Effects of freeze–thawing cycles on desorption behaviors of PAH-contaminated soil in the presence of a biosurfactant: a case study in western Canada
Bibliographic record
Abstract
Many regions in Canada are facing increasing environmental threats posed by oil and gas exploitation and transportation. These contaminated lands are inevitably subjected to seasonal and diurnal freeze–thawing cycles (FTCs). However, knowledge about the effect of FTCs on the behaviours of hydrophobic contaminants during the aging process of soil is limited. This study investigated the desorption characteristics of phenanthrene in aging soils in the presence of the biosurfactant rhamnolipid under diurnal and seasonal FTC treatments. It was found that the presence of rhamnolipid in soil during the aging process was able to increase the desorption efficiency of phenanthrene. In the presence of rhamnolipid above 100 mg L−1, FTCs could inhibit the sequestration of phenanthrene. Soil moisture and rhamnolipid concentration are two major factors affecting this effect. High moisture content and FTC frequency could lead to lower desorption in the early stage of FTCs due to the increased specific surface area. The sequestration of phenanthrene was less effectively hindered under seasonal FTCs than diurnal FTCs. The results from this study have important implications for understanding the role of surfactants in cold-region soil aging, and for the improvement of site remediation strategies of PAH contaminated soil in cold regions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".