1. Probabilistic methods for aggregate and cumulative exposure to pesticides Marc Kennedy Risk and Numerical Sciences team marc.kennedy@fera.gsi.gov.uk marc.kennedy@fera.gsi.gov.uk Willem Roelofs, Vicki Roelofs, Hilko van der Voet

2. Potential human exposure to pesticides www.acropolis-eu.comwww.browseproject.eu

3. POPULATION Individual Food consumption Activities Operator activities Worker activities Resident activities Bystander activities Consumer Product use Operator exposure Worker exposure Resident exposure Bystander exposure Consumer Product exposure Non-dietary exposure contributions (internal and external) Pesticides: EUROPOEM database or BROWSE model Pesticides: BREAM or BROWSE model Pesticides: EUROPOEM database BREAM or BROWSE model Pesticides & biocides: CONSEXPO model BROWSE/EFSA surveys or user-specified Specified by user MCRA Consumption Database MCRA Recipes Database MCRA Pesticide Residues Database Residues in food MCRA model Absorption factors Dietary exposure contribution (external) TOTAL AGGREGATE EXPOSURE (INTERNAL or EXTERNAL) Biocides: BEAT/ART models Vet meds: case by case Pesticides: EUROPOEM or BROWSE model Biocides: BEAT/ART models Vet meds: case by case Repeat calculation for large sample of individuals Conceptual model Cumulative Aggregated

4. Cumulative dietary exposure: Data sources, uncertainties Food consumption survey data  Per country of consumption (target population) Pesticide monitoring data  Multiple residues per sample  Vast majority of measurements ND (<LOR) impossible to estimate correlations reliably Pesticide usage survey data  Use and co-use of pesticides on a single crop

5. Estimate correlations from monitoring data? 5355 possible pair-wise correlations to estimate, based on UK data (119 foods, 10 triazoles measured):  1.1% have at least 2 samples with positive residues in both  0.1% have at least 10 samples with positive residues in both Conclusion: very little information available on correlation between residues  Information will have to be obtained from elsewhere Our model combines monitoring and usage data

6. Illustration: Triazole residues in carrots DifenoconazoleTebuconazoleNumber of samples <LOR 68 0.03<LOR1 0.01 1 <LOR0.01 (10), 0.02 (9), 0.03 (2), 0.04 (4), 0.06 (1)26 Limited residue monitoring data (2009, Pesticide Residue Committee)

7. Pesticide Usage Survey data Field treatment type (triazoles) Percent of total GB Carrot, parsnips and celery crop (2007) none46.08 Azoxystrobin/difenoconazole0 Azoxystrobin/difenoconazole+8.63 Difenoconazole2.21 Difenoconazole+0.43 Tebuconazole3.62 Tebuconazole+38.30 Azoxystrobin/difenoconazole, Tebuconazole+0.72 Field treatment type (triazoles) Percent of total GB Parsnip crop (2007) none26.40 Difenoconazole1.71 Tebuconazole47.49 Difenoconazole, Tebuconazole24.40 Field treatment type (triazoles) Percent of total GB Carrot crop (2007) none46.08 Difenoconazole5.27 Tebuconazole33.39 Difenoconazole, Tebuconazole15.26 Combined treatments over the year (field-year level) Individual combinations applied (field-treatment level) Relatively few tank mixes, so we’ll assume uncorrelated amounts

8. Uncertain field size distributions Model accounts for: Dependence between treatment and field size distribution Limited survey data – does it matter?

9. Independent LN distributions for amounts of D, T Using PUS increases precision Residue data alone (2 points) provide some information Tebuconazole Difenoconazole Residues only PUS + residues Point estimate for p0

10. Operators Many uncertain/ variable inputs, little data

11. Code Emulator: 2 code runs Gaussian process response surface (meta-model) Emulator estimate interpolates data Emulator uncertainty grows between data points

12. 3 code runs Adding another point changes estimate and reduces uncertainty

13. 5 code runs And so on…with enough runs, emulator becomes surrogate for original model, and we can derive uncertainty/sensitivity measures

14. Sensitivity analysis for one of the outputs (preliminary bystander model) Main & joint effects for adult spray output Uncertainty due to emulation is small, for estimating these ‘average effects’

15. Partitioning the output variation Main input contribution (or additional interaction pair)% output variance Boom height23.0 Crop height13.0 Wind angle9.2 Forward speed8.5 Wind speed8.2 Boom height, Crop height3.8 Boom height, Forward speed3.2 Boom height, Wind angle2.7 Wind speed, crop height2.1 Boom height, wind speed1.8 Wind angle, crop height1.7 Number of nozzles1.5 Main effect contributions Joint effect contributions

16. Simulated spray outputs distributed along the x-axis extra variability around the ‘mean bystander deposit’ for a given spray level Monte Carlo estimate, 10,000 runs of the emulator with independent distributions for Wind Speed, Wind angle, Boom height (variability in real conditions during a single spray event)

17. Integrating Acropolis and Browse using 2D Monte Carlo Variability CDF: exposure for all individuals in population Uncertainty Repeat many times with different model parameter values (e.g. sampled from posterior), or via bootstrap variability uncertainty Integration Acropolis & Browse exposures both coded as 2DMC simulation matrices Scale Browse matrix to correspond to exposure on same scale as dietary (internal dose) Add dietary and non-dietary matrices