Intensive Spatiotemporal Characterization of the Tire Wear Toxin 6PPD Quinone in Urban Waters
Bibliographic record
Abstract
= 41-95 ng/L) found widely distributed in urban environments. Most monitoring efforts have relied on relatively few discrete samples collected at select locations across rain events. Early work has revealed that 6PPDQ concentrations vary widely over time and space, raising questions about when and where to collect samples. Here, we employ condensed phase membrane introduction mass spectrometry, a high-throughput analysis approach to characterize spatiotemporal variability of 6PPDQ in urban streams. Analytical method detection limits of 0.3-8 ng/L and a duty cycle of 2.5 min/sample enabled high-throughput adaptive sampling. Temporal sampling revealed dynamic 6PPDQ concentrations, with significant changes occurring over minutes during peak rainfall. Spatial variability was characterized at multiple sites along three watercourses during the first significant precipitation of autumn 2023 on central Vancouver Island, B.C., Canada. Site-specific concentrations suggest attenuation of 6PPDQ after point source inputs by some combination of physical (dilution, sorption) or chemical (degradation) processes. This is the first report of an intensive sampling campaign describing the spatiotemporal distribution of 6PPDQ, highlighting the need for careful consideration of sampling strategies to evaluate the risk and impact of 6PPDQ in urban waterways.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".