PFAS profiles in biosolids, composts, and chemical fertilizers intended for agricultural land application in Quebec (Canada)
Bibliographic record
Abstract
Biosolids, sewage sludge, and composts are applied to agricultural land for nutrient recovery and soil organic matter replenishment, aligning with sustainable development goals. However, they may contain per- and polyfluoroalkyl substances (PFAS) that can enter the food chain through plant bioaccumulation and leaching into the groundwater. This study analyzed 80 PFAS compounds in sewage sludge, biosolids, commercial composts, and chemical fertilizers in Quebec, Canada, using UHPLC-HRMS (Orbitrap Q-Exactive). PFAS concentrations ranged from 18 to 59 µg/kg in commercial composts, 9.8 to 213 µg/kg in pulp and paper sludge, 15 to 705 µg/kg in sewage sludge, 12 to 1310 µg/kg in biosolids, and 14.6 µg/kg on average in biosolids ash. Dominant PFAS classes included diPAPs, sulfonamides, PFCAs, and PFSAs. High diPAPs concentrations indicated widespread use in domestic, commercial, or industrial applications. This study also observed a negligible correlation between soil organic carbon and PFAS concentration in the biowastes signifying a stronger influence due to different WWTP configurations, the quality of the wastewater inputs and other medium's properties that could affect PFAS partitioning to the biowastes. Environmental assessments showed PFAS loads of up to 30 µg/kg soil from a single application, within some regulatory limits. However, repeated applications could lead to PFAS accumulation in soil, posing risks to crops and groundwater.
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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) | 0.002 | 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".