Vertical distribution and migration of microplastics in soils from Fars Province, Southwest Iran
Bibliographic record
Abstract
Microplastics (MPs) in soils are becoming increasingly recognised as important terrestrial contaminants, yet their vertical distribution below the plough layer remains poorly understood. Here, we examined 76 genetic soil horizons from 27 profiles along four transects spanning managed and unmanaged land uses in Fars Province, southwest Iran, to depths of up to 140 cm. A total of 392 MPs were recovered from 71 of 76 samples, comprising 342 fibres (87.2%), 48 fragments or films and two spherules, with concentrations reaching about 320 particles kg-1 of dry soil. Raman analysis of 98 representative particles showed a dominance of polyamides, polyesters and polyolefins. MP abundance, size and polymer type were heterogeneous among sites and horizons, and only a few statistically significant relationships with soil properties were observed (notably inverse relationships with sand at Darab and silt at Sarvestan). MPs occurred throughout the profiles, including the deepest horizons, with no consistent size- or polymer-related fractionation with depth. These observations indicate sustained MP accumulation in soils and suggest that downward transport can occur under arid to semi-humid conditions through percolation, drying-cracking of the substrate and bioturbation. Because strong local point sources were not evident at most sites and fibres dominated the assemblage, atmospheric deposition is considered a major contributor at the soil surface, although additional source-specific data are needed to confirm this inference. The persistence and vertical mobility of MPs in Fars soils indicate potential risks to subterranean ecosystems and groundwater quality.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".