Role of heavy metals and organic matter on sorption and mobility of polycyclic aromatic hydrocarbons in soil : implications for remediation
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are a group of abundant contaminants in contaminated sites having many adverse effects on human health and the ecosystem. Due to the many common sources of PAHs and heavy metals, many sites are contaminated by both groups. Co-presence of these contaminants can affect their sorption/desorption in the soil environment, affecting their fate, transport, and remediation processes. This research project advanced the understanding of sorption behavior of PAHs co-existing with heavy metals in soil. Three types of artificially blended clay and clay minerals (kaolinite, kaolinite+sand, kaolinite+sand+bentonite) and a real spiked clayey soil sample were investigated. The synergistic effect of organic matter (humic acid) with heavy metals on enhancing the sorption of PAHs was tested and confirmed for the first time. Different single and combined solutions were used to enhance the desorption of PAHs from the soil. Two non-ionic surfactants (Triton X-100 and Tween 80) with EDTA showed the capability to simultaneously remove these PAHs (acenaphthene, fluorene and fluoranthene) and these prominent heavy metals (Ni, Pb, and Zn) from the soil sample. Results also showed that the co-presence of metal contaminants and soil organic matter can decrease the mobility and desorption of PAHs with the results that the efficiency of soil washing/flushing remediation could decrease in such cases. Our findings also show that the rate of desorption of PAHs is reduced by the co-presence of organic matter and heavy metals in soil. This affects the cost and time of remediation of sites contaminated by mixed heavy metals and PAHs. Our findings were confirmed through column soil flushing of a real natural soil sample by combined enhancing solutions.
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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 | 0.001 | 0.000 |
| 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".