Mercury and methylmercury in snowpacks, snowmelt, and tailings ponds of the Athabasca Oil Sands Region, Alberta, Canada
Bibliographic record
Abstract
The Alberta Oil Sands Region (AOSR) is the third largest proven oil reserve in the world and one of Canada’s major economic drivers. Industrialized extraction of this resource has resulted in the release of contaminants from various sources, such as stack emissions, volatilization and leakage of chemicals from tailings ponds, increased erosion due to land disturbance, and blowing dust from landscape disturbance, road activity, and open-pit mines. Among the contaminants released to the environment from industrial activities are organic pollutants, such as polycyclic aromatic hydrocarbons and naphthenic acids, sulphur dioxide and nitrogenous oxide species, secondary organic aerosols and the 13 elements (Ag, Ar, Be, Cd, Cr, Cu, Hg, Ni, Pb, Se, Sb, Tl, Zn) considered priority pollutants elements under the U.S. Environmental Protection Agency. Mercury (Hg) is also a contaminant of concern in the AOSR, especially methylmercury (MeHg), which is a potent vertebrate neurotoxin that biomagnifies through food webs to concentrations that may be of concern to consumers including humans. Total Hg (THg: all forms of Hg in a sample) concentrations downstream of the AOSR and THg and MeHg loadings in snowpacks have also been found to be elevated; however, sources of this Hg are not known. In this thesis, I: 1) quantify THg and MeHg concentrations in four AOSR tailings ponds varying in composition, age, surface area, and volume; and 2) determine if MeHg is actively produced in AOSR snowpacks and melted snow, or associated with the particles deposited there. I show that surface and sub-surface water concentrations of THg (unfiltered 0.30 ± 0.14 ng/L; filtered 0.26 ± 0.12 ng/L) and MeHg (unfiltered 0.15 ± 0.20 ng/L; filtered 0.08 ± 0.11 ng/L) were low in the four tailings ponds, with the highest concentrations observed in the oldest pond. In mature fine tailings that settle out in the ponds, concentrations of THg (58.6 ± 50.2 ng/g) and MeHg (0.23 ± 0.16 ng/g) were also low, with the highest concentrations also observed in the oldest pond. Overall, these ponds are not likely a major source of THg or MeHg to downstream freshwater ecosystems into which they slowly leak, though further assessment of the source of the MeHg in the oldest pond should be pursued. To determine if MeHg is produced within snowpacks and/or melted snow of the AOSR, I used Hg stable isotope incubation experiments at four sites located varying distances from the major industrial developments. Results from these experiments demonstrated that the potential rate of MeHg production was low in snowpacks (km = 0.001–0.004 d-1) and non-detectable in melted snow, except at one site (km = 0.0007 d-1), and that in situ production is therefore unlikely an important source of MeHg to AOSR snowpacks. Concentrations of MeHg on particles (pMeHg) in snowpacks increased linearly with distance from the upgraders (R2 = 0.71, p <0.0001); however, snowpack total particle and pMeHg loadings decreased exponentially over this same distance (R2 = 0.49, p = 0.0002; R2 = 0.56, p <0.0001). Thus, at near-field sites, total MeHg loadings in snowpacks were high due to high particle loadings, even though particles originating from industrial activities are not MeHg rich compared to those originating from natural sources at distant sites. More research is required to identify snowpack particle sources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".