The control of air flow during loud soprano singing

Rothenberg, Martin; Miller, Donald G.; Molitor, Richard; Leffingwell, Dolores

doi:10.1016/s0892-1997(87)80010-3

Cited by 9 publications

(10 citation statements)

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“…The mean (SD) PEF for simulated coughing was 532.08 (75.65) L/min and FEV1 of 5.92 (0.1) L. Likewise, the mean (SD) PEF for simulated talking was 148.35 (43.29) L/min and FEV1 of 1.79 (0.07) L. Both simulated exhalation levels are within range of previous studies. [37][38][39][40] The aerosol particle concentration was measured at the one-meter frontal sensor during the last minute of all no-mask (control) runs and resulted in concentration levels and distribution that indicates good polydisperse particle generation (online supplementary figure 3). The mean (SD) concentrations for simulated talking generated peak concentrations of 32,199 (3,683) for 0.3µm particle diameters representing aerosols, and 201(42) for 10µm particle diameters representing droplets.…”

Section: Resultsmentioning

confidence: 99%

“…The exhalation airflows were calibrated to simulate peak expiratory flow (PEF) of coughing with a range 507L/min to 650L/min and PEF for talking of approximately 120L/min as established by previous research. [37][38][39][40] The typical air flows for talking are similar to that of singing. [40] Four laser scattering particle concentration sensors (Plantower PMS5003) were placed at specific locations inside a non-airtight fume hood (online supplementary figure 2) to detect aerosol dispersion directly downward, laterally from the mid-line, and 1 meter forward of the mouth.…”

Section: Design and Methods: Comparative Effectiveness Researchmentioning

confidence: 99%

“…[37][38][39][40] The typical air flows for talking are similar to that of singing. [40] Four laser scattering particle concentration sensors (Plantower PMS5003) were placed at specific locations inside a non-airtight fume hood (online supplementary figure 2) to detect aerosol dispersion directly downward, laterally from the mid-line, and 1 meter forward of the mouth. Preliminary testing of the configuration identified that the optimal position when used with various masks in this fume hood was 43 cm below the level of the mouth for all sensors.…”

Section: Design and Methods: Comparative Effectiveness Researchmentioning

confidence: 99%

“…The exhalation airflows were calibrated to simulate peak expiratory flow (PEF) of coughing with a range 507L/min to 650L/min, and PEF for talking of approximately 120L/min as established by previous research. [44-47] The typical air flows for talking are similar to that of singing. [47]…”

Section: Objective Design and Methodsmentioning

confidence: 99%

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Quantifying Respiratory Airborne Particle Dispersion Control Through Improvised Reusable Masks: The Physics of Non-Pharmaceuptical Interventions for Reducing SARS-COV-2 (COVID-19) Airborne Transmision

Edwards

Widrick

Potember

et al. 2020

Preprint

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Objective: To determine the effectiveness of non-medical grade washable masks or face coverings in controlling airborne dispersion from exhalation (both droplet and aerosol), and to aid in establishing public health strategies on the wearing of masks to reduce COVID-19 transmission. Design: This comparative effectiveness study using an exhalation simulator to conduct 94 experiment runs with combinations of 8 different fabrics, 5 mask designs, and airflows for both talking and coughing. Setting: Non-airtight fume hood and multiple laser scattering particle sensors. Participants: No human participants. Exposure: 10% NaCl nebulized solution delivered by an exhalation simulator through various masks and fabrics with exhalation airflows representative of "coughing" and "talking or singing." Main Outcomes and Measures: The primary outcome was reduction in aerosol dispersion velocity, quantity of particles, and change in dispersion direction. Measurements used in this study included peak expiratory flow (PEF), aerosol velocity, concentration area under curve (AUC), and two novel metrics of expiratory flow dispersion factor (EDF) and filtration efficiency indicator (FEI). Results: Three-way multivariate analysis of variance establishes that factors of fabric, mask design, and exhalation breath level have a statistically significant effect on changing direction, reducing velocity or concentration (Fabric: P = < .001, Wilks' Λ = .000; Mask design: P = < .001, Wilks' Λ = .000; Breath level: P = < .001, Wilks' Λ = .004). There were also statistically significant interaction effects between combinations of all primary factors. Conclusions and Relevance: The application of facial coverings or masks can significantly reduce the airborne dispersion of aerosolized particles from exhalation. The results show that wearing of non-medical grade washable masks or face coverings can help increase the effectiveness of non-pharmaceutical interventions (NPI) especially where infectious contaminants may exist in shared air spaces. However, the effectiveness varies greatly between the specific fabrics and mask designs used.

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Section: Resultsmentioning

confidence: 99%

Section: Design and Methods: Comparative Effectiveness Researchmentioning

confidence: 99%

Section: Design and Methods: Comparative Effectiveness Researchmentioning

confidence: 99%

Section: Objective Design and Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying Respiratory Airborne Particle Dispersion Control Through Improvised Reusable Masks: The Physics of Non-Pharmaceuptical Interventions for Reducing SARS-COV-2 (COVID-19) Airborne Transmision

Edwards

Widrick

Potember

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, an average airflow for normal speech can be around 0.15 l/s for a passage reading, 13 but can reach significantly higher peak values for a soprano singing up to 2 l/s. 77 …”

Section: Cfd Modellingmentioning

confidence: 99%

Numerical investigation of respiratory drops dynamics released during vocalization

Peña-Monferrer¹,

Antao²,

Sawko³

2021

Physics of Fluids

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Release of drops from a human body has been the focus of many recent investigations because of the current COVID-19 pandemic. Indirect virus transmission from asymptomatic individuals has been proved to be one of the major infectious routes and difficult to quantify, detect, and mitigate. We show in this work a detailed and novel numerical investigation of drops released during vocalization from a thermal manikin using a large eddy simulation coupled with Lagrangian tracking of drops. The vocalization experiment was modeled using existing data from the literature for modeling exhaled airflow, emission rate, and size distribution. Particular focus was on the definition of the boundary conditions for the exhalation process. Turbulence was compared with experimental data for the near mouth region for 75 exhalation breathing cycles and showed the sensitivity of different modeling assumptions at the mouth inlet. The results provide insights of special interest for understanding drop dynamics in speech-like exhalation modes, modeling the mouth inlet boundary conditions, and providing data for verifying other more simplified models.

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Sound Production in Birds: Acoustics and Physiology Revisited

Gaunt¹,

Nowicki²

1998

Animal Acoustic Communication

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The control of air flow during loud soprano singing

Cited by 9 publications

References 1 publication

Quantifying Respiratory Airborne Particle Dispersion Control Through Improvised Reusable Masks: The Physics of Non-Pharmaceuptical Interventions for Reducing SARS-COV-2 (COVID-19) Airborne Transmision

Quantifying Respiratory Airborne Particle Dispersion Control Through Improvised Reusable Masks: The Physics of Non-Pharmaceuptical Interventions for Reducing SARS-COV-2 (COVID-19) Airborne Transmision

Numerical investigation of respiratory drops dynamics released during vocalization

Sound Production in Birds: Acoustics and Physiology Revisited

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