Transfer and transport of microplastics from biosolids to agricultural soils and the wider environment
Bibliographic record
Abstract
Between April to November of 2017, microplastics (MPs) were analysed in biosolids from two separate suppliers, and in the soils of three agricultural fields to which they were applied, in Ontario, Canada. Soils of a control site with no history of biosolid application were also examined. High MP concentrations of between 8.7 × 103 MP kg−1 and 1.4 × 104 MP kg−1 were found in biosolids samples. Lower MP concentrations observed in Provider 2 biosolids may be due to storage, settling and supernatant removal prior to applications. Annual MP additions to agricultural soils across Ontario were estimated at between 4.1 × 1011 and 1.3 × 1012 particles. All fields receiving biosolids had higher soil pre-treatment MP concentrations than the control. The field with the greatest number of previous biosolid treatments had the highest pre-treatment soil MP concentrations; suggesting some MP retention in soils between applications. Immediately following biosolids applications, two fields demonstrated significant increases in soil MP concentrations, with preferential retention of MP fibers over fragments observed, while a reduction in soil MP concentrations were observed in the third. Surprisingly, only one field demonstrated a net gain in soil MPs over the course of the study. At all three fields, >99% of MPs applied in biosolids in 2017 were unaccounted for. The study suggests that despite adhering to applicable legislation, biosolids applications at all sites likely result in high rates of MP export. This study is the first to track MP transport through soils following their application in biosolids, and contributes to filling current knowledge gaps regarding export of MPs to aquatic systems from the terrestrial environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".