Heavy metal transport in soil contaminated by residual light non-aqueous phase liquids (LNAPLs)
Bibliographic record
Abstract
This paper presents an experimental study on mixed soil contamination, more specifically on heavy metal behaviour in soil contaminated by residual non-aqueous phase liquids (NAPLs). Remediation of mixed contaminated sites is a complex technical goal because of the presence of physically and chemically different contaminants and potential interactions between them. Commonly encountered contaminants in mixed contaminated soils include light and dense organic liquids (LNAPLs, DNAPLs) and heavy metals. This study investigated interactions between three residual LNAPLs and three heavy metals (Cd, Cu, Pb) in a carbonated soil. The objectives of the study were to (i) establish the presence of interactive processes in the behaviour of the contaminants, with a focus on the influence of residual LNAPL on heavy metal transport and retention; and (ii) determine the nature of these interactions. Results showed that the LNAPL having the highest residual saturation enhanced heavy metal mobility and decreased heavy metal retention by the soil. On the other hand, the geochemical distribution of heavy metals was not significantly modified by chemical interactions with the residual LNAPLs. Specific modifications of Pb and Cu geochemical distributions rather appeared to be the result of modifications of soil hydrodynamics by residual LNAPL.Key words: contaminated soil, mixed contamination, heavy metals, LNAPL, preferential flow, contaminant mobility.
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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) | 0.000 | 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) | 0.000 | 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".