Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 173

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Indoor-Air Microbiome in an Urban Subway Network: Diversity and Dynamics

Open access
Leung, Marcus H. Y., Wilkins, David, Li, Ellen K. T., Kong, Fred K. F., Lee, Patrick K. H.·2014·Applied and Environmental Microbiology

Abstract

Subway systems are indispensable for urban societies, but microbiological characteristics of subway aerosols are relatively unknown. Previous studies investigating microbial compositions in subways employed methodologies that underestimated the diversity of microbial exposure for commuters, with little focus on factors governing subway air microbiology, which may have public health implications. Here, a culture-independent approach unraveling the bacterial diversity within the urban subway network in Hong Kong is presented. Aerosol samples from multiple subway lines and outdoor locations were collected. Targeting the 16S rRNA gene V4 region, extensive taxonomic diversity was found, with the most common bacterial genera in the subway environment among those associated with skin. Overall, subway lines harbored different phylogenetic communities based on α- and β-diversity comparisons, and closer inspection suggests that each community within a line is dependent on architectural characteristics, nearby outdoor microbiomes, and connectedness with other lines. Microbial diversities and assemblages also varied depending on the day sampled, as well as the time of day, and changes in microbial communities between peak and nonpeak commuting hours were attributed largely to increases in skin-associated genera in peak samples. Microbial diversities within the subway were influenced by temperature and relative humidity, while carbon dioxide levels showed a positive correlation with abundances of commuter-associated genera. This Hong Kong data set and communities from previous studies conducted in the United States formed distinct community clusters, indicating that additional work is required to unravel the mechanisms that shape subway microbiomes around the globe.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1128/aem.02244-14

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

See all 173

Metadata

Authors
Leung, M. H. Y., Wilkins, D., Li, E. K. T., Kong, F. K. F., & Lee, P. K. H.
Venue
Applied and Environmental Microbiology
Published
2014-11
Volume
80
Issue
21
Pages
6760-6770
ISSN
  • 0099-2240 (ISSN-L)
  • 1098-5336
OpenAlex
W2031658644
Cited by
173
References
4
FWCI
7.78
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Indoor-Air Microbiome in an Urban Subway Network: Diversity and Dynamics. https://app.cochat.ai/papers/10.1128/aem.02244-14
The paper
Leung, M. H. Y., Wilkins, D., Li, E. K. T., Kong, F. K. F., & Lee, P. K. H. (2014). Indoor-Air Microbiome in an Urban Subway Network: Diversity and Dynamics. Applied and Environmental Microbiology. https://doi.org/10.1128/aem.02244-14

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.