Wood oil biodegradation in the marine environment: Behavior, mechanism, and impact
Bibliographic record
Abstract
The demand for biomass pyrolysis oil (e.g., wood oil) is increasing with global carbon neutrality. As shipping of such oils expands, the risk of accidental spills also rises, posing threats to the marine ecosystem. Biodegradation represents the ultimate fate of spilled oil in marine environments; however, the biodegradation behavior and mechanism of wood oil spills and the associated environmental impacts remain largely unexplored. In this study, we assessed the biodegradation of fresh, weathered, and chemically dispersed wood oil under northern Atlantic Ocean conditions (4 °C and 20 °C), comparing it with conventional crude oil Alaska North Slope (ANS). Oil component changes and suspended aggregate formation during biodegradation, and the toxicological impacts of oil post-biodegradation were systematically investigated. Phenols and aldehydes were predominant biodegradable constituents (27 %-56 %) in wood oil, undergoing mineralization and ring-opening. Two oil-degrading indigenous bacterial strains Alcanivorax. sp and Exiguobacterium. sp exhibited similar pathways when degrading wood oil. Wood oil biodegradation formed lignin-based aggregates and altered marine snow structure, increasing benthic exposure potential. Chemical dispersant Corexit 9500A facilitated wood oil biodegradation but showed low dispersion efficiency. Despite faster biodegradation, wood oil residues exhibited tenfold higher toxicity than crude oil. Diminished biodegradation rates at the lower temperature intensified toxic effects, increasing risks to northern communities and the fragile marine ecosystem. This study provides previously undocumented insights into the wood oil spill behavior in marine environments, expanding the knowledge base needed to support marine biofuel spill response planning and associated risk management strategies.
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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 | 0.001 |
| 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".