Compound specific isotope analysis of hexachlorocyclohexane isomers: a method for source fingerprinting and field investigation of <i>in situ</i> biodegradation
Bibliographic record
Abstract
RATIONALE: The manufacturing and uses of hexachlorocyclohexane (HCH) have resulted in a serious environmental challenge and legacy. This study highlights the ability of compound specific isotope analysis (CSIA) to distinguish among various HCH sources and to support the evaluation of the potential for in situ biodegradation in contaminated groundwater. METHODS: Tests were conducted to verify the absence of significant isotope fractionation during HCH sample pre-concentration including dichloromethane extraction, solvent exchange into iso-octane, and H2SO4 clean-up, and analysis by gas chromatography/combustion-isotope ratio mass spectrometry (GC/C-IRMS). The method was then applied to four Technical Grade (TG) HCH mixtures procured from different sources and to groundwater samples from a contaminated site. RESULTS: The pre-concentration method enabled determination of carbon isotope ratios (δ(13)C values) of HCH isomers with no significant isotopic fractionation. The TG-HCH mixtures had significantly different δ(13)C values. Moreover, for any given TG-HCH, all isomers had δ(13)C values within 1.1‰ of each other - a distinctly uniform fingerprint. At the HCH-contaminated field site, compared with source wells, downgradient wells showed significant (up to 5.1‰) enrichment in (13)C and the δ(13)C values of the HCH isomers were significantly different from each other. CONCLUSIONS: A method was successfully developed for the CSIA of HCH isomers that showed potential for HCH source differentiation and identification of HCH in situ biodegradation. At the HCH-contaminated site, the observed preferential isotopic enrichment of certain isomers relative to others for a given source allows differentiation between biodegraded and non-biodegraded HCH.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".