Evaluation of asbestos exposure resulting from simulated application of spiked talcum powders.

This study characterizes airborne asbestos exposures resulting from the adult application of cosmetic talc body powders spiked with known concentrations of tremolite. Raw talc ores were spiked with 0.005% and 0.1% asbestiform or non-asbestiform tremolite. Personal samples were collected during 16 simulated events, including puff and shaker application and associated clean-up activities. Airborne fiber levels (PCM) were not significantly different for simulations involving talc spiked with asbestiform and non-asbestiform tremolite (p = 0.6104). For application and clean-up of talc spiked with 0.005% asbestiform tremolite, 2 of 24 (8.3%) samples were above the LOD for TEM (0.003 f/cc). For application of talc spiked with 0.1% asbestiform tremolite, 21 of 24 (87.5%) were above the LOD for TEM. The corresponding mean PCME asbestos concentrations were 0.016 f/cc for puff and shaker for samples collected in the first 15 min, 0.002 f/cc for puff and 0.004 f/cc for shaker in the second 15 min, and 0.005 f/cc for puff and 0.013 f/cc for shaker for the full 30 min. Mean PCME concentrations for samples collected during clean-up following application of talc spiked with 0.1% asbestiform tremolite were 0.003 f/cc for samples collected in the first 15 min following puff application, 0.005 f/cc for samples collected in the second 15 min following shaker application, and 0 f/cc for the remaining clean-up samples. Using the EPA's exposure factors, we determined the range of cumulative asbestiform fiber exposures that would result from product use, assuming asbestiform tremolite was present at 0.1%.

Beyond descriptive statistics: using additional analyses to determine the technological feasibility of meeting a new exposure limit.

In determining whether a new permissible exposure limit is technologically feasible, the Occupational Safety and Health Administration (OSHA) and various courts have used poorly defined criteria such as whether "most employers most of the time" are able to comply with a standard. This vague definition creates problems when employers try to determine the best way to protect their workers and estimate the costs to remain in compliance with the permissible exposure limit. This article proposes a more rigorous approach to determine feasibility. By utilizing the best available statistical methods, employers and rule makers can better understand the variability within existing exposure data to determine the feasibility of new exposure limits. There are several readily available statistical tools that can be used for this purpose. To illustrate these techniques, a subset of data from the foundry industry and analysis from the OSHA respirable crystalline silica rulemaking proceeding are compared to methods published by the National Institute for Occupational Safety and Health in 1977 and a more sophisticated Bayesian approach. The results of this analysis suggest that complying with a new permissible exposure limit is more challenging than what is implied by OSHA's analysis, and calls into question its method of determining compliance. In the same vein, OSHA should move away from assessing compliance based on individual measurements and instead use a statistical approach to determine if a workplace is in compliance. These changes will encourage employers to better characterize occupational exposures, and will ultimately lead to better protection for employees while also providing employers protection from violations due to one-off overexposures.

Optimal object association in theDempster-Shafer framework.

Object association is a crucial step in target tracking and data fusion applications. This task can be formalized as the search for a relation between two sets (e.g., a sets of tracks and a set of observations) in such a way that each object in one set is matched with at most one object in the other set. In this paper, this problem is tackled using the formalism of belief functions. Evidence about the possible association of each object pair, usually obtained by comparing the values of some attributes, is modeled by a Dempster-Shafer mass function defined in the frame of all possible relations. These mass functions are combined using Dempster's rule, and the relation with maximal plausibility is found by solving an integer linear programming problem. This problem is shown to be equivalent to a linear assignment problem, which can be solved in polynomial time using, for example, the Hungarian algorithm. This method is demonstrated using simulated and real data. The 3-D extension of this problem (with three object sets) is also formalized and is shown to be NP-Hard.