Probabilistic risk assessment Modelling exposure and effects

Transcription

1 Probabilistic risk assessment Modelling exposure and effects Udo Hommen Outline Focus on risk assessment for plant protection products Definitions Example of a tiered assessment Standard Risk Assessment Probabilistic refinement of effects Probabilistic refinement of exposure Probabilistic consideration of recovery potential Summary 1

2 What are probabilistic risk assessments? Risk = probability of hazard, e.g. probability of an event multiplied by the costs So, why probabilistic risk assessments? EUFRAM 2006 Probabilistic risk assessments use probabilities or probability distributions to quantify one or more sources of variability and/or uncertainty in exposure and/or effects and the resulting risk. Variability = real variation in factors that influence risk Uncertainty = limitations in knowledge about factors that influence risk Thus, a probabilistic risk assessment should answer three questions: How bad? How often? How sure? Standard (aquatic) risk assessment under 91/414 EEC Exposure assessment Predicted Environmental Conc. based on model calculations or tables for different scenarios PEC Effect assessment Set of standardized single species tests in the laboratory LC50, EC50, NOECs Risk characterization Toxicity Exposure Ratio TER = EC50 / PEC Deterministic assessment using point estimations resulting on one number Yes or No decision: magnitude, frequency and uncertainty of hazard not quantified TER is compared with trigger value considering uncertainty in the assessment 2

3 91/414/EEC Annex VI Requirements Standard assessment designed to be conservative, screening level assessment to focus resources and identify areas for higher tier assessment Aim is to reduce type 2 errors (false negatives), consequently many type 1 errors (false positives) Uniform principles (Annex VI, 91/414/EEC): no authorization shall be granted if TER acute < 100 or TER chronic < unless it is clearly established through an appropriate risk assessment that under field conditions no unacceptable impact on the viability of exposed species occurs => Option to conduct higher tier risk assessments Example Plant protection product One application per year in orchards Application rate = 1290 g as/ha Exposure mainly due to spray drift Fast degradation in water => Focus on acute effects 3

4 Exposure assessment: standard spray drift scenario Application Wind at 2-5 m s m Bare ground 0.3 m Distance Ganzelmeier et al. 1995: Many experiments on flat surfaces 90 th percentile used Resulting in tables of Drift% depending on distance for different cultures For our example: PEC(10m) = 15 µg/l Drift deposition (% of field rate) Distance from treated area (m) Effect assessment: Acute standard test set Test species Duration L(E)C50 Algae 4d 700 Daphnia 2d 16 Chironomus 1 d 128 Trout 4 d 610 Blugill 4 d 750 Lowest L(E)C50 4

5 Example first tier acute assessment L(E)C50 of the most sensitive species Toxicity TER = Exposure PEC - predicted environmental concentration µg/l = µg/l Lower TERs indicate higher risk. Under EU legislation TERacute <100 indicates need for further refinement TER << Trigger = 100 Refined risk assessment should be conducted Higher-tier risk assessment Objective: realism and/or uncertainty Exposure assessment Refine worst case assumptions in PEC calculations (realism ) Effect assessment Tests under more realistic exposure scenarios (realism ) Tests with more life stages or populations (uncertainty realism ) Tests with additional species (uncertainty ) Micro- / mesocosm studies (realism uncertainty ) Ecological modelling (realism uncertainty?) Risk Characterisation Probabilistic assessment Refine the point estimate (TER) considering variability and uncertainty explicitly in exposure and/or effect assessment 5

6 Refined exposure assessment Using the whole distribution of the Ganzelmeier experiments From Schaefer et al Refined effect assessment: SSD 1 Use of additional single species tests with invertebrates and SSD calculation 100% 80% Percentile of species 60% 40% 20% 0% Carbaryl concentration (µg / L) 6

7 Refined effect assessment: SSD Differentiation of species Other less sensitive arthropods and molluscs (LC50 > 100 µg/l) Probability Amphipods, mayflies (20 µg/l < LC50 < 100 µg/l) Planktonic filter feeders (Cladocera) and two shrimps 1. Species living in high current waters (i.e. stoneflies) LEC50, EC50 [µg/l] Refined effect assessment: SSD Probability LEC50, EC50 [µg/l] HC5 = 5.2 µg/l => TER = 5.2 / 15 = 0.3 7

8 Risk characterisation: Combining PEC distr. and SSD 1.0 PEC distribution Probability Species Sensitivity Distribution 0.2 Joint Probability Curve LEC50, EC50 [µg/l] Up to 10% of species experiencing exposures exceeding their LC50s expected in 38 % of the ditches. Or: In 62 % of the ditches more than 10 % of the species LC50 is exceeded. The median estimate for the average proportion of species exceeding their LC50s is 37.6%. In the 90th percentile ditch, it is estimated that 90% of species will experience an exposure exceeding their LC50. Or: In 10 % of the ditches, the LC50 of more than 90 % is exceeded. From Schaefer et al Options for further refinements Site specific refinements of exposure model e.g. consideration of variability in dimension of ditches distance between ditch and edge of field drift mitigation by vegetation variability of wind direction Conduct higher tier studies e.g. a mesocosm study to test effects under more realistic conditions effects on populations and communities incl. indirect effects recovery Use ecological modelling to extrapolate effects measured on individuals to populations or communities estimate recovery times 8

9 Toxicant [µg/l] Modelling to link exposure, toxicity and ecology Objective: Probabilistic assessment of the recovery times of cladocerans dc dt = k c Time [days] Exposure Toxicity Effects, Recovery, Risk 1 Effect = slope ln( c) 1+ ln( EC50) Effect Toxicant [µg/l] dn dt Ecology N = r N 1 K Estimation of recovery times Recovery time 9

10 m distance 5 m distance 10 m distance 15 m distance 20 m distance Carbaryl (µg / L) Probabilistic approach using Monte-Carlo simulations Probability of exceedence Exposure (PEC) 100% Percent of species affected 80% 60% 40% 20% Aquatic LC50 values for Carbaryl: Plankton filter feeder 0% Carbaryl concentration (µg / L) Toxicity (EC50) ln(r-value) Normal % cumulative Ecology (r-value) ln(r-value) Monte-Carlo runs for 3 m distance 100 Percent (cumulative) median recovery time = 16 d 95th percentile = 42 d Probability of recovery within 3 weeks = 60 % Recovery time [d] 10

11 Summary 1 Standard risk assessment (for plant protection products) Toxicity Exposure Ratio: Deterministic point estimate, no real information about risk (e.g. how bad, how often) Very conservative due to worst case assumptions, especially for exposure Uncertainty and variability considered in the trigger values (assessment factors) of 10 or 100 Higher tier risk assessment Many options for higher tier studies or models to increase realism and/or reduce uncertainty resulting in higher TER and/or lower trigger values Reduction of trigger values by expert judgement, no quantitative consideration of uncertainty or variability Probabilistic risk assessment Quantification of variability and uncertainty Summary 2: Probabilistic risk assessment Probabilistic methods might be seen as complicated and difficult to communicate data hungry a departure from current schemes of assessment a magic wand to make unwanted risk disappear Colin Brown (2005) Probabilistic methods are another tool in the toolbox already in use under 91/414/EC (e.g. SSDs, 90th centile exposure) able to fit into existing schemes of refinement not a magic wand! a way to use and provide more information a way to support more robust decisions Need for 3 Ts: Tools Training Trees (decision trees ) 11

12 Questions / more information udo.hommen@ime.fraunhofer.de Diagrams for risk characterisation 1. Overlay plots 2. Exceedance risk distribution Proportion of exposures exceeding the EC50 for 10% or more species has a median estimate of 0% and 95% confidence interval 0% - 10%. 3. Cumulative risk distribution 4. Cumulative profile plot The 90th percentile exposure will exceed the EC50 for 1% of species (median estimate) with a 95% confidence interval of 0-10% At the 90th percentile exposure, 1% of species will exceed their toxicity endpoint (95% confidence interval 0% - 8%). The area under the curve in Figures 2 and 4, and the area above the curve in Figure 3, are all equal to the mean percentage species exceeding their EC50, averaged over all exposures. In this case, the median estimate for the average is 0.3% species exceeding their endpoint, with a 95% confidence interval of 0% - 6%. 12

13 Risk characterisation method From Schaefer et al Calculation of sampling uncertainties From Schaefer et al