Aided Phytoremediation to Clean Up Dioxins/Furans-Aged Contaminated Soil: Correlation between Microbial Communities and Pollutant Dissipation
Bibliographic record
Abstract
To restore and clean up polluted soils, aided phytoremediation was found to be an effective, eco-friendly, and feasible approach in the case of many organic pollutants. However, little is known about its potential efficiency regarding polychlorinated dibenzo-p-dioxins and furans-contaminated soils. Thus, phytoremediation of aged dioxins/furans-contaminated soil was carried out through microcosm experiments vegetated with alfalfa combined with different amendments: an arbuscular mycorrhizal fungal inoculum (Funneliformis mosseae), a biosurfactant (rhamnolipids), a dioxins/furans degrading-bacterium (Sphingomonas wittichii RW1), and native microbiota. The total dioxins/furans dissipation was estimated to 23%, which corresponds to 48 ng.kg−1 of soil, after six months of culture in the vegetated soil combined with the four amendments compared to the non-vegetated soil. Our findings showed that the dioxins/furans dissipation resulted from the stimulation of soil microbial enzyme activities (fluorescein diacetate hydrolase and dehydrogenase) and the increase of bacterial abundance, richness, and diversity, as well as fungal diversity. Amplicon sequencing using Illumina MiSeq analysis led to identification of several bacterial (Bacillaceae, Sphingomonadaceae) and fungal (Chaetomium) groups known to be involved in dioxins/furans degradation. Furthermore, concomitant cytotoxicity and dioxins/furans concentration decreases were pointed out in the phytoremediated soil. The current study demonstrated the usefulness of combining different types of amendments to improve phytoremediation efficacy of aged dioxins/furans-contaminated soils.
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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".