Characterizing the Microplastic Content of Biosolids in Southern Ontario, Canada
Bibliographic record
Abstract
Abstract The application of biosolids to agricultural land has been identified as a major pathway of microplastic (MP) pollution to the environment. Very little research, however, has been done on the MP content of biosolids within Canada. Fifteen biosolid samples from different treatment processes (liquid, dewatered, pelletized, and alkali-stabilized) were collected from 11 sources across southern Ontario to quantify and characterize the MP load within them. All samples exhibited MP concentrations ranging from 188 200 (±24 161) to 512 000 (±28 571) MPs/kg dry weight and from 4122 (±231) to 453 746 (±38 194) MPs/kg wet weight. Field amendment of these biosolids can introduce up to 3.73 × 106 to 4.12 × 108 MP/ha of agricultural soil. There was no significant difference in the MP concentrations of liquid, dewatered, and pelletized samples; but a reduction in MP content was observed in alkali-stabilized biosolids. Fragments composed 57.6% of the MPs identified, while 36.7% were fibers. In addition, MPs showed an exponential increase in abundance with decreasing size. Characterization of MPs confirmed that polyester was the most abundant, while polyethylene, polypropylene, polyamide, polyacrylamide, and polyurethane were present across the majority of biosolid samples. The results of the present study provide an estimate of the potential extent of MP contamination to agricultural fields through the amendment of biosolids. Environ Toxicol Chem 2024;43:793–806. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| 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".