An Urban Stormwater Contaminant Signature: Defining Priority Contaminants for Urban Stormwater Research
Bibliographic record
Abstract
Stormwater ponds (SWPs) are a common feature of urban landscapes, designed to manage runoff and reduce flooding. Increasingly, they are also recognized as seminatural habitats supporting aquatic biodiversity. However, SWPs receive complex mixtures of contaminants from surrounding urban areas, and the extent of contamination within the ponds themselves remains underexplored. Most studies have focused on outflows or a narrow set of targeted analytes, limiting our understanding of the exposure risks for organisms residing within these systems. To address this gap, we assessed contaminant profiles in 21 SWPs across a highly urbanized city in Ontario, Canada, using three complementary sampling approaches: time-integrated water samples, biofilm on artificial substrates, and organic diffusive gradients in thin films (o-DGTs). Across all sites, we detected 200 organic compounds, including pesticides, pharmaceuticals, industrial chemicals, and compounds, linked to vehicles and infrastructure. Additionally, we documented widespread chloride and fecal contamination and elevated levels of traffic-related metals in biofilms. From these data, we identified a set of frequently detected and environmentally relevant contaminants, which we term the urban stormwater contaminant signature (USCS). This proposed list may support the development of targeted monitoring strategies and help focus future research on mixture toxicity and risk to aquatic biota. Given the apparent ecological role of SWPs and the range of stressors they contain, assessing cumulative exposures is critical for understanding the potential impacts of urban runoff on resident organisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".