Parameterization of a Brazilian scenario in the USEPA Pesticide in Water Calculator tool to estimate the environmental exposure of pesticide in surface waters
Bibliographic record
Abstract
Abstract The current pesticide registration process in Brazil is mainly hazard-based and does not consider exposure and therefore risk. However, the scenario prompted changes and discussions about risk assessments by Brazilian environmental regulatory agencies. The US Environmental Protection Agency's (EPA) Pesticide in Water Calculator (PWC) model is used as a regulatory tool for aquatic exposure assessment in Canada and the USA for exposure evaluation of agrochemical products; nevertheless, the available scenarios only consider North American local conditions. This work aims to demonstrate a parametrization of the PWC model for a Brazilian scenario, considering the active ingredient glyphosate and sugarcane agronomic practices. The estimated environmental concentrations (EECs) obtained were compared with two standard EPA scenarios. Essential parameter data to build a specific local scenario were collected from the literature and official Brazilian databases. The EECs (1-in-10 years) of glyphosate according to the conditions established were 1.427 μg L−1 (1st day), 0.382 µg L−1 (21st day), and 0.2027 µg L−1 (60th day). These values can be used as exposure elements in acute and chronic risk assessments considering the agricultural practices used in the developed scenario. A 4.45-fold and 1.28-fold difference was found comparing the 1-day (1-in-10 years) average concentration of the Brazilian scenario with two EPA standard scenarios. Such a difference may affect the outcome of risk assessments, affecting regulatory decisions. This demonstrates the importance of generating more realistic scenarios for Brazil, yielding surface water EECs that consider local conditions. Integr Environ Assess Manag 2022;18:1387–1398. © 2021 SETAC KEY POINTS Estimated environmental concentrations (EECs) generated by standard EPA-PWC scenarios can differ greatly from the ones when local conditions are considered and such difference may affect the outcome of the risk assessment, and regulatory decisions. Developing local PWC scenarios, instead of developing a new exposure calculator is a more straightforward approach since it is possible build on a previously regulatory approved and validated tool. It is feasible for local regulatory, industry and academic scientific groups to build PWC local scenarios from the selection of specific local data—such as meteorological information, soil characteristics, runoff parameters—to be used as an exposure element in risk assessments. Considering that Brazil has continental extensions with different climates, soils, and representative crops, the development of local scenarios for representative regions is crucial to obtain more realistic EECs addressing aquatic risk assessments appropriately.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".