Airborne bacteria are an important component of atmospheric fine particulate matter (PM2.5), yet the interactions between microbial communities and organic compounds remain poorly characterized. This study investigated the impact of alkane chain length on bacterial dynamics in outdoor PM2.5 using correlation analysis, generalized additive models, and network-based approaches. Among individual alkane species, C30 (n-triacontane) showed a consistent positive association with bacterial concentration in both simple and partial correlation analyses, whereas C20 (n-eicosane) and C24 (n-tetracosane) exhibited significant negative associations only after controlling for collinearity among alkanes. Grouped alkane classes (C20–C24, C25–C29, C30– C35) did not show statistically significant nonlinear effects on bacterial concentration in models using the full dataset. However, temperature demonstrated a strong nonlinear effect and acted as a modifier of alkane-bacteria relationships. Stratified generalized additive models revealed that under high-temperature conditions (≥ 14oC), all three alkane groups showed significant and localized nonlinear associations with bacterial concentration, with the strongest positive response observed for C30–C35 (p = 0.0011). Network analysis indicated that mid-chain alkanes (C20–C25) were positively linked to metabolically versatile genera such as Pseudomonas, Caldalkalibacillus, Pseudarthrobacter, Pigmentiphaga, and Janthinobacterium, whereas long-chain alkanes (C26–C35) were negatively associated with genera including Methylosinus, Pelomonas, and Pedomicrobium. These results suggest that alkane chain length acts as an ecological filter structuring bacterial communities present in PM2.5 and that hightemperature conditions (≥ 14oC) enhance these interactions by altering alkane phase behavior and particle stability. Understanding these coupled chemical and biological processes is therefore critical for anticipating future changes in air quality and emerging health risks.

Abstract
1. 서 론
2. 실험재료 및 방법
    2.1 PM2.5 포집 방법
    2.2 세균 농도 및 군집 구조 분석 방법
    2.3 알케인 분석 방법
    2.4 통계 분석 방법
3. 결과 및 고찰
    3.1 PM2.5 내 알케인과 박테리아 농도의 기초적 연관성 평가
    3.2 환경요인 제거 후 비선형 효과 및 온도 조건별차이
    3.3 PM2.5 내 알케인과 박테리아 속의 상호작용
4. 결 론
감사의 글
References

• 강수경(이화여자대학교 인간중심인공지능연구원) | Sookyung Kang (Human-Centered Artificial Intelligence Research Institute, Ewha Womans University)
• 이지이(이화여자대학교 환경공학과) | Ji Yi Lee (Department of Environmental Science & Engineering, Ewha Womans University)
• 조경숙(이화여자대학교 환경공학과) | Kyung-Suk Cho (Department of Environmental Science & Engineering, Ewha Womans University) Corresponding author
• 이지영(이화여자대학교 데이터사이언스학과) | Jenny Jiyoung Lee (Department of Data Science, Ewha Womans University)