Environmental exposure assessment of co‐formulants in plant protection products under REACH

Abstract

It is a regulatory requirement to assess co‐formulants in plant protection products (PPP) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) legislation. The standard environmental exposure assessment framework for chemicals under REACH is a multicompartmental mass‐balanced model and, at the local scale, is designed for use with urban (wide dispersive) or industrial (point source) emissions. However, the environmental release of co‐formulants used in PPP is to agricultural soil and indirectly to waterbodies adjacent to a field and, for sprayed products, to the air. The Local Environment Tool (LET) has been developed to assess these specific emission pathways for co‐formulants in a local‐scale REACH exposure assessment, based on standard approaches and models used for PPP. As such, it closes a gap between the standard REACH exposure model's scope and REACH requirements to assess co‐formulants in PPP. When combined with the output of the standard REACH exposure model, the LET includes an estimate of the contribution from other nonagricultural background sources of the same substance. The LET is an improvement over the use of higher tier PPP models for screening purposes because it provides a simple standardized exposure scenario. A set of predefined and conservatively selected inputs allows a REACH registrant to conduct an assessment without requiring detailed knowledge of PPP risk assessment methods or typical conditions of use. The benefit to the co‐formulant downstream user (formulators) is a standardized and consistent approach to co‐formulant assessment, with meaningful and readily interpretable conditions of use. The LET can serve as an example to other sectors of how to address possible gaps in the environmental exposure assessment by combining a customized local‐scale exposure model with the standard REACH models. A detailed conceptual explanation of the LET model is provided here together with a discussion on its use in a regulatory context. Integr Environ Assess Manag 2023;00:1–11. © 2023 BASF SE, Bayer AG et al. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

Performance Evaluation Of The Deep Learning System For Weed

“…The primary objective of this study is to determine whether AutoML techniques can compete with manuallydesigned architectures. Three primary contributions are presented in this paper: (1) a procedure with two stages that utilizes AutoML to for deep learning component extraction and classifier ensembles for plant identification; (2) we exclusively used open-source AutoML frameworks for our implementation, along with two publicly accessible datasets, to facilitate transparent and reproducible research. (3) This study aims to evaluate the reliability and susceptibility of AutoML systems to overfitting using noisy data samples.…”

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