Emissions of pesticides in the European Union: a new regional-level dataset.

We present a European Union (EU)-wide dataset of estimated quantities of active substances of plant protection product applied on crops (also called "emissions"). Our estimates are derived from data reported by eight EU countries and extrapolated to encompass all EU regions using regression models. These models consider both climate and agricultural land use data. This allows us to spatially represent pesticide use at NUTS Level 3 of the European statistical mapping units, and within various agricultural land cover classes in each region. We compare our estimates with aggregated data provided by EUROSTAT and with independent, detailed data for the United Kingdom, highlighting an error typically within one order of magnitude. Our estimates can provide insights into the distribution and patterns of pesticide use in the EU around the year 2015. The estimate is most reliable for Western and Southern Europe. Outside these regions, data scarcity makes extrapolation more uncertain, potentially limiting the ability to accurate depict regional variations in pesticide use.

Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets.

The EFSA 'Guidance on tiered risk assessment for edge-of-field surface waters' underscores the importance of in silico models to support the pesticide risk assessment. The aim of this work was to use in silico models starting from an available, structured and harmonized pesticide dataset that was developed for different purposes, in order to stimulate the use of QSAR models for risk assessment. The present work focuses on the development of a set of in silico models, developed to predict the aquatic toxicity of heterogeneous pesticides with incomplete/unknown toxic behavior in the water compartment. The generated models have good fitting performances (R2: 0.75-0.99), they are internally robust (Q2loo: 0.66-0.98) and can handle up to 30% of perturbation of the training set (Q2 lmo: 0.64-0.98). The absence of chance correlation was guaranteed by low values of R2 calculated on scrambled responses (R2 Yscr: 0.11-0.38). Different statistical parameters were used to quantify the external predictivity of the models (CCCext: 0.73-0.91, Q2 ext-Fn: 0.53-0.96). The results indicate that all the best models are predictive when applied to chemicals not involved in the models development. In addition, all models have similar accuracy both in fitting and in prediction and this represents a good degree of generalization. These models may be useful to support the risk assessment procedure when experimental data for key species are missing or to create prioritization lists for the general a priori assessment of the potential toxicity of existing and new pesticides which fall in the applicability domain.

Cumulative dietary exposure to a selected group of pesticides of the triazole group in different European countries according to the EFSA guidance on probabilistic modelling.

The practicality was examined of performing a cumulative dietary exposure assessment according to the requirements of the EFSA guidance on probabilistic modelling. For this the acute and chronic cumulative exposure to triazole pesticides was estimated using national food consumption and monitoring data of eight European countries. Both the acute and chronic cumulative dietary exposures were calculated according to two model runs (optimistic and pessimistic) as recommended in the EFSA guidance. The exposures obtained with these model runs differed substantially for all countries, with the highest exposures obtained with the pessimistic model run. In this model run, animal commodities including cattle milk and different meat types, entered in the exposure calculations at the level of the maximum residue limit (MRL), contributed most to the exposure. We conclude that application of the optimistic model run on a routine basis for cumulative assessments is feasible. The pessimistic model run is laborious and the exposure results could be too far from reality. More experience with this approach is needed to stimulate the discussion of the feasibility of all the requirements, especially the inclusion of MRLs of animal commodities which seem to result in unrealistic conclusions regarding their contribution to the dietary exposure.