Estimating downwind concentrations of viable airborne microorganisms in dynamic atmospheric conditions

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Abstract
A Gaussian plume model has been modified to include an airborne microbial survival term that is a best-fit function of laboratory experimental data of weather variables. The model has been included in an algorithm using microbial source strength and local hourly mean weather data to drive the model through a summer- and winter-day cycle. For illustrative purposes, a composite airborne "virus" (developed using actual characteristics from two viruses) was used to show how wind speed could have a major modulating effect on near-source viable concentrations. For example, at high wind speeds such as those occurring during the day, or with short travel times, near-source locations experience high viable concentrations because the microorganisms have not had time to become inactivated. As the travel time increases, because of slow wind speed or longer distances, die-off modulation by sunshine, relative humidity, temperature, etc., potentially becomes increasingly predominant.