Arctic's hidden hydrocarbon degradation microbes: investigating the effects of hydrocarbon contamination, biostimulation, and a surface washing agent on microbial communities and hydrocarbon biodegradation pathways in high-Arctic beaches
Bibliographic record
Abstract
BACKGROUND: Canadian Arctic summer sea ice has dramatically declined due to global warming, resulting in the rapid opening of the Northwest Passage (NWP), slated to be a major shipping route connecting the Atlantic and Pacific Oceans by 2040. This development elevates the risk of oil spills in Arctic regions, prompting growing concerns over the remediation and minimizing the impact on affected shorelines. RESULTS: This research aims to assess the viability of nutrient and a surface washing agent addition as potential bioremediation methods for Arctic beaches. To achieve this goal, we conducted two semi-automated mesocosm experiments simulating hydrocarbon contamination in high-Arctic beach tidal sediments: a 32-day experiment at 8 °C and a 92-day experiment at 4 °C. We analyzed the effects of hydrocarbon contamination, biostimulation, and a surface washing agent on the microbial community and its functional capacity using 16S rRNA gene sequencing and metagenomics. Hydrocarbon removal rates were determined through total petroleum hydrocarbon analysis. Biostimulation is commonly considered the most effective strategy for enhancing the bioremediation process in response to oil contamination. However, our findings suggest that nutrient addition has limited effectiveness in facilitating the biodegradation process in Arctic beaches, despite its initial promotion of aliphatic hydrocarbons within a constrained timeframe. Alternatively, our study highlights the promise of a surface washing agent as a potential bioremediation approach. By implementing advanced -omics approaches, we unveiled highly proficient, unconventional hydrocarbon-degrading microorganisms such as Halioglobus and Acidimicrobiales genera. CONCLUSIONS: Given the receding Arctic sea ice and the rising traffic in the NWP, heightened awareness and preparedness for potential oil spills are imperative. While continuously exploring optimal remediation strategies through the integration of microbial and chemical studies, a paramount consideration involves limiting traffic in the NWP and Arctic regions to prevent beach oil contamination, as cleanup in these remote areas proves exceedingly challenging and costly.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".