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Development and Validation of Exposure Models for Construction Industry: Tier 2 Model

건설업 유해화학물질 노출 모델의 개발 및 검증: Tier-2 노출 모델

  • Published : 2014.06.30

Abstract

Objectives: The major objective of this study was to develop a tier 2 exposure model combining tier 1 exposure model estimates and worker monitoring data and suggesting narrower exposure ranges than tier 1 results. Methods: Bayesian statistics were used to develop a tier 2 exposure model as was done for the European Union (EU) tier 2 exposure models, for example Advanced REACH Tools (ART) and Stoffenmanager. Bayesian statistics required a prior and data to calculate the posterior results. In this model, tier 1 estimated serving as a prior and worker exposure monitoring data at the worksite of interest were entered as data. The calculation of Bayesian statistics requires integration over a range, which were performed using a Riemann sum algorithm. From the calculated exposure estimates, 95% range was extracted. These algorithm have been realized on Excel spreadsheet for convenience and easy access. Some fail-proof features such as locking the spreadsheet were added in order to prevent errors or miscalculations derived from careless usage of the file. Results: The tier 2 exposure model was successfully built on a separate Excel spreadsheet in the same file containing tier 1 exposure model. To utilize the model, exposure range needs to be estimated from tier 1 model and worker monitoring data, at least one input are required. Conclusions: The developed tier 2 exposure model can help industrial hygienists obtain a narrow range of worker exposure level to a chemical by reflecting a certain set of job characteristics.

Keywords

References

  1. Cherrie JW, Schneider T, Spankie S, Quinn M. A new method for structured, subjective assessments of past concentrations. Occup Hyg 1996;3:75-83
  2. Cherrie JW, Schneider T. Validation of a new method for structured subjective assessment of past concentrations. Ann Occup Hyg 1999;43(4):235-245 https://doi.org/10.1093/annhyg/43.4.235
  3. Cochran WG. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 1968;24:295-313 https://doi.org/10.2307/2528036
  4. Gilks WR, Richardson S. Analysis of disease risks using ancillary risk factors, with application to job-exposure matrices. Statist Med 1992;11(11):1443-1463 https://doi.org/10.1002/sim.4780111104
  5. Gronewold AD, Borsuk ME. Improving Water Quality Assessments through a Hierarchical Bayesian Analysis of Variability. Environ Sci & Tech 2010;44(20):7858-7864 https://doi.org/10.1021/es100657p
  6. Hewett P, Logan P, Mulhausen J, Ramachandran G, Banerjee S. Rating exposure control using bayesian decision analysis. J Occup Environ Hyg 2006;3(10):568-581 https://doi.org/10.1080/15459620600914641
  7. Kaplan D, Chen J. A Two-step Bayesian approach for propensity score analysis: simulations and case study. Psychometrika 2012;77:581-609 https://doi.org/10.1007/s11336-012-9262-8
  8. Lee EG, Kim SW, Feigley CE, Harper M. Exposure models for the prior distribution in Bayesian decision analysis for occupational hygiene decision-making. J Occup Environ Hyg 2013;10(2):97-108 https://doi.org/10.1080/15459624.2012.748627
  9. Lee JH, Lee KS, Hong MK. Evaluation of the Application of a European Chemical Risk Assessment Tool in Korea. J Korean Soc Occup Environ Hyg 2012;22(3):191-199
  10. McCandless LC, Gustafson P, Austin PC. Bayesian propensity score analysis for observational data. Statisticsin Medicine 2009;28:94-112 https://doi.org/10.1002/sim.3460
  11. Nicas M, Jayjock M. Uncertainty in exposure estimates made by modeling versus monitoring. AIHA J 2002;63(3):275-283 https://doi.org/10.1080/15428110208984714
  12. Ramachandran G, Kandlikar M. Bayesian analysis for inversion of aerosol size distribution data. J Aerosol Sci 1996; 27(7):1099-1112 https://doi.org/10.1016/0021-8502(96)00005-5
  13. Ramachandran G. Restrospective expoosure assessment using Bayesian methods. Ann Occup Hyg 2001;45(8):651-667 https://doi.org/10.1093/annhyg/45.8.651
  14. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983;70:41-55 https://doi.org/10.1093/biomet/70.1.41
  15. Su ZM, Adkison MD, Van Alen BW. A hierarchical Bayesian model for estimating historical salmon escapement and escapement timing. Can J Fisher and Aquat Sci 2001;58(8):1648-1662 https://doi.org/10.1139/f01-099
  16. Vadali M, Ramachandran G, Mulhausen J. Exposure modeling in occupational hygiene decision making. J Occup Eviron Hyg 2009;6(6):353-362 https://doi.org/10.1080/15459620902855161