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Record W3212238096 · doi:10.5194/egusphere-egu21-1735

Fate of pesticide residues in vegetative filter strips in long-term exposure assessments: VFSMOD development and analysis

2021· article· en· W3212238096 on OpenAlexaboutno aff
Rafael Muñoz‐Carpena, Stefan Reichenberger, Robin Sur, Klaus Hammel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceSurface runoffPesticideSurface waterPesticide residueStormBuffer stripHydrology (agriculture)Environmental engineeringMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Inclusion of quantitative mitigation of pesticides in regulatory environmental risk assessment (ERA) using common agricultural field conservation practices is a critical need recently identified by experts in North America and EU [1]. Currently, mitigation by vegetative filter strips (VFS) is available by coupling the event-based model VFSMOD in continuous simulations within current long-term higher-tier surface water ERA frameworks (EU FOCUS SWAN, US EPA PWC, PRMA Canada, California CDPR PREM, etc.). In this case, the field management and pesticide-laden surface runoff at the edge of the field is calculated by the model PRZM and VFSMOD routes it from the edge of field through a VFS of desired characteristics to estimate potential load reductions before entering the aquatic environment, simulated by the receiving water body model (FOCUS TOXSWA, EPA VVWM). While under proper settings VFS could effectively reduce pesticide concentrations in surface water below thresholds of concern- what happens to the residues trapped in the VFS? The current ERA VFS framework uses a highly risk-conservative assumption, whereby the pesticide trapped in the VFS undergoes degradation between storm events and the surface residue (soil mixing layer and adsorbed to trapped sediment) is remobilized in full and added to the incoming pesticide load in the next event in the series. While risk conservative, this initial approach is not consistent with the nonuniform pesticide redistribution and extraction with depth used in the model PRZM within current ERA, and it has also been found too conservative for highly sorbed compounds with high specific toxicity like pyrethroids and others. The objective of this study is to develop a complete VFSMOD component to quantify the fate of VFS pesticide residues between runoff events for use in long-term ERA simulations. This includes realistic assumptions of the fate of the residues, including non-linear pesticide redistribution in the soil, mass balance of the VFS soil mixing layer and sediment trapped, degradation between runoff events, and partial remobilization and carryover of the remaining residue to the next event. Initial sensitivity and limited testing with existing field data are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.275
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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