Hamborg Bodil

Jens Hinge Jensen

Annette Andersen

Tue Petersen

Christensen

in food. A comparison of the deterministic and probabilistic approach

Keywords: Probabalistic modelling, Risk assessment Additives/Contaminants: Pesticides, Residues Food Types: Fruit, Vegetables

Dietary exposure assessment of Danish consumers to dithiocarbamate residues in food. A comparison of the deterministic and probabilistic approach

Introduction

Dithiocarbamates are some of the most often used pesticides in the EU. These pesticides are also some of the most frequently detected pesticides in national monitoring programs (Poulsen et al. 2005[START_REF] Caldas | Dithiocarbamates residues in Brazilian food and the potential risk for consumers[END_REF][START_REF]Monitoring of Pesticide Residues in Products of Plant Origin in the European Union[END_REF].

Dithiocarbamate residues were found in 7.1% of the samples in the monitoring period [1998][1999][2000][2001][2002][2003] in Denmark, and are among the six pesticides, which contribute most to the intake of pesticides from fruit and vegetables in the Danish market (Poulsen et al. 2005). Residues are determined as carbon disulphide (CS 2 ) (limit of quantification (LOQ) 0.1 mg/kg as CS 2 ) in fruit and vegetables [START_REF] Andersen | Results from the monitoring of pesticide residues in fruit and vegetables in the Danish market 1998-99[END_REF].

To estimate the exposure to pesticides, both deterministic and probabilistic modelling can be used. For legislation purposes the deterministic point estimate approach is used to estimate the exposure to acute toxic pesticides today in the EU. The calculations of the point estimate are performed using the equations defined by JMPR (FAO/WHO 1997, FAO/WHO 2002). The point estimate approach is conservative since one high level for both consumption and residue level is used at the same time. As described in the EFSA opinion on acute dietary assessment [START_REF] Efsa | Opinion of the Scientific Panel on Plant protection products and their Residues on acute dietary intake assessment of pesticide residues in fruit and vegetables[END_REF], the deterministic approach is not suitable to estimate the protection for the total population. This is because that this approach only considers the exposure from one commodity at a time and does not takes into account the contribution of individual commodities to the total intake of a pesticide. Assessing the acute and chronic exposure using the probabilistic approach takes into account the variation in food consumption, pesticides residue levels and body weight by using a Monte Carlo simulation.

In the present work, a probabilistic and deterministic estimation of the acute and chronic exposure to dithiocarbamate residues for the Danish population is presented.

Materials and methods

Dithiocarbamate residue data

Dithiocarbamate residue data were taken from the Danish pesticide monitoring program during the period 1998-2003 (Poulsen et al.) (Poulsen et al.). The calculations include analyses for dithiocarbamate residues from 5721 samples of fruit and vegetables from 26 commodities (only commoditites, where dithiocarbamates were found, are included). 3 Dithiocarbamate residues were found in 34 commodities, but aubergines, peas with pod, exotic vegetables, spring onions, herbs, figs, currants and other small fruits (355 samples) were not included because of lack of data for consumption. Findings in these commodities were few and consumptions presumedly small. No distinctions were made between imported commodities and those of Danish origin in the calculations.

Residues were determined as CS 2 (LOQ = 0.1 mg/kg) in the fruit and vegetables samples [START_REF] Andersen | Results from the monitoring of pesticide residues in fruit and vegetables in the Danish market 1998-99[END_REF]. Samples analysed did not include Brassica crops, as these are known to give false positives by the method used [START_REF] Perz | CS2 Blinds in Brassica Crops: False Positive Results in the Dithiocarbamate Residue Analysis by the Acid Digestion Method[END_REF]. Papayas (included in the group "Exotic fruits") were analysed without pulp or seeds [START_REF] Lund | Effect of samplehandling on verification of dithiocarbamate contents in papaya[END_REF]. For intake calculations, residue levels below the LOQ were considered as zero.

Processing factors

There is not described much about processing factors for dithiocarbamates in the literature. [START_REF] Caldas | Dietary exposure of Brazilian consumers to dithiocarbamate pesticides -A probabilistic approach[END_REF] used 0.26 for cooking, 0.3 for washing of apple, strawberry, carrot, lettuce and tomato and 0.08 for peeling of orange, banana and papaya. Since most commodities in which dithiocarbamate residues were found in this study can be eaten raw, except for melon, which is peeled, processing factors for cooking and peeling was not applied in the calculation. In the EU washing is not considered as a processing factor for most commodities as it is assumed that they can be eaten unwashed.

Food consumption data

The food consumption data were obtained from the nationwide dietary survey conducted in 2000-2002 by the Department of Nutrition, Technical University of Denmark [START_REF] Andersen | Estimation of the acute dietary exposure to pesticides using the probabilistic approach and the point estimate methodology[END_REF]. The survey included 3299 adults aged 15-75 years old and 231 children aged 4-6 years old. The survey used a seven-day prospective food record with a pre-coded (semi-closed) questionnaire that included answering categories for the most commonly eaten foods and dishes in the Danish diet. The survey was distributed equally over the 7 days of the week and over the whole year to take into account possible seasonal variation in dietary habits. Holidays were excluded.

The questionnaire was organised according to a normal daily meal pattern. The body weight of the participant was noted and used in the calculations. In the calculations of the deterministic acute and chronic exposure, an average body weight of 74 kg was used for adults and 22 kg for children. Chronic intake calculation. The National Estimated Daily Intake (NEDI) of dithiocarbamate residues was calculated by multiplying the mean consumption for the consumer group with the mean level of dithiocarbamate residues for each commodity.

The total chronic intake for dithiocarbamate residues was calculated by summing up the individual intakes from all commodities. The sum was compared to the ADI of maneb and mancozeb (see below).

Probabilistic intake calculations

The probabilistic exposure assessment was conducted using the Monte Carlo Risk Assessment (MCRA 4.0) system, an internet-based program developed by RIKILT -Institute of Food Safety, Wageningen UR, in the Netherlands [START_REF] Boer | MCRA a web-based program for Monte Carlo Risk Assessment[END_REF].

The outputs from the intake distribution generated after the simulation were specified at percentiles P50, P90, P95, P99, P99.9 and P99.99. The calculations have been performed as described by [START_REF] Boon | Calculations of dietary intake to acrylamide[END_REF].

Acute intake calculation. For the estimation of the daily acute exposure, 100 000 randomly drawn consumption patterns selected from the consumption database were multiplied with randomly selected dithiocarbamate residues giving an empirical estimate of the intake distribution for the acute exposure of dithiocarbamate residues.

The default variability factors 7, 5 and 1 were used as fixed factors in the calculations.

Currently there are no guidelines on how to incorporate variability into the probabilistic approach (Boon et al. 2004). 5 Chronic intake calculation. The chronic intake calculations were performed by multiplying randomly drawn consumption patterns from the food consumption database with the mean residue levels of dithiocarbamate per food and then summing up over all food. The mathematical model used for estimating the chronic intake is proposed by statisticians at the Iowa State University and described by [START_REF] Nusser | A semi-parametric transformation approach to estimating usual intake daily intake distributions[END_REF] and [START_REF] Guenther | Development of an approach for estimating usual nutrient intake distributions at the population level[END_REF].

Toxicological data for dithiocarbamates

In the EU the six dithiocarbamates maneb, mancozeb, metiram, propineb, thiram and ziram are registered for use. In Denmark only mancozeb and maneb are registered for use. The residue definition for the dithiocarbamates is the sum of maneb, mancozeb, metiram, propineb, thiram, ziram expressed as CS 2 . The analytical methodology applied to analyse dithiocarbamates in food did not discriminate among the compounds used on the crops. The ADI for both mancozeb and maneb is 50µg/kg/bw/day and since these substances are the most frequently used of the dithiocabamates in the EU this ADI was used in the chronic risk assessment. The ARfDs for mancozeb and maneb are 600 µg/kg bw/day and 200 µg/kg bw/day, respectively. The ARfD for maneb was used in the risk assessment in this study. Range for the six dithiocarbamates is 7-50 µg/kg bw/day for the ADI and 80-600 µg/kg bw/day for the ARfD [START_REF]Monitoring of Pesticide Residues in Products of Plant Origin in the European Union[END_REF].

Results and discussion

Residue data and food consumption data.

In Table I, the frequencies, mean levels and highest residues found in 5721 samples, accounting for contributions from 26 commodities of fruits and vegetables are shown.

[Insert Table I about here] Dithiocarbarmate residues were found in 7.1% of all the samples analyzed in the monitoring period 1998-2003(Poulsen et al. 2005)). In a Brazilian study [START_REF] Caldas | Dithiocarbamates residues in Brazilian food and the potential risk for consumers[END_REF]) dithiocarbamate residues were found in 60.8% of the samples. Nine commodities were analysed by Caldas; five of these (papaya, banana, apple, strawberry, tomato) were included in the Danish program for dithiocarbamate residues but much lower residue frequencies were found. In Denmark apricot (26.6%), pear (20.6%), spinach (12.9%), exotic fruits including papaya (12.6%) and apple (12.1%) [Insert Table II about here] Table II shows a summary of the food consumption data obtained from the nation-wide survey. Apple followed by carrot, banana, tomato, cucumber and pear showed the highest mean consumption levels for all consumers (total population; both consumers and non-consumers), while melon followed by apple, pear, banana, table grapes and peaches and nectarine had the highest mean consumption on consumption days only (only days with consumption included). Exotic fruit followed by tomato, cucumber, sweet pepper and carrots had the highest percentage of consumption days for both adults and children. That exotic fruit had the highest percentage of consumption days is probably due to that this group includes different fruits.

Intake distribution. Figure 1 shows percentiles of the acute intake distribution, summed for all commodities, calculated by the probabilistic approach. It is seen that 0.1% of the adults and children have an estimated intake higher than 11 µg/kg bw/day and 28 µg/kg bw/day, respectively. The maximum intake is 86 µg/kg bw/day and 138 µg/kg bw/day for adults and children, respectively. Thus, the acute intake is below the ARfD of 200 µg/kg bw/day for maneb, indicating that there is no acute risk for the Danish consumers from dithiocarbamate residues.

[Insert Figure 1 about here] Acute intake calculation Total intake. In Figure 2 the contribution (%) per commodity to the total acute intake as estimated by the probabilistic approach is shown. Nine commodities out of 26 contributed to 94 % of the total intake of dithiocarbamate residues. Apples followed by pear and cucumbers contributed most for both adults and children. Comparison of the deterministic point estimate and the probabilistic approach. The point estimate approach was compared with the probabilistic approach following the same approach as Boon et al. (2004). The results are shown in Table III. Since the point estimate approach is calculated for consumers only the probabilistic approach is also performed for consumers only. For comparison, calculations for the total population (including consumers and non-consumers) are also presented. The large portion size expressed as the 97.5% percentile used in the point estimate calculation is derived from the same survey [START_REF] Andersen | Estimation of the acute dietary exposure to pesticides using the probabilistic approach and the point estimate methodology[END_REF] as the consumption data used in the probabilistic calculation. To compare the outcome from the point estimate approach with the probabilistic approach, the 99.9% percentile (P99.9) of the exposure was used.

In the USA this percentile is used by the US EPA (US EPA 2000c). In the work example performed by (Boon et al. 2004) this percentile was also chosen for comparison.

[Insert Table III about here]

Point estimates vs. probabilistic modelling, consumers only. Table III shows that the exposures calculated by the point estimate approach in general were higher or comparable to the exposures at P99.9 for consumers only. For apricot however at the P99.9 the exposure was much higher (x3) for children than that calculated with the point estimate approach. Apricot has only 0.2 % (n=4) consumption days for children which included 3 persons. One of the persons had an intake of 150 g and a body weight of 16 kg, and the other two children had consumptions from 10-30 g and body weights of 22 kg and 19 kg, respectively. The fact that one child out of the three children had such a high intake combined with a bodyweight, which is lower than the mean body weight of 22 kg can explain the much higher exposure level calculated by the probabilistic approach than by the deterministic approach. In both cases, however, the exposure was still below 100 % of the ARfD of 200 µg/kg bw/day. Compared to the exposure at P99.99 the point estimates were generally lower. The exposure levels for children were higher than for adults.

Probabilistic modelling, consumers only approach vs. total population. Exposure levels calculated with probabilistic approach for the total population (including consumers and non-consumers) were always lower than the corresponding levels for the consumers only (Table III). As mentioned before, this is due to the fact that the total population includes both consumers and non-consumers of a certain crop. Because a person can be consumer of a certain crop containing the pesticide of interest and non-consumer of other crops containing the pesticide of interest the consumers only approach is not suitable when considering the exposure levels for a pesticide via the consumption of more than one crop. Only the total population approach is suitable for this (Boon et al. 2004[START_REF] Efsa | Opinion of the Scientific Panel on Plant protection products and their Residues on acute dietary intake assessment of pesticide residues in fruit and vegetables[END_REF]. For rarely eaten crops as e.g. apricot, which was only consumed by 0.2% (n=4) of the child population, the exposure for the total population was 0.0% at P99.9, while it was 56% of the ARfD using the consumers only approach (Table III).

Due to the risk of dilution for rarely eaten crops and to protect the small number of consumers both (Boon et al, 2004) and the US EPA (US EPA 2000c) recommend also to perform a consumers only or a point estimate calculation for rarely eaten crops.

Chronic dietary risk assessment

To compare the deterministic approach with the probabilistic approach estimates have been calculated for both children (4-6 years, 231 persons) and adults (15-75 years, 3299 persons). Figure 3 shows the mean chronic intake calculated by the deterministic approach as well as the mean and percentiles calculated by the probabilistic approach.

[Insert Figure 3 about here] Using the deterministic approach, the chronic intake for adults and children was respectively 0.08 µg/kg bw/day and 0.22 µg/kg bw/day corresponding to 0.16% and 0.44% of the ADI of 50 µg/kg bw. The mean intake calculation in the probabilistic approach was nearly similar to the mean intake calculated by the deterministic approach. At the P99.9% percentile, the intake for adults and children were 0.35 µg/kg bw/day (0.70% of the ADI) and 0.76 µg/kg bw/day (1.5% of the ADI), respectively.

Using the probabilistic approach instead of the deterministic approach in the calculation of the chronic intake gives insight of the exposure at different percentiles. Furthermore the uncertainties are quantified and the variability in the population is taken into account. The highest intake was estimated to 2.35 µg/kg bw/day (4.7% of the ADI) for both adults and children. Thus, the chronic exposure for Danish consumers to dithiocarbamate residues is far below the acceptable intake exposure using the ADI for mancozeb or maneb in the risk assessment. 2006) has also performed a probabilistic risk assessment of dithiocarbamates to the Brazilian population. They found that the general population and children was exposed to 6.9 and 13.5 µg/kg bw/day at the P99.9% percentile, which were higher than the exposure, calculated for the Danish consumers, but still acceptable compared to the ADI. In the study ½ LOQ was used to correct for undetected residues in the calculation, which could increase the calculated exposure as the LOQ is relatively high. When the number of samples without detected residues is high, the contribution to the intake will also be relatively high. In addition, the frequencies of dithiocarbamate residues were higher in the Brazilian study than in the present Danish study.

Conclusion

The aim of this study was to compare the outcome of probabilistic risk assessment and deterministic risk assessment to dithiocarbamates for both acute and chronic intake estimations and to see if the comparison between the point estimate approach and the probabilistic approach for the acute intake showed the same reported by Boon et al (2004).

For the acute intake calculations the comparison showed that the point estimate approach generally was higher or comparable to the exposure at the P99.9 as calculated with the probabilistic approach. Since the point estimate approach is very easy to perform and understand this approach could be used as first choice in a tiered process (US EPA 2000c). If this calculation indicates that there might be a problem with the acute intake, the probabilistic approach could then be applied to refine the calculations. Neither the point estimate, nor the probabilistic, consumers only approach can be used for estimating exposure to a pesticide from more than one crop.

In this case the probabilistic total population approach should be used. On the other hand, the risk of dilution for rarely eaten crops could be masked, unless a probabilistic consumer only calculation or a point estimate calculation is performed. These conclusions are in accordance with those reported by Boon et al (2004).

Comparison of the chronic intake using both the deterministic and the probabilistic approach showed nearly the same estimate of the mean value. However, when using the probabilistic approach it is possible to estimate the exposure at different percentiles In both calculation methods it was shown that there is no health risk for the Danish consumers to the dithiocarbamate residue levels found in the Danish monitoring program.

F

  . The acute dietary intake or short-term exposures (the point estimate) were estimated by using the equations for the National Estimate of Short Term Intake (NESTI) (FAO/WHO 2002). The large portion sizes were derived from the Danish National Consumptions survey from 2000-2002[START_REF] Andersen | Estimation of the acute dietary exposure to pesticides using the probabilistic approach and the point estimate methodology[END_REF]). In the calculations the default variability factors 7, 5 and 1 were used according to JMPR (FAO/WHO 2002). The unit weight was derived from the reference "Typical weights for food, 1996" (Johanna Haraldsdóttir et al.) and highest residue found in the monitoring program was used in the calculations.

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  g. P99.9 and P99.99. The variability in the populations eating habits are taken into account and uncertainties is quantified.

  had the highest frequency of samples containing residues at or above the LOQ of 0.1 mg/kg. Samples of spinach, cucumber, melon, apricot, pear, table grape, sweet pepper, lettuce, beans with pods and carrots had contents above the EU-MRLs(Poulsen et al. 2005).

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http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

Acknowledgements

The authors wish to thank the Rikilt Institute in Wageningen for permission to use the Monte Carlo Risk Assessment program in the calculations.


Wendie Liliane Claeys

Simon De Voghel

Jean-François Schmit

Valérie Vromman

Luc Pussemier

Wendie ; Claeys

Fasfc

Exposure assessment of the Belgian population to pesticide residues through fruit and vegetable consumption

Keywords: Exposure assessment, Probabalistic modelling Additives/Contaminants: Pesticides Exposure assessment, probabilistic modelling, pesticides, fruit, vegetables 26 concentration include storage, transport, shelf life, use patterns, lab-to-lab variation

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Concerning the consumption data recorded by the IPH, a selection had to be made 108 and certain commodities had to be combined as a group (e.g. consumption data on 109 Chinese, Savoy, red and white cabbage, and sauerkraut were aggregated into the 110 group "cabbage") in order to match the matrices analysed in the residue monitoring 111 campaign of the FASFC.

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The total dataset, including zero intakes ("zero consumption days"), was used as 113 part of producing an "average" diet for long-term consumer exposure assessment since 114 the main interest of this study is to evaluate the probability that the ADI is exceeded.

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When one is however interested in assessing the safety of actually eating a 116 commodity, it is preferred to use the "consumers only" approach [START_REF] Van Raaij | Cumulative exposure to cholinesterase 535 inhibiting compounds: a review of the current issues and implications for policy RIVM 536 report 320108001[END_REF] 117 [START_REF] Hamilton | Pesticide residues in food -506 acute dietary exposure[END_REF]).

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Estimation of exposure 120

The dietary exposure to pesticides (mg kg body weight -1 day -1 ) was calculated based

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In a first phase, the exposure was calculated deterministically. For a given 124 residue/food combination, the average residue concentration was multiplied with the 125 average consumption as well as with the 97.5 th percentile of consumption of the whole 126 population in order to have an idea of the chronic exposure of the population to 127 pesticide residues.

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In a second phase, the exposure was evaluated in more detail by a probabilistic 129 approach. Hereto, the Monte Carlo technique was applied. This technique involves the 130 random sampling of each probability distribution within the model to produce hundreds 131 or even thousands of scenarios (also called iterations or trials). Each probability is 132 sampled in a manner that reproduces the distribution's shape. The distribution of the

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In order to have a good representation of consumption and pesticide residue data, 140 both parametric and non-parametric approaches were evaluated. Fitting of probability 141 distributions to the data in the parametric approach was performed using BestFit 142 (Palisade, NY, US). However, due to the large number of zero consumption days and 143 of 'non-detects' in the residue concentration data, most of the consumption and 144 contamination data could not be fitted by one of the distributions, and preference was 145 given to the non-parametric approach. As such, model inputs (consumption and 146 pesticide residue concentration data) were described by a discrete, uniform distribution.

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In this non-parametric approach the collected data points themselves are considered to

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(e.g. 97.5 th , 99.9 th percentile), can also be calculated from each bootstrap sample. This 185 will generate a bootstrap distribution of 500 97.5 th , 99.9 th percentiles, etc. The bootstrap 186 distribution now characterises the uncertainty due to sampling uncertainty of the 187 original data set (Boon et al. 2004).

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As is illustrated in Figure 2, the exposure distributions are extremely right-skewed.

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Except for chlorpyriphos and lambda-cyhalothrin, the exposure estimates at the P99.99 235 are approximately 2 to 3 times higher than the exposure at the P99.9, which is 3 (to 10) 236 times higher than the exposure at P97.5. Because both food consumption and residue 237 monitoring distributions are right-skewed, the resulting product is also a right-skewed 238 distribution. It can be questioned if the consumption data set and the monitoring data 239 set are robust enough to support exposure values calculated at the P99.9 and higher.

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These values can be more sensitive to uncertainties in data collection (sample size, 241 reporting mistakes such as over reporting, analytical uncertainties) making these 242 estimations of exposure less reliable. This is illustrated in Table III, where the 95 % 243 confidence intervals of the higher percentiles of exposure for some residue/food

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Comparison of the deterministic and probabilistic analysis 278

Comparison between the deterministic and probabilistic approach has to be done with 279 care. Historically, the P97.5 has been chosen as the upper percentile for deterministic 280 acute intake assessments, i.e. the P97.5 of consumers for the specific food and the

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P97.5 residue level, which in combination amounts to the P99.94 for residue intake 282 [START_REF] Hamilton | Pesticide residues in food -506 acute dietary exposure[END_REF].

(100 x (1-((1-0.975)x(1-0.975))))

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In the present study, the deterministic and probabilistic P97.5 exposure values are

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Additionally, it has to be remarked that in the deterministic approach, total exposure 289 for a given residue was obtained by summing exposures from all residue/food 290 combinations, whereas deterministic intake assessments should deal with only one 291 food at a time. Generally, summing point estimates across foods leads to an estimate 292 of food consumption that is extremely high and unrealistic. Because of the inherent 293 conservatism in the point estimate, summing these estimates over commodities would 294 be equivalent to assuming that large portions of all commodities (P97.5 of a 295 consumption distribution) are consumed on 1 day or frequently by one individual and 296 that all these commodities are contaminated. For a realistic estimate of short-term 297 dietary exposure through multiple foods, a probabilistic approach is recommended 298 [START_REF] Hamilton | Pesticide residues in food -506 acute dietary exposure[END_REF]. Nevertheless, when calculating the total exposure by the probabilistic approach, 300 potential correlations between daily consumption rates of different food items need to 301 be taken into account. As a pragmatic approach, recorded days were considered as 302 such, without discriminating between variation among individuals and variation among 303 days (within individuals), resulting in a distribution of "person-days". The drawback of 304 this approach is that it cannot be determined if few persons are at risk but each of them 305 at many days, or that many persons are at risk but each of them only rarely (Pieters et 306 al. 2005). Since the focus of the present study concerns mainly the intake of frequently 307 exposed individuals, the intake was compared to the ADI rather than to the ARfD.

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Upper percentiles of exposure 310 Since in exposure assessment of pesticides, the regulatory threshold risk is at the 311 upper tail of the exposure distribution (P99 and higher), also the P99.9 and P99.99 312 values of exposure are presented in Table II. From this table, it can be concluded that 313 except for chlorpropham, the probability that a person exceeds the ADI by eating one 314 or more servings of contaminated a food item is less than 0.1%. The relatively high 315 exposure to chlorpopham is solely due to 1 crop, namely potatoes (Figure 2a).

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Next to chlorpropham, a relatively high intake in terms of % ADI is also observed for 317 dimethoate and to a lesser extent, for imazalil. This high exposure value for dimethoate 318 is mainly due to a high or frequent consumption of cherries and lettuce (Figure 2c).

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With respect to imazalil, citrus fruit, orange and mandarin in particular, contributed most 320 to the intake (Figure 2b).

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Different profiles can be distinguished between the high exposure levels. Exposure 322 to imazalil is relatively high since the amount of commodities on which it was detected 323 and its use is authorized, is high. For chlorpropham on the other hand, the high 324 exposure was due to some high levels of pesticide residue detected on a single food The effect of consumption data on the outcome can be illustrated by Table III [START_REF] Wright | Refinements in acute dietary exposure assessments for 542 chlorpyrifos[END_REF][START_REF] Hamey | A practical application of probabilistic modelling in assessment of dietary 502 exposure of fruit consumers to pesticide residues[END_REF]. When comparing exposure assessments in 342 literature, some caution has to be taken. One difficulty is that studies are performed 343 using different (national) food consumption databases. The way in which food 344 consumption data of different countries has been and is collected, is not harmonised 345 and can vary considerably. This diversity is related to the population addressed (e.g.

346

children included or not), the method of data collection (24-h recall, dietary method), 347 duration of the survey, number of respondents involved, coding of food consumption 348 data and method of quantifying amount consumed (actual weighing vs. estimations on 349 the basis of portion sizes) [START_REF] Kroes | Assessment of intake from the diet[END_REF]. These variables should be kept in mind 350 when comparing exposure assessments using different databases.

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Another critical aspect is the sampling of commodities for pesticide residue 352 analysis. This is not always according to an ad random procedure, but often based on a risk approach (directed or targeted sampling). The use of these data to estimate 354 dietary exposure may thus lead to an overestimation of exposure.

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In order to be able to compare exposure assessment results internationally, 356 standardized ways of collecting and reporting data is needed.

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The exposure estimates presented here for chlorpyriphos, compare favourably to

359

estimates published in literature. The US FDA's total Diet studies from 1989 and 1990 360 estimated chlorpyriphos exposure at 0.041 x 10 -4 mg/kg bw/day for children aged 14 to 361 16 years (FDA 1991;FDA 1990). This estimate is similar to the average value given for 362 the lower bound scenario in Table II (0.045 x 10 -4 mg/kg bw/day). Based on monitoring 363 programmes, [START_REF] Wright | Refinements in acute dietary exposure assessments for 542 chlorpyrifos[END_REF] estimated a P99.9 exposure of 4.8 x 10 -4 mg/kg 364 bw/day for the US population, and 9.4 x 10 -4 mg/kg bw/day for children between 1 and 365 6 years old, which is of the same order of magnitude as calculated for the higher 366 percentiles of exposure in Table II.

367

In a Dutch study where a probabilistic model of dietary exposure of infants to 368 pesticides was compared with a duplicate diet study, the 95 % confidence interval for 369 the P99 of exposure to chlorpyriphos was 0.2-4.3 x 10 -4 mg/kg bw/day for the duplicate 370 diet study assuming a log-normal exposure distribution and 24.5-43.3 x 10 -4 mg/kg 371 bw/day for the probabilistic model in which variability and processing was taken into 372 account an non-detects were replaced with the limit of reporting or LOR (Boon et al.

373

2003). The latter value exceeds even the worst case (non-detects equal to LOQ) 374 P99.99 value estimated in this study (14.2 x 10 -4 mg/kg bw/day). This can be amongst 375 others due to the fact that in the Dutch study toddlers aged 8 to 12 months were 376 considered and the sampling strategy was designed to include as many as children as 377 possible that were fed home-made meals of fruits and vegetables.

378

In a Swiss study, the probabilistic intake to chlorpropham was assessed for potato III).

394

Nevertheless, exposure values for imazalil were lower. In 2005 and with respect to the 395 considered food items, two MRL exceedings were reported for chlorpropham on 396 potatoes (non-harmonised, national MRL of 5 mg/kg), whereas 6 MRL exceedings 397 were reported for dithiocarbamates including 1 for cress, 3 for lettuce, 1 for peas and 1 398 for radishes (EC MRLs of 0.3, 3-5, 0.1 and 0.05 mg/kg respectively) (FASFC 2006). In 399 terms of exposure, chlorpropham is ranked higher than the dithiocarbamates.

400

Based on results, it can be concluded that chronic intakes of 25 selected pesticide 401 residues are rather low compared to the ADI (mostly < 1 % of ADI) and that the public 402 safety in Belgium seems to be generally under control in terms of (single) pesticide 403 intakes through consumption of fruit and vegetables. However, some residues such as 404 chlorpropham, imazalil, dithiocarbamates, dimethoate, lambda-cyhalothrin and 405 chlorpyriphos, needed to be considered more closely since for a high consumer 406 (P97.5) the intake can reach 23% of the ADI for imazalil, 15% for chlorpropham, 14% indicated that except for chlorpropham, the probability to exceed the ADI will be much 410 lower than 0.1% when exposed to one of the selected pesticides.

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Considering acute exposure to pesticide residues, interpretation of exposure levels 412 determined in this study in terms of % of the ARfD will indicate the absence of acute 413 risks, even for high consumers, given that ADI values for pesticides are lower than 414 ARfD values (Table II).

416

It seems that the exposure to the pesticide residues considered is mainly due to the 417 consumption of only one or two food items (e.g. potatoes in case of chlorpropham, 418 citrus fruit in case of imazalil). With regard to fruit and vegetables, attention should also 419 be given to chemical contaminants other than pesticides such as natural (e.g.

427

It has to be noted that the present study focused on fruits and vegetables only.

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Therefore, it is an underestimation of the total exposure of pesticides studied. On the 429 other hand, processing factors were ignored, whereas fruit and vegetables are often Further research could also deal with exposure assessment related to the presence 438 of multiple residues on a single food or serving, i.e. cumulative dietary intake 439 assessment. Cumulative exposure to various residues of pesticides in food is a 440 potential area of concern and gaining more and more interest. This issue is especially 441 relevant for pesticides with a common mechanism of toxicity (e.g. organophosphates, 442 carbamates) (EFSA 2007;[START_REF] Van Raaij | Cumulative exposure to cholinesterase 535 inhibiting compounds: a review of the current issues and implications for policy RIVM 536 report 320108001[END_REF][START_REF] Jensen | Cumulative risk assessment of the intake of 513 organophosphorus and carbamate pesticides in the Danish diet[END_REF].

444

Finally, it has to be noted that compared to point estimates, making use of 445 probabilities and revealing the extreme upper tail of the exposure distribution may 446 create difficulties for both consumers and regulators in terms of their understanding of 447 the assessment. However, a full probability distribution also enables more informed 448 decisions to be made for policy and regulation. Regulation based on unrealistic 449 exposure estimates could lead to unnecessary restrictions on products, or potentially 450 put consumers at risk. It is therefore important to understand the influence that sources 451 of uncertainty exert on predictions of exposure and to include this knowledge in the 452 decision making process [START_REF] Ferrier | Assessment of uncertainty in a 496 probabilistic model of consumer exposure to pesticide residues in food[END_REF].

454

Conclusion 455

In 2005, the FASFC controlled 1,322 samples of fruit and vegetables. Pesticide 456 residues were found in 56% of the samples and standards were exceeded for 7.9% of 457 the samples (FASFC 2006). These numbers could give unnecessary rise to 458 consumers' concern. A more nuanced and different picture is obtained when the actual 459 exposure of consumers to pesticide residues is considered. Based on results of the 460 present study, chronic exposure to pesticide residues due to the consumption of fruit 461 and vegetables seems to be under control, even for frequent consumers.

527

The risk management question governs the method. RIVM report 320011001/2005.

F o r P e e r R e v i e w O n l y

Table I. Total exposure in terms of % ADI of selected pesticide residues based on a deterministic exposure assessment approach, based an average residue concentration and an average and 97.5 th percentile of consumption.

121

  on consumption data and individual body weights of the IPH diet study, and residue 122 monitoring data of the Belgian FASFC monitoring programme 2005.

  exposure assessment, Monte Carlo simulations were performed 136 with 10000 iterations. The model input distributions were randomly sampled by Latin 137 Hypercube sampling. Calculations were performed by means of the commercially 138 available software package @Risk ® (Palisade Corporation, Version 4.5.5, NY, US).

148

  form the distribution function and all possible values have the same probability of 149 occurrence[START_REF] Vose | Risk Analysis -A quantitative guide[END_REF]).

  assessment, the estimated distribution typically consists of many 164 measurements below the minimum pesticide residue concentration reported (limit of 165 quantification or LOQ). Such samples do not necessarily indicate that pesticide 166 residues are not present in the sample, but only that the analytical method could not 167 detect the possible amount of pesticide present. Therefore, samples with residue levels 168 below the LOQ ("non-detects") were assigned either 0, ½ LOQ or LOQ, corresponding 169 to a lower, middle and upper bound (worst case) scenario. 170 171 Uncertainty of exposure estimates 172 The probabilistic method in itself generates estimates of variability in the form of an 173 exposure distribution (1-dimensional Monte Carlo model). To estimate the uncertainty 174 of the exposure estimate, it is necessary to apply other methods, such as bootstrap 175 sampling (2-dimensional Monte Carlo model). Bootstrap sampling is a method to 176 assess the reliability of percentiles of exposure and is a means to study the stability of 177 the tail of the exposure distribution. With this method a sample of n observations (food 178 consumption levels, residue levels) is resampled from the original database to obtain a 179 bootstrap sample of n observations. Sampling is performed with replacement, so that 180 every observation can occur more than once in the bootstrap sample. By repeating this 181 process e.g. 500 times, 500 bootstrap samples are obtained, which may be considered 182 as alternative data sets that might have been obtained during sampling from the 183 population of interest. Each statistic that can be calculated from the original data set

  screening, the exposure to pesticide residues was calculated by a 192 deterministic approach on a total of 25 residues. These residues were selected out of 193 the 200 residues analysed by the FASFC in 2005 based on their frequency of detection 194 (> 2% of analysed samples exceeded the limit of quantification, LOQ). The residue 195 concentration in the food commodities was assumed to be equal to the average 196 concentration in order to take the variation of the concentrations into account. A rough estimate of the total exposure to a given pesticide residue X was obtained by summing 198 exposures from all residue X / food combinations considered. The total exposure was 199 compared with the ADI and expressed in terms of % ADI. Results for the lower, middle 200 and upper bound scenario for samples with a residue concentration below the LOQ are 201 given in TableI, together with their detection frequency and the ADI.

  Figure 1 presents 202 an overview of the exposure for the average and the 97.5 th percentile (P97.5) of 203 consumption according to a scenario where the residue concentration of non-detected 204 samples is equal to half the LOQ of the analytical method used (middle bound 205

  244combinations are given, including chlorporpham on potatoes, imazalil on oranges and 245 dimethoate on cherries and lettuce. These 95% confidence intervals246were calculated by the bootstrap sampling method. or the number of samples with a residue concentration below 252 the LOQ is rather high. As can be observed from TableI, treatment of so-called left 253 censored data can have a large effect on the deterministically obtained outcome. For 254 example, considering the results based on the P97.5 consumption, the intake of 255 lambda-cyhalothrin or of the dithiocarbamates exceeds the intake of chlorpropham for 256 the upper bound scenario. For the lower and middle bound scenarios on the other . (2006) evaluated the effect of uncertainty on the exposure estimate 272 using scenarios reflecting different assumptions related to sources of uncertainty. The 273 most influential uncertainty issue was the distribution type used to present input 274 variables. Other sources that most affected the model output were non-detects, unit-to-275 unit variability and processing.

284

  not comparable since they are essentially based on different notions. The 285 deterministically obtained exposure values are based on percentiles of consumption, 286 whereas the probabilistically determined results are actual percentiles of exposure. In 287 contrast, the average exposure values are very similar.

  330 occasionally. Their consumption is seasonal resulting in extreme right-censored 331 consumption data (1.2% non-zero consumption days). Due to the high detection 332 frequency of dimethoate on cherries, exceeding of the ADI may occur for a minority of 333 consumers (P99.99), although rarely. From this example, it is also clear that caution 334 should be taken when interpreting high exposure percentiles without knowledge of the 335 confidence interval. Performing a 1-dimensional Monte Carlo simulation could result in 336 extreme values of a confidence interval leading to unnecessary concern. 337 338 Comparison with other studies 339 Different probabilistic exposure assessment studies of pesticides from food are 340 reported in literature, but are still scarce (Brüschweiler 2004; Boon et al. 2003; López et 341 al. 2003;

  93 x 10 -4 and 0.0025 mg/kg bw/day respectively for a person weighing 60 kg. Both 382 results are lower than observed in this study, where the average intake of 383 chlorpropham by uncooked and unpeeled potatoes was calculated as 0.0021 mg/kg 384 bw/day. As remarked by the author, calculated exposures depend strongly on the 385 processing factors applied, which differ markedly in literature. in the introduction, detection frequency and the number of 389 samples for which the MRL is exceeded, give an indication on residues and food 390 commodities to prospect, but lack information in terms of food safety. This can be 391 illustrated by comparing e.g. chlorpropham with imazalil and the dithiocarbamates. The 392 detection frequency of chlorpropham in potatoes was lower than the one for imazalil in 393 mandarin or oranges (69% compared to 87 and 100% respectively) (Table

Page

  407for the dithiocarbamates and 10% for lambda-cyhalothrin and dimethoate, and 9% for 408 chlorpyriphos for the middle bound scenario. Nevertheless, probabilistic analysis 409

  420 mycotoxins), environmental (e.g. cadmium, PCBs) or process contaminants (e.g. 3-421 MCPD, acrylamide). As such, e.g. exposure to chlorpropham (and other sprouting 422 inhibitors) on potatoes could be linked to the acrylamide issue. It has been shown that 423 more acrylamide is formed during cooking of potatoes that have been stored at cold 424 temperature (Claeys et al. 2005). If less potatoes are stored in cold-storage depots, it is 425 not inconceivable that the application of sprouting inhibitors like chlorpropham 426 increases.

  430 peeled, cooked or boiled before consumption, resulting in an overestimation of the 431 actual exposure to pesticide residues. Other variables affecting the pesticide residue

  . Residues in food -1989. Journal -Association of Official Analytical Chemists 62: Rueda C, Armentia A, Rodríguez M, Cuervo L, Ocio J. 2003. Validation and sensitivity 523 analysis of a probabilistic model for dietary exposure assessment to pesticide residues 524 with a Basque Country duplicate diet study. Food Additives & Contaminants 20(Suppl. 1): 525 S87-S101. 526 Pieters M, Ossendorp B, Slob W. 2005. Probabilistic modelling of dietary intake of substances.

Figure 2 .

 2 Figure 2. Contribution of different food items to the total exposure (in terms of % ADI, middle bound scenario) of chlorpropham (a), imazalil (b), dimethoate (c), dithiocarbamates (d), chlorpyriphos (e) and lambda-cyhalothrin (f).

  Figure 1.

[Insert Table I and Figure 1 about here] 208 209

	218	Discussion
	219	Deterministic analysis
	220	Based on the P97.5 of consumption, relatively high exposure values were observed for
	221	chlorpropham, imazalil, dithiocarbamates, dimethoate, lambda-cyhalothrin and
	222	chlorpyriphos in the deterministic analysis (Table I, Figure 1). Chlorpropham is a
	223	selective systemic herbicide and plant growth regulator belonging to the N-
	224 225 226	phenylcarbamate group of pesticides. It is mainly applied as sprouting inhibitor in F potato storage. Imazalil is a systemic fungicide used to control a wide range of fungi on o r fruit, vegetables, and ornamentals. It is also used as a seed dressing and for
	227	P postharvest treatment of citrus, banana, and other fruit to control storage decay.
	228	e Dithiocarbamates are widely used antisporulant (contact) fungicides that are very often
	229 230	associated with other fungicides. Lambda-cyhalothrin is a pyrethroid insecticide. e r Dimethoate and chlorpyriphos are organophosphate insecticides. These pesticides
	231	R were selected for further probabilistic analysis.
	232		e
		Probabilistic analysis	e i v
			w
	210	Probabilistic exposure assessment	O n l
	211	Table II presents the exposure values based on a probabilistic assessment approach y
	212	for chlorpropham, imazalil, dithiocarbamates, dimethoate, lambda-cyhalothrin and
	213	chlorpyriphos. Figure 2 gives more specific information on the contribution of several
	214	food items to the exposure of a given pesticide residue, and this for the middle bound
		scenario.
			9/20

  . For 327 example, the P97.5 exposure to dimethoate by the consumption of cherries is zero and 328 the P99.99 exposure values range from zero to values higher than the ADI, although a 329 detection frequency of 84.6% was observed. Cherries are food items that are eaten

mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk Food Additives and Contaminants F o r P e e r R e v i e w O n l yTable II .

 II Total exposure in terms of % ADI of selected pesticide residues based on a probabilistic exposure assessment approach.

		Food Additives and Contaminants				Page 22 of 31
	Average consumption 0 LOQ/2 LOQ 0.0 0.1 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.3 0.5 0.0 0.0 0.0 4.3 4.4 4.4 0.1 0.9 1.7 0.0 0.0 0.1 0.3 0.5 0.6 0.3 1.8 3.4 0.1 0.3 0.4 1.7 2.1 2.5 0.0 0.0 0.0 0.2 0.4 0.6 0.0 1.0 2.1 0.3 0.3 0.3 0.2 0.6 1.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.5 0.5 0.0 0.1 0.1 P97.5 0 LOQ/2 LOQ 4.49 34.65 34.40 36.20 119.61 133.41 153.35 223.9 187.81 281.07 P 97.5 consumption Detection frequency (%) 1 0 LOQ/2 LOQ 2.0 (1020) 0.0 1.3 2.6 18.9 (328) 1.3 1.7 2.1 11.7 (213) 1.4 1.6 1.7 16.2 (376) 1.0 3.0 5.0 5.0 (20) 0.0 0.2 0.3 41.4 (104) 14.5 14.8 15.1 3.3 (509 0.9 8.5 16.2 10.3 (367) 0.3 0.6 0.9 9.6 (197) 2.4 9.9 17.5 16.3 (857) 2.7 14.2 25.6 14.3 (28) 0.9 1.9 2.9 26.0 (323) 20.0 22.6 25.1 2.1 (47) 0.0 0.0 0.1 15.8 (865) 1.8 3.8 5.7 1.6 (862) 0.1 10.1 20.2 8.6 (70) 0.0 0.6 1.1 9.0 (299) 2.1 5.4 8.7 2.5 (245) 0.0 0.2 0.3 3.7 (646) 0.1 1.3 2.5 8.9 (651) 2.1 6.3 10.6 14.5 (379) 0.1 0.8 1.4 Average P99.9 P99.99 LOQ/2 LOQ 0 LOQ/2 LOQ 0 LOQ/2 LOQ 4.26 2.14 2.58 15.99 16.82 17.36 54.19 52.35 59.55 92.40 117.89 78.42 0.44 0.63 0.00 1.63 3.25 55.92 56.61 63.70 125.52 173.04 150.20 1.83 3.41 2.22 5.27 9.55 11.66 11.74 18.68 31.77 14.91 30.49 0.79 1.51 0.68 3.24 5.96 3.62 6.54 10.62 5.84 7.87 14.19 1.04 2.05 0.00 4.45 8.87 1.63 8.93 17.04 7.50 10.12 21.30 F o r P e e r R e v i e w ADI (mg kg -1 bw day -1 ) Azoxystrobin 0.1 Benomyl group 0.03 Boscalid 0.04 Bromide ion 1.0 Chlormequat 0.05 Chlorpropham 0.05 Chlorpyriphos 0.01 Cyprodinil 0.03 Dimethoate 0.001 Dithiocarbamates 0.05 Ethephon 0.03 Imazalil 0.025 Imidacloprid 0.06 Iprodione 0.06 Lambda-cyhalothrin 0.005 Linuron 0.003 Methomyl 0.0025 Oxadixyl 0.125 Pirimicarb 0.035 Procymidone 0.025 Propamocarb 0.29 ADI (mg kg -1 bw day -1 ) ARfD (mg kg -1 bw day -1 ) Detection frequency (%) 1 0 0.05 0.5 41.3 (104) 4.26 0.025 0.05 26 (323) 1.65 0.001 0.01 9.6 (197) 0.25 0.05 0.2-0.6 2 16.4 (861) 0.26 0.01 0.1 5.3 (509) 0.05 0.005 0.0075 1.9 (855) 0.01 1 total number of samples is indicated between brackets Chlorpropham Imazalil Dimethoate Dithiocarbamates Chlorpyriphos Lambda-cyhalothrin 2 ARfD of Maneb and Mancozeb respectively O n l y
	Tebuconazole	0.03	17.0 (47)	0.0	0.0	0.0	0.1	0.3	0.4
	Thiabendazole	0.1	17.5 (331)	0.2	0.3	0.3	2.8	3.0	3.2
	Tolclophos-methyl	0.064	16. (238)	0.0	0.0	0.0	0.0	0.1	0.1
	Tolyfluanid 1 total number of samples is indicated between brackets. Numbers are based on selected food items in this study and not on the total of samples reported in 0.1 17.4 (493) 0.0 0.1 0.1 0.5 0.7 0.9
	the monitoring programme 2005 (FASFC 2006).

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Table III .

 III Consumer's exposure in % ADI [95% confidence interval] for different residue-food commodities (probabilistic approach). Exposure assessment by the deterministic approach for the middle bound scenario (LOR/2 for residue concentrations < LOR) in terms of % ADI (based on average residue concentration and average and P97.5 consumption).

	Figure 1.
		Detection	% of non-		P97.5		P99.9			P99.99
		frequency (%) 1	zero consumption	0	LOQ/2	LOQ	0	LOQ/2	LOQ	0	LOQ/2	LOQ
			days
	Chlorpropham
	Potatoes	69.4 (62) 31.5 F 67.0 o r
			P
			e
			e r
				R
				e
				v i e
					w
					O n l
						y
							1

Processing factors (peeling, cooking, boiling, …) and variability factors were not

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 [Insert Table II and Figure 2 about here]

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Programme on Reduction of Pesticides and Biocides. Prof. W. Steurbaut (UGent), Prof.

Commission Recommendation 2005/178/EC.

127a-145a.

Fytoweb, a Belgian website supported by the Federal Public Service Health, Food


Kerstin ; Schoeberl

Dirk W Lachenmeier
email: lachenmeier@web.de

Helmut Reusch

Constanze Sproll

Occurrence of benzene as heat-induced contaminant of carrot 1 juice for babies in a general survey of beverages 2 3

Keywords: Chromatography -GC/MS, Chromatography -Headspace, Market basket survey, Survey Additives/Contaminants: Benzene, Additives general, Metals, Process contaminants Food Types: Baby food, Beer, Beverages, Fruit juices Benzene, benzoic acid, beverages, soft drinks, fruit juice, carrot juice, baby 28 food 29

A survey of benzene contamination of 451 beverage samples using headspace sampling 13 in combination with gas chromatography and mass spectrometry (HS-GC/MS) with a 14 quantification limit of 0.13 µg l -1 was conducted. Artefactual benzene formation during 15 headspace sampling was excluded by only mildly heating at 50°C and adjustment of 16 sample pH to 10. The incidence of benzene contamination in soft drinks, beverages for 17 babies, alcopops and beer-mixed drinks was relatively low with average concentrations 18 below the EU drinking water limit of 1 µg l -1 . Significantly higher concentrations were 19 only found in carrot juices, with the highest levels in carrot juices specifically intended 20 for infants. About 94% of 33 carrot juices for infants had detectable benzene levels with 21 an average concentration of 1.86 ± 1.05 µg l -1 . The benzene contamination of beverages 22 was significantly correlated to iron and copper concentrations, which act as catalyst in 23 benzene formation. The formation of benzene in carrot juices was predominantly caused 24 by a heat-induced mechanism, which explains the higher occurrence in infant carrot 25 juices that are considerably heated to exclude microbiological contamination. 26 27

Introduction 30

Benzene is one of the contaminants with the highest level of evidence for 31 carcinogenicity. For example, it was classified as carcinogenic to humans (Group 1) by 32 the International Agency for Research on Cancer (IARC 1987). Active as well as 33 passive smoking, automobile exhaust, and driving or riding in automobiles are 34 postulated as the most important pathways for benzene exposure [START_REF] Wallace | Environmental exposure to benzene: An update[END_REF]. Since 35 high levels of benzene metabolites are frequently reported among children and non-36 smoking workers without occupational exposure, [START_REF] Johnson | A critique of benzene exposure in the general 407 population[END_REF] hypothesize that 37 there may significant sources of benzene that are hitherto unidentified. 38 39 Since early 1990s, concerns about benzene contamination of food have been raised. 40

Several sources can contribute to the occurrence of benzene in foods. Benzoic acid, a 41 widely used food preservative, may decarboxylate to benzene in the presence of ascorbic 42 acid [START_REF] Gardner | Benzene production from decarboxylation of benzoic 395 acid in the presence of ascorbic acid and a transition-metal catalyst[END_REF]. The formation of benzene from benzoic acid is 43 influenced by the presence of transition-metal catalysts (for example, Cu(II) or Fe(III) 44 ions) and is dependent on pH, UV light or temperature [START_REF] Gardner | Benzene production from decarboxylation of benzoic 395 acid in the presence of ascorbic acid and a transition-metal catalyst[END_REF]45 McNeal et al. 1993;[START_REF] Chang | Studies on benzene formation in beverages[END_REF][START_REF] Barshick | Determination of benzene 365 content in food using a novel blender purge and trap GC/MS method[END_REF]. Benzene may be 46 introduced into foods through leaching from various packaging materials or storage 47 environments, from contamination of water supplies, or may be formed during 48 irradiation processes [START_REF] Barshick | Determination of benzene 365 content in food using a novel blender purge and trap GC/MS method[END_REF]. Another source of benzene in soft drinks 49 and beer is contaminated carbon dioxide used for carbonation [START_REF] Long | From cobalt to chloropropanol: de tribulationibus aptis cerevisiis 412 imbibendis[END_REF]Wu et al. 50 2006). [START_REF] Carrillo-Carrión | Liquid-liquid 373 extraction/headspace/gas chromatographic/mass spectrometric determination of 374 benzene, toluene, ethylbenzene, (o-, m-and p-)xylene and styrene in olive oil using 375 surfactant-coated carbon nanotubes as extractant[END_REF], 54 static or purge and trap headspace sampling [START_REF] Page | A Survey of Benzene in Fruits 422 and Retail Fruit Juices, Fruit Drinks, and Soft Drinks[END_REF]Gardner and Lawrence 55 1993;[START_REF] Mcneal | Survey of Benzene in 414 Foods by Using Headspace Concentration Techniques and Capillary Gas-415 Chromatography[END_REF][START_REF] Chang | Studies on benzene formation in beverages[END_REF][START_REF] Barshick | Determination of benzene 365 content in food using a novel blender purge and trap GC/MS method[END_REF]Fabietti et al. 56 2001;[START_REF] Cao | Determination of benzene in 370 soft drinks and other beverages by isotope dilution headspace gas 371 chromatography/mass spectrometry[END_REF] or headspace solid-phase dynamic extraction (Ridgway et al. 57 2007). Headspace solid-phase microextraction (HS-SPME) in combination with HPLC 58 was proposed for determination of benzene in fruit juices [START_REF] Clasadonte | Determination of benzene and 380 toluene in fruit juice by solid phase microextraction (SPME) and HPLC[END_REF]. The 59 recent finding of benzene in drinks has raised concerns over analytical methodology, 60 especially heating during the purge-and-trap step that might lead to artefactual formation 61 of benzene [START_REF] Hileman | Dispute over benzene in drinks[END_REF]. 62 63 There are only a few surveys that are currently available about benzene in food. Page et 64 al. (1992) determined benzene levels ranging from 0.018 to 3.83 µg kg -1 in 97 Canadian 65 samples of various fruits, juices and drinks. [START_REF] Mcneal | Survey of Benzene in 414 Foods by Using Headspace Concentration Techniques and Capillary Gas-415 Chromatography[END_REF] showed that food 66 without added benzoates contained lower levels than some foods and beverages 67 containing both ascorbic acid and sodium benzoate. In a study of 60 soft drinks from 68 Italy [START_REF] Fabietti | Investigation into the benzene and toluene 391 content of soft drinks[END_REF], none of the samples were found to have benzene levels above 69 the World Health Organization (WHO) guideline value of 10 µg l -1 for benzene in 70 drinking water [START_REF] Who | Guidelines for drinking-water quality[END_REF]. Out of 150 soft drinks sampled in the United Kingdom, 71 107 did not contain detectable levels of benzene but four contained average levels above 72 10 µg kg -1 (Food Standards Agency 2006). In a sample of 68 Australian flavoured 73 beverages, 38 contained trace levels of benzene ranging from 1 to 40 µg kg -1 (Anon 74 2006). From 84 Chinese lager beers, six contained detectable benzene concentrations up 75 to 7.1 µg l -1 [START_REF] Wu | Investigation into benzene, 449 trihalomethanes and formaldehyde in Chinese lager beers[END_REF] which contained either no benzene or levels below 5 µg kg -1 [START_REF] Meadows | Benzene in beverages[END_REF]. Cao et al. 78 (2007) assessed benzene levels in 124 Canadian beverages, and found that 40% of the 79 samples contained detectable benzene levels and two products had benzene levels above 80 10 µg l -1 . 81 82 In our study, which is the first survey of benzene in German food products, we 83 concentrated on product groups that were previously described as likely to contain 84 benzene such as beverages containing ascorbic and benzoic acid. We focused on 85 products sold as baby or infant food and intended for the most susceptible consumer 86 group [START_REF] Murray | Environmental hazards to children's health in 437 the modern world[END_REF]. In addition, we included certain alcoholic beverages in the 87 survey, because ethanol in alcoholic beverages was recently evaluated by the IARC as 88 carcinogenic to humans (Group 1) (Baan et al. 2007;[START_REF] Iarc | Alcoholic Beverage Consumption and Ethyl Carbamate (Urethane)[END_REF]. The IARC evaluation 89 emphasizes the importance and necessity of avoiding and controlling carcinogenic 90 contaminants in alcoholic beverages, since synergistic effects between ethanol and other 91 contaminants are possible [START_REF] Lachenmeier | Consequences of IARC re-evaluation of alcoholic beverage 409 consumption and ethyl carbamate on food control[END_REF]). In the case of benzene, such an 92 interaction with ethanol is likely. This is because significant deviations of acute toxicity 93 were demonstrated for the binary mixture [START_REF] Tichý | Risk assessment of 439 mixtures: possibility of prediction of interaction between chemicals[END_REF][START_REF] Rucki | Acute toxicity of binary mixture benzene-ethanol and 432 partition coefficient K(ow) of benzene and ethanol[END_REF]) and 94 both substances may be metabolized by cytochrome P450 in humans (Rana and Verma 95 2005;[START_REF] Seitz | Molecular mechanisms of alcohol-mediated carcinogenesis[END_REF] the food group and sample number to collect but not specific brands. We had initially 107 requested for food groups "soft drinks", "alcopops", "beer-mixed drinks", and 108 "beverages for babies/infants" that have been previously described to be susceptible to 109 benzene contamination. Due to our risk-oriented sampling approach (Roth et al. 2007), 110 we then increased the sample numbers of conspicuous food groups. For example, after 111 the first detection of comparably high benzene levels in carrot juices, we had 112 specifically requested the food inspectors to collect further samples of carrot juices 113 intended for normal consumption as well as carrot juices intended as baby food for 114

Quantitative determination of benzene 118

Benzene was purchased from Sigma-Aldrich (Taufkirchen, Germany). Benzene-d 6 was 119 obtained from Merck (Darmstadt, Germany). The analysis of benzene was done using 120 static headspace (HS) sampling in combination with gas chromatography and mass 121 spectrometry (GC/MS). For sample preparation, 10 ml of sample was placed in a 20 ml 122 headspace vial. After adjustment with KOH solution (10%, m/m) to pH 10, the solution 123 was spiked with 100 µl of benzene-d 6 (100 µg l -1 in methanol) as internal standard. The 124 vials were tightly sealed and homogenized using a vortex mixer. 125

The HS-GC/MS system used for analysis was an Agilent model 6890 Series Plus gas 126 chromatograph in combination with a CTC Combi PAL autosampler and an Agilent 127 5973N mass selective detector. Acquisition and analysis of data were performed using 128 standard software supplied by the manufacturer. The samples were incubated in the 129 agitator oven of the autosampler at 50°C for 30 min. After that, 1000 µL of sample 130 headspace were injected into the GC/MS system using split injection mode (split ratio 131 2:1). Substances were separated on a fused silica capillary column (Optima 624, 60 m x 132 0.25 mm I.D., film thickness 1.4 µm). Helium with a constant flow rate of 1.0 ml min -1 133 was used as carrier gas. Temperature program: 35°C hold for 1 min, 10°C min -1 up to 134 240°C, hold for 10 min. The temperatures for the injection port, ion source, quadrupole 135 and interface were set at 250°C, 230°C, 150°C and 250°C, respectively. 136 To determine retention times and characteristic mass fragments, electron impact (EI) 137 mass spectra at 70 eV of the analytes were recorded by total ion monitoring. The 138 retention time was 11.70 min benzene and 11.65 min for benzene-d 6 . For quantitative 139 analysis, the chosen diagnostic mass fragments were monitored in the selected ion 140 monitoring (SIM) mode. Benzene: m/z 78 as target ion and m/z 77 as qualifier ion; 141 benzene-d 6 : m/z 84 as target ion and m/z 82 as qualifier ion. For quantification, peak 142 area ratios of the analytes to the internal standard were calculated as a function of the 143 concentration of the substances. 144 The method was validated using two authentic samples analyzed seven times. The 145 precision of the method never exceeded 6.6% (coefficient of variation) and the trueness 146 never exceeded 5.3% (as compared to spiked concentrations), indicating good assay 147 accuracy. Determined according to DIN 32645, the limit of detection was 0.04 µg l -1 and 148 the limit of quantitation was 0.13 µg l -1 . 149 150

Quantitative determination of possible benzene precursors and catalysts 151

A subset of samples (n=165) was analyzed for benzoic acid, ascorbic acid, copper and 152 iron, which were previously discussed to influence benzene formation in food. Benzoic 153 acid was determined using HPLC according to the German reference method (Anon 154 1984). L-Ascorbic acid was analyzed using a commercially available enzymatic test-kit 155 (R-Biopharm, Darmstadt, Germany). Copper and iron were analyzed using atomic 156 absorption spectroscopy, according to the German reference method (Anon 1993). 157 158

Experiment about artefactual benzene formation during headspace sampling 159

The possible formation of benzene during the headspace sampling procedure was 160 studied using a full-factorial experimental design. The experiment was conducted using 161 a model solution containing sucrose (60 g l -1 ), citric acid (4 g l -1 ) and ascorbic acid (0.4 162 g l -1 ). Three variables were studied: benzoic acid concentrations (6 levels: 1, 10, 50, 163 100, 500 and 1000 mg l -1 ), pH (2 levels: 3 and 10) and temperature (2 levels: 50°C and 164 In order to evaluate possible formation mechanisms of benzene in carrot juice with a 168 minimum amount of experiments, the information gleaned from each experiment and 169 the relationships between the experiments have to be fully exploited. This was done 170 using a D-optimal design type [START_REF] Box | Statistics for Experimenters[END_REF][START_REF] Montgomery | Design and Analysis of Experiments[END_REF]). The D-optimal 171 algorithm was used because it chooses an ideal subset of all possible combinations and 172 significantly reduces the number of required experiments compared to standard design 173 types. 174

The carrot juice used in this experiment was freshly prepared from organic carrots using 175 a juice extractor (centrifuge-type, Starmix, Reichenbach, Germany). The heating 176 temperature was studied at four levels (unheated, 100°C, 125°C, 150°C) and the heating 177 time was varied at three levels (30 min, 60 min, 120 min). The experiments were 178 conduced with 10 ml of juice directly in the headspace vials that were measured 179 afterwards without opening to avoid any loss of benzene. 180 181

Statistics 182

The experimental designs and calculations were done using the Software Package 183 Design Expert V6 (Stat-Ease Inc., Minneapolis, Minnesota, USA). The experiments 184 were evaluated using Analysis of Variance (ANOVA) to find the significance of 185 variables and their interactions in the models. The models were checked for consistency 186 by looking at the lack of fit and possible outliers. 187

The survey data were evaluated using standard statistical packages for Windows. 188

Statistical significance was assumed at below the 0.05 probability level. Groups of two 189 cases were compared using t-tests. One-way ANOVA was used to test whether three or 190 more cases had the same mean including the Bonferroni post hoc means comparison. 191

Results and Discussion

197

Artefactual benzene formation during headspace sampling 198

The results of the headspace sampling experiment are shown in Figure 1. The ANOVA 199 results prove that benzoic acid concentration (p=0.0008), as well as pH (p<0.0001) and 200 temperature (p<0.0001) all have a significant influence on benzene formation. However, 201 benzene formation that would lead to false-positive results (that is, results above LOQ 202 of 0.13 µg l -1 ) only occurs at low pH values in combination with high temperatures and 203 high concentrations of benzoic acid. Our results verify the observation by Cao et al. 204 (2007) that formation of artefacts may occur at 100°C in a 0.5 h time-period, but not at 205 22-24°C. We suppressed artefactual benzene formation by setting the incubation 206 temperature during headspace GC to 50°C and by pH adjustment of the samples to pH 207 10. All samples in our market survey and formation experiments were measured under 208 these conditions. 209

[Insert Figure 1 about here] 210 211

Survey of benzene in German foods 212

The results of the determination of benzene in different soft drinks, drinks intended for 213 babies and infants (drinks with fruits, vegetables and/or tea), alcopops, beer-mixed 214 I about here] 217 218 Using ANOVA, it was proved that benzene concentrations in different food groups 219 showed highly significant differences (p<0.0008). The concentrations in carrot juices 220 and especially in carrot juices intended for infants were higher than those in all other 221 groups of beverages. The carrot juices for normal consumption contained average 222 benzene in concentrations below 1 µg l -1 , whereas carrot juices intended for infants had 223 significantly (p<0.0001) higher concentrations clearly above 1 µg l -1 . Approximately 224 88% of all carrot juices for infants had benzene contaminations above the EU drinking 225 water limit of 1 µg l -1 (European Council 1998). 226 227The lowest concentrations were detected in other drinks for babies and infants as well as 228 beer-mixed drinks. Soft drinks and alcopops contained slightly higher values. However, 229 only 2% of all soft drink samples had benzene contaminations above the EU limit, and 230 only one sample had a very high benzene concentration of 41.8 µg l -1 , which was also 231 above the WHO limit of 10 µg l -1 . We were unable to provide an explanation for this 232 high level in the soft drink, and the levels of the analyzed precursors and catalysts were 233 inconspicuous. The samples from the same product charge, which were simultaneously 234 analyzed, had benzene levels below 1 µg l -

Precursors and catalysts influencing benzene formation in beverages 237

A multivariate data analysis was conducted to gain a first overview of the dataset of 165 238 samples analyzed for benzene and the possible precursors and catalysts benzoic acid, 239 ascorbic acid, copper and iron. The results of the principal components analysis (PCA) 240 are shown in Figure 3. The correlation loadings plot shows that benzene appears to be 241 closely correlated to copper and iron, which influence the PC1 component. Benzoic acid 242 and ascorbic acid are inversely correlated on the PC2 component. The results of PCA 243 were confirmed by univariate data analysis (Table II). The copper and iron 244 concentrations were significantly higher in benzene-positive samples than in benzene-245 negative samples. Table III shows that the concentration of benzene has a relation to that 246 of copper and iron. However, there also appear to be other -so far unknown -influences 247 as the metal concentrations show a large dispersion, and some benzene-free cases had 248 higher metal concentrations than benzene-positive ones. The influence of copper and 249 iron was not unexpected due to the experiments in model solutions by Gardner et al. 250 (1993). Our study suggests that a metal-catalyzed formation mechanism at least 251 influences benzene formation in real food. We also can partially confirm the observation 252 of [START_REF] Gardner | Benzene production from decarboxylation of benzoic 395 acid in the presence of ascorbic acid and a transition-metal catalyst[END_REF] that there may possibly be non-linear effects or confounding 253 between different constituents on the formation on benzene. For example, Gardner et al. 254 (1993) detected a maximum of benzene production at 1.0 nM CuSO 4, higher 255 concentrations of this metal resulted in a small decrease in benzene concentration. And 256 even when there was no CuSO 4 in the solution, significant amounts of benzene were 257 still produced. In the same study, it was also observed that addition of FeSO 4 in the 258 absence of CuSO 4 led to benzene formation, but at higher concentrations of FeSO 4 the 259 benzene production decreased. In model solutions, there appears to be a dependency 260 appears to be true for our real food matrices; however, the analysis of our data was 262 unable to detect interactions between both metals or a certain metal concentration that 263 leads to maximum benzene formation. Presumably, other influences (e.g. temperature) 264 may have larger influences than the metals and lead to confounding of the data. 265

[Insert Figure 3 & Tables II and III about here] 266

In contrast to the clear influence of copper and iron, benzoic acid concentration tended 267 to be higher in benzene positive samples but not on a statistically significant level 268 (Table III). The PCA scores plot (Figure 3) shows that only some soft drink samples 269 containing benzoic acid were also high in benzene. But our results show that the 270 presence of benzoic acid was not causative for benzene formation in our samples and 271 that other mechanisms appeared to be more predominant. No relation at all could be 272 proven between ascorbic acid and benzene. Our results proved the inconsistency 273 detected between levels of benzene and benzoate salts or ascorbic acid previously 274 described by [START_REF] Cao | Determination of benzene in 370 soft drinks and other beverages by isotope dilution headspace gas 371 chromatography/mass spectrometry[END_REF]. In contrast, earlier studies had confirmed a clear 275 connection between benzoic acid and benzene, which was dependent on the 276 concentration of benzoic acid in the beverages and showed large interactions with the 277 concentration of ascorbic acid [START_REF] Gardner | Benzene production from decarboxylation of benzoic 395 acid in the presence of ascorbic acid and a transition-metal catalyst[END_REF][START_REF] Page | A Survey of Benzene in Fruits 422 and Retail Fruit Juices, Fruit Drinks, and Soft Drinks[END_REF]McNeal et al., 278 1993). Therefore, it is likely that due to the efforts of the beverage industry to avoid 279 benzoic acid especially in combination with ascorbic acid, the benzene formation 280 mechanism from those precursors is nowadays only of secondary relevance, but still 281 may be causative if high concentrations of benzoic acid are used as preservative. an amount unacceptable from the public health viewpoint and in particular at a 320 toxicological level shall be placed on the market. Furthermore, contaminant levels shall 321 be kept as low as reasonably can be achieved by following good practices. 322 323 It is also reasonable to apply the EU drinking water limit of 1 µg l -1 as basis to evaluate 324 other beverages. According to our results, it is possible to manufacture beverages with 325 benzene contamination below 0.5 µg l -1 . Therefore we think that soft drinks with 326 benzene contamination above 1 µg l -1 are in offence against Council Regulation (EEC) 327

No 315/93 and should be removed from the market. 328 In general, our study confirms the established view that food is not an important 331 pathway for benzene exposure [START_REF] Wallace | Environmental exposure to benzene: An update[END_REF]. However, some food groups seem to 332 have a higher risk of benzene contamination than others, and a systematic study of 333 exposure to benzene in the complete human food chain is missing so far. In our study, 334 carrot juices formed the food group with the highest susceptibility to benzene 335 contamination due to heat-induced formation. In the nutrition of babies and infants, 336 carrot juices belong to the standard program as a complimentary food for infants from 337 four months, when breast milk or infant formulas alone can no longer meet the baby's 338 growing nutritional requirements. For this reason, significant consumption of carrot 339 juice per kg of bodyweight is possible in infants. It would be certainly interesting to 340 study if the exposure to benzene containing baby food may provide an answer to the 341 elevated levels of benzene biomarkers in children, whose origin was so far unexplained. 342 [START_REF] Johnson | A critique of benzene exposure in the general 407 population[END_REF]). As toxicology does not allow establishment of safe benzene levels 343 above zero, any exposure at all to benzene and benzene-containing products should be 344 avoided [START_REF] Mehlman | Carcinogenic effects of benzene: Cesare Maltoni's contributions[END_REF]. This is especially true in the case of exposure of children to 345 benzene. Since there are two infant carrot juices without benzene contamination in our 346 The correlation loadings show that benzene is highly correlated to both iron and copper 469 concentrations. 470 471 Figure 4. Heat-induced formation of benzene in carrot juice. Significant amounts of 472 benzene are formed if the carrot juice is heated at temperatures higher than 100°C for 473 over 30 min. Freshly pressed organic carrots without any additives were used for this 474 experiment. 475

  of benzene in food usually apply GC/MS and different 53 sample clean-up techniques like liquid-liquid extraction

  infants and young children in the sense of Commission Directive 2006/125/EC 115 (Commission of the European Communities, 2006). 116 117

  test was used to evaluate the significance of linear relations.Box and whisker 192 plots were used for visualization of data (box 25 th -75 th percentile, line in the box: 193 median, whiskers: minimum and maximum (max. 1.5 times the length of the inner 194 quartiles), data points outside are outliers). 195 196

  well as carrot juices for the general consumer and infants are shown in 215 Table I and Figure 2. 216 [Insert Figure 2 & Table

  of the various constituents and benzene formation. The same 261

  0007) and heating time (p=0.0020), which supports our theory. The formation of 307 benzene in carrot juices appears to be predominantly heat-induced. It is so far unknown 308 if the metals play a role in this heat-induced mechanism. Further research is needed to 309 determine if elimination of the metals could minimize the benzene formation during the exceptions, benzene exposure of the consumer due to soft drinks and 315 alcoholic beverages on the German market appears to be very low and nearly negligible 316 in consideration of the exposition to benzene from other sources. According to Council 317 Regulation (EEC) No 315/93 laying down Community procedures for contaminants in 318 food (Council of the European Communities 1993), no food containing a contaminant in 319

  Figure Legends454 455 Figure 1. Artefactual benzene formation during headspace analysis. The experiment was 456

  . The U.S. Food and Drug Administration (FDA) tested 76 more than 100 soft drinks and other beverages from the United States. Almost all of 77

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	101	Between 2006 and 2007, 451 samples submitted to the CVUA Karlsruhe have been
	102	analysed for benzene. As part of official food control in the German federal state Baden-
	103 104	Württemberg, our institute covers the district of Karlsruhe in North Baden (Germany) F r o with a population of approximately 2.7 million. The sampling has been done by local
	105 106	authorities directly at food producers or retail trade. The samples have been randomly P e selected and collected by government food inspectors, as our institute has requested only
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