How many microplastic particles are present in Canadian biosolids?
Bibliographic record
Abstract
Application of treated sewage sludge (biosolids) from wastewater treatment plants (WWTPs) to farmlands is an important pathway through which microplastic particles (MPs) enter terrestrial ecosystems. Yet, microplastic concentrations in Canadian biosolids have only been estimated in samples from four WWTPs previously. We aimed to fill this knowledge gap by quantifying microplastics in biosolids from 22 WWTPs located in nine provinces and two commercial fertilizer producers in Canada. All samples had substantial microplastic concentrations ranging from 228 to 1353 particles per gram dry weight (median = 636 particles), which are orders of magnitude greater than MPs reported from earlier investigations of biosolids from other countries. Fibers (median: 86%) were the most common type of MPs observed, followed by fragments (median: 13%). There were no statistically significant differences in the amount of microplastics observed in the biosolids from different geographical regions, WWTP types, and sludge treatment processes. This suggests that diverse combinations of local sewershed characteristics, site-specific treatment approaches, and daily flow at WWTPs may be influencing concentrations of microplastics in biosolids. Our results indicate that microplastic concentrations in biosolids are substantially higher than they are in other environmental matrices, and this has important implications to managing microplastic pollution in terrestrial ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".