Rapid Detection and Identification of Biogenic Aerosol Releases and Sources

Biogenic aerosols can be important contributors to aerosol chemistry, cloud droplet and ice nucleation, absorption and scattering of radiation, human health and comfort, and plant, animal, and microbial ecology. Many types of bioaerosols, e.g., fungal spores, are released into the atmosphere in response to specific climatological and meteorological conditions. The rapid identification of bioaerosol releases is thus important for better characterization of the above phenomena, as well as enabling public officials to respond quickly and appropriately to releases of infectious agents or biological toxins. One approach to rapid and accurate bioaerosol detection is to employ sequential, automated samples that can be fed directly into an image acquisition and data analysis device. Raman spectroscopy-based identification of bioaerosols, automated analysis of microscopy images, and automated detection of near-monodisperse peaks in aerosol size-distribution data were investigated as complementary approaches to traditional, manual methods for the identification and counting of fungal and actinomycete spores. Manual light microscopy is a widely used analytical technique that is compatible with a number of air sample formats and requires minimal sample preparation. However, a major drawback is its dependence on a human analyst's ability to distinguish particles and accurately count, size, and identify them. Therefore, automated methods, such as those evaluated in this study, have the potential to provide cost-effective and rapid alternatives if demonstrated to be accurate and reliable. An exploratory examination of individual spores for several macro- and microfungi (those with and without large fruiting bodies) by Raman microspectroscopy found unique spectral features that were used to identify fungi to the genus level. Automated analyses of digital spore images accurately recognized and counted single fungal spores and clusters. An automated procedure to discriminate near-monodisperse bioaerosol peaks from those for polydisperse ambient particulate matter successfully found biogenic peaks in both simulated and real mixtures of spores and urban and rural aerosols.