• Article

A model to predict the breathing zone concentrations of particles emitted from surfaces

Activity based sampling (ABS) is typically performed to assess inhalation exposure to particulate contaminants known to have low, heterogeneous concentrations on a surface. Activity based sampling determines the contaminant concentration in a person's breathing zone as they perform a scripted activity, such as raking a specified area of soil, while wearing appropriate sample collection instrumentation. As an alternative approach, a probabilistic model based on aerosol physics and fluid dynamics was developed to predict the breathing zone concentration of a particulate contaminant emitted from a surface during activities of variable intensity. The model predicted the particle emission rate, tracked particle transport to the breathing zone, and calculated the breathing zone concentration for two scenarios. One scenario used an Eulerian model based on a Gaussian concentration distribution to quantify aerosol exposure in the trailing wake of a moving object. The second scenario modeled exposure in a quiescent environment. A Lagrangian model tracked the cumulative number of individual particles entering the breathing zone volume at a particular time. A Monte Carlo simulation calculated the breathing zone concentration probability distribution for each scenario. Both models predicted probability distributions of asbestos breathing zone concentrations that bracketed experimentally measured personal exposure concentrations. Modeled breathing zone concentrations were statistically correlated (p-value < 0.001) with independently collected ABS concentrations. The linear regression slope of 0.70 and intercept of 0.03 were influenced by the quantity of ABS data collected and model parameter input distributions at a site broader than those at other sites


Thornburg, J., Kominsky, J., Brown, G., Frechtel, P., Barrett, W., & Shaul, G. (2010). A model to predict the breathing zone concentrations of particles emitted from surfaces. Journal of Environmental Monitoring, 12(4), 973-980. https://doi.org/10.1039/b919385e

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