Field leaching study - Inverse estimation of degradation and sorption parameters for a mobile soil metabolite and its pesticide parent.

Pesticide concentration measurements from field studies under real-world conditions can improve the derivation of more representative modelling input parameters for the exposure assessment of agrochemicals in the authorization process of plant protection products. The pertinent guidance documents foresee the application of inverse modelling approaches in combination with environmental fate and transport models to estimate e.g., soil dissipation rates that are solely based on microbial degradation and are not lumped with contributions from other dissipation processes such as leaching, plant uptake, volatilization and photodegradation. Field leaching studies can be used to estimate both degradation and sorption of chemicals in the soil matrix. In this study, inverse modelling of environmental fate parameters is presented based on solute concentrations from a field leaching study sampling pore water from five different depths down to 1.5 m. The leaching model PEARL and the universal optimization tool PEST were coupled, and sorption and degradation of the fungicide fluopicolide and its soil metabolite BAM (2,6-dichlorobenzamide) were quantified. Soil degradation half-lives were not different from results obtained in regular field degradation studies sampling residues in the total soil matrix (236 d vs. 158 d for fluopicolide and 53 d vs. 45 d for BAM); whereas a sorption increase with time (time-dependent sorption) was observed for the parent compound. This work aims at pointing out the feasibility to include field leaching studies with measurements at different soil depths in regulatory exposure assessment, since a statistically significant derivation of degradation and sorption parameters is presented, along with low uncertainties in the estimated parameter values of ±10%.

Recalibration and cross-validation of pesticide trapping equations for vegetative filter strips (VFS) using additional experimental data.

Vegetative filter strips (VFS) are widely used for mitigating pesticide inputs into surface waters via surface runoff and erosion. To simulate the effectiveness of VFS the model VFSMOD is frequently used. While VFSMOD simulates infiltration and sedimentation mechanistically, the reduction of pesticide load in surface runoff by the VFS is calculated with the empirical Sabbagh equation. This multiple regression equation has not been widely accepted by regulatory authorities, because its reliability has not been sufficiently demonstrated yet. A major drawback is the small number of calibration data points (n = 47). To corroborate and improve the predictive capability of the Sabbagh equation, additional experimental VFS data were compiled from the available literature. The enlarged dataset (n = 244) was used to recalibrate the Sabbagh equation, the recently proposed Chen equation and a set of "reduced" Sabbagh equations with fewer independent variables, with ordinary least squares (OLS) regression and to test an alternative, regression-free mass balance approach. The Sabbagh equation fitted the dataset slightly better than the Chen equation (coefficient of determination R2 = 0.82 vs. 0.79). The purely predictive mass balance approach performed slightly worse (Nash-Sutcliffe Efficiency NSE = 0.74), but significantly better than the Sabbagh and Chen equations with their old coefficients. In a k-fold cross validation analysis to assess the predictive capability of the various regression equations, both the full Sabbagh and the reduced Sabbagh equations with two or more variables outperformed the Chen equation. Finally, a maximum-likelihood-based calibration and uncertainty analysis were conducted for the Sabbagh equation using the DREAM_ZS algorithm and two different likelihood functions. The DREAM simulations corroborated the parameter values obtained with OLS regression. The study confirmed the suitability of the Sabbagh equation for regulatory modelling of pesticide trapping in VFS. However, the regression-free mass balance approach turned out to be a viable alternative.