The Use of Alkaline Hydrolysis As a Novel Strategy for Chloroform Remediation: The Feasibility of Using Construction Wastes and Evaluation of Carbon Isotopic Fractionation
Bibliographic record
Abstract
Laboratory and field-scale pilot experiments were performed to evaluate the feasibility of chloroform degradation by alkaline hydrolysis and the potential of δ(13)C values to assess this induced reaction process at contaminated sites. In batch experiments, alkaline conditions were induced by adding crushed concrete (pH 12.33 ± 0.07), a filtered concrete solution (pH 12.27 ± 0.04), a filtered cement solution (pH 12.66 ± 0.02) and a pH 12 buffer solution (pH 11.92 ± 0.11). The resulting chloroform degradation after 28 days was 94, 96, 99, and 72%, respectively. The experimental data were described using a pseudo-first-order kinetic model, resulting in pseudo-first-order rate constant values of 0.10, 0.12, 0.20, and 0.05 d(-1), respectively. Furthermore, the significant chloroform carbon isotopic fractionation associated with alkaline hydrolysis of chloroform (-53 ± 3‰) and its independence from pH in the admittedly limited tested pH range imply a great potential for the use of δ(13)C values for in situ monitoring of the efficacy of remediation approaches based on alkaline hydrolysis. The carbon isotopic fractionation obtained at the lab scale allowed the calculation of the percentage of chloroform degradation in field-scale pilot experiments where alkaline conditions were induced in two recharge water interception trenches filled with concrete-based construction wastes. A maximum of approximately 30-40% of chloroform degradation was achieved during the two studied recharge periods. Although further research is required, the treatment of chloroform in groundwater through the use of concrete-based construction wastes is proposed. This strategy would also imply the recycling of construction and demolition wastes for use in value-added applications to increase economic and environmental benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".