Simultaneous targeted and non-targeted analysis of contaminants in fertilizers in Quebec, Canada
Bibliographic record
Abstract
In this study, an LC-MS based analytical method was developed and validated for the simultaneous targeted analysis (14 bisphenols and 14 plasticizers) and suspect screening of other plastic-related contaminants in various types of fertilizers. The ultrasound-assisted extraction method showed overall satisfactory performances, achieving a median absolute recovery of 85 % for the target compounds in different types of fertilizers. The method was applied to sixteen different types of fertilizers, including fertilizing residual materials ( n = 8 types), one cattle manure , and seven mineral fertilizers collected in Quebec, Canada in 2022 and 2023. Relatively higher levels of the targeted bisphenols and plasticizers were detected in some fertilizing residual materials, such as municipal biosolids and deinking residues. 4-Hydroxyphenyl 4-isoprooxyphenylsulfone (D-8) and bis(2-ethylhexyl) phthalate (DEHP) were dominant contaminants in these matrixes, with concentrations up to 35.6 and 64.7 μg g −1 dw, respectively. A non-targeted workflow was successfully applied to municipal biosolids and deinking residues, and >30 contaminants were identified across multiple chemical families at level 1 identification confidence, with most of them previously unreported in various types of fertilizers. For example, new color developers, N-(2-((Phenylcarbamoyl)amino)phenyl)benzenesulfonamide (NKK-1304) and 2,4-bis(phenylsulfonyl)phenol (DBSP), were reported in deinking residues. This work illustrates the complexity of the contaminant mixtures in fertilizers such as municipal biosolids and deinking residues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".