Compound Class-Specific Temporal Trends (2021–2023) of Tire Wear Compounds in Suspended Solids from Toronto Wastewater Treatment Plants
Bibliographic record
Abstract
Tire wear compounds (TWCs) have received increasing attention due to their ubiquitous environmental occurrence and toxicity. In this study, the temporal trends of 23 TWCs in two Toronto wastewater treatment plants were systematically investigated through a two-year wastewater surveillance. Through an optimized analytical method, 20 TWCs were detected across 161 weekly influent suspended solid samples at a total concentration of 273–52,500 ng/g dw. Phenyl- p -phenylenediamines (PPDs), N -(1,3-dimethylbutyl)- N ′-phenyl- p -phenylenediamine-quinone (6PPD-Q), and 1,3-diphenylguanidine (DPG) showed a strong co-occurrence, and their concentration spikes were coincident with both flow rates of influents and precipitation, which was not observed for other TWCs. Therefore, stormwater runoff is a major source of PPDs, PPD-Qs, and DPG, but not other TWCs. The temporal trends of N -1,3-dimethylbutyl- N′ -phenyl- p -phenylenediamine (6PPD) transformation products were further determined. Among six detected transformation products, four compounds including N -(1,3-dimethylbutyl)- N′ -phenyl- p -quinonediimine (6QDI) showed a strong co-occurrence with 6PPD but not with 4-hydroxydiphenylamine (4-HDPA) and N -phenyl- p -phenylenediamine (4-ADPA). Rapid hydrolysis of 4-ADPA to 4-HDPA was observed ( t 1/2 = 35.7 h), suggesting that 4-ADPA, rather than 6PPD, is the major precursor leading to the formation of 4-HDPA in wastewater. The compound class-specific temporal trends of TWCs in wastewater suggest the existence of distinct emission sources of TWCs in addition to traffic-related stormwater runoff.
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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 | 0.000 | 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) | 0.001 | 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".