Development of a Flow Imaging Microscopy-based Method for Rapid and High-throughput Measurement of Fiber Count and Length Distributions
BON KI KU, Pramod Kulkarni, Centers for Disease Control and Prevention, NIOSH
Abstract Number: 111
Working Group: Health-Related Aerosols
Abstract
Measurement of the distribution of airborne fiber length and number count in an air sample is important to assess exposures to asbestos and elongated mineral particles (EMPs) in workplace atmospheres. Exposure to noncommercial EMPs with the potential for asbestos-like health effects is an emerging issue. Phase contrast microscopy (PCM; NIOSH method 7400) is commonly used for assessing inhalation exposure to airborne asbestos or other EMPs; however, the method is labor- and time-intensive, especially for dilute samples. In addition, PCM lacks the capability to accurately count, size, and identify all fibers collected on filters with robust counting statistics. In this study, a flow imaging microscopy (FIM) method was investigated for measurement of number and fiber length distribution. First, monodisperse polystyrene latex count and size standards with microspheres in the diameter range of 2 μm -70 μm were used to examine counting and sizing accuracies of the FIM method, with objective lens magnifications of 4X and 10X for spherical particles. Then, test glass fibers were either i) prepared as a suspension in deionized water from bulk fiber powder or were ii) aerosolized by vortex shaking of glass fiber powder and collected on cascade mesh micro-screens with different pore sizes to obtain fiber samples with different lengths. Sizing accuracy of the FIM for spherical particles was verified in the range 5-70 μm with less than 13 % and 3 % difference for 4X and 10X, respectively. FIM measurement of fiber length distributions with 10X for geometric mean lengths in the range of 14.0 to 20.0 μm was in reasonable agreement with the PCM, resulting in about a 12.4.% difference. The study demonstrates that the high-throughput measurement capability of FIM shows promise for conducting fiber analysis of workplace air samples at significantly reduced time and/or cost and robust counting statistics.