## 4 Discussion  
This study is the first comparative analysis to quantitatively characterize baseline differences between closely situated natural and urban environments in terms of specific PM features and the combined genomic sequencing analysis of microbiome content. The statistical analysis of particle features indicates that the two locations can be differentiated based on complex shape parameter analysis of airborne particles. In particular, particle width emerged as the most robust distinguishing feature, with statistically significant differences observed in the 0.52-1.66 \(\upmu\)m range. This difference likely reflects a higher concentration of elongated, rod-shaped particles in one of the environments. Additionally, findings from non-quantitative genomic analysis suggest that certain bacterial strains may contribute to these particle-based quantitative differences (Tables 4 and 5). The results demonstrate the successful identification of a broad spectrum of microbial taxa, especially bacteria (Tables 4 and 5, Figures 8 and 9). Notably, the data revealed significant differences in microbial community composition and PM feature distributions between the sampling sites (urban parking area and forest/lake site), suggesting that environmental factors may influence the detection of bioagents, which could be either intentionally or unintentionally released into the air. Furthermore, the findings highlight the potential of combining air-based environmental DNA sampling with quantitative phase imaging (QPI) microscopy as a non-invasive tool for monitoring microbial diversity. \begin{table}
\begin{tabular}{c c c} \hline
**No** & **Microorganism** & **Size Approx.** \\ \hline
1 & _Escherichia coli_ & 1–1.5 \(\times\) 2–6 \(\upmu\)m \\
2 & _Escherichia marnotae_ & 0.5–1 \(\times\) 1–2.5 \(\upmu\)m \\
3 & _Salmonella enterica_ & 0.7–1.5 \(\times\) 2–5 \(\upmu\)m \\
4 & _Shigella flexerii_ & 0.4–0.6 \(\times\) 1–3 \(\upmu\)m \\
5 & _Marinobacterium_ sp. LSUCC0821 & 0.5–0.7 \(\times\) 1.6–2.3 \(\upmu\)m \\
6 & _Acinetobacter seifertii_ & 1.0–1.5 \(\times\) 1.5–2.5 \(\upmu\)m \\
7 & _Acinetobacter johnsonii_ & 0.9–1.6 \(\times\) 1.5–2.5 \(\upmu\)m \\
8 & _Moraxella osloensis_ & 1–1.5 \(\times\) 1.5–2.5 \(\upmu\)m \\
9 & _Pseudomona olevorans_ & 0.5–1.0 \(\times\) 1.5–3 \(\upmu\)m \\
10 & _Pseudomona denitrificans_ & 0.8 \(\times\) 1 \(\upmu\)m \\
11 & _Pseudomona_ sp. R32 & 0.5 \(\times\) 0.8 \(\upmu\)m \\
12 & _Brevendomonas neajgansensis_ & 0.4 \(\times\) 2.3 \(\upmu\)m \\
13 & _Mesorhizobium opportunistium_ & 0.4–0.9 \(\times\) 1.2–3 \(\upmu\)m \\
14 & _Rhizobium_ sp. Changiran 2 & 0.5–0.9 \(\times\) 1–3 \(\upmu\)m \\
15 & _Sinorhizobium meliloti_ & 0.8 \(\times\) 2 \(\upmu\)m \\
16 & _Roseomonas_ sp. FDA ARGOS\_362 & 0.9 \(\times\) 2.2 \(\upmu\)m \\
17 & _Sphingomonas_ sp. HMP9 & 0.3–0.8 \(\times\) 1–2.7 \(\upmu\)m \\
18 & _Comamonas testosterone_ & 0.5 \(\times\) 2 \(\upmu\)m \\
19 & _Hydrogenophag_ sp. BA0156 & 0.4–0.5 \(\times\) 0.7–1.3 \(\upmu\)m \\
20 & _Rhodoferax saidenbuchenisis_ & 0.9 \(\times\) 1.8 \(\upmu\)m \\
21 & _Sandaracimus anylolytica_ & 0.8 \(\times\) 4.5 \(\upmu\)m \\
22 & _Flavobacterium anntineine_ & 0.3 \(\times\) 1.7 \(\upmu\)m \\
23 & _Acinetomyces oris_ & 0.4 \(\times\) 1 \(\upmu\)m \\
24 & _Corynebacterium ulcerans_ & 0.5 \(\times\) 3–5 \(\upmu\)m \\
25 & _Dernacoccus nishinomiyaensis_ & 0.9 \(\times\) 1.6 \(\upmu\)m \\
26 & _Rhodoluna lacicola_ & 0.05 \(\upmu\)m \\
27 & _Rathayibacter_ sp. VKM Ac-2759 & 0.5–0.7 \(\times\) 1.1–2 \(\upmu\)m \\
28 & _Glutamicibacter anilaitensis_ & N/A \\
29 & _Cutibacterium acnes_ & 0.4–0.7 \(\times\) 3–5 \(\upmu\)m \\
30 & _Aeronomicobium_ sp. 592 & 0.5 \(\times\) 1.2 \(\upmu\)m \\
31 & _Bacillus coagulans_ & 0.9 \(\times\) 3–5 \(\upmu\)m \\
32 & _Lactococcus lactis_ & 0.5 \(\times\) 1.5 \(\upmu\)m \\ \hline \end{tabular}
\end{table} 
Table 5: The list of microorganisms detected in the parking/urban site with their approx. sizes. Figure 8: Phylogenetic relationships among microorganisms detected in forest/lake location.