A Washing Procedure to Mobilize Mixed Contaminants from Soil
Bibliographic record
Abstract
We conducted a laboratory study to assess the feasibility of a washing process with nonionic and anionic surfactants in combination with ethylenediaminetetraacetate (EDTA) for the simultaneous mobilization of heavy metals and polychlorinated biphenyls (PCBs) from a field-contaminated soil. Unit processes consisting of complexometric extraction and surfactant-assisted mobilization were combined with reagent regeneration and detoxification steps to generate innocuous products. Ten minutes of ultrasonic mixing of the soil with a combination of 30 mL L(-1) surfactant suspension and a sparing quantity (2 mmol) of EDTA mobilized appreciable quantities of PCBs, virtually all of the available Cd, Cu, Mn, and Pb, and lesser amounts of the Zn, Ni, and Cr but only small quantities of Al and Fe. Relative to individual reagents, combinations of surfactant (Brij 98, Triton X-301, or Triton XQS-20) with EDTA did not influence PCB extraction efficiencies perceptibly. Of the three surfactants, the Brij 98 proved to be the most efficient for three successive extractions with a single charge, mobilizing 83% of the PCBs, whereas companion extractions that used fresh reagent each time mobilized 87% of the soil PCB content. The decreased PCB mobilization with the same quantity of anionic surfactant (71 or 68%) resulted from losses during the EDTA regeneration process with zero-valent Mg. In toto, these studies demonstrate that PCB compounds and selected heavy metals can be coextracted efficiently from soil with three successive washes with the same washing suspension containing EDTA and a nonionic surfactant.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".