Traffic-related sources may dominate urban water contamination for many organic contaminants
Bibliographic record
Abstract
Abstract Urban runoff and wastewater/sewage input are majorly responsible for the contamination of urban streams. In streams where wastewater input is not a considerable input, the importance of urban runoff as a mechanism of contaminant transport and delivery from urban surfaces to receiving waters is even more apparent. Extensive studies on two such streams in Southern Ontario, Canada yielded data on the occurrence and levels of multiple contaminant groups (polycyclic aromatic hydrocarbons and quinones, benzotriazoles (BTs), BT ultraviolet stabilizers, organophosphate esters, herbicides) and the influence of factors such as temperature, rainfall characteristics, and land use. Here, we collectively examined the data from these studies to identify any trends and further insights. Using concentration-discharge relationships, we found that the transport dynamics of many particle-bound compounds are strikingly similar to each other, and to that of suspended solids in which they were quantified, suggesting a single, predominant source. Similar urban to rural ratios across compound groups and strong correlations with road density further support the existence of a dominant source and point to traffic as this source, respectively. Although road traffic had not previously been implicated as a major source of many of the investigated compound groups, their uses suggest that traffic-related sources are very plausible. Overall, this work highlights that traffic is a major source of a surprisingly wide array of organic contaminants to urban surfaces, and subsequently to nearby streams.
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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 | 0.002 |
| 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".