key: cord-0784412-nt378esz
authors: Edwards, N. J.; Widrick, R.; Potember, R.; Gerschefske, M.
title: Quantifying Respiratory Airborne Particle Dispersion Control Through Improvised Reusable Masks
date: 2020-07-14
journal: nan
DOI: 10.1101/2020.07.12.20152157
sha: ecf2a2b2e476c0b453697971b8b8a0f49023433d
doc_id: 784412
cord_uid: nt378esz

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' {Lambda} = .000; Mask design: P = < .001, Wilks' {Lambda} = .000; Breath level: P = < .001, Wilks' {Lambda} = .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.

 The strength of this study is that it offers quantitative evidence on the effectiveness of wearing non-medical improvised masks in controlling airborne dispersion of particles from exhalation.  Study can aid in establishing public health strategy that encourage the wearing of masks or face coverings for reducing airborne transmission of infectious disease in shared air spaces.  A limitation of this study is that is uses an exhalation simulator with the PPE industry standard NaCl test solution for particle generation rather than a clinical study with exhalation of biomaterial particles.  The particle sensors used had a limited ability to detect fast moving aerosol clouds from coughing or talking with no-mask applied.

In light of the current pandemic from rapid transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) and significant morbidity, there has been inconsistent medical guidance given to the public regarding the wearing of non-medical improvised fabric masks or face coverings to reduce the transmission of COVID-19. If the SARS-CoV-2 aerosol is considered with an ability to infect for more than 3 hours with TCID50 of greater than 10 2 as noted in a recent laboratory study [1] then the understanding the effectiveness of non-medical masks and face coverings to control human exhalation aerosol dispersion has significant importance for broad public health infectious disease strategy, especially with asymptomatic or pre-symptomatic populations.

Of concern, recent studies show that bio-droplets of all sizes are generated from normal exhalation [2] [3] [4] [5] with 80-90% of droplets from human exhalation in the size range of 0.1-1µm, [6, 7] those from a cough can travel 23 to 27 feet (7-8 m) which is well beyond the recommended social distances of six feet or two meters, and smaller aerosols (≤5µm) stay aloft in the air and pose a greater risk for severe infection. [8, 9] In addition, social distancing is difficult to accomplish since many essential locations like grocery stores have aisles are narrow and result in the proximity of patrons being closer than 1 meter; reduced distance correlates to increased transmission of COVID-19. [10, 11] A lack of definitive data on establishing the effectiveness of using non-medical masks or face coverings has resulted in medical practitioners giving broad public health guidance based on professional judgement only. Existing guidance includes statements that facial coverings may offer minimal protection from small infectious particles, may only reduce large particulate matter, or only remind users to not touch their face considering infectious disease transmission from hand to face. A number of previous studies have been conducted to understand if wearing of masks reduce community infections of common diseases such as influenza, however most are inconclusive due to the application of masks post-exposure or lack of strict wearing compliance by study participants. [12] [13] [14] Only a few well-executed studies conclude the prophylactic wearing of medical grade masks reduce community transmission of influenza or RSV. [15, 16] To make matters worse, the lack of definitive guidance has also led to social and political debates on the wearing of masks or face coverings [17] [18] [19] and deters the acceptance of any new public health strategy for reducing airborne transmission of infectious diseases.

Prior studies have established the filtration efficiency of a variety of fabrics but do not consider reducing COVID-19 transmission by controlling airborne dispersion of human exhalation. [20] [21] [22] [23] [24] [25] [26] [27] Other research investigates only forward dispersion of particles from coughing or sneezing by measuring a on a single optical plane, [8, 28] or on the aerodynamics of exhalation particles inside various rooms. [29] [30] [31] [32] Several clinical studies showing reductions in virus shedding when wearing face masks,[2,3,5,33-35] but neither the clinical nor experimental studies have fully characterized the effectiveness of non-medical grade reusable masks in controlling aerosol dispersion of human exhalation particles in terms of three-dimensional direction, velocity, and particle concentration of various diameters in real world environments.

The goal of this research is to determine the statistically significant factors and effectiveness of non-medical grade washable masks or face coverings in the control of aerosol dispersion of human exhalation, and to aid in establishing public health strategies or policies on the wearing of masks. [36] Although broad clinical studies on the use of non-medical masks would offer results directly correlated to the community reduction of infectious diseases, this original research offers the experimental results that establish a basis for conducting such a study along with a more comprehensive set of effectiveness measurements for mask designs.

We conducted a comparative effectiveness study using a randomized full factorial design of experiments with 10% NaCl nebulized solution and an exhalation simulator to conduct 94 experiment runs with combinations of 8 different fabrics, 5 mask designs, no-mask as a control, and exhalation airflows (PEF and FEV1) that represent both talking and coughing. The experiment also included randomized runs of no-mask applied as the control and a preliminary comparison with the performance of a MERV13 air filter media which has similar electrostatic filtration properties to the NIOSH N95 standard (95% filtration efficiency of 0.3µm particles).

The exhalation simulator was constructed similar to previous research, [37, 38] but with some differences. The exhalation simulator was driven by a dry compressed air expansion chamber and timing-controlled relay, with a port for the small volume jet nebulizer, in-line spirometer, and a corrugated tube to emulate a trachea before exiting the mouth of the CPR manikin. Details on the simulator equipment are in (online supplementary figure 1 ). 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. 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. The frontal sensor represents an approximate halfway point of a 2 meter or 6 feet social distance.

A NaCl aqueous solution was selected as a polydisperse test aerosol which is also used as the exposure for NIOSH N95 respirator test methods. 10% NaCl was used to generate a sufficient quantity of particles for the open-air fume hood environment and also to stay beneath the PMS sensor maximum (65,535 particle count per 0.1L for any given size). The aerosol was produced by nebulizing the solution at 103 kPa (15 psi) for 5 seconds into the aerosol chamber of the exhalation simulator followed by a 3 ms delay before exhalation from the manikin through the applied masks. The simulated exhalations were driven by timing controlled compressed air at 827 kPa (120 psi) for "coughing" and 206 kPa (30 psi) for "talking".

The intervention was provided by non-medical grade washable masks sourced from local materials that were available during the COVID-19 supply chain interruptions. The fabrics were selected are shown in (online supplementary table 1) (fabric bolts were unavailable) which All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07.12.20152157 doi: medRxiv preprint included natural fibers, polyesters, and other materials. The mask designs were selected from variety of community-based designs which included a bandana style, surgical mask style, folded no-sew, a simple mask with earloops, and a stylistic mask that had more coverage of the nose. Microscopic images of the fabric weave and fibers were taken (Keyence VHX-S660E) to further understand and explain the results. More details regarding the fabrics and masks designs and the basic test procedure are also included in the (online supplementary appendix).

The primary outcome was to measure any significant reduction in aerosol dispersion velocity, quantity of particles, and change in dispersion direction. Measurements used in this study included peak expiratory flow (PEF), forced expiratory volume (FEV1), as well as aerosol arrival time, time to peak concentration, aerosol velocity, area under curve (AUC) for first minute and last minute as shown in Figure 1 . A change in direction from sensor 5, reduction in velocity, or AUC are considered a positive effect. Two novel metrics of Filtration Efficiency Indicator (FEI) and Expiratory Flow Dispersion Factor (EDF) are established in this study to present quantitative values that give relative indicators to the dispersion control performance of non-medical masks using simple and repeatable measurement techniques of the research. A description of all measurements and outcomes are presented in Table 1 . Indicates general filtration of mask design / fabric by comparing residual particle concentration with mask applied to nomask (control) after system has equalized. Max value of the control is used to give

Ratio of particle conc.

All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07.12.20152157 doi: medRxiv preprint worst case scenario. This is a unitless ratio.

Quantifies the overall reduction in aerosol velocity and direction from a mask application. This is a unitless ratio of the air velocity from exhalation and velocity of the first arriving aerosol coupled with direction. PEF velocity is calculated from a standard volumetric flow formula.

= 1 − ,

a Since the data collected is a series of discrete measurements, the AUC calculation is similar to a summation of trapezoidal areas but accounts for the ascending and descending edges.

FEI is a ratio of the particle concentration remaining after exhalation through a mask compared to no-mask and provides a quantitative indicator that aids in the filtration performance characterization. It is a ratio of remaining particle concentration (AUC4 to 5 min) with a mask applied compared to the worst-case AUC of no-mask applied. Since this experiment does not lend itself to directly measure the particle concentration in the aerosol chamber prior to exhalation nor does it use the same calibrated equipment, test orifice and tube size, airflow dynamics, and other equipment from the NIOSH N95 test standard, [41] the filtration efficiency indicator values are relative to this experiment. Similarly, many other recent studies seeking to establish the filtration efficiencies of non-medical grade masks or fabrics have the same constraint on relativity of results. However, while the actual values are relative to this study the FEI measurement technique is broadly applicable to all exhalation dispersion studies.

We also present EDF as a measurement of the reduction in particle velocity and change in direction when a mask is applied. As shown in Table 1 , it is the ratio of cloud velocity at the first arriving sensor to that of the exhalation airflow velocity derived from the PEF measurement. The theory of EDF is based on the airflow from exhalation simulator (bounded volume) and aerosol velocity in fume hood (unbounded turbulent airflow) which are correlated by Bernoulli's ideal-gas law and further described in the field of kinetic theory of gases. Full derivation of the volumetric flow formula is provided in literature; [42] in this study the airflow of PEF equals the cross-sectional area of the spirometer multiplied by the average velocity of the air stream shown in Equation (1) = ̅

(1)

Where:

The inside diameter of the MIR SmartOne spirometer was measured to be 28.67 mm which allows for an area calculation. Using algebraic relationships, the formula for calculating the velocity of the exhalation using PEF measurement is shown in Equation (2) along with the unit conversion to meters per second.

• . ×

(2) All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07.12.20152157 doi: medRxiv preprint Sample Size: The overall sample size from the full factorial combination of 40 distinct masks, several randomly inserted test runs of no-mask as the control, and 2 exhalation levels resulted in 94 experiment runs. The overall experiment with four sensors and average sampling rate of 1 second, generated over 1.694 million time-series sensor data measurements for this study. The sample size of n=94 resulted in 2,496 measurements for each dependent response variable across all particle diameters.

Multivariable and multivariate analysis was conducted from the perspective of a null hypothesis that non-medical improvised masks do not affect the dispersion or offer source control. This study determines if the three independent variables (mask designs, fabrics and breathing levels) have a statistically significant effect on any of the dependent responses (direction, AUC0 to 1 min, FEI, and EDF) at various sensor locations. Three-way multivariate analysis of variance (MANOVA) is used to simultaneously understand the significance of multiple effects from the independent variables and their correlations while minimizing type I statistical errors (false positives). Details on the use of MANOVA and validation against the assumptions of the data, including homogeneity of covariance, normality, independence of observations and multicollinearity [43] [44] [45] [46] [47] are provided in (online supplementary description of statistical methods).

MATLAB version R2019a was used to import the raw data files, compute the response variable values, and calculate summary statistics. SPSS version 1.0.0.1327 was used to perform MANOVA. To additionally validate the statistical results and measured outcomes, graphical analysis of the data was also performed to identify any anomalies that were not expected in the response variables.

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. Likewise, simulated coughing generated peak concentrations of 27,731 (9,837) for 0.3µm particle diameters, and 131(27) for 10µm particle diameters. Table 2 shows descriptive summary statistics of variables with respect to the primary dispersion direction and gives some insight into the generalized responses. The best overall performing mask is the surgical style with internal non-woven layers [AUC0 to 1 min = 7.721x10 5 (6.606x10 5 ), FEI = .468(.158), EDF = .993(.005)]. The best overall fabric depends on a desired characteristic of reduced velocity and direction or increased filtration performance, a general comparison of EDF across mask designs is shown in Figure 2 . The velocity-ratio related performance for nomask applied, EDF = .984(.009), indicates the overall slowdown of particles due to turbulence All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07.12.20152157 doi: medRxiv preprint and aerodynamics in an open air system. The large standard deviations represent the divergence between the responses for each exhalation breath level (visible in (online supplementary figures 6 -13 ) and also indicate that the interactions between multiple factors and the multivariate responses. In addition, the mean velocities for no-mask are, in some cases, lower than certain masks or fabrics which is related to a PMS sensor's sampling rate limitation. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. The results from MANOVA on the complete data set are reported in Table 3 Table 3 shows that the results using Pillai's Trace are also significant (in case the MANOVA assumptions of homogeneity of variance-covariance were violated). Therefore, the null hypothesis that masks or face coverings have no effect on exhalation dispersion or source control is rejected. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. The statistics of Wilks Lambda and Pillai's trace (Table 3) converge at η 2 = .996 and indicate that 99.6% of the variance of dependent variables are associated with exhalation breath levels of talking or coughing. It should also be noted that there were statistically significant interaction effects between fabric, mask design, breath levels and the combination of all three independent variables also reported in Table 3 (Fabric*Mask Design, Fabric*Breath level, Mask design*Breath level, Fabric*Mask design*Breath level). In some cases, the between-subject effects were marginally significant (P-value closer to .05), however the vast majority of individual between-subject effects 30/35 (85.7%) are significant. A full multivariate analysis of variance and multivariate tests of between-subject effects and interactions are provided in (online  supplementary table 2 and 3) .

Conclusively, this quantitative comparative effectiveness study establishes that the application of improvised non-medical grade mask designs or fabric combinations were statistically significant in reducing airborne dispersion of particles from exhalation as defined by direction, velocity, AUC0 to 1 min, FEI and EDF. The statistically significant interaction effects between combinations of all primary factors and partial η 2 values further establish the strong correlation of outcomes to fabrics used, mask design, and exhalation breath levels. This foundational research offers an orthogonal but complimentary result to previous research on respiratory protection and personal protective equipment which exclusively looks at inhalation filtration and airflow pressure gradients that support respiration.

When considering airborne dispersion control (also known as source control in some literature) it is important to understand the primary mechanisms that affect the dispersion. The field of filtration theory offers significant understanding with the primary mechanisms for the respiratory use case: Interception, Brownian Diffusion, Inertial Impaction, and Sieving or blocking filtration. [48] [49] [50] [51] Since the fibers and fabric meshes are typically larger than small aerosols or infectious particles at 5µm diameters or smaller, the first three filtration mechanisms are most applicable to this study and other studies in the field of personal protective equipment (PPE). Special emphasis is placed on inertial impaction and Brownian diffusion to disrupt the velocity and direction of airborne dispersion. Further discussion on filtration mechanisms is provided in (online supplementary appendix).

All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07.12.20152157 doi: medRxiv preprint One observation from this study is that the effectiveness in dispersion varies greatly between the specific fabrics and mask design combinations. For example, the factors of fabric, mask design, and the interaction of fabric and exhalation breath level that have significant effect on FEI, while other factors and interactions are not significant (online supplementary table 3) . This suggests that a fabric's dynamic characteristics such as pliability (i.e. conforms to the face for fit and coverage) and dynamic response to airflow force (i.e. stretch characteristics) have an effect on the overall filtration of exhaled particles. Ad hoc test data of a stretch fabric commercially available mask is consistent with this statement (online supplementary table 5). Further materials analysis and characterization is justified to fully understand this observation however it does emphasize that proper wearing of masks [52] [53] [54] is important for PPE usage as well as dispersion control. Characterization of fabric thickness, fiber density and weave, as well as layering will also aid in establishing accurate predictor coefficients of a dispersion control linear regression model for specific fabrics and masks.

The strength of this study is that it offers quantitative evidence on the effectiveness of nonmedical improvised masks for helping to establishing public health strategies or policies that encourage the wearing of masks or face coverings. Fundamentally the effectiveness of nonpharmaceutical interventions (NPI) can be increased by reducing exhalation particle dispersion and is especially important where infectious contaminants may exist in shared air spaces. An overall public health strategy must consider the additive effect of wearing masks and face coverings for inhalation filtration (PPE) and that of dispersion and source control. However, the strategy would need to account for the non-ideal performance of various fabrics and masks, where the ideal particle dispersion performance would offer 95% filtration efficiencies and dispersion contained to the user's body. Combining this research with recent community SIR modeling [55] can help provide significant insights to the public health strategy. To summarize, it would be of most benefit for all people in community settings to wear masks and get full effect of controlling exhalation particle dispersion to reduce transmission of highly infectious respiratory diseases such as COVID-19.

One limitation of this study is that it provides approximations of human exhalation using polydisperse NaCl solution rather than actual exhalation. Real human exhalation adds additional compositions of particles that can be smaller than 0.3µm in diameters, moisture, proteins, gases, and other bio material[56] so the longer term effectiveness of masks for source or dispersion control cannot be directly established from this data, however this study utilizes industry and NIOSH accepted proxy for testing respiratory barriers of NaCl. Another limitation was the PMS sensor performance: measurement minimum of 0.3µm particles and a slower intake fan speed limited its ability to accurately measure all characteristics of fast moving particle clouds from that of no-mask applied. Regardless, the sensor data and experiment design were sufficient to determine statistical conclusions on the effects of wearing masks and face coverings of different fabrics and designs. Future works should consider using a large test chamber and more sensors to result in more accurate measurement of airborne dispersion and turbulent airflows.

All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07. 12.20152157 doi: medRxiv preprint The results show that the application of various non-medical grade mask designs or fabric combinations were statistically significant in reducing airborne dispersion of particles from exhalation during coughing and talking as well as singing. However, the effectiveness varies greatly between the specific fabrics and mask designs used. The best overall performing mask design is a surgical style with internal non-woven layers, while the best overall fabric depends on a desired characteristic of reduced velocity, change in direction, or increased filtration performance. Conclusively this study can aid in establishing public health strategies or policies that encourage the wearing of masks or face coverings to increase the effectiveness of nonpharmaceutical interventions (NPI) especially where infectious contaminants may exist in shared air spaces.

Mr. Edwards was the principal investigator and primary author, Ms. Widrick created the design of experiment and conducted the mathematical analysis, Dr. Potember assisted in conducting background research and guided the approach to experimentation and rigor of analysis, Mr. Gerschefske assisted in the laboratory configuration and testing.

We extend our appreciation to The American Red Cross of Southeastern Colorado and El Paso County Public Health for allowing the use of CPR training equipment and manikin to rapidly conduct this study. We also acknowledge Mr. Asher Edwards for his contributions to the early code development for the data acquisition system, and Ms. Lydia Edwards for providing continuous awareness of open news research and public vetting of concepts. 56 Popov TA. Human exhaled breath analysis. Ann Allergy Asthma Immunol 2011;106:451-6. doi:10.1016/j.anai.2011.02.016 Figure 1 : Examples of the measurements performed on the time-series data from all experiment runs for each of the four sensors and particle sizes of 0.3µm, 0.5µm. 1µm, 2.5µm, 5µm, 10µm MaskDesign All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07.12.20152157 doi: medRxiv preprint

Aerosol emission and superemission during human speech increase with voice loudness

The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission

The Size Distribution of Droplets in the Exhaled Breath of Healthy Human Subjects

Size distribution and sites of origin of droplets expelled from the human respiratory tract during expiratory activities

Turbulent Gas Clouds and Respiratory Pathogen Emissions: Potential Implications for Reducing Transmission of COVID-19

Collection, particle sizing and detection of airborne viruses

Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis

Natural ventilation for infection control in health-care settings

Face Mask Use and Control of Respiratory Virus Transmission in Households

Review of economic evaluations of mask and respirator use for protection against respiratory infection transmission

National Academies of Sciences E. Rapid Expert Consultation on the Effectiveness of Fabric Masks for the COVID-19 Pandemic

Mask use, hand hygiene, and seasonal influenza-like illness among young adults: A randomized intervention trial

Respiratory Syncytial Virus (RSV) Infection Rate in Personnel Caring for Children With RSV Infections: Routine Isolation Procedure vs Routine Procedure Supplemented by Use of Masks and Goggles

Trump shares tweet that says masks represent 'slavery and social death' -Business Insider

Mandatory Masks Aren't About Safety, They're About Social Control

The mask wearing debate is dividing America. And the messaging isn't getting any clearer

Assessment of Fabric Masks as Alternatives to Standard Surgical Masks in Terms of Particle Filtration Efficiency

Multilayer nonwoven fabrics for filtration of micron and submicron particles

Simple respiratory protection--evaluation of the filtration performance of cloth masks and common fabric materials against 20-1000 nm size particles

Aerosol Filtration Efficiency of Common Fabrics Used in Respiratory Cloth Masks

Evaluating the efficacy of cloth facemasks in reducing particulate matter exposure

The Ultimate Guide to Homemade Face Masks for Coronavirus. Smart Air Filters

Effectiveness of Common Fabrics to Block Aqueous Aerosols of Virus-like Nanoparticles

Visualizing the effectiveness of face masks in obstructing respiratory jets

COVID-19 Outbreak Associated with Air Conditioning in Restaurant

No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity

Novel Coronavirus (COVID-19) Pandemic: Built Environment Considerations To Reduce Transmission

Towards aerodynamically equivalent COVID19 1.5 m social distancing for walking and running

How Sneeze Particles Travel Inside An Airplane | Popular Science

Testing the Efficacy of Homemade Masks: Would They Protect in an Influenza Pandemic

Respiratory source control using a surgical mask: An in vitro study

Utility of Substandard Face Mask Options for Health Care Workers During the COVID-19 Pandemic

To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

Dispersion and Exposure to a Cough-Generated Aerosol in a Simulated Medical Examination Room

A Cough Aerosol Simulator for the Study of Disease Transmission by Human Cough-Generated Aerosols

Exhaled air dispersion during coughing with and without wearing a surgical or N95 mask

The control of air flow during loud soprano singing

National Institute for Occupational Safety and Health. TEB-APR-STP-0059: Determination of Particulate Filter Efficiency Level for N95 Series Filters Against Solid Particulates for Non-powered

A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

7 Robustness of ANOVA and MANOVA test procedures

Handbook of Applied Multivariate Statistics and Mathematical Modeling

7 -Multivariate Analysis of Variance and Covariance

Information Point: Wilks' lambda

Filtration Theory for Granular Beds

Determining Impaction Efficiencies of Mist Collection Equipment

Ullmann's Encyclopedia of Industrial Chemistry

Masks and Coronavirus Disease 2019 (COVID-19)

Recommendation Regarding the Use of Cloth Face Coverings, Especially in Areas of Significant Community-Based Transmission

Use of Cloth Face Coverings to Help Slow the Spread of COVID-19

A modelling framework to assess the likely effectiveness of facemasks in combination with 'lock-down' in managing the COVID-19 pandemic