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

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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 (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.

Original languageEnglish
Pages (from-to)834-842
Number of pages9
JournalAerosol and Air Quality Research
Volume12
Issue number5
DOIs
Publication statusPublished - 2012 Oct

All Science Journal Classification (ASJC) codes

  • Environmental Chemistry
  • Pollution

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