Effects of soil properties and biosurfactant on the behavior of PAHs in soil-water systems
Bibliographic record
Abstract
The interactions among biosurfactant, soil components and PAHs govern the efficiency of biosurfactant enhanced remediation, which was still poorly studied. In this study, we investigated effects of biosurfactant and soil properties on sorption and desorption of phenanthrene (PHE) and pyrene (PYR) in soil – water systems. Two kinds of soil samples (ditch and under plant) from the same petroleum contaminated site in western Canada were applied. The results indicate that soil organic matter (SOM) was the predominant factor that affects PAHs sorption onto soil. The SOM content in ditch soil was half of that in under plant soil, therefore ditch soil showed less sorption affinity to PAHs than under plant soil. We also examined the combined effects of soil DOM and biosurfactant on desorption of PAHs. The results indicated that more PAHs were desorbed from ditch soil than the under plant soil under the combined conditions. The SOM was still the key factor that determined desorption of PAHs. Besides, competitions among PAHs, DOM and surfactant for sorption sites exist. In high solute concentration system, the competition for sorption site was more severely than low concentration system and more PAHs were sequenced in soil phase in high PAH concentration system. Also in low biosurfactant system, less PAHs were desorbed from soil. The study results should be helpful in broadening knowledge of biosurfactant enhanced bioremediation of PAHs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".