Evaluating <scp>TCE</scp> Abiotic and Biotic Degradation Pathways in a Permeable Reactive Barrier Using Compound Specific Isotope Analysis
Bibliographic record
Abstract
Abstract A pilot‐scale zero valent iron ( ZVI ) Permeable Reactive Barrier ( PRB ) was installed using an azimuth‐controlled ‐vertical hydrofracturing at an industrial facility to treat a chlorinated Volatile Organic Compound ( VOC ) plume. Following ZVI injection, no significant reduction in concentration was observed to occur with the exception of some multilevel monitoring wells, which also showed high levels of total organic carbon ( TOC ). These patterns suggested that the zero valent iron was not well distributed in the PRB creating leaky conditions. The geochemical data indicated reducing conditions in these areas where VOC reduction was observed, suggesting that biotic processes, associated to the guar used in the injection of the iron, could be a major mechanism of VOC degradation. Compound‐Specific Isotope Analysis ( CSIA ) using both carbon and chlorine stable isotopes were used as a complementary tool for evaluating the contribution of abiotic and biotic processes to VOC trends in the vicinity of the PRB . The isotopic data showed enriched isotope values around the PRB compared to the isotope composition of the VOC source confirming that VOC degradation is occurring along the PRB . A batch experiment using site groundwater collected near the VOC source and the ZVI used in the PRB was performed to evaluate the site‐specific abiotic isotopic fractionation patterns. Field isotopic trends, typical of biodegradations were observed at the site and were different from those obtained during the batch abiotic experiment. These differences in isotopic trends combined with changes in VOC concentrations and redox parameters suggested that biotic processes are the predominant pathways involved in the degradation of VOCs in the vicinity of the PRB .
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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".