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The ART of Modelling Exposure

The ART of Modelling Exposure. Martie van Tongeren Thursday 14 May 2009 Petroleum Safety Authority Stavanger, Norway. The ART Project. TNO: Erik Tielemans (Project Leader), Jody Schinkel, Wouter Fransman, Hans Marquart,

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The ART of Modelling Exposure

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  1. The ART of Modelling Exposure Martie van Tongeren Thursday 14 May 2009 Petroleum Safety Authority Stavanger, Norway

  2. The ART Project TNO: Erik Tielemans (Project Leader),Jody Schinkel, Wouter Fransman, Hans Marquart, IOM: Martie van Tongeren, John Cherrie, Peter Ritchie, Anne Sleeuwenhoek, Sally Spankie HSL: Nick Warren, Kevin McNally BAuA: Martin Tischer NRCWE: Thomas Schneider University of Utrecht: Hans Kromhout

  3. Content • The ART model • Mechanistic model • Database/similarity algorithms • Worked example • Timeline for ART • Further developments • Summary

  4. Need for higher Tier approach for REACH First Tier inherently conservative • Simple, easy-to-use, inexpensive • Higher Tier approaches needed for subgroups • As simple as possible, but not simpler • A generic higher Tier tool increases cost-effectiveness and speed of RA • Case-by-case approach is alternative Tier 1 Reduced complexity Generic higher tier Increased precision Specific approach

  5. Exposure database, with contextual information Update and calibrated Mechanistic model Similarity module to select data for risk assessment Bayesian process to combine data and model output Exposure estimates for risk assessment ART approach New developments on: • Mechanistic models • Bayesian statistics • Databases • Software • Validation

  6. Schematic Structure of ART

  7. Mechanistic Model • Based on previous work by Cherrie et al. (1996) and Cherrie and Schneider (1999) • Used for Stoffenmanager (Marquart et al., 2008) • Conceptual Model for inhalation exposure (Tielemans et al., 2008)

  8. Example from pharmaceutical industry (From Cherrie et al., 2008)

  9. Measured and predicted benzene exposure in China

  10. Conceptual model for inhalation exposure

  11. ART Mechanistic Model

  12. Exposure determinants – substance emission potential • Dustiness: • particle size (distribution) • aggregation/coalescence/cohesion/friability • moistness of product (if not related to airborne capture sprays) • solidity/intactness/corrosion/surface modification of bound materials • Volatility: • Process temperature • Mole/weight fraction of mixture • Activity coefficients • partial vapour pressure

  13. Example of assigned factors for LEV

  14. Algorithms

  15. Calibration of Mechanistic model • From relative scores to concentration unit (e.g. mg m-3) • Revise model if necessary • Requires exposure measurement data • Currently collecting from TNO, IOM, HSE, GSK, BOHS, metal industry, etc. • More good quality exposure and contextual data very welcome!

  16. Building of Mechanistic Model

  17. Linking mechanistic model with database • Identify relevant data using similarity algorithms • Quantitative measure of similarity between ES and scenarios for which exposure data exists • Rank data sets to degree of analogy to the ES • Bayesian update is determined by • Level of similarity • Number of data points

  18. Making full use of all information + BDA Uncertainty Model Data Updated estimate

  19. Evolving system Mechanistic model Larger applicability domain More detailed insights into MFs Additional data for calibration Increased accuracy Additional data for Bayesian update

  20. Worked example ART: dye manufacture • Manual scooping and dumping of powdered dyes • 2hrs of exposure related tasks. No relevant exposure during rest of shift • Fairly hazardous compounds • Full shift exposure to inhalable dust

  21. Dye manufacture: user input Intrinsic emission potential (dustiness) 5 classes 0.1 Firm granules, flakes or pellets 10 Extremely fine and light powders • Localized control • 12 classes • No control • 0.1 Wet suppression • 0.1 LEV • 0.01 LEV and partial enclosure • 0.001 LEV and complete enclosure • …… • ……

  22. Dye manufacture: mechanistic model Calibration based on approximately 500 measurements Final calibration will be based on several thousands of measurements

  23. Dye manufacture: exposure measurements • Completely analogous data • Most likely added by the user • 21 measurements, 6 sites • Median: 0.19 mg m-3 • Range: 0.06 - 1.29 mg m-3 • GSD: 2.8 • First update uses data from just one company • Second update uses all measurement data

  24. Dye manufacture: updated predictions

  25. Dye manufacture: updated predictions

  26. Dye manufacture: predictions

  27. Where do we stand now? • Mechanistic model nearly completed • Calibration of mechanistic model ongoing • Software development ongoing • Beta version ART will be published in Autumn 2009 • Web-based user-interface • Calibrated mechanistic model • User can enter fully analogous exposure data (no link to database) • Bayesian update of exposure estimate (median and other percentiles)

  28. Where do we stand now? • Starting add-on projects with metal industry and a petrochemical company • Upon completion Beta version will be updated (expected end 2009/beginning 2010) • Funding for development of the full ART model (inhalation) is not complete. Further funding required for: • ART database • Software development for full ART (linking mechanistic model with database) • Validation • Discussions ongoing to resolve the budget shortage

  29. Limitations • Only inhalation exposure, hopefully in the near future we will also look at dermal exposure (discussions with Dutch and UK Governments) • Gasses, metals and fibres not (yet) explicitly addressed in ART • Add-on project funded by European metals industry • Gasses and fibres need to be addressed at later stage

  30. Summary (1) • Research project has made good progress • ART Beta version 1.1 will be available Autumn 2009 • ART model will be available end 2010 • Approach makes efficient use of modelled exposure estimates and measurements • ART facilitates higher tier exposure assessment under REACH • Provides estimates of whole distribution • Evolving system: • inclusion of any new data that becomes available • Improving mechanistic model

  31. Summary (2) • Approach facilitates sharing of exposure data down and up the supply chain • Additional discussions are needed on issues related to variability and uncertainty • Which percentile of distribution should be used in RA? • What level of precision is required?

  32. More information… • Cherrie JW, Schneider T, Spankie S et al. (1996) A new method for structured subjective assessments of past concentrations. Occup Hyg; 3:75–83. • Cherrie JW, Schneider T (1999) validation of a new method for structured subjective assessment of past concentrations. Ann Occup Hyg; 43:235-45 • Tielemans E, Warren N, Schneider T et al. (2007) Tools for regulatory assessment of occupational exposure: development and challenges. J Expo Sci Environ Epidemiol; 17 (Suppl. 1):S72-80 • Fransman W, Schinkel J, Meijster T et al. (2008) Development and evaluation of an exposure control efficacy library (ECEL). Ann Occup Hyg; 52:567-575 • Tielemans E, Schneider T, Goede H, et al. (2008) Conceptual model for assessment of inhalation exposure: defining modifying factors. Ann Occup Hyg; 577-586

  33. Thank you for your attention! Questions? or email me at Martie.van.Tongeren@iom-world.org

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