Three Complementary Sampling Approaches Provide Comprehensive Characterization of Pesticide Contamination in Urban Stormwater
Bibliographic record
Abstract
Urban areas are expanding rapidly and experience diverse and complex contamination of their surface waters. Addressing these issues requires different tools to describe exposures and predict toxicological risk to exposed biota. We surveyed 21 stormwater management ponds in Brampton, Ontario using three types of sampling methods deployed concurrently: time-integrated water sampling, biofilms cultured on artificial substrates, and organic-diffusive gradients in thin films (o-DGT) passive samplers. Our objective was to compare pesticide occurrences and concentrations to inform monitoring in stormwater ponds, which reflect pesticide pollution in urban areas. We detected 82 pesticides across the three sampling matrices, with most detections occurring in o-DGT samplers. The in situ accumulation of pesticides in o-DGTs during deployment and the high analytical sensitivity achieved establishes o-DGTs as excellent tools for capturing the mixtures of pesticides present. Water and biofilm sampling demonstrated that pesticide concentrations available for uptake are relatively low, with most below toxicological thresholds. Yet our results demonstrate that urban areas are subject to a wide range of pesticides, including herbicides, insecticides, and fungicides, and underscores the urgency of research to quantify the risks of chronic exposure to this chemical mixture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".