Abstract

Air sampling is traditionally used to assess exposure to airborne contaminants. However, failing to account for exposure variability by taking only a few measurements, and failing to record information on exposure determinants for the time periods monitored, substantially limit the conclusions one can draw from the data. Mathematical modeling offers a partial solution to this problem, and is a useful adjunct to exposure monitoring. The modeling approach is illustrated with two examples; the model predictions agree with limited air monitoring data. The first example is estimating short-term benzene exposure levels (specifically, 15-min time weighted averages, TWAs) among delivery truck drivers while offloading gasoline to small above-ground tanks. The benzene mass emission rate from the tank headspace is assumed to be constant and a function of the benzene content of the gasoline, temperature, and tank fill rate. Vapor is emitted from the fill hole into a surrounding near field zone which contains the driver’s breathing zone; airflow into the near field depends on the local random air speed. Monte Carlo simulation is used to examine variability in 15-min TWA exposure; a substantial proportion are predicted to exceed the 15-min TWA benzene exposure limit of 5 ppm. The second example is estimating ethylene oxide (ETO) gas levels in a well-mixed room due to use of a desktop medical sterilizer device. The variable ETO mass emission rate was modeled based on information provided in the product literature, and was used to derive a general equation for the room ETO concentration given a set of room volume and dilution ventilation values. For a poorly ventilated room, the model predicted that, the average concentration during the sterilization phase could exceed the 8-hr TWA ETO exposure limit of 1 ppm, and that during the sterilization and aeration phases the 15-min TWA ETO limit of 5 ppm could be exceeded.

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