Inhalation of expiratory droplets in aircraft cabins

Gupta, JK; Chen, Lin; Chen, Qingyan

doi:10.1111/j.1600-0668.2011.00709.x

Cited by 54 publications

(39 citation statements)

References 38 publications

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“…by Gupta, Lin, and Chen (2011). Tetra mesh (0.1-0.5 cm) was used in the near-patient zone, and hexahedral mesh (3 cm) was used in the rest of the space.…”

Section: Figurementioning

confidence: 99%

The Effects of Patient Movement on Particles Dispersed by Coughing in an Indoor Environment

2014

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This study investigates the influence of a moving patient on the transport characteristics of coughing particles by dynamic meshing and the Lagrangian particle tracking method. Through simulation, particle movement during the initial coughing, patient movement, and stationary flow phases was examined. Two different particle sizes (5 μm and 10 μm) were used and yielded small differences in the initial airflow after coughing. During the patient-moving phase, the temporal particle distribution in the upper, breathing, lower, and near-floor zones were simulated. The amplified air-velocity field induced by the moving patient enhanced the particle entrainment, thus increasing the risk of contamination (defined as particle system entropy) in the whole room. Both temporal and final particle distribution during the stationary flow phase were studied. Walking speed affected the particle distribution in the traveling direction, but not in the vertical or lateral directions at the end of the simulation. Turbulence dispersion played a critical role in the spread of the particles through the coughing and patientmoving phases.

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“…by Gupta, Lin, and Chen (2011). Tetra mesh (0.1-0.5 cm) was used in the near-patient zone, and hexahedral mesh (3 cm) was used in the rest of the space.…”

Section: Figurementioning

confidence: 99%

The Effects of Patient Movement on Particles Dispersed by Coughing in an Indoor Environment

2014

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“…Experiments and simulations show that particles <2 μm in diameter could be distributed widely, albeit at a low concentration, from a single source throughout an aircraft cabin ( 22 ). Influenza outbreaks in a train and an aircraft cabin with nonoperating air conditioning showed wide distribution of secondary cases, suggestive of airborne transmission ( 23 , 24 ).…”

Section: Discussionmentioning

confidence: 99%

Protection by Face Masks against Influenza A(H1N1)pdm09 Virus on Trans-Pacific Passenger Aircraft, 2009

Zhang¹,

Peng²,

Ou³

et al. 2013

Emerg. Infect. Dis.

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In response to several influenza A(H1N1)pdm09 infections that developed in passengers after they traveled on the same 2 flights from New York, New York, USA, to Hong Kong, China, to Fuzhou, China, we assessed transmission of influenza A(H1N1)pdm09 virus on these flights. We defined a case of infection as onset of fever and respiratory symptoms and detection of virus by PCR in a passenger or crew member of either flight. Illness developed only in passengers who traveled on the New York to Hong Kong flight. We compared exposures of 9 case-passengers with those of 32 asymptomatic control-passengers. None of the 9 case-passengers, compared with 47% (15/32) of control-passengers, wore a face mask for the entire flight (odds ratio 0, 95% CI 0–0.71). The source case-passenger was not identified. Wearing a face mask was a protective factor against influenza infection. We recommend a more comprehensive intervention study to accurately estimate this effect.

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“…The combined Lagrangian and superimposition method can provide very similar results to those of the pure Lagrangian method with a lower computing cost (Gupta et al 2011b;Chen et al 2015b). Therefore, the combined method was used for the simulations in the present study.…”

Section: Lagrangian Modelmentioning

confidence: 94%

Comparing the Markov Chain Model with the Eulerian and Lagrangian Models for Indoor Transient Particle Transport Simulations

Chen

Liu

Lin

et al. 2015

Aerosol Science and Technology

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Correctly predicting transient particle transport in indoor environments is crucial to improving the design of ventilation systems and reducing the risk of acquiring airborne infectious diseases. Recently, a new model was developed on the basis of Markov chain frame for quickly predicting transient particle transport indoors. To evaluate this Markov chain model, this study compared it with the traditional Eulerian and Lagrangian models in terms of performance, computing cost, and robustness. Four cases of particle transport, three of which included experimental data, were used for this comparison. The Markov chain model was able to predict transient particle transport indoors with similar accuracy to the Eulerian and Lagrangian models. Furthermore, when the same time step size (Courant number 1) and grid number were used for all three models, the Markov chain model had the highest calculation speed. The Eulerian model was faster than the Lagrangian model unless a super-fine grid was used. This investigation developed empirical equations for evaluating the three models in terms of computing cost. In addition, the Markov chain model was found to be sensitive to the time step size when the Courant number is larger than 1, whereas the Eulerian and Lagrangian models were not.

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Inhalation of expiratory droplets in aircraft cabins

Cited by 54 publications

References 38 publications

The Effects of Patient Movement on Particles Dispersed by Coughing in an Indoor Environment

The Effects of Patient Movement on Particles Dispersed by Coughing in an Indoor Environment

Protection by Face Masks against Influenza A(H1N1)pdm09 Virus on Trans-Pacific Passenger Aircraft, 2009

Comparing the Markov Chain Model with the Eulerian and Lagrangian Models for Indoor Transient Particle Transport Simulations

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