Temporal effect of MgO reactivity on the stabilization of lead contaminated soil
Bibliographic record
Abstract
Elevated soil lead (Pb) concentrations are a global concern owing to the toxic effects of this heavy metal. Solidification/stabilization (S/S) of soils using reagents like Portland cement (PC) is a common approach for the remediation of Pb contaminated sites. However, it has been reported that under long-term field conditions, the performance of PC treatments can diminish significantly. Therefore, novel reagents that provide longer-term stabilization performance are needed. In this study, four magnesium oxide (MgO) products of different reactivity values were applied (5 wt%) to a Pb contaminated clayey soil. The short-term (1–49 days) and long-term (25–100 years) temporal stabilization effects were investigated by laboratory incubation and accelerated ageing methods, respectively. The concentration of Pb in Toxicity Characterization Leaching Procure (TCLP) leachate was ~14 mg/L for the untreated soil; ~1.8 times higher than the TCLP regulatory level (5 mg/L). Only one day after treatment with MgO, the leachate concentration was reduced to below the regulatory level (a reduction of 69.4%–83.2%), regardless of the MgO type applied. However, in the long-term accelerated ageing experiments, only treatments using the most reactive MgO type could provide leachate concentrations that were consistently below the TCLP threshold throughout the 100 years of simulated ageing. The soil treated with the MgO of lowest reactivity was the first to exceed the regulatory level, at simulated year 75. It is thus demonstrated that MgO reactivity has a significant effect on its long-term effectiveness for contaminated soil stabilization. This is attributed to differences in their specific surface area and readiness to carbonate, which may facilitate the immobilization of Pb in the long term. It is also noteworthy that compared to PC, reactive MgO is more environmentally friendly owing to lower energy consumption and reduced CO 2 emissions during its manufacture.
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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 | 0.001 |
| 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) | 0.002 | 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".