Dynamic airborne mycobiome in the metropolitan city transit system is driven by seasonality and station type
Bibliographic record
Abstract
ABSTRACT Subway transit systems serve as the primary transportation mode in metropolitan areas. The quality of the airway in transit plays a crucial role in human health as commuters and workers are exposed to microbes transmitted from passengers and circulated within built environments. The diversity and dynamics of bacterial microbiome in these environments have been relatively well studied. However, the fungal communities remain poorly investigated. In this study, we conducted a year-long comprehensive investigation of the mycobiomes within the world’s second largest subway system and analyzed the seasonal dynamics of fungal composition across intercity hub stations, urban hub stations, and suburban stations. We found a high diversity of the subway mycobiome that varied seasonally and was influenced by various environmental factors, such as particulate matter (PM2.5) levels and average humidity. Fungal diversity was higher in months with elevated PM2.5 pollution. Autumn exhibited increased diversity and peaks in the distribution of human pathogenic fungi. Furthermore, it was determined that station types exert a significant influence on the diversity of pathogenic fungi, with interchange stations (train and airport transfer stations) showing the highest diversity, while suburban stations showed the lowest. The core taxa of the mycobiome comprised several genera including ubiquitous fungi commonly found in soil and outdoor environments (e.g., Alternaria and Cladosporium ), as well as potential plant and human pathogens (e.g., Phoma and Fusarium ), indicating a potential risk to public health. Our study demonstrated the seasonal and spatial dynamics of mycobiomes in the Beijing subway system and revealed the factors/mechanisms that shape the indoor fungal communities. Understanding the patterns and processes of mycobiome community is important for infection prevention and public health management. IMPORTANCE Respiratory infections and allergic reactions caused by airborne fungi have received considerable public attention; however, fungal communities remain poorly investigated. This research performed the first year-long investigation of airborne mycobiome in the world's largest subway system. We found that the fungal diversity peaks in autumn and at stations with higher PM2.5 levels. Intercity hubs exhibit the highest diversity of pathogenic fungi and the least seasonal fluctuation. Suburban stations revealed a reduced diversity of human pathogens but an elevated presence of plant pathogens. Core fungal taxa in subways include both common soil fungi (e.g., Alternaria and Cladosporium ) and potential plant and human pathogens (e.g., Phoma , Fusarium , and Rhinocladiella ) that pose potential health risks. These results are crucial for infection prevention and public health management in city transit systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".