[Two new anthropometric panels of respirator fit test for Chinese adult workers].

Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi

Key Lab of Environment and Health and Department of Occupational and Environment Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan 430030, China.

Published: July 2009

Objective: To develop two new respirator fit test panels using data from anthropometric survey of Chinese adult workers: bivariate panel and principal component analysis (PCA) panel for meeting the requirements of respirator fit design and testing and evaluate the applicability of the two different fit test panels for current Chinese workers.

Methods: An anthropometric database based upon anthropometric data of current Chinese workers was used for panel design. The bivariate panel was based upon the bivariate distribution of the face length and face width measurements, and the PCA panel was developed using principal components analysis of 10 stable facial dimensions (minimum frontal breadth, face width, bigonial breadth, face length, interpupillary distance, head breadth, nose protrusion, nose breadth, nasal root breadth, subnasale-sellion length). The distribution of Chinese workers across the panels was used to analyze the panels' applicability for Chinese workers.

Results: The bivariate panel and PCA panel developed in this study consisted of 10 cells and 8 cells respectively, covered 96.9% (male 95.4%, female 98.4%) and 96.5% (male 95.1%, female 98.1%) of the current Chinese workers, respectively. The distribution of workers across both panels was uniform. Each cell contained 4.6% - 21.7% of the population in the bivariate panel and 10.4% - 14.6% of the population in the PCA panel. The LANL full-face piece respirator fit test panel covered only 70.9% of the Chinese workers, It was thought to be no longer adequate for the Chinese adult workers.

Conclusion: New respirator fit test panels based on updated anthropometric database and distributions specific to Chinese workers may be more beneficial for applications in China.

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