33Populations are exposed to mixtures of pesticides through their diet on a daily basis. The 34 question of which substances should be assessed together remains a major challenge due to the 35 complexity of the mixtures. In addition, the associated risk is difficult to characterise. The 36EuroMix project (European Test and Risk Assessment Strategies for Mixtures) has developed 37 a strategy for mixture risk assessment. In particular, it has proposed a methodology that 38 combines exposures and hazard information to identify relevant mixtures of chemicals 39 belonging to any cumulative assessment group (CAG) to which the European population is 40 exposed via food. For the purposes of this study, food consumption and pesticide residue data 41 in food and drinking water were obtained from national surveys in nine European countries. 42Mixtures of pesticides were identified by a sparse non-negative matrix underestimation 43 (SNMU) applied to the specific liver steatosis effect in children from 11 to 15 years of age, and 44 in adults from 18 to 64 years of age in nine European countries. Exposures and mixtures of 144 45 pesticides were evaluated through four different scenarios: (1) chronic exposure with a merged 46 concentration dataset in the adult population, (2) chronic exposure with country-specific 47 concentration datasets in the adult population, (3) acute exposure with a merged concentration 48 dataset in the adult population, and (4) chronic exposure with a merged concentration dataset 49in the paediatric population. The relative potency factors of each substance were calculated to 50 2 express their potency relative to flusilazole, which was chosen as the reference compound. The 51 selection of mixtures and the evaluation of exposures for each country were carried out using 52 the Monte Carlo Risk Assessment (MCRA) software. 53Concerning chronic exposure, one mixture explained the largest proportion of the total variance 54 for each country, while in acute exposure, several mixtures were often involved. The results 55showed that there were 15 main pesticides in the mixtures, with a high contribution of imazalil 56 and dithiocarbamate. Since the concentrations provided by the different countries were merged 57 in the scenario using merged concentration data, differences between countries result from 58 differences in food consumption behaviours. These results support the approach that using 59 merged concentration data to estimate exposures in Europe seems to be realistic, as foods are 60 traded across European borders. The originality of the proposed approach was to start from a 61 CAG and to integrate information from combined exposures to identify a refined list of mixtures 62 with fewer components. As this approach was sensitive to the input data and required significant 63 resources, efforts should continue regarding data collection and harmonisation among the 64 different aspects within the pesticides regulatory framework, and to develop methods to group 65 substances and mixtures to characterise the r...
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This paper presents a cost-effective and validated method for the determination of 172 pesticide residues monitored during a survey study on 27 commercially available Cypriot wines (red/ white, dry/sweet). Sample preparation involved ethyl acetate extraction of pesticides from wines. For the validation of the method, blank samples were spiked with a solution of 172 pesticides (such as insecticides, fungicides, herbicides) at three levels such as 0.01, 0.05 and 0.1 mg/kg. The samples were analyzed by LC-MS/MS (liquid chromatography tandem mass spectrometry). The validation study was in accordance with the DG SANCO (DirectorateGeneral for Health and Consumers) guidelines [Document No. SANCO/12495/2012. Method validation and quality control procedures for pesticide residues analysis in food and feed]. The scope of validation included recovery, linearity, limits of quantification and precision. Recoveries of the majority of compounds were found to be in the range of 70-120% and were characterized by precision lower than 20%. Fifteen out of the 27 real samples analyzed were positive with pesticide residues and all observed values were much lower than the MRLs.
Cyprus alongside with another 4 countries has participated successfully in the Grant Agreement GP/EFSA/DATA/2016/01-GA 02, entitled: "Strategic Partnership with Cyprus on Data Quality". The project was co-financed by EFSA, aiming to help both EFSA and data providers from Member States to possess data of high quality in a quantitatively manageable way. The main objective of the grant agreement was the establishment of the data governance, coordination and improvement of the quality of the data submitted to EFSA, in the four domains of: Chemical occurrence, Zoonoses, Pesticide Residues and Veterinary Medicinal Product Residues. The project objectives have been achieved (a) by establishing a national data governance in Cyprus with the assignment of a National Data Coordinator and the definition of related Standard Operating Procedures, (b) by assigning a dedicated person responsible for collecting and transmitting the data to EFSA (Domain Specific Data Stewards) and by developing Key Performance Indicators (KPI) for quantifying the quality of the data transmitted to EFSA and (c) by enhancing the local LIMS software to provide better electronic communication with EFSA (supporting SSD2 transmissions) and making more robust the local automatic data management and validation. Cyprus has been submitting electronically data (for the three domains) to EFSA since 2011, using an in-house LIMS software where all the information concerning the samples and their analysis is recorded, stored and adapted for transmitting the mandatory elements to EFSA. However, no documented procedures were in place for ensuring or improving the data transmitted to EFSA, and no written procedures existed for the data governance at national level either. With the implementation of this project, the procedures were documented and performance indicators were developed, securing the data quality. National data governance was established. The development of ways to monitor, improve and enhance the quality of the data will ensure the long term quality of the data.
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