The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
How do low wind speeds and high levels of air pollution support the spread of COVID-19?
Open accessAbstract
The pandemic of coronavirus disease 2019 (COVID-19) is generating a high number of infected individuals and deaths. One of the current questions is how climatological factors and environmental pollution can affect the diffusion of COVID-19 in human society. This study endeavours to explain the relation between wind speed, air pollution and the diffusion of COVID-19 to provide insights to constrain and/or prevent future pandemics and epidemics. The statistical analysis here focuses on case study of Italy and reveals two main findings: 1) cities with high wind speed have lower numbers of COVID-19 related infected individuals; 2) cities located in hinterland zones (mostly those bordering large urban conurbations) with little wind speed and frequently high levels of air pollution had higher numbers of COVID-19 related infected individuals. Results here suggest that high concentrations of air pollutants, associated with low wind speeds, may promote a longer permanence of viral particles in polluted air of cities, thus favouring an indirect means of diffusion of the novel coronavirus (SARS-CoV-2), in addition to the direct diffusion with human-to-human transmission dynamics.
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Cites
Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1
van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2004973 · Cited by 9,951
Coccia, M. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138474 · Cited by 689
Self-citationRole of the chronic air pollution levels in the Covid-19 outbreak risk in Italy
Fattorini, D., & Regoli, F. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.114732 · Cited by 616
Investigation of effective climatology parameters on COVID-19 outbreak in Iran
Ahmadi, M., Sharifi, A., Dorosti, S., Jafarzadeh Ghoushchi, S., & Ghanbari, N. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138705 · Cited by 515
Bashir, M. F., MA, B. J., Bilal, Komal, B., Bashir, M. A., Farooq, T. H., Iqbal, N., & Bashir, M. (2020) · Environmental Research · doi:10.1016/j.envres.2020.109652 · Cited by 452
Feng, Y., Marchal, T., Sperry, T., & Yi, H. (2020) · Journal of Aerosol Science · doi:10.1016/j.jaerosci.2020.105585 · Cited by 406
Impact of weather on COVID-19 pandemic in Turkey
Şahin, M. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138810 · Cited by 391
Zhong, J., Zhang, X., Dong, Y., Wang, Y., Liu, C., Wang, J., Zhang, Y., & Che, H. (2018) · Atmospheric Chemistry and Physics · doi:10.5194/acp-18-247-2018 · Cited by 297
Relationship between COVID-19 and weather: Case study in a tropical country
Rosario, D. K. A., Mutz, Y. S., Bernardes, P. C., & Conte-Junior, C. A. (2020) · International Journal of Hygiene and Environmental Health · doi:10.1016/j.ijheh.2020.113587 · Cited by 260
Air Pollution and the Novel Covid-19 Disease: a Putative Disease Risk Factor
Martelletti, L., & Martelletti, P. (2020) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-020-00274-4 · Cited by 253
Cited by
Coccia, M. (2022) · Environmental Research · doi:10.1016/j.envres.2021.111678 · Cited by 282
Self-citationThe relationship between macro-socioeconomics determinants and COVID-19 vaccine distribution
Roghani, A., & Division of Epidemiology, U. O. U. S. O. M. (2021) · AIMS Public Health · doi:10.3934/publichealth.2021052 · Cited by 45
Lai, A., Chang, M. L., O'Donnell, R. P., Zhou, C., Sumner, J. A., & Hsiai, T. K. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2021.146464 · Cited by 25
Metadata
- Authors
- Coccia, M.
- Venue
- Atmospheric Pollution Research
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 12
- Issue
- 1
- Pages
- 437-445
- ISSN
- 1309-1042 (ISSN-L)
- OpenAlex
- W3092480211
- Cited by
- 258
- References
- 27
- FWCI
- 19.64
- Type
- journal article
- Language
- en
- Open access
- green
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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.
