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- Publisher Website: 10.1038/s41598-021-02523-5
- Scopus: eid_2-s2.0-85120637800
- PMID: 34853387
- WOS: WOS:000725094400007
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Article: Outdoor PM2.5 concentration and rate of change in COVID-19 infection in provincial capital cities in China
Title | Outdoor PM<inf>2.5</inf> concentration and rate of change in COVID-19 infection in provincial capital cities in China |
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Authors | |
Issue Date | 2021 |
Citation | Scientific Reports, 2021, v. 11, n. 1, article no. 23206 How to Cite? |
Abstract | This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = −0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = −0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = −0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R. |
Persistent Identifier | http://hdl.handle.net/10722/336834 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Han, Yang | - |
dc.contributor.author | Lam, Jacqueline C.K. | - |
dc.contributor.author | Li, Victor O.K. | - |
dc.contributor.author | Crowcroft, Jon | - |
dc.contributor.author | Fu, Jinqi | - |
dc.contributor.author | Downey, Jocelyn | - |
dc.contributor.author | Gozes, Illana | - |
dc.contributor.author | Zhang, Qi | - |
dc.contributor.author | Wang, Shanshan | - |
dc.contributor.author | Gilani, Zafar | - |
dc.date.accessioned | 2024-02-29T06:56:51Z | - |
dc.date.available | 2024-02-29T06:56:51Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Scientific Reports, 2021, v. 11, n. 1, article no. 23206 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336834 | - |
dc.description.abstract | This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = −0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = −0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = −0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R. | - |
dc.language | eng | - |
dc.relation.ispartof | Scientific Reports | - |
dc.title | Outdoor PM<inf>2.5</inf> concentration and rate of change in COVID-19 infection in provincial capital cities in China | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1038/s41598-021-02523-5 | - |
dc.identifier.pmid | 34853387 | - |
dc.identifier.scopus | eid_2-s2.0-85120637800 | - |
dc.identifier.volume | 11 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | article no. 23206 | - |
dc.identifier.epage | article no. 23206 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.isi | WOS:000725094400007 | - |