New insights into pesticide occurrence and multicompartmental monitoring strategies in stream ecosystems using periphyton and suspended sediment
Bibliographic record
Abstract
Streams are susceptible to pesticide pollutants which are transported outside of the intended area of application from surrounding agricultural fields. It is essential to monitor the occurrence and levels of pesticides in aquatic ecosystems to comprehend their effects on the aquatic environment. The common sampling strategy used for monitoring pesticides in stream ecosystems is through the collection and analysis of grab water samples. However, grab water sampling may not effectively monitor pesticides due to its limited ability to capture temporal and spatial variability, potentially missing fluctuations and uneven distribution of pesticides in aquatic environments. Monitoring using periphyton and sediment sampling may offer a more comprehensive approach by accounting for accumulative processes and temporal variations. Periphyton are a collective of microorganisms that grow on hard surfaces in aquatic ecosystems. They are responsive to chemical and biological changes in the environment, and therefore have the potential to act as a cost-effective, integrated sampling tool to monitor pesticide exposures in aquatic ecosystems. The objective of this study was to assess pesticides detected through periphyton, suspended sediment, and conventional grab water sampling methods and identify the matrix that offers a more comprehensive characterization of a stream's pesticide exposure profile. Ten streams across Southern Ontario were sampled in 2021 and 2022. At each stream site, water, sediment and periphyton, colonizing both artificial and natural substrates, were collected and analyzed for the presence of ~500 pesticides. Each of the three matrices detected distinctive pesticide exposure profiles. The frequency of detection in periphyton, sediment and water matrices were related to pesticides' log Kow and log Koc (P < 0.05). In addition, periphyton bioconcentrated 22 pesticides above levels observed in the ambient water. The bioconcentration factors of pesticides in periphyton can be predicted from their log Kow (simple linear regressions, P < 0.05). The results demonstrate that sediment and periphyton accumulate pesticides in stream environments. This highlights the importance of monitoring pesticide exposure using these matrices to ensure a complete and comprehensive characterization of exposure in stream ecosystems.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".