Remediation of a clay soil contaminated with phenanthrene by using <scp>MgO</scp> and forced carbonation
Bibliographic record
Abstract
Abstract BACKGROUND The Stabilization and Solidification (S/S) technique is one of the most cost‐effective, low‐risk, and efficient remedial technologies for contaminated soils. In this research, the effect of MgO (Magnesium Oxide) on the remediation of a clay soil contaminated with phenanthrene with and without forced carbonation was studied by the S/S method. The effects of various parameters in reducing the mobility and leaching of phenanthrene were considered, including the percentage of MgO and curing time with and without forced carbonation, as well as the duration of carbonation. RESULTS The results of the leaching tests showed that by using 5% MgO, the initial concentration of phenanthrene (32.7 mg kg−1) is changed to 9.16, 5.77, and 5.1 mg kg−1 for curing times of 7, 14, and 28 days. In addition, for the mixture of contaminated soil with 0.25% MgO and by using forced carbonation, the initial concentration is changed to 7.9, 4.3, and 3.73 mg kg−1 after 4, 8, and 24 h of injection of CO2. The S/S mechanisms for the removal of phenanthrene from soil include immobilization, adsorption, and encapsulation mechanisms. These stages are dependent on the hydration products of MgO. CONCLUSION A comparison of the results of the leaching tests without and with forced carbonation showed that forced carbonation can reduce the concentration of phenanthrene in a few hours with a small percent of MgO compared to the case without forced carbonation. Therefore, forced carbonation provides a cost‐effective method for the remediation of contaminated soil. © 2022 Society of Chemical Industry (SCI).
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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".