Use of Postregistration Monitoring Data to Evaluate the Ecotoxicological Risks of Pesticides to Surface Waters: A Case Study with Chlorpyrifos in the Iberian Peninsula
Bibliographic record
Abstract
Chemical monitoring data sets such as those provided by the implementation of the Water Framework Directive (WFD) offer opportunities to evaluate the ecological risks of pesticides under large spatiotemporal scales and to evaluate the protectiveness of the current prospective risk-assessment framework. As a case study, we used the monitoring data set for the insecticide chlorpyrifos to perform a probabilistic risk assessment for Iberian surface-water ecosystems. The specific objectives of the study were 1) to assess the occurrence of chlorpyrifos in relation to different agricultural production land uses, 2) to assess the spatiotemporal variation in the exceedance of the European WFD short- and long-term environmental quality standards (maximum allowable concentration environmental quality standard [MAC-EQS] and annual average [AA] EQS), and 3) to perform a probabilistic risk assessment for freshwater invertebrates. A database that contains chlorpyrifos concentrations from 14 600 surface water samples taken between 2012 and 2017 in the Iberian Peninsula (Spain and Portugal) was analyzed, and chlorpyrifos was detected in 21% of these samples. The MAC-EQS was exceeded in 2% of the cases, whereas the AA-EQS was exceeded in 18% of the cases. The majority of the exceedances took place in the littoral areas of the eastern and southeastern parts of the Iberian Peninsula, particularly in areas with dominant citrus production during late spring, late summer, and autumn. The present study indicates unacceptable risks posed by chlorpyrifos to Iberian surface waters over the study period, although it was approved for use in Europe. The present study supports the need to perform further postregistration monitoring assessments with other pesticides following similar approaches, which can help to identify possible pesticide-misuse practices and improvements of the prospective risk-assessment framework. Environ Toxicol Chem 2021;40:500-512. © 2020 SETAC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".