Remediation of a heavy metal and PAH-contaminated sediment by a rhamnolipid foam
Bibliographic record
Abstract
Industrial contaminants have long term and sometimes irreversible adverse effects on humans, animals and the ecosystem. As a result attention has been focused on the effects of these contaminants. One of the concerning environmental issues these days is contaminated sediments. The presence of aquatic organisms in sediments makes them important in the well-being and health of these organisms. If sediments become contaminated, they can pose a threat to sediment dwelling habitants and through these microorganism that can be spread even to humans. Nowadays a major concern that government regulators and related industry can face is the protection and investigation of the quality of aquatic sediments. A protection that can be done through different pathways includes remediation. \nAn investigation was made into evaluating the capability of a rhamnolipid biosurfactant (JBR425) in the form of a foam for treatment of polycyclic aromatic hydrocarbon (PAH) contaminated fresh water sediments that also have elevated levels of Pb, Zn and Ni. Studies commenced by evaluating foam characteristics followed by performing column tests. To do this, dewatered non-dried sediments were put in a column and the biosurfactant was injected in the form of a foam or a liquid solution. The pressure gradient was monitored during flushing tests to avoid possible problems due to high pressure. Foam quality of the rhamnolipid varied between 85% and 99% with stabilities from 15 to 43 min. PAH and metal removal were then evaluated for sediment samples from sector 103 of the Port of Montreal in Montreal, Quebec with different initial concentrations of target contaminants. Among PAHs, pyrene; benz(a)anthracene and chrysene had concentrations above thresholds according to Quebec Sediment Quality Criteria. Highest removal for PAHs was obtained by a 99% quality foam produced by 0.5% rhamnolipid solution after 20 pore volumes. Removal efficiency (due to mobilization) for the biosurfactant foam was 44.6% of pyrene, 30% of benz(a)anthracene and 37.8% of chrysene while total removal efficiency (mobilization + volatilization) for the biosurfactant foam was 56.4% of pyrene, 41.2% of benz(a)anthracene and 45.9% of chrysene. With biosurfactant liquid solution at the same pH as above mentioned foam (pH 6.8), maximum removal (mobilization) was 31.4% of pyrene, 20.5% of benz(a)anthracene and 27% of chrysene. Here no volatilization of PAHs was observed. Deionized water (DI) did not remove any PAH. For metals, highest removal was achieved using 0.5% rhamnolipid foam (99% quality, pH 10.0). These were 53.3% of Ni, 56.8% of Pb and 55.2% of Zn. Removal efficiencies were reduced between 11% - 13% for metals when a 0.5% rhamnolipid solution was used. DI water removed only 16% for both Pb and Ni individually and 17% of Zn at pH 10.0. From these analyses, lower pH (6.8) rhamnolipid showed higher removal efficiencies for PAHs while it was not as successful for metals. As for metals, pH 10 was proven to be the best. It is concluded that, rhamnolipid foam could be a non-toxic and effective method of remediating PAH and heavy metal contaminated soil/sediments. Further efforts will be required to optimize the performance of the foam.
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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".