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Volume 12, No. 5, October 2012, Pages 834-842 PDF(706 KB)  
doi: 10.4209/aaqr.2012.04.0107   

Assessing Long-Term Oil Mist Exposures for Workers in a Fastener Manufacturing Industry Using the Bayesian Decision Analysis Technique

Hsin-I Hsu1, Mei-Ru Chen2, Shih-Min Wang3, Wong-Yi Chen1, Ya-Fen Wang4, Li-Hao Young3, Yih-Shiaw Huang5, Chung Sik Yoon6, Perng-Jy Tsai1,3

1 Department of Environmental and Occupational Health, Medical College, National Cheng Kung University, 138, Sheng-Li Road, Tainan 70428, Taiwan
2 Department of Occupational Safety and Health, Chung Hwa University of Medical Technology, 89 Wenhwa 1st St., Rende Shiang, Tainan 71703, Taiwan
3 Department of Occupational Safety and Health, College of Public Health, China Medical University, 91, Hsueh-Shih Road, Taichung 40402, Taiwan
4 Department of Bioenvironmental Engineering, Chung Yuan Christian University, 200, Chung Pei Road, Chung-Li 320, Taiwan
5 The Industrial Safety and Health Association of the Republic of China, F. 6, 10, Sec. 6, Roosevelt Road, Taipei 116, Taiwan
6 Institute of Health and Environment, School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea




Collecting multiple and long-term samples is necessary to accurately describe the exposure profile of a similar exposure group (SEG), but only a few industries can afford to do this because of the costs and manpower needed. In the present study, measured oil mist concentrations (Cm, n = 11) were randomly collected on eleven days during one year (serving as the likelihood distribution in Bayesian decision analysis (BDA)), and daily fastener production rates (Pr, n = 250) were used as a surrogate for predicting the yearlong oil mist exposure concentrations (Cp) (serving as the prior distribution in BDA). The resulting BDA posterior distributions were used to assess the long-term oil mist exposures to threading workers in a fastener manufacturing industry. The feasibility of the proposed methodology was finally examined with reference to the effects of the sample size of the Cm. The results show that threading workers experienced more severe thoracic and respirable oil mist exposure than exposure to the inhalable fraction. Using Pr as a surrogate was adequate to explain ~92% of the variations in Cm. By combining Cp and Cm, our results suggest that the BDA technique adopted in this work was effective in predicting workers’ long-term exposure. By judging the consistency of the resulting posterior exposure ratings, this study suggests that the proposed methodology could be feasible, even when the sample size of Cm is set as low as 3.



Keywords: Oil mist; Exposure assessment; Bayesian decision analysis; Predictive model.



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