Applications of data on the mobility of heavy metals in contaminated soil to the definition of site-specific remediation criteria
Bibliographic record
Abstract
More than a decade of research on the fractionation of heavy metals in soil has led scientists and engineers to the conclusion that the mobility of heavy metals can be defined by the relative sensitivity of their solid species to environmental stresses. The following paper presents a procedure to obtain the fractionation of heavy metals during the acidification of the soil and discusses the use such data. The procedure used combines an acid–base titration of the soil with the Tessier scheme of sequential chemical extractions (SCEs). The soil was strongly buffered against acidification by the dissolution of solid carbonates. Cadmium and zinc were nevertheless significantly dissolved by acidification from pH 8 to pH 5. All other heavy metals were dissolved once pH 5 was attained. The most soluble heavy metals were those that had formed less stable acid-soluble and reducible solid species. These results are used as an example to demonstrate the importance of knowing the buffering capacity of the soil and the fractionation of heavy metals to obtain a more rigorous assessment of the mobility of heavy metals and to determine site-specific remediation criteria. Key words: contaminated soil, heavy metals, mobility, sequential chemical extractions, buffering capacity, remediation criteria.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".