High-Frequency Sampling of Small Streams in the Agroecosystems of Southwestern Ontario, Canada, to Characterize Pesticide Exposure and Associated Risk to Aquatic Life
Bibliographic record
Abstract
The temporal dynamics of pesticide concentrations in streams remains poorly characterized in southwestern Ontario, a region of the province where land use is dominated by agriculture. Understanding the magnitude and duration of pulsed exposures to pesticides in these small streams is critical when estimating the risk of pesticides to these aquatic ecosystems. The present study investigated the application of a high-frequency water sampling approach paired with the collection of flow data to characterize the pulsed exposure of pesticides to small streams in southwestern Ontario. Six sites along 2 different streams with different magnitudes of agricultural land use in their upstream catchments were sampled using half-day composite samples from July to October 2018 and from May to September 2019. A total of 1043 samples were collected over the 2 yr, of which 210 were analyzed. Samples for analysis were chosen based on flow, water level, and precipitation data. Liquid and gas chromatography coupled with tandem mass spectrometry was used to measure >500 pesticides in each water sample. A total of 35 different compounds were detected over the 6 sampling sites. For pesticides that were detected in >10% of water samples above the method quantification limit, a deterministic risk assessment using water quality guidelines and a probabilistic risk assessment using species sensitivity distributions were performed. The calculated hazard quotients showed that 2,4-D, atrazine, metolachlor, and metribuzin exceeded a level of concern of 1 at the highest concentrations detected. In all cases, hazard concentrations that would be protective of 95% of species from the species sensitivity distributions were greater than the 95th centile of the environmental exposure distributions, meaning that the risk from the pesticides was low. Environ Toxicol Chem 2020;39:2570-2587. © 2020 SETAC.
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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 | 0.001 |
| 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) | 0.001 | 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".