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Dietary exposure to nitrate and nitrite occurs via three main sources; occurrence in (vegetable) foods, food additives in certain processed foods and contaminants in drinking water. While nitrate can be converted to nitrite in the human body, their risk assessment is usually based on single substance exposure in different regulatory frameworks. Here, we assessed the longterm combined exposure to nitrate and nitrite from food and drinking water. Dutch monitoring data (2012)(2013)(2014)(2015)(2016)(2017)(2018) and EFSA data from 2017 were used for concentration data. These were combined with data from the Dutch food consumption survey (2012)(2013)(2014)(2015)(2016) to assess exposure. A conversion factor (median 0.023; range 0.008-0.07) was used to express the nitrate exposure in nitrite equivalents which was added to the nitrite exposure. The uncertainty around the conversion factor was taken into account by using conversion factors randomly sampled from the abovementioned range. The combined dietary exposure was calculated for the Dutch population (1-79 years) with different exposure scenarios to address regional differences in nitrate and nitrite concentrations in drinking water. All scenarios resulted in a combined exposure above the acceptable daily intake for nitrite ion (70 µg/kg bw), with the mean exposure varying between 95-114 µg nitrite/kg bw/day in the different scenarios. Of all ages, the combined exposure was highest in children aged 1 year with an average of 250 µg nitrite/kg bw/day. Vegetables contributed most to the combined exposure in food in all scenarios, varying from 34%-41%. Food additive use contributed 8%-9% to the exposure and drinking water contributed 3%-19%.Our study is the first to perform a combined dietary exposure assessment of nitrate and nitrite while accounting for the uncertain conversion factor. Such a combined exposure assessment overarching different regulatory frameworks and using different scenarios for drinking water is a better instrument for protecting human health than single substance exposure.

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...

No abstract

Uncertainty analysis is an important component of dietary exposure assessments in order to understand correctly the strength and limits of its results. Often, standard screening procedures are applied in a first step which results in conservative estimates. If through those screening procedures a potential exceedance of health-based guidance values is indicated, within the tiered approach more refined models are applied. However, the sources and types of uncertainties in deterministic and probabilistic models can vary or differ. A key objective of this work has been the mapping of different sources and types of uncertainties to better understand how to best use uncertainty analysis to generate more realistic comprehension of dietary exposure. In dietary exposure assessments, uncertainties can be introduced by knowledge gaps about the exposure scenario, parameter and the model itself. With this mapping, general and model-independent uncertainties have been identified and described, as well as those which can be introduced and influenced by the specific model during the tiered approach. This analysis identifies that there are general uncertainties common to point estimates (screening or deterministic methods) and probabilistic exposure assessment methods. To provide further clarity, general sources of uncertainty affecting many dietary exposure assessments should be separated from model-specific uncertainties.