Impact of iron concentration and ph on zero‐valent iron dechlorination of DDT for brownfields
Bibliographic record
Abstract
Abstract Soil contamination with persistent pesticides such as dichloro‐diphenyl‐trichloroethane (DDT) is a major issue at many brownfield sites. A technology that can be used to treat DDT‐contaminated soil using surfactants is to enhance the migration of the contaminants from the soil phase to the liquid phase, followed by the dechlorinating of the mobilized DDT in the liquid phase using zero‐valent iron (ZVI). The DDT degradation using ZVI occurs under anaerobic conditions via reductive reactions. The effect of the iron concentration on the dechlorination rate is assessed in the range of 1 to 40 percent (weight to volume) for remediation of a DDT‐contaminated site in Ontario, Canada. The optimum percentage of iron is found to be 20 percent at which the dechlorination rates of DDT and 1,1‐dichloro‐2,2‐bis(p‐chlorophenyl)ethane (DDD) were 4.5 and 0.6 mg/L/day, respectively. While mixing of the reaction solution is shown to be important in providing the iron surface available for the dechlorination reaction throughout the reaction solution, there is no significant difference between batch and fed‐batch mode of adding iron to the dechlorination process. Low pH values (pH = 3) increased the dechlorination rates of DDT and DDD to 6.03 and 0.75 mg/L/day, respectively at a 20 percent iron concentration, indicating increased dechlorination rates in acidic conditions. © 2010 Wiley Periodicals, Inc.
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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 | 0.001 |
| 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".