Mix-Tool: An Edge-of-Field Approach to Predict Pesticide Mixtures of Concern in Surface Water From Agricultural Crops.

Current regulation on the authorization of plant protection products (PPPs) in the European Union is limited to the evaluation of ecological risks for the single active substances they contain. However, plant protection treatments in agriculture often consist of PPPs already containing more than one active substance; moreover, each cropped field receives multiple applications per year, leading to complex pesticide mixtures in the environment. Different transport processes lead to a multitude of heterogeneous and potentially toxic substances that, for example, may reach water bodies and act simultaneously on natural freshwater ecosystems. In this context, the development of methodologies and tools to manage risks of pesticides mixtures is imperative to improve the current ecological risk assessment procedures and to avoid further deterioration of ecological quality of natural resources. The present study suggests new procedures for identifying pesticide mixtures of potential concern released from agricultural crops in surface water. The approach follows the European Union regulatory context for the authorization of PPPs in the market (edge-of field risk assessment) and requires the use of Forum for the Co-ordination of pesticide fate models and their Use (FOCUS) models (Step 3 and 4) for calculating the concentrations in surface water of mixture components on a daily basis. Moreover, it uses concentration addition models to calculate the toxic potency of the pesticide mixtures released by a treated crop. To implement this procedure, we developed a simple Microsoft-Excel-based tool. We also considered two case studies (maize and apple tree), representative of Italian agricultural scenarios for annual and perennial crops. Moreover, we compared results with 3 years of monitoring data of surface water bodies of the Lombardia region (northern Italy) where the two crops are largely present. Environ Toxicol Chem 2022;41:2028-2038. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Pesticides groundwater modelling relies on input data characterised by a high intrinsic variability: Is the resulting risk for groundwater credible?

In the framework of Regulation (EC) 1107/2009, concerning the placing of plant protection products (PPP) on the market, FOCUS models are used to predict active substances concentration in groundwater. The predicted environmental concentration in groundwater (PECGW) are influenced by active substance specific parameters, namely DT50, KOM and Freundlich coefficient (1/n), whose minimal variation in certain combinations of intervals significantly affects PECGW output. Considering that minimal variation are intrinsic in all laboratory studies, this approach may lead to not acceptable variations in the results for regulatory purposes. In the present article, PECGW were calculated for all maize crop scenarios, using 808 dummy active substances with different combinations of DT50, KOM and 1/n values, in order to quantify the influence of each single parameter on the final result of PEARL and PELMO models. The results obtained were used to create a classification system for the input parameters KOM and DT50 in order to minimise the input uncertainty effects. Even if this approach is scientifically viable yet, due to its conservative nature, it cannot be considered suitable in the regulatory framework, where acceptability of an active substance is strictly related to the limit value of 0.1 µg/L. Nevertheless, this classification system could represent an important screening or preliminary assessment to plan pesticide monitoring programmes. Based on the results of this analysis, it is believed that the assessment of pesticide leaching into groundwater should be revised to take into account this variability. Considering that both PEARL and PELMO FOCUS models deal with interaction between a chemical and a complex system like soil and weather, the selection of input data cannot pretend to rely on single specific number. Considering that intrinsic uncertainty cannot be eliminated from experimental work, a revision of the criteria used to identify the proper input data and a thorough revision of the actual groundwater modelling is recommended.