Simulation of polycyclic aromatic compounds in the Athabasca River Basin: integrated models and insights
Bibliographic record
Abstract
The environmental risks associated with polycyclic aromatic compounds (PACs) stemming from oil sands mining have become a concern in the Athabasca River basin (ARB), Alberta, Canada. Their complex environmental behavior complicates mechanistic, basin-scale modeling, making it difficult to assess the relevance of their sources, fate/transport, and potential impacts on exposed organisms. To address these challenges, a Python-based Soil Water Assessment Tool-Load Calculator (SWAT-LC) was developed and coupled with SWAT and Water Quality Analysis Simulation Program 8 (WASP8) models to describe PACs' behavior in the ARB, including transport via surface runoff, soil lateral flow, groundwater baseflow, direct flux, and outcrop/sediment erosion. Chrysene, naphthalene, C4-phenanthrenes/anthracenes, and C4-dibenzothiophenes were selected to demonstrate the applicability of our modeling approach for simulating PACs of petrogenic and pyrogenic origins, diverse physico-chemical properties, and varying environmental relevance. The simulation results indicated that including the description of flow-driven natural outcrop erosion processes significantly enhanced model performance, while the temperature-dependent mechanism showed potential for improving erosion process characterization. Overall, the model performed well for chrysene, C4-phenanthrenes/anthracenes, and C4-dibenzothiophenes (NSE = 0.19∼0.75, d = 0.66∼0.95, PBIAS = -23∼47 % at middle and downstream stations), but its performance was weaker for naphthalene (NSE = -2.16∼-0.40, d = 0.35∼0.53, PBIAS = 17∼51 % at all reference stations). Nonetheless, by integrating a comprehensive set of mechanistic processes, this model is now well-suited for scenario testing, especially for representative PACs that have major environmental and health relevance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".