The bacteria we breathe in every day

In a recent study published in the journal PNAS, researchers studied global airborne bacterial communities to understand their community structure and biogeographic distribution patterns. Additionally, they examined their interactions with other Earth microbiomes, particularly surface habitats.

Study: Global airborne bacterial community – interactions with terrestrial microbiomes and anthropogenic activities. Image Credit: Lightspring / Shutterstock


The atmosphere is the most intact microbial habitat on Earth, and airborne bacteria are the most complex and dynamic communities that influence Earth’s microbiomes. There are more than 1 × 104 bacterial cells/m3 and hundreds of unique taxa in the air. Large-scale studies have systematically documented the microbial characteristics of soil, ocean, and human waste. Furthermore, they suggested a relationship between airborne microbiomes and surface environments. However, there is a lack of studies documenting airborne microorganisms, especially regarding their community structure.

Microbes do not live in isolation. Instead, they have multiple ecological relationships, ranging from mutualism to competition. Thus, determining their biogeographic distribution patterns and their interactions with other Earth microbiomes, which define their origins, could shed light on the effects of climate/environmental change and anthropogenic activities.

About the study

In the present study, the researchers first developed a dataset of global airborne bacteria to assess their degree of similarity and interrelationships. This dataset included 76 newly collected airborne particle samples combined with 294 samples collected for previous studies at 63 sites around the world. Sampling sites varied in elevation and geography and encompassed ground level to rooftops (1.5 m to 25 m high) to mountains to 5380 m above sea level. sea, densely populated urban cities and the distant Arctic Circle.

The team obtained the dataset for comparison from the Earth Microbiome Project (EMP), which accumulated more than 5,000 samples from 23 surface environments. The reference catalog of airborne bacteria contained over 27 million non-redundant 16S ribosomal RNA (rRNA) gene sequences.

In addition, researchers constructed a global airborne community co-occurrence network encompassing 5,038 significant correlation relationships (Spearman’s ρ > 0.6) among 482 connected operational taxonomic units (OTUs). OTUs are analytical units grouped by DNA sequence similarity in microbial ecology. Finally, the team used structural equation modeling (SEM) to explore the mechanisms behind microbial communities. Similarly, they calculated the total effects of environmental filtering and bacterial interactions on community formation.

The structure of airborne bacterial communities distributed worldwide. (A) Locations where air samples and environmental data were collected around the world. (B) The number, proportion, and relative abundance of global basal OTUs compared to those of remaining bacterial OTUs. (C) The taxonomic composition of global core bacteria at the phylum and class level. (D) The global airborne bacterial community co-occurrence network. The connections (edges) represent a strong (Spearman’s ρ > 0.6) and significant (p

Study results

There were 10,897 taxa detected from 370 individual air samples, and most bacterial sequences belonged to five phyla. Firmicutes, Alphaproteobacteria, Gammaproteobacteria, Actinobacteriaand Bacteroidetes constituted respectively 24.8%, 19.7%, 18.4%, 18.1% and 8.6% of these bacterial sequences. The abundance-occupancy relationship (AOR) between samples occupied by a bacterial taxon and its average mass in global air showed a sigmoid curve, similar to the pattern observed for the distribution of wild animals and plants on Earth.

Air is a fluid and dynamic ecosystem allowing the long-range transport of the bacterial communities it carries. However, its bacterial community appeared well-connected to local environments, particularly source contributions and air quality conditions resulting from anthropogenic activities. Reduced environmental filtering effects and high contributions from human-related sources resulted in lower biomass loads, higher abundances of pathogenic bacteria, and more destabilized network structures.

Notably, compared to their counterparts in topsoil and marine environments, airborne bacteria were not tightly interconnected, with an average intranode connection of 5.24. They had a random clustering approach and the topology had low resistance to changes. The observed distant relationships and loose network clusters suggest that the airborne bacterial community is more susceptible to disruption depending on environmental conditions that typically lead to drastic changes in bacterial composition. The functions of atmospheric bacterial taxa have been inferred based on their genetic information in other habitats.

The team found potential associations between airborne bacterial communities and other surface microbial habitats. The total estimated abundance of global airborne bacteria (1.72 × 1024 cells) were comparable to those in the hydrosphere and one to three orders of magnitude smaller than those in other habitats (e.g. soil).

Of the 23 major terrestrial habitats studied in the current study, terrestrial air showed more similarities to human and animal environments, while marine air showed a closer relationship to ocean systems. Additionally, assessments based on Bayesian methods have shown that characteristics of the corresponding surface environment determine the dominant sources of airborne bacteria. Notably, human-related sources have contributed more to airborne bacteria in urban areas, especially at terrestrial sites, a finding that has been largely ignored in previous emissions modeling studies.

Role of airborne bacteria in the microbial world of the Earth. (A) Estimated global microbial abundance and richness in various habitats. The overall richness (S) and total abundance (N) in the corresponding habitats show a scaling relationship (the dashed orange line is the 95% prediction interval). Wealth was predicted from the lognormal model using Nmaximum inferred from our sequencing data (filled circles) or Nmaximum predicted from the dominance scaling law (open circles). The estimated values ​​of S and N for each habitat are, in themselves, a global sum. Some S and N were derived from previous studies. (B) A Bray-Curtis-based non-metric multidimensional scaling (NMDS) plot shows that different microbial habitats harbor different bacterial communities on Earth (n=5,189). The Bray-Curtis distance was calculated to represent dissimilarities in the composition of bacterial communities. (C) Earth’s bacterial co-occurrence network shows the interconnecting relationships between 23 major microbial habitats. The connections (edges) represent a strong (Spearman’s ρ > 0.7) and significant (p

The authors noted no substantial disparity in the richness of airborne bacterial communities between urban and natural areas within the same latitude range. However, geographical location played a role. Thus, the evenness of bacterial communities was much lower in urban air. For example, the relative abundance of pathogenic species, Burkholderia and Pseudomonas, was higher in urban areas than in natural areas (5.56 and 2.50% versus 1.44 and 1.11%). Additionally, bacteria contributed less to particulate matter (PM) mass in urban areas than in natural areas, indicating that urbanization has increased the proportion of non-biological particles in the air (e.g., dust).

The pathogens most at risk of mortality, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosaand Enterobacter (ESKAPE) were more abundant in urban air. The co-occurrence network of urban airborne bacterial communities indicated that anthropogenic impacts destabilized their network structure, which, in turn, also altered the bacterial taxonomic composition.

The authors noted that several factors impact airborne bacterial communities, for example, geographic locations as well as typical environmental factors. Biotic interactions between key and core bacterial communities, as well as bacterial richness, interacted significantly. Of all the deterministic processes, environmental filtering was the primary determinant of the structure and distribution of airborne microbial communities.


In summary, nearly 46.3% of airborne bacteria came from surrounding environments, and stochastic processes primarily shaped community assembly. Moreover, the distinguishing characteristic of airborne bacteria in urban areas was its increasing proportion composed of potential human-borne pathogens. Finally, airborne bacterial source profiles affected a significantly higher percentage of structural variation than air quality and local weather (43.7% versus 29.4% and 25.8%), such as as assessed by analysis of distribution of variations (APV).

Journal reference:

  • Global Airborne Bacterial Community – Interactions with Terrestrial Microbiomes and Human Activities, Jue Zhao, Ling Jin, Dong Wu, Jia-wen Xie, Jun Li, Xue-wu Fu, Zhi-yuan Cong, Ping-qing Fu, Yang Zhang, Xiao-san Luo, Xin-bin Feng, Gan Zhang, James M. Tiedje, Xiang-dong Li, PNAS 2022, DOI:, https://www.pnas. org/doi/full/10.1073/pnas.2204465119

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