The analysis of the coherent data on nonextractable (bound) residues (NER) from the literature and EU pesticide registration dossiers allows the identification of general trends, in spite of the large variability and heterogeneity of data. About 50% of the pesticides reviewed exhibit a low proportion of NER (less than 30% of the initial amount) while only 12% of pesticides have a proportion of NER exceeding 70%. The lowest proportion of NER was found for dinitroanilines (<20%), and the largest value was obtained for carbamates, and in particular dithiocarbamates. The presence of chemical reactive groups, such as aniline or phenol, tends to yield a larger proportion of NER. NER originating from N-heteroatomic ring were found to be lower than those from phenyl-ring structures. Among the environmental factors affecting the formation of NER, microbial activity has a direct and significant effect. Concerning the NER uptake or their bioavailability, consistent data suggest that only a small percentage of the total amounts of NER can be released. The analysis of NER formation kinetics showed that incubation experiments are often stopped too early to allow a correct evaluation of the NER maturation phase. Therefore, there is a need for longer term experiments to evaluate the tail of the NER formation kinetics. Still, the heterogeneity of the NER data between pesticides and for specific pesticides calls for great care in the interpretation of the data and their generalization.

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Simulations of pesticide fate in soils are often based on persistence models developed nearly 30 years ago. These models predict dissipation in the field on a daily basis by correcting laboratory degradation half‐lives for actual soil temperature and moisture content. They have been extensively applied, but to date no attempt has been made to evaluate existing studies in a consistent, quantitative way. This paper reviews 178 studies comparing pesticide soil residues measured in the field with those simulated by persistence models. The simulated percentage of initial pesticide concentration at the time of 50% measured loss was taken as a common criterion for model performance. The models showed an overall tendency to overestimate persistence. Simulated values ranged from 12 to 96% of initial pesticide concentrations with a median of 60%. Simulated soil residues overestimated the target value (50% of initial) by more than a factor of 1.25 in 44% of the cases. An underestimation by more than a factor of 1.25 was found in only 17% of the experiments. Discrepancies between simulated and observed data are attributed to difficulties in characterizing pesticide behavior under outdoor conditions using laboratory studies. These arise because of differences in soil conditions between the laboratory and the field and the spatial and temporal variability of degradation. Other possible causes include losses in the field by processes other than degradation, deviations of degradation from first‐order kinetics, discrepancies between simulated and actual soil temperature and moisture content, and the lack of soil‐specific degradation parameters. Implications for modeling of pesticide behavior within regulatory risk assessments are discussed.

Sensitivity analyses using a one-at-a-time approach were carried out for leaching models which have been widely used for pesticide registration in Europe (PELMO, PRZM, PESTLA and MACRO). Four scenarios were considered for simulation of the leaching of two theoretical pesticides in a sandy loam and a clay loam soil, each with a broad distribution across Europe. Input parameters were varied within bounds reflecting their uncertainty and the influence of these variations on model predictions was investigated for accumulated percolation at 1-m depth and pesticide loading in leachate. Predictions for the base-case scenarios differed between chromatographic models and the preferential flow model MACRO for which large but transient pesticide losses were predicted in the clay loam. Volumes of percolated water predicted by the four models were affected by a small number of input parameters and to a small extent only, suggesting that meteorological variables will be the main drivers of water balance predictions. In contrast to percolation, predictions for pesticide loss were found to be sensitive to a large number of input parameters and to a much greater extent. Parameters which had the largest influence on the prediction of pesticide loss were generally those related to chemical sorption (Freundlich exponent nf and distribution coefficient Kf) and degradation (either degradation rates or DT50, QTEN value). Nevertheless, a significant influence of soil properties (field capacity, bulk density or parameters defining the boundary between flow domains in MACRO) was also noted in at least one scenario for all models. Large sensitivities were reported for all models, especially PELMO and PRZM, and sensitivity was greater where only limited leaching was simulated. Uncertainty should be addressed in risk assessment procedures for crop-protection products.