios until simulated air concentrations matched those recently reported by Environment Canada (INV). Finally, they calculated and compared emissions densities (t/km²·y) for PHE, PYR, and BaP for all three emissions scenarios—AIR, TP, and INV—in addition to global per-country emission estimates compiled by Shen et al.

Model simulations using only direct emissions to air resulted in air, water, soil, and foliage concentrations of PHE, PYR, and BaP that tended to fall toward or below the minimum measured in the AOSR and other boreal environments.

Model simulations using the TP scenario for all three PAHs resulted in concentrations in most media that are within the range of measured concentrations.

The results of the TP simulations reaffirm that emissions estimates for the AOSR that take into account only direct emissions to air do not appear to be adequate representations of actual emissions in the region. Furthermore, indirect emissions of PAHs from secondary sources, such as tailings ponds to the atmosphere, may be a more significant contributor of oil sands PAHs to the AOSR atmosphere relative to direct emissions to air.

—Parajulee and Wania

Tailing ponds are not the end of the journey for the pollutants they contain. PAHs are highly volatile, meaning they escape into the air much more than many people think.

—Abha Parajulee

The higher levels of PAHs the UTSC scientists’ model predicts are consistent with what have been measured in samples taken from areas near and in the Athabasca Oil Sands Region.

Parajulee and Wania’s model also factors in additional PAHs that are released during the transport and storage of other waste materials from oil sands operations.

The pair of researchers modeled only three types of PAHs—PHE, PYR, and BaP—which they believe are representative of many other types of air pollutant. Still, they say, their model indicates better monitoring data and emissions information could improve the understanding of the environmental impact of the oil sands even further.

Our study shows that emissions of polycyclic aromatic hydrocarbons estimated in environmental impact assessments conducted to approve developments in the Athabasca oil sands region are likely too low. This finding implies that environmental concentrations in exposure-relevant media, such as air, water, and food, estimated using those emissions may also be too low. The potential therefore exists that estimation of future risk to humans and wildlife because of surface mining activity in the Athabasca oil sands region has been underestimated.

—Parajulee and Wania

Resources

• Abha Parajulee and Frank Wania (2014) “Evaluating officially reported polycyclic aromatic hydrocarbon emissions in the Athabasca oil sands region with a multimedia fate model,” PNAS doi: 10.1073/pnas.1319780111

• Frank Wania, Knut Breivik, N. Johan Persson, Michael S. McLachlan (2006) “CoZMo-POP 2 – A fugacity-based dynamic multi-compartmental mass balance model of the fate of persistent organic pollutants,” Environmental Modelling & Software, Volume 21, Issue 6, Pages 868-884 doi: 10.1016/j.envsoft.2005.04.003

• Donald Mackay and Sally Paterson (1986) “Fugacity revisited. The fugacity approach to environmental transport.” Environ. Sci. Technol. 16 (12), pp 654A–660A doi: 10.1021/es00106a001