Identifying an indicator compound for progress monitoring during in-situ thermal treatment of coal tar and creosote
Bibliographic record
Abstract
In situ thermal treatment (ISTT) has been used to treat sites impacted by coal tar and creosote when stringent remediation objectives must be met over short timeframes. There is a need to identify an indicator compound that can be used to track progress during the treatment of these complex semi-volatile non-aqueous phase liquids (NAPLs) to complement soil sampling typically conducted once treatment is complete. This study outlines an approach to track ISTT progress and support shutdown decisions based on mass removal objectives using a series of laboratory experiments to investigate changes in semi-volatile NAPL composition during thermal treatment. Sand, water and semi-volatile NAPL were heated, and the recovery of polycyclic aromatic hydrocarbons (PAHs) was monitored by sampling and analysis of condensate. PAHs were predominantly removed between 260 °C and 455 °C, with early-stage condensate composed of higher volatility PAHs and later-stage condensate composed of lower volatility PAHs. Experimental results showed that intermediate-volatility PAHs (e.g., phenanthrene) could be used as an indicator compound to infer treatment progress with respect to both higher and lower volatility PAHs. Monitoring an indicator compound during ISTT of semi-volatile NAPL could provide higher confidence in treatment progress than conventional monitoring techniques and allow for more accurate shutdown decisions. • Detailed monitoring of semi-volatile NAPL condensate during thermal treatment. • Phenanthrene recovery provided information about treatment progress of other PAHs. • Intermediate volatility PAHs are recommended as indicator compounds. • Indicator compound recovery may inform practitioners when to shut down heating.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".