Bioaerosols are crucial for cloud and rain formation and pose significant risks to human health. Especially since COVID-19, people have become increasingly concerned about bioaerosols in the atmosphere. However, existing biological indicators in air quality metrics have certain limitations, and the methods are costly and complicated. This study uses the microbial metabolic activity in atmospheric particles as an indicator of microbial concentration. Based on one - year atmospheric monitoring data from Beijing's Huairou District and using Principal Component Analysis (PCA) and (Variable Importance in Projection) VIP analysis, it reveals the relationship between environmental factors and PM_2.5 microbial activity, taking into account the lagged response of microbes to environmental factors. Significant seasonal variations in microbial activity within PM were observed, with higher levels recorded in winter and under fair weather conditions compared to other seasons and weather conditions. PM_2.5 concentration had a significant negative correlation with MA in PM_2.5. the significant negative correlation between O₃ and CO in air pollutants and MA in PM_2.5 is stronger than that in PM_10. meteorological factors such as air temperature and humidity were more strongly negatively correlated with MA in PM_2.5 than in PM₁₀ on a year-round time scale. Notably, according to the spearman correlation coefficients between environmental factors and microbial activity at different particle sizes, the microbial activity in PM_2.5 was more sensitive to environmental factors than the microbial activity in PM₁₀. Seasonal variations in microbial activity within PM_2.5 were primarily driven by a combination of air pollution and meteorological factors, with ozone exerting the greatest influence on PM_2.5 microbial activity in summer. Additionally, environmental factors improved the accuracy of microbial activity predictions in summer compare to other seasons. These findings provide strong evidence that environmental factors can be used to predict bioaerosol pollution, offering a cost-effective alternative to traditional monitoring methods, at the same time, contributing to the understanding of bioaerosol contamination.
 

Particulate matter; Microbial Activity; Environmental factor; Prediction