Food and Chemical Toxicology 47 (2009) 2890–2898

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Food and Chemical Toxicology

journal homepage: www.elsevier .com/ locate / foodchemtox

Probabilistic acute dietary exposure assessments to captan and tolylfluanidusing several European food consumption and pesticide concentration databases

Polly E. Boon a,*, Kettil Svensson b, Shahnaz Moussavian b, Hilko van der Voet c, Annette Petersen d,Jiri Ruprich e, Francesca Debegnach f, Waldo J. de Boer c, Gerda van Donkersgoed a, Carlo Brera f,Jacob D. van Klaveren a, Leif Busk b

a RIKILT – Institute of Food Safety, Wageningen University and Research Centre, Wageningen, The Netherlandsb National Food Administration, Research and Developmental Department, Uppsala, Swedenc Biometris, Wageningen University and Research Centre, Wageningen, The Netherlandsd National Food Institute, Technical University of Denmark, Department of Toxicology and Risk Assessment, Søborg, Denmarke National Institute of Public Health, Prague, CHFCH Brno, Czech Republicf Istituto Superiore di Sanità, Rome, Italy

a r t i c l e i n f o

Article history:Received 17 August 2008Accepted 27 January 2009

Keywords:Electronic platformHarmonisationFood consumption dataConcentration dataProbabilistic modelling

0278-6915/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.fct.2009.01.040

Abbreviations: BW, body weight; CZ, Czech ReEuropean Food Safety Authority; E-platform, electrounion; IT, Italy; LOR, limit of reporting; MCRA, Mprogram; NL, Netherlands; RAC, Raw agricultural comWorld Health Organisation.

* Corresponding author. Tel.: +31 317 480 379; faxE-mail address: polly.boon@wur.nl (P.E. Boon).

a b s t r a c t

Probabilistic dietary acute exposure assessments of captan and tolylfluanid were performed for the pop-ulations of the Czech Republic, Denmark, Italy, the Netherlands and Sweden. The basis for these assess-ments was national databases for food consumption and pesticide concentration data harmonised at thelevel of raw agricultural commodity. Data were obtained from national food consumption surveys andnational monitoring programmes and organised in an electronic platform of databases connected toprobabilistic software. The exposure assessments were conducted by linking national food consumptiondata either (1) to national pesticide concentration data or (2) to a pooled database containing all nationalpesticide concentration data. We show that with this tool national exposure assessments can be per-formed in a harmonised way and that pesticide concentrations of other countries can be linked tonational food consumption surveys. In this way it is possible to exchange or merge concentration databetween countries in situations of data scarcity. This electronic platform in connection with probabilisticsoftware can be seen as a prototype of a data warehouse, including a harmonised approach for dietaryexposure modelling.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Internationally, harmonisation of risk assessment proceduresregarding adverse chemicals present in food has been and is stilldiscussed by e.g. the European Food Safety Authority (EFSA) (EFSA,2007a), the European Union (EU) (SSC, 2000, 2003), and the WorldHealth Organisation (WHO) (IPCS, 2004). An important part of thisprocess is the harmonisation and accessibility of national food con-sumption and chemical concentration data, as well as a harmo-nised procedure to estimate the exposure (as part of the riskassessment) to these chemicals. Regarding the quality and avail-ability of harmonised food consumption data in Europe progress

ll rights reserved.

public; DK, Denmark; EFSA,nic platform; EU, European

onte Carlo Risk Assessmentmodity; SE, Sweden; WHO,

: +31 317 417 717.

was made in the EFCOSUM project (Brussaard et al., 2002). Thisproject described the quality of food consumption databases usedin the EU, showing that the collection of food consumption datawithin Europe is not harmonised (Verger et al., 2002). This diver-sity is related to the population addressed (e.g. children includedor not), method of data collection (24-h recall, dietary method),duration of the survey, number of respondents involved, categori-sation of food consumption data and method of quantifyingamount consumed (actual weighing vs. estimations on the basisof portion sizes). The EFCOSUM project identified the 24-h recallmethod as the most suitable method to obtain internationallycomparable new data on population means and distributions of ac-tual food intake, to be conducted at least twice per respondent(Brussaard et al., 2002). For the estimation of infrequently con-sumed foods, it was recommended to use food frequencyquestionnaires.

Chemical concentration data are collected within Europe atMember State level and are known to differ in quality and avail-ability due to lack of a harmonised procedure of collection. In view

P.E. Boon et al. / Food and Chemical Toxicology 47 (2009) 2890–2898 2891

of this the Scientific Committee of EFSA (EFSA, 2005) recom-mended that EFSA should try to get access to chemical concentra-tion data within Europe and create a European data warehouse,where the information is either stored or made accessible throughlinks to existing databases. In addition to the lack of harmonisationof the methods used to obtain food consumption and chemicalconcentration data, also the manner in which risk assessments(including exposure assessments) are performed between varioustypes of chemicals is not harmonised. This was already recognizedby the former EU Scientific Steering Committee which stressed theneed for harmonised risk assessment procedures in Europe (SSC,2000, 2003).

Building on these European developments we developed anelectronic platform (or E-platform) of national food consumptionand chemical concentration databases from several EU countries(the Czech Republic, Denmark, Italy, the Netherlands, and Sweden).The databases in the E-platform are connected via internet to prob-abilistic software for dietary exposure modelling. With this plat-form exposure assessments were performed in a harmonisedway. The categorisation of the different databases was harmonisedas described in Boon et al. (2009), making it possible to merge orexchange concentration data between countries, so that countrieswith data scarcity can supplement their data with that of othercountries. In the E-platform concentration databases are presentregarding different adverse chemicals (pesticides, mycotoxinsand glycoalkaloids). In this paper we focus on concentration dataof pesticides.

The E-platform has already been used and described in 2007 byEFSA in an opinion on acute dietary exposure (EFSA, 2007b). Sincethen, the platform has been updated and made more complete. Theaim of this paper is to demonstrate the potentials of the E-platformin combination with probabilistic software by analysing the resultsof probabilistic dietary exposure assessments using several Euro-pean food consumption and pesticide concentration databases. Insensitivity analyses we address how the results are affected byassumptions regarding the concentration assigned to samples witha concentration below limits of reporting (LORs) such as encoun-tered in practice, and by inclusion of factors for processing effectsand unit variability in the assessment (FAO/WHO, 1997; Harriset al., 2000). This work presents a platform that can be seen as aprototype of a data warehouse, including a harmonised approachfor dietary exposure modelling.

2. Materials and methods

2.1. National food consumption surveys

All national food consumption databases included in the E-platform are used fornational food safety purposes in the respective countries. The data used in this pa-per are described shortly below (see also Table 1). For more detailed descriptionswe give references per food consumption survey.

2.1.1. Czech Republic (CZ)In CZ a food consumption survey (SISP04) was conducted between Novem-

ber 2003 and 2004 covering a 1-year period (Ruprich et al., 2006). In this study2177 persons aged 10–90 years and 413 persons aged 4–9 years were asked

Table 1Characteristics of national consumption data from Czech Republic (CZ), Denmark(DK), Italy (IT), the Netherlands (NL) and Sweden (SE).

Country Year Method of consumptiondata collection

Population

Age (y) Number ofrespondents

CZ 2003–2004 2 � 24 h recall 4–90 2590DK 2000–2003 7-d record 4–75 4120IT 1994–1996 7-d record 0–94 1978NL 1997–1998 2-d record 1–97 6250SE 1997–1998 7-d record 18–75 1211

about their eating habits via two 24-h recalls. The repeated recall was withina period of 1–6 months after the first recall and addressed another day ofthe week. Amounts consumed were estimated using either photographs of por-tions for the most frequently consumed meals, or measuring guides, such asspoons and cups.

2.1.2. Denmark (DK)For DK food consumption levels derived from the National Food Consumption

Survey conducted in 2000–2003 were used (Lyhne et al., 2005). In this survey4068 persons aged 4–75 years were asked to record their food consumption duringseven consecutive days using the 7-d dietary record method. Amounts consumedwere estimated using photographs of portion sizes or household measures (e.g.cups and spoons).

2.1.3. Italy (IT)For IT food consumption data collected by the Italian National Institute of Nutri-

tion (INRAN) during the period of 1994–1996 was used (Turrini et al., 2001). In thissurvey information on food consumption levels was gathered for 1978 individualsaged 0–94 years during seven consecutive days using the 7-d dietary record meth-od. Amounts consumed at home were weighed using precision scales, while foodsconsumed outside were estimated using photographs.

2.1.4. Sweden (SE)Food consumption data from SE was that of the ‘Riksmaten’ study (Becker and

Pearson, 2002). This is a dietary study performed in 1997–1998 among 1211respondents aged 18–75 years. Participants were asked to record their food con-sumption during seven consecutive days using the 7-d dietary record method. Asin Denmark, amounts consumed were estimated using photographs of portion sizesor household measures.

2.1.5. The Netherlands (NL)The food consumption data from the Netherlands was that of the Dutch Na-

tional Food Consumption Survey of 1997–1998 (Anonymous, 1998; Kistemakeret al, 1998). In this survey 6250 persons aged 1–97 years recorded their food intakeover two consecutive days using the 2-d dietary record method. Amounts con-sumed were weighed accurately.

All the national food consumption databases covered all seasons of the year aswell as all days of the week, excluding holidays and festive periods due to diver-gent food habits during those periods. Apart from food consumption data alsonon-food characteristics were obtained in all surveys, including sex, age and bodyweight.

The food consumption data of the countries was harmonised at the level of rawagricultural commodity (RAC), as described in Boon et al. (2009). This approach waschosen because chemical analyses of pesticides are predominantly performed inRACs, and defined RACs are comparable between countries. In short, foods classifiedin the national food consumption surveys were converted to RACs using the Dutchfood conversion model (Van Dooren et al., 1995; Boon et al., 2009). This food con-version model was chosen because it was the most elaborate food conversion modelavailable. In this model, amounts of foods classified in the Dutch food consumptionsurvey are converted to equivalent amounts of their RAC ingredients. So for exam-ple, 100 g of food ‘spinach, cooked’ is converted to 167 g RAC ‘spinach’. By linkingfoods classified in the national food consumption surveys of CZ, DK, IT and SE tothe most appropriate Dutch food, resembling best the food in question, as manyfoods as possible were translated to their RAC ingredient(s). These RAC ingredientswere classified according to the FAO/WHO Codex Classification of Foods and AnimalFeeds (Codex Alimentarius, 1993). This resulted in five national food consumptiondatabases with food intake estimates converted to RAC intake estimates and withRACs classified in a harmonised way. By using the same classification system forRACs analysed for pesticide residues in monitoring programmes (see Section 2.2),pesticide concentration data can be linked to consumption. For more details seeBoon et al. (2009).

Age groups addressed in the national food consumption surveys differed. Rec-ognizing the effect of age on exposure (especially children have a higher level ofexposure per kg body weight), we selected the same age range for each survey toperform the dietary exposure calculations. The common age group available forall countries was 18–75 years (Table 1).

2.2. National monitoring programmes for pesticides

EU Member States regularly perform analyses of pesticides on RACs destined forhuman consumption to monitor the occurrence of pesticide residues and to checkcompliance of RACs with the maximum residue limits as set in Regulation (EC)No. 299/20081. These analyses are performed as part of national monitoring pro-grammes undertaken by the Member States’ authorities and as part of an EU-wideprogramme co-ordinated by the European Commission (EC, 2007). In this paper we

1 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:097:0067:0071:EN:PDF.

Table 2National monitoring concentrations of captan and tolylfuanid in Czech Republic (CZ), Denmark (DK), Italy (IT), the Netherlands (NL) and Sweden (SE), analysed in 2002–2003.

Country Captan Tolylfluanid

Number of RACsa

analysedNumber of samplesanalysed

Number of samplesPLORb

Number of RACsanalysed

Number of samplesanalysed

Number of samplesPLOR

CZ 65 (8)c 1291 18 (1.4)d 65 (4) 1312 11 (0.8)DKe 77 (4) 639 42 (6.6) 77 (6) 437 68 (16)IT 59 (8) 6337 151 (2.4) – – –NL 95 (15) 4091 236 (5.8) 95 (16) 4211 336 (8.0)SE 69 (7) 1405 180 (13) 69 (8) 1370 126 (9.2)

a RAC = raw agricultural commodity.b LOR = limit of reporting.c Between brackets the number of RACs with at least one sample with a level PLOR.d Between brackets the percentage of samples with a level PLOR.e Data from 2003.

2 http://www.who.int/foodsafety/chem/gems/en/.

2892 P.E. Boon et al. / Food and Chemical Toxicology 47 (2009) 2890–2898

consider national monitoring results of two pesticides, captan and tolylfluanid, sam-pled in the period 2002–2003. All countries in our study had monitoring results forthese two pesticides, except Italy where tolylfluanid was first permitted in 2004and not monitored before. Table 2 gives an overview of the concentration data percompound and country. The RACs analysed were all classified via the Codex Classifi-cation system (Codex Alimentarius, 1993), in order to link concentrations to nationalconsumption levels (see Section 2.1), and to allow pooling of concentration data in apesticide concentration database containing the monitoring results of all countries inthis study.

When reporting pesticide concentrations analysed in monitoring programmesto national governments or the EU, actual concentrations are only reported whenthey exceed a certain level. In practice, it is often unclear what this level represents.In this paper we term this level the limit of reporting (LOR), and we assume no spe-cial properties for it. The LOR should not be confused with well-defined perfor-mance characteristics of the analytical method, e.g. the limit of detection (LOD)or the limit of quantification (LOQ), which are levels of reliable detection and reli-able quantification, respectively (Mocak et al., 1997). The LOR is just the concentra-tion below which results were reported as ‘less-than’ in the national monitoringprogrammes. In the exposure assessments reported in this paper, samples with con-centrations below LOR were assumed to contain no pesticide, unless stated other-wise. We addressed the sensitivity of the exposure to this assumption in asensitivity analysis (see Section 2.4).

2.3. Dietary exposure assessment model

We calculated the exposure to captan and tolylfluanid in CZ, DK, IT, NL and SEby combining national consumption databases at RAC level with either nationalpesticide concentration databases or a merged pesticide concentration databasecontaining all national pesticide concentrations of the five countries. To performthese calculations in a harmonised way, all national exposures were assessed usingthe same program, namely the Monte Carlo Risk Assessment program (MCRA), ver-sion 6.1. This program is part of the E-platform and available for registered users(De Boer and Van der Voet, 2007). MCRA implements statistical methods that coverboth acute and chronic dietary exposure. In this paper we studied the acute dietaryexposure. MCRA uses for this Monte Carlo simulations in which daily consumptionpatterns of RACs are re-sampled from a consumption database with food intakeestimates converted to RAC intake estimates and combined with concentrationssampled randomly from a pesticide concentration database for the relevant RACsconsumed. Summing over RACs provides an empirical estimate of the acute dietaryexposure distribution for the compound of interest. All estimated exposures are ad-justed for the individual’s self-reported body weight (BW) and expressed as dailyexposure in lg/kg BW/d. The reported percentiles of the exposure distributionare P50, P95, P97.5, P99, and P99.9. The number of Monte Carlo simulations peranalysis was 100,000.

This procedure was followed for the different countries to estimate the acutedietary exposure to captan and tolylfluanid. To quantify the uncertainties in theexposure calculations due to sampling uncertainty of consumption and concentra-tion data, the bootstrap method (Efron, 1979; Efron and Tibshirani, 1993) was used.With this method a bootstrap database is generated of the same size as the originaldatabase for both the consumption and concentration database by sampling withreplacement from the original databases. These two bootstrap databases are thenused for the Monte Carlo acute exposure calculation and derivation of the relevantpercentiles. Repeating this process 100 times resulted in a bootstrap distribution foreach percentile, which was used to derive a confidence interval around it. Thus, afull exposure assessment consisted of generating 100 consumption and 100 concen-tration bootstrap databases and calculating the exposure with 100,000 iterationsfor each of the 100 cases. From the resulting bootstrap distributions per percentilewe obtained a 95% uncertainty interval around each percentile by computing the2.5% and 97.5% points of the distribution.

2.4. Sensitivity analyses

Sensitivity analyses were performed using the consumption and concentrationdatabase from the Netherlands. This country was selected because preliminary re-sults already showed that it had the highest national exposures for both pesticides(see Section 3.3).

2.4.1. ProcessingPesticide analyses are performed mostly in RACs which generally undergo a cer-

tain form of processing before consumption. Processing is known to affect pesticideconcentrations, mainly leading to a reduction (e.g. FAO, 2005; Zabik et al., 2000).Ignoring the effect of processing may therefore result in an overestimation of theexposure, but may at times also underestimate pesticide concentrations in con-sumed foods (e.g. dried foods like raisins may contain higher concentrations thanthe RAC grape).

In a sensitivity analysis we examined how the use of processing factors affectedthe exposure to captan and tolylfluanid in the Netherlands. We used for this theprocessing factors from the EFSA opinion on acute dietary exposure (EFSA,2007b; Table 3). Whereas MCRA also allows for describing processing effects witha distribution, fixed processing factors were applied in the current assessments.

2.4.2. Variability in pesticides concentrations between individual units of compositesamples

Pesticide analyses in monitoring programs are typically performed in compositesamples of RACs (e.g. apples are analysed in samples consisting of 12 units each),while people usually consume smaller portions of a fruit or vegetable (e.g. one unit).Concentrations analysed in a composite sample may originate from just one unit ofthe commodity, and consumers may thus be confronted with such a concentrationin a single unit (e.g. one apple) rather than the averaged concentration as analysedin composite samples. To account for this possibility, unit variability factors wereintroduced in acute dietary exposure assessment of pesticides. The variability factoris defined as the P97.5e divided by the mean of the distribution of concentrationsfound for single units of a RAC (FAO, 2002). In this paper, we use the same variabil-ity factors as normally used in the deterministic approach: 7 for RACs with a unitweight between 25 and 6250 g, 5 for RACs with a unit weight above 250 g and 3for RACs head cabbage and head lettuce. For the unit weights of the RACs we usedthose collected by the WHO and published on the WHO website (updated May 1,20032). For RACs with a missing unit weight we selected the most likely unit weightof a similar RAC.

In the point estimate approach, used for worst case approaches per RAC in apreventive paradigm, one fixed level of the variability factor is applied. In the prob-abilistic approach however, where all RACs are considered together for the calcula-tion of the total exposure, this would be over-conservative: it is unlikely that onewould eat the unit of every RAC containing the highest concentration. Therefore,an approach was developed which simulates the variability of pesticide concentra-tions on units in a composite sample by defining the variability factor as a modelparameter (De Boer and Van der Voet, 2007; Boon et al., 2008). In short, a beta dis-tribution of unit pesticide concentrations in a composite sample is assumed. Thisdistribution is bounded by 0 and the highest concentration possible for one of theunits of the composite sample, which is the composite sample concentration mul-tiplied with the number of units in the sample. The beta distribution has twoparameters, which we took to be the composite sample mean concentration andthe variability factor. Together with the maximum value these parameters deter-mine the shape of the distribution. For a variability factor very close to 1 the distri-bution resembles a single spike around the composite mean concentration. Forlarger variability factors the distribution will become broader, but is still bounded

Table 3Processing factors for captan and tolylfluanid per raw agricultural commodity (RAC)and processing type.

RAC Processing type Processing factor

Captan Tolylfluanid

Apple Canned/conserved – 0Juicing 0.9 0.1Marmelade/jam – 0Sauce/puree 0.8 0.4

Aubergine/egg plant Juicing – 0.4Sauce/puree – 1.1

Currant (red, white, black) Juicing – 0.6Marmelade/jam – 0.3Washing/cleaning – 0.8

Gooseberry Juicing – 0.6Marmelade/jam – 0.3Washing/cleaning – 0.8

Pear Canned/conserved – 0Juicing – 0.1Marmelade/jam – 0Sauce/puree – 0.4

Strawberry Canned/conserved – 0.2Marmelade/jam – 0.2Washing/cleaning – 0.6

Grape Drying – 3.3Juicing – 0.9

Tomato Juicing 0.5 0.4Canned/conserved 0.5 –Sauce/puree 1.2 1.1

P.E. Boon et al. / Food and Chemical Toxicology 47 (2009) 2890–2898 2893

by the lower and upper values. The number of units in a composite sample was de-rived from EU guidance document ‘Guidelines for the generation of data concerningresidues as provided in Annex II part A, Section 6 and Annex III, part A, Section 8 ofDirective 91/414/EEC concerning placing plant protection products on the market’,Appendix B3.

2.4.3. Concentrations assigned to samples with concentrations below the limit ofreporting

For some samples it is only reported that the pesticide concentration is below acertain limit, here designated the limit of reporting (LOR). Such samples are oftencalled non-detects, but in reality the concentration can be anywhere between zeroand LOR. When calculating the exposure to pesticides, assumptions have to bemade regarding the concentration to be assigned to these non-detects. In the sen-sitivity analyses we studied the influence on the percentiles of exposure of assign-ing ½LOR and LOR to these samples, instead of zero. In the Dutch database the LORof captan and tolylfluanid was 0.05 mg/kg for all RACs.

2.5. An electronic platform of national consumption and concentration databasesconnected to software for probabilistic modelling (E-platform)

The E-platform consists of a central data server, that also runs the software, andof a network of decentralized data servers where the input data is located, like foodconsumption data, concentration data and other relevant input variables (e.g. pro-cessing factors). These data servers can be located everywhere, for example at thenational institutes responsible for maintaining and updating the input databases.The servers can be accessed by the central system via internet. Databases can bestored in three ways: (1) at a shared area on the central data server; (2) at a userarea on the central server, or (3) on any of the decentralized data servers. This struc-ture allows for an optimal assignment of tasks respecting responsibilities for riskassessment and database maintenance.

3. Results

3.1. National assessments of acute dietary exposure to captan andtolylfluanid

In Table 4 the estimated acute dietary exposures to captan forpopulations aged 18–75 years in CZ, DK, IT, NL and SE are listed,as well as the five RACs contributing most to the upper 5% of the

3 http://ec.europa.eu/food/plant/protection/resources/app-b.pdf.

exposure distribution. In Fig. 1A the percentiles of exposure to cap-tan are shown graphically. The exposure to captan was zero up tothe P90 in all countries (Table 4). From P90 onwards there was anever more pronounced difference in exposure with the highestexposure in NL. The uncertainty analyses show that the Dutchexposure percentiles from P90 onwards were outside the confi-dence intervals of the corresponding exposure percentiles of theother countries (Table 4). Apple was the dominating source ofexposure to captan for all five countries, followed by grape andpear (Table 4).

In Fig. 1B the estimated exposure for captan in the populationsincluding all ages present in the national consumption databases(see Table 1) were plotted. In comparison with the previous results(Fig. 1A), for two populations (NL and CZ) the exposure was almostdoubled.

In Table 5 and in Fig. 2A and B similar results are presented fortolylfluanid. As for captan, the highest exposure to tolylfluanid wasfound for NL, but less pronounced than for captan. Apple was againthe dominating source of exposure for all four countries (Table 5).For NL and DK also pear contributed largely to the exposure (>25%).

3.2. Linking national food consumption data to pooled concentrationdatabase

Table 6 lists the estimated exposure percentiles per country forcaptan and tolylfluanid when national consumption data werelinked to the pooled pesticide concentration database. Calculationswere again performed for the populations aged 18–75 years. Formost countries the exposure to captan increased at the higher per-centiles when sampling concentrations from the pooled databaseinstead of the national database (Tables 4 and 6). However, forNL the exposure decreased. For tolylfluanid the P99.9 of exposuresincreased significantly for DK when sampling from the pooled con-centration database, while for the other countries the exposurewas hardly affected (Tables 5 and 6).

3.3. Sensitivity analyses

The resulting exposures in the sensitivity analyses were com-pared to the basic scenario (see Section 3.1), in which no process-ing and variability factors were applied, and samples with aconcentration below limit of reporting (LOR) were assumed to con-tain no pesticide residue.

3.3.1. Processing and unit variabilityThe influence of incorporating the effect of processing and/or

unit variability on the exposure to captan or tolylfluanid was stud-ied in the Dutch adult population, aged 18–75 years. The resultsare listed in Table 7. Including processing in the assessment de-creased the exposure, but only slightly, and resulted in exposureestimates that were still within the 95% confidence interval ofthe basic scenario. Including unit variability in the assessment in-creased the exposure significantly for the P99.9 of the distributionfor both compounds. This percentile was even outside the 95% con-fidence interval of the basic scenario. Including both input vari-ables, which is the most realistic situation, P99.9 for bothcompounds was higher than the estimates of the basic scenario,and comparable to the scenario with only unit variability.

3.3.2. Concentrations assigned to samples with a concentration belowlimit of reporting

The effect of assigning either ½LOR or LOR to samples with apesticide concentration below LOR on the upper percentiles ofexposure is listed in Table 7. P99 and P99.9 were not affected inboth scenarios compared to the situation where these sampleswere assumed to contain no residue.

CZ DKIT

NLSE

P50 P9

0 P95 P9

9P9

9.90

5

10

15

20

25

Country Percentile of exposure

CZ DKIT

NLSE

P50 P9

0 P95 P9

9P9

9.90

5

10

15

20

25

Exp

osur

e (u

g/kg

BW

/d)

Country Percentile of exposure

A B

Fig. 1. (A) National percentiles of acute dietary exposure to captan (in lg/kg BW/d) calculated by linking national consumption data for the adult population (18–75 years)with national captan concentrations of Czech Republic (CZ), Denmark (DK), Italy (IT), the Netherlands (NL) and Sweden (SE). (B) As in 1A but now for the populations withages as given in Table 1. Samples with a concentration below the limit of reporting were assumed to contain no pesticide.

Table 4National percentiles of acute dietary exposure to captan (in lg/kg BW/d) calculated by linking national consumption data for the adult population (18–75 years) with nationalcaptan concentrations of Czech Republic (CZ), Denmark (DK), Italy (IT), the Netherlands (NL) and Sweden (SE), including the 95% lower and upper confidence limit. On the rightside the top 5 of raw agricultural commodities (RACs) contributing most to the upper 5% of the exposure distribution are listed per country. Samples with a concentration belowlimit of reporting were assumed to contain no pesticide.

Country Percentiles of exposure (lg/kg BW/d) RACs contributing most to the upper 5% of exposuredistribution (relative contribution (%))

P50 P90 P95 P99 P99.9 1 2 3 4 5

CZ 0.00 0.00 0.00 1.00 4.3 Apple Grape Pear Lettuce head Apricot(0.00–0.00) (0.00–0.00) (0.00–0.22) (0.32–1.74) (1.5–7.5) 70 12 9.3 5.5 1.4

DK 0.00 0.00 0.03 0.33 1.5 Apple Pear Grape Strawberry –(0.00–0.00) (0.00–0.01) (0.00–0.53) (0.14–0.66) (0.63–2.3) 86 9.4 4.9 0.1

IT 0.00 0.00 0.15 2.1 6.6 Apple Pear Peach Apricot Egg plant(0.00–0.00) (0.00–0.00) (0.08–0.19) (1.62–2.5) (5.4–7.6) 84 14 0.9 0.6 0.6

NL 0.00 0.16 0.58 3.3 11 Apple Pear Strawberry Grape Apricot(0.00–0.00) (0.11–0.22) (0.43–0.75) (2.3–4.2) (8.2–15) 89 3.8 2.3 2.2 1.2

SE 0.00 0.00 0.11 0.98 2.7 Apple Grape Pear Apricot Strawberry(0.00–0.00) (0.00–0.00) (0.03–0.18) (0.62–1.3) (2.0–3.5) 97 1.4 0.5 0.4 0.2

Table 5Percentiles of acute dietary exposure to tolylfluanid (in lg/kg BW/d) calculated by linking national consumption data for the adult population (18–75 years) and nationaltolylfluanid concentrations of Czech Republic (CZ), Denmark (DK), the Netherlands (NL) and Sweden (SE), including the 95% lower and upper confidence limit. On the right sidethe top 5 of raw agricultural commodities (RACs) contributing most to the upper 5% of the exposure distribution are listed per country. Samples with a concentration below limitof reporting were assumed to contain no pesticide.

Country Percentiles of exposure (lg/kg BW/d) RACs contributing most to the upper 5% of exposuredistribution (relative contribution (%))

P50 P90 P95 P99 P99.9 1 2 3 4 5

CZ 0.00 0.00 0.00 0.29 1.9 Apple Grape Pear Strawberry –(0.00–0.00) (0.00–0.97) (0.00–0.00) (0.01–1.1) (0.31–2.70) 89 7.6 2.3 1.2

DK 0.00 0.00 0.07 0.54 1.7 Apple Pear Raspberries Strawberry –(0.00–0.00) (0.00–0.02) (0.03–0.12) (0.30–0.71) (1.02–2.2) 52 45 1.8 1.7

NL 0.00 0.09 0.23 0.70 2.22 Apple Pear Currant Tomato Raspberry(0.00–0.00) (0.07–0.12) (0.19–0.28) (0.57–0.85) (1.6–2.8) 49 28 7.4 7.0 4.7

SE 0.00 0.00 0.01 0.23 1.1 Apple Strawberry Onion Cucumber Pear(0.00–0.00) (0.00–0.09) (0.00–0.04) (0.15–0.35) (0.63–1.5) 81 6.0 5.6 5.0 1.9

2894 P.E. Boon et al. / Food and Chemical Toxicology 47 (2009) 2890–2898

4. Discussion

In this paper the use of an electronic platform (E-platform) ofharmonised national consumption and concentration databasesconnected to probabilistic exposure software is demonstrated.With this platform national exposure assessments can be per-

formed using a harmonised exposure methodology. Also concen-tration data can be shared and used in a national assessment.This E-platform can thus be seen as a prototype of how data shar-ing can be achieved, including a harmonised approach for dietaryexposure modelling. This may be of special interest in the Euro-pean context where harmonisation has become a key issue.

CZDK

NLSE

P50 P9

0 P95 P9

9

P99.

90

1

2

3

4

5

Country Percentile of exposure

CZDK

NLSE

P50 P9

0 P95 P9

9

P99.

90

1

2

3

4

5E

xpos

ure

(ug/

kg B

W/d

)

CountryPercentile of

exposure

A B

Fig. 2. (A) National percentiles of acute dietary exposure to tolylfluanid (in lg/kg BW/d) calculated by linking national consumption data for the adult population (18–75years) with national tolylfluanid concentrations of Czech Republic (CZ), Denmark (DK), the Netherlands (NL) and Sweden (SE). (B) As in 1A but now for the populations withages as given in Table 1. Samples with a concentration below the limit of reporting were assumed to contain no pesticide.

Table 6Percentiles of acute dietary exposure to captan and tolylfluanid (in lg/kg BW/d) calculated by linking national consumption data for the adult population (18–75 years) of CzechRepublic (CZ), Denmark (DK), Italy (IT), the Netherlands (NL) and Sweden (SE) to a pooled concentration database containing concentrations of captan and tolylfluanid of all fivecountries, including the 95% lower and upper confidence limit. Samples with a concentration below limit of reporting were assumed to contain no pesticide.

Country Percentiles of dietary exposure (lg/kg BW/d)

P50 P90 P95 P99 P99.9

CaptanCZ 0.00 0.00 0.14 2.2 8.6

(0.00–0.00) (0.00–0.00) (0.06–0.23) (1.5–2.8) (6.1–12)DK 0.00 0.07 0.48 2.6 9.8

(0.00–0.00) (0.03–0.14) (0.33–0.67) (2.0–3.3) (6.7–13)IT 0.00 0.00 0.36 2.9 8.9

(0.00–0.00) (0.00–0.05) (0.23–0.60) (2.0–4.2) (6.1–11)NL 0.00 0.04 0.25 2.0 7.6

(0.00–0.00) (0.02–0.06) (0.18–0.34) (1.5–2.4) (5.2–9.6)SE 0.00 0.00 0.11 1.5 5.9

(0.00–0.00) (0.00–0.00) (0.03–0.18) (1.1–2.0) (4.1–9.2)

TolylfluanidCZ 0.00 0.05 0.21 0.75 2.0

(0.00–0.00) (0.02–0.08) (0.17–0.28) (0.60–0.91) (1.4–2.5)DK 0.00 0.18 0.38 1.0 2.6

(0.00–0.00) (0.15–0.23) (0.32–0.45) (0.88–1.2) (2.0–3.1)NL 0.00 0.12 0.27 0.79 2.3

(0.00–0.00) (0.09–0.15) (0.23–0.33) (0.65–0.97) (1.6–2.7)SE 0.00 0.06 0.16 0.57 1.5

(0.00–0.00) (0.03–0.08) (0.12–0.21) (0.42–0.69) (1.0–1.9)

P.E. Boon et al. / Food and Chemical Toxicology 47 (2009) 2890–2898 2895

4.1. Exposure assessments using national or pooled concentrations

The national assessments using national consumption and pes-ticide concentration data showed clear differences in estimatedexposures between countries, even when addressing a commonage group (18–75 years). Important factors that could explainthese differences are differences in pesticide concentrations(including monitoring practices and differences in limit abovewhich concentrations are reported) and dietary habits (includingfood classification systems used to record dietary habits, and per-centage of foods converted to raw agricultural commodity(RAC)). Below these variables are discussed in some detail. A fulldiscussion on differences in food consumption and pesticide con-centrations between countries is beyond the scope of this paper.

4.1.1. Pesticide concentrationsWe found a higher exposure to both captan and tolylfluanid in

the Netherlands (NL) compared to the other four countries (Tables

4 and 5). A possible explanation is that the number of RACs ana-lysed for the two compounds was higher in NL, and that NL hada high percentage of RACs with at least one sample with a concen-tration above the limit of reporting (Table 2). Also the analysedconcentrations tended to be higher in NL. In Table 8 we listedthe monitoring results in apple, the RAC contributing most to theexposure of captan and tolylfluanid in all countries (Tables 4 and5). An explanation for this could be a difference in monitoring prac-tices between countries. Sampling in NL could be more targeted atpossibly contaminated crops (e.g. based on past knowledge). Fu-ture research could be aimed at addressing this hypothesis.

Another possible explanation for differences in exposures be-tween countries is a difference in the minimum concentrationabove which concentrations are reported (LOR). In NL concentra-tions are reported when exceeding the concentration at whichthe amount of a compound can be quantified. In the other coun-tries the policy followed by the laboratories was unknown to us.For proper risk assessments of pesticides it is essential that in

Table 7Effect of including processing and unit variability, and assigning a concentration tosamples with a level below the limit of reporting (LOR; 0.05 mg/kg), on percentiles ofacute dietary exposure to captan and tolylfluanid in the Dutch adult population aged18–75 years.

Scenarioa Captan Tolylfluanid

P99 P99.9 P99 P99.9

Basic 3.3 11 0.70 2.2(2.3–4.2) (8.2–15) (0.57–0.85) (1.6–2.8)

Basic + proc 3.1 9.7 0.61 1.7(2.1–4.3) (7.4–12.3) (0.52–0.71) (1.4–2.2)

Basic + var 3.0 17 0.91 3.5(2.5–4.2) (14–24) (0.76–1.1) (3.0–4.5)

Basic + var + proc 3.1 16 0.71 3.4(2.3–4.1) (12–20) (0.61–0.90) (2.5–4.0)

Basic + 1/2LOR 3.2 10 0.78 2.3(2.4–4.3) (7.8–12) (0.66–0.90) (1.8–2.6)

Basic + LOR 3.2 10 0.90 2.5(2.3–4.0) (7.7–12) (0.81–1.0) (1.9–2.7)

a Basic = exposure estimated without processing and unit variability factors inthe calculations, and samples with concentrations below limit of reporting (LOR)equal to zero; Basic + proc = including processing factors; Basic + var = includingvariability factors; Basic + var + proc = including processing and unit variabilityfactors; Basic + ½ LOR = replacement of samples with levels below LOR by ½LOR;Basic + LOR = replacement of samples with levels below LOR by LOR.

2896 P.E. Boon et al. / Food and Chemical Toxicology 47 (2009) 2890–2898

the future the limit concentration(s) below which no pesticide res-idues are reported, should be included in the monitoringdatabases.

4.1.2. Food consumptionFor both pesticides apple is the dominating source of exposure

in all countries. Thus, differences in the amount of apple consumedand percentage of consumption days are key questions to address.Table 8 lists for the five different countries the mean consumptionand the percentage of consumption days for apple. In NL apple wasconsumed more frequently (Table 8). However, the mean appleconsumption was highest in DK.

Differences in estimated consumption levels are due to realdifferences in dietary habits between countries, but also to differ-ences in dietary methods used and food categorisation. In theDanish database, for example, consumptions were recorded atingredient level and not, as in the databases of IT, SE and NL pre-dominantly at food level. In the database of CZ a combination wasused: foods were recorded partly as foods as eaten and partly asingredients. This explains why the number of foods that con-tained RAC ‘apple’ as an ingredient was very low in DK and CZcompared to the other three countries (Table 8). How this differ-ence in food recording affects the estimated exposure is unclear.In the Danish and Czech databases for example it is not clear

Table 8Consumption and concentration details of captan and tolylfluanid for raw agricultural commdatabase containing concentrations of captan and tolylfluanid of Czech Republic (CZ),concentration below limit of reporting were assumed to contain no pesticide.

Country Consumption details

Mean consumption(g)

% Consumptiondays

Number of foods consumedcontaining apple as ingredien

CZ 61 35 7DK 84 50 5IT 52 33 28NL 55 55 58SE 45 34 25Pooled – – –

a Samples reported with concentrations above limit of reporting.

which foods as eaten containing apple as ingredient were in-cluded. Does this for example also include apple present in smallquantities in foods like fruit yoghurt or fruit salad? Includingthese small quantities will reduce the mean consumption of theRAC ingredient apple, but increase the percentage consumptiondays with a positive consumption of apple. Not including thesefoods in an assessment will result in lower estimated exposurescompared to estimates using databases that contain these foods,like in NL.

An important asset of the E-platform is the possibility of usingconcentrations from or sharing concentrations between countries.Whether this is a useful approach will depend on whether nationalmonitoring programs are representative of RACs on the interna-tional market or only of those nationally produced, and on whetherthere are real differences in, for example, the concentrations be-tween countries. Also differences in monitoring practices (targetedvs. random sampling, coverage of RACs) and laboratory perfor-mance (e.g. LOR) should be addressed. When using concentrationsfrom another country in a national exposure assessment, concen-trations could be selected from the country that has the most sim-ilar concentrations of that pesticide (or all pesticides). Anotherpossibility is to pool all concentrations in one overall database. Inview of the international trade developments with RACs traversingthe whole of Europe to reach consumers this is an option that maycome close to what an European consumer may encounter in reallife. Merging dietary habits is less obvious. Some calculations per-formed here suggested that consumptions levels were more simi-lar than pesticide concentrations. However, this conclusion isbased on results from five countries in Europe with a little biasto northern Europe (3 out of 5), as well as the fact that this studywas limited to only two pesticides predominantly present in apple.It is evident that the EU population is a population with a vastdiversity in dietary patterns.

Linking national consumption data to a pooled concentrationdatabase showed large changes in estimated exposure to captanfor almost all five countries. For all countries the exposure in-creased, except for NL for which the exposure decreased. Thischange in exposure was due to the change in concentrations of pri-marily apple: the mean captan concentration decreased from0.16 mg/kg to 0.08 mg/kg for NL (Table 8). Also this example showsthat differences in monitoring concentrations determine to a largeextent differences in exposure.

4.2. Sensitivity analyses

The influence of three additional input parameters on the esti-mated exposure, namely processing, unit variability and assigningconcentrations to samples that were reported to be below a limitwere examined.

odity ‘apple’ per country, including concentration details in the pooled concentrationDenmark (DK), Italy (IT), the Netherlands (NL) and Sweden (SE). Samples with a

Concentration details

Captan Tolylfluanid

t% Positivesamplesa

Mean Concentration(mg/kg)

% Positivesamples

Mean Concentration(mg/kg)

9.5 0.03 6 0.0110 0.01 10 0.0117 0.07 – –30 0.16 24 0.0317 0.05 8 0.0118 0.08 20 0.02

P.E. Boon et al. / Food and Chemical Toxicology 47 (2009) 2890–2898 2897

Most processing practices resulted in a reduction in the con-centration of captan and tolylfluanid in the foods consumed com-pared to the RAC analysed (Table 3). However, the effect oncaptan exposure was minimal (Table 7), due to relatively highfactors for only two different processing types of apple (Table3). For tolylfluanid, due to the large number of processing factorsavailable for different RAC – processing type combinations a de-crease in exposure was observed when included in the assess-ment (Table 7). Including unit variability factors in the analysisresulted in higher exposures (Table 7). However, it should be rea-lised that the concept of modelling processing factors and unitvariability probabilistically is new and will very likely be devel-oped further in the coming years.

In this paper we examined the effect of assigning LOR or ½LORto samples which were only reported to be below LOR. When per-forming exposure calculations to pesticides within Europe, sam-ples with a pesticide concentration below LOR are normallyconsidered to be real zeroes. In reality this will only be true forthose samples that have not been treated with the pesticide. Inthe United States the Environmental Protection Agency (EPA)has developed a methodology to assign concentrations to thesesamples based on percentage crop treated (EPA, 2000). In Europeno information on this is available, and will very likely not be-come available in the near future. Because in reality only partof the crop will be treated with a compound assigning ½LOR orLOR to all non-detect samples is expected to overestimate theexposure.

There is a growing awareness that the probabilistic approach,assessing the whole diet and addressing both variability and uncer-tainty, may become standard practice in all kinds of risk assess-ments in the future. This includes assessments regardingcompounds with a common mechanism of action (cumulativeexposure), exposure via different routes (aggregated exposure)and risk-benefit analyses.

4.3. Conclusions and recommendations

We developed an E-platform of harmonised national consump-tion and concentration databases to perform national exposureassessments using either national concentrations or data from apooled concentration database. The Scientific Committee of EFSAhas recommended in an Opinion on Exposure Assessment thatEFSA should try to get access to occurrence data in Europe and cre-ate a data warehouse, where the information is either stored ormade accessible through links to existing databases (EFSA, 2005).The E-platform described in this paper is an example of how thiscould be realised.

Examination of data quality needs attention when developingthis tool further or implementing it for use by for example riskassessors in Europe or for EFSA. Also harmonisation of food con-sumption and pesticide concentrations at an international level isimportant to make it possible to compare exposure results be-tween countries. Guidance should be given on how to use the plat-form, including probabilistic exposure modelling.

Conflict of interest statement

The authors declare that there are no conflicts of interest.

Acknowledgements

This work has been partly funded by the EU 6.FP project ‘‘Pro-moting Food Safety through a New Integrated Risk Analysis Approachfor Foods” (SAFEFOODS, Food-CT-2004-506446).

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