Microplastic fate in a chronosequence of biosolid‐amended agricultural soil in Southern Ontario, Canada
Bibliographic record
Abstract
Abstract Municipally sourced biosolids are commonly used as cost‐effective fertilizers, diverting material from landfills and contributing to the circular economy. However, biosolids contain high concentrations of microplastics (MPs), which are emerging contaminants of concern due to their ubiquity in the environment. Despite this, there is a lack of environmentally relevant field studies. In 2022, composite topsoil samples (0–15 cm depth) were collected from seven agricultural fields in Southern Ontario, Canada, representing a chronosequence of biosolid applications ranging from 1 to 9 years since amendment and a control (untreated) field. MP particles down to 20 μm in size were extracted by density separation, enumerated, characterized by stereomicroscope and polymers identified using attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectroscopy. Here, we report on the characteristics, abundance and polymer type of MP particles in the study area to assess their fate in biosolid‐amended soils. The average MP concentration among fields was 6.87 ± 1.47 MP g −1 (3.43 ± 0.74 mg MP kg −1 ). Additionally, the MP soil pool increased with repeated applications of biosolids. The dewatered biosolid plastic content of 8816 ± 1809 MP g −1 dry weight (11.6 ± 17.5 g MP kg −1 dry weight) was used to estimate a mean MP loading of 94.5 ± 10.9 kg ha −1 to each field per application, suggesting that 7% of the MP soil pool persisted over time. Quantifying the MP pool in biosolid‐amended agricultural soil will inform evidence‐based plastic policy changes in our global effort to understand and reduce plastic pollution.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".