Monitoring Biodegradation of Ethene and Bioremediation of Chlorinated Ethenes at a Contaminated Site Using Compound-Specific Isotope Analysis (CSIA)
Bibliographic record
Abstract
Chlorinated ethenes are commonly found in contaminated groundwater. Remediation strategies focus on transformation processes that will ultimately lead to nontoxic products. A major concern with these strategies is the possibility of incomplete dechlorination and accumulation of toxic daughter products (cis-1,2-dichloroethene (cDCE), vinyl chloride (VC)). Ethene mass balance can be used as a direct indicator to assess the effectiveness of dechlorination. However, the microbial processes that affect ethene are not well characterized and poor mass balance may reflect biotransformation of ethene rather than incomplete dechlorination. Microbial degradation of ethene is commonly observed in aerobic systems but fewer cases have been reported in anaerobic systems. Limited information is available on the isotope enrichment factors associated with these processes. Using compound-specific isotope analysis (CSIA) we determined the enrichment factors associated with microbial degradation of ethene in anaerobic microcosms (ε = -6.7‰ ± 0.4‰, and -4.0‰ ± 0.8‰) from cultures collected from the Twin Lakes wetland area at the Savannah River site in Georgia (United States), and in aerobic microcosms (ε = -3.0‰ ± 0.3‰) from Mycobacterium sp. strain JS60. Under anaerobic and aerobic conditions, CSIA can be used to determine whether biotransformation of ethene is occurring in addition to biodegradation of the chlorinated ethenes. Using δ(13)C values determined for ethene and for chlorinated ethenes at a contaminated field site undergoing bioremediation, this study demonstrates how CSIA of ethene can be used to reduce uncertainty and risk at a site by distinguishing between actual mass balance deficits during reductive dechlorination and apparent lack of mass balance that is related to biotransformation of ethene.
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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 | 0.001 |
| 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".