Using fluorescence spectroscopy to measure the biodegradation of naphtha in Athabasca oil sands tailings ponds
Bibliographic record
Abstract
This study used fluorescence spectroscopy and solid phase microextraction (SPME) to develop a time-efficient and cost-effective method to measure naphtha biodegradation in Athabasca Oil Sands Region mature fine tailings (MFT) samples. Diverse microbial communities in tailings ponds can readily utilize certain fractions of the available naphtha and support methanogenesis (Holowenko et al. 2000). This has negative implications for the environment as it results in the release of methane, a potent greenhouse gas (Holowenko et al. 2000). As such, there is a need to better track this biodegradation to determine the amount of naphtha within the ponds and the rate at which methane is released. Direct fluorescence analysis by extracting all hydrocarbons could not track changes in naphtha concentrations (ranging from 0-1%) because of high background from bitumen hydrocarbons. When using SPME, with the addition of a silicone polymer to the MFT suspension, there was a multiphase equilibrium of the naphtha hydrocarbons, and less extraction of the bitumen background hydrocarbons. After equilibration in the MFT sample, the polymer was transferred to ethanol, and the hydrocarbons were extracted into the pure solvent phase to be analyzed. Results indicated that while SPME does help reduce the background bitumen signal, samples must still be diluted. This makes it more difficult to track small changes in naphtha over time. However, results do support the use of the polymer providing linear results, good repeatability, and high desorption in the 270-290 nm naphtha region. Thus, SPME can be used to detect naphtha degradation when it ranges from >25-100%. The values generated from the SPME fluorescence calibration were used in a multiphase equilibrium model to estimate the volume of bitumen in a sample as well as the amount of naphtha in the water and bitumen phases. Results indicated that the %volume bitumen for MFT was 1.01±0.26% which is within the literature range for the % bitumen in MFT by volume, and that the % naphtha in the water phase was ~10%. These estimates can be used in future remediation studies that require an understanding of bitumen-diluent volumes in MFT such as those looking to track biodegradation over time.
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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 | 0.001 | 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".