Evaluation of Airborne Crocidolite Fibers at an Asbestos- Cement Plant
Van Orden, Drew R, Richard J Lee, Matthew S Sanchez, Matthew D Zock, Edward B Ilgren, Yumi Kamiya, “Evaluation of Airborne Crocidolite Fibers at an Asbestos- Cement Plant”, Annals of Respiratory Medicine, Case Study, July 26, 2012
Construction Materials, EH&S, Materials Characterization, Mining & Minerals, Particle Characterization
In many developing countries, asbestos is still used for friction products, insulation, and asbestos cement products. However, there is very limited modern data evaluating the airborne concentrations of asbestos fibers in operating asbestos plants. We have conducted an Industrial Hygiene survey, using state of the art PCM and TEM analysis, of a Bolivian asbestos cement plant that uses crocidolite during the production of roofing shingles and water tanks. The study is the first to use modern analytical measurements in an uncontrolled asbestos environment. Thus, this study offers insight into the historical airborne exposures at asbestos plants operating before modern dust control measures were introduced or airborne fiber levels regulated. The results suggest that fiber concentrations are far in excess of those for which current models would predict the onset of asbestos disease and mesothelioma.
Significantly, the plant and community do not have an elevated incidence of asbestos disease or mesothelioma. Airborne fiber levels during comminution and fiber sizing ranged from 100 to as much as 1,000 f/cc. Fiber levels in the mixing, forming and finishing areas ranged from 0.5 to 9 f/cc. Based on the number of overloaded samples and estimates of their concentration, historical estimates of exposure may be underestimated by an order of magnitude. Also, an excellent correlation (1:1) between the PCM and TEM fiber concentrations (for fibers _0.25 um diameter) were obtained, raising questions about the conventional wisdom that the PCM systematically undercounts fibers. The results of this study further indicate that current models may overestimate the risk of asbestos disease and mesothelioma.
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