Estimation of the Aerodynamic Sizes of Single Bacterium-Laden Expiratory Aerosols Using Stochastic Modeling with Experimental Validation

Abstract

The aerodynamic size of pathogen-laden expiratory aerosols plays an important role in their dispersion in air and deposition onto surfaces, both of which are related to the spread of infectious respiratory diseases. The size of bacterial cells is on a similar scale to the size of expiratory aerosols, but because some bacterial cells are nonspherical, bacterium-laden expiratory aerosols often have irregular shapes and highly variable aerodynamic sizes. An algorithm that can estimate their aerodynamic sizes is highly desirable in studying their physical transport and to assess the subsequent exposure level and infection risk. In this study, an algorithm based on stochastic modeling was developed to predict the distribution of the aerodynamic size of bacterium-laden expiratory aerosols. The applicability of the algorithm was tested experimentally by conducting biological air sampling using a multi-stage impactor in a test facility. The proposed algorithm was used to predict the size profile of simulated expiratory aerosols encasing a strain of benign rod-shaped bacterium. Simulated bacterium-laden expiratory aerosols were generated using a cough machine with a solution containing the bacteria. Air at three different positions was then sampled to obtain the size profile of bacterium-laden aerosols at each position. The results were compared to the prediction by the algorithm and by another method, which simply considers the evaporative shrinkage of the expiratory aerosols and neglects the inclusion of the pathogen. It was found that the prediction by the proposed algorithm generally matched the measured results much better than the method that neglects the inclusion of the bacterium. Limitations of the current algorithm and further research and development are also discussed in this article.

Copyright 2012 American Association for Aerosol Research

Acknowledgments

This research was financially supported by the Research Grants Council of the Hong Kong SAR Government through the GRF 611509 grant.