In the aftermath of a release of microbiological agents, environmental sampling must be conducted to characterize the release sufficiently so that mathematical models can then be used to predict the subsequent dispersion and human health risks. Because both the dose-response and environmental transport of aerosolized microbiological agents are functions of the effective aerodynamic diameter of the particles, environmental assessments should consider not only the total amount of agents but also the size distributions of the aerosolized particles. However, typical surface sampling cannot readily distinguish among different size particles. This study evaluates different approaches to estimating risk from measurements of microorganisms deposited on surfaces after an aerosol release. For various combinations of sampling surfaces, size fractions, HVAC operating conditions, size distributions of release spores, uncertainties in surface measurements, and the accuracy of model predictions are tested in order to assess how much detail can realistically be identified from surface sampling results. The recommended modeling and sampling scheme is one choosing 3, 5, and 10 μm diameter particles as identification targets and taking samples from untracked floor, wall, and the HVAC filter. This scheme provides reasonably accurate, but somewhat conservative, estimates of risk across a range of different scenarios. Performance of the recommended sampling scheme is tested by using data from a large-scale field test as a case study. Sample sizes of 10-25 in each homogeneously mixed environmental compartment are sufficient to develop order of magnitude estimates of risk. Larger sample sizes have little benefit unless uncertainties in sample recoveries can be reduced.
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 December 2014.
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#22,374,895
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