Pathways to Refining. Exposure Analysis:

Transcription

1 environmental failure analysis & prevention health technology development Exposure Analysis: Pathways to Refining Regulatory Risk Assessments Rick Reiss Exponent November 4, 2009 A leading engineering & scientific consulting firm dedicated to helping our clients solve their technical problems. 1

2 Outline of Presentation Risk assessment paradigm Mathematical modeling Examples of problems and solutions using models and additional data collection Probabilistic exposure modeling Analysis of uncertainty and variability 2

3 The Established Risk Assessment Process 3

4 The Tiered Risk Assessment Process 4

5 Exposure Assessment Tools Concentration (how much) Frequency (how often) Duration (how long) 5 5

6 Options at Exposure Assessment Tiers Variable Lower Tiers Higher Tiers Concentration Worst-case measurements Simple models with default assumptions Frequency & duration Default assumptions Professional judgment Upper-bound estimates Site or scenario-specific measurements Advanced models Surrogate data Population-specific surveys Expert solicitation Exposure estimates Combine upper-bound concentration and frequency & duration estimates Probabilistic exposure assessment 6

7 Mathematical modeling Definition: A model that is expressed in formal mathematics using equations statistical relationships, or a combination of the two. Although values, judgment, and tacit knowledge are inevitably embedded in the structure, assumptions, and default parameters, computational models are inherently quantitative, relating phenomena through mathematical relationships and producing numerical results. National Academy Report Models in Environmental Regulatory Decision Making

8 Types of Mathematical Models 8

9 Consumer Product Exposure Models and Tools A number of exposure models may be useful for evaluating consumer product exposures under different scenarios These models include default assumptions or require data on consumer product use patterns and behaviors These models address variability (and uncertainty) in different ways 9

10 Different Consumer Product Models/Tools OPP SOPs MCCEM WPEM ConsExpo SHEDS-Multimedia E-FAST ChemSTEER PHED PROMISE ECETOC TRA EUSES RISKOFDERM DERMAL AIRPEX SCIES BEAT 10

11 How Models Fit into the Grander Scheme 11

12 Models for Different Environmental Media Air models (dispersion and airshed) Indoor air Groundwater Surface water Industrial hygiene 12

13 Basics of Air Dispersion Modeling: Gaussian Dispersion 13

14 AERMOD Output 14

15 Puff Modeling Improvement to Plume Modeling (3-D Meteorology) 15

16 CALPUFF Output 16

17 CMAQ Analysis 17

18 Different Tiers of Air Models Models Examples Features Screening SCREEN3 DOS-based Assumes unidirectional wind Runs in minutes Refined ISC AERMOD CALPUFF Airshed CMAQ CAMx DOS-based (but commercial Windows versions) Use historical, hourly meteorological data Simple chemistry Steady-state (ISC and AERMOD) Minutes to hours to run Up to 50 km (more with CALPUFF) Complex chemistry, accounts for interaction between source emission Models in space and time National-scale spatial range Days of prep; hours to days to run 18

19 Problem: Bystander Exposure to Fumigants 19

20 Issues to Solve Estimation of emission rate from field Extrapolation of field measurements to the myriad of meteorological conditions that might occur Prediction of buffer zones to provide safety to bystanders Do all this without unnecessarily limiting farmers access to critical growing tools 20

21 Issues and solutions for fumigant problem Issue Solution Estimate emission rate Field studies under different conditions combined with modeling to back-calculate emissions Extrapolation of field measurements to different conditions Dispersion modeling Estimation of buffer zones Adaption of dispersion modeling tools to estimate concentrations in a way to develop buffer zones estimates 21

22 Fumigant Field Study Designs ' 30' 30' 30' 30' 11 Indirect Flux 6 Design Air ' Direct Flux Setup Samplers 150cm 90cm 141' 14 1' 9 55cm 30' 30' ' 2 33cm 15cm 141' 10 Feet

23 Back-calculate flux rate using dispersion model: using a model in reverse y = x R 2 = Original Data Linear (Original Data) Modeled Concentration (ppm) 23 Measured Concentration (ppm)

24 Cumulative Mass Loss 24

25 Development of a Mathematical Model Problems with existing dispersion modeling tools: Somewhat hard to use Do not output results in a probabilistic manner Concentrations are not outputted in an easy way for estimating buffer zones Not easy to deal with multiple fields emitting at once Lots of post-processing would be required Solution: Probabilistic Exposure and Risk model for FUMigants (PERFUM) 25

26 Aside: Reiss Rules for Naming a Model! Probabilistic Exposure and Risk model for FUMigants (PERFUM) Needs to be catchy and memorable Some relation to the topic (FUM = fumigants) Inside humor is ok (PERFUM has an odor; many fumigants also do!) Don t be too rigid is making acronym Can skip one word in title (model) Can borrow multiple letters from one word (FUMigants) Goal, within 1 year, be able to Google your name and model name and get at least 10 hits! 26

27 PERFUM Approach Run ISCST3 (embedded in model) with 5 years of historical meteorological data Average concentrations over duration according to toxicity endpoint Estimate concentrations in all directions around the field Exposure probability Use measured diurnal profile of flux rates Start time of application is a critical variable Cast results in a probabilistic format 27

28 PERFUM Receptor Grid 28

29 PERFUM Receptor Grid 29

30 Estimation of Buffer Zones 30 MOE < 100 MOE > 100 5% Whole field buffer zone Maximum concentration buffer zone

31 Addressing Uncertainty in Flux Rates 2-D Monte Carlo Modeling 31

32 PERFUM Features Full percentile distributions of buffer lengths for whole field and maximum concentration profiles 1 st to 99 th percentile, 99.9, th percentiles Repeats calculations for up to 10 user-supplied application rates Takes advantage of linearity between flux and concentration in ISCST3 Useful for developing buffer zone tables 32

33 PERFUM Output Buffer lengths on a monthly basis Useful for seasonal analysis Models any field size and orientation up to 40 acres More than 90 error and warning messages Includes algorithms for multiple fields 33

34 Exposure to swimming pool chemicals dealing with extreme default assumptions Issue: Regulators were assuming that children swim 3 hours/day and adults 5 hours/day. Us: That s unreasonably long! Regulators: Says who. Do you have any data to contradict our assumption? Us: Well, no. Solution: Conduct a survey of swim coaches 34

35 Outline of the Survey Children and adult competitive swimmers Interviewed 45 Masters swim coaches Identified from trade association with their cooperation High-level competitive swimmers Interviewed 45 collegiate swim coaches 15 from each division Identified from an Internet search 35

36 Results of Swim Coach Survey Children (7-10) Children (11-14) - Typical Children (11-14) - High-end Masters Collegiate Survey Generated SWIMODEL 36 Time (hours per year)

37 Monte Carlo Analysis Monte Carlo analysis is an important statistical methodology for higher-tiered exposure assessments The use of probability distributions is particularly useful when there is significant variability among consumer products, use patterns, and activities These methods can also be used to address uncertainty in exposure factors (such as in the use professional judgment to fill data gaps) 37

38 Example of Tiered Approach Using ConsExpo Parameter Range of Inputs Tier 1 Inputs Tier 2 Inputs Duration (minutes) Normal distribution Mean 20, Std. Dev. 7 Product amount (grams) Triangular distribution Min 3, Mid 10, Max 30 Room volume (ft 3 ) , Triangular distribution Min 1000, Mid 12,000, Max 30,000 Air exchange rate (hr -1 ) Triangular distribution Min 0.1, Mid 1, Max 3 38

39 Tier 1 Result - Deterministic 49.6 mg/m 3 39

40 Tier 2 Result - Probabilistic Mean=1.8 mg/m 3 90 th =3.4 mg/m 3 99 th =7.8 mg/m 3 40

41 Model Evaluation Although model validation became a common term for judging model performance, it has been argued persuasively (e.g., Oreskes et al., 1994) that complex computational models can never be truly validated, only invalidated. The contemporary phrase for what one seeks to achieve in resolving model performance with observation is evaluation. NRC Report Models in Environmental Regulatory Decisionmaking 41

42 Issues in Evaluating Models 42

43 Elements of Model Validation Scientific basis Computational infrastructure Peer review Quality assurance and quality control Quality of data inputs Testing with simple analytical solutions Corroboration with observations Sensitivity and uncertainty analysis Transparency 43

44 Summary Exposure modeling is a useful tool for refining regulatory risk assessments There are many available models for different purposes Models must address uncertainty and variability Models can never be truly validated, but rigorous model evaluation is critical 44

45 QUESTIONS???? 45