Methane Production and Isotopic Fingerprinting in Ethanol Fuel Contaminated Sites
Bibliographic record
Abstract
Biodegradation of organic compounds in groundwater can be a significant source of methane in contaminated sites. Methane might accumulate in indoor spaces posing a hazard. The increasing use of ethanol as a gasoline additive is a concern with respect to methane production since it is easily biodegraded and has a high oxygen demand, favoring the development of anaerobic conditions. This study evaluated the use of stable carbon isotopes to distinguish the methane origin between gasoline and ethanol biodegradation, and assessed the occurrence of methane in ethanol fuel contaminated sites. Two microcosm tests were performed under anaerobic conditions: one test using ethanol and the other using toluene as the sole carbon source. The isotopic tool was then applied to seven field sites known to be impacted by ethanol fuels. In the microcosm tests, it was verified that methane from ethanol (δ¹³C = -11.1‰) is more enriched in ¹³C, with δ¹³C values ranging from -20‰ to -30‰, while the methane from toluene (δ¹³C = -28.5‰) had a carbon isotopic signature of -55‰. The field samples had δ¹³C values varying over a wide range (-10‰ to -80‰), and the δ¹³C values allowed the methane source to be clearly identified in five of the seven ethanol/gasoline sites. In the other two sites, methane appears to have been produced from both sources. Both gasoline and ethanol were sources of methane in potentially hazardous concentrations and methane could be produced from organic acids originating from ethanol along the groundwater flow system even after all the ethanol has been completed biodegraded.
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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 | 0.001 |
| 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".