Environmental Impacts of Remediation of a Trichloroethene-Contaminated Site: Life Cycle Assessment of Remediation Alternatives
Bibliographic record
Abstract
The environmental impacts of remediation of a chloroethene-contaminated site were evaluated using life cycle assessment (LCA). The compared remediation options are (i) in situ bioremediation by enhanced reductive dechlorination (ERD), (ii) in situ thermal desorption (ISTD), and (iii) excavation of the contaminated soil followed by off-site treatment and disposal. The results showed that choosing the ERD option will reduce the life-cycle impacts of remediation remarkably compared to choosing either ISTD or excavation, which are more energy-demanding. In addition to the secondary impacts of remediation, this study includes assessment of local toxic impacts (the primary impact) related to the on-site contaminant leaching to groundwater and subsequent human exposure via drinking water. The primary human toxic impacts were high for ERD due to the formation and leaching of chlorinated degradation products, especially vinyl chloride during remediation. However, the secondary human toxic impacts of ISTD and excavation are likely to be even higher, particularly due to upstream impacts from steel production. The newly launched model, USEtox, was applied for characterization of primary and secondary toxic impacts and combined with a site-dependent fate model of the leaching of chlorinated ethenes from the fractured clay till site.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".