Field metabolomics and laboratory assessments of anaerobic intrinsic bioremediation of hydrocarbons at a petroleum‐contaminated site
Bibliographic record
Abstract
Field metabolomics and laboratory assays were used to assess the in situ anaerobic attenuation of hydrocarbons in a contaminated aquifer underlying a former refinery. Benzene, ethylbenzene, 2-methylnaphthalene, 1,2,4- and 1,3,5-trimethylbenzene were targeted as contaminants of greatest regulatory concern (COC) whose intrinsic remediation has been previously reported. Metabolite profiles associated with anaerobic hydrocarbon decay revealed the microbial utilization of alkylbenzenes, including the trimethylbenzene COC, PAHs and several n-alkanes in the contaminated portions of the aquifer. Anaerobic biodegradation experiments designed to mimic in situ conditions showed no loss of exogenously amended COC; however, a substantive rate of endogenous electron acceptor reduction was measured (55 ± 8 µM SO(4) day(-1)). An assessment of hydrocarbon loss in laboratory experiments relative to a conserved internal marker revealed that non-COC hydrocarbons were being metabolized. Purge and trap analysis of laboratory assays showed a substantial loss of toluene, m- and o-xylene, as well as several alkanes (C(6)-C(12)). Multiple lines of evidence suggest that benzene is persistent under the prevailing site anaerobic conditions. We could find no in situ benzene intermediates (phenol or benzoate), the parent molecule proved recalcitrant in laboratory assays and low copy numbers of Desulfobacterium were found, a genus previously implicated in anaerobic benzene biodegradation. This study also showed that there was a reasonable correlation between field and laboratory findings, although with notable exception. Thus, while the intrinsic anaerobic bioremediation was clearly evident at the site, non-COC hydrocarbons were preferentially metabolized, even though there was ample literature precedence for the biodegradation of the target molecules.
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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".