Ecotoxicity Studies for On-Site Disposal of Decant Water During Oil Spills: A Review
Bibliographic record
Abstract
Mechanical oil recovery, a most used tool for oil spill response, can generate a considerable proportion of oil-contaminated water (10–70%). Large storage space is commonly required to transport the recovered oil and water mixture to shore. Transit and transportation consume loads of time, money, and resources while reducing oil recovery’s overall efficiency and capacity. Techniques of on-site treatment and disposal of oily wastewater provide benefits for oil recovery by freeing storage space. However, the high petroleum content of decant water can lead to uncertain risks, if discharged into the marine ecosystem. Insufficient ecological toxicity data and research limit the standardization and establishment of regulative tools. To fill the knowledge gaps, this review comprehensively summarized recent studies on the potential impacts of the organic composition in decant water, including oil–water accommodated fractions, dispersed oil droplets, and other related chemicals, on various marine species (i.e., bacterium, invertebrates, fishes, plants, reptiles, and mammals). The toxicity effects and the ecological endpoints of oils, TPH, and PAHs on different species were discussed. Recommendations for future ecological impacts and decant water composition were provided to support the on-site disposal of the water fraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".