Simulation Analysis of Inhalable Dust Sampling Errors Using a Multi-Component Error Model

Abstract

Measurements to characterize inhalable aerosol exposure are subject to random error even after sources of systematic error have been eliminated. For a fixed aerosol sampler geometry the random errors are due to the variability of measured and unmeasured parameters including ambient variables, quantification technique, and operation parameters. In this discussion we apply a multi-component error estimation model to size selective aerosol sampling with the well-known Institute of Occupational Medicine (IOM) inhalable aerosol sampler. Random errors due to typical variations in sampler flow control, timing, and mass determination were small, being approximately 3%. Similarly, random errors due to variations in wind velocity were reasonably small at approximately 10%. However, the bias introduced by wind velocity was notable, ranging from peak values of 17 to 27% depending on aerosol mass median aerodynamic diameter and geometric standard deviation. This modeling indicated that the combined influence of variations in sampler flow control, timing, mass determination, and ambient wind velocity on IOM performance appeared to be less than approximately 10%; however, bias at moderate wind velocities was shown to be important for the IOM sampler as suggested by other studies. The effects of sampler placement, angle of incidence of ambient wind velocity on the sampler, and head orientation of the exposed person are unknown at this time and need additional research.