Seasonal Variations and Spatial Distribution of Indoor Airborne Microorganisms in a University Campus in Beijing: A Metagenomic Study

doi:10.21203/rs.3.rs-6789478/v1

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Seasonal Variations and Spatial Distribution of Indoor Airborne Microorganisms in a University Campus in Beijing: A Metagenomic Study

https://doi.org/10.21203/rs.3.rs-6789478/v1

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published 18 Dec, 2025

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As densely populated urban areas, the spatiotemporal distribution characteristics of airborne microorganisms on university campuses are critical to the health of students and faculty, as well as urban ecological safety. This study employed impact sampling and metagenomic sequencing techniques to systematically analyze the concentration, particle size distribution, and community characteristics of airborne microorganisms across different seasons (autumn, winter) and sites (male dormitory, female dormitory, classroom, computer room) at the Shunyi Campus of Capital Medical University. The average concentration of airborne microorganisms was 553.38 ± 277.06 CFU/m³. Concentrations varied by location, ranking from highest to lowest as: male dormitory (598.18 ± 314.59 CFU/m³) > computer room (566.55 ± 248.40 CFU/m³) > classroom (555.80 ± 246.19 CFU/m³) > female dormitory (495.09 ± 258.27 CFU/m³). Microbial concentration in winter (696.55 ± 306.36 CFU/m³) was significantly higher than in autumn (410.20 ± 138.94 CFU/m³, P < 0.001), with particle sizes predominantly concentrated between 0.65–2.1 µm (Stages VI and V). Metagenomic analysis revealed Bacillota (52%) and Pseudomonadota (19%) as the dominant phyla. Species diversity was significantly higher in winter than in autumn (Shannon index, P = 0.02), with the classroom exhibiting the highest number of species (2155). Opportunistic pathogens, such as Stutzerimonas stutzeri, were detected. Principal Coordinate Analysis (PCoA) and PERMANOVA indicated that seasonal variation was the primary driver of community structure (R²=0.152, P = 0.003), with location having a lesser impact. These findings demonstrate that both seasonal shifts and human activity shape the airborne microbiome in university indoor environments and provide a scientific basis for indoor air quality management and microbial risk prevention strategies in campus settings.

University campus

Airborne microorganisms

Particle size distribution

Metagenome sequencing

Microbial diversity

According to data from the Global Burden of Disease Study (2021), ambient particulate matter air pollution accounts for 8.0% of the overall global disability-adjusted life-years (DALYs), ranking first among 88 evaluated risk factors(Collaborators, 2024). As the main carriers of bioaerosols, fine particulate matter, such as PM2.5, PM10, serve as primary carriers for biological components. These particles can transport biological components like bacteria, viruses, fungi, allergenic pollen, and mold spores. Excessive concentrations may cause respiratory diseases and increase the risk of spreading infectious diseases(Aghapour et al., 2022). China's latest Indoor Air Quality Standard (GB/T18883-2022)(Health et al., 2022) has lowered the permissible threshold for total airborne bacteria concentration from 2500 CFU/m³ to 1500 CFU/m³, reflecting strengthened regulatory attention to the health risks posed by airborne microorganisms. As densely populated environments where students and stuff typically remain for 8 to 10 hours per day, university campuses have distinct public relevance in the prevention and control of indoor microbial air pollution.

Studies have shown that airborne microbial concentrations in many schools exceed recommended safety thresholds(XN et al., 2024). In some universities, monitoring data indicate that total bacteria counts can reach up to 7.0×10⁴ CFU/m³, exceeding the GB/T18883-2002 standard by a factor of 28(MY et al., 2018). The concentration of airborne microorganisms is closely associated with ambient particulate matter levels(Brągoszewska & Mainka, 2024), and microbial aerosols with different particle sizes have different effects on human health(Jiang et al., 2022). Particle sizes smaller than 5 µm can penetrate deep into the respiratory tract, while those between 0.5 and 2 µm tend to deposite in the alveolar region, posing particularly significant health risks(Aghapour et al., 2022; Jiang et al., 2022).

However, most existing studies on airborne microorganisms in campus environments are limited by sampling methods, and only a few have employed dedicated air microbial samplers(CM et al., 2019), resulting in a lack of data on microbial particle size distribution. Prior research has shown that the particle size distribution of culturable microorganisms in air generally follows a unimodal pattern. For example, Ye Jin et al.(J, 2021) reported that the peak microbial concentrations were primarily observed at 1.1ཞ 2.1 µm (Stage V), especially in summer, and more than 90% of culturable bacteria and fungi were smaller than 4.7 µm (Stage III and above). Yu et al.(DJ et al., 2011) found that respirable fungal particles accounted for 60.5% of total fungal particles. Sun Fan et al.(F et al., 2019) showed that bacteria and fungi with particle sizes smaller than 4.7 µm accounted for 75.6% and 93.0%, respectively. These findings highlight the critical role of microbial particle size distribution in assessing potential health risks associated with airborne microorganisms.

Despite a substantial body of literature reporting the abundance, community composition and diversity of airborne microorganisms on university campuses(GQ & T, 2022; HQ, 2022), notable differences exist in the dominant bacterial genera across different geographical regions and climatic conditions. For example, in high-humidity areas such as Chongqing(H et al., 2014) and Nanjing(HL, 2009), Micrococcus, Bacillus and Pseudomonas are predominant, whereas in the arid region of Xinjiang(DD, 2018), dominant genera include Microbacterium, Acinetobacter, and Brevundimonas. In the northern coastal region of Shandong Province(M et al., 2019), Micrococcus luteus, Nocardia, and Achromobacter denitrificans, prevail, while in the southern coastal province of Zhejiang(W et al., 2014) and Fujian(Li et al., 2019), Bacillus subtilis and Bacillus megatherium are dominant. These findings suggest that optimizing indoor microbial communities’ structures require richer and more region-specific monitoring data.

Given the current scarcity of research data on airborne microorganisms in campus environments, this study selected the Shunyi Campus of Capital Medical University — characterized by a typical temperate monsoon climate, centralized heating, and high population density — as a representative case. Airborne microbial samples were collected using a six-stage Anderson air sampler and analyzed via metagenomic sequencing to assess microbial quantity, particle size distribution, species composition, and relative abundance of airborne microorganisms across different locations and seasons. This study aims to elucidate the distribution patterns of indoor airborne microorganisms in a typical temperate university setting and to provide a scientific basis for indoor air quality management and public health interventions in similar environments, ultimately contributing to the improvement of campus air quality and the protection of students and staff health.

2.1 Airborne microbiological sample collection and culture

In accordance with the Indoor Air Quality Standard (GB/T18883-2022)(Health et al., 2022), air sampling in this study was performed using an impact method with a six-stage Anderson air sampler (BY-300 type). Sampling was conducted at the Shunyi Campus of Capital Medical University (39.86° N, 116.35° E) during the autumn and winter seasons. The selected sampling sites included four locations closely associated with students’ daily activities: male dormitories (20 m², 4 occupants per room), female dormitories (20 m², 4 occupants per room), classroom (50 m², accommodating 40 students) and computer rooms (48 m², accommodating 45 persons). For rooms with an area between 25 and 50 m² (classrooms, and computer rooms), two sampling points were set up, and the higher microbial count between the two was recorded. For small rooms (< 25 m², dormitories), one sampling point was used. At each sampling point, three parallel measurements were taken, and the average value was calculated. Sampling was conducted during two distinct periods: autumn (September to October 2023) and winter (November to December 2023). For each functional area, sampling was scheduled during peak occupancy periods: in classrooms and computer rooms, samples were collected during class breaks 90 minutes after the start of lectures; in dormitories, sampling was conducted between 16:00 and 17:00. The sampling height was set between 1.2 and 1.5 meters, with a sampling time is 10 minutes and the flow rate of 28.3 L/min. For each site and season, approximately 20% of available rooms were randomly selected based on actual usage conditions.

The collected air samples were cultured on nutrient agar (CM107, Beijing Luqiao), a medium suitable for bacterial growth, at 36℃±1°C for 48 hours. After incubation, the total number of colonies on each plate was counted. The concentration of airborne microorganisms in indoor air (CFU/m³) was calculated according to Eq. (1):

Number of airborne microorganisms (CFU/m³) =\(\:\frac{1000N}{QT}\) (1)

Where N represents the total number of colonies counted on all plates (CFU); Q is the preset flow rate of the sampler (L/min), and T is the sampling time (min).

2.2 Microbial sample collection

A volume of 2ཞ4 mL of sterile physiological saline was added to each counted agar plate. Using a sterile inoculation loop, the surface of the plate was gently scraped to ensure that all culturable microorganisms were eluted into the saline solution. Eluates from the same sampling site were pooled into a single centrifuge tube and centrifuged at 3000Íg for 10 min at 4 ℃ using a refrigerated centrifuge. Subsequently, the supernatant was discarded, and the precipitated fraction was collected and frozen to be uniformly used for subsequent assay analysis.

2.3 Metagenomic sequencing and bioinformatics analysis

Read-based metagenomic sequencing was employed to analyze the microbial samples. Metagenomics, also referred to as environmental genomics or microbiome sequencing, is a DNA-based technique that enables the direct characterization of microbial species and their functional composition from environmental samples under natural conditions(Be et al., 2015).

In this study, genomic DNA was extracted from autumn and winter samples using the CTAB method. DNA concentration, integrity, and purity (OD₂₆₀/₂₈₀>1.8) were detected using an Agilent 5400 system (Agilent, USA). DNA libraries were constructed using the NEB Next® Ultra TM DNA Library Prep Kit for Illumina (NEB, USA, Catalog #: E7370L). After quality assessment, the samples were sheared into 350 bp fragments using a Covaris ultrasonicator. Subsequent steps included end repair, A-tail, adapter ligation, fragment size selection, PCR amplification, and purification to complete library preparation. The PCR-amplified products were purified by the AM Pure XP system (Beverly, USA), and the library quality was evaluated with the Agilent 5400 system. The final library concentration was quantified by QPCR (1.5 nM). Qualified libraries were pooled according to effective concentration and sequencing output requirements, and paired-end sequencing (2 × 150 bp) was performed on an Illumina NovaSeq platform. Raw metagenomic data were obtained, representing the microbial communities (bacteria and fungi) present in the samples.

To ensure the reliability of sequencing data, Raw reads were preprocessed using KneadData. The specific processing steps were as follows: ① Adapter trimming and quality filtering were performing using Trimmomatic with the parameters ILLUMINACLIP: adapters_path:2:30:10 to remove adapter sequences, SLIDINGWINDOW:4:20 to discard low-quality reads (average quality score threshold ≤ 20), MINLEN:50 to eliminate reads shorter than 50 bp. ② To remove potential host-derived contamination, host genome alignment was conducted using Bowtie2 (Bowtie2, http://bowtie-bio.sourceforge.net/bowtie2/index.shtml.) with --very-sensitive parameter. Reads aligning to the host genome were filtered out to retain non-host, high-quality sequences for downstream analysis. ③Quality control effectiveness was further assessed using Fast QC, ensuring the integrity and usability of the filtered reads(Langmead & Salzberg, 2012; Martin, 2011; Schmieder & Edwards, 2011).

For taxonomic profiling, all valid reads were annotated using Kraken2 (v2.0.7-beta) against a custom microbial nucleotide database constructed by integrating NCBI RefSeq and GTDB genomes. Subsequently, Bracken (v2.0) was employed to re-estimate the relative abundance at nine taxonomic ranks based on Bayesian modeling of the Krahen2 output(Brum et al., 2015; Lu et al., 2017; Mandal et al., 2015; Wood & Salzberg, 2014).

2.4 Statistical analyses

All statistical analyses and visualizations were conducted in R software (version 3.3.2)(Core Writing et al., 2009). First, the non-parametric Kruskal-Wallis test was employed to evaluate differences in total microbial colony counts across different seasons and locations. To analyze differences in relative abundances of microbial taxa obtained from sequencing, a two-way analysis of variance (ANOVA) was performed. Considering that the Chao1 diversity index estimates the theoretical richness based on observed data (providing a closer approximation to true species richness), it was used to characterize alpha diversity(Chao, 1987). Next, Principal Coordinate Analysis (PCoA) based on Bray-Curtis dissimilarity(Bray & Curtis, 1957) was conducted using the vegan package (version 2.5-7)(Core Writing et al., 2009) to explore beta diversity, i.e., the dissimilarity in microbial community composition among different seasons and sampling locations. The PERMANOVA test was applied to assess whether seasonal variation and sampling location significantly influenced microbial community composition at the phylum level. Finally, indicator species analysis was performed using the IndVal method in the labdsv package (version 1.8.0)(Dufrene & Legendre, 1997) to identify microbial taxa at the phylum level that serve as potential indicators for specific seasons and locations.

3.1 Microbial population distribution

The distribution of airborne microbial concentrations across different seasons and locations on campus is shown in Fig. 1. The overall mean bacterial concentration was 553.38 ± 277.06 CFU/m³. Among the sampling locations, the highest average concentrations were observed in male dormitories (598.18 ± 314.59 CFU/m³), followed by computer rooms (566.55 ± 248.40 CFU/m³), classrooms (555.80 ± 246.19 CFU/m³), and female dormitories (495.09 ± 258.27 CFU/m³). As shown in the table (Online Resource 1) However, Kruskal-Wallis H testing revealed no statistically significant differences in bacterial concentrations among the four settings (X² = 3.09 P = 0.378). Seasonal variation analysis showed a marked increase in bacterial concentrations during winter (696.55 ± 306.36 CFU/m³) compared to autumn (410.20 ± 138.94 CFU/m³), as determined by the Mann-Whitney U-test (Z=-5.48, P< 0.001). Although seasonal differences were not statistically significant in classroom (Z=-1.01 P = 0.31), winter levels were significantly elevated in male dormitories (Z=-3.75 P< 0.001), female dormitories (Z=-3.26 P = 0.001), and computer rooms (Z=-2.88 P = 0.004), indicating environment-specific seasonal responses in bacterial load.

3.2 Microbial particle size distribution

As illustrated in Fig. 2, the particle size distribution profiles of airborne microorganisms were generally consistent across all sampling locations within the same season. The lowest proportions were detected at Stages I or II, while the highest concentrations were consistently observed at Stage V or VI. In autumn, microbial concentrations increased from Stage I to a peak at Stage VI (30.09%) before declining. In contrast, winter samples peaked at Stage VI (35.26%), with concentrations reaching up to 369.26 CFU/m³, more than twice the autumn maximum of 158.72 CFU/m³.

Among the four locations, peak concentrations were observed in classrooms and computer rooms. Notably, the Stage VI microbial count in the computer rooms during winter (369.26 ± 47.55 CFU/m³) was significantly higher than that in autumn (77.47 ± 34.11 CFU/m³, P < 0.001). The male and female dormitories exhibited similar size distribution trend; however, winter Stage VI concentrations were higher in female dormitories (251.05 ± 225.79 CFU/m³) compared to male dormitories (154.48 ± 94.54 CFU/m³). Across all sampling sites, fine particles (Stage V-VI) accounted for over 40% of the total microbial count, with winter concentrations in the computer room reaching 69.47%, the highest among all locations.

3.3 Sequencing results Analysis

3.3.1 Microbial composition

Meatagenomic monitoring revealed that airborne microorganisms in the campus were predominantly Gram-positive bacilli. Figure 3 shows the taxonomic composition and relative abundances of airborne microorganisms at the phylum and genus levels. At the phylum level, Pseudomonodota dominated the air samples from student dormitories and computer rooms in autumn accounting for more than 50% of the total microbial community, while the dominant phylum in classrooms was Bacillota. In winter, however, Bacillota became the dominant phylum across all sampling sites, with the most pronounced shift in observed in the computer room. At the genus level, Moraxella was predominant in student dormitories during autumn, Stutzerimonas in computer rooms, and Bacillus in classrooms. In winter, Bacillus became the dominant genus at all sites.

3.3.2 Shared and unique microbial species

To assess the overlap and specificity of culturable microbial species between seasons, a Venn diagram was generated (Fig. 4). A total of 3,451 microbial species were detected across autumn and winter samples, of which 1,769 species (51.26%) were shared between the two seasons. This indicates notable seasonal variation in microbial composition at the species level, with winter samples harboring a greater number of unique species than those from autumn.

To further investigate differences in microbial species (at the species level) among sampling locations, a Venn diagram was generated (Fig. 5). A total of 3451 cultivable microbial species were detected across the four sites, with 856 (24.80%) shared among all locations. Several species were shared by two or three locations. In terms of both unique species and overall microbial richness, the ranking of locations was classroom > female room > male room > computer room.

3.3.3 Alpha diversity of microbial communities

Alpha diversity of microbial communities across seasons is shown in Fig. 6A. Statistical analysis revealed that αlpha diversity was significantly higher in winter than in autumn (F = 5.88, P< 0.05), with greater within-group variation observed in winter samples. Figure 6B compares alpha diversity among different sampling locations. Pairwise comparisons indicated no statistically significant differences between locations (F = 1.51, P> 0.05). Among them, classroom samples exhibited the greatest within-group variation, while computer rooms samples had the highest average diversity.

3.3.4 Beta diversity of microbial communities

Principal Coordinate Analysis (PCoA) based on Bray-Curtis distance revealed that seasonal variation accounted for 47.94% of the variability in microbial community structure, suggesting that seasonal turnover is the dominant factor driving differences in airborne microbial communities on campus (Figure. 7). This was further supported by PERMANOVA test (P = 0.003), while the influence of site heterogeneity was not statistically significant (P = 0.124). The above results indicated that seasonal environmental factors (e.g., temperature, humidity) play a more substantial role in shaping airborne microbial communities than local site-specific heterogeneity.

3.3.5 Microbial taxa co-occurrence network analysis

To identify key microbial taxa across different campus sites, a co-occurrence network analysis was performed using the top 200 most abundant taxa, irrespective of seasonal variations. As shown in Fig. 8, the dominant taxa were primarily affiliated with the phylum Bacillota, which exhibited the highest internal connectivity and outnumbered taxa from other phyla. The Pseudomonadota also occupied a prominent position in the network, although its intra-phylum associations were comparatively weaker. Actinomycetota was represented by fewer taxa, but these demonstrated strong internal correlations and were also highly connected to Bacillota members. Frequent interactions were observed between taxa from Bacillota and Pseudomonadota, suggesting that both phyla play central roles in shaping the microbial community structure and dominate the airborne microbiota in the studied environment.

Using a six-stage Andersen sampler combined with metagenomic sequencing, this study systematically investigated the concentration levels, particle size distribution, and community diversity of indoor airborne microorganisms across different functional spaces at the Shunyi Campus of Capital Medical University. The results revealed that airborne microbes were predominantly concentrated in the 0.65 ~ 2.1 µm particle size range, with a stronger aggregation trend toward smaller particles observed during winter. This suggests enhanced transmissibility and potentially increased health risks. Seasonal variation emerged as the primary driver of microbial community structure shifts, while human activity and spatial function also contributed to differences in microbial composition, indicating that the indoor air micro-ecosystem in campus environments is highly responsive to environmental conditions.

4.1. Microbial abundance and particle size distribution

The results of this study showed that the average concentrations of indoor airborne microorganisms at the Shunyi Campus of Capital Medical University was significantly lower than those reported at several other universities, including Tibet University, Guizhou University, and Chang'an University(CM et al., 2019). According to the Indoor Air Quality Standard (GB/T18883-2022)(Health et al., 2022), microbial concentrations at all sampling sites in this study met the national standard threshold of 1500 CFU/m³.

A significant seasonal variation was observed, with microbial concentrations in winter being markedly higher than in autumn. This trend contrasts with the "low in winter, high in summer" pattern reported at Zhejiang University of Traditional Chinese Medicine(YX et al., 2018) and Guizhou University(MY et al., 2018) One possible explanation lies in the unique indoor heating conditions prevalent in Beijing during winter. The heating system increases the temperature differential between indoor and outdoor environments, while more tightly sealed doors and windows reduce ventilation. These factors jointly contribute to the accumulation and resuspension of microorganisms carried on organic substrates such as human skin flakes and textile fibers(Frankel et al., 2012; Nguyen et al., 2021).

Airborne microbial concentrations varied across different functional areas of the campus. Among dormitories, the most pronounced intergroup differences were observed in male dorm rooms, potentially attributable to variations in residents’ hygiene practices and living habits. Dormitories with adequate ventilation and regular cleaning exhibited lower microbial concentrations, whereas poor ventilation and practices such as indoor drying of clothes likely promoted microbial accumulation(Wang et al., 2023) Additionally, the computer rooms displayed relatively high microbial concentrations, possibly due to high occupant density and heat convection from electronic equipment, which enhances particle resuspension—consistent with findings reported by Lou(Lou et al., 2023).

The aerodynamic diameter of bioaerosols plays a critical role in determining their airborne residence time and potential health impacts. Particles within the range of 0.5ཞ 2 µm exhibit high penetrability, allowing them to reach the alveolar regions of the lungs and potentially cause infections or allergic reactions(Rao et al., 1996). They may also act as carriers for airborne pathogens, thereby increasing the risk of respiratory disease transmission(Guzman, 2021). In the present study, airborne microbial particle on campus were predominantly concentrated within the range of 0.65ཞ 1.1 µm (Stage VI) and 1.1ཞ 2.1 µm (Stage V), with this trend being especially pronounced in winter (up to 69.47% in the computer room). These findings are consistent with previous reports by Sun Fan(F et al., 2019) and Ye Jin(J, 2021), which also identified dominant microbial size distributions within Stages III–V. This seasonal shift may be attributed to increased indoor-outdoor temperature differentials during winter, frequent donning and doffing of heavy clothing, and poor ventilation, all of which contributed to the prolonged suspension of fine particles(Benabed & Boulbair, 2022). From a particle size perspective, these results suggest that airborne microorganisms may pose a significant respiratory health risk to students and staff, highlighting the need for improved ventilation strategies and routine air quality monitoring to mitigate exposure.

4.2. Microbial diversity and its potential impact on human health

The present study found that while the dominant bacterial genera remained largely consistent across seasons and indoor sites, their relative abundance varied. The dominant genera in all seasons and sites were Bacillus, Moraxella, Staphylococcus, and Priestia. Notably, the autumn microbial communities exhibited a certain degree of specificity. In addition to the aforementioned genera, Stutzerimonas and Micrococcus were also detected, with Stutzerimonas showing clear dominance, particularly in the computer rooms setting.

Moraxella species are widely distributed on the mucosal surfaces of the human upper respiratory tract. Among them, Moraxella_osloensis has been identified as a highly enriched commensal in the Chinese populations, frequently colonizing the skin, respiratory tract, and other mucosal surfaces(Funaki et al., 2016; Li et al., 2021). Although typically considered an opportunistic pathogen, M. osloensis may cause respiratory infections, conjunctivitis, meningitis, and spinal infections in immunosuppressed individuals or those with underlying health conditions(Maruyama et al., 2018; Masse-Chabredier & Mizony, 2007; Yi et al., 2022). Its high abundance in dormitory air samples suggests a close association with human activity, further highlighting the significantly influence of indoor human presence on airborne microbial composition and providing evidence for the potential airborne transmission of respiratory diseases.

The genus Stutzerimonas (formerly classified under Pseudomonas), which was detected at high relative abundance in autumn, is a newly reclassified genus within the family Pseudomonadaceae. It is widely distributed in the environment and is considered a rare opportunistic pathogen(Gomila et al., 2022). In immunocompromised individuals, it can cause infections such as wound infections, pulmonary infections, and urinary tract infections(F, 2020; Gomila et al., 2022; Park et al., 2013). Additionally, Micrococcus, a common saprophytic genus in the environment, is prevalent in soil and air and forms part of the normal microbiota of mammalian skin, oral cavity, oropharynx, and upper respiratory trac(Kloos & Musselwhite, 1975). The dominant species identified in this study, Micrococcus_luteus, may act as an opportunistic pathogen in immunosuppressed populations, causing serious infections such as intracranial abscesses, pneumonia, and endocarditis(Erbasan, 2018; YM et al., 2006). Both genera are non-spore-forming and exhibit poorl cold tolerant, which may explain their relatively low abundance during winter(Gomila et al., 2022; Kloos & Musselwhite, 1975). Furthermore, the computer rooms’ high equipment density, heat generation, and inadequate ventilation often create a warm, enclosed environment favorable for the survival and proliferation of thermophilic bacteria like Stutzeriomonas.

The predominant genera identified in this study generally exhibited strong environmental resilience, with some, such as Bacillus, also originating from the human body. These findings suggests that the composition of indoor air microbial communities is strongly influenced by human activities. Therefore, it is essential to mitigate microbial transmission risks at the source by promoting health education, optimizing indoor spatial design, and avoiding excessive crowding.

4.3. Seasonal variation and human activities shape the airborne microbial diversity

4.3.1 Effect of seasonal variations on microbial communities

The results indicated that both species richness and diversity indices of airborne microbial communities were significantly higher in compared to autumn (Fig. 4), and only 51.26% of shared species between the two seasons. This highlights the strong regulatory effect of seasonal environmental factors on community structure. Indoor heating during winter elevates room temperatures while reducing ventilation, creating a relatively enclosed, dry, and warm microenvironment that favors the survival and enrichment of microorganisms with strong thermal and cold tolerant. Dominant taxa such as Bacillus benefit from this environment by forming stress-resistant endospores(Basta & Annamaraju, 2025), hus gaining ecological advantage. In contrast, the mild climate and more frequent ventilation in autumn facilitate microbial dispersion, resulting in relatively lower community diversity.

Microbial community structures in different indoor environments exhibited pronounced spatial heterogeneity across seasons, shaped jointly by site-specific functions and environmental conditions. In autumn, microbial communities in student dormitories and computer rooms were predominantly composed of the phylum Pseudomonadota, particularly genera such as Pseudomonas and its newly classified genus Stutzerimonas, which showed strong adaptability in low-oxygen, enclosed conditions. In contrast, classrooms, adjacent to green areas, were influenced by plant-associated microbial inputs, leading to Bacillus becoming the dominant taxon(Miletto & Lindow, 2015). During winter, the combined effects of heating and reduced ventilation facilitated the dominance of phylum Bacillota across all environments, with the cold-resistant Bacillus genus further consolidating its ecological niche. Notably, although the species richness in computer rooms was relatively low, their stable temperature and humidity may support the reactivation of dormant microbes, thereby increasing microbial diversity indices. This observation suggests that closed, thermally stable microenvironments may promote microbial diversity restoration to some extent.

In summary, temporal variation in airborne microbial communities is influenced not only by seasonal climatic factors (e.g., temperature and ventilation), but also by site-specific functional characteristics (e.g., occupant density and ventilation efficiency) and by microbial input pathways (e.g., soil resuspension and human carriage). A deeper understanding of these interactive factors can facilitate more accurate assessments of airborne microbial exposure risk in different functional areas of school campuses, thereby informing targeted public health interventions.

4.3.2 Site-specific differences and the influence of human activities on airborne microbial communities

In the analysis of site-specific differences, although the variations in airborne microbial diversity across different functional areas did not reach statistical significance, classrooms exhibited greater within-group variability, while computer rooms showed higher average diversity (Fig. 6). These differences may be attributed to the stability of the user population and the functional characteristics of the spaces.

Classrooms are occupied by fixed student groups, with relatively uniform sources of microbial input. However, differing activity patterns among classes result in substantial intra-group variation. Microbial communities may develop a degree of “settlement” among class members, whereby specific microbial taxa spread within a limited area and maintain local stability(Lee et al., 2021), thus intensifying spatial heterogeneity. In contrast, computer rooms are shared across grades and disciplines, with a high turnover of users and diverse backgrounds, contributing to more varied microbial inputs. In addition, the continuous heat dissipation from electronic equipment and high frequency of use may create a relatively stable microenvironment, facilitating microbial survival and promoting greater diversity.

Therefore, beyond seasonal factors, both the composition of occupants and the functional use of indoor spaces play crucial roles in shaping the structure of airborne microbial communities. Figure 7 further demonstrates that seasonal environmental factors (e.g., temperature and humidity) are the primary drivers of shifts in microbial community structure in the campus environment, while spatial heterogeneity plays a secondary role. Against the backdrop of global climate change, these findings highlight the importance of considering seasonal climatic conditions in regulating microbial ecological adaptation and aerosol transmission mechanisms(Li et al., 2022).

4.3.3 Key species analysis of microbial communities

Among the key taxa identified in this study, Bacillota and Pseudomonadota were dominant, with a strong correlation observed between the two (Fig. 8). Bacillota may participate in certain plant physiological processes and play important roles in decomposition and soil amelioration(Ayaz et al., 2022), while Pseudomonadota are primarily involved in the degradation of organic matter and contribute to soil microbial communities(Yasen et al., 2025). As shown in the network analysis (Fig. 8), Bacillota occupied a central position in the ecological network, suggesting that it may perform key ecological functions within the campus microbial community. Its dominance may be attributed to its ability to form endospores that are resistant to heat and environmental stress, allowing for long-term survival and widespread dispersal in indoor environments. Although Pseudomonadota constituted the second most abundant phylum, the relatively weak inter-species associations suggest it may play a less central ecological role than Bacillota. Actinomycetota were detected at a lower rate, which may be related to their specific nutritional and ecological requirements, such as high humidity or particular organic substrates(Georjon et al., 2023). Despite their low abundance, the strong internal correlations within Actinomycetota, as well as their close association with Bacillota, suggest a potential synergistic role in the microbial community, especially in ecological functions such as organic matter decomposition and soil amendment(Wrobel et al., 2024). Overall, Bacillota and Pseudomonadota together form the core network structure of the airborne microbial community in the campus environment. Bacillota dominate the structure due to their high adaptability and dispersal ability, while Pseudomonadota provide complementary support in ecological functionality. The synergy between the two may be a key mechanism driving the maintenance of the microbial community structure and the realization of ecological functions.

The findings of this study hold broader significance. The observed seasonal microbial variations (e.g., increased winter concentrations and fine particle fractions), linked to heating and ventilation patterns, likely have relevance in regions globally with similar climates and building features (e.g., heating systems), highlighting the importance of addressing indoor air quality during heating seasons in these areas. Furthermore, the identified dominant genera (e.g., Bacillus, Moraxella) and opportunistic pathogens (e.g., Stutzerimonas stutzeri) reflect the widespread influence of human activity on indoor microbiomes and indicate potential health risks common to high-density settings worldwide, such as schools and offices. Therefore, this research provides valuable insights for understanding and improving air microbial monitoring, risk assessment, and intervention strategies in comparable indoor environments globally.

This study systematically analyzed the spatiotemporal distribution patterns of indoor airborne microorganisms in a university campus and assessed their potential health risks. The results revealed that microbial concentrations were significantly higher in winter than in autumn, with particle sizes predominantly ranging from 0.65 to 2.1 µm (Stage V ~ VI), potentially increasing health risks. Macrogenomic sequencing identified Bacillota and Pseudomonadota as dominant taxa. Conditional pathogens such as Stutzerimonas stutzeri were detected in computer rooms, indicating potential public health hazard in these environments. Notably, temperature and humidity changes caused by indoor heating, along with human activity patterns (e.g., poor ventilation, high occupancy), were identified as key drivers of microbial community dynamics. These findings reveal how seasons and space use together shape airborne microbes, offering a basis for targeted campus air quality interventions.

Based on the findings, we recommend the implementation of intelligent ventilation systems during winter to balance heating with adequate air circulation. In addition, routine microbial surveillance should be conducted in high-risk areas such as computer rooms and dormitories. Incorporating particle size-specific indicators into national indoor air quality standards is also essential to more accurately assess and manage bioaerosol-related health risks. This study has some certain limitations. First, the data were collected only during autumn and winter, limiting the ability to capture year-round microbial dynamics. Future research should extend sampling across all seasons. Second, reliance on culture-based methods may have underestimated the diversity of non-culturable taxa such as strict anaerobes and viruses. Third, the absence of continuous monitoring of environmental parameters (e.g., temperature, humidity, PM2.5) restricts the ability to link microbial changes to environmental drivers.

Future work will aim to quantify the relationship between microbial exposure and health outcomes through interdisciplinary approaches, offering both theoretical insights and practical strategies for ensuring a healthy and safe campus environment.

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Funding

This work was supported by National Key Research and Development Program of China (Grant numbers 2023YFC260510 and 2023YFC2605104).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose

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Availability of data and material

The datasets generated and analysed during the current study are available in the submitted forms

Author contributions

Xiaoqin Chen was responsible for the conceptualization and methodology design of the study, provided overall supervision, and contributed to both the original draft preparation and final manuscript review and editing. Yuheng Liu, Xunuo Deng, Shenglun Ma, and Junde He were involved in sample collection and processing. In addition, Yuheng Liu and Xunuo Deng performed data curation and formal analysis, and contributed to the original draft writing. Xuezheng Ma acquired funding for the project and critically reviewed and edited the manuscript. Zihua Xu and Liping Ren were responsible for preparing the necessary equipment and materials for the study. All authors have read and approved the final version of the manuscript.

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No competing interests reported.

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Seasonal Variations and Spatial Distribution of Indoor Airborne Microorganisms in a University Campus in Beijing: A Metagenomic Study

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Airborne microbiological sample collection and culture

2.2 Microbial sample collection

2.3 Metagenomic sequencing and bioinformatics analysis

2.4 Statistical analyses

3. Results

3.1 Microbial population distribution

3.2 Microbial particle size distribution

3.3 Sequencing results Analysis

3.3.1 Microbial composition

3.3.2 Shared and unique microbial species

3.3.3 Alpha diversity of microbial communities

3.3.4 Beta diversity of microbial communities

3.3.5 Microbial taxa co-occurrence network analysis

4. Discussion

4.1. Microbial abundance and particle size distribution

4.2. Microbial diversity and its potential impact on human health

4.3. Seasonal variation and human activities shape the airborne microbial diversity

4.3.1 Effect of seasonal variations on microbial communities

4.3.2 Site-specific differences and the influence of human activities on airborne microbial communities

4.3.3 Key species analysis of microbial communities

5. Conclusions and outlook

Declarations

References

Additional Declarations

Supplementary Files

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