Carbon Isotope Analysis to Evaluate Nanoscale Fe(O) Treatment at a Chlorohydrocarbon Contaminated Site
Bibliographic record
Abstract
Remediation of groundwater contaminated by chlorinated hydrocarbons via in situ technologies such as direct injection of nanoscale zero valent iron (ZVI, Fe(O)) particles is increasingly common. However, assessing target compound degradation by abiotic processes is difficult because (1) the injection may displace the contaminant plume so that concentration measurements alone are often inconclusive and (2) biodegradation may also occur, making it challenging to identify and evaluate the abiotic degradation component. In this study, trichloroethylene (TCE) and 1,1,1‐trichloroethane (1,1,1‐TCA) were treated in a highly heterogeneous hydrogeologic setting. The purpose of this study was to evaluate the potential for compound‐specific stable isotope analysis (CSIA) to monitor the effectiveness of ZVI injection by assessing TCE and 1,1,1‐TCA degradation. Prior to ZVI injection, carbon isotope measurements demonstrated biodegradation of TCE by native microorganisms. This in situ biodegradation was quantified by measuring the enrichment of 13 C in TCE samples downstream of the suspected source. When ZVI was injected through only two injection wells, no changes in TCE and 1,1,1‐TCA isotope signatures were detected compared to preinjection values. In contrast, when ZVI was injected through 11 wells covering a greater portion of the contaminated area, 5 out of 10 monitoring wells showed further enrichment of 13 C in either TCE or 1,1,1‐TCA, indicating additional target compound transformation. The abiotic nature of this TCE transformation was confirmed through temporal trends in carbon isotope values of the putative transformation products cis ‐dichloroethylene ( cis ‐DCE), ethene and ethane. This demonstrates the usefulness of CSIA in distinguishing abiotic vs. biotic transformation in the field.
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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) | 0.001 | 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".