A predictive model for determining asbestos concentrations for fibers less than five micrometers in length
The controversy of whether small asbestos fibers are biologically significant has not been resolved. The present standard method for evaluating asbestos fiber concentrations in workroom air excludes fibers less than 5 micron long even though it has been shown that small fiber concentrations dominate...
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Published in | Environmental research Vol. 43; no. 1; p. 31 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Netherlands
01.06.1987
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Subjects | |
Online Access | Get more information |
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Summary: | The controversy of whether small asbestos fibers are biologically significant has not been resolved. The present standard method for evaluating asbestos fiber concentrations in workroom air excludes fibers less than 5 micron long even though it has been shown that small fiber concentrations dominate in a dust cloud. This research project was conducted to develop a mathematical model whereby one could predict small (less than 5 micron length) asbestos fiber concentration based on the fiber count concentration determined by phase contrast microscope analysis. Dry chrysotile asbestos was aerosolized into a chamber and sampled by membrane filtration. Segments from each filter were analyzed by both the NIOSH technique using phase contrast microscopy (PCM) and scanning electron microscopy (SEM) at 2000 X for fiber concentrations. A linear relationship was found to exist between the natural logarithm of the SEM-determined concentration and the natural logarithm of the PCM-determined concentration (r = 0.852). Using these data, a mathematical model was developed to predict SEM concentrations based on PCM counts. This model may have application in retrospective epidemiological studies for estimating small fiber exposure levels to determine if small fibers play a role in disease production. The greatest utility would be in those retrospective studies where the only exposure information available is based on PCM counts. |
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ISSN: | 0013-9351 |
DOI: | 10.1016/S0013-9351(87)80054-3 |