Baseline Particulate Matter Characteristics and Microbial Composition in Air Samples from Natural and Urban Environments: A First Combined Genomic and Microscopy Analysis

Abstract

Featured Application: The rapid detection and identification of biological agents, crucial for medical diagnostics, environmental monitoring, and biodefense, remains a significant challenge. Combining microscopic analysis with genetic studies enables the identification of biomass, and when paired with the growing field of deep machine learning, these methods could swiftly detect changes in particulate matter and microbial composition, particularly in the context of chemical, biological, radiological, and nuclear (CBRN) threats. Conducting such analyses on a large scale holds significant potential for creating a unique database, enabling the comparison of airborne biomass and accelerating the detection of anomalies, such as the intentional or unintentional release of bacteria into the environment. This study examines the differences in particulate matter (PM) properties and microbial compositions between natural and urban environments, providing foundational data for environmental monitoring and biothreat detection. Air samples were collected during the spring and early summer from two distinct locations: a forest/lake area, and an urban parking lot adjacent to a high-traffic roadway. Quantitative phase imaging microscopy and genomic sequencing were employed to characterize particle size distributions, statistical properties, and microbial community structures in these environments. The results revealed significant differences in PM properties between the two locations. Urban air exhibited higher particle concentrations that reflect pollution sources, whereas the natural environment displayed greater variability in particle size and distribution, correlating with diverse biological content. Genomic sequencing showed a lower diversity of microbial communities compared to the forest/lake area but with greater uniformity. To sum up, by integrating optical microscopy and genomic sequencing, this research demonstrates the feasibility of establishing environmental baselines for PM characteristics and bio-component diversity. The findings underscore the potential of combining real-time optical sensing with genomic tools for early biothreat detection and improved environmental monitoring in diverse settings.