Phosphate-solubilizing microorganisms for sustainable remediation of heavy metal(loid)-contaminated soils: underlying mechanisms and practical implications
Bibliographic record
Abstract
Soil heavy metal(loid) pollution remains one of the most critical environmental challenges worldwide, and increasing attention has been paid to remediation strategies. Phosphate-solubilizing microorganisms (PSMs) have been identified as promising bioremediation microorganisms due to their multifunctional roles in nutrient cycling and heavy metal(loid) stabilization or mobility. This review provided the remediation mechanisms of heavy metal(loid)-contaminated soils by PSMs, including surface adsorption, chelation with siderophores, organic acids, and exopolysaccharides. During the remediation process, PSMs could also enhance the soil available phosphorus which could further immobilized heavy metal(loid)s. The PSMs could also combine with hyperaccumulators and soil amendments to increase the remediation efficiency through altering the bioavailability of heavy metal(loid)s. The remediation efficiency of PSMs could be influenced by soil characteristics, environmental conditions, and heavy metal(loid) properties, which relate with the PSMs activity. The PSMs application may also result in soil acidification, disrupt the native microbial communities, and increase the mobilization of heavy metal(loid)s, which could not be ignored. This work will provide guidelines for the safe and effective remediation of metal(loid)-contaminated soils using PSMs. Future research should aim to optimize PSM application on enhancing the remediation efficiency of contaminated soils and decreasing the ecological risks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".