Remediation of Dicofol Type Ddts-Contaminated Sediments by Ferrous Activated Sodium Persulfate Oxidation
Bibliographic record
Abstract
In recent years, contamination by dicofol-type DDTs has attracted immense concern as a new source of DDT pollution. In this study, sediment samples from a dicofol manufacturing factory in Tianjing, China exhibited serious DDT contamination [p,p'-DDE (115.27 mg kg-1) and p,p'-DDT (11.84 mg kg-1)]. Results of the batch experiments showed that total DDT degradation rates increase as S2O82?/Fe2+ molar ratios increase. The S2O82-/Fe2+ molar ratios used in this study were as follows: 60/10 < 10/30 < 20/30 < 60/50 < 60/20 < 40/30 < 60/40 < 60/30 < 80/30. Their corresponding degradation rates were 31, 43, 52, 69, 70, 71, 72, 89, and 91 ug g-1, respectively. The optimal S2O82-/Fe2+ molar ratio was 60/30, which resulted in 64% and 96% degradation of p,p'-DDE and p,p'-DDT, respectively. However, when an excessive amount of ferrous ion was used (<S2O82-/Fe2+ molar ratio of 60/30), then competition for SO4? between ferrous ion and DDTs resulted in decreased DDT degradation efficiency and increased persulfate decomposition (represented by the generated amount of sulfate). Our results implied that a slow and steady production of sulfate free radicals is favorable for DDT degradation, and that Fe2+ availability plays an important role in controlling persulfate reactions activated by ferrous ion. Fe2+-activated persulfate oxidation may be significant in developing environment friendly and fast-remediation options for DDT-contaminated sediments and soil. Therefore, this study contributes to current knowledge on remediating DDT contamination.
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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) | 0.001 | 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) | 0.000 | 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 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".