Environmental risk assessment procedures for the authorization of pesticides at European Union (EU) and national level must keep pace with scientific progress and must be updated regularly. The standardized FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) approach for pesticide exposure assessment in surface waters consists of four steps. Mitigation measures for spray drift and surface runoff (vegetative filter strips, VFS) can be assessed in Step 4. The SWAN (Surface Water Assessment eNabler) tool for higher-tier exposure assessment implements fixed VFS efficiencies according to the FOCUS Landscape and Mitigation report. Since 2012, SWAN has also included the option to apply runoff mitigation using the mechanistic, event-based model VFSMOD. Since then, substantial scientific efforts have been made to further improve VFSMOD and its underlying assumptions and process descriptions. Moreover, new EU soil profile and land cover data have become available. The objective of this study was therefore to derive updated SWAN-VFSMOD scenarios for Europe. VFSMOD was run for 1897 combinations of FOCUS scenario and soil profile, two rainfall/runoff events, two example compounds, a VFS length of 10 m and two different lower boundary options. Spatial cumulative distribution functions (CDFs) of pesticide load reduction efficiency ∆P were subsequently calculated for each scenario. The four CDFs corresponding to the most relevant input factor combination were selected. In each case, the soil profile corresponding to the 10th percentile ∆P was used for parameterizing VFSMOD in SWAN. The new SWAN-VFSMOD scenarios were found to be more conservative than the previous ones. The largest proportion of the observed decrease in ∆P was due to the replacement of the pesticide trapping equation. Compared with the current standard fixed efficiency approach, the new approach yielded predominantly less conservative, but occasionally more conservative ∆P and exposure estimates, which reflects an increase in realism and accuracy.

Assessment of the leaching potential of pesticides and their metabolites is an important part of the authorization procedure for pesticides in Europe. To protect groundwater quality, it must be demonstrated that concentrations of active substances in the upper groundwater do not exceed 0.1 μg/L before a pesticide can be approved for use. For the purpose of exposure assessment, this concentration limit is imposed on the water leaching downward at 1 m depth in the soil profile. For a given substance and application pattern, this leaching concentration can vary in space by several orders of magnitude, due to variation in site conditions, most importantly soil properties and climate. Spatially distributed leaching modelling (SDLM) is a methodology for exposure assessment over large spatial extents, dealing with this spatial variability in a comprehensive way. It involves performing simulations for many parametrizations representative for a spatial region and can be used to generate maps or calculate spatio-temporal percentiles of leaching concentrations. While such tools are already used in exposure assessment at national level in several EU member states, no generally accepted SDLM tool is available at the European level. In 2020, a working group of Society of Environmental Toxicology and Chemistry (SETAC) was formed with the purpose to develop a harmonized framework for SDLM across Europe (EU27 + UK). A first version of an SDLM—referred to as GeoPEARL-EU—was built around the pesticide leaching model PEARL, a field-scale model of pesticide fate in the soil-plant system. PEARL mechanistically simulates pesticide behaviour in a 1D soil column based on explicit descriptions of transport in the liquid and gas phases, sorption to the solid phase, degradation, volatilisation, and plant-uptake. Soil moisture content and fluxes are provided by the SWAP hydrological model. PEARL is used in regulatory exposure assessment for groundwater and soil. Furthermore, a spatially distributed tool based on PEARL (GeoPEARL) is used for exposure assessment in the Netherlands. To apply PEARL to Europe, pan-European gridded datasets were collected for several variables, including soil texture, pH, soil organic carbon, weather, irrigation patterns and crop area. These datasets were used to develop a set of parametrizations covering the variability of climate and soil conditions in Europe. To this end, all 1x1 km grid cells for the EU27 + UK were partitioned into approximately 10,000 clusters using k-means clustering, based on several soil- and climate-related variables relevant for leaching vulnerability. Subsequently, a representative grid cell was selected for each cluster, which was used to obtain the data required to parameterize PEARL from the spatial data sets. Pedotransfer functions were used to derive soil hydraulic parameters. We will present results from GeoPEARL-EU for several test cases with specific attention to the effect of the spatial aggregation approach on the model predictions. Moreover, we discuss how the tool could be used in the tiered approach of the regulatory exposure assessment for groundwater in the EU.

Mechanistic effect modelling is becoming increasingly important for environmental risk assessment in the framework of pesticide authorization. For instance, the European Food Safety Authority (EFSA) has judged the model for the aquatic macrophyte test organism Lemna as “ready for use.” Nevertheless, national regulatory authorities are still hesitant to accept mechanistic effect modelling studies.In order to increase the confidence in the Lemna model, in this study we performed a two-step global sensitivity analysis (GSA) of the harmonized model. GSA notably allows identifying and ranking the importance of i) toxicokinetic (TK) and toxicodynamic (TD) parameters, ii) physiological and ecological parameters, iii) environmental driving variables, and iv) initial conditions.In a first step a Morris sensitivity screening was conducted to filter out non-influential input factors. In a second step, a true variance-based GSA was carried out with the Sobol method. The GSA was conducted for four different concentration levels and three different exposure regimes: constant, pulsed and realistic. Moreover, two different sets of input distributions of TKTD parameters were examined. The target variables were the effects of the pesticide on Lemna biomass and average growth rate.Both Morris and Sobol GSA showed that for a specific substance three physiological parameters (optimum and minimum growth temperature, maximum photosynthesis rate) and the initial biomass BM0 were more important than the five TKTD parameters. Hence, for predictive applications of the model outside a laboratory context, BM0 must be chosen carefully, and uncertainty in the main physiological parameters must be reduced to a minimum.

Estimating emissions of chemical pesticides used in agriculture is an essential component in evaluating the potential toxicity-related impacts on humans and ecosystems in various comparative risk and impact assessment frameworks, such as life cycle assessment, environmental footprinting, absolute environmental sustainability assessment, chemical substitution, and risk prioritization. Emissions related to drift deposition-usually derived from drift experiments-can reach non-target areas, and vary as a function of crop characteristics and application technique. We derive cumulative drift deposition fractions for a wide range of experimental drift functions for use in comparative and mass-balanced approaches. We clarify that cumulative drift deposition fractions require to integrate the underlying drift functions over the relevant deposition area and to correct for the ratio of deposition area to treated field area to arrive at overall mass deposited per unit mass of applied pesticide. Our results show that for most crops, drift deposition fractions from pesticide application are below 0.03 (i.e. 3% of applied mass), except for grapes and fruit trees, where drift fractions can reach 5% when using canon or air blast sprayers. Notably, aerial applications on soybeans can result in significantly higher drift deposition fractions, ranging from 20% to 60%. Additionally, varying the nozzle position can lead to a factor of five differences in pesticide deposition, and establishing buffer zones can effectively reduce drift deposition. To address remaining limitations in deriving cumulative drift deposition fractions, we discuss possible alternative modelling approaches. Our proposed approach can be implemented in different quantitative and comparative assessment frameworks that require emission estimates of agricultural pesticides, in support of reducing chemical pollution and related impacts on human health and the environment.

To protect human health, wildlife and the aquatic environment, “safe uses” of pesticides are determined at the EU level while product authorization and terms of use are established at the national level. In Sweden, extra precaution is taken to protect drinking water, and permits are therefore required for pesticide use within abstraction zones. This paper presents MACRO-DB, a tool for assessing pesticide contamination risks of groundwater and surface water, used by authorities to support their decision-making for issuing such permits. MACRO-DB is a meta-model based on 583,200 simulations of the physically-based MACRO model used for assessing pesticide leaching risks at EU and national level. MACRO-DB is simple to use and runs on widely available input data. In a qualitative comparative assessment for two counties in Sweden, MACRO-DB outputs were in general agreement with groundwater monitoring data and matched or were more protective than the national risk assessment procedure for groundwater.

The most widely implemented mitigation measure to reduce transfer of surface runoff pesticides and other pollutants to surface water bodies are vegetative filter strips (VFS). The most commonly used dynamic model for quantifying the reduction by VFS of surface runoff, eroded sediment, pesticides and other pollutants is VFSMOD, which simulates reduction of total inflow (∆Q) and of incoming eroded sediment load (∆E) mechanistically during the rainfall-runoff event. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (∆P). Since errors in ∆Q and ∆E propagate into ∆P, for strongly-sorbing compounds an accurate prediction of ∆E is crucial for a reliable prediction of ∆P. The most important incoming sediment characteristic for ∆E is the median particle diameter (d50). Current d50 estimation methods are simplistic, yielding fixed d50 based on soil properties and ignoring specific event characteristics and dynamics. We derive an improved dynamic d50 parameterization equation for use in regulatory VFS scenarios based on an extensive dataset of 93 d50 values and 17 candidate explanatory variables compiled from heterogeneous data sources and methods. The dataset was analysed first using machine learning techniques (Random Forest, Gradient Boosting) and Global Sensitivity Analysis (GSA) as a dimension reduction technique and to identify potential interactions between explanatory variables. Using the knowledge gained, a parsimonious multiple regression equation with 6 predictors was developed and thoroughly tested. Since three of the predictors are event-specific (eroded sediment yield, rainfall intensity and peak runoff rate), predicted d50 vary dynamically across event magnitudes and intensities. Incorporation of the improved d50 parameterization equation in higher-tier pesticide assessment tools with VFSMOD provides more realistic quantitative mitigation in regulatory US-EPA and EU FOCUS pesticide risk assessment frameworks. The equation is also readily applicable to other erosion management problems.

Estimating emissions of chemical pesticides used in agriculture is an essential component in evaluating the potential toxicity-related impacts on humans and ecosystems in various environmental assessment frameworks, such as life cycle assessment, environmental footprinting, and exposure and risk prioritization. Drift deposition—usually derived from drift experiments—drives differences in pesticide emission distributions, and varies as a function of crop characteristics and application technique. We provide guidance on how to interpret and implement the wider range of experimental drift results in mass balance approaches for estimating cumulative pesticide emissions. We propose that drift deposition functions are integrated over the relevant deposition area, are applied only once per pesticide application scenario, and are corrected for the ratio of deposition area to treated field area to arrive at mass deposited per unit mass of applied pesticide. Finally, we discuss possible alternative approaches for estimating drift deposition related emissions, which address some of the current limitations when deriving emissions from crop- and application technique specific drift experiments in impact assessment contexts. Our guidance aims at aiding practitioners to derive and interpret cumulative pesticide emissions for application in quantitative and comparative assessment frameworks, in support of reducing chemical pollution and related impacts on human health and the environment.

BACKGROUNDOne of the most important sources of pesticide pollution of surface waters is runoff and erosion from agricultural fields after rainfall. This study analyses the efficacy of different risk mitigation measures to reduce pesticide runoff and erosion inputs into surface waters from arable land excluding rice fields. RESULTSThree groups of risk mitigation measures were quantitatively analyzed: vegetative filter strips, micro-dams in row crops and soil conservation measures. Their effectiveness was evaluated based on a meta-analysis of available experimental data using statistical methods such as classification and regression trees, and exploratory data analysis. Results confirmed the effectiveness of vegetative filter strips and micro-dams. Contrary to common assumption, the width of vegetative filter strips alone is not sufficient to predict their effectiveness. The effectiveness of soil conservation measures (especially mulch-tillage) varied widely. This was in part due to the heterogeneity of the available experimental data, probably resulting from the inconsistent implementation and the inadequate definitions of these measures. CONCLUSIONBoth vegetative filter strips and micro-dams are effective and suitable, and can therefore be recommended for quantitative assessment of environmental pesticide exposure in surface waters. However, the processes of infiltration and sedimentation in vegetative filter strips should be simulated with a mechanistic model like Vegetative Filter Strip Modeling System, VFSMOD. The reduction effect of micro-dams can be modelled by reducing the runoff curve number, e.g., in the pesticide root zone model, PRZM. Soil conservation measures are in principle promising, but further well-documented data are needed to determine under which conditions they are effective. & COPY; 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

The FOOTPRINT Soil Type (FST) system has been derived during the FOOTPRINT project (2006-2009) to facilitate spatially distributed hydrological and solute transport modelling at national or EU scale. The basic idea of this approach is to classify the soil typological units (STUs) of a national or European soil database into a limited number of soil types (FSTs) in order to reduce the number of unique soil-climate combinations for the later numerically expensive simulations. The FST code consists of a hydrological class (the FOOTPRINT Hydrologic Group), a topsoil and a subsoil texture code and an organic matter profile code. The FST system is model-independent, but complete parameterization methodologies were established during FOOTPRINT for MACRO, a 1-D dual permeability model for simulating water flow and solute transport in macroporous soils at field level. In this study we i) translated the latest version of the German soil map 1:200,000 (BUEK200) into FSTs, ii) derived representative profiles for all FSTs with arable land use, and iii) parameterized these representative profiles in MACRO. The 3648 STUs with arable land use in the BUEK200 were classified into 226 FSTs. Area proportions covered by the different FSTs are highly skewed: The 13 FSTs with the largest areas already cover 50 % of the total arable land. The hydrological class of each FST indicates whether artificial drainage is needed to allow arable landuse, and a map of potentially drained arable land was derived for Germany accordingly. A representative soil profile was established for every FST by depth-based averaging over all soil profiles belonging to the same FST. Special care had to be taken to ensure that mineral soil layers were not mixed with peat or hard rock layers. The plausibility of the representative FST profiles and their MACRO parameterization was checked with water balance simulations. The present case study for the BUEK200 soil database demonstrates the potential of the FST system for spatially distributed hydrological and solute transport modelling at large scale based on national soil databases.

The most widely implemented mitigation measure to reduce transfer of pesticides and other pollutants to surface water bodies via surface runoff are vegetative filter strips (VFS). The most commonly used model for assessing the reduction of surface runoff, eroded sediment and pesticide inputs into surface water by VFS is VFSMOD, which simulates reduction of total inflow (∆Q) and reduction of incoming eroded sediment load (∆E) mechanistically. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (∆P). Since errors in ∆Q and ∆E will propagate to ∆P, for strongly sorbing compounds, an accurate prediction of ∆E is crucial for a reliable prediction of ∆P. The most important parameter characterizing the incoming sediment in VFSMOD is the median particle diameter d50, which is currently fixed to 20 µm in the regulatory tool SWAN 5.01. The objective of this study was to derive an improved, generic d50 parameterization methodology that can be readily used for regulatory VFS scenarios.A test dataset of d50 values and explanatory variables has been compiled from heterogeneous data sources. The established test dataset (n = 93) was analysed using Machine Learning techniques (Random Forest, Gradient Boosting) and multiple regression analysis (MLR). With the help of the knowledge gained with Machine Learning, a MLR equation with six predictor variables was established and thoroughly tested. Since three of the predictors are event-specific (eroded sediment yield, rainfall intensity and peak runoff rate), the predicted d50 values vary between runoff events according to their magnitude and intensity.A modified version of SWAN-VFSMOD containing the improved d50 parameterization method was run for a number of contrasting compounds and application scenarios. The obtained ∆E and ∆P values as well as the resulting pesticide concentrations in surface water and sediment (PECsw/sed) were compared with the current FOCUS step4 approach.

<p>In Sweden farmers are legally obliged to apply for permits for pesticide use if their land lies within a drinking water abstraction zone. The standalone modelling tool MACRO-DB 4 developed by the Swedish University of Agricultural Sciences (SLU) is available for risk assessment and decision support. MACRO-DB 4 is used by local authorities, farmers/landowners and consultants, and is based on the well-established leaching model MACRO 5.2. However, the software is costly to maintain and slow for end users. Hence, a robust meta-model of MACRO-DB 4 was developed and integrated in a web-based tool (MACRO DB Steg2 v.5) that is fast, easy to maintain, and easy to understand for stakeholders. The meta-model (implemented as an R package) is based on i) a large number of MACRO simulations for the whole agriculturally relevant area of Sweden, and ii) a trilinear interpolation tool. The simulations comprised 18 climates, 72 soils, 1 typical crop (spring cereals), 3 application seasons (spring, summer and autumn), and 150 dummy compounds consisting of a grid of normalized Freundlich coefficient Koc, degradation half-life at reference conditions DT50 and Freundlich exponent. Target variables were i) the mean leaching flux concentration over 20 years at 2 m depth (PECgw), and ii) the 20-year mean concentration in large surface water bodies (PECsw; based on pesticide inputs via drainage and baseflow) . The meta-model performs a trilinear interpolation (in the three-dimensional space of Koc, DT50 and Freundlich exponent) for log10 of PECgw or PECsw, respectively. Different crops are taken into account in the web-based tool by adjusting the pesticide interception fraction according to the BBCH stage of the crop to be modelled at the time of application. In order to identify the most important input factors for PECgw and PECsw, a variance-based Global Sensitivity Analysis (GSA) was performed for the MACRO meta-model using the Sobol’ method. This method allows to i) identify first-order (direct) and higher-order (interaction) effects for each input factor (climate, soil, application season, Koc, DT50 and Freundlich exponent), and ii) rank the input factors according to their importance.</p>

Regulatory exposure assessments (EA) are a cornerstone in the registration of new or existing agrochemicals in the EU, USA and many other countries to preserve ecosystem health and water quality. VFSMOD (Muñoz-Carpena et al., 1999; 2004) is a numerical, storm-based design model that quantifies the performance of vegetative filter strips- VFS (grass buffers) to mitigate pesticide runoff into surface waters. The model has been coupled into current USA and EU long-term (20 to 30 yr), higher-tier regulatory pesticide EAs to estimate potential pesticide load reductions by the VFS before entering the aquatic environment. A new comprehensive modeling component has been added to VFSMOD to quantify the fate of pesticides residue on the VFS between events in the context of long-term simulations. The aim of this study is to present the verification of the assumptions and accuracy of the new pesticide residue component within VFSMOD in the context of continuous simulations by exploring a wide range of conditions (Koc, half-life, remobilization processes) of importance to improve current understanding of quantitative mitigation of pesticides in the regulatory environment. The results show the complex interaction in the VFS amongst surface pesticide runoff, sedimentation, infiltration and leaching, degradation, evapotranspiration and soil moisture change, and mixing layer remobilization that produces emergent behavior in the amount of pesticide residue potentially remobilized in the next event in the series.

Surface runoff from agricultural fields is a major input pathway of pesticides into surface waters. The aim of this project was to i) analyze the effectiveness of various mitigation measures to reduce pesticide runoff and erosion inputs into surface waters, ii) assess the suitability of the measures found effective for use in the quantitative environmental exposure assessment for authorization of plant protection products (PPP), and iii) make recommendations how the potentially suitable measures could be applied in risk assessment of PPP in Germany. Following a literature analysis, 16 risk mitigation measures were presented to five experts for evaluation of effectiveness, cost-effectiveness, controllability, current distribution and dissemination potential. Measures finally selected for quantitative analysis belong to 3 groups: vegetative filter strips (VFS), soil conservation measures and micro-dams in row crops. Subsequently, the effectiveness of the recommended measures was evaluated based on experimental data using different statistical methods (e.g. CART, MLR, graphical methods). The quantitative analysis confirmed the effectiveness of VFS and micro-dams. For soil conservation measures (especially mulch-till), the evaluated data showed highly variable results. This was partly caused by the heterogeneity of the experimental data, which also made it difficult to aggregate the results of different studies. The following conclusions were drawn: Both VFS and micro-dams can be recommended for application in quantitative environmental exposure assessment for pesticides. However, infiltration and sedimentation in VFS should be simulated with a mechanistic model such as VFSMOD. The effect of micro-dams can be modelled as a reduction of the runoff Curve Number (CN). The runoff modelling should be carried out with a model such as PRZM which adjusts the CN daily based on soil water content.

Quantitative mitigation of pesticides entering surface water using vegetative filter strips (VFS) is currently available within the regulatory SWAN tool for EU FOCUS STEP 4 simulations. For the VFSMOD model option, field estimates of surface runoff, sediment and pesticide loads simulated with the model PRZM are routed through the VFS where VFSMOD estimates the reductions of total inflow (dQ), eroded sediment (dE) and pesticide (dP) loads before the remaining runoff enters the waterbody. The reduced runoff is handed over to the TOXSWA aquatic model to calculate predicted environmental concentrations in surface water (PECsw). Brown et al. (2012) proposed VFSMOD parametrization rules including the selection of VFS soils and other characteristics for use in the FOCUS R1 to R4 (Rx) SWAN scenarios. The rules apply to free-draining soils, described in VFSMOD by the Green-Ampt model extended for unsteady rainfall. However, in some EU regions, the presence of a seasonal shallow water table (sWT) is common. In these cases, the VFS efficiency can be limited, depending on water table depth (WTD) and soil type. VFSMOD incorporates a sWT mechanistic infiltration component that has proven successful to predict sWT effects in VFS experiments. This component requires soil hydraulic characteristics, described by e.g. the Mualem-van Genuchten (MvG) equations.The main objective of this study is to identify Rx representative VFS soils to study the effects of sWT on pesticide mitigation for a combination of illustrative storms and pesticides, as well as on PECsw from long-term SWAN simulations.The selection and testing of the Rx VFS soils seeks to reflect a 90th-percentile worst case in space of dP. The multicriteria adopted in the soil selection evaluate not only dP, but also the percentile of important soil parameters for noWT (Ks, Sav) and sWT infiltration conditions (fillable pore volume fpv). The framework consisted of 4 steps: (a) soil spatial soil database analysis for VFS Rx mitigation scenarios; (b) selection of VFS candidate soils; (c) analysis of effects of sWT and sorption on dP for individual storm events; (d) Effect of sWT on long-term STEP 4 SWAN VFS mitigation simulations. For (a), representative soil profiles and area coverage for each of the EU Rx were obtained by combining the latest EU JRC soil profile databases SPADE2 and SPADE14. Each multilayer soil was aggregated into single-layer depth-weighted profiles, and MvG parameters were estimated using HYPRES pedotransfer functions (PTF). Water table depths (WTD) were set at equilibrium with TOXSWA median surface water level, and Sav and fpv were calculated by numerical integration from MvG characteristics. For (b), 10644 VFSMOD simulations were run for all combinations of soils, T=1 and 10 yr storms, high/low Koc pesticides, and sWT/noWT conditions. Candidate Rx VFS soils were selected for the most conservative case (low Koc=100 Kg/L pesticide, T=10 yr storm) and noWT to achieve the target spatial 90th percentile worst case of pesticide load reduction by the VFS.The implementation of the new sWT VFS mitigation component provides a more realistic description of pesticide reduction in accordance with STEP 4 EU FOCUS objectives.

The most widely implemented mitigation measure to reduce transfer of pesticides to surface water bodies via surface runoff are vegetative filter strips (VFS). To reliably model the reduction of surface runoff, eroded sediment and pesticide load by VFS an event-based model is needed. The most commonly used model for this purpose is VFSMOD. VFSMOD simulates reduction of total inflow (∆Q) and reduction of incoming eroded sediment load (∆E) mechanistically. These variables are subsequently used to calculate the reduction of pesticide load (∆P). While ∆P can be relatively well predicted from ∆Q, ∆E and some other variables, errors in ∆Q and ∆E will propagate to ∆P. Hence, for strongly sorbing compounds, an accurate prediction of ∆E is crucial. The most important parameter characterizing the incoming sediment in VFSMOD is the median particle diameter d50. The objective of this study was to derive a generic d50 parameterization methodology for sediment trapping in VFSMOD that can be readily used for regulatory VFS scenarios. Four studies with 16 hydrological events were selected for modelling. A first set of VFSMOD simulations, following the SWAN-VFSMOD sediment parameterization with d50 = 20 µm yielded a general overestimation of ∆E. Consequently, a maximum-likelihood-based calibration and uncertainty analysis with the DREAM-ZS algorithm was performed for the 16 events. The resulting d50 values were all in the low range (1.3-5.4 µm) and did not allow to establish a robust relationship to predict a wider range of d50 from the available explanatory variables. To increase the sample size and the range of d50 values, the comprehensive Kinston dataset for a loamy sand in North Carolina was calibrated with DREAM-ZS. Calibration was performed separately for each hydrological event. Further data points with measured particle size distributions in run-on were assimilated from the literature. The extended test data set of d50 values and explanatory variables was analysed using an extended multiple linear regression (MLR) approach and Classification and Regression Trees (CART). A good calibration of event totals and outflow hydrographs could be achieved for most events and VFS treatments of the Kinston site. The calibrated d50 values yielded a wider range (2-16 µm) than the initial 16 events. The improved d50 parameterization method derived with MLR/CART will be adopted in the next version of SWAN-VFSMOD to provide more realistic quantitative mitigation within FOCUS STEP4.

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.

Surface runoff from agricultural fields is a major input pathway of pesticides into surface waters. The aim of this project was to i) analyze the effectiveness of various mitigation measures to reduce pesticide runoff and erosion inputs into surface waters, ii) assess the suitability of the measures found effective for use in the quantitative environmental exposure assessment for authorization of plant protection products (PPP), and iii) make recommendations how the potentially suitable measures could be applied in risk assessment of PPP in Germany. Following a literature analysis, 16 risk mitigation measures were presented to five experts in the field. Measures finally selected for quantitative analysis belong to 3 groups: vegetative filter strips (VFS), soil conservation measures (including no-till) and microdams in row crops. VFS effectiveness was analysed with CART (Classification and Regression Trees) using the dataset compiled by Reichenberger et al. (2019). CART was performed for three target variables: i) relative reduction of total inflow by the VFS (ΔQ), ii) relative reduction of sediment load (ΔE), and relative reduction of pesticide load (ΔP). The main data sources for soil conservation measures were a plot database with annual runoff volumes and soil losses (Maetens et al., 2012), a literature review (Fawcett et al., 1994) and a field study with event-based data (Erlach, 2005), while for microdams the principal source were the data compiled by Sittig et al. (2020). The following conclusions were drawn from the analysis: VFS can be recommended for application in quantitative risk assessment. However, infiltration and sedimentation should be simulated with a mechanistic model such as VFSMOD. Due to the high variability of results and limited availability of high-quality data, effectiveness of mulch-till could not be quantified sufficiently well. It can therefore not be recommended for now as a regulatory mitigation measure. Before recommending no-till as a regulatory mitigation measure for surface runoff and erosion, the question of potentially increased pesticide loss via leaching and drainage should be clarified. Microdams in row crops can also be recommended as a regulatory mitigation measure, since they have shown to be effective and their effect can be modelled as a reduction of the runoff Curve Number. However, elaborating a CN table for e.g. the FOCUS scenarios would require an in-depth analysis of the available data.

The most widely implemented mitigation measure to reduce transfer of pesticides and other pollutants to surface water bodies via surface runoff are vegetative filter strips (VFS). To reliably model the reduction of surface runoff, eroded sediment and pesticide inputs into surface water by VFS in a risk assessment context, an event-based model is needed. The most commonly used dynamic, event-based model for this purpose is VFSMOD. VFSMOD simulates reduction of total inflow (ΔQ) and reduction of incoming eroded sediment load (ΔE) mechanistically. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (ΔP). There are several options in VFSMOD to calculate ΔP, notably the empirical Sabbagh equation (either with original or revised regression coefficients) and a regression-free, mechanistic mass-balance approach (Reichenberger et al., 2017).Four studies with 16 hydrological events were selected from the experimental data compiled by Reichenberger et al. (2019), representing different levels of data availability and uncertainty. A first set of VFSMOD simulations, with parameterization according to the settings in the tool SWAN-VFSMOD, was run with the aim to compare the performance of the different pesticide trapping equations. The simulations yielded a general overestimation of ΔE, suggesting that the SWAN-VFSMOD parameterization of sediment filtration is too optimistic. However, a reliable prediction of ΔE is important for the reliability of predicted ΔP, in particular for strongly sorbing compounds.In a second step, a maximum-likelihood-based calibration and uncertainty analysis with the DREAM-ZS algorithm was performed for each hydrological event and the target variables ΔQ and ΔE. Overall a good match of measured ΔQ and ΔE was achieved, but only a few parameters could be well constrained.In a third step, in order to reduce the observed equifinality, the hydraulic parameters were fixed to the best parameter sets obtained during the second phase, and only sediment filtration parameters were calibrated with DREAM-ZS.The most important parameter characterizing the incoming sediment in VFSMOD is the median particle diameter DP. A set of empirical equations to predict DP from soil texture (Foster et al., 1985) was used as supporting information in the calibration of DP.The poster will present an improved, generic parameterization methodology for sediment trapping in VFSMOD that can be used for regulatory VFS scenarios.

Vegetative filter strips (VFS) are commonly implemented in the field to mitigate runoff pesticide inputs into surface waters and protect aquatic ecosystems. The efficiency of this mitigation practice can be evaluated within the current regulatory high-tier, long-term environmental risk assessments (ERA) in combination with VFSMOD, an established and commonly used numerical model for the analysis of runoff, sediment, and pesticide transport in VFS. For every rainfall/runoff event in the long-term time series, VFSMOD takes the PRZM calculated edge-of-the-field surface runoff, eroded sediment yield, and dissolved and particle-bound pesticide load. It then calculates infiltration, sedimentation and pesticide trapping in the VFS during the event, and the outflow into the downslope aquatic body for further calculations and risk analysis. Importantly, at the end of each event, VFSMOD calculates the amount of pesticide residue retained in the filter (sediment-bound and infiltrated in the liquid phase), its degradation until the next event in the series, and the fraction of pesticide residue that is remobilized and added to the next runoff event. In earlier VFSMOD versions, full remobilization of the pesticide residue sorbed to sediment and that dissolved in the soil surface mixing layer (typically the top 0.5-5 cm) was calculated conservatively. Recent VFSMOD ERA applications for very highly-sorbed (i.e. pyrethroids) or persistent pesticides indicate that the full remobilization scheme might be too conservative in some cases. In this work, we evaluate new alternative partial remobilization schemes in VFSMOD, i.e. no remobilization of adsorbed residues, but full remobilization of dissolved residues in the mixing layer, or alternatively just a fraction of the mixing layer by diffusive exchange with the runoff. We evaluate the effects of the alternative remobilization schemes on observed total VFS pesticide reductions from available field data. In addition, employing global sensitivity analysis, we assess the relative importance of the alternative remobilization model structures in the context of the expected field variability of other known drivers of VFS efficiency (hydrology, soils, vegetation, pesticide chemical characteristics). The study provides science-based recommendations for future high-tier pesticide ERA with VFS mitigation.