The effect of sub-boiling temperatures on mass transfer from former manufactured gas plant residuals
Bibliographic record
Abstract
The dissolution of polycyclic aromatic hydrocarbons (PAHs) from coal tar at former manufactured gas plant (FMGP) sites is a long-term threat to groundwater quality. The dissolution rate is often limited by an increase in the viscosity of the non-aqueous phase liquid (NAPL) as the lower molecular weight compounds are depleted over time, and this slow mass transfer prevents the effective application of remediation technologies that rely on NAPL-to-water mass transfer to remove or degrade mass. Increasing subsurface temperatures has the potential to increase mass transfer at FMGP sites by increasing PAH solubility and reducing NAPL viscosity. This study investigated the mass transfer of PAH compounds from a synthetic NAPL mixture and FMGP residual at 25, 50 and 80 °C using well-mixed batch experiments. Effective solubilities increased by up to an order of magnitude and mass transfer rate coefficients increased by up to a factor of 45. Enhancements were greater for higher molecular weight compounds, and for the more complex FMGP NAPL compared to the synthetic mixture due to a more substantial decrease in NAPL viscosity. Simulations using a screening-level model demonstrated the potential for sub-boiling temperature to increase NAPL mass removal at FMGP sites, with increases in concentration up to a factor of seven, and 6 to 87 % of mass remaining after heating to 80 °C for 120 days compared to 25 °C.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".