Extraction, Analysis and the Role of Co-contaminants on Sulfolane Biodegradation
Bibliographic record
Abstract
Sulfolane’s extensive use in various oil and gas industries has led to its increased environmental contamination throughout Alberta. The impact of co-contaminants and complex matrices in sulfolane polluted areas can produce challenges in analytical and remediation efforts and generate vastly different results from those obtained in a laboratory setting. Particularly, the low regulatory guidelines of 0.18 mg/kg in soils and 0.09 mg/L in groundwater has created challenges for commercial testing laboratories as these levels are often too close to their instrument detection limits. Potential interferences and false positives have become a cause for concern especially in complex matrices with high organic content. Similarly, groundwater bioremediation efforts can be positively or negatively impacted depending on the type of co-contaminant present alongside sulpholane. A spiking study was conducted to investigate sulfolane analytical challenges faced by various testing labs when analysing sulfolane in peat and clay soils, as well as groundwater samples. It was observed that soil spiked with high sulfolane concentrations (>0.5mg/kg) resulted in more reliable data compared to low concentrations (<0.5mg/kg), with mineral soils providing more reproducible data than the highly-organic peat soil. Similarly, groundwater analysis also provided less variable results in higher concentrations (0.5 mg/L) than lower levels (0.1 mg/L). Soil water extraction efficiency of sulfolane improved with an increase in soil to water ratio, however, clay soils will require additional aliquots to achieve maximum recovery. GC-MS analysis demonstrated that organic soils can produce false positives. Therefore, at low concentrations, interferences, loss of sulfolane due to biodegradation and sample heterogeneity will significantly impact results. Impact of co-pollutants (As (III), fulvic acid, and diisopropanolamine) on sulfolane biodegradation in groundwater was also investigated in this research. Three concentrations of As (III) (low, medium and high) were observed to have negligible impact on sulfolane degradation. Similarly, at environmentally relevant concentrations, fulvic acid did not significantly effect sulfolane removal. Diisopropanolamine, however, was observed to positively and negatively impact sulfolane removal depending on the biological, chemical and physical characteristics of the water matrix used.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".