Effects of soil properties and biosurfactant on the behavior of PAHs in soil-water systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it