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

Hsin-I Hsu; Mei-Ru Chen; Shih-Min Wang; Wong-Yi Chen; Ya-Fen Wang; Li-Hao Young; Yih-Shiaw Huang; Chung Sik Yoon; Perng-Jy Tsai

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 Tsai ¹^,3

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

Received: April 30, 2012
Revised: June 30, 2012
Accepted: June 30, 2012
Download Citation: ||https://doi.org/10.4209/aaqr.2012.04.0107

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Cite this article:
Hsu, H.I., Chen, M.R., Wang, S.M., Chen, W.Y., Wang, Y.F., Young, L.H., Huang, Y.S., Yoon, C.S. and Tsai, P.J. (2012). Assessing Long-Term Oil Mist Exposures for Workers in a Fastener Manufacturing Industry Using the Bayesian Decision Analysis Technique. Aerosol Air Qual. Res. 12: 834-842. https://doi.org/10.4209/aaqr.2012.04.0107

ABSTRACT

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 (C_m, 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 (C_p) (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 C_m. 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 C_m. By combining C_p and C_m, 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 C_m is set as low as 3.