The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Inhalable Microorganisms in Beijing’s PM2.5 and PM10 Pollutants during a Severe Smog Event
Open accessAbstract
Particulate matter (PM) air pollution poses a formidable public health threat to the city of Beijing. Among the various hazards of PM pollutants, microorganisms in PM2.5 and PM10 are thought to be responsible for various allergies and for the spread of respiratory diseases. While the physical and chemical properties of PM pollutants have been extensively studied, much less is known about the inhalable microorganisms. Most existing data on airborne microbial communities using 16S or 18S rRNA gene sequencing to categorize bacteria or fungi into the family or genus levels do not provide information on their allergenic and pathogenic potentials. Here we employed metagenomic methods to analyze the microbial composition of Beijing's PM pollutants during a severe January smog event. We show that with sufficient sequencing depth, airborne microbes including bacteria, archaea, fungi, and dsDNA viruses can be identified at the species level. Our results suggested that the majority of the inhalable microorganisms were soil-associated and nonpathogenic to human. Nevertheless, the sequences of several respiratory microbial allergens and pathogens were identified and their relative abundance appeared to have increased with increased concentrations of PM pollution. Our findings may serve as an important reference for environmental scientists, health workers, and city planners.
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.
Integrity signals
Registry
Cites
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.
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
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
Self-citationGreengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P., & Andersen, G. L. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.03006-05 · Cited by 10,891
A metagenome-wide association study of gut microbiota in type 2 diabetes
Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. (2012) · Nature · doi:10.1038/nature11450 · Cited by 6,762
Metagenomic microbial community profiling using unique clade-specific marker genes
Segata, N., Waldron, L., Ballarini, A., Narasimhan, V., Jousson, O., & Huttenhower, C. (2012) · Nature Methods · doi:10.1038/nmeth.2066 · Cited by 1,776
Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus
Nierman, W. C., Pain, A., Anderson, M. J., Wortman, J. R., Kim, H. S., Arroyo, J., Berriman, M., Abe, K., Archer, D. B., Bermejo, C., et al. (2005) · Nature · doi:10.1038/nature04332 · Cited by 1,391
Effect of relative humidity on the aerodynamic diameter and respiratory deposition of fungal spores
Reponen, T., Willeke, K., Ulevicius, V., Reponen, A., & Grinshpun, S. A. (1996) · Atmospheric Environment · doi:10.1016/1352-2310(96)00128-8 · Cited by 170
Lee, S.-A., & Liao, C.-H. (2014) · Science of The Total Environment · doi:10.1016/j.scitotenv.2013.07.104 · Cited by 48
Cited by
Comunian, S., Dongo, D., Milani, C., & Palestini, P. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17124487 · Cited by 464
Dynamic Human Environmental Exposome Revealed by Longitudinal Personal Monitoring
Jiang, C., Wang, X., Li, X., Inlora, J., Wang, T., Liu, Q., & Snyder, M. (2018) · Cell · doi:10.1016/j.cell.2018.08.060 · Cited by 218
Bourdrel, T., Annesi-Maesano, I., Alahmad, B., Maesano, C. N., & Bind, M.-A. (2021) · European Respiratory Review · doi:10.1183/16000617.0242-2020 · Cited by 224
SARS-CoV-2 infection, COVID-19 pathogenesis, and exposure to air pollution: What is the connection?
Woodby, B., Arnold, M. M., & Valacchi, G. (2021) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14512 · Cited by 160
Xie, J., Jin, L., Luo, X., Zhao, Z., & Li, X. (2018) · Environmental Science & Technology Letters · doi:10.1021/acs.estlett.7b00561 · Cited by 161
Metadata
- Authors
- Cao, C., Jiang, W., Wang, B., Fang, J., Lang, J., Tian, G., Jiang, J., & Zhu, T. F.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-01-23
- Volume
- 48
- Issue
- 3
- Pages
- 1499-1507
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W127352138
- Cited by
- 669
- References
- 7
- FWCI
- 25.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
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.
