Pesticide residues in European agricultural soils – A hidden reality unfolded

Pesticide use is a major foundation of the agricultural intensification observed over the last few decades. As a result, soil contamination by pesticide residues has become an issue of increasing concern due to some pesticides' high soil persistence and toxicity to non-target species. In this study, the distribution of 76 pesticide residues was evaluated in 317 agricultural topsoil samples from across the European Union. The soils were collected in 2015 and originated from 11 EU Member States and 6 main cropping systems. Over 80% of the tested soils contained pesticide residues (25% of samples had 1 residue, 58% of samples had mixtures of two or more residues), in a total of 166 different pesticide combinations. Glyphosate and its metabolite AMPA, DDTs (DDT and its metabolites) and the broad-spectrum fungicides boscalid, epoxiconazole and tebuconazole were the compounds most frequently found in soil samples and the compounds found at the highest concentrations. These compounds occasionally exceeded their predicted environmental concentrations in soil but were below the respective toxic endpoints for standard in-soil organisms. Maximum individual pesticide content assessed in a soil sample was 2.05 mg kg−1 while maximum total pesticide content was 2.87 mg kg−1. This study reveals that the presence of mixtures of pesticide residues in soils are the rule rather than the exception, indicating that environmental risk assessment procedures should be adapted accordingly to minimize related risks to soil life and beyond. This information can be used to implement monitoring programs for pesticide residues in soil and to trigger toxicity assessments of mixtures of pesticide residues on a wider range of soil species in order to perform more comprehensive and accurate risk assessments.</p

BLUE, BLUP and the Kalman filter: some new results

In this contribution, we extend ‘Kalman-filter’ theory by introducing a new BLUE–BLUP recursion of the partitioned measurement and dynamic models. Instead of working with known state-vector means, we relax the model and assume these means to be unknown. The recursive BLUP is derived from first principles, in which a prominent role is played by the model’s misclosures. As a consequence of the mean state-vector relaxing assumption, the recursion does away with the usual need of having to specify the initial state-vector variance matrix. Next to the recursive BLUP, we introduce, for the same model, the recursive BLUE. This extension is another consequence of assuming the state-vector means unknown. In the standard Kalman filter set-up with known state-vector means, such difference between estimation and prediction does not occur. It is shown how the two intertwined recursions can be combined into one general BLUE–BLUP recursion, the outputs of which produce for every epoch, in parallel, the BLUP for the random state-vector and the BLUE for the mean of the state-vector

Distributional theory for the DIA method

The DIA method for the detection, identification and adaptation of model misspecifications combines estimation with testing. The aim of the present contribution is to introduce a unifying framework for the rigorous capture of this combination. By using a canonical model formulation and a partitioning of misclosure space, we show that the whole estimation–testing scheme can be captured in one single DIA estimator. We study the characteristics of this estimator and discuss some of its distributional properties. With the distribution of the DIA estimator provided, one can then study all the characteristics of the combined estimation and testing scheme, as well as analyse how they propagate into final outcomes. Examples are given, as well as a discussion on how the distributional properties compare with their usage in practice

Fast gas chromatographic residue analysis in animal feed using split injection and atmospheric pressure chemical ionisation tandem mass spectrometry

Significant speed improvement for instrumental runtime would make GC–MS much more attractive for determination of pesticides and contaminants and as complementary technique to LC–MS. This was the trigger to develop a fast method (time between injections less than 10 min) for the determination of pesticides and PCBs that are not (or less) amenable to LC–MS. A key factor in achieving shorter analysis time was the use of split injection (1:10) which allowed the use of a much higher initial GC oven temperature. A shorter column (15 m), higher temperature ramp, and higher carrier gas flow rate (6 mL/min) further contributed to analysis-time reduction. Chromatographic resolution was slightly compromised but still well fit-for-purpose. Due to the high sensitivity of the technique used (GC–APCI-triple quadrupole MS/MS), quantification and identification were still possible down to the 10 μg/kg level, which was demonstrated by successful validation of the method for complex feed matrices according to EU guidelines. Other advantages of the method included a better compatibility of acetonitrile extracts (e.g. QuEChERS) with GC, and a reduced transfer of co-extractants into the GC column and mass spectrometer.We would like to acknowledge Jack Cochran from Restek for stimulating discussions on fast GC and inlet optimisation. The authors are grateful to Serveis Centrals d’Instrumentació Científica (SCIC), University Jaume I for the use of GC-Xevo TQ-S. The authors acknowledge the financial support of Generalitat Valenciana, as research group of excellence PROMETEO II/2014/023 and also for the development of the ISIC project on Collaborative Research on Environment and Food-Safety (ISIC/2012/016). The Dutch Ministry of Economic Affairs is acknowledged for financially supporting this work (WOT-02-001-017)