Identification of Multiple Sources of Groundwater Contamination by Dual Isotopes
Bibliographic record
Abstract
Chlorinated solvents are one of the most commonly detected groundwater contaminants in industrial areas. Identification of polluters and allocation of contaminant sources are important concerns in the evaluation of complex subsurface contamination with multiple sources. In recent years, compound-specific isotope analyses (CSIA) have been employed to discriminate among different contaminant sources and to better understand the fate of contaminants in field-site studies. In this study, the usefulness of dual isotopes (carbon and chlorine) was shown in assessments of groundwater contamination at an industrial complex in Wonju, Korea, where groundwater contamination with chlorinated solvents such as trichloroethene (TCE) and carbon tetrachloride (CT) was observed. In November 2009, the detected TCE concentrations at the study site ranged between nondetected and 10,066 µg/L, and the CT concentrations ranged between nondetected and 985 µg/L. In the upgradient area, TCE and CT metabolites were detected, whereas only TCE metabolites were detected in the downgradient area. The study revealed the presence of separate small but concentrated TCE pockets in the downgradient area, suggesting the possibility of multiple contaminant sources that created multiple comingling plumes. Furthermore, the variation of the isotopic (δ(13) C and δ(37) Cl) TCE values between the upgradient and downgradient areas lends support to the idea of multiple contamination sources even in the presence of detectable biodegradation. This case study found it useful to apply a spatial distribution of contaminants coupled with their dual isotopic values for evaluation of the contaminated sites and identification of the presence of multiple sources in the study area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".