Predictive modelling of microbial contamination and antibiotic resistance risks in subway bioaerosols: correlation with environmental factors in central China

Despite the global prevalence of subway systems, the environmental drivers of bioaerosol microbial communities and antibiotic resistance genes (ARGs) remain poorly characterized. This research investigated the microbial composition, ARG distribution, and associated health risks in subway bioaerosols across central China, using high-throughput 16S rRNA sequencing and polymerase chain reaction (qPCR). This study employs response surface methodology (RSM) to model and predict microbial-ARG dynamics in subway bioaerosols identifying key environmental factors. Statistical approaches like Redundancy Analysis (RDA) were applied to assess patterns and environmental influences on microbial and ARG profiles. Results revealed an average culturable bacterial concentration of 133 CFU/m³, with peak levels at station entrances (230 CFU/m³) and minimal detection on platforms (35 CFU/m³). Dominant genera included Bacillus, Staphylococcus, and Enterococcus, while humidity and temperature correlated significantly with pathogen abundance (Escherichia coli, Pseudomonas). ARG profiling identified high temporal variability, with beta-lactamase, multidrug, and tetracycline resistance as predominant. RDA results revealed that many bacterial genera were positively associated with humidity, temperature, and lighting, while wind speed had a negative correlation. Fine particles (≤3.3 μm) comprised the majority of bioaerosols, while health risk assessment indicated that the health risk due to inhalation of culturable bacteria was within acceptable limits. This study highlights the role of environmental factors in shaping dynamics of subway microbes and ARGs and provides a framework for monitoring airborne health risks in urban transit systems.