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. 2025 Jun 27;28(7):113015.
doi: 10.1016/j.isci.2025.113015. eCollection 2025 Jul 18.

Integrating air microbiome for comprehensive air quality analysis

Sofya Pozdniakova  1 , Akira Uchida  2 , Alejandro Fontal  1 , Lídia Cañas  1 , Samuel Santamaria  1 , Lim Yee Hui  2 , Irvan Luhung  2 , Stephan C Schuster  2 , Xavier Rodó  1   3 , Sílvia Borràs  1
Affiliations

Integrating air microbiome for comprehensive air quality analysis

Sofya Pozdniakova et al. iScience. .

Abstract

Air quality monitoring typically overlooks the biological composition of airborne particles, despite its relevance to human health. This study evaluated the feasibility of using filters from high-volume air samplers, widely employed in air quality networks, to analyze bioaerosol content through shotgun metagenomic sequencing. We developed a DNA extraction method for ultra-low biomass samples and assessed the impact of sampling duration, particle size selection, and filter material on microbial diversity. Our findings show that prolonged continuous sampling reduces species detection, while larger particle size selectors capture a broader range of microbial content, particularly fungi. Comparisons with a dedicated bioaerosol sampler confirmed that these filters can yield comparable results. This work demonstrates that existing air quality infrastructure can be leveraged for airborne microbiome monitoring, offering a practical and cost-effective approach to integrate biological data into routine assessments and support a more comprehensive understanding of air quality and its implications for public health.

Keywords: Environmental monitoring; Microbiology; Microbiome.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Air biomass collection using HVS Four experiments for air biomass collection were conducted: Experiment 1: sampling duration (1 day vs. 3 or 6 days); Experiment 2: sampling head (PM2.5 vs. PM10); Experiment 3: filter material (quartz vs. PTFE coated glass filters); Experiment 4: sampling device (HVS vs. SASS) (A). Top 40 species identified in samples and blanks using shotgun metagenomics sequencing (blanks are used as a reference). Red box indicates blank samples (B). See also Figures S2 and S3, and Table S1.
Figure 2
Figure 2
Experiment 1. Impact of sampling duration, continuous vs. discrete, on microbial diversity Sankey diagram representing the common and unique species observed in continuous vs. average discrete sampling (A). Top 40 airborne species detected in samples collected over 3 or 6 days continuous vs. 1-day discrete sampling for 3 or 6 days, respectively. Average (av) of 3- or 6-day discrete sampling is shown in the plots (B). See also Figures S4 and S8.
Figure 3
Figure 3
Experiment 2 Microbial diversity of PM2.5 and PM10 fractions collected for 1 day. Taxonomic profiling of organisms uniquely detected in PM2.5 (A right panel) and PM10 (A left panel) fractions. Top 40 airborne species with varied abundance detected in PM2.5 and PM10 average samples (B). See also Figures S4, S9, and S10, and Table S2.
Figure 4
Figure 4
Experiment 3 Impact of filter material on the diversity of collected airborne microorganisms. Top 40 airborne species detected in samples collected on either micro-quartz fiber (QZ) or PTFE-coated glass fiber (PTFE) filters during 24 h over 3 different days (A). Beta diversity of air samples collected on QZ and PTFE filters evaluated by Bray-Curtis and Jaccard PCoA (B). See also Figure S4.
Figure 5
Figure 5
Experiment 4 Sampling devices, comparison of HVS vs. SASS. A. Overall abundance of top 40 identified species from the two different sampling devices. Data is averaged from the 3 different days of the same experimental setting (A). Occupancy analysis of bacterial pathogens detected in HVS and SASS samplers. Red indicates detection of at least one read (B). Beta diversity of air samples collected by HVS and SASS evaluated by Bray-Curtis and Jaccard PCoA (C). See also Figures S4 and S11.
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