Compound Class-Specific Temporal Trends (2021–2023) of Tire Wear Compounds in Suspended Solids from Toronto Wastewater Treatment Plants
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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 it