Oil sands process-affected water composition effect on Henry's law constants for polycyclic aromatic compounds: Theory and experiment
Bibliographic record
Abstract
Oil sands process-affected water (OSPW) is a source of atmospheric emission for polycyclic aromatic compounds (PACs), compounds known to have toxic effects on humans. Estimating emissions and assessing the chemical fate of PACs requires measured or predicted physical-chemical properties such as Henry's law constants (H), that can be used to predict chemical transfer into the atmosphere. OSPW is a complex water-based mixture that is highly variable in composition and nature and contains both organic and inorganic ions. This study uses COSMO-RS solvation theory to estimate and compare Henry's law constants for a set of PACs in both water and theoretically modelled OSPW, to assess the expected deviation that occurs from pure water H values due to the ionic content within OSPW. Experimental measurements of Henry's law constants for PACs in pure water and OSPW using EVA-coated passive dosing and sampler beads were also made in support of our theoretical predictions. For the theory work, OSPW composition data for the Athabasca oil sands in Alberta were used to model a simulated OSPW environment with realistic sodium, chloride, fluoride, sulfate, potassium, bicarbonate, and naphthenic acid concentrations. Theory results indicate that the combined presence of these ions at OSPW concentrations has a negligible effect on H values, causing on average a 3% or 0.014 log unit deviation. By comparison, temperature has a much larger influence on H values, with estimations showing an average 0.20 log unit increase for a 5 °C increase in temperature. The experimental results demonstrate that Henry's law constants can be accurately and precisely measured with this technique in pure water but with less precision in OSPW. Nevertheless, the experimental results support the conclusion that Henry's law constants for OSPW can be accurately estimated assuming a pure water phase.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".