Evaluating vacant middle seats and masks as Coronavirus exposure reduction strategies in aircraft cabins using particle tracer experiments and computational fluid dynamics simulations
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Evaluating vacant middle seats and masks as Coronavirus exposure reduction strategies in aircraft cabins using particle tracer experiments and computational fluid dynamics simulations

Filetype[PDF-2.80 MB]


English
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  • Alternative Title:
    Eng Rep
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  • Description:
    Aircraft cabins have high-performance ventilation systems, yet typically hold many persons in close proximity for long durations. The current study estimated airborne virus exposure and infection reductions when middle seats are vacant compared to full occupancy and when passengers wear surgical masks in aircraft. Tracer particle data reported by U.S. Transportation Command (TRANSCOM) and CFD simulations reported by Boeing were used along with NIOSH data, to build nonlinear regression models with particle exposure and distance from particle source as variables. These models that estimate exposure at given distances from the viral source were applied to evaluate exposure reductions from vacant middle seats. Reductions averaged 54% for the seat row where an infectious passenger is located and 36% for a 24-row cabin containing one infectious passenger, with middle seats vacant. Analysis of the TRANSCOM data showed that universal masking (surgical masks) reduced exposures by 62% and showed masking and physical distancing provide further reductions when practiced together. For a notional scenario involving 10 infectious passengers, compared with no intervention, masking, distancing, and both would prevent 6.2, 3.8, and 7.6 secondary infections, respectively, using the Wells-Riley equation. These results suggest distancing alone, masking alone, and these practiced together reduce SARS CoV-2 exposure risk in increasing order of effectiveness, when an infectious passenger is present.
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  • Source:
    Eng Rep.
  • Pubmed ID:
    36718395
  • Pubmed Central ID:
    PMC9878082
  • Document Type:
    Journal Article
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