Engineered floating wetlands as a secondary oil spill remediation strategy for freshwater shorelines
Bibliographic record
Abstract
Conventional oil spill recovery methods are not desirable for sensitive freshwater shorelines due to damage caused from physical recovery efforts. This, in addition to the residual oil, causes long term impacts to the environment. With increasing production and transport of oil across Canada, there is a need for non-invasive alternatives for oil spill remediation. This thesis has assessed the use of Engineered Floating Wetlands (EFW) as a non-invasive strategy for remediating oil spills in sensitive habitats, through a series of in-lake model oil spill and microcosm experiments conducted at the International Institute for Sustainable Development Experimental Lakes Area. The in-lake experiments monitored changes to the EFW root microbial community upon exposure to model spills of diluted bitumen and conventional heavy crude oil following primary recovery in shoreline enclosures. These experiments found that EFWs support a diverse microbial community, with high richness and evenness of prokaryotes, and variable dominance of eukaryotes. While direct roles in degradation were not assessed, total polycyclic aromatic compounds (PACs) declined to near background conditions in the aqueous environment after ~60-70 days, likely a result of various biotic and abiotic processes. To confirm whether plants can enhance removal of PACs, a microcosm experiment was conducted to assess the removal of 1 mg/L phenanthrene by three emergent plants from freshwater over 21 days. All treatments resulted in successful removal of phenanthrene (≥ 89% from initial measured concentration). Typha sp. had a greater removal rate than Carex lasiocarpa, but not C. utriculata. However, the control with no vegetation also resulted in successful phenanthrene removal, likely from biostimulation. There were no differences between the control and planted microcosms, however Typha generally had greater removal rates than the control. While all treatments had successful removal, it was clear there were plant specific functions influencing water quality and removal. This thesis has demonstrated that EFWs may be a successful alternative to conventional strategies for sensitive environments. A number of recommendations are made throughout this thesis to help advance knowledge in this field to protect freshwater ecosystems. Specifically, future research is recommended to further identify factors driving successful bioremediation mediated by EFWs.
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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) | 0.000 | 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".