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Updated: Nov 27, 2025

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
Published on: January 7, 2019
Survey of background microbial index in inhalable particles in Beijing
Hanlin Chen1, Rui Du1, Yongtao Zhang1
1College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Abstract:
As a potential transmission route for diseases, aerosols have an important impact on human health. At present, research concerning the biological components of atmospheric particulate matter (PM) is of increasing interest. However, previous research has mainly focused on serious pollution conditions, creating a knowledge gap regarding background atmospheric microbes. In this study, we observed the atmosphere of Huairou in Beijing for one year, analyzed the characteristics of the physiological metabolic activity of the microorganisms as an index to determine the air quality, and further explored the microbial communities. From January 2018 to January 2019, a total of 157 days of microbial activity data for PM2.5 and PM10 were obtained through the use of a modified fluorescein diacetate (FDA) hydrolysis method. Our results showed that there was no significant difference between the microbial activity of PM2.5 and PM10, even though there was significant seasonal variation. At increasing pollution levels, the results showed that the microbial activity decreased at first, and then increased as the conditions worsened. The microbial community of PM2.5 was analyzed using the high-throughput sequencing method. There were significant seasonal differences in species richness and community diversity of bacteria in PM2.5, whereas there was variation only in its fungi species richness. Notably, the microbial community dominated by bacteria has a significant influence on microbial activity. From the perspective of microbial community composition, this study uncovered the possible causes of microbial activity variation and identified the key bacteria and fungi. These results will provide a theoretical basis for both improving air biological pollution predictions and ambient air quality evaluations.
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