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Unique atmospheric microbiota patterns for 31 major Chinese cities

相对物种丰度 相对湿度 生物 丰度(生态学) 生态学 空气污染 微粒 环境科学 地理 气象学
作者
Lu Zhang,Maosheng Yao
出处
期刊:Atmospheric Environment [Elsevier BV]
卷期号:315: 120143-120143 被引量:13
标识
DOI:10.1016/j.atmosenv.2023.120143
摘要

Microbial aerosol deposition and exposure represent a major health threat toward humans and ecology. Here, particulate matter (PM) samples from a total of 465 automobile air conditioning (AC) filters from 31 major Chinese cities were analyzed for the airborne patterns of bacterial, fungal and RNA viral communities and their influencing factors. Atmospheric microbiota were shown to vary greatly among 31 major cities. The relative abundances of some bacterial genera were extremely high, e.g., in Changsha PM about 20 times than other some cities for Brevibacterium. Significant correlations were found between latitude and bacteria diversity indexes (Shannon and Simpson index: negative correlation) as well as the relative abundance of some bacterial, fungal and viral genera (p-value <0.05). The relative abundances of genus Aspergillus were found much higher in PM samples from Changsha (51.22%) and Shanghai (23.25%) than other cities. Varying RNA virus structures were detected in 16 cities’ samples with family Virgaviridae abundant from over half of the 16 cities. Using redundancy (RDA), variance partition (VPA) and aggregated boosted tree (ABT) analysis, the work showed that environmental factors (including geography factors), especially relative humidity, have played a leading role on the fungal structures, followed by air pollutants and social factors. In contrast, social factors, especially the secondary industry, had the greatest impact on the bacteria community structures, followed by environmental factors and air pollutants. However, only temperature was shown to be correlated with the relative abundances of top 20 bacteria and fungi genera (random forest regression R2 = 0.774). Here, distinctive microbiota patterns were detected being formed by environmental, social-economic and ecological factors, and further influenced by interactions among the microbial agents across 31 geophysical locations.
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