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

Procedures for environmental risk assessment for pesticides are under continuous development and subject to debate, especially at higher tier levels. Spatiotemporal dynamics of both pesticide exposure and effects at the landscape scale are largely ignored, which is a major flaw of the current risk assessment system. Furthermore, concrete guidance on risk assessment at landscape scales in the regulatory context is lacking. In this regard, we present an integrated modular simulation model system that includes spatiotemporally explicit simulation of pesticide application, fate, and effects on aquatic organisms. As a case study, the landscape model was applied to the Rummen, a river catchment in Belgium with a high density of pome fruit orchards. The application of a pyrethroid to pome fruit and the corresponding drift deposition on surface water and fate dynamics were simulated. Risk to aquatic organisms was quantified using a toxicokinetic/toxicodynamic model for individual survival at different levels of spatial aggregation, ranging from the catchment scale to individual stream segments. Although the derivation of landscape-scale risk assessment end points from model outputs is straightforward, a dialogue within the community, building on concrete examples as provided by this case study, is urgently needed in order to decide on the appropriate end points and on the definition of representative landscape scenarios for use in risk assessment.

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

Natural and seminatural habitats of soil living organisms in cultivated landscapes can be subject to unintended exposure by active substances of plant protection products (PPPs) used in adjacent fields. Spray-drift deposition and runoff are considered major exposure routes into such off-field areas. In this work, we develop a model (xOffFieldSoil) and associated scenarios to estimate exposure of off-field soil habitats. The modular model approach consists of components, each addressing a specific aspect of exposure processes, for example, PPP use, drift deposition, runoff generation and filtering, estimation of soil concentrations. The approach is spatiotemporally explicit and operates at scales ranging from local edge-of-field to large landscapes. The outcome can be aggregated and presented to the risk assessor in a way that addresses the dimensions and scales defined in specific protection goals (SPGs). The approach can be used to assess the effect of mitigation options, for example, field margins, in-field buffers, or drift-reducing technology. The presented provisional scenarios start with a schematic edge-of-field situation and extend to real-world landscapes of up to 5 km × 5 km. A case study was conducted for two active substances of different environmental fate characteristics. Results are presented as a collection of percentiles over time and space, as contour plots, and as maps. The results show that exposure patterns of off-field soil organisms are of a complex nature due to spatial and temporal variabilities combined with landscape structure and event-based processes. Our concepts and analysis demonstrate that more realistic exposure data can be meaningfully consolidated to serve in standard-tier risk assessments. The real-world landscape-scale scenarios indicate risk hot-spots that support the identification of efficient risk mitigation. As a next step, the spatiotemporally explicit exposure data can be directly coupled to ecological effect models (e.g., for earthworms or collembola) to conduct risk assessments at biological entity levels as required by SPGs. Integr Environ Assess Manag 2024;20:263-278. © 2023 Applied Analysis Solutions LLC and WSC Scientific GmbH and Bayer AG and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Evaluating and Explaining the Variability of Honey Bee Field Studies across Europe Using BEEHAVE.

To assess the effect of plant protection products on pollinator colonies, the higher tier of environmental risk assessment (ERA), for managed honey bee colonies and other pollinators, is in need of a mechanistic effect model. Such models are seen as a promising solution to the shortcomings, which empirical risk assessment can only overcome to a certain degree. A recent assessment of 40 models conducted by the European Food Safety Authority (EFSA) revealed that BEEHAVE is currently the only publicly available mechanistic honey bee model that has the potential to be accepted for ERA purposes. A concern in the use of this model is a lack of model validation against empirical data, spanning field studies conducted in different regions of Europe and covering the variability in colony and environmental conditions. We filled this gap with a BEEHAVE validation study against 66 control colonies of field studies conducted across Germany, Hungary, and the United Kingdom. Our study implements realistic initial colony size and landscape structure to consider foraging options. Overall, the temporal pattern of colony strength is predicted well. Some discrepancies between experimental data and prediction outcomes are explained by assumptions made for model parameterization. Complementary to the recent EFSA study using BEEHAVE, our validation covers a large variability in colony conditions and environmental impacts representing the Northern and Central European Regulatory Zones. Thus we believe that BEEHAVE can be used to serve the development of specific protection goals as well as the development of simulation scenarios for the European Regulatory Zone. Subsequently, the model can be applied as a standard tool for higher tier ERA of managed honey bees using the mechanistic ecotoxicological module for BEEHAVE, BEEHAVEecotox . Environ Toxicol Chem 2023;42:1839-1850. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

An Evaluation of the BEEHAVE Model Using Honey Bee Field Study Data: Insights and Recommendations.

A lack of standard and internationally agreed procedures for higher-tier risk assessment of plant protection products for bees makes coherent availability of data, their interpretation, and their use for risk assessment challenging. Focus has been given to the development of modeling approaches, which in the future could fill this gap. The BEEHAVE model, and its submodels, is the first model framework attempting to link 2 processes vital for the assessment of bee colonies: the within-hive dynamics for honey bee colonies and bee foraging in heterogeneous and dynamic landscapes. We use empirical data from a honey bee field study to conduct a model evaluation using the control data set. Simultaneously, we are testing several model setups for the interlinkage between the within-hive dynamics and the landscape foraging module. Overall, predictions of beehive dynamics fit observations made in the field. This result underpins the European Food Safety Authority's evaluation of the BEEHAVE model that the most important in-hive dynamics are represented and correctly implemented. We show that starting conditions of a colony drive the simulated colony dynamics almost entirely within the first few weeks, whereas the impact is increasingly substituted by the impact of foraging activity. Common among field studies is that data availability for hive observations and landscape characterizations is focused on the proportionally short exposure phase (i.e., the phase where colony starting conditions drive the colony dynamics) in comparison to the postexposure phase that lasts several months. It is vital to redistribute experimental efforts toward more equal data aquisition throughout the experiment to assess the suitability of using BEEHAVE for the prediction of bee colony overwintering survival. Environ Toxicol Chem 2019;38:2535-2545. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

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

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.