1. PROBABILISTIC DIETARY EXPOSURE ASSESSMENT TO PESTICIDE RESIDUES

2. STRUCTURE Applications of probabilistic exposure assessments Pesticide conceptual model Exposure assessment to chlorpyrifos Input data Model settings Results of assessment Information on uncertainty Contribution of food items

3. APPLICATIONS Risk assessment for pesticide authorisation Risk assessment of registered pesticides Characterisation of variability and uncertainty Identification of the main contributions to the intake

4. EXPOSURE ASSESSMENT N Iterations Frequency Pear Residue Apple Residue Orange Residue Exposure = Σ(Consumption * Residue) / Bodyweight Consumption P A O 2 1 N 4 3

5. Exposure assessment

6. PESTICIDE CONCEPTUAL MODEL PESTICIDE RESIDUE INTAKE FOOD SURVEY PESTICIDE RESIDUE MONITORING PROGRAMME CONSUMPTIO OF RAW AGRICULTURAL COMMODITY (RAC) ADJUSTED RESIDUE (PAC)

10. ACUTE EXPOSURE ASSESSMENT PESTICIDE: Chlorpyrifos POPULATION: Infants of the Basque Country 8 to 12 months old PERIOD:1 day

12. INPUT DATA  Food consumption dataFood diary and recipes. Basque Country  BodyweightFood diary and recipes. Basque Country  Pesticide residue dataMonitoring programmes CCAA Spain MRLs spanish legislation

13. INPUT DATA  Observed data Data(0.08,0.24,0.039,0.26,0.68,0.43,0.20,0.06,0.63,0.61,1.6,0.38,0. 53,0.94)  Histogram  Parametric distribution Lognorm(1.43,0.83) 0.0 0.4 0.8 0.31.01.72.43.2 3.9 Lognorm

15. UNIT-TO-UNIT VARIABILITY Composite sample MEAN ANALYSIS ACUTE EXPOSURE ?

16. UNIT TO UNIT VARIABILITY OPTIONS No variability adjustment Concentration = mean for all units Variability GVDSP raw lab data Laboratory data for individual units Variability GVDSP lognormal Concentration in units described by a lognormal distribution Variability RIKILT No. Units in composite sample Concentration in units described by a Bernouilli distribution

17. LOGNORMAL VARIABILITY R Log(R,f(R,  )) Distribution residues units x r1 +x r3x r2 + Intake = Distribution of residues Composite samples Consumption: 3 apples

20. MEDIAN MEAN sd 95 th p Minimun 99 th p

21. 1550 90 95 97.5 98 99 99.5 99.9 2.5

22. 97.5 th p 99 th p

23. VALIDATION: Cumulative distributions

24. CHARACTERISING UNCERTAINTY  Processing factors: with vs. without  No analysisMRL vs. 0  Samples < LORLOR vs 0  Variability with vs. without REFERENCE MODEL: WITH PROCES. WITH VARIAB MRL LOR

25. 97.5 th P No proc factors Reference model LOR = 0 MRL = 0 No variability

26. CONTRIBUTION OF FOOD ITEMS 95th Percentile