Xplicit, a novel approach in probabilistic spatiotemporally explicit exposure and risk assessment for plant protection products

Abstract

The quantification of risk (the likelihood and extent of adverse effects) is a prerequisite in regulatory decision making for plant protection products and is the goal of the Xplicit project. In its present development stage, realism is increased in the exposure assessment (EA), first by using real-world data on, e.g., landscape factors affecting exposure, and second, by taking the variability of key factors into account. Spatial and temporal variability is explicitly addressed. Scale dependencies are taken into account, which allows for risk quantification at different scales, for example, at landscape scale, an overall picture of the potential exposure of nontarget organisms can be derived (e.g., for all off-crop habitats in a given landscape); at local scale, exposure might be relevant to assess recovery and recolonization potential; intermediate scales might best refer to population level and hence might be relevant for risk management decisions (e.g., individual off-crop habitats). The Xplicit approach is designed to comply with a central paradigm of probabilistic approaches, namely, that each individual case that is derived from the variability functions employed should represent a potential real-world case. This is mainly achieved by operating in a spatiotemporally explicit fashion. Landscape factors affecting the local exposure of habitats of nontarget species (i.e., receptors) are derived from geodatabases. Variability in time is resolved by operating at discrete time steps, with the probability of events (e.g., application) or conditions (e.g., wind conditions) defined in probability density functions (PDFs). The propagation of variability of parameters into variability of exposure and risk is done using a Monte Carlo approach. Among the outcomes are expectancy values on the realistic worst-case exposure (predicted environmental concentration [PEC]), the probability p that the PEC exceeds the ecologically acceptable concentration (EAC) for a given fraction of habitats, and risk curves. The outcome can be calculated at any ecologically meaningful organization level of receptors. An example application of Xplicit is shown for a hypothetical risk assessment for nontarget arthropods (NTAs), demonstrating how the risk quantification can be improved compared with the standard deterministic approach.

A spatiotemporally explicit modeling approach for more realistic exposure and risk assessment of off‐field soil organisms

“…Corresponding explicit AEs will have to be provided in future RAs, quantifying effect magnitudes in space and time for a defined biological entity (e.g., individuum, population, functional group) and attributes (e.g., behavior, survival and/or growth, abundance and/or biomass). Although the presented xOffFieldSoil approach currently has a focus on exposure assessment (Model design section below), its underlying concepts are built to address the full SPG framework (Bub et al, 2020; Schad, 2013; Schad & Schulz, 2011).…”

“…Endpoints can focus on exposure values (as in the case study herein), soil function, or populations of soil organisms, if corresponding population effect models are integrated into xOffFieldSoil. Such model adaptations are possible as xOffFieldSoil is built on a generic modular landscape modeling framework (Figure 2; Schad, 2013). The aims of this framework are (i) to provide concepts and a toolbox to build landscape models capable of propagating variability to model outcome while complying with fundamental principles of MC modeling (e.g., outcome pattern to represent a possible status of the modeled system); (ii) to enable the integration of existing models as components (also to facilitate model validation); (iii) to assure consistent data and information states within the model; and (iv) to transparently build AEs that directly link to the attributes, risk dimensions, and scales defined in SPGs (e.g., EFSA PPR Panel, 2017; EFSA Scientific Committee, 2016).…”

“…Components provide major model functionality (e.g., spray‐drift or runoff exposure calculation) and are built by wrapping existing models (e.g., PRZM) or by developing new ones (e.g., “RunoffFilter1”). The implementation of xOffFieldSoil is based on a generic modular landscape modeling framework (Schad, 2013). The light gray boxes represent xOffFieldSoil components that were not used in the case study, although they do exist or are under development (full scheme in Supporting Information: Figure , https://github.com/xlandscape/xOffFieldSoilRisk).…”

Aquatic Risks at the Landscape Scale: A Case Study for Pyrethroid Use in Pome Fruit Orchards in Belgium

“…The agricultural management component generates input data for the model on application characteristics with regard to, e.g., location, timing, application rate, and used equipment. The spray drift component , simulates the spray drift deposition per square meter along field edges per day. For each orchard, the application date is picked randomly within a user-defined application window.…”