Geology and Geomorphology Drive Polycyclic Aromatic Compound Concentrations and Composition in Rivers Draining the Alberta Oil Sands
Bibliographic record
Abstract
Large-scale open-pit bitumen mining operations in Alberta, Canada, have raised concerns about contaminant releases to downstream ecosystems and communities. Among the contaminants of concern are polycyclic aromatic compounds (PACs), a toxic group of organic pollutants prevalent at high concentrations in bitumen. Here, we quantify PAC concentrations, loads, and yields in four rivers draining watersheds impacted by mining. These rivers also actively erode and incise the bitumen-bearing McMurray Formation, which has complicated previous attempts to distinguish natural from anthropogenic inputs. We collected 998 water quality samples from locations both upstream and downstream of mining, analyzed them for a broad suite of 48 unsubstituted and alkylated PAC homologues, and compared their compositional fingerprints to potential natural and anthropogenic sources. Erosion of bituminous outcrops, rather than industrial pollution, is the main driver behind PAC input and riverine transport, as supported by (i) discrepancies in loads and yields among watersheds with varying levels of industrial development, (ii) responses to hydrologic changes with respect to the distribution of mapped outcrops, and (iii) shifts in PAC relative abundances. This study presents evidence that PAC concentrations are primarily controlled by the presence of eroding bitumen-rich outcrops in conjunction with hydrographic profiles, rather than industrial impacts.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".