Decontamination of metals and polycyclic aromatic hydrocarbons from slag-polluted soil
Bibliographic record
Abstract
Metallurgy is an industrial activity that is one of the largest contributors to soil contamination by metals. This contamination is often associated with organic compound contamination; however, little research has been aimed at the development of simultaneous processes for decontamination as opposed to treatments to heavy metals or organic compounds alone. This paper presents an efficient process to decontaminate the soils polluted with smelting by-products rich in Cu, Zn and polycyclic aromatic hydrocarbons (PAHs). A simultaneous treatment for metals and PAHs was also tested. The process is mainly based on physical techniques, such as crushing, gravimetric separation and attrition. For the finest particle size fractions, an acid extraction with H2SO4 was used to remove metals. The PAH removal was enhanced by adding surfactant during attrition. The total metal removals varied from 49% to 73% for Cu and from 43% to 63% for Zn, whereas a removal yield of 92% was measured for total PAHs. Finally, a technical-economic evaluation was done for the two processes tested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".