## 1 Introduction
However, no comparative analysis has yet been conducted to investigate the relationship between the genomic sequencing data of bacterial species and the PM characteristics of airborne particles within the bacterial size range across distinct geolocations sampled in the same season. Understanding the variability in PM size distributions and statistical characteristics across geolocations with distinct activity profiles, combined with the genomic sequence analysis of bacterial components, is essential for establishing a baseline for airborne biocomponent and biothreat detection [16]. Such baselines are critical for informed decision-making in response to both incident-related and naturally occurring outbreaks. To explore baseline differences in PM ranges and genomic bacterial content, two geolocations with contrasting activity profiles, yet in close proximity, were selected for air sampling: an urban area near a high-traffic roadway and a natural environment within a forested region. Due to recent technical advancements and several promising efforts to develop low-cost, portable microscope prototypes, artificial intelligence-powered microscopy has emerged as a practical tool for environmental air quality monitoring [13, 17, 18]. Additionally, this technology has proven useful for air bacterial content detection in scenarios driven by chemical, biological, radiological and nuclear (CBRN) threats, public safety, and security needs [19, 20]. However, comparative statistical data on the baseline size and shape characteristics of airborne particles remain unavailable for samples collected from nearby environmental sites with distinct activity profiles. Biological agents, especially bacteria, are very diverse and can be characterized by their diverse structure, shapes, and sizes. In regard to the bacteria size, the average range is from 0.2 m to 2 m, which allows them to be visualized by optical microscopy techniques [21]. The diversity of bacteria shapes allows us to identify them within their genus and distinguish them from other bacterial genera. On the basis of bacteria shapes, they can be classified into four categories: spherical bacteria, spiral bacteria, rod-shaped bacteria, and filamentous bacteria. Besides the four main bacteria shapes, irregular shapes are also present: appendage, pleomorphic, star-shaped, sheathed, stalk, spindle-shaped, lobed, trichome-forming, or rectangular. When examining the bacteria arrangements, they may group in pairs (diplo), in groups of four (tetrads), in grape like (staphylo) formations, or in chains (strepto) [22]. These characteristics can be tabulated and can create a form of a catalogue--database. The primary objective of this study was to assess the statistical differences in PM characteristics (including features like diameter, area, perimeter, length, and width) across two sites with contrasting human and natural activity profiles within a localized region. The secondary objective was to analyze the microbial, specifically bacterial, content of air samples from these locations. The rapid detection and identification of biological agents, whether for medical diagnostics, environmental monitoring, or biodefense, is a critical challenge in contemporary science. The results obtained from microscopic examinations combined with genetic studies, which enable the identification of the observed biomass, form the basis for the development of advanced techniques. These, in conjunction with the rapidly developing technology of deep machine learning, will allow for the rapid detection of changes in the characteristics of PM and microbial composition, particularly within the scope of CBRN threats.