Microbial responses to marine oil spills: impacts of salinity, dispersant application and oil properties
Bibliographic record
Abstract
Marine oil spills can cause catastrophic impacts on ecosystems and human life. Natural attenuation by indigenous oil-degrading bacteria is one of the vital weathering processes that can result in oil mitigation. Various environmental factors, oil spill response options, and types of spilled oils would affect microbial physiologies for oil biotransformation. This thesis aims to uncover the effects of salinity, dispersant application, and oil property on microbial responses to oil biodegradation. To reveal the salinity effects on oil biodegradation, a halotolerant oil-degrading bacterium, Exiguobacterium sp. N4-1P, was tested as a model. The microbial eco-physiological strategy for salinity-mediated crude oil biodegradation was proposed for the first time. The impacts of dispersant application on oil biodegradation under diverse salinities were also evaluated, which showed that dispersant addition could override the oil biodegradation barriers at hyper-salinities primarily through enriching cell abundance. Increased production of unconventional heavy crude oils has led to increased marine transportation and spill risks. The effects of dispersants on the natural attenuation of the dilbit (diluted bitumen) within microbial communities over time were comprehensively evaluated using a metagenomic/metatranscriptomic approach. We found that dispersant has short-term inhibiting effects, but over the long term, its effects are insignificant. In addition, magnetic nanoparticles decorated bacteria (MNPB) were developed for responding to a simulated heavy crude oil attachment. A strategy named “access-dispersion-recovery” was proposed, and it led to enhanced mitigation of heavy crude oil pollution. The responses of an Alcanivorax species isolated from the North Atlantic Ocean for degrading alkanes and plastics were also studied. Experimental results indicated that the well-recognized obligate alkane-degrader Alcanivorax tied to ocean hydrocarbon cycles could also strongly degrade plastics. The existing biogeochemical processes involved in hydrocarbon biodegradation may aid in the ecosystem’s resilience to the impact of the new anthropogenic plastic-based carbon. The outputs of the thesis could advance the understanding of sophisticated marine crude oil biodegradation processes, generate potentially promising remediating tools for oil spill responses, provide new insights into marine hydrocarbon degradation, and benefit decision-making for adopting oil spill responding options.
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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 | 0.001 | 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".