Infectious Disease Transmission 

in Airliner Cabins 

*

Qingyan “Yan” Chen, Ph.D.; James J. McDevitt, Ph.D., CIH; Jitendra K. 

Gupta, Ph.D.; Byron W. Jones, P.E., Ph.D.; Sagnik Mazumdar, Ph.D.; 

Stephane B. Poussou, Ph.D.; and John D. Spengler, Ph.D. 

National Air Transportation Center of Excellence for 

Research in the Intermodal Transport Environment (RITE) 

Airliner Cabin Environment Research Program 

Harvard School of Public Health; Kansas State University; and Purdue 

University 

February 22, 2012

Report No. RITE-ACER-CoE-2012-01

*Authors’ names appear alphabetically.
NOTICE 

___________

This document is disseminated under the sponsorship 

of the U.S. Department of Transportation in the interest 

of information exchange. The United States Government 

assumes no liability for the contents thereof. 

___________ 

This work was funded by the U.S Federal Aviation Administration 

Office of Aerospace Medicine under 

Cooperative Agreements 07-C-RITE and 10-C-RITE. 

___________ 

This publication is available in full-text from 

the publications Web site of the 

National Air Transportation Center of Excellence for 

Research in the Intermodal Transport Environment (RITE) at: 

www.acer-coe.org
A-1 

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

RITE-ACER-CoE-2012-01 

4. Title and Subtitle 5. Report Date 

Infectious Disease Transmission in Airliner Cabins February 22, 2012 6. Performing Organization Code 

 7. Author(s) 8. Performing Organization Report No. 

Qingyan “Yan” Chen, James J. McDevitt, Jitendra K. Gupta, Byron W. 

Jones, Sagnik Mazumdar, Stephane B. Poussou, and John D. Spengler 

 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) Harvard School of Public Heath Boston, Massachusetts 

Kansas State University Manhattan, Kansa 11. Contract or Grant No. Purdue University West Lafayette, Indiana 07-C-RITE and 10-C-RITE 12. Sponsoring Agency name and Address 13. Type of Report and Period Covered Office of Aerospace Medicine Technical Report 

2/1/2009 to 12/31/2011 

 

Federal Aviation Administration 800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 15. Supplemental Notes Work was accomplished under Public law 108-76. 16. Abstract Airborne disease transmission has always been a topic of wide interest in various fields for decades. This report shows 

the study of the entire disease transmission route in an airliner cabin from the infectious agents generated by an index 

patient to the fellow passengers and crew members. The investigation developed exhalation/inhalation models for 

coughing, breathing and talking with human subjects. The transport of these expiratory droplets in an aircraft was 

investigated using the CFD methods. It was found that the bulk airflow pattern in the cabin played the most important 

role on the droplet transport. The deterministic and probabilistic approaches (Wells Riley equation) can be used to 

quantify the risk of infection based on the inhaled infectious dose. By assuming an index passenger with influenza the 

deterministic approach can calculate the amount of influenza virus ribonucleic acid (RNA) particle inhaled by each 

passenger and the probabilistic approach can determine the risk of infection for each passenger. Further experimental 

and CFD investigations revealed that in-flight movement of passengers and crew members had a significant impact on 

infectious disease transmission inside airliner cabins. The investigation showed that the movement of a passenger/crew 

member could be the reason behind the transmission of SARS viruses to passengers seated as far as seven rows from 

the infected passenger during the SARS outbreak in 2003. An important route for disease transmission is by deposition 

of droplets or particles on surfaces followed by contact with the surfaces. Experimental methods to measure particle 

deposition in a full-scale cabin mockup were developed. This investigation has also developed a better understanding of 

surface contamination in the air cabin through laboratory investigation and a pilot field study. The laboratory 

investigation focused on evaluating survival of influenza virus on surfaces and developing a field, microbial, surface 

sampling protocol for use in the air cabin environment. The results of our investigation show that influenza survival on 

stainless steel surfaces are impacted by relative humidity. 

 

17. Key Words 18. Distribution Statement 

Virus, influenza, SARS, models, experiment, risk, 

airflow, relative humidity, particles, deposition, 

movement, in-flight 

 

 

 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 172 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 
‐ 1 ‐ 

EXECTIVE SUMMARY 

Airborne disease transmission has always been a topic of wide interest in various 

fields for decades. The transmission of bacterial and viral pathogens from inanimate 

surfaces to susceptible persons is a concern with regard to infectious disease 

transmission. For infection to occur, the pathogenic organisms must be present and 

survive on the environmental surface until such a time that the pathogen is transferred to 

a susceptible host at the appropriate route of entry. The air cabin is a unique environment 

for disease transmission. The air cabin has a high occupation density with a high 

passenger through-put. While seated for hours at a time, “sick” individuals have ample 

opportunity to contaminate their surrounding area. Once surfaces in the air cabin become 

contaminated, susceptible persons in the immediate area may come into contact with the 

contaminated surfaces. 

The transmission of airborne disease starts when the infectious agents such as the 

influenza virus or Mycobacterium tuberculosis are exhaled from an infected person. The 

infectious agents are then dispersed and finally inhaled by a susceptible person. The 

transmission of infectious diseases in indoor environments, especially in aircraft cabins 

may represent great risk as they have high occupant density and need to be investigated. 

With the recent advancements, Computational Fluid Dynamics (CFD) has become a 

powerful tool for predicting the transmission of airborne diseases in indoor environments. 

The CFD simulations need precise thermo-fluid boundary conditions for the 

exhalation/inhalation during various respiratory events and reliable flow models to 

accurately predict the transport of the infectious agents. 

The present study first developed exhalation/inhalation models for coughing, 

breathing and talking. The study conducted measurements on the exhaled/inhaled flow 

rate, flow direction and area of mouth/nose opening with human subjects. The flow rate 

variation over time can be defined as a combination of gamma functions for a cough; 

sinusoidal function for breathing, and a constant for talking process. The variables 

required to define these flow rate functions can be obtained from the physiological details 

of a person. The direction of the exhalation jet and the area of mouth/nose opening did 

not vary significantly during these processes. Though variation among people existed but 

had no correlation with the physiological details of a person. Thus a mean value for these 

parameters can be used as boundary condition. In summary, a set of mathematical 

equations were developed to provide the flow boundary conditions to the CFD 

simulations for the coughing, breathing and talking processes. The equations account for 

the variation in the flow boundary conditions with time and people. 

The droplets exhaled by an infectious person are the carriers of infectious agents. 

The transport of these expiratory droplets in an aircraft was investigated using the CFD 

methods. The developed exhalation/inhalation boundary conditions were used. A sevenrow, twin-aisle, fully-occupied cabin with index passenger seated at the center was 

investigated. The droplets exhaled were from a single cough, a single breath and a 15-s 

talk of the index passenger. The droplets were tracked by using Lagrangian method and 

their evaporation was modeled. It was found that the bulk airflow pattern in the cabin 

played the most important role on the droplet transport. The droplets were contained in 

the row before, at, and after the index patient within 30 s and dispersed uniformly to all 

the seven rows in 4 minutes. For the cough case, the total airborne droplet fraction 
‐ 2 ‐ 

reduced to 48%, 32%, 20%, and 12% after 1, 2, 3 and 4 minutes respectively. Similar 

observations were made for the breathing and talking cases. 

The study further developed methods to predict the spatial and temporal 

distribution of expiratory droplets for any flight duration based on the 4 minutes of CFD 

simulations. The CFD simulations indicated that the local droplet concentrations were 

higher in the zone, where the expiratory droplets first reached. The droplets concentration 

then reduced due to the constant dispersion and removal of the droplets from the outlets. 

The variation in the droplet concentration in the vicinity of the passengers with time 

indicated that the droplets got well mixed in the cabin in 3 minutes, thus perfectly mixed 

conditions can be assumed beyond 3 minutes. The droplets from multiple exhalations 

from the index passenger were found to follow similar tracks. This indicates that the 

airflow in most of the cabin was steady. It is proposed that the concentration of the 

droplets in a zone can be obtained by adding the concentrations in the zone from all the 

exhalations taken place until that time provided with a time shift. Finally, the amount of 

droplets inhaled by the susceptible passengers was calculated for 4 hours of flight 

duration under three scenarios. 

The study used the deterministic and probabilistic approaches (Wells Riley 

equation) to quantify the risk of infection based on the inhaled infectious dose. The 

inhaled dose was calculated using the amount of inhaled droplets and the infectious dose 

contained in a droplet. A case with index passenger suffering with influenza was 

analyzed. For the deterministic approach, the amount of influenza virus ribonucleic acid 

(RNA) particle inhaled by each passenger was calculated. For the probabilistic approach, 

the risk of infection for each passenger was evaluated based on the amount of inhaled 

quanta. The effectiveness of masks in reducing the risk of infection was also explored. 

Further experimental and CFD investigations revealed that in-flight movement of 

passengers and crew members can change the airflow and contaminant transmission 

inside airliner cabins. Experiments were performed using a one-tenth scale, water-based 

empty cabin model to facilitate measurements and to generate high quality experimental 

data using Particle Image Velocimetry (PIV) and Planar Laser-Induced Fluorescence 

(PLIF). The results indicate that seats and passengers tend to obstruct the lateral 

transmission of contaminants and restrict the spread to the aisle of the cabin if a 

contaminant is released from a moving body. Investigations showed that movement could 

be the reason behind the transmission of SARS contaminants to passengers seated as far 

as seven rows from the infected passenger during the SARS outbreak in 2003. 

An important route for disease transmission is by deposition of droplets or 

particles on surfaces followed by contact with the surfaces. There are few experimental 

measurements that can be used to aid in developing accurate CFD models. To fill this 

need, experimental methods to measure particle deposition in a full-scale cabin mockup 

were developed. These methods use microphotographs to count particles on surfaces 

following controlled particle releases in the cabin. These measurements are ongoing. 

When completed, the resulting data set will be combined with the corresponding data set 

for airborne concentrations already collected to relate deposition rates to airborne 

concentrations. 

This investigation has also developed a better understanding of surface 

contamination in the air cabin through laboratory investigation and a pilot field study. 

The laboratory investigation focused on evaluating survival of influenza virus on surfaces 
‐ 3 ‐ 

and developing a field, microbial, surface sampling protocol for use in the air cabin 

environment. While the pilot field study focused on developing a rational method for 

evaluating microbial contamination on air cabin surfaces. The results of our investigation 

show that influenza survival on stainless steel surfaces are impacted by relative humidity. 

There was a trend of increasing influenza inactivation with increasing relative humidity. 

The inactivation rates for influenza virus were statistically higher at 75% compared to 25 

and 50% RH. The low RH in the air cabin may be conducive to influenza survival and 

increase infection risk. Recovery of bacteria from surfaces varied and was inconsistent 

among a variety of swab types. The sampling kit used in the pilot field study recovered a 

wide variety of bacteria, but only a single type of virus. The strategy of sampling aircraft 

at the end of the flight day, and prior to cleaning, provided an efficient means of 

evaluating microbial surface contamination on a variety of aircraft. 
‐ 4 ‐ 

TABLE OF CONTENTS 

EXECTIVE SUMMARY .............................................................................................................................1

Part 1: Respiratory Exhalation/Inhalation Models and Assessment of Airborne  

Infection Risk in Commercial Aircraft Cabins                   8

1. INTRODUCTION.............................................................................................................................9

1.1 EXHALATION AND INHALATION MODELS FOR RESPIRATORY EVENTS ........................................................... 11

1.2 PREDICTION OF INFECTION SPREAD IN AN AIRCRAFT CABIN ...................................................................... 11

2. THE STATE OF THE ART................................................................................................................13

2.1 EXHALATION AND INHALATION DURING RESPIRATORY EVENTS .................................................................. 13

2.1.1 Coughing................................................................................................................................. 13

2.1.2 Breathing ................................................................................................................................ 15

2.1.3 Talking .................................................................................................................................... 15

2.2 PREDICTION OF TRANSPORT OF EXPIRATORY DROPLETS IN AN AIRCRAFT CABIN............................................ 15

2.2.1 Considerations for CFD Modeling ........................................................................................... 15

2.2.2 Prediction of Expiratory Droplet Transport in an Aircraft Cabin............................................. 16

2.3 EVALUATION OF RISK OF INFECTION IN AN AIRCRAFT CABIN...................................................................... 17

2.4 CONTROLLING THE RISK OF INFECTION ................................................................................................. 18

2.4.1 Effectiveness of Masks against the Infectious Agents............................................................ 18

2.5 SUMMARY ...................................................................................................................................... 21

3. FLOW CHARACTERIZATION OF A COUGH .....................................................................................23

3.1 MEASUREMENT METHODS ................................................................................................................ 23

3.2 RESULTS......................................................................................................................................... 24

3.2.1 Flow Rate of a Cough.............................................................................................................. 24

3.2.2 Flow Direction of a Cough....................................................................................................... 30

3.2.3 Mouth Opening Area .............................................................................................................. 32

3.3 DISCUSSION .................................................................................................................................... 34

3.4 CONCLUSIONS ................................................................................................................................. 34

4. FLOW CHARACTERIZATION OF BREATHING AND TALKING............................................................36

4.1 MEASUREMENT METHODS ................................................................................................................ 36

4.2 RESULTS......................................................................................................................................... 37

4.2.1 Breathing ................................................................................................................................ 37

4.2.2 Talking .................................................................................................................................... 43

4.3 DISCUSSION .................................................................................................................................... 46

4.4 CONCLUSIONS ................................................................................................................................. 48

5. TRANSPORT OF EXPIRATORY DROPLETS IN AN AIRCRAFT CABIN..................................................49

5.1 RESEARCH METHOD ......................................................................................................................... 49

5.1.1 Domain of Study ..................................................................................................................... 49
‐ 5 ‐ 

5.1.2 Numerical Schemes................................................................................................................. 50

5.1.3 Case Setup .............................................................................................................................. 53

5.1.4 Grid Independence and Cabin Section End Conditions ........................................................... 58

5.2 RESULTS......................................................................................................................................... 63

5.2.1 Steady‐State Airflow in the Cabin........................................................................................... 63

5.2.2 Evaporation of Droplets in the Cabin...................................................................................... 65

5.2.3 Transport of Droplets Exhaled from the Coughing ................................................................. 66

5.2.4 Transport of Droplets Exhaled from the Breathing................................................................. 69

5.2.5 Transport of Droplets Exhaled from the Talking..................................................................... 71

5.3 DISCUSSION .................................................................................................................................... 73

5.4 CONCLUSIONS ................................................................................................................................. 74

6. INHALATION OF DROPLETS IN AN AIRCRAFT CABIN .....................................................................75

6.1 RESEARCH METHODS........................................................................................................................ 75

6.1.1 Extension of Droplet Concentration for Real Flight Durations................................................ 75

6.1.2 Droplets Inhaled by the Passengers due to Multiple Exhalations from the Index Passenger. 79

6.1.3 Case Setup .............................................................................................................................. 82

6.2 RESULTS......................................................................................................................................... 83

6.3 DISCUSSION .................................................................................................................................... 84

6.4 CONCLUSIONS ................................................................................................................................. 85

7. EVALUATION OF AIRBORNE RISK OF INFECTION IN AN AIRCRAFT CABIN ......................................86

7.1 RISK ASSESSMENT MODELS................................................................................................................ 86

7.2 CASE DESCRIPTION........................................................................................................................... 87

7.3 AMOUNT AND VIABILITY OF DOSE ....................................................................................................... 88

7.3.1 Deterministic Models.............................................................................................................. 88

7.3.2 Probabilistic Model: Wells Riley Equation .............................................................................. 88

7.4 RESULTS......................................................................................................................................... 89

7.4.1 Risk of Infection from Influenza using the Deterministic and Probabilistic Models................ 89

7.4.2 Effect of N‐95 Mask ................................................................................................................ 91

7.5 DISCUSSION .................................................................................................................................... 93

7.6 CONCLUSION................................................................................................................................... 93

8. INFLUENCE OF MOVING BODY THROUGH EXPERIMENTAL AND CFD INVESTIGATIONS IN A SMALL‐

SCALE CABIN .......................................................................................................................................94

8.1 RESEARCH METHODOLOGIES.............................................................................................................. 94

8.2 EXPERIMENTAL METHOD................................................................................................................... 95

8.2.1 Experimental Setup................................................................................................................. 95

8.2.2 Experimental Techniques........................................................................................................ 97

8.3 CFD MODELING .............................................................................................................................. 99

8.4 RESULTS AND DISCUSSION ............................................................................................................... 100

8.4.1 Cross‐sectional Flow ............................................................................................................. 100

8.4.2 Longitudinal Flow ................................................................................................................. 108

8.4.3 Contaminant Transport ........................................................................................................ 110

8.5 CONTAMINANT TRANSMISSION DUE TO MOVEMENT IN THE SARS INFECTED CABIN.................................... 113

8.6 CONCLUSIONS ............................................................................................................................... 118
‐ 6 ‐ 

9. CONCLUSIONS AND FUTURE WORK........................................................................................... 119

9.1 CONCLUSIONS ............................................................................................................................... 119

9.2 RECOMMENDATION FOR FUTURE STUDIES .......................................................................................... 122

10. REFERENCES.......................................................................................................................... 124

Part 2: Measurement of Particle Deposition Behavior in an Aircraft Cabin        135

11. INTRODUCTION ........................................................................................................................... 136

12. EXPERIMENTAL METHODS ........................................................................................................... 137

13. RESULTS ...................................................................................................................................... 141

14. CONCLUSIONS AND NEXT STEPS................................................................................................... 144

15. REFERENCES ................................................................................................................................ 145

Part 3: Role of Relative Humidity in the Inactivation of Influenza Viruses on Steel

Surfaces and Microbial Surface Sampling in the Air Cabin              146

16. INTRODUCTION..................................................................................................................... 147

17. BACKGROUND....................................................................................................................... 149

17.1 RELATIVE HUMIDITY AND INFLUENZA SURFACE SURVIVAL ...................................................................... 149

17.2 SURFACE SAMPLING PROTOCOL DEVELOPMENT ................................................................................... 149

17.3 FIELD STUDY ................................................................................................................................. 150

18. METHODS ............................................................................................................................. 152

18.1 TEST ORGANISMS .......................................................................................................................... 152

18.1.1 Influenza Virus.................................................................................................................. 152

18.1.2 Bacteria ............................................................................................................................ 152

18.2 RELATIVE HUMIDITY AND INFLUENZA SURFACE SURVIVAL ...................................................................... 153

18.2.1 RH Control Chamber......................................................................................................... 153

18.2.2 RH Survival Tests .............................................................................................................. 153

18.2.3 Data Analysis.................................................................................................................... 154

18.3 SURFACE SAMPLING PROTOCOL DEVELOPMENT ................................................................................... 154

18.3.1 Swab Selection.................................................................................................................. 154

18.3.2 Swab Surface Testing ....................................................................................................... 155

18.3.3 Surface Testing Data Analysis .......................................................................................... 156

18.4 FIELD STUDY ................................................................................................................................. 156

18.4.1 Field Study Sampling Plan ................................................................................................ 156

18.4.2 Field Study Sample Processing ......................................................................................... 157

19. RESULTS................................................................................................................................ 158

19.1 RELATIVE HUMIDITY AND INFLUENZA SURFACE SURVIVAL ...................................................................... 158

19.2 SURFACE SAMPLING PROTOCOL DEVELOPMENT ................................................................................... 159

19.3 FIELD STUDY ................................................................................................................................. 161

20. DISCUSSION.......................................................................................................................... 166

20.1 RELATIVE HUMIDITY AND INFLUENZA SURFACE SURVIVAL ...................................................................... 166
‐ 7 ‐ 

20.2 SURFACE SAMPLING PROTOCOL DEVELOPMENT AND FIELD STUDY........................................................... 167

21. CONCLUSIONS....................................................................................................................... 170

22. REFERENCES.......................................................................................................................... 171
‐ 8 ‐ 

Part 1:

Respiratory Exhalation/Inhalation Models and 

Assessment of Airborne Infection Risk in Commercial 

Aircraft Cabins 

Prepared by 

Jitendra K, Gupta, PhD., Sagnik Mazumdar, Ph.D., Stephane B. Poussou, Ph.D., and 

Qingyan “Yan” Chen, Ph.D. 

School of Mechanical Engineering 

Purdue University 

West Lafayette, Indiana 

February 10, 2012 
‐ 9 ‐ 

1. INTRODUCTION 

Airborne infection has always been a major source of morbidity and mortality 

worldwide (Corbett et al., 2003). There are common airborne infectious diseases such as 

cold and influenza, and severe ones as Severe Acute Respiratory Syndrome (SARS), 

tuberculosis (TB) and avian flu. Dye et al. (1999) evaluated the global burden of TB. 

About 22 countries were found to have TB infections with a total of 1.87 million deaths. 

Influenza epidemics were found to cause about 47,200 deaths each year in the United 

States with similar figures in Europe (Viboud et al., 2004). The Spanish flu of 1918-19 

(H1N1) was by far the most lethal flu pandemic of the 20th century. According to the 

World Health Organization (WHO) estimates, it infected about one-quarter of the global 

population and took more than 40 million lives(WHO, 2002). Not only avian flu could be 

deadly; there were 8098 people infected by SARS and 774 of them died according to 

statistics from the World Health Organization (WHO, 2004). In a recent swine flu 

epidemic (2009) about 0.6 million people (laboratory confirmed) got infected (WHO, 

2009). The list of such social and economic disruption caused by these and many more 

airborne diseases is endless. Thus it is important to understand, predict and control the 

transmission of airborne diseases to trim down the deaths. 

Airborne disease transmission starts from infectious agents exhaled from an 

infected person (Cole and Cook, 1998). Then the infectious agents disperse in the air and 

are finally inhaled by a susceptible person. The infectious agents can spread at indoor and 

outdoor spaces. At the outdoors the infectious droplets can rapidly disperse and quickly 

render to a nonviable stage because of sky/sun light (Edwards and Dragsted, 1952). On 

the other hand, the indoor spaces are confined with low air exchange rate as compared to 

outdoors and thus may have major risk for infection transmission (Houk et al., 1998 

WHO 1998; WHO 1999 and Mangili and Gendreau, 2005). Moreover, people spend 90% 

of their time indoors (Klepeis et al., 2003 and Aliaga and Winqvist, 2003). Thus it is 

important to investigate the transmission of infectious diseases in indoor environments 

such as office spaces (Kenyon et al., 2000), hospitals (WHO, 1999), and aircraft cabins 

(WHO, 1998). 

The airborne disease transmission during air travel has been given high 

importance (WHO, 1998). This is because aircraft cabins have high occupancy rate and 

long exposure time, people sit in quite proximity of each other, and cannot leave the 

cabin, and thus would very likely be infected by airborne infectious agents if there is a 

pandemic. Some of the TB outbreaks were believed to be caused by in-flight infection 

transmission (Kenyon et al., 1996). The SARS infected passengers in the flight from 

Hong Kong to Beijing (Olsen et al., 2003) were believed to get infected due to the inflight transmission. Mangili and Gendreau (2005) reviewed the risks of airborne infection 

spread among the passengers inside the aircraft cabin from various respiratory outbreaks 

and suggested that in-flight infection disease transmission is of greatest risk. Moreover, 

now it has become more of a public issue as more than two billion people are traveling 

each year (Gendreau and DeJohn, 2002) in commercial flights. Thus the current study is 

focused on investigating airborne disease transmission in aircraft cabins. 

The airborne disease transmission can be predicted by computer simulations or 

experimental methods. The experimental methods are expensive, time consuming and 

less flexible to changes, though they can provide realistic information (Zhang, 2007a; 
‐ 10 ‐ 

Zhang, 2007; Sze To et al. 2009 and Yan et al., 2009). It may not be feasible to conduct 

experiments on a real aircraft cabin with passengers and crew members as the cost 

involved could be extremely high. Zhan (2007) estimated that the experimental studies 

for a few rows can easily go up to a million dollar and may not represent real cabin 

conditions. The aircraft cabins are long and thus it may really take long time to conduct 

measurements. The computational methods on the other hand are inexpensive, fast and 

more flexible to changes in comparison with the experiments. The main computational 

methods include multizone models (Axley, 2007), zonal models (Megri and Haghighat, 

2007), fast fluid dynamics (FFD) (Zuo and Chen, 2009) and computational fluid 

dynamics (CFD) (Norton and Sun, 2006). The aircraft cabin is a complex geometry and 

the airflow and contaminant transport is three-dimensional (Zhang et al., 2009). The 

multizone models assume uniform air temperature, and chemical-species concentration in 

a zone, which is not a valid assumption for large spaces like aircraft cabins. The zonal 

based models are fast but can give approximate results as it considers macroscopic flow 

between the zones. Thus it may not provide intricate details required for the prediction of 

infection transmission. The FFD based models are faster than the CFD models but are not 

as accurate as the CFD models especially when the flow is turbulent (Zuo and Chen, 

2009). On the other hand, the CFD based models are relatively time consuming but can 

provide accurate information. The CFD models can effectively predict airborne 

contaminant transport in commercial aircraft cabins (Lin et al., 2005a and 2005b; Wan et 

al., 2009; Yan et al., 2009 and Zhang, 2007) and the associated non-uniformities. 

Moreover, CFD predictions of contaminant transport matched reasonably well with the 

experimental data (Zhang, 2007; Wan et al., 2009 and Yan et al., 2009). Thus for the 

current investigations, the CFD simulations are used for predicting the airborne disease 

transmission. 

Accurate prediction of the transmission by these CFD simulations requires, 

precise information on boundary conditions (Zhang, 2007) such as, sources and sinks of 

infectious agents and appropriate flow models such as, turbulence and dispersion model 

(Zhang, 2007). The exhaled air of an infected human is the source of infectious agents. 

These agents enter the body of the victim through inhalation, deposit on a surface, or are 

extracted through a ventilation exhaust, which are called sinks. There are a number of 

turbulence models (Zhang, 2007) and dispersion models (Zhang and Chen, 2007c) that 

are used to study the indoor environment airflow. The selection of the model depends on 

the application, available computational resources and time. To our knowledge there is no 

comprehensive literature on the exhalation and inhalation characterization from various 

respiratory events. Also the available literature on the prediction of in-flight disease 

transmission though gives quite an insight on the airflow and dispersion but has its pros 

and cons. These are discussed in details in the state of the art chapter. Thus it is required 

to develop models for exhalations/inhalations from various respiratory events and further 

investigate the in-flight disease transmission. 
‐ 11 ‐ 

Figure 1.1 CFD simulation layout for the prediction of airborne disease transmission. 

1.1 Exhalation and Inhalation Models for Respiratory Events 

The present study first developed models for the exhalations and inhalations, 

which can be used as thermo-fluid boundary conditions in CFD simulations. The thermofluid boundary conditions include the flow rate, direction, area of mouth/nose opening, 

duration of the event, temperature, and droplet size distribution. These parameters are 

transient and can have considerable variation among people. But most of the CFD 

simulations (Zhu et al., 2006a, 2006b and Zhao et al., 2005) for infectious agent 

dispersion predictions have treated these boundary conditions as arbitrary periodic profile 

with assumed direction, temperature, and a ballpark number for the area of the mouth or 

nose opening. This assumption could be due to scarcity of reliable exhalation/inhalation 

models. Thus it is required to develop models, which can provide accurate information on 

these boundary conditions. 

The main exhalation/inhalation modes include coughing, sneezing, talking, and 

breathing. All these exhalation modes can introduce significant amount of infectious 

agents. The coughing and sneezing have a higher exhalation velocity and droplet 

concentration but a lower event frequency, while the breathing and talking have a lower 

velocity and droplet concentration but a higher event frequency. This investigation is 

focused on the coughing, breathing and talking processes. The study conducted 

measurements on the exhaled/inhaled flow rate, flow direction and area of mouth/nose 

opening with human subjects, and developed a set of equations to provide the thermofluid boundary conditions for coughing, breathing and talking processes. 

1.2 Prediction of Infection Spread in an Aircraft Cabin 

The transport of droplets exhaled from the coughing, breathing and talking of an 

index passenger was investigated in a seven row section of a twin aisle cabin using CFD 

methods. The developed exhalation/inhalation models along with the appropriate 

information of the expiratory droplets distribution were fed in the CFD simulations. The 

index passenger could release the infectious agents/droplets through multiple exhalations 

for several hours during the air travel. Performing CFD studies for all such possible 

combinations of exhalations for realistic flight durations is impractical. Therefore a 

method was developed to extend the information on the droplet obtained from the single 

cough, single breath and 15s talking CFD simulations of 4 minutes to multiple exhalation 

scenarios over realistic flight durations. Using the spatial and temporal distribution of 

expiratory droplets the amount of droplets inhaled by the passengers was calculated. The 

amount of infectious dose inhaled by the passengers and the corresponding risk to the 
‐ 12 ‐ 

passengers was then calculated. Based on the comprehensive literature review the 

reduction in the risk obtained by using the N-95 mask was also calculated. 
‐ 13 ‐ 

2. THE STATE OF THE ART 

This chapter first shows the available studies on the flow characterization of 

exhalation and inhalation during coughing, breathing, and talking. The available literature 

on the requirements for the CFD modeling for predicting the transport of expiratory 

droplets is then presented. The literature on the transport of expiratory contaminants and 

risk prediction in an aircraft cabin is then explored. Finally, a comprehensive review on 

protection provided by the masks is presented. 

2.1 Exhalation and Inhalation during Respiratory Events 

This section discusses the available literature on the exhalation and inhalation 

flow rate, flow direction and area of opening of the mouth/nose during coughing, 

breathing and talking processes. 

2.1.1 Coughing 

Figure 1 shows the typical flow generated from a cough over time. Information 

about cough flow rate is available from the literature (Leiner et al., 1966; Mahajan et al., 

1994 and Singh et al., 1995), such as Cough Peak Flow Rate (CPFR), Cough Expired 

Volume (CEV) that is the area under the curve, and Peak Velocity Time (PVT). The 

information is of great medical interests (Lamb et al., 1993), but cannot be used as 

boundary conditions in the CFD simulations. However, the information provides good 

insight of the flow behavior. Leiner et al. (1966) correlated CPFR with the height and age 

of human subjects through a regression analysis. Mahajan et al. (1995) further developed 

relations between CPFR, CEV and PVT. 

Later, Singh et al. (1995) corrected the relationship between CPFR and PVT for 

different genders and found the difference caused by the larynx size in male and female. 

The total cough expired volume measured by Zhu et. al (2006b) had a variation of 0.8-2.2 

liter with an average of 1.4 liter. This was low compared to the studies by Mahajan et al. 

(1994), who observed a variation of up to 5 L with an average of about 3 L. Though these 

studies gave some information on cough but did not quantify the dynamics of the cough 

profile. 
‐ 14 ‐ 

Figure 2.1 Cough flow rate variation with time. 

There were a number of studies visualizing the coughing process that could be 

used to create boundary conditions for CFD simulations. Jennison (1942) visualized 

cough through high speed photography using strobe (Edgerton and Barstow 1959). Their 

main emphasis was on droplet size distribution but he also revealed qualitatively the 

droplet dispersion to 2-3 feet from mouth. Settles et al. (1995) captured the thermal 

plume coming out of a cough through Schlieren imaging. But none of these studies could 

characterize the transient process or direction of the jet. Particle Image Velocimetry 

(PIV) (Afshari et al., 2002; Badeau et al., 2002; VanSciver; Zhu et al., 2006b and Chao et 

al., 2009) measurements were also performed to capture the flow field. Mahajan et al. 

(1995) pointed out that the PVT was in the order of a few milliseconds. Hence to capture 

the cough jet variation and its direction, it becomes essential to have a high frequency of 

measurements. PIV measurements by Afshari et al. (2002) and Badeau et al. (2002) were 

done at an interval of 67 ms. The interval was too large to capture the flow features. Chao 

et al. (2009) investigated the maximum and average velocities but the velocity variation 

with time was not studied. The PIV studies by VanSciver et al. gave detailed flow 

information but with an even larger interval of 267 ms. The efforts by Zhu et al. (2006b) 

were also with a large interval of 70 ms. Therefore, none of these studies could be used to 

characterize the direction of a cough jet for the CFD simulations. The measurements of 

the flow rate and direction must be performed with a frequency of 100 Hz or higher. 

The area of mouth opening is required to obtain the velocity from the flow rate. 

The PIV studies by Zhu et al. (2006b) indicated the peak cough velocity varied from 6 to 

22 m/s with an average of 11.2 m/s. To our best knowledge, little experimental studies 

were available on the area of mouth opening during a cough. Most researchers have 

assumed (Zhu et al., 2006a, 2006b and Zhao et al., 2005) the area to be a few square 

centimeters. Thus the available literature provides good understanding of the flow 

behavior of the coughing but not the information that can be used as boundary condition 

for CFD simulations. 
‐ 15 ‐ 

2.1.2 Breathing 

Some information on breathing airflow is available in the literature, such as 

Respiration Frequency (RF), Minute Volume (MV), and Tidal Volume (TV). The RF is 

defined as the number of breaths in a minute; MV the volume expired in a minute; and 

TV the volume expired in a single breath. These parameters are of medical interest and 

had been studied thoroughly (Handbook of Physiology, 1986 and Altman et al., 2004). 

The RF, MV, and TV vary with age, height, weight, body surface area, sex, posture and 

physical load (Handbook of Physiology, 1986 and Altman and Dittmer, 1971). Flowvolume studies by Araujo et al., (2004) were on evaluating the effect of physical load but 

not on quantifying the flow rate variation over time. All these studies provide a good 

insight but not the information as boundary conditions for CFD simulations. 

2.1.3 Talking 

Most of the past efforts on characterizing a talking process were focused on 

establishing droplet size distribution (Duguid, 1946; Jennison, 1942 and Wells et al., 

1939). Duguid (1946) revealed that the velocities of speech can go up to 16 m/s. 

Jennision (1942) found that consonants, like ‘F’, when pronounced loudly can generate 

high flow. Wells et al., (1939) used the Tyndall effect to visualize the pronunciation of 

the letters ‘P’ and ‘T’. But none of the studies quantified the flow rate transience. The 

information is incomplete for CFD simulations because the flow rate and direction of 

talking jets and opening area of mouth/nose are not available. 

Thus it is required to study the exhalation and inhalation during the coughing, talking and 

breathing processes and develop models that can provide the flow rate, flow direction of 

the jet and the area of mouth/nose opening during the processes. 

 

2.2 Prediction of Transport of Expiratory Droplets in an Aircraft Cabin 

This section explores the factors to be considered for modeling the expiratory 

droplet transport in an aircraft cabin. The contaminant transport in an aircraft cabin and 

the associated risk studies are then presented.

2.2.1 Considerations for CFD Modeling 

Selection of appropriate control volume and geometrical details: The aircraft 

cabin with seats, passengers, airflow inlets and outlets, is a complex and big geometry. 

The airflow and the contaminant transport in the cabin are three-dimensional and nonuniform (Singh et al., 2002 and Zhang et al., 2009). Thus it is essential to account for the 

geometrical details of the whole cabin configuration including the passengers (Singh et 

al., 2002). Another important thing is to decide on the domain of study, i.e. how may 

rows? Zhang et al. (2009) suggested that only a few rows may not represent real cabin 

conditions. The SARS infection in the flight from Hong Kong to Beijing spread in 7 rows 

(Olsen et al., 2003). 
‐ 16 ‐ 

Information on the boundary conditions for the control surfaces: The boundaries 

(control surfaces) in the aircraft cabin are the walls of the cabin, airflow inlets and outlets 

of the cabin, seat walls, passenger body, nose and mouth opening of the passengers. 

Numerical simulations are very sensitive to these boundary conditions (Zhang, 2007). 

Appropriate flow models: The selection of equations to be solved, such as, 

turbulence model and the particle transport model (Zhang, 2007) is very crucial for 

accurate prediction of transport of infectious agents. Moreover, the expiratory droplets 

are volatile and can quickly evaporate (Morawska, 2007). Thus it is important to model 

the evaporation of the expiratory droplets. 

The selection of turbulence model is another crucial step in particle transport and 

deposition pattern. Zhang (2007) evaluated the performance of various turbulence models 

for indoor environments. He compared the performance of indoor zero-equation model 

(Chen and Xu, 1998), the RNG k-ε model (Yakhot and Orszag, 1986), a low Reynolds 

number k-ε model (LRN-LS) (Launder and Spalding, 1974), the SST k-ω model (SST) 

(Menter, 1994), a modified v2f model (v2f-dav) (Davidson et al., 2003), Reynolds stress 

model (RSM-IP) (Gibson and Launder, 1978), the large eddy simulation (LES) (LESDyn) (Germano et al., 1991 and Lilly 1992), and the detached eddy simulation (DES-SA) 

(Shur et al., 1999) under natural convection, forced convection, mixed convection, and 

strong buoyant flows. Airflow in aircraft cabins is mixed convection in nature. Zhang, 

(2007) recommended v2f-dav and RNG k- models for mixed convection scenario in 

terms of efficiency, accuracy and robustness. 

The transport of particle in CFD is usually predicted either through Eulerian or 

Lagrangian method. The selection of particle transport method depends on the problem 

and the associated objectives. Eulerian methods are used to predict the particle 

concentration distribution (Murakami et al., 1992; Mazumdar et al., 2008 and Yan et al., 

2009) while the Lagrangian methods are used to predict the particle dispersion pattern 

(Loomans and Lemaire, 2002; Zhang et al., 2006 and Wan et al., 2009). Zhang and Chen, 

2007c compared the two methods and recommended Lagrangian method for transient 

prediction of particle transport. He found out that Eulerian methods requires small time 

steps and higher number of iteration per time step to converge, thus is time consuming. 

Moreover, Lagrangian method can predict detailed particle distribution. 

In summary, for accurately and efficiently modeling the transport of expiratory 

droplets in an aircraft cabin, it is essential to consider the geometrical details along with 

the passengers and seats, appropriate domain (7 rows or may be full cabin), precise 

boundary condition on the surfaces (including exhalation/inhalation) and appropriate 

turbulence (RNG k-), dispersion models (Lagrangian), and evaporation models. 

2.2.2 Prediction of Expiratory Droplet Transport in an Aircraft Cabin 

There are a number of studies on in-flight contaminant transport (Aboosaidi et al., 1991; 

Lin et al., 2005a; Lin et al., 2005b; Zhang et al., 2009; Yan et al., 2009 and Wan et al., 

2009) 

Aboosaidi et al. (1991) considered an empty cabin but Singh et al. (2002) pointed out that 

the airflow inside the cabin can change significantly if heating load of the passengers is 

considered. Investigations by Lin et al. (2005a) and Lin et al. (2005b) provided great 

insight for the airflow in the aircraft cabin. But the domain consisted of just 2 rows, 
‐ 17 ‐ 

which may not be sufficient (Olsen et al., 2003; Mazumdar et al., 2008 and Zhang and 

Chen, 2009). 

Zhang et al. (2009) studied contaminant transport in a twin aisle aircraft cabin for 

4 rows. They pointed out that the bulk airflow governs the track of contaminants. But 

again only a few rows were considered, which may not represent real cabin conditions. 

Yan et al. (2009) studied the contaminant transport in a 5-row, twin-aisle cabin 

with passengers. They have used tracer gas to investigate the transmission and used 

standard k- model. The study provided meaningful results but again may lack accuracy 

as unsteady particle tracking (Zhang, 2007) and RNG k- model (Zhang, 2007 and Wan 

et al., 2009) were not used, which are best suited for the aircraft cabin environment 

simulation. 

Recently, Wan et al. (2009) have studied the transport and deposition of 

polydispersed droplets in a 3-row-two-aisle cabin configuration using Lagrangian 

method. The passengers were treated as cylinders with heat source. The passenger seated 

at the last row at the center was used as an infection source and was provided exhalation 

velocity of 10.6 m/s over a 1 s of time period with an opening of 0.38 cm2

 for the last 

passenger seating at the center. They have evaluated the infection risk due to inhalation 

and from direct contact with the surfaces. They pointed out that the fate of the particle 

depends upon its size. The study provides a good insight but only three rows are 

considered with passengers as cylinders and the source boundary conditions are not 

reliable, i.e. cough lasts for a fraction of a second and the mouth opening is about 4 cm2. 

It is thus required to further investigate the transport of contaminants (expiratory 

droplets) by considering more number of rows and realistic passenger geometry and 

precise boundary conditions for the exhalation and inhalation using the CFD methods. 

Moreover, as these detailed CFD simulations could be time consuming (Gupta et al., 

2010b), it is not practical and feasible to perform the CFD simulations for realistic flight 

times for a multiple exhalation scenario from the index passenger. Therefore, it is 

required to develop methods to predict the spatial and temporal distribution of expiratory 

droplets and thus, the amount of inhaled droplets by the passengers for realistic flight 

durations. 

 

2.3 Evaluation of Risk of Infection in an Aircraft Cabin 

The droplets inhaled by the passengers do not necessarily reflect active infectious 

agents and therefore the risk of infection. It is thus required to develop methods to 

quantify the risk of infection in an aircraft cabin. 

The airflow in the aircraft cabins is not uniform (Yan et al., 2009).The pulse 

release of infectious agents by the index passenger through coughing, breathing or talking 

makes the infectious agent transport transient. Therefore, there will be a spatial and 

temporal variation in the risk of infection to the passengers based on the amount of 

inhaled infectious agent. 

Walkinshaw, 2010 predicted the risk of infection from influenza in an aircraft 

cabin. He quantified the amount of influenza virus ribonucleic (RNA) particles inhaled 

by the passengers assuming perfectly mixed environment under transient conditions. Ko 

et al., 2004 and Jones et al., 2009 used the multizone models to quantify the risk based on 

the dose response model under steady and unsteady scenarios respectively. Wan et al., 
‐ 18 ‐ 

2009 performed detailed steady-state CFD simulations and quantified the risk of infection 

from influenza in an aircraft cabin. Risk from inhalation and surface contact was 

quantified using the dose-response model. 

These studies account for either the spatial or the temporal variation of risk of 

infection in an aircraft cabin but not for both the variations except the transient zonal 

models (Jones et al., 2009). Thus it is required to develop risk models based on the spatial 

and temporal distribution of amount of inhaled infectious agents. 

2.4 Controlling the Risk of Infection 

After predicting the risk of infection, it is required to explore ways to mitigate the 

risk. The risk of infection from the infectious agents can be brought down by reducing the 

level of infectious agents in the inhaled air of the occupants. It can be done by either 

cleaning/decontaminating the bulk air or reducing the level of infectious agents locally in 

the breathing zone of the occupants. 

The bulk air can be cleaned by increasing the ventilation rate. This will increase 

the rate of removal and will dilute the infectious agents. But increasing the ventilation 

rate increases the energy consumption. The other way could be by sterilizing the bulk air 

using the ultra violet (UV) rays with wavelength ranging from 254 to 260 nm (Weiss et 

al., 2007). McLean, (1961) found that the incidence of influenza reduced to 2% from 

19% by installing the UV light in a hospital. The UV rays are constrained well above the 

head level and the UV rays sterilize the air in this zone. The convection currents 

generated by the buoyancy and mechanical ventilation mixes the air and that is how all 

the air gets sterilized. The germicidal effects of UV rays are good for humidity levels 

below 70% (Laurell and Ronge, 1955) and thus can be used for indoor environment such 

as office spaces and hospitals. The other light disinfection techniques include photocatalysis (Zhao and Yang, 2003) and X-rays (Raber and McGuire, 2002). But these 

techniques cannot be used in aircraft cabins as the cabins do not have enough height. The 

chemicals, such as the vaporized hydrogen peroxides can also be used to disinfect the 

bulk air (Block, 1991) but is good for unoccupied cabins. 

The other way of reducing the risk of infection is to reduce the level of infectious 

agents locally in the inhaled air of the occupants. This can be done by filtering the air 

available for inhalation, using the protection device, such as masks (Weiss et al., 2007) or 

by supplying clean air to the breathing zone using personalized ventilation (Melikov, 

2004 and Cermark et al., 2006). Both of these techniques can be used for occupied 

aircraft cabins. 

The current study presents a brief summary of existing literature on protection 

provided by the surgical and respirator masks against the infectious agents. The effect of 

personalized ventilation used in aircraft cabins, commonly known as gasper on the 

inhalation of infectious agents will be investigated in future. 

2.4.1 Effectiveness of Masks against the Infectious Agents 

Masks are equipped with filters that purify the air passing them. It can be used to 

filter the air exhaled by an infected wearer (surgical masks) or to filter the air inhaled by 
‐ 19 ‐ 

the wearer (respirator mask). In both the cases ,the role of masks is to bring down the 

level of contaminants in the inhaled air of the occupants. 

The current study reviewed the investigations on the performance of surgical and 

respirator masks, when used to filter the air inhaled by the wearer. The performance or 

effectiveness of a mask is defined by the amount of contaminants present in the inhaled 

air relative to the amount of contaminants in the surrounding air. The air inhaled by the 

wearer can come through the filter (gets purified) and face seal (leakages, not purified). 

Thus the level of protection provided by a mask depends on the filtration performance 

and the amount of leakages from the face seal. The filtration performance of a mask can 

vary with the particle size, particle type, and the flow velocities. 

National Institute of Occupational Safety and Health (NIOSH) has standard test 

protocols for testing the filtration performance of respirator masks and certifying them. 

Respirator masks are rated as N, R, or P for protection against oils. N stands for not 

resistant to oil, R if somewhat resistant to oil and P for strongly resistant to oil. They also 

have a filtration rating, tested for most penetrating particle size. The ratings are 95, 99 or 

100 and indicate that the mask filtered out 95%, 99% or 99.97% of airborne particles 

with most penetrating size. The N-95 is the most popular and widely available mask. 

For the surgical masks, the FDA recommends (FDA, 2004) the particulate 

filtration efficiency (PFE) and bacterial filtration efficiency (BFE) tests (Oberg and 

Brosseau, 2008). Oberg and Brosseau, (2008) found that particle penetration through the 

surgical masks when tested using the NIOSH or Occupational Safety and Health 

Administration (OSHA) protocol is much higher than the PFE and BFE tests. Grinshpun 

et al., (2009), Lee et al., (2008) and Oberg and Brosseau, (2008) tested the surgical masks 

and found that the total penetration through the mask ranges from 10-50% and is way 

higher than the commonly used respirator mask (N-95). Therefore the current survey is 

focused on the studies on evaluating the performance of N-95 masks. 

NIOSH certifies the N-95 masks by testing them against NaCl particles with mass 

median diameter of 300 nm (most penetrating particle size) at a constant flow rate of 85 

l/min (Code of Federal Regulation Title 42, 1994). They use scanning and differential 

based particle sizer. As these tests are performed under specific conditions, the scientific 

community in the area raised following concerns 

1. Particle Size and type: Do NaCl particles of size of 300 nm have the most 

penetration among the wide range infectious agents and droplets exhaled by an 

infectious occupant? 

2. Flow conditions: Does a constant flow rate of 85 l/min represent the realistic 

inhalation transience? 

3. Instrumentation: Is the instrumentation used accurate enough to capture all the 

particle sizes? 

4. Human subjects versus manikins or Portacount: What will be the filtration 

capacity if human subjects are used? 

5. Leakages: How much is the leakage from the face seal? 

To investigate these concerns a comprehensive review was performed. 

Particle Size and type: The size of infectious viruses and bacteria range from 0.08 

to 0.14 m (Ksiazek et al., 2003 and Mandell et al., 1995) and 0.6 to 1.5 m (Nevalainen 

et al., 1993) respectively. The size of droplets carrying these agents can vary with the 
‐ 20 ‐ 

exhalation process. The droplets exhaled during coughing, breathing, and talking range 

from 0.62 to 15.9 m (Yang et al., 2007), 0.3 to 5 m (Fabian et al., 2008), and 1 to 100 

m (Duguid, 1946) respectively. The particle size associated with infectious agents or the 

droplets thus range from 0.08 to 100 m. It is required to investigate if the NaCl particles 

of size 0.3 m have the most penetration for this range. Balazy et al., (2006) evaluated 

the performance of N-95 masks against the MS2 virus (similar to pathogens) of size 10 to 

80 nm. The filter penetration was found to be less than 7%. Brosseau et al., (1997) tested 

16 respirator masks against the 0.55 m latex sphere and Mycobacterium Abscessus and 

found that the median penetration of the particle was 2, 0.4 and 0.02% for dust mist, dust 

fume mist and high efficiency particulate air filter. Qian et al., (1998) tested three N95 

masks from different manufacturers against NaCl particles of size 0.1 to 1 m and 

bacteria of shape and size similar to Mycobacterium Tuberculosis (B. Subtilis and B. 

Megatherium, size 1-2 m). They found that the penetration through the N95 masks was 

well below 5% against all the particles. Willeke and Qian, (1998) also had similar 

conclusions. Coffey et al., (1999) tested 21 models of N95 masks against ambient 

particles and indicated that the penetration from the masks can go as high as 16% under 

fit testing. Balazy et al., (2005) found that the penetration through the N95 masks for the 

NaCl particles ranging from 0.01 to 0.6 m was less than 7%. They found that most of 

the penetration takes place at around 0.04 to 0.05 m size. Rengasamy et al., (2008) 

tested five models of N95 masks against silver and NaCl particle of size 0.004 to 0.03 m 

and 0.02 to 0.4 m respectively and found that they provide expected level of protection. 

Lee et al., (2008) found that the penetration of particles of size 0.04 to 1.3 m for the four 

models of N95 masks tested could be more than 10% in some cases. But this also 

includes the leakages that might be taking place through the face seal. Eshbaugh et al., 

(2009) tested the N95 masks particles of size 0.02 to 2.9 m and found that the mask 

provided expected level of protection under constant flow rate of 85 l/min. The studies 

covered the range of particle from 4 nm to 3 m. The most penetrating size was found to 

lie in the range of 20 to 60 nm. It was found that the penetration from the filter for most 

of the masks was below 5% but can go up to 10% even under tight fit testing. It is hard to 

comment if the particle type had any influence but the penetration for all the biological 

and non-biological particle remained below 10%. 

Flow conditions: The penetration through the masks increases with increase in 

flow rate (Eshbaugh et al., 2009; Balazy et al., 2005 and Brosseau et al., 1997). The 

normal breathing minute volume and peak flow rate range from 4 to 16 liter and 10 to 50 

l/min respectively (Gupta et al., 2009), which is lower than the flow rate of 85 l/min at 

which NIOSH certifies the masks. Therefore for normal breathing, penetration would be 

less than the penetration predicted by the NIOSH test for the particle size. But during 

heavy breathing the peak velocities may be high. Eshbaugh et al., (2009) found that under 

cyclic breathing and constant flow conditions the penetration through most of the N95 

masks was below 10% even for flow as high as 360 l/min. Balazy et al., (1995) and 

Brosseau et al., (1997) tested N95 masks at lower flow rate and found that the penetration 

was well below 5%. In summary, under the normal breathing conditions the penetration 

was well below 5%. 

Instrumentation: Researchers have used different instrumentation for measuring 

the particle concentrations to evaluate the filtration performance of the masks. 

Appropriate care must be taken to understand the filtration performance values as these 
‐ 21 ‐ 

instruments could measure a limited range of particle size. Eninger et al., (2008) pointed 

out that the TSI portacount (instrument deployed by NIOSH) can capture diameters of 

size 100 nm or more. In order to measure the nano size particles, Anderson seven stage 

sampler (Brosseau et al., 1997) or the differential mobility analyzer and condensation 

particle counter (Balazy et al., 2006) can be used. Therefore, the penetration predicted by 

NIOSH does not include the penetration of particle below 100nm. 

Human Subjects versus manikin: The masks are usually tested either by putting 

them on human subjects (Coffey et al., 1999 and Grinshpun et al., 2009), manikins 

(Balazy et al., 2005 and Cho et al., 2009), or doing a fit testing where it is tightly fitted 

(Coffey et al., 1999 and Rengasamy et al., 2008) and the particle concentration on both 

the sides of the filter is measured. The fit test can account for the filtration penetration but 

not the leakages. Masks tested on manikin under realistic breathing profile may account 

for total penetration but may not reflect the realistic fitting with the face (Eninger et al., 

2008). Therefore the masks tested on human subjects can only provide a realistic 

estimation on total penetration (Eninger et al., 2008). 

Leakages: The ambient contaminant can enter through the masks from the gaps 

between the face and the masks (Coffey et al., 1999) in addition to through the filter. 

Therefore the total penetration could be higher than the penetration from the filter 

material. Coffey et al., (1999) have performed tests on human subjects and found out that 

the average penetration without fit testing was 33%, while with fit testing it was 4%. This 

clearly indicates that the face seal leakages could be way higher than the filter 

penetration. Recent study by Grinsphun et al., (2009) on human subjects found that the 

face seal leakages from the N-95 respirators ranged from 2 to 7%, while the penetration 

from the filter was less than 1% for particle size range 0.04 to 1 m. Cho et al., (2008) 

performed similar studies using manikins for particle size range 0.85 to 4 m under 

cyclic breathing. He found that the penetration from the face seal could be 2 to 8 times 

the filter penetration. Though the penetration was less than 1.2%, which could be due to 

the higher particle sizes and manikins. In addition to face seal, there could be leakages 

from the exhalation valve seal provided in some of the N95 masks. Lee et al., (2008) 

conducted the total penetration tests on human subjects using the OSHA protocol and 

found that the masks with and without the valve have the same performances. They also 

found that the total penetration can be higher than 10% for particle size range of 0.04 to 

1.3 m. 

In a nutshell, the total penetration through the mask for the particle size of ranging 

from 0.04 to 4 m was 10% for most of the N-95 masks under normal breathing 

conditions. Though some of the measurements have indicated average total penetration of 

33%, which could be due to very poor fitting of the masks. Thus for the further analysis, 

we considered the penetration from the masks including the face seal leakages to be 10%. 

2.5 Summary 

There is no comprehensive analysis on exhalation and inhalation during coughing, 

breathing, and talking that can provide boundary conditions for the CFD simulations. The 

CFD simulations are highly sensitive to the boundary conditions and need information on 

thermo-fluidic boundary conditions for these exhalations/inhalations to accurately predict 

the infectious agent transport. It is thus required to characterize the flow dynamics of 
‐ 22 ‐ 

these exhalations/inhalations. Therefore our investigations were first focused on 

developing exhalation/inhalation models to provide flow boundary conditions for 

coughing, breathing and talking by conducting experiments with human subjects. 

There are a number of studies on transport of contaminants in aircraft cabins. 

These studies provided good insight on the airflow and contaminant transport but have 

their pros and cons. In most of the studies only a few rows (3-5 rows) wide domain is 

investigated. But Mazumdar and Chen (2008) and Olsen et al. (2003) have pointed out 

that the infection can spread up to 7-13 rows. Some of the investigations were performed 

on an empty cabin but Singh et al. (2002) pointed out that the occupancy and associated 

thermal load need to be considered to accurately predict the contaminant transport. Most 

of these studies have assumed the exhalation and inhalation boundary conditions for 

various respiratory events. In some of the investigations, Eulerian method was used to 

predict the infection transmission but Lagrangian method can provide better results under 

transient conditions (Zhang and Chen, 2007c). Thus, this study further investigated the 

expiratory droplet transport in an aircraft cabin with passengers using the 

exhalation/inhalation boundary condition developed in the first phase and appropriate 

turbulence, dispersion and evaporation models. 

As the infectious (index) passenger can release the contaminants through a 

number of exhalations during his air travel, it is required to develop methods to predict 

the spatial and temporal distribution of expiratory droplets and the amount of such 

droplets inhaled by the passengers for realistic flight times. It is also required to evaluate 

the spatial and temporal distribution of risk of infection in an aircraft cabin. We extended 

the information obtained from the four minutes of CFD simulations of expiratory droplet 

transport from a single exhalation to realistic flight duration and quantified the risk of 

infection. 

Studies on evaluating the performance of masks have indicated that the total 

penetration from the masks for the virus, bacteria and droplet size range was 10%. This 

could be helpful in reducing the risk of infection. Therefore the risk of infection for a 

scenario with passengers wearing masks should be evaluated. We quantified the 

reduction in risk if masks are used by the passengers. 
‐ 23 ‐ 

3. FLOW CHARACTERIZATION OF A COUGH 

Coughing is defined as a violent release of air from the lungs following the 

opening of the glottis. It is a pulse process and lasts for just a fraction of second. But it 

has a much higher velocity and droplet concentration, which makes it the prime source 

for introducing infectious agents. 

In this chapter, first the experimental methods to measure the flow rate, flow 

direction and area of opening of mouth of a cough are discussed. The results obtained on 

these measured parameters are then discussed. A set of simple equations is developed that 

can be used to generate boundary conditions for the flow rate, direction, and area of 

mouth opening for a cough. As has been mentioned earlier, the boundary conditions 

should also include the exhaled air temperature and droplet distribution; hence 

appropriate models from literature are identified for them. 

3.1 Measurement Methods 

The present study measured flow rates, flow directions and mouth opening areas 

of coughs. A Spirometer based on Fleish type pneumotachograph (Sancho et al., 2004 

and Bongers and O’Driscoll, 2006) was used to measure the cough flow rates generated 

over time with a frequency of 330 Hz. The Spirometer is consisted of numerous capillary 

tubes in parallel and gives the flow proportional to the pressure drop. It is based on 

Poiseuille’s Law, which states that, under capillary conditions, in a straight rigid tube, 

flow is proportional to pressure loss per unit length. The flow directions were visualized 

through moderate-speed photography (120Hz) with 1 Mega Pixel resolution. Cigarette 

smoke was used as seeding fluid. The cigarette smoke particle size is about 0.2 m 

(Klepeis and Nazaroff, 2002) in diameter and the measured temperature of the cigarette 

smoke was close to exhaled air temperature. Thus the exhaled smoke jet should closely 

follow the cough air jet profile. The mouth opening areas were measured through the 

moderate speed photography (120Hz). All the measurements were conducted with 

vertical head posture as shown in Fig. 3.1. The head posture may affect the flow rate and 

flow direction in particular. But as the velocities during a cough are high, gravity effects 

may be negligible and the direction with respect to mouth may not change significantly. 

All the measurements were performed over 12 female and 13 male subjects to 

obtain realistic flow features with an approval from the Institutional Review Board for 

human subject experimentation. Normal healthy subjects were recruited for the study. 

The subjects were first given an overview of the research and were told about the risk 

involved in the measurements. Every subject signed a consent form before proceeding for 

the measurements. 

The flow rate measurements were done by placing a mask on subject’s face. The 

mask had ports for mouth and nose and the ports were connected to a filter then further to 

the Spirometer as shown in Fig 3.1. 
‐ 24 ‐ 

Figure 3.1 Subject with a mask holding the Spirometer during test. 

Two types of flow measurements, single cough and sequential cough, were 

conducted. The single cough coughed just once while sequential cough coughed twice. 

Each test was repeated three times for every subject. 

The flow visualization study was performed on smoker subjects. A light source 

was placed beneath a subject face to throw light upwards. The light source along with a 

dark background helped the flow visualization. The subjects were asked to exhale smoke 

out through coughing. The measurements of mouth opening area were performed over 8 

male and 8 female subjects. The subjects were asked to cough and the front views close 

to their lips were captured for the whole event. 

3.2 Results 

This section presents the experimental results on cough flow dynamics obtained 

from this investigation. 

3.2.1 Flow Rate of a Cough 

The flow rate characterization of a cough is to develop a simple mathematical 

model describing the boundary conditions for CFD simulations. 

Single Cough 

Fig. 3.2 shows the flow rate generated from a typical cough over time from a 

subject. The results indicate that the cough began with a very short inhalation (<1% of the 

total exhaled air volume), a very high acceleration afterwards in exhalation and 

subsequently a decay. The inhalation volume was very small and may be neglected. 
‐ 25 ‐ 

Figure 3.2 Cough flow rate variation with time. 

Figure 3.3 illustrates the transient flow of a cough for all the subjects studied. A 

large variation existed among the subjects. For example, the difference between the 

maximum and minimum values of CPFR, CEV and PVT for the male subjects is about 

200%, 300% and 100% respectively. Table 3.1 summarizes the variation range of the 

CPFR, PVT and CEV for the subjects. Thus, neither the cough flow characteristics from 

a subject can be used to represent the whole population, nor a standard cough exists. 

 

Figure 3.3 Cough flow rates for the 25 subjects. 

Table 3.1 Variation observed in cough flow characteristics. 

To describe the flow characteristics, this study used non-dimensional parameters. 

The dimensionless flow rate is defined as: 
‐ 26 ‐ 

. CPFR

Flowrate M  (3.1) 

The dimensionless time is defined as: 

. PVT

Time

  (3.2) 

 Figure 3.4 is exactly the same as Fig. 3.3 but in non-dimensional form. With the 

re-arrangement, one can see that when  < 1.2 all the curves are coincident. After that 

time the variation existed but the trend looks similar. Moreover, it can be seen that the 

dimensionless flow rate variation with time closely follows a gamma probability function 

till about 1.2 dimensionless time. Also, it was found that by superimposing another 

gamma probability distribution function, the inflation zone including the cough flow rate 

decay can be captured. 

Figure 3.4 Dimensionless cough flow rates from the 25 subjects. 

Equations (3.3) and (3.4) show these functions. The dimensionless flow rate for 

dimensionless time less than 1.2 is given by: 

for 1.2. ( b )c

) ca exp(

M b1

1 1

1

b 1

1













 

 (3.3) 

The dimensionless flow rate for dimensionless time greater than 1.2 is given by: 

for 1.2. ( b )c

) c

( 1.2 ) a ( 1.2 ) exp(

( b )c

) ca exp(

M 2

2

1

1

b

2 2

2

b 1

2

b

1 1

1

b 1

1



  







 











 

 

(3.4) 
‐ 27 ‐ 

Eq. (3.3) for  < 1.2 that contains only CPFR and Eq. (3.4) for  ≥ 1.2 that contains 

CPFR, PVT and CEV. Finally, a non linear least square curve fitting analysis was 

performed in MATLAB to obtain the optimum values of the variables required to define 

the gamma probability distribution function for each cough and are given by: 

 

 (3.5) 

Fig. 3.5 compares the measured flow rate with the fitted one obtained from equations 

(3.3) and (3.4) for a subject. Such a good agreement was found for all the subjects. 

Figure 3.5 Comparison of the flow rate calculated by Eqs. (3.3) and (3.4) for a single cough from 

a subject with the measured data. 

Please note that the CPFR, PVT and CEV are unknown for a new subject. In 

order to apply the two equations to a new subject, it is essential to link these variables to 

the subject’s characters, such as height, weight, and gender. Our regression analysis by 

the solid lines shown in Fig. 3.6 indicates that these medical parameters were interrelated. Figs. 3.6(a) and 3.6(b) show the variation of the CEV with the CPFR for the male 

and female subjects, respectively. The figures show a positive correlation between all the 

parameter (r>0.5, p<0.005 for all). Since the CEV increases with the CPFR, this implies 

that the higher the peak flow rate the higher would be the total volume exhaled. The 

CPFR for the female subjects were lower than that for the male subjects. Mahajan et al. 

. b 1

1.8

c

10.457, PVT CPFR

2.158 CEV b

a , PVT CPFR

CEV

a

c 0.428,

b 3.338,

a 1.680,

2

2

2

2 1

1

1

1

 





  

  






‐ 28 ‐ 

(1994) proposed using a single equation to correlate the CEV with the CPFR for both 

genders. As shown in Fig. 3.6, their regression lines deviate significantly from our data. 

Our two regression lines that distinguish the genders fit the best to the data. 

Similarly, Figs. 3.7(a) and 3.7(b) show the variation of the PVT with the CPFR 

for the male and female subjects, respectively. The results again indicate that the PVT 

increased with the CPFR. The PVT for the female subjects was higher than that for the 

male subjects. This could be attributed to the difference in their larynx sizes as indicated 

by Singh et al. (1995). The slope of the PVT versus the CPFR variation is similar to that 

obtained by him, but the intercept is higher by about 50 ms in our measurements. Our 

measurements were performed at 330 Hz and can capture the initial period, which could 

have got missed out in previous studies. 

 (a) (b) 

Figure 3.6 The relationship between CEV and CPFR for (a) male and (b) female subjects. 

(a) (b) 

Figure 3.7 The relationship between PVT and CPFR for (a) male and (b) female subjects. 

The cough expired volume (CEV) for male subjects can be written as: 

CEV(l )  0.138CPFR(l / s )  0.2983. (3.6) 

The cough expired volume (CEV) for female subjects can be written as: 

CEV(l )  0.204CPFR(l / s)  0.043. (3.7) 
‐ 29 ‐ 

The peak velocity time (PVT) for male subjects can be written as: 

PVT( ms )  1.360CPFR(l / s)  65.860. (3.8) 

The peak velocity time (PVT) for female subjects can be written as: 

PVT( ms )  3.152CPFR(l / s )  64.631. (3.9) 

With the two sets of equations and the CPFR, one can determine the cough flow rate over 

time by Eqs. (3.3) and (3.4). By using our data and multiple regression analyses, this 

study found that the CPFR depends mainly on subject height. For the male subjects, the 

subject weight has some impact on the CPFR as well. The regression analyses lead to the 

following two equations for calculating the CPFR from the height and weight of a 

subject. For male population the regression equation is given by: 

CPFR(l / s )  8.8980  6.3952h( m )  .0346w( kg ). (3.10) 

For female population the regression equation can be written as: 

CPFR(l / s )  3.9702  4.6265h( m ). (3.11) 

The two equations can calculate the CPFR with an error no greater than 20% by 

comparing it with the data in our database. The equations indicate that the CPFR 

increases with the height and weight of the subject. 

Thus, from the physiological information such as the height, weight, and gender, 

the CPFR can be calculated using equations (3.10) and (3.11). The CEV and PVT can be 

calculated from the CPFR using equations (3.6) to (3.9). With the CPFR, CEV and PVT, 

the dimensionless flow rate can be obtained from equations (3.3) and (3.4). Equations 

(3.1) and (3.2) can be utilized to convert the dimensionless flow rate into the dimensional 

one. Hence, the flow rate over time for a single cough can be obtained from the 

physiological information. 

Sequential Cough 

A sequential cough is defined one cough followed immediately by the other. Fig. 

3.8 shows the flow generated for a sequential cough from a subject. The flow behavior 

for the two coughs in the sequential cough was similar to that of a single cough and can 

also be described by equations (3) and (4). The first cough closely followed the features 

of a single cough while the second one was a scaled down of the first one. Tables 3.2 and 

3.3 compare the sequential coughs with the single coughs from the male and female 

subjects, respectively. The PVT for the two coughs in the sequential coughs was 

approximately the same as that of the single coughs while the CPFR and CEV for the 

second cough were approximately 0.5.-0.6 times of the single coughs. 
‐ 30 ‐ 

Figure 3.8 Flow rate over time for a sequential cough from a subject. 

Table 3.2 Relationship between sequential and single coughs for male. 

Table 3.3 Relationship between sequential and single coughs for female. 

Thus, the CPFR, PVT and CEV for the first cough can be obtained from equations 

(3.6) to (3.11), while those for the second cough should be corrected with Tables 3.2 and 

3.3. The flow rate can then be obtained by equations (3.3) and (3.4) for the two coughs. 

3.2.2 Flow Direction of a Cough 

The flow direction of a cough was obtained from flow visualization. Figure 3.9 

shows the sequence of images taken at 120 Hz for a typical cough process from t=0 to 

36/120 seconds. The first three images were for t=0 to 3/120 seconds, when the mouth 

was closed. Then the images were from t=9/120 to 36/120 seconds at an interval of 1/120 

seconds. At t=9/120 seconds, the mouth completely opened up but the smoke came out 

after t=11/120 seconds. 
‐ 31 ‐ 

Figure 3.9 The side view of a cough process recorded with a frequency of 120 Hz. 
‐ 32 ‐ 

Figure 3.10 The cough jet direction from the side and front views. 

The downward jet can be defined with two angles, 1 and 2, as shown in Fig. 

3.10. The jet has negligible spread in this view. Fig. 3.11 shows little variation in the 

cough angles for all the subjects. The 95% confidence bounds for the mean angles can be 

determined by: 

15 5 , 1      (3.12) 

and 

40 4 . 1      (3.13) 

The two mean angles can be used as boundary conditions for CFD modeling. 

Figure 3.11 Variation in the two Cough angles among all the subjects. 

3.2.3 Mouth Opening Area 

The mouth opening area is defined as the area between the lips during a cough. 

Figure 3.9 shows the sequence of images during a cough. Initially the mouth was closed 
‐ 33 ‐ 

and then opened up fully at t=9/120 seconds. The mouth remained opened for the rest of 

the cough process. This can be further confirmed from the images of the front view for all 

the subjects. Fig. 3.12 shows the mouth opening area over time for a subject. It can be 

seen that the mouth opening area was almost constant when there was flow from the 

mouth. 

Figure 3.12 Change of mouth opening area during a cough. 

Figure 3.13 Variation in mouth opening area during a cough with the heights of all the subjects. 

Fig. 3.13 shows the variation in mouth opening area during a cough with the 

heights of all the subjects. But the figure does not indicate any clear trend. Hence this 

study proposed to use an average value. The mean mouth opening area for the female 

subjects was smaller than that of the male subjects. The mouth opening area for the male 

subject is given by: 

Mouth Opening Area 4.00 0.95 cm .

2   (3.14) 

For female subjects it is given by: 

Mouth Opening Area 3.37 1.40 cm .

2   (3.15) 
‐ 34 ‐ 

3.3 Discussion 

This investigation has developed a set of simple equations that can be used to 

generate boundary conditions (or a source model) for predicting infectious virus transport 

by CFD due to coughing. Our effort focused on the flow rate, flow direction, and air 

velocity that can be determined from flow rate and mouth opening area. The inputs 

required are height, weight, and gender of a person. 

The boundary conditions required by CFD simulations should also include the 

temperature of the exhaled air and the droplet distribution. The study by Hoppe (1981) 

can be utilized to obtain the exhaled air temperature under various ambient temperature 

and humidity. He found that ambient temperature is the most influential parameter. There 

are a few studies on cough droplet size measurements (Duguid, 1946; Fairchild and 

Stampfer, 1987; Papineni and Rosenthal, 1997; Fennelly et al., 2004; Yang et al., 2007; 

Hersen et al., 2008; Chao et al., 2009 and Morawska et al., 2009). The measurements 

were done over healthy subjects (Duguid, 1946; Fairchild and Stampfer, 1987; Papineni 

and Rosenthal, 1997; Yang et al., 2007; Hersen et al., 2008; Chao et al., 2009 and 

Morawska et al., 2009) and the subjects infected by TB (Fennelly et al., 2004) and with 

cough and cold (Hersen et al., 2008). The latest measurements(Yang et al., 2007; Hersen 

et al., 2008) indicated that the droplet concentration was of the order of 106 and the size 

varied from 0.1 to 10 m. The study by Yang et al. (2007) indicated a linear trend with 

mean cough flow rate. Hence by knowing the mean cough flow rate from our model, the 

droplet concentration can be obtained. 

Figure 3.14 Droplet distribution for a cough (Yang et al., 2007). 

3.4 Conclusions 

In this section our efforts were focused on characterizing the flow behavior of 

cough. The flow rate, flow direction and mouth opening area were measured for 25 

human subjects, which could be used as boundary conditions in CFD simulations. 
‐ 35 ‐ 

The flow rate can be defined as a combination of gamma probability distribution 

functions. The variables in the functions can be represented by medical parameter i.e. 

CPFR, PVT and CEV. These medical parameters are related to the height, weight, and 

gender of a person. 

A sequential cough was found to be the combination of two single coughs. The 

first one behaved approximately the same as that of a single cough, while the second one 

was a scaled down version of the first one. 

The cough flow direction was visualized through the moderate speed photography 

using smoke. The flow direction did not vary very much among the subjects and can be 

determined by two angles. 

The mouth opening area was constant during a cough. The mouth opening area 

varied among the subjects but without a clear correlation with the subject height. Hence 

this study proposed to use a mean mouth opening area during a cough. 
‐ 36 ‐ 

4. FLOW CHARACTERIZATION OF BREATHING AND TALKING 

Breathing is defined as the regular intake of oxygen and outflow of carbon 

dioxide through the mouth or nose. As compared to coughing, which is pulse exhalation, 

breathing is periodic in nature. The process of talking on the other hand is highly 

nonspecific and can change significantly from person to person and per the matter of 

conversation. As conversations can be for minutes or hours, thus talking can be treated as 

a continuous process averaged over the time of the conversation. The flow rates in 

breathing and talking are lower when compared to coughing. But as they can have higher 

event frequency, its contribution in introducing the contaminants could be significant. 

In this chapter, first, the experimental methods to measure the flow rate, flow 

direction and the area of opening of mouth/nose during normal breathing and talking are 

discussed. The results obtained from the experiments are then analyzed. A set of simple 

equations was developed to generate boundary conditions for the flow rate, direction, and 

the area of mouth/nose opening during breathing and talking. Similar to the study of the 

cough, the appropriate temperature and droplet models for breathing and talking are 

identified from the literature to generate boundary conditions for them. 

4.1 Measurement Methods 

The present study measured flow rates, jet direction, and the area of opening of 

the mouth/nose for breathing and talking. The instrumentation, population, and research 

protocol were similar to cough measurements as has been explained in section 3. But the 

exercises that have been performed were different. The most common postures are sitting 

and standing; and the common routes of breathing are through the mouth and the nose. 

Thus the measurements were conducted with these variations, with a total of four sets of 

measurements, as described in Table 4.1. 

Table 4.1 Design of experiments for breathing. 

Organ Posture 

Mouth Sitting 

Nose Standing 

The flow rate measurements for talking consisted of three exercises. The first set 

was based on articulating letters from the alphabet. An alphabet set consists of 6 letters: 

F, E, C, O, S and T. F, S, and T were found to have the maximum exhalation flow rates, 

while E, C, and O are the most frequently used letters. Peak velocities of these 

measurements were analyzed. The second set of measurements was based on enunciating 

the numbers from one to ten. This was done to correlate the number of droplets with flow 

rates as the measurements on droplet sizes were done for this kind of exercise (Papineni 

and Rosenthal, 1997). The third set was based on the pronunciation of a reading rainbow 

passage (Fairbanks, 1940). This passage contains a mixture of oral and nasal consonants 

in the approximate proportion found in everyday speech (Seaver et al., 1991) and, hence, 
‐ 37 ‐ 

provides a reflection of the possible combination of flow rates that can be found in a 

conversation. The airflow rates from both the subject’s mouth and nose were recorded 

during this exercise. 

4.2 Results 

4.2.1 Breathing 

In this section experimental results on normal breathing under various postures 

with mouth and nose are investigated. The normal breathing flow rate can be best 

represented by a sine wave for all the subjects, and for all the breathings shown in table 

4.1, although the time of inhalation was shorter than that of exhalation. Fig. 4.1 shows the 

typical flow rate variation over time. 

Figure 4.1 Flow generated over time for a subject for normal breathing. 

The functional form of the flow rate during breathing can be expressed as: 

Flow Rate for Breathing  a sin( t ) (4.1) 

where,  and a can be calculated from Equations (4.2) – (4.4). As inhalation and 

exhalation time periods are different, subscript x can be replaced by “in” for inhalation 

and “out” for exhalation. The amplitude of the sine wave is given by: 

. 2

TV

a x

x

 (4.2) 

The tidal volume (TV) is defined as: 

. 2RF RF

MV ( RF RF ) TV

in out

in  out  (4.3) 

The  is defined as:
‐ 38 ‐ 

. 30

RFx

x

   (4.4) 

The MV and RF can vary with the subject, organ of breathing or human posture, and so 

will “a” and “”. We conducted statistical tests to investigate the influence of organ of 

breathing and human posture on MV and RF. The RF and thus MV can change with the 

organ of breathing due to the differences in the routes of respiration (Douglas et al., 

1983), but the hypothesis testing through the paired t tests indicated no significant 

differences in the current data. Human posture affects the activity of the abdominal 

muscle and thus may influence the breathing (Kera and Maruyama, 2005). The paired t 

test indicated no significant differences in the MV and RF due to the change in the 

posture. This is in agreement with studies by Kera and Maruyama, (2005). Therefore 

further analysis was performed on the nose breathing in sitting posture. 

Our study indicated that the minute volume was proportional to the body surface 

area, which is in agreement with the literature (Goldman and becklake, 1959; Baldwin et 

al., 1948, and Robinson, 1938). The body surface area can be obtained from the height 

and weight of a person (Gehan et al., 1970). Figs. 4.2(a) and (b) show the variation of 

minute volume with the body surface area of the male and female subjects, respectively. 

All the values lie between the confidence intervals from several previous studies. One of 

the main reasons for the wide spread could be the wide variation in ethnicity of the 

subjects in the current study. It was found that the minute volume for the female subjects 

was lower than that for the male subjects. 

(a) (b) 

Figure 4.2 Variation of minute volume with the body surface area of (a) the male subjects and (b) 

the female subjects. 

The relationship between the minute volume and the body surface area was 

obtained by a linear regression analysis. The correlation coefficient for both groups of the 

subjects was found to be more than 0.4 for a p less than 0.05. The relationship for the 

male and female subjects can be described by Equations (4.5) and (4.6) respectively. 

For male population, minute volume (MV) can be written as: 
‐ 39 ‐ 

MV (liter ) 5.225 BSA( m ). 2

male   (4.5) 

For female population, minute volume (MV) can be written as: 

MV (liter ) 4.634 BSA( m ). 2

female   (4.6) 

The confidence interval for the slope for the male and female subjects was from 4.838 to 

5.868 liter/m2

 and 4.421 to 5.16 liter/m2, respectively. 

Figures 4.3(a) and (b) show the variation in respiratory frequency for the male and 

female subjects, respectively. The exhalation time was longer than the inhalation for both 

the genders. The multiple regression analysis showed that the respiratory frequency was 

mainly dependent on the height. With an increased height that may be associated with an 

increased lung capacity, the frequency would be decreased for the same minute volume. 

For the male subjects, a slight dependency on weight was also found. An increase in 

weight increased the frequency and, hence, reduced the time period of each breath. 

Therefore, a heavier person typically has a shorter breath. Equations (4.7) – (4.10) give 

the relationship between the respiration frequency and the physiological parameters for 

the male and female subjects. 

 (a) (b) 

Figure 4.3 Variation of respiratory frequency for (a) the male subjects and (b) the female 

subjects. 

For Male 

The obtained regression equation for respiratory frequency for inhalation is given 

by: 

RF 55.55 32.86 H( m ) 0.2602W ( kg ). in    (4.7) 

The regression equation for respiratory frequency for exhalation is given by: 
‐ 40 ‐ 

RF 77.03 45.42H( m ) 0.2373W( kg ). out    (4.8) 

For Female 

Similarly for the female population, the equation for respiratory frequency for 

inhalation is given by: 

RF 46.43 18.85H( m ). in   (4.9) 

The equation for respiratory frequency for exhalation can be written as: 

RF 54.47 25.48H( m ) out   (4.10) 

where, RF is respiration frequency, H body height, and W body weight. 

Thus, with physiological parameters of a person, the minute volume and 

respiratory frequencies can be obtained using equations (4.5) – (4.10). The amplitude and 

frequency of the breathing sine wave can then be calculated from the MV, RFin, and RFout

through equations (4.2) – (4.4). Finally the flow rate over time can be obtained with the 

“a” and “”values via equation (4.1). 

The nose breathing direction was investigated by visualizing the jets from the 

front and side views. The jets can be better defined with two angles from each view. Fig. 

4.4 shows these views and the angles for one of the jets. The m and m are the mean side 

and front angle, while the s and s are the front and side spreading angle, respectively. 

Figure 4.4 Angles needed to define the direction of a nose breathing jet. 

The mean side and front angles did not vary much among the subjects as shown in 

Fig. 4.5. Hence, an average angle calculated from these mean angles could be used for 

the nose breathing direction. Equations (4.11) and (4.12) give a 95% confidence bound 

for the side and front mean angles. The mean side angle is given by: 

60 6 . m      (4.11) 

The mean front angle is given by: 
‐ 41 ‐ 

69 8 . m      (4.12) 

Figure 4.5 The mean angles for nose breathing. 

Similar variation was observed for the spreading angle though there was variation 

among the subjects as shown in Fig. 4.5. Thus, averaged mean and spreading angles with 

95% confidence bounds are proposed and are given by equations (4.13) and (4.14) 

respectively. The side spreading angle is given by: 

23 14 . s      (4.13) 

The front spreading angle is given by: 

21 10 . s      (4.14)

Figure 4.6 shows a mouth breathing jet. The mouth breathing jet was discharged 

approximately in the horizontal direction with a spreading angle of 300. Table 4.2 shows 

variations of the mouth breathing angles among the subjects. 

Figure 4.6 Visualization of a mouth breathing jet. 

The mouth breathing jet was observed to have a negligible spread from the front and, 

hence, the side angle is deemed sufficient to define the direction of the jet. The side 

spread can be described by a single value: 
‐ 42 ‐ 

30 . s    (4.15) 

Table 4.2 Variation in jet angle from mouth breathing among different subjects. 

Subjects 1 2 Spreading angle s (= 1 + 2) 

A 11 23 34 

B 13 20 33 

C 11 16 27 

D 10 17 27 

This investigation found that the nose opening area did not change during normal 

breathing for a subject but variation existed among some of the subjects as shown in 

Figure 4.7. The mean nose opening area for the female subjects was smaller than that of 

the male subjects. Equations (4.16) and (4.17) give the mean nose opening area with 95% 

confidence bounds for the male and female subjects respectively. 

The mean nose opening area for male subjects is given by: 

Mean Nose Opening Area 0.71 0.23 cm .

2   (4.16) 

The mean nose opening area for female subjects is given by: 

Mean Nose Opening Area 0.56 0.10 cm .

2   (4.17) 

Figure 4.7 Variation in nose opening area during breathing with body surface areas. 

The mouth opening was measured over time during normal mouth breathing and 

did not change during the process, thus can be modeled as a constant. Figure 4.8 shows 

the results obtained in correlation with the body surface areas for all the subjects. The 

results do not show a correlation. Thus an average value is proposed for modeling the 

mouth breathing. Equations (4.18) and (4.19) shows the average value of the mouth 

opening area with 95% confidence bound for the male and the female subjects 

respectively. 
‐ 43 ‐ 

Figure 4.8 Correlation of mouth opening area during breathing with body surface area. 

The mouth opening area for male subjects can be written as: 

Mouth Opening Area 1.20 0.52 cm .

2   (4.18) 

The mouth opening area for the female subjects can be written as: 

Mouth Opening Area 1.16 0.67 cm .

2   (4.19) 

4.2.2 Talking 

This section first discusses the flow rate, jet direction and mouth opening area 

measured or observed during talking. As mentioned in the research design, the talking 

study consisted of three parts: counting from one to ten, pronouncing six letters, and 

reading a passage. 

Figure 4.9 shows the flow rate over time for all the three exercises. The peak flow 

rates of counting three times consecutively numbers two, three, eight, and ten were higher 

than others. The same was observed for alphabets F, S, and T than the other alphabets. 

The flow rate over time for the passage was found to be irregular. The breathing during 

talking was not normal; most of the outflow (i.e. exhalation) took place from the mouth, 

while inhalation through the nose. Table 4.3 shows the volume of exhalation and 

inhalation from the nose and mouth of a subject. The air volume imbalance was probably 

due to the leakages from the mask used to collect the flow. As it would be difficult to 

accurately describe the irregular flow, a time averaged flow rate was obtained from this 

study. As most of the exhalation took place through mouth, this average flow rate can be 

used as boundary conditions for the mouth for modeling the exhalation of talking. 

Similarly, as most of the inhalation took place from the nose, the average flow rate can be 

used as a boundary condition at the nose for modeling inhalation of talking. 
‐ 44 ‐ 

Figure 4.9 Flow rate measured for a subject: (a) from the mouth by counting the numbers three 

times consecutively, (b) from the mouth by pronouncing the alphabets three times consecutively, 

(c) from the mouth for reading the passage, and (d) from the nose for reading the passage. 

Table 4.3 Air volume inhaled and exhaled from the nose and mouth of a subject during the 

passage reading. 

 Mouth Nose Total 

Volume inhaled (L) 4.02 23.92 27.94 

Volume exhaled (L) 21.96 3.89 25.85 

Imbalance (%) 7.5 

Figures 4.10 (a) and (b) show the measured airflow rate for pronouncing the 

alphabets (Aavg), counting the numbers (Navg) and reading the passage (Pavg) for the male 

and female subjects respectively. It was found that the Aavg, Navg and Pavg for both 

genders increase with the body surface area (BSA). This behavior is quite similar to 

breathing. In breathing, the minute volume (volume exhaled in a minute) increases 

linearly with BSA (Goldman and Becklake, 1959). Thus a linear regression analysis was 

performed on the talking flow rate data to obtain a relationship similar to breathing 

(equation 4.5 or 4.6). Equation (4.20) shows the relationship for various talking flow 

rates. Table 4.4 shows the value of the slope (m) for various exercises for the male and 

female populations. The relationship for the average flow rate from talking can be written 

as: 

Avg. Flow rate(l / s ) m BSA( m ). 2   (4.20) 
‐ 45 ‐ 

(a) (b) 

Figure 4.10 Measured airflow rate for counting the numbers (Navg), pronouncing the alphabets 

(Aavg) and reading the passage (Pavg) vs. body surface area (BSA) for (a) the male subjects and 

(b) the female subjects. 

Table 4.4 Value of slope (m) as defined in equation 20 obtained from the regression analysis. 

 Aavg Navg Pavg

Male 0.3808±0.0864 0.4306±0.1200 0.0965±0.0072 

Female 0.3000±0.0746 0.2566±0.0624 0.0888±0.0095 

The talking process was visualized to determine jet directions. As no single event 

can define a talking process, its direction should be determined by equation (4.15) for 

mouth breathing jet. The mouth opening during talking is the area between the lips when 

certain words/letters are said. As the lip movement is continuous during talking, the 

mouth opening varies over time. Fig. 4.11 shows the change in the mouth opening area 

over time for a subject when he/she counted numbers from one to ten. Initially the mouth 

opening area increased and then stayed almost unchanged and finally reduced to zero. 

The variation in the mouth opening area while reading the rainbow passage would be the 

sequence of the variations in mouth opening areas when the words in the passage are 

said. Thus the average of these mouth opening areas can be used to model the talking 

process. The variation in the mouth opening area when numbers from one to ten are said 

could be a representative of mouth opening area for various words in the rainbow 

passage. Hence, this study recommends using a time averaged area, averaged over all the 

numbers for modeling the talking process with passage flow rates. 
‐ 46 ‐ 

Figure 4.11 Mouth opening area variation with time when a subject counted numbers from one to 

ten. 

Figure 4.12 shows the variation in this average area of mouth opening with the 

height for all the subjects. No clear trend with height or any other physiological 

parameter was observed. Thus, an average area of mouth opening is proposed for both of 

the genders and is given by equation (4.21). The area of mouth opening during talking for 

male and female subjects can be written as: 

Area of mouth Opening for talking for male and female 1.8 0.03 cm .

2   

(4.21) 

Figure 4.12 Variation in average mouth opening with height for talking. 

4.3 Discussion 

In a manner similar to the study of coughing, this investigation has developed a 

set of simple equations that can be used to generate boundary conditions (or a source 

model) for breathing and talking. Our efforts have focused on the flow rate, flow 

direction, and air velocity which can be most accurately determined from the flow rate 
‐ 47 ‐ 

and mouth opening area. The inputs required are the height, weight, and gender of a 

person. 

As was mentioned earlier, the boundary conditions required by CFD simulations 

should also include the temperature of the exhaled air and the droplet distribution. By 

working with methods similar to the cough studies by Hoppe, (1981), one can obtain the 

exhaled air temperature for breathing and talking. There are a few studies on breathing 

droplet size measurements (Fairchild and Stampfer, 1987; Papineni and Rosenthal, 1997; 

Edwards et al., 2004 and Fabian et al., 2008). The measurements were done over healthy 

subjects (Fairchild and Stampfer, 1987; Papineni and Rosenthal, 1997 and Edwards et al., 

2004) and the subjects infected by influenza (Fabian et al., 2008). Most of the 

measurements were performed on mouth breathing. The measurements (Fairchild and 

Stampfer, 1987; Edwards et al., 2004 and Fabian et al., 2008) indicated that the droplet 

concentration was of the order of 103 and the size varied from 0.3 to 5 m. Fig. 4.13 

shows the distribution. 

Figure 4.13 Droplet size distribution for breathing (Fabian, 2008). 

There have been only a few studies conducted on talking droplet size 

measurements (Duguid, 1946 and Fairchild and Stampfer, 1987). The measurements 

were conducted on healthy subjects. The measurements indicated that the droplet 

concentration was of the order of 250 and that the size varied from 5 to 75 m. Table 4.5 

shows the distribution. 
‐ 48 ‐ 

Table 4.5 Droplet distribution for talking (Duguid, 1946). 

Droplet Diameter (mm) No of droplets Counting loudly 1-100 

1-2 1 

2-4 13 

4-8 52 

8-16 78 

16-24 40 

24-32 24 

32-40 12 

40-50 6 

50-75 7 

75-100 5 

100-125 4 

125-150 3 

150-200 2 

200-500 1 

500-1000 3 

4.4 Conclusions 

This study was focused on characterizing the flow dynamics of breathing of 

human beings. The flow rate, flow direction, and mouth/nose opening area were 

measured during normal breathing and talking for 25 healthy human subjects, which 

could be used as boundary conditions for CFD simulations. 

The breathing flow rate variation with time was sinusoidal. The amplitude and 

frequency of the sine function were related to body height, weight, and gender. No 

significant difference in the breathing flow rate was observed due to either a change of 

breathing organ or posture. The flow direction of the exhaled jet during breathing can be 

characterized by two front angles and two side angles for nose breathing and one sideangle for mouth breathing. The breathing flow directions did not change much among the 

subjects tested. The mouth and nose opening areas were constant during breathing. 

The talking flow rate was found to be irregular in nature and, hence, is defined as 

an average flow rate over time. The flow rate was related to body surface area which can 

be obtained from the height and weight of a person. As no single event can define talking 

direction, it is proposed to use the directions of normal mouth breathing. The mouth 

opening area during talking was found to vary with time and words. A time averaged 

area, averaged over numbers, is therefore proposed for talking. The mouth opening area 

varied among subjects, but no clear trend was observed; thus, a mean value is proposed 

for the mouth opening area during talking. 
‐ 49 ‐ 

5. TRANSPORT OF EXPIRATORY DROPLETS IN AN AIRCRAFT CABIN 

The airborne infectious agents are typically carried by the droplets exhaled by a 

patient with infectious disease such as influenza or tuberculosis. The droplets disperse in 

the air and then inhaled by a susceptible person. It is essential to study the transport of 

these expiratory droplets in the cabin air to understand how a passenger could become 

infected. The previous chapters discussed the flow models developed for the exhalations 

such as, coughing, breathing and talking. This chapter is focused of the prediction of 

transport of droplets exhaled from the coughing, breathing and talking of an index 

passenger in an aircraft cabin. The CFD methods were adopted and the developed 

exhalation/inhalation boundary conditions were used to study the transport. The research 

methods are first presented and finally the results obtained are analyzed. 

5.1 Research Method 

The domain under study is first described. The numerical schemes and boundary 

conditions used in the simulations are then presented. 

 

5.1.1 Domain of Study 

This investigation studied droplet transport due to coughing, breathing, and 

talking in a seven-row, twin-aisle, fully-occupied cabin as shown in Fig. 5.1. The index 

patient sat in the middle as marked by purple color. The passenger seats were with an 

inclination of 15o to the back from the vertical, as passengers generally prefer to sit in this 

position for a long flight. For talking passengers were modeled facing each other. A 

mouth opening of 4 cm2 and a nose opening of 0.71 cm2 (for each nostril) was used for 

each passenger as measured by our studies (Gupta et al., 2009 and 2010a). 

A

B

C D E

F G

7

6

5

4

3

2

1

(a) 
‐ 50 ‐ 

(b) 

Figure 5.1 Section of a seven-row, twin-aisle, fully occupied cabin used for (a) Coughing and 

breathing case (b) Talking case. 

5.1.2 Numerical Schemes 

The cabin was studied for airflow, temperature distribution, humidity distribution 

and expiratory droplet transport. A commercial CFD solver FLUENT was used to solve 

the appropriate equations for the problem. The airflow in the cabin is three dimensional, 

incompressible, and turbulent; and is mixed convection in nature. The effect of change in 

density due to change in temperature was taken into account, the rest of the physical 

properties were assumed to be constant. Mean values for these properties were used at the 

mean temperature. 

The airflow and temperature was obtained by solving the mass balance equation, Navier 

Strokes equations for momentum balance, and the energy conservation equation. 

Equation (5.1) shows the general form of the conservation equations and is given by: 

S . x x xu

t j

,effective

j j

j  

      





















  





 (5.1) 

Table 5.1 shows the values of parameters for the equations. The effect of 

temperature on density was accounted by using the ideal gas equation for density 

calculations (incompressible ideals gas assumptions). As a mixture of water vapor and air 

was modeled, an effective density was used for the simulations. For turbulence the 

renormalization group (RNG) k- model was used (Yakhot et al., 1986). The model is 

similar to standard k- model but has different constants and a few additional terms and 

function in the transport equation of k and . The additional functions and terms improves 

the predictions for highly swirl flows, rapidly strained flows and has analytical formula 

for effective viscosity, which accounts for the low Reynolds number effects. Equation 

(5.1) also shows the general form of the turbulent transport equation for k and  while the 
‐ 51 ‐ 

table 5.1 provides the specific values for it. A standard wall function approach was 

adopted. 

Table 5.1 Values of parameters and constants for the transport equations. 

Equation  ,effective S Constants 

Continuity 1 0 - - 

 momentum 

ui, uj ond uk 

(velocity 

Components)

eff dx j

dp  - 

Energy T 

(Temperature) (k/Cp)eff Heat Sources - 

Species Water vapor 

concentration 

D+t/Sct

(D is the 

Molecular 

Diffusivity) 

Mass sources 

mt is turbulent 

viscosity and Sct 

is turbulent 

Schmidt Number 

Turbulent 

Kinetic 

Energy (k) 

k t k,t    / Gk    GB

i

B i t T t

ij ij

k t

t

x

T G g

S S S

G S

k C



 







( / )

2 ;

;

;

,

2

2

  



  

Turbulent 

Dissipation 

() 

 t ,t /     

C G k C k k / / * 2

2

*

1     

1.0

1.0; 1.3;

0.09; 0.9;

1.44; 1.92;

.

. .

.

*

1

*

1



 

 

 

C t

k t t

C T t

C C



 







 

 The discrete phase (expiratory droplets) was tracked using 

Lagrangian scheme, while the continuous phase (air) is solved using Eulerian scheme. 

The discrete phase is solved from the calculations of the continuous field. A force 

balance is applied to calculate the trajectory of the discrete phase as given by: 

i

d

i d

D d

d F g ( ) F (u u ) dt

du

    



  (5.2) 

where i represents the direction of force. 

The first term on the right hand side is the drag force term, and Fi represents the 

additional forces in the ith direction. In the current problem the drag force and the gravity 

are the important forces and need to be considered. The drag force can be written as: 

. 24

C Re

d

18 F D

2

d d

D



  (5.3) 

The Reynolds number can be written as: 
‐ 52 ‐ 

. u u dRe d d d



                  (5.4)   

       

The expiratory droplets consisted on liquid and solid matter. The liquid matter 

was assumed to be volatile and is 90% of the total volume of the expiratory droplets 

(Potter et al., 1963). The evaporation of the expiratory droplets was also modeled through 

analytical equations. The droplets will evaporate if the droplet temperature (Td) is higher 

than the vaporization temperature of the volatile liquid (Tvap) and if it has volatile matter 

(mass) in it. The droplet will continue to evaporate until it reduces down to its non 

volatile content or it completely evaporates. In the present case as we have 10% non 

volatile matter, it will evaporate till that point if its temperature is higher than dew point 

temperature (Tdp). The rate of evaporation is given by: 

N k (C C ) i  c i,s  i, (5.5) 

where, Ni is the molar flux of vapor (kgmolm-2s

-1), kc is the mass transfer coefficient 

(m/s), Ci,s is the vapor concentration at the droplet surface (kgmolm-3), Ci,,∞ is the vapor 

concentration in the bulk gas (kgmolm-3). 

The concentration of vapor at the surface of the droplet is evaluated by assuming 

the pressure to be the saturation pressure at the droplet temperature and is given by: 

d

sat d

i,s RT

p (T ) C  (5.6) 

where, the relationship between saturation pressure and droplet temperature can be 

obtained from Flatau et al. (1992). 

The concentration of the vapor in the bulk gas is obtained by using the vapor bulk 

mole fraction (Xi) and the bulk mixture properties (p, T) and is given by: 

. RT

p Ci,  Xi (5.7) 

The mass transfer coefficient can be obtained from 

d

i,m

c AB d

D

k  Sh (5.8) 

where ShAB is the Sherwood number for the binary mixture (water and air in our case) 

and Di,m is the diffusion coefficient of the vapor in the bulk (m2

/s). 

Sherwood number can be obtained from following relationship (Ranz and 

Marshall, 1952): 

Sh 2.0 0.6 Re Sc . 1 / 2 1 / 3

AB   d (5.9) 
‐ 53 ‐ 

Sc is the Schmidt number given by: 

. DSc

 i,m

  (5.10) 

The mass of the droplet is then reduced according to the following relationship: 

m t t m t N M t d (   )  d ( )  i w,i (5.11) 

where Mw,i is the molecular weight of the evaporating species. 

Heat transfer in the droplet is calculated through the energy conservation i.e 

Energy stored = Energy obtained from convection + latent heat transfer; and can be 

written as: 

fg

d

d d

d

d p h

dt

dm hA T T

dt

dT

m C  (   )  (5.12) 

where, Cp is the droplet heat capacity (Jkg-1K-1), h is the convective heat transfer 

coefficient (Wm-2K-1), T∞ is the bulk mixer temperature, and hfg is the latent heat of 

vaporization (Jkg-1). 

The second order upwind discretization schemes were used for all the variables 

except pressure. Pressure discretization was based on PRESTO scheme (FLUENT, 

2005). The momentum and pressure equations were coupled through SIMPLE algorithm 

(Patankar, 1980). For the transient transport of droplets a time step size of 0.05s was 

used. For coughing period a time step of 0.001s was compared with 0.05s and no 

significant differences in the cough jet behavior were observed, therefore a time step of 

0.05s can also be used for the exhalation period. 

5.1.3 Case Setup 

The droplet transport simulations used the steady-state airflow pattern in the cabin 

as the initial condition. The airflow was created by the air supplied from and exhausted 

by the environmental control systems and the thermal boundary conditions on the cabin 

surfaces and passengers. Figure 5.2 and Table 5.2 show the boundary schematic and 

conditions. The air exchange rate for the supply inlet conditions was 33.7 ACH. 
‐ 54 ‐ 

Aisle

Base 2 Base 2 Base 1

Supply Inlets

Outlets Outlets

Passenger Seat

Top wall 1

Top wall 2 Top wall 2

Left 

Wall

Right 

Wall

Figure 5.2 Boundary surfaces in the cabin. 

The boundary conditions and the exhalation jet direction for coughing, breathing 

and talking processes of the passengers were specified according to our previous study 

(Gupta et al., 2009 and 2010a). The flow boundary conditions at the nose and mouth of 

the index passenger were different depending on whether he/she was coughing, breathing 

or talking. For rest of the passengers, the mouth was assumed to be closed all the time 

except for the talking case where the talking boundary conditions were implemented for 

the passengers in conversation. In real situations all the passengers, including the index 

passenger will breathe. The passengers can breathe differently, thus an asynchronous 

(random) breathing pattern was assigned for each passenger to simulate one such case. 

The random breathing pattern was assigned to these passengers such that every passenger 

had a random breathing minute volume, breathing frequency and start of the breathing 

cycle. Figure 5.3 shows the representative breathing pattern assigned to the passengers in 

a row. 
‐ 55 ‐ 

Figure 5.3 Random breathing distribution provided at the nose of passengers. 

Figure 5.4 shows the velocity boundary conditions at the mouth and nose 

respectively for the infected passenger for the coughing case. Figure 5.5 and 5.6 shows 

the velocity boundary conditions for the breathing and talking cases respectively. It 

should be noticed that there was no exhalation from the nose during the coughing period 

and once the coughing process was over there was no exhalation from the mouth while 

there was an immediate inhalation from nose. User defined routines (UDFs) were 

developed to implement these boundary conditions in FLUENT. 

Table 5.2 Boundary conditions. 

Boundary 

Surface Velocity 

Temperature 

(

o

C) Humidity ratio Droplet 

Human body No slip 31 zero diffusive flux Reflect 

Seat No slip Adiabatic zero diffusive flux Reflect 

Top Wall1 No slip 23.8 zero diffusive flux Reflect 

Top Wall2 No slip 26.4 zero diffusive flux Reflect 

Left and Right 

Side wall No slip 24.5 zero diffusive flux Reflect 

Base 1 (Side) No slip 24.4 zero diffusive flux Reflect 

Base 2 

(center) No slip 25.1 zero diffusive flux Reflect 

Aisle No slip 24.1 zero diffusive flux Reflect 

Inlets 

2.88 m/s (x=0,y=-

0.3,z=±0.95), TI=10% 19.3 0.003 (20%RH) Escape 

Outlets Outflow Escape 

Mouth and 

nose of the 

infected 

passenger - 

33 (Hoppe, 

1981) 0.007 (50% RH) Reflect 

Mouth of rest 

of the 

passenger - 31 zero diffusive flux Reflect 

Nose of rest of 

the passenger 

No slip (Steady) / 

Random 

breath(Unsteady) 

31 (Steady)/ 

33 

(Unsteady) 0.007 (50% RH) Reflect 

Back and front Periodic 
‐ 56 ‐ 

(a) 

(b) 

Figure 5.4 Flow boundary condition for the index passenger for the coughing case at the (a) 

mouth and (b) nose. 

Figure 5.5 Flow boundary condition for the index passenger for the breathing case at the nose. 
‐ 57 ‐ 

‐4

‐3

‐2

‐1

0

1

2

3

4

0 5 10 15 20 25 30 35 40

Velocity (m/s)

Time

Mouth of passengers in

conversation

Nose: Infected passenger

Nose: Passenger in

conversation

Figure 5.6 Flow boundary condition for the passengers in conversation for the talking case. 

The current investigations were performed using mono-dispersed droplets for the 

coughing, breathing and talking cases. Therefore it was assumed that all the droplets 

exhaled during a particular exhalation were of one size. A single size droplet and the total 

amount of droplets exhaled during the exhalation were selected from the literature and 

used in the simulation. There is a wide range of literature on expiratory droplet size and 

concentration measurements for coughing, breathing, and talking processes (Duguid, 

1946; Fairchild and Stampfer, 1987; Edwards et al., 2004; Yang et al., 2007 and Fabian et 

al., 2008). There were differences in the finding as these measurements were performed 

on different subjects using different methods. Yang et al., (2007) found that the average 

mode size of droplets exhaled during coughing was 8.35 m, while the droplet 

concentration was of the order of 103

 per cm3. Therefore droplet size of 8.5 m and 

concentration of 106

 per cough (considering the order of volume exhaled during the 

cough) was used for the current investigation. Fabian et al., (2008) found that the droplets 

exhaled by the influenza infected subjects were mostly in the range of 0.3 to 0.5m. The 

total droplet concentration ranged from 67 to 8500 particles per liter of air (geometric 

mean of 724). Fairchild and Stampfer, (1987) and Edwards et al., (2004) also indicated 

that the amount of droplets exhaled during breathing were of the order of 103

 per liter. 

Thus for the breathing simulated in the current investigation, we obtained 525 droplets 

exhaled per breath considering 103

 droplets exhaled per liter. A mean droplet size of 0.4 

m and concentration of 525 per breath was thus used for the breathing exhalation 

simulations. Duguid, (1946) measured the size distribution of droplets exhaled during 

talking (counting from 1 -100) but did not quantify the droplet concentration 

(droplets/cm3

). There was no single dominating size for talking (Duguid, 1946); therefore 

a mean size (30 m) was calculated and used in the current investigations. Fairchild and 

Stampfer found that the droplet concentration exhaled during talking was of the order of 
‐ 58 ‐ 

103 droplets per liter. Therefore droplet size of 30 m and concentration of 103 droplets 

per liter, which implies 2250 droplets exhaled during the 15 seconds of talking, was used 

for the CFD simulations. Table 5.3 shows the mean droplet diameter and number of 

droplets used in the study. 

Table 5.3 Droplet size and concentration for various exhalation activities. 

Exhalation Droplet Diameter (m) Number of droplets 

Coughing 8.5 106 per cough 

Breathing 0.4 525 per breath 

Talking 30 2250 for 15 sec of talk 

Zhang (2007) reviewed the particle deposition studies in indoor environment and 

have indicated that the particle deposition velocity for the current range of droplet 

diameter is 10-6 to 10-4 m/s. The particle loss coefficient for the aircraft cabin for this 

range comes out to be .01 to 1 (h-1), this is much less than the air exchange rate in the 

aircraft cabins (25-40 ACH). ACH is defined as the air change per hour. A recent 

experimental study by Chao et al. (2008) indicated that the particle deposition fraction for 

this range of droplet diameter was around 10%-20% for 6-12 ACH exchange rates in 

hospital environment. An experimental study by Sze To et al. (2009) found out about 

75% of particles by mass deposits in aircraft cabins. They had around 90% of the 

particles of size more than 100 microns and they would have higher tendency to deposit. 

As the particle deposition rate for the droplets of size 0.16 to 12 micron with 25-40 ACH 

is less than 10%, no deposition on the surfaces was assumed in the current study. Thus, 

these droplets reflect from the solid surfaces with a low coefficient of restitution (Zhang 

et al., 2009). This means that droplet will be stationary when it reaches the wall and thus 

can only be picked by the bulk airflow. 

The random walk model (FLUENT) that was used to account for the turbulent 

dispersion of expiratory droplets is stochastic in nature and thus it is critical to estimate 

the optimum number of trajectories required to obtain a stable distribution of the 

expiratory droplets. The total number of droplets in the vicinity of the nose of each 

passenger under steady state was calculated using 20, 200, 2000, and 20000 trajectories. 

The calculations were performed 10 times for each case. It was observed that the mean 

of the total number of droplets was within ±9% and ±12.5% of the 97.5% confidence 

interval for most of the passengers for 20000 and 2000 trajectories respectively. It should 

be noticed that more the number of trajectories higher will be the computation efforts. 

Especially in unsteady cases where these many trajectories will be required every time 

step. As the differences in the uncertainties associated with the total amount of droplets 

calculated using the 20000 and 2000 trajectories are low, 2000 droplet trajectories were 

used every time step for the further analysis for the coughing case. Similarly for 

breathing and talking 320 (±20%) and 2000 (±10%) droplet trajectories were used 

respectively. A total of 16000, 11840 and 600000 droplet trajectories were used for the 

coughing, breathing and talking cases respectively. 

5.1.4 Grid Independence and Cabin Section End Conditions 
‐ 59 ‐ 

This investigation performed a grid sensitivity analysis to obtain an optimum grid 

for the further analysis. A grid with 5 mm size on mouth, nose and face of the passengers, 

20 mm size on the rest of the body of the passenger and seats, and 40 mm elsewhere was 

created. More than 98.5% of the cells had equi-size skew angle of less than 0.7. The 

width of the each cell was then decreased to half to obtain the finer grid. The number of 

tetrahedral elements for the coarse and find grids was 1.5 million and 10 million 

respectively. 

Grid Sensitivity 

The grids generated were solved for airflow and temperature distribution. The 

velocities and temperatures at various planes and along the center line were compared. 

Fig. 5.7 shows the planes and the table 5.4 shows the comparison of the area averaged 

velocity and temperature for the planes. 

Figure 5.7 Planes for comparing the averaged velocities and temperatures. 

It can be seen from the table that the difference between the velocities and 

temperatures is below 10% and 0.60

C respectively. The velocities and temperatures were 

compared along the lines at a lateral plane located at the center of the domain. Figure 5.8 

shows the location of the lines and Fig. 5.9 (a), (b) and (c) shows the velocity and 

temperature variation along the line 1, 2 and three respectively for the two grids. 

Table 5.4 Comparison of area averaged velocity and temperatures for the two grids at planes 

shown in Fig. 5.7. 
‐ 60 ‐ 

Figure 5.8 Location of the lines located centrally from top to bottom in the domain. 

The trend of the velocity and temperature along the lines were similar for both the 

grids. The differences in the velocity along the lines exist near the walls and are of the 

order of 0.1 m/s. The finer grid over predicts the temperature by about 0.60

C. As the finer 

grid is about 8 times of the coarser grid in size and the differences in the velocity and 

temperature are not significant, we decided to do the further analysis on the coarser grid. 
‐ 61 ‐ 

(a) 

(b) 

(c) 

Figure 5.9 Velocity and temperature variation along the lines shown in Fig. 5.8 for the two grids 

(a) line 1 (b) line 2 (c) line 3. 

Cabin Section End Condition 

Table 5.5 shows the comparison of the area averaged velocities and temperatures 

at the planes shown in Fig. 5.7 for the adiabatic and periodic boundary conditions 

provided at the ends (front and back) of the cabin. It can be seen that the maximum 

differences in the velocity and temperature is 6% and 0.250C. 
‐ 62 ‐ 

Table 5.5 Comparison of area averaged velocity and temperatures for the two boundary 

conditions at planes shown in Fig. 5.7. 

Figures 5.10 (a), (b), and (c) show the variation in velocity and temperature along 

the lines shown in Fig. 5.8. It can be seen that the trend of velocity and temperature 

variation along all the lines is similar. The maximum difference in the velocity was 

observed to be 0.06 m/s. The differences in the temperature were observed to be 

negligible. It was observed that the case with periodic boundary condition needs to be 

more under-relaxed and thus takes more time to converge to the steady state condition. 

Thus any of these boundary conditions can be applied at the ends as both show similar 

trends in airflow and temperature distribution for the cabin. 

(a) 

(b) 
‐ 63 ‐ 

(c) 

Figure 5.10 Velocity and temperature variation along the line shown in Fig. 5.8 for the two 

boundary conditions for (a) line 1 (b) line 2 (c) line 3. 

5.2 Results 

5.2.1 Steady-State Airflow in the Cabin 

Our studies on the transient transport of droplets exhaled from coughing, 

breathing, and talking of the index patient in the cabin used the steady state airflow 

pattern as the initial condition. Figure 5.11 (a) shows the velocity vectors in a cross 

section located in front of the index patient and Figures 5.11 (b), (c), (d), (e), and (f) in 

longitudinal sections through the index patient (column D), column C, aisle, column B 

and the column A respectively. The airflow pattern was similar to those obtained by 

Zhang and Chen (2007b) and Zhang et al. (2009). The cold air from the supply inlets 

flowed to the sides along the top wall and a part of it existed through the outlets located at 

the bottom sides. The other part and the thermal plumes created two large re-circulations 

in a cross section. The airflow pattern clearly indicates the mixed convection in a cross 

section. The air velocities at the top, center and window zones (Figure 5.11 (a)) of the 

cabin ranged from 0.6 to 0.8 m/s, 0.2 to 0.3 m/s, and 0.3 to 0.4 m/s respectively. It should 

be noticed that the flow was approximately symmetrical about the center line 

(longitudinal direction) and the airflow current on the top were strong in the lateral 

direction. The strong airflow near the vicinity of the passengers as shown in Figure 5.11 

(b) and 5.11 (c) was due to the thermal plumes around the human bodies. The air 

velocities around these passengers ranged from 0.2 to 0.3 m/s (Figure 5.11 (b)). The 

airflow was upwards and towards the back. The airflow in this zone was natural 

convection dominated While for the plane along the aisle (Figure 5.11(d)) the airflow was 

upwards and was to the front especially in the lower zone (0.2-0.3 m/s). The airflow 

around the passengers seated on A and B as shown in Fig. 5.11 (e) and (f) was forced 

convection dominated. The flow was towards the front along the body. This clearly 

shows the mixed convection nature of flow in the cabin. 
‐ 64 ‐ 

1 m/s

(a) 

(b) 

(c) 

(d) 
‐ 65 ‐ 

(e) 

(f) 

Figure 5.11 Velocity fields in the cabin on (a) cross section through the index patient and 

longitudinal section through (b) the index patient (Column D) (c) column C (d) the aisle (e) 

column B (f) column A. 

5.2.2 Evaporation of Droplets in the Cabin 

Figure 5.12 shows the change in diameter due to evaporation for the droplets 

exhaled through coughing, breathing and talking. All these droplets evaporated to their 

nuclei in less than 0.3 seconds. The droplets with smaller diameter had higher rate of 

evaporation and evaporated quickly. The droplets generated from breathing were smallest 

(0.4 m) and evaporated in 0.005 s. The droplets produced from talking were the largest 

(30 m), so they took 0.3 s to evaporate. The time required for the evaporation of these 

droplets agrees with the finding by Morawska (2006). It should be noticed that none of 

these droplets evaporated completely because they contained 10% non-volatile matter. 

The expiratory droplets from coughing, breathing and talking reduced to 4, 0.19, and 14 

m, respectively, after evaporation completed. The diameter of these droplets was small 

and thus their deposition on the surfaces was minimal. 
‐ 66 ‐ 

Figure 5.12 Evaporation of droplets from coughing, breathing and talking. 

5.2.3 Transport of Droplets Exhaled from the Coughing 

Figure 5.13 shows temporal distributions of the droplets exhaled due to a single 

cough of the index patient in a perspective and the side view. The droplets shown are 

0.001 times the actual number of droplets (1×106) contained in a cough. The diameter of 

the droplets shown in the figure is 1 mm (relative to cabin dimensions), which is two 

order of magnitude larger than the actual one. This was done to clearly visualize the 

droplet movement. 

The droplets first followed the air jet caused by the cough, which pushed the 

droplet cloud down forwards. The simulated cough jet was impulsive in nature with peak 

velocity of 9m/s. The cough jet penetrated to a distance of about 45 cm axially. The air 

velocities in this zone decayed to around 0.3 m/s after 0.5 seconds. As the background 

cabin velocities around the index passenger ranged from 0.2 to 0.3 m/s, the cough expired 

droplets followed the bulk airflow soon after the cough was over. Gupta et al., 2009 also 

observed a similar jet behavior. Though the bulk airflow and the cough jet velocities in 

their case were not exactly the same but the cough penetration during the cough period in 

the domain was found to agree well. Since the jet momentum was weak, the droplets 

followed the bulk airflow in the cabin. The side view showed that the droplets moved 

upwards within 5s and went towards the backside of the index patient due to the bulk 

airflow pattern as shown in Figure 5.11(b). The droplets reached the cabin ceiling and 

then went to the side walls in 10s due to the jet flow from the environmental control 

system. The majority of the droplets reached the passengers sitting at the window seats in 

about 10-12 s. Due to the high droplet concentration and short life of the droplets 

(pathogens in the droplets may be alive), the passengers seated at these locations (5A, 

5G, 4A, and 4G) may have relatively higher risk of infection. The droplets then moved to 

the lower level of the cabin because of the re-circulation airflow as shown in Figure 

5.11(a). When the remaining particles came back the proximity of the index patient, it 

took around 20s. These droplets then dispersed in the air in the proximity of the index 
‐ 67 ‐ 

patient. Figure 5.13(a) also shows the temporal values of the total airborne droplet 

fraction (df), defined as the number of airborne droplets in the cabin over the total amount 

of droplets exhaled. In 20 s, the df become 0.74 (or 74%) that indicates that about 26% of 

the expiratory droplets exhausted from the outlets These droplets dispersed to all the 

seven rows in a minute, but most of the droplets were contained to the row in front, at, 

and behind of the index patient. Thus the risk of infection for the passengers sitting in 

these rows can be significantly higher than those sitting in the other rows. The df reduced 

to 48%, 32%, 20%, and 12% at t = 1, 2, 3 and 4 minutes, respectively. The delay was 

slow. 

It took 4 weeks to run the 4-minute real-time simulation on the 8-parallelprocessor computer cluster. The computing time was very long for such a short section of 

cabin. As the droplet distribution become rather uniform in 4 minutes, a homogenously 

mixed condition could be used afterwards. 
‐ 68 ‐ 

(a) 
‐ 69 ‐ 

(b) 

Figure 5.13 Temporal distributions of droplets due to a single cough from the index patient: (a) a 

perspective view and (b) the side view. 

5.2.4 Transport of Droplets Exhaled from the Breathing 

Very similarly, Figure 5.14 shows the temporal distributions of the droplets 

exhaled through a single breath from the index patient. The number of droplets shown is 

1/3 the actual and the diameter of the droplet shown is 1 mm for better visualization. The 

droplet number from the breath is 3 to 4 magnitude order smaller than that from the 

cough. 
‐ 70 ‐ 

(a) 
‐ 71 ‐ 

(b) 

Figure 5.14 Temporal distributions of droplets due to a single breath from the index patient: (a) a 

perspective view and (b) the side view. 

The index patient started to exhale at t = 1.85 s. Since the jet from the breath was 

much weaker than the cough, the droplets went upwards very quickly due to the bulk 

airflow pattern. The weak jet did create a slightly different droplet distribution in the first 

few seconds, but not very evident. In general, the droplet distributions and the df for the 

breath were similar to those for the cough, but the amount of droplets were much smaller. 

5.2.5 Transport of Droplets Exhaled from the Talking 

This investigation also simulated the droplet distributions in the cabin when the 

index patient talked with the left passenger (4C) for 15 s. Figure 5.15 (a) and (b) show the 

temporal distributions of the droplets. Similar to the coughing and breathing cases, the 

number of droplets shown is increased by 2.5 times compared with the actual for better 

visualization. The index patient was modeled facing to passenger 4C so the droplets 

exhaled moved to the left side of the index patient. 

It is very interesting to see that all the droplets exhaled initially went to the left 

side of the cabin that was very different from the coughing and breathing case. This is 

because the head of the index patient turned to the left and the symmetrical bulk airflow 
‐ 72 ‐ 

brought the droplets to the left side of the cabin. Although some droplets could go to the 

right side of the cabin at the second recirculation due to diffusion, the amount of the 

droplets was significantly less than that to the left cabin. Please also note that the talk 

lasted for 15 s so the droplets were generated continuously during this period. This 

different source character had some effects on the df in initial time. The droplet 

distribution was uniform in 4 minutes. 

(a) 
‐ 73 ‐ 

(b) 

Figure 5.15 Temporal distributions of droplets due to a 15 s talk from the index patient: (a) a 

perspective view and (b) the side view. 

5.3 Discussion 

The overall flow pattern predicted by the CFD methods was in agreement with the 

previous literature. The expiratory droplet dispersion was in accordance with the exhaled 

jet and bulk airflow in the cabin. Zhang et al., (2007a) studied the contaminant (scalar) 

transport in a twin aisle cabin under a steady release. They used CFD methods and 

validated it against experiments. The contaminant trajectories predicted by them agrees 

qualitatively with the current investigations. A controlled experimental study is still 

required for a quantitative validation on the droplet evaporation and dispersion 

predictions. The validation can aid researchers identify the limitations of the CFD 

methods used in the current investigations and improve the prediction on the transient 

transport of expiratory droplets. The index passenger location influences the expiratory 

droplet transport as the airflow in different at different locations. 

Moreover, the present study was focused on single exhalation event but the reality 

is a continuous process with the combination of multiple exhalation events. It is not 

practical to use the CFD simulations for such a simulation because it would take months 

of computing time to simulate a one hour flight for an entire cabin. It is required to 
‐ 74 ‐ 

develop methods to extend the information for realistic flight durations. It is also required 

to develop methods to quantify the exposure risk to the fellow passengers. 

5.4 Conclusions 

This investigation studied the transport of the expiratory droplets from an index 

patient seated at the center of a seven-row, twin-aisle, fully occupied aircraft cabin by 

using a commercial CFD program (FLUENT 2005). The exhalation events studied were a 

single cough, a single breath, and talk for 15 s by using the models developed by us as 

discussed in chapter 3 and 4. The simulations were performed for four minutes of real 

time starting from a steady-state airflow and temperature distribution. The study led to 

the following conclusions: 

The droplets exhaled from the cough of the index patient followed mainly the 

bulk airflow. The droplet concentration in the vicinity of the passengers reduced over 

time due to the removal from the outlets and dispersion of the droplets. The total airborne 

droplet fraction reduced to 48%, 32%, 20%, and 12% after the droplets entered the cabin 

air for 1, 2, 3 and 4 minutes, respectively. Most of the droplets were transported within 

one row of the index patient in the first 30 s and then could be transported to the entire 

seven-row cabin with a uniform droplet distribution in 4 minutes. 

The droplets exhaled from the breath of the index patient behaved similarly as 

those from the cough, but the number was much smaller. The expiratory droplets 

generated during the talking of the index patient were contained on the left side of the 

cabin since the patient turned the head to the left. 
‐ 75 ‐ 

6. INHALATION OF DROPLETS IN AN AIRCRAFT CABIN 

In the last chapter we developed the CFD methods to predict the spatial and 

temporal distribution of droplets exhaled from a single cough, single breath and 15s of 

talking of the index passenger. The simulations were performed for four minutes of flight 

time and took four weeks of computational time on an 8-parallel-processor computer 

cluster. A commercial air flight could last between one to 20 hours from gate to gate. 

Therefore, it is not practical and feasible to perform the CFD simulation for realistic 

flight durations for multiple exhalations from the index passenger. 

This chapter presents the methods developed to predict the spatial and temporal 

distribution of expiratory droplets and quantifies the amount of inhaled droplets by each 

passenger in an airliner cabin for realistic flight durations for multiple exhalations from 

the index passenger. This chapter first presents the method to extend the data obtained 

from the four minutes of the CFD simulations (discussed in the last chapter) to realistic 

flight durations. The method to obtain the information on droplet concentrations for a 

multiple exhalation scenario from the single exhalation cases is then discussed. The 

method to quantify the amount of droplets inhaled by the passengers then presented. The 

cases studied to investigate the effect of type of exhalations are then presented. 

6.1 Research Methods 

6.1.1 Extension of Droplet Concentration for Real Flight Durations

As observed in the last chapter that the droplet distributions could be highly nonuniform in an airliner cabin. To assess the exposure risk of fellow passengers caused by 

the droplets exhaled by an index patient, it is important to know the actual number of 

droplets inhaled by each passenger. It is required to know the time variation in the droplet 

concentration around the nose of the passengers to calculate the droplets inhaled by them. 

As passengers may move their heads around, a zone of 0.0283 m3

 (1 ft3) around the nose 

of each passenger, as shown in Fig. 6.1, was constructed and a time variation in the 

droplet concentration in these zones was obtained from the CFD simulations. A user 

defined routine (UDF) was developed to calculate the droplet concentration in these 

zones and was integrated with the CFD simulations. 

(a) (b) 

Figure 6.1 The zone of 0.305 m × 0.305 m × 0.305 m around the nose region of a passenger for 

determining the average droplet concentration. 
‐ 76 ‐ 

This study defined  (droplet fraction-s/m3) as the average number of expiratory 

droplets in the vicinity of a passenger relative to the total number of droplets exhaled by 

the index passenger). The  was summed over time in this investigation as: 

t

vN

N (t ) (t )

t

t 0 t

i    

 (6.1) 

where ν is the zone volume around the passenger, Ni is of droplets in the zone around the 

i

th passenger at time t, and Nt is the total number of droplets exhaled for the exhalation of 

the index patient. 

Figure 5.1 (a) shows the cabin configuration and nomenclature for the passengers. 

The analysis for the configuration with index passenger seated at the seat 4D is presented 

as an example. As observed in the CFD simulations that the expiratory droplet cloud 

from the coughing of the index passenger 4D moved in the cabin with the bulk flow. The 

local droplet concentrations in the zones where the droplet cloud reached first were as 

high as the cloud was dense for the initial period. It was observed that the droplets 

eventually dispersed to all seven rows, but the droplet concentrations in the row furthest 

from the index passenger were relatively low. Therefore, the droplet concentration for the 

passengers seated only in the 3rd, 4th (index passenger), and 5th rows is discussed here. 

Figure 6.2 shows the average cumulative droplet fraction () for the passengers 

seated in A (window), B (aisle), C (aisle), and D (center of the row) seats, respectively, 

for the expiratory droplets from a single cough by index passenger 4D. As shown in Fig. 

6.2, the droplets from passenger 4D reached passengers 5A, 5B, 4A, and 4B in about 10s. 

Therefore, there were sudden increments in  for these passengers at around 10s. The rate 

of increment was higher for 5A and 4A as the droplets first passed through these zones. 

The droplet cloud then reached the passenger seated in 4C and dispersed in the 3rd, 4th

and 5th rows. The rate of increment in  for the passengers seated in these rows was 

higher during the initial period (<1 min). 
‐ 77 ‐ 

(a) 

(b) 
‐ 78 ‐ 

(c) 

(d) 

Figure 6.2 Average cumulative droplet fraction ( over time for passengers due to a cough from 

the index passenger for (a) Column A (b) Column B (c) Column C (d) Column D. 

The CFD studies indicated that the droplets dispersed in the cabin in about 1 

minute, but they did not perfectly mix in the cabin. In fact, Nazaroff et al. (1998) and 

Wan and Chao (2007) indicated that a perfectly mixed condition is merely an assumption 

and cannot be achieved. The variation in  clearly confirms that perfectly mixed 

conditions cannot be assumed for the initial (<1 min) period due to its sharp changes. 

However, the  increments after 3 minutes were steady and similar for the 

passengers. This indicates a state close to a perfectly mixed state. If perfectly mixed 
‐ 79 ‐ 

conditions could be assumed after 3 minutes (180s), the average droplet fraction 

concentration can be written as: 

V

n (180)exp(-Q(t 180)/V) c(t) t   (6.2) 

where, nt(180) is the total droplet fraction at 180 s, i.e., the total number of droplets in the 

cabin at 180s divided by the total number of droplets exhaled, Q is the total supply flow 

rate to the cabin, and V is the volume of the cabin. 

Therefore, a semi-analytical approach can be adopted to obtain the average 

droplet fraction concentration (c(t)) in the vicinity of the passengers. The CFD simulation 

can be used to obtain the c(t) for the first 3 minutes after the droplet release and equation 

(6.2) to obtain c(t) beyond 3 minutes. It should be noted that the average droplet fraction 

concentration given by equation (6.2) is also the rate of increment in the average 

cumulative droplet fraction (). Figure 6.2 compares (t) calculated using the semianalytical approach with the CFD simulations for t>3 minutes. The differences were 

smaller than 10% and the perfectly mixed method is considered validated for t > 3 

minutes. Therefore, one can use the CFD method to obtain the droplet concentrations 

around the passengers for the initial period of 3 minutes and equation (6.2) for beyond 3 

minutes. 

Although not presented here in detail, one can expect that the  for the droplets 

from the breathing and talking of passenger 4D could be different from those of 

coughing, but the method developed above can be used for breathing and talking. 

6.1.2 Droplets Inhaled by the Passengers due to Multiple Exhalations from the Index 

Passenger 

An index passenger normally exhales droplets through multiple events, such as 

combined breathing, coughing, and talking. To carry out CFD simulations for such 

combined events can be very time consuming. Thus, it is important to further develop the 

above semi-analytical method for the multiple exhalations from the index passenger. 

This investigation started from using continued breathing as an example. An 

unsteady CFD simulation was carried out for a case with 10 consecutive exhalations from 

the normal breathing of index passenger 4D. The turbulent flow and the breathing of the 

passengers made the local airflow in the cabin unsteady. However, the droplets from all 

the exhalations followed similar trajectories, even though the airflow in the cabin was 

unsteady. This indicates that the bulk flow in most of the domain was almost steady. 

Therefore, the information on the droplet concentration obtained using the CFD 

simulations for a single breath exhalation from the index passenger can be superimposed 

multiple times to obtain the droplet concentration in the cabin for the multiple exhalations 

of the index passenger using the following equation: 

, Ct C t t i bi j () ( )    (6.3) 
‐ 80 ‐ 

where, Ci(t) is the average droplet concentration in the breathing zone of the ith passenger 

at time t and Cb,i(t-tj) is the average droplet concentration in the zone at time t-tj due to the 

breathing started at tj. 

If the principle can be extended for coughing and talking, the droplet 

concentration in a breathing zone can be obtained by summing up that from all the 

exhalations taking place until current time with a time shift: 

C (t ) C (t t )C (t t )C (t t ) i c,i i b,i j ta,i k (6.4) 

where, Cc,i(t-ti) is the average droplet concentration in the breathing zone at time t-ti due 

to the coughing starting at ti, and Cta,i(t-tk) is the average droplet concentration in the zone 

at time t-tk due to the talking starting at tk. The Cc,i, Cb,i, and Cta,i can be obtained from the 

CFD simulations for the droplets exhaled from a single cough, breathing, and talking, 

respectively. 

To validate the model developed, Figure 6.3 compares the droplet fraction in the 

breathing zones with 10 consecutive exhalations from the index passenger predicted by 

CFD with the droplet fraction calculated by the super-imposition method. The variation 

trend of the droplet fraction was similar in the two methods but with differences in the 

absolute values. The differences could be attributed to the inherent flow transience and 

the transient breathing. The high frequency variations were very difficult to capture by 

the simplified super-imposition method. Figure 6.4 further compares the  predicted by 

the two methods. Again, the two methods yielded very similar results. Thus, this 

investigation considers the super-imposition method to be validated. 

(a) 
‐ 81 ‐ 

(b) 

Figure 6.3 A comparison between the droplet fraction predicted by the CFD model and the superimposition method for passengers seated in (a) 3A, 4A, and 5A seats and (b) 3D, 4D, and 5D 

seats.

Figure 6.4 A comparison of the predicted by the CFD and super-imposition methods for 

passengers sitting in the 4th , 5th , and 6th rows. 
‐ 82 ‐ 

The total amount of droplets inhaled by each passenger can be calculated by 

integrating the breathing profile of each passenger with the droplet concentration in the 

breathing zone: 

r (t )  q (t )C (t ) t i i i  (6.5) 

where qi is the rate of inhalation (volume/time) of the ith passenger and is zero during 

exhalation, and Ci is the average droplet concentration (number of droplets/volume) in 

the zone around the ith passenger. Our breathing measurements have developed methods 

to obtain qi based on the physiological details of the passengers. 

Figure 6.5 shows a representative variation of the cumulative droplets inhaled by 

a passenger, the droplet concentration in the breathing zone, and the breathing flow rate. 

The droplets inhaled by the passenger were zero initially as the droplet concentration in 

the breathing zone was zero. 

Figure 6.5 A representative variation in droplet fraction and the cumulative droplets inhaled. 

6.1.3 Case Setup 

With the method to extend the CFD data of 3 minutes to the whole flight and the 

method of super-imposition to calculate the droplet concentration in the breathing zones, 

this investigation has studied the droplets inhaled by different passengers in the cabin 

shown in Fig. 5.1 (a) during a 4-hour flight. MATLAB codes were developed to calculate 

the amount of inhaled droplets using the method of super-imposition. Since the index 

passenger can exhale droplets through coughing, breathing, talking, or any combination 

of these exhalations, three hypothetical cases were analyzed and are as follows: 
‐ 83 ‐ 

1) Breathing only: This case assumed that the index passenger exhaled 525 droplets 

for every breathing exhalation, as calculated by Gupta et al., (2010b) using the 

measurements performed by Fabian et al., (2008) and Edwards et al., (2004) on 

the amount of droplets exhaled during breathing. 

2) Coughing and breathing: The studies by Hsu et al. (1994) and Loudon and Brown 

(1967) indicated that the cough frequency can vary from 12 to 35 and 3 to 48 

coughs per hour, respectively. By using an average from these studies, this case 

assumed 100 coughing exhalations for the 4 hour flight. Since the index passenger 

would also breathe normally, the 100 coughing exhalations were randomly 

sandwiched between the breathing. It was further assumed that the index 

passenger exhaled 106 droplets for each cough, as calculated by Gupta et al., 

(2010b) using the studies conducted by Yang et al., (2007). 

3) Talking and breathing: This case assumed that the index passenger would talk for 

30 minutes during the flight. The talking exhalations were also coupled with the 

breathing exhalations. It was assumed that the index passenger released 150 

droplets every second through the talking, as calculated by (Gupta et al., 2010b) 

using the studies of Fairchild and Stampfer, (1987). 

6.2 Results 

Figure 6.6 presents the total amount of droplets inhaled by the fellow passengers 

due to the exhalations from index passenger 4D for the three cases by using the research 

method developed and validated in the previous sections. The results are for passengers 

seated on the 3rd, 4th, and 5th rows. The number of droplets inhaled by the index passenger 

is not shown as it is not meaningful. The inhaled droplets varied with location and type of 

exhalation from the index passenger. 
‐ 84 ‐ 

Figure 6.6 The droplets inhaled by the passengers seated in the 3rd, 4th and 5th rows for regular 

breathing, 100 coughs combined with regular breathing, and 30 minutes of talking along with 

regular breathing by the index passenger. 

The droplets inhaled by these passengers, generated by the exhalations from the 

regular breathing of the index passenger, varied from 300 to 2000 depending on the 

movement of the droplets. The droplets first went to passenger 5A and 5G but stayed 

there only for a short period of time due to the high air velocity in that zone. These 

droplets then went to passengers in 4C, 4D, 4E, 5C, 5D and 5E; thus, the number of 

droplets inhaled by these passengers was high. The droplet cloud reached the 3rd row 

after some time, then the droplet concentration decreased, so the number of droplets 

inhaled by passengers seated in this row was relatively low. 

When the coughing was combined with regular breathing, the droplets inhaled by 

the fellow passengers varied from 18000 to 90000. This was because a cough contains 

106 droplets, while a breath contains only 525. Clearly, the exhalation from a cough 

carries more risk to fellow passengers than the breathing. The droplets inhaled by 

passengers in 5A, 5G and 5D were relatively fewer than those inhaled by the passengers 

in row 4. This is because the high velocity of the cough jet pushed the droplets to the 

front of the index passengers, so fewer droplets moved to the back row (5th row) (as 

discussed in the last chapter). 

For the case in which talking was combined with regular breathing, the droplets 

inhaled by the fellow passengers varied from 300 to 1700. The talking exhaled droplets 

inhaled by the passenger in 4C were relatively more than those inhaled by the other 

passengers because the passenger was in conversation with the index passenger. The 

droplets inhaled by the passengers and the distribution pattern were similar to the case 

that had only breathing. This implies that the rate of release of droplets from talking was 

similar to rate of release from breathing. As the combined effect of breathing and talking 

is shown, the order of effective amount of droplets inhaled is a cumulative of both the 

processes. 

It was observed that the number of droplets inhaled was linear with time. This 

means that the droplets inhaled in an 8-hour flight would be twice as many, and in a 2-

hour flight, half those of the 4-hour flight. 

6.3 Discussion 

The investigation calculated the droplets inhaled by the passengers seated in the 

row and the neighboring rows of the index passenger for three common scenarios. The 

droplets inhaled by the fellow passengers were of the order of 103 due to the breathing 

and talking in the 4-hour flight and 105 due to the coughing. It should be noted that not all 

these droplets contained active infectious agents. The study provided only an estimate of 

the relative risk of infection to the passengers. 

The information on the viability and the amount of the infectious agents should be 

added to this model to predict the spatial distribution of infection risk in the cabin. The 

model accounts for the time and spatial variation of parameters such as the breathing flow 

rate and the droplet concentration. The model can be easily coupled with the viability and 

amount of infectious agents. 
‐ 85 ‐ 

6.4 Conclusions 

The methods to predict the transport of droplets exhaled from combinations of 

breathing, talking, and coughing of an index passenger in an aircraft cabin for the whole 

flight duration were developed. Since the droplets got well mixed in the cabin air 3 

minutes after they were released, the method used combined CFD and mixed condition 

models for the prediction. It was found that the droplet distribution from single 

exhalations can be superimposed to obtain the droplet distribution for multiple exhalation 

scenarios. The droplets inhaled by a passenger can consequently be determined for the 

whole flight duration. 

The number of droplets inhaled by the fellow passengers due to the droplets 

exhaled from regular breathing, 100 coughs sandwiched with regular breathing, and 30 

minutes of talking combined with regular breathing from the index passenger in a 4-hour 

flight were calculated. The coughs generated the most droplets and may impose a very 

high risk on the fellow passengers if the droplets contained active infectious agents. The 

risks due to the breathing and talking were almost two orders of magnitude smaller than 

the risk due to coughing. 
‐ 86 ‐ 

7. EVALUATION OF AIRBORNE RISK OF INFECTION IN AN AIRCRAFT 

CABIN 

In the previous chapter a method to predict the amount of droplets inhaled by the 

passengers for realistic flight durations was presented. As not all of these droplets may 

contain the infectious agents, the number of droplets inhaled may not represent the risk of 

infection. This chapter first presents a method to quantify the risk of infection based on 

the amount of infectious agents inhaled by the passengers in the aircraft. Using this 

method the reduction in the risk to the passengers was also calculated for a scenario with 

the passengers wearing masks. 

7.1 Risk Assessment Models 

The risk of infection can be quantified using either the deterministic or the 

probabilistic (stochastic) models. Haas et al., (1999) discussed these models in details. 

Recently Sze To and Chao, (2010) have reviewed these models. For both the models it is 

required to calculate the dose inhaled by the susceptible occupants. The dose could be 

represented as the tissue culture infectious dose (TCID50), colony forming unit (cfu), 

influenza virus RNA particle or quanta (Wells, 1955). To calculate the dose inhaled by 

the occupants, the information on the dose exhaled by the infectious person during 

various exhalations such as coughing, sneezing, breathing or talking is required. There 

are a few studies that quantified the dose exhaled for some of the airborne infectious 

diseases. Alford et al., (1966) quantified the dose exhaled by the influenza patients in 

terms of TCID50. Yeager et al., (1967) measured the amount of colony forming units in 

patients with tuberculosis. Recently Fabian et al., (2009) and Milton et al., (2010) 

conducted controlled experiments and quantified the amount of influenza virus RNA 

particles exhaled from the breath and cough of influenza infected people. The dose 

exhaled during various epidemics was also calculated in terms of quanta (Qian et al., 

2009 and Rudnick and Milton, 2003). Quanta is a term defined by Wells, 1955 that 

indicates that if a person inhales one quanta the probability of him getting infected is 1-

1/e. Using the information on the amount of dose exhaled and the dose inhaled can be 

calculated using the equation given by: 

d (t )   f (t  t ) p (t ) C (t ) t i  0 i i  (7.1) 

where, di(t) is the dose inhaled by the ith passenger till time t, f is the amount of infectious 

agents contained in a droplet,  is the viability of the infectious agent, i.e. the fraction of 

total infectious agents that are active at time t. This is generally defined with reference to 

the time, the infectious agents are exhaled. t0 is the time when the infectious agents are 

exhaled, therefore it is a function of t-t0. pi is the rate of inhalation (volume/time) for the 

i

th passenger and is zero during exhalation. (Gupta et al., 2009) and Ci is the 

concentration of droplets in the vicinity of the ith passenger. 

For the deterministic models a person will get infected only if he inhales more 

than or equal to his tolerance dose (Haas et al., 1999). This tolerance dose can vary based 
‐ 87 ‐ 

on the type of infectious agent and the immunity of the susceptible person. But to our 

knowledge there is no study that quantifies the tolerance dose in terms of TCID50, cfu or 

influenza virus RNA particles. Therefore even though the dose inhaled can be quantified 

the risk can’t be quantified. The inhaled dose can give indication on the relative risk of 

infection. 

On the other hand the probabilistic models are based on probability distribution 

such as Poisson’s or Beta-Poisson’s distribution. The probabilistic models do not tell if a 

person will get infected or not, but will estimate the probability of a person to get 

infected. Some of the most popular probabilistic models are dose-response model (Haas, 

1983) and Wells-Riley model (Riley et al., 1978). Recently Sze To and Chao, (2010) 

have reviewed the pros and cons of these models. Equation (7.2) shows the mathematical 

form of the probability of risk given by Wells-Riley equation and is given by: 

t 1 exp  N . Q

Iqp 1 exp SD P     s 











     (7.2) 

The dose response model can be mathematically written as: 

1 exp  rN

S

D P     (7.3) 

where, P is the probability of infection, D is the number of developed (secondary) 

infection cases, S is the number of susceptible cases, I is the primary infected cases, q is 

the quanta release rate, p is the pulmonary ventilation rate, Q is the ventilation rate, r is 

the contact rate (or capacity to cause infection) and N is the inhalation dose. Ns represents 

the infectious inhalation dose under perfectly mixed steady state scenario. The value of r 

depends on the human response to the airborne infection. Some of the studies on mice for 

tubercle found that the best fitted r value is one (Wells, 1955). Recently Jones et al., 

(2009) suggested a value of 0.0218 based on infection in monkeys. Therefore the 

accuracy of probability of risk will depend on the value of r. Similarly the calculation of 

risk using the Wells-Riley approach requires information on quanta. The information on 

quanta for various epidemics was calculated (Riley et al., 1978; Rudnick and Milton, 

2003 and Qian et al., 2009) using the Wells-Riley equation (Riley et al., 1978) . The 

quanta values were back calculated by substituting the number of susceptible and infected 

people, ventilation rate and pulmonary rate, and the time of exposure. Therefore the 

quanta calculations were not direct. As the method on predicting the quanta exhaled is 

not direct, people have debated on its accuracy (Sze To and Chao, 2010). Even though 

the method is not direct but as the information (quanta) is the infectious dose, we have 

used the Wells-Riley equation to quantify the risk of infection in an aircraft cabin. 

To summarize, we have used the deterministic and probabilistic model (WellsRiley equation) to quantify the amount of infectious agents inhaled and probability of 

infection respectively in an aircraft. Finally the reduction in risk was estimated if the 

passengers wear masks. 

7.2 Case Description 
‐ 88 ‐ 

A fully occupied Boeing 767 flight with index passenger suffering from influenza 

and seated at the center of the row was studied. The risk of infection for the passengers 

seated in the row of the index passenger and the neighboring rows was calculated for a 

four hours of air travel. The CFD studies were performed under the ventilation rate of 

33.7 ACH and therefore we assumed the same ventilation rate for the four hours of air 

travel. The amount of dose inhaled (influenza virus RNA particle or the quanta) by each 

passenger was first calculated based on the amount of dose exhaled by the index 

passenger as discussed in the next section. Similar calculations on the dose inhaled were 

performed assuming that the protection factor provided by the masks is 10 (as discussed 

in detail in literature review section). Finally the risk of infection was calculated. 

7.3 Amount and Viability of Dose 

 

7.3.1 Deterministic Models 

The dose for the deterministic models was defined in terms of the influenza virus 

RNA particles. Fabian et al., (2009) and Milton et al., (2010) measured the rate of 

exhalation of influenza virus RNA particles from the cough and breathe of influenza 

infected patients. It was observed that an influenza infected subject can exhale 0.01 to 2 

influenza virus RNA particles per minute (geometric mean 0.1) through breathing. The 

amount of such influenza virus RNA particles for a cough combined with one minute of 

breathing ranged from 0.1 to 20000 per minute (geometric mean 3.1) and 0.1 to 100000 

per minute (geometric mean 5) for coarse and fine particles respectively. Therefore a 

cough alone can contain 8 (geometric mean) influenza virus RNA particles. Harper, 

(1961) found that the viability of the influenza virus does not change significantly in four 

hours and thus it was assumed to be 1 (=1). Hsu et al., (1994) and Loudon and Brown, 

(1967) indicated that the cough frequency can vary from 12 to 35 and 3 to 48 per hour 

respectively. Thus average of 25 cough exhalations an hour was considered. No breathing 

exhalations were considered as the amount of infectious agents exhaled from cough was 

higher than breathing. This information along with the breathing profile and droplet 

concentration as obtained from the method discussed earlier can be used in equation (7.1) 

to calculate the inhaled amount. 

7.3.2 Probabilistic Model: Wells Riley Equation 

The dose inhaled for the Wells-Riley model was calculated in terms of quanta. 

The rate of exhalation of quanta was calculated based on the influenza epidemic case 

(Moser et al., 1979). A Boeing 737 flight got delayed for 4.5 hours due to engine failure. 

There were 54 people on board while 29 remained on board throughout the delay. One 

index passenger was on board and 25 secondary infection cases were observed. Rudnick 

and Milton, (2003) formulated an unsteady Wells-Riley equation and calculated the 

quanta release rate for this case under low air exchange rate. The quanta release rate was 

79 and 128 per hour for 0.1 and 0.5 ACH respectively. We took an average and 

considered a quanta release rate of 103 per hour. Assuming if the same infection spread 

would have taken place under ventilation rate of 33.7 ACH, we found that the quanta 

release rate would have been 5226 per hour. As the quanta is an active quantity we 
‐ 89 ‐ 

assumed the value of  to be one. The amount of quanta inhaled was then calculated 

similarly using equation (7.1) and assuming that all the quanta was exhaled through 

coughing. The probability of infection was calculated for both the quanta release rate. 

7.4 Results 

This section first presents the results on the risk predicted using the deterministic 

and probabilistic models. The reduction in risk for a case with everyone except the index 

passenger is wearing the N-95 masks was then calculated. 

7.4.1 Risk of Infection from Influenza using the Deterministic and Probabilistic Models 

Figures 7.1 shows the risk of infection from influenza for the passengers seated in 

the row and the neighboring rows of the index passenger, using the deterministic and 

probabilistic approaches respectively, for a four hours air travel. The risk evaluated using 

the deterministic approach is represented as inhaled influenza virus RNA particles, as 

shown in Figure 7.1 (a). The index passenger was located at seat D. It should be noticed 

that the dose inhaled by the passengers seated in the vicinity of the index passenger were 

high. The dose inhaled by passenger 5A was high even though the seat is far from the 

index passenger. It is because the droplet exhaled moved backwards and then towards the 

window on the back and index passenger rows. The amount inhaled was in accordance 

with the droplet movement. It can be seen that the amount of influenza virus RNA 

particles inhaled for four hours of flight was less than one but it does not mean that the 

risk was low or high, as the influenza virus RNA particles can be more than one influenza 

virus. Moreover the tolerance dose for influenza for the passengers is not known. Thus 

any comment on who all can get infected can’t be made. But the relative risk of infection 

to the passengers can be observed. 

(a) 
‐ 90 ‐ 

(b) 

(c) 

Figure 7.1 Risk of infection from influenza for the passengers in a 4 hours flight (a) Deterministic 

approach: inhaled influenza virus RNA particles (b) Probabilistic approach: probability of 

getting infected with influenza for the release rate of 103 quanta per hour (c) release rate of 5226 

quanta per hour. 

On the other hand, the risk evaluated using the probabilistic approach is 

represented as the probability of getting infected from influenza (Probability influenza) 

from an index passenger. Figure 7.1 (b) and (c) show the Probability influenza for the 

release rate of 103 quanta per hour (low ventilation) and 5226 quanta per hour (33.7 

ACH) respectively. The Probability influenza is proportional to the amount of quanta of 

influenza inhaled. Therefore the Probability influenza was higher for the passengers 

seated close to the index passenger and 5G and 5A as explained earlier. Higher the 

probability, more are the chances of getting the infection. Considering the average 

probability for the passengers, it was found that there is a probability of getting 3 

(rounded off) people infected out of 20 for quanta release rate of 103 per hour. This is 

much lower than the observed case of 25 secondary infection cases out of total 29 
‐ 91 ‐ 

passengers (Moser et al., 1979). This is because flight case happened under no or low 

ventilation, while our calculations were performed assuming a full ventilation of 33.7 

ACH. This clearly explains the importance of the ventilation system. Similar calculations 

on average probability for the quanta release rate of 5226 per hour indicated that there is 

a probability of all 20 people getting infected with influenza. This is because the quanta 

release rate of 5226 per hour was back calculated assuming a full ventilated aircraft and 

25 secondary infection cases among 29 total passengers. 

7.4.2 Effect of N-95 Mask 

Figure 7.2 shows the risk of infection from influenza for the case with everyone 

except the index passenger is wearing the mask. As discussed earlier the mask can 

provide a protection factor of 10, therefore the amount of influenza virus RNA particles 

would be 1/10th of inhaled without the masks, as shown in Fig. 7.2 (a). If the index 

passenger also wears the mask the amount would be even lower. 

(a) 
‐ 92 ‐ 

(b) 

(c) 

Figure 7.2 Risk of infection from influenza for the passengers in a 4 hours flight for a case with 

the passengers except the index passenger wearing the masks (a) Deterministic approach: 

inhaled influenza virus RNA particles (b) Probabilistic approach: probability of getting infected 

with influenza for the release rate of 103 quanta per hour (c) release rate of 5226 quanta per 

hour. 

Similarly the quanta inhaled with the masks would be 10% of the quanta inhaled 

without the mask and thus the Probability influenza with the mask would be lower than 

without mask as shown in Figure 7.2 (b) and (c). Considering the average probability of 

infection for the passengers wearing mask case, it was found that 0 (rounded off) of 20 

and 11 out of 20 passengers may get infected for the quanta release rate of 103 and 5226 

per hour respectively. This is lower than the average probability of infection without the 
‐ 93 ‐ 

mask case which was 3 out of 20 and 20 out of 20 for the quanta release rate of 103 and 

5226 per hour respectively. This clearly indicates the usability of the masks. 

7.5 Discussion 

The model quantified the amount of dose inhaled based on the dose exhaled from 

the data collected by Fabian et al., (2009) and Milton et al., (2010) and the influenza 

epidemic case (Moser et al., 1979). As the dose exhaled can vary from index passenger’s 

infectious conditions, the dose inhaled and thus the probability of getting infected by the 

susceptible passengers would be a direct function of it. Total protection level provided 

by the N95 respirator mask was considered to be 10% based on the available literature 

and the values can vary based on the fitting. 

7.6 Conclusion 

A method to predict the spatial and temporal risk of infection in an aircraft based 

on the deterministic and probabilistic approaches was discussed. Using the methods 

developed in earlier sections the spatial distribution of dose inhaled by the passengers 

was calculated. Based on the dose inhaled the risk of infection was quantified for the 

passengers seated in the row and the neighboring rows of the index passengers with 

influenza for a four hours of flight. 

For the deterministic approach the dose was quantified as the number of influenza 

virus RNA particles inhaled, but as the tolerance dose for the influenza virus RNA 

particles is not known nothing can be said on the infection risk to the passengers. 

For the probabilistic approach the Wells Riley equation was used to quantify the 

probability of influenza based on the spatial distribution of quanta inhaled by the 

passengers. It was found that the probability of influenza can be reduced by using N95 

respirator masks. 
‐ 94 ‐ 

8. INFLUENCE OF MOVING BODY THROUGH EXPERIMENTAL AND CFD 

INVESTIGATIONS IN A SMALL-SCALE CABIN 

In addition to the supply airflow condition from the diffusers, movement of 

individuals inside enclosed environments such as airliner cabins has been shown to 

significantly influence contaminant distribution and personal exposure (Brohus et al., 

2006; Bjørn and Nielsen, 2002; Bjørn et al., 1997). Passengers seated as far as 7 rows 

from the infected passenger were affected during the SARS contamination incident on the 

Air China flight (Olsen et al., 2003), whereas previous investigations, which did not 

consider the effects of moving crew and passengers, concluded that in-flight transmission 

of a disease should remain confined to within 2 rows of a contagious passenger (NRC 

2002; Kenyon et al., 1996; Jeffrey et al., 1993). The infection of some of the crew 

members on the Air China flight raises concerns about the mechanisms of transmission, 

i.e. whether their movement promoted the SARS transmission. This chapter is an attempt 

to provide an understanding of the influence of moving crew members and passengers on 

flow and contaminant transport in an airliner cabin. 

8.1 Research Methodologies 

In general, the investigation of the influence of human movements on airborne 

contaminant transmission in airliner cabins can be done experimentally or through 

computer simulations, the latter being more practical and cost-efficient. Numerical 

simulations of movement in a cabin can be handled directly or indirectly. But direct 

methods are more time-expensive because they involve moving and dynamic grids to 

simulate body movement. Matsumoto et al. (2004) used a grid deformation approach to 

generate a computational mesh around a moving body in an empty room. A similar CFD 

approach was used in studying the airflow due to a walking person and a sliding door in 

an isolation room (Shih et al., 2007). Edge et al. (2005) applied unsteady Reynoldsaveraged Navier–Stokes (RANS) simulations in a study of contaminant transport in the 

wake of a stationary human model. Choi and Edwards (2008) further modeled 

contaminant transport induced by human walking motion in simplified situations, with a 

person walking from one room to another and a person walking from a room into a long 

hallway. They used a human kinematics model to generate a realistic human walking 

motion. 

In contrast, indirect methods model movements in an approximate way such as 

using a distributed momentum source (Zhai et al., 2002) or a turbulent kinetic energy 

source (Brohus et al., 2006). Such methods have been used in the study of contaminant 

transmission in complex computational domains such as orthopedic surgical rooms. 

Indirect methods are computationally faster, as they require no remeshing, and are 

therefore preferred over direct methods. Moreover, any real-life body displacement 

pattern may be modeled using indirect methods rather than direct methods, which might 

be beyond practical reach, but at the expense of accuracy (Brohus et al., 2006). Both 

direct and indirect numerical simulations of people’s movements have their own 

advantages and disadvantages, yet no conclusive indication of the reliability of these 
‐ 95 ‐ 

CFD modeling techniques in predicting flow and contaminant transport in airliner cabins 

and other enclosed environments can be drawn. As these CFD models use 

approximations, their reliability is always questionable unless validated by high quality 

experimental data (Chen and Srebric, 2002). 

The literature on experimental investigations of diverse enclosed environments 

indicates a significant influence of movement on the airflow and transport of 

contaminants (Matsumoto and Ohba, 2004; Bjørn and Nielsen, 2002; Bjørn et al., 1997; 

Mattsson and Sandberg, 1996). But these studies lacked sufficient spatial and temporal 

resolutions for validation of CFD models. Moreover, the studies recently conducted by 

Zhang et al. (2005; 2009) and Kühn et al. (2009) emphasized the difficulty of measuring 

and understanding the complex airflow inside a full-scale cabin mockup, even under 

steady-state conditions. The large volume of the enclosure and the complications arising 

from obstructions such as seats and simulated passengers prevented obtaining detailed 

data. The presence of body movements would make it even more difficult to acquire data 

with meaningful resolutions. Small-scale water models produce reasonably well-resolved 

data to permit an understanding of flow and contaminant transport in ventilated buildings, 

e.g. Lin and Linden (2002), Thatcher et al. (2004), Settles (2006), and Finlayson et al. 

(2004). As a consequence, the present study uses a simplified small-scale cabin mockup 

inside a water tank in an effort to generate the high-quality experimental data required to 

validate the performance of an associated CFD model. However, changes in the physical 

scale and working fluid further complicate the interpretation of equivalent effects to a full 

scale (Thatcher et al., 2004). 

In order to address the influence of body movements on flow and contaminant 

transport in airliner cabins, the present work is divided into two chapters. This chapter 

presents the experimental results from the small-scale cabin mockup and discusses the 

performance of a companion CFD model. 

8.2 Experimental Method 

8.2.1 Experimental Setup 

Figure 8.1(a) shows the small-scale, water-based experimental test facility 

consisting of an upside-down cabin mockup. The cabin was made using a transparent 

semi-circular pipe 45 cm in diameter and 2.44 m in length. The one-tenth scaled mockup, 

fully submerged in a glass tank, is equivalent to a cabin with 28 rows of economy-class 

seats. Water temperature was monitored during each experiment to maintain isothermal 

conditions inside the cabin so that the buoyancy effects could be neglected. The interior 

of the model cabin was free of obstructions to eliminate secondary flow features. To 

simulate the environmental control system (ECS) of commercial airliner cabins, 

provisions were made to inject water through an overhead duct of the inlet diffuser 

assembly (Fig. 8.1(a)). Water was pumped to the overhead pipe from a 550-liter tank. To 

achieve uniform inflow to the cabin, the water entered a settling chamber through 23 pipe 

fittings and was then supplied to the cabin through 48 elongated openings cut along the 

length, where a T-shaped diffuser diverted the fluid laterally to both sides of the cabin 

cross section. Water was extracted from two outlets located near the side walls of the 
‐ 96 ‐ 

cabin at floor level. An automated mechanism placed above the experimental facility 

traversed the body (0.02 m thick x 0.05 m wide x 0.17 m tall) along the longitudinal 

direction of the cabin. In order to isolate the effects of the moving body, measurements 

were done with (ventilation case) and without (baseline case) the environmental control 

system (ECS), respectively. 
‐ 97 ‐ 

(a) 

(b)

Figure 8.1 (a) The small-scale experimental test facility of the cabin mockup, and (b) the CFD 

model of the test facility. 

8.2.2 Experimental Techniques 

Particle Image Velocimetry (PIV) was employed in measuring the velocity 

distribution inside the water tank, using a dual 50 mJ/pulse NewWave Gemini Nd:YAG 

laser (Raffel et al., 2007). Table 4-1 presents the configuration of the PIV system. The 

time interval between two successive laser pulses was typically between 1500 and 2500 

μs. A digital camera (2,048×2,048 pixels PowerView 4MP) was used to acquire images. 

The CCD camera and laser were positioned to capture cross-sectional and longitudinal 

flow images (Fig. 8.2). The flow visualization images were obtained using 14 μm 

diameter silver-coated, hollow-sphere particles with a specific gravity of 1.7. A program 

was written (Labview) to synchronize the motion of the moving body with the data 

acquisition system. The body speed was 0.175 m/s at the model scale, which is equivalent 

to movement through two economy-class rows per second in an actual airliner cabin. The 

time and velocity scales of the tracer particles under such conditions are shown in Table 

4-2. Since the tracer particles have a Stokes number much less than unity, the particles 

are expected to follow the fluid faithfully (Samimy and Lele, 1991). The physical 

Cabin

Body

Overhead duct

of Inlet Diffuser

Traverse

mechanism

Cabin

   Inlet

Body

  T‐shaped

Outlets
‐ 98 ‐ 

resolution of the flow field was 3.5×3.5 mm. The experimental uncertainties in the body 

velocity, the flow rate, and the PIV system were 0.4%, 4%, and 5.2%, respectively 

(Poussou, 2008). 

Table 8.1 Configuration of the PIV system. 

Component Description Characteristics 

Laser New Wave, Gemini PIV 

Model #105355 

Nd:YAG, 15 Hz, 532 nm 

5 ns pulse, 50 mJ/pulse 

Camera TSI Inc., PowerView 4MP 

Model #630159 

12-bit gray levels 

2048x2048 pixels 

Nikon AF Micro-NIKKOR 60mm 

Nikon 62mm Circular Polarizer II 

Synchronizer TSI Inc., LaserPulse 

Model #610034 

Computer Dell T3400, Intel Dual Core 2.4 GHz, 2 Gb RAM 

Software TSI Inc., Insight 3G v8.0.5 

Optics TSI Inc., cylindrical lenses 

TSI Inc., spherical lenses 

-15, -25, -50 mm 

250, 500, 1000 mm 

Particles Laboratory for Experimental 

Fluid Mechanics at Johns 

Hopkins University 

Silver coating 

14 μm diameter 

Specific Gravity = 1.7 

(

a) (b)

Measurement settings 

 Baseline With Ventilation 

Flow rate (L/s) 0 2.5 

Cross-sectional 

view 

d1 = 16.6 cm 

d2 = 83.1 cm 

 d3 = 1 cm 

d1 = 9.4 cm 

 d2 = 34.2 cm 

 d3 = 1 cm 

Longitudinal 

view 

d1 = 16.6 cm 

 d2 = 103 cm 

d1 = 9.4 cm 

 d2 = 63.8 cm 

Figure 8.2 The measurement settings for (a) cross-sectional and (b) longitudinal flow 

visualizations. 
‐ 99 ‐ 

Table 4-2. Tracer particle dynamics. 

Parameter Value 

Particle response time, τp 

Flow time scale, τf

37.5 μs 

3.6 ms 

Gravitational settling time 

Centripetal settling time 

3.2 μm/s 

723 μm/s 

Particle Stokes number, St = τp/τf 0.0104 

Planar Laser-Induced Fluorescence (PLIF) was used in visualizing contaminant 

transport inside the small-scale airliner cabin (Freymuth, 1993). Contaminant was 

simulated by injecting a dye (uranine, C20H10O5.2Na) into the flow and observing their 

fluorescence under laser illumination. In the present study, the dye was released from the 

lateral sides of the moving body (the red arrows in Fig. 8.2 (a)). The configuration details 

of the PLIF system are shown in Table 4-3. The dye was delivered from an 18-liter 

container, pressurized to 48 kPa by compressed air which emanated from the moving 

body at flow rates of 25 mL/s. Upon injection into the flow, the dye was illuminated by a 

1-mm-thick laser sheet and recorded by a video camera, as described in Table 4-3. 

Cylindrical optical lenses were used to form the laser sheet. Continuous movies of the 

flow were recorded and converted to frame sequences using standard video-editing 

methods (Adobe Premiere CS3). The frame sequences of the dye transport were used to 

qualitatively test the CFD model. 

Table 8.3 Configuration of the PLIF system. 

Component Description Characteristics 

Dye ScholAR Chemistry, CAS #518-47-8 Disodium salt, C20H10O5.2Na 

Laser 1 

Laser 2 

New Wave, Gemini PIV, Model 

#10535 

Lexel, Model #85 

Nd:YAG, 532 nm 

Ar-ion, 514.5 nm 

Camera TSI Inc., PowerView 4MP, Model 

#630159 

12-bit gray levels, 7.25 fps 

2048x2048 pixels 

8.3 CFD Modeling 

The CFD model used a second-order upwind scheme and the SIMPLE algorithm. 

The Re-Normalization Group (RNG) k-ε was used to model the turbulent flow. 

Compared to other turbulence models, RNG k-ε was observed to deliver the best 

performance in terms of accuracy, computing efficiency, and robustness for modeling 

indoor environments (Zhang et al., 2007). Note that the above CFD model would predict 

time-averaged flow field, while the flow visualization images captured using PIV were 

instantaneous. Hence, the average flow fields computed by CFD were compared with 

phase-averaged flow fields measured by PIV. 
‐ 100 ‐ 

The CFD geometry used for this study is shown in Fig. 8.1(b). Most of the 

geometrical features of the experimental test facility were accurately modeled, including 

the pipelines of the simulated environmental control system that supply water to the cabin 

through the inlet diffusers. The CFD model used a combined dynamic and static mesh 

scheme. The computational domain was divided into two separate sections: section 1 for 

the moving body (dynamic mesh, 0.25 million cells) and section 2 for the rest of the 

cabin (static mesh, 4.4 million cells), as illustrated in Fig. 8.3. Only 5.3 % of the total 

meshes inside the domain were dynamic, thus reducing the computing time required for 

remeshing. The maximum mesh size inside the cabin was 5 mm. The dynamic layering 

meshing scheme was used to keep the grid size in the dynamic zone constant, which 

reduced the uncertainties due to the size variation of the computational mesh (Brohus et 

al., 2006). A commercial CFD program, FLUENT (Fluent, 2005), is used for this study. 

The interactions between the static and dynamic sections were done using interfacing, 

which can reduce the accuracy of the flow predictions (Tezduyar, 2004). User defined 

functions were implemented in FLUENT to define and track the movement of the body. 

The cabin condition was isothermal. Near wall treatment was done using standard wall 

functions. The speed of the body in the water was 0.175 m/s, which is equivalent to a Re 

of 9500 based on body width. 

Figure 8.3 Layout of meshing scheme in the cabin section. 

Both cases, i.e., with and without ventilation, were computed in a 4-node Linux 

cluster. Each node had five processors (1.8 GHz AMD 64) and 4GB of memory. The 

time step used for the computations was 0.01 s. 

8.4 Results and Discussion 

8.4.1 Cross-sectional Flow 

Figure 8.4 shows instantaneous velocity fields in a cross section at Frame 4 

obtained during different runs (runs 1, 50, and 100, respectively), along with the averaged 

velocity field obtained over 100 runs. The speed of the body in the water was 0.175 m/s. 

Frame 4 was acquired when the back of the body was at a distance of 8.25 cm from the 

fixed laser sheet (see Figure 8.2). Figure 8.4 shows strong intermittency in the flow. 

Vertical symmetry in the wake was observed in the mean velocity field. Averaged 

velocity field using 25, 50, and 100 measurements is shown in Fig. 8.5. A vertically 
‐ 101 ‐ 

symmetric wake is visible after phase-averaging 25 measurements. 

Experiment 1 Experiment 50 

Experiment 100 Average of 100 experiments 

Figure 8.4 Instantaneous and averaged velocity fields at Frame 4 for the baseline case 

Average of 25 experiments Average of 50 experiments 

Average of 100 experiments 
‐ 102 ‐ 

Figure 8.5 The averaged velocity fields at Frame 4 for the baseline case.

Figure 8.6(a) shows the mean flow fields of 100 measurements for baseline flow 

at Frames 1, 4, and 7, which were acquired when the back of the body moved 1, 8.25, and 

15.5 cm past the laser sheet (refer to Fig. 8.2), respectively. A strong downwash in the 

wake of the moving body is shown in Fig. 8.6, which was produced by the two 

symmetric eddies around the top corners. Once the two eddies approached the cabin 

floor, they spread to the sides and dissipated. The disturbance created by the moving 

body on the stagnant flow field diminished very rapidly after this process. 

Frame 1: Measured Frame 1: Computed 

Frame 4: Measured Frame 4: Computed 

Frame 7: Measured Frame 7: Computed 
‐ 103 ‐ 

(a) (b) 

Figure 8.6 The (a) measured and (b) computed mean flow fields at Frames 1, 4, and 7 for the 

baseline case. 

Frame 4 Frame 7 
‐ 104 ‐ 

Figure 8.7 Quantitative estimates of measured and computed velocity magnitudes and its 

components at Frames 4 and 7 at half the different height of the moving body for the baseline 

case. 

Figure 8.6(b) shows the corresponding computed flow field for Frames 1, 4, and 

7, respectively. Side-by-side comparison using Figs. 8.6 and 8.7 indicated that the CFD 

model is able to predict the development of the two-eddy system. The predicted flow 

patterns agree although noticeable differences existed with respect to the vortex aspect 

ratio. Reduction of mesh size and shifting of the interface boundary between the static 

and dynamic mesh zones did not alter the CFD predictions. Separated flows behind bluff 

bodies are typically difficult to model, e.g., the transient RANS simulations by Lubcke 

(2001). However, the CFD model can reasonably describe the flow physics of the 

baseline case based on the agreement of the results with the experimental data suggests, 

which is necessary to study of contaminant transport in airliner cabins. 

The cross-sectional flow of the ventilated cabin, when the ECS is operating, is 

shown in Figs. 8.8 and 8.9 at different instants. As in the baseline case, Fig. 8.7 shows the 

mean flow fields at t=0s and at Frames 4 and 7. The injection of water coming into the 

cabin through the overhead duct of the inlet diffuser assembly (refer to Fig. 8.1(a)) is 

evident from the CFD results in Fig. 8.8(b). The PIV measurements in Fig. 8.8(a) do not 

show these incoming jets as the measurement window was focused inside the cabin. The 

flow field at t=0s corresponds to the undisturbed flow generated by the ECS before the 

body started its movement. CFD captured the flow across the cross section much more 

accurately than in previous attempts (Zhang et al., 2009). This is due to accurate 

modeling of inlet geometrical features of the experimental setup. The average crosssectional airflow (at t=0 s) shows a quasi-symmetric vortex structure. The assembly of 

the experimental setup did not permit perfect symmetry in the flow. The particular 

section shown in Fig. 8.8 showed the best flow symmetry. 

The interacting flow is shown in Frames 4 and 7 in Figs. 8.8 and 8.9. The strong 

downwash flow behind the moving body is still visible, but the lateral spread of the 

moving body wake was limited due to the opposing flow from the ECS. The evolution of 

the downwash was profoundly perturbed by the ventilation. As the body traversed 

through the measurement plane, the location of the vortex cores at each side of the cabin 

moved downward in the direction of the side walls. The CFD model was able to capture 

this transient vortex development. However, discrepancies between the results of the 
‐ 105 ‐ 

CFD model and the experiments are evident. This stems from the sensitivity of the 

overall flow to the computed wake behind the moving body. 
‐ 106 ‐ 

t=0: Measured t=0: Computed 

Frame 4: Measured Frame 4: Computed 

Frame 7: Measured 

(a) 

Frame 7: Computed 

(b)

Figure 8.8 The (a) measured and (b) computed mean flow fields at t=0s and at Frames 4 and 7 

for the case with ventilation. 
‐ 107 ‐ 

Frame 4 Frame 7 

Figure 8.9 Quantitative estimates of measured and computed velocity magnitudes and its 
‐ 108 ‐ 

components at Frames 4 and 7 at half the height of the moving body for the case with ventilation. 

8.4.2 Longitudinal Flow 

A thorough analysis of the perturbed flow in the wake of the body along the cabin 

length was conducted for the baseline and ventilation cases. Instantaneous PLIF and PIV 

measurements were complemented by CFD computations, as shown in Fig. 8.10. Flow 

recirculation due to flow separation could be observed (Figs. 8.10(b) and (c)). Fig. 

8.10(c) shows only a small area of the flow due to the limited image size captured by the 

PIV. The flow features behind the moving body, approximately inclined at 45o from the 

vertical as seen in the PLIF measurements, were also captured by CFD (comparing Figs. 

8.10(a) and (c)). However, the longitudinal flow computed behind the moving body was 

stronger than that observed in the PIV measurements, with over prediction of longitudinal 

momentum transfer. Earlier vertically elongated eddy rings showed lesser momentum 

transfer in the lateral directions. However CFD captured the fundamental flow features. 

(a) 

(b) 

(c) 
‐ 109 ‐ 

(d) 

Figure 8.10 (a) Instantaneous flow visualization; (b) mean measured longitudinal flow field; (c) 

mean computed longitudinal flow field; and (d) the computed dye concentration for the baseline 

case. 

Figure 8.11 shows the measured and computed results of the longitudinal flow 

with ventilation by the ECS. The injection of water coming into the cabin through the 

overhead duct of the inlet diffuser assembly (refer to Fig. 8.1(a)) is evident from the CFD 

results in Fig. 8.11(c). The PIV measurements in Fig. 8.11(b) do not show these incoming 

jets as the measurement window was focused inside the cabin. The CFD model was able 

to capture the vortex above the moving body for this case (Figs. 8.11(b) and (c)). The 

contaminant dye was observed to convect to higher vertical locations (Figs. 8.11 (a) and 

(d)). The mixing in the wake was modified and a shorter longitudinal recirculation region 

was observed for the ventilation case versus the baseline case (Figs. 8.10(c) and 11(c)). 

The flow structures recirculating at 45o behind the body for the baseline case were 

disrupted due to the interaction of wake behind the moving body and the flow from the 

ECS. 

 (a) 

(b) 
‐ 110 ‐ 

(c) 

 (d) 

Figure 8.11 (a) Instantaneous flow visualization; (b) mean velocity field; (c) mean computed 

longitudinal flow field; and (d) the computed dye concentration for the case with ventilation. 

8.4.3 Contaminant Transport 

Figures 8.12 and 8.13 show a qualitative comparison of contaminant dye transport 

across the cabin cross section for the baseline and ventilation cases, respectively. Twentyfive instantaneous PLIF images were averaged to obtain the measured mean 

concentration field. A limited number of realizations were acquired because the water 

tank had to be cleaned after each dye injection experiment. Twenty-five measurements 

are typically sufficient to observe vertical symmetry in the wake, as shown in Fig. 8.5. 

The maximum contaminant concentration occurred behind the top two corners of the 

moving body in both cases. The CFD model predicted the dye intensity profile and the 

location of maximum intensity. The intensity of the fluorescent dye across the cross 

section was lower for the ventilation case due to mixing, thus making a comparison of the 

measured and computed dye intensities difficult. Overall, the maximum concentration 

across the cabin cross section was lower for the ventilation case, as one would expect. 
‐ 111 ‐ 

Frame 2: Measured Frame 2: Computed 

Frame 4: Measured 

(a) 

Frame 4: Computed 

(b)

Figure 8.12. (a) Measured and (b) computed mean dye concentrations at Frames 2 and 4 for the 

baseline case. 
‐ 112 ‐ 

Frame 2: Measured Frame 2: Computed 

Frame 4: Measured 

(a) 

Frame 4: Computed 

(b)

Figure 8.13 (a) Measured and (b) computed mean dye concentrations at Frames 2 and 4 for the 

ventilation case. 

A quantitative comparison between maximum concentrations across the cabin 

cross section as the body moves through it is shown in Fig. 8.14. The light intensity in the 

region behind the top edges was compared for both the experimental and the numerical 

data, as it typically showed maximum levels when compared to the rest of the flow field 

due to maximum dye concentration. Image processing was done using computational 

software (MATLAB). At Frame 4, the CFD model predicted that the ventilation would 

decrease the maximum concentration by 23% relative to the baseline case, compared with 

20% as observed during the experiments, although the absolute magnitudes were 

different. The CFD predictions were within ±5% of the experimental measurements for 

the other frames as well. Therefore, CFD captured the comparative strength of the dye 

concentration between the two cases, despite the complex transient flow phenomena. 

Moreover, CFD predicted that the maximum dye concentration across the cabin cross 

section would decay by more than 30% for both cases in less than a second, i.e. from 

Frame 4 to Frame 10, as the body moved through. Further quantitative comparisons could 

not be performed due to limitations associated with the experimental technique. 
‐ 113 ‐ 

Figure 8.14 Comparison of the decay in maximum concentration (CFD) and dye intensity (PLIF) 

for the baseline and the ventilation case. 

8.5 Contaminant Transmission due to Movement in the SARS Infected Cabin 

The validated CFD model was used in investigating the influence of movement in 

the SARS infected Air China Flight 112 from Hong Kong to Beijing (Olsen et al., 2003). 

Previous studies have reported that the risk of transmission without such movement 

should remain within 2 rows of the contagious passenger (NRC 2002). But passengers 

seated as far as 7 rows ahead of the contagious passenger were infected during the SARS 

contamination incident. This study thus examined whether movement of crew or 

passengers along the aisle of the cabin could be the reason behind the SARS contaminant 

transmission. 

The study assumed that the air entered the cabin through four linear diffusers as 

shown in Fig. 8.15: two of the diffusers, placed at the ceiling above the aisle, injected air 

downwards into the cabin while the other two diffusers at the side walls located below 

the storage bins injected air inwards towards the aisle. The airflow rate into the cabin was 

10 L/s per passenger (Zhang and Chen, 2007) and was distributed equally among the four 

inlets. Air was exhausted through outlets placed in the side walls close to the floor. A 

fully occupied 15-row cabin section with a seat pitch of 0.86 m was computed. The 

thermo-fluid conditions along with the passenger and the seat model were similar to that 

used by Mazumdar and Chen (2008). 
‐ 114 ‐ 

Figure 8.15 Schematic of one of the rows for the Air China Flight 112 cabin. 

To investigate whether movement can aid transmission of contaminants to 7 rows, 

this study assumed that a gaseous contaminant was released passively from the infected 

passenger at a uniform rate of 25 mL/s as is done in the rest of this chapter. The effect of 

body movement was studied on two contaminant initial distribution patterns: one was for 

a pulse contaminant released for 30 s and the other had an initial steady-state contaminant 

distribution simulating a continuous release. A previous comprehensive study done by the 

authors in a 4-row twin-aisle cabin concluded that the wake behind the moving body 

traps contaminants and carries them along the cabin (Mazumdar and Chen, 2007). 

Moreover, as the body stops moving, the contaminants carried by the wake spread to 

passengers seated near the aisle. This previous experience with a 4-row twin-aisle cabin 

was used in designing the case for the 15-row single-aisle SARS-infected mockup. It was 

assumed that once the cabin has the aforementioned initial contaminant distribution 

pattern, the body starts its movement from the rear end of the cabin at a speed of 1.75 m/s 

and finally stops 7 rows ahead of the infected passenger without making any intermediate 

stops. The duration of the movement was 5.2s. The movement was modeled using the 

combined dynamic and static mesh scheme since the indirect method under predicted the 

downwash flow behind the moving body. The time step used in the computations was 

0.01s. 

Figure 8.16 shows the breathing level contaminant distribution inside the cabin 

for a pulse contaminant released for 30 s. The high concentration zone was localized to 2 

rows near the infected passenger at t=30s. As the body moved along the aisle, the wake 

carried the contaminant localized in the 2 rows to 7 rows in front. Once the body stopped 

its movement, the contaminant carried in the wake was distributed to passengers seated 

near the aisle. A similar phenomenon is seen in Fig. 8.17 with an initial steady-state 

contaminant distribution for a continuous release. Even though the initial contaminant 

Inlet
‐ 115 ‐ 

concentration inside the cabin was higher, movement increased the contaminant 

concentration near the passenger seated 7 rows ahead of the infected passenger even for a 

continuous release. It can be observed that the initial steady-state contaminant 

distribution for a continuous release was higher behind the infected passenger. The 

thermal plume of the infected passenger transported the contaminant backward as a 

passive release scenario was studied. If the contaminant is released via a coughing or a 

sneezing process, the local distribution of contaminants can vary (Zhang, 2007). Yet the 

fact that movement can aid transmission of contaminants to 7 rows or more would remain 

true even for other release scenarios as well. 

 The contaminant concentration at the breathing level of an aisle passenger 

seated 7 rows in front of the infected passenger is shown in Fig. 8.18. The movement of a 

body along the aisle increases the maximum contaminant concentration at the breathing 

level of the aisle passenger by an order of magnitude for the case of a continuous release 

with initial steady-state contaminant distribution. For the pulse release case, the 

maximum concentration is about an order of magnitude lower than that of the continuous 

release case. Without movement, the contaminant would not have been transmitted to 

passengers sitting 7 rows ahead for the pulse release case. Moreover, the design of the 

cabin airflow system in the Air China flight prevented immediate flushing out of the 

transmitted contaminants to the aisle passenger as is evident from the transient 

concentration plots in Fig. 8.18. Hence, movement of the crew and passengers along the 

aisle of the cabin greatly enhanced the risk of contamination from the infected passenger. 
‐ 116 ‐ 

 t=30s 

 t=32.6s 

 t=35.2s 

 t=36s 

Figure 8.16 Contaminant transport trends due to movement of a person along the aisle for a 

pulse release from the infected passenger. 

Infected passenger
‐ 117 ‐ 

t=0s (steady state) 

 t= 2.6s 

 t=5.2s 

 t=6s 

Figure 8.17 Contaminant transport trends due to movement of a person along the aisle of the 

cabin with initial steady state contaminant distribution for a continuous release from the infected 

passenger. 

Infected passenger
‐ 118 ‐ 

(a) (b) 

Figure 8.18 Contaminant concentration at a location near the breathing level of the aisle 

passenger seated 7 rows in front of the infected passenger (a) with an initial steady-state 

contaminant distribution for a continuous release and (b) with a pulse release. 

8.6 Conclusions 

This chapter used a small-scale, water-filled model to investigate the effect of a 

moving body on flow and contaminant transport inside an airliner cabin. The small-scale 

model generated high quality experimental data with good spatial and temporal 

resolutions for CFD model validation. CFD validation was done for two different 

conditions: one with the cabin ECS (ventilation case) and the other without (baseline 

case). PIV was used to measure the flow under these two conditions. The measurements 

revealed a strong downwash flow along the vertical centerline of the moving body for the 

baseline case. The evolution of the downwash was profoundly perturbed by the flow from 

ECS in the ventilation case. A shorter longitudinal recirculation region was also observed 

for the ventilation case when compared to the baseline case. The CFD model captured 

these fundamental flow features reasonably well. 

The contaminant transport predicted by the CFD model was further validated 

using PLIF measurements. The contaminant dye was observed to convect to higher 

vertical locations for the ventilation case. The maximum contaminant concentration was 

found behind the top two corners of the moving body for both cases. CFD can accurately 

capture this phenomenon. The CFD model could quantitatively estimate remarkably well 

the strength of the dye concentration observed across the cabin cross section in the two 

cases. 

Movement of a crew member or passenger along the cabin aisle can carry 

contaminant from its source to many rows away. The body movement may have caused 

the transmission of SARS pathogen from the infected passenger to fellow passengers 

seated as far as seven rows away in Air China Flight 112 in 2003. 

5.2 s 35.2 s
‐ 119 ‐ 

9. CONCLUSIONS AND FUTURE WORK 

This chapter first summarizes the key conclusions from the study and then 

discusses the future scope of the research. 

9.1 Conclusions 

Airborne infection has always been a major source of morbidity and mortality 

worldwide. Thus it is important to understand, predict and control the transmission of 

airborne infection to trim down the deaths. Previous investigations have indicated that the 

infectious disease transmission in indoor environment, especially in aircraft cabins 

represents great risk and needs to be studied. It was found that the CFD simulations can 

accurately predict the disease transmission if are provided with accurate boundary 

conditions and flow models. The exhaled air is the source of the infectious agents and 

people can get infected by inhaling the infectious agents, which are generally carried by 

the expiratory droplets. It was found that there is no comprehensive study that can 

provide flow boundary conditions to the CFD simulations for the exhalations/inhalations 

from human beings. Thus, the present study first developed these models. 

The flow rate, flow direction of the jet, and the area of opening measurements were 

conducted on 25 human subjects for coughing, breathing, and talking processes. The 

cough flow rate was found to be a combination of gamma probability distribution 

functions. The variables in the functions can be represented by medical parameter i.e. 

CPFR, PVT, and CEV. These medical parameters were related to the height, weight, and 

gender of a person. A sequential cough was found to be the combination of two single 

coughs. The first one behave approximately the same as that of a single cough, while the 

second one was a scaled down version of the first one. The cough flow direction did not 

vary very much among the subjects and can be determined by two angles. The mouth 

opening area was constant during a cough. The mouth opening area varied among the 

subjects but without a clear correlation with the subject height. Hence this study proposed 

to use a mean mouth opening area during a cough 

The breathing flow rate variation with time was sinusoidal. The amplitude and 

frequency of the sine function were related to body height, weight, and gender. No 

significant difference in the breathing flow rate was observed due to either a change of 

breathing organ or posture. The flow direction of the exhaled jet during breathing can be 

characterized by two front angles and two side angles for nose breathing and one sideangle for mouth breathing. The breathing flow directions did not change much among the 

subjects tested. The mouth and nose opening areas were constant during breathing. 

The talking flow rate was found to be irregular in nature and, hence, is defined as 

an average flow rate over time. The flow rate was related to body surface area which can 

be obtained from the height and weight of a person. As no single event can define talking 

direction, it is proposed to use the directions of normal mouth breathing. The mouth 

opening area during talking was found to vary with time and words. A time averaged 

area, averaged over numbers, is therefore proposed for talking. The mouth opening area 

varied among subjects, but no clear trend was observed; thus, a mean value is proposed 

for the mouth opening area during talking. 
‐ 120 ‐ 

To summarize, the exhalation/inhalation flow models provided the information on 

the flow rate, flow direction and the area of nose/mouth opening during coughing, 

breathing and talking for human beings and can be represented as a set of simple 

mathematical equations. These equations account for the variation with time and people; 

and can be easily fed in a CFD simulation to provide thermo-fluidic boundary conditions 

for the exhalations/inhalations. The set of equations developed can be combined with 

appropriate infectious agents and droplet distribution information from available 

literature. Figure 9.1 shows the exhalation/inhalation model layout. 

Figure 9.1 Source/Sink Model layout for generating flow boundary conditions for various 

exhalations/inhalations. 

Previous investigations have also indicated that in addition to boundary conditions 

it is highly essential to select appropriate turbulence and dispersion model along with the 

suitable control volume (domain). Thus the study further investigated the transport of 

expiratory droplets that may carry the infectious agents, in aircraft cabin using the CFD 

methods and addressing the issues in the previous studies. 

This investigation studied the transport of the expiratory droplets from an index 

patient seated at the center of a seven-row, twin-aisle, fully occupied aircraft cabin by 

using a commercial CFD program (FLUENT 2005). The airflow and temperature 

distribution was first studied under steady state conditions. A grid sensitivity analysis was 

carried out and the optimum grid was used for the further analysis. Moreover as the 

boundary conditions at the ends of the cabin are unknown, two kinds of boundary 

conditions, adiabatic wall and periodic, were implemented and compared to evaluate the 

sensitivity of the boundary conditions at the ends on the overall flow. It was found that 

any of these boundary conditions can be used as the difference found in airflow and 

temperature distribution was insignificant. The steady state solution was then used as 

initial condition to study the droplet transport exhaled from a single cough, single breath 

and 15s of talking. The developed exhalation/inhalation boundary conditions were then 

fed in the CFD simulation. The RNG k- turbulence model was used and the monodispersed droplet tracking was performed through Lagrangian scheme. The simulations 
‐ 121 ‐ 

were performed for four minutes of real time starting from a steady-state airflow and 

temperature distribution. 

The droplets exhaled from the cough of the index patient followed mainly the 

bulk airflow. The droplet concentration in the vicinity of the passengers reduced over 

time due to the removal from the outlets and dispersion of the droplets. The total airborne 

droplet fraction reduced to 48%, 32%, 20%, and 12% after the droplets entered the cabin 

air for 1, 2, 3 and 4 minutes, respectively. Most of the droplets were transported within 

one row of the index patient in the first 30 s and then could be transported to the entire 

seven-row cabin with a uniform droplet distribution in 4 minutes. 

The droplets exhaled from the breath of the index patient behaved similarly as 

those from the cough, but the number was much smaller. The expiratory droplets 

generated during the talking of the index patient were contained on the left side of the 

cabin since the patient turned the head to the left. 

In real situations the index passenger can exhale droplets from multiple 

exhalations over several hours of flight duration. The CFD simulations provided a good 

insight on the expiratory droplet movement from various exhalations for four minutes of 

time but took 4 week of computational time on a 8 parallel processor cluster. Therefore 

performing the CFD simulations for multiple exhalations from the index passenger over 

several hours of flight is not practical and feasible. Hence, the study further developed 

methods to predict the transport of droplets exhaled from combinations of breathing, 

talking, and coughing of an index passenger in an aircraft cabin for the whole flight 

duration. Since the droplets got well mixed in the cabin air 3 minutes after they were 

released, the method used combined CFD and mixed condition models for the prediction. 

It was found that the droplet distribution from single exhalations can be superimposed to 

obtain the droplet distribution for multiple exhalation scenarios. The droplets inhaled by a 

passenger can consequently be determined for the whole flight duration. 

The number of droplets inhaled by the fellow passengers due to the droplets 

exhaled from regular breathing, 100 coughs sandwiched with regular breathing, and 30 

minutes of talking combined with regular breathing from the index passenger in a 4-hour 

flight were calculated. The coughs generated the most droplets and may impose a very 

high risk on the fellow passengers if the droplets contained active infectious agents. The 

risks due to the breathing and talking were almost two orders of magnitude smaller than 

the risk due to coughing. 

The amount of droplets inhaled may not represent the risk of infection as these 

droplets may not necessarily contain active infectious agents. Therefore, a method to 

predict the spatial and temporal risk of infection in an aircraft based on the deterministic 

and probabilistic approaches was proposed. Using the methods developed to quantify the 

amount of inhaled droplets, the spatial distribution of dose inhaled by the passengers was 

calculated. Based on the dose inhaled the risk of infection was quantified for the 

passengers seated in the row and the neighboring rows of the index passengers suffering 

from influenza for a four hours of flight. For the deterministic approach the dose was 

quantified as the number of influenza virus RNA particles inhaled but as the tolerance 

dose for the influenza virus RNA particles is not known nothing can be said on the 

infection risk to the passengers. For the probabilistic approach the Wells Riley equation 

was used to quantify the probability of influenza based on the spatial distribution of 
‐ 122 ‐ 

quanta inhaled by the passengers. It was found that the probability of influenza can be 

reduced by using N95 respirator masks. 

In-flight movement of passengers and crew members inside the airliner cabin had 

changed the airflow and contaminant transmission observed. Experiments performed in 

an empty one-tenth scale water-based cabin model showed a strong downwash flow 

along the vertical centerline of the moving body. The flow from the environmental 

control system of the airliner cabin profoundly perturbed the evolution of the downwash. 

Maximum contaminant concentration was found behind the two shoulder corners of the 

moving body. The CFD results indicate that seats and passengers do tend to obstruct the 

lateral transmission of contaminants and restrict the spread to the aisle of the cabin if a 

contaminant is released from a moving body. CFD results also predicted that movement 

can aid transmission of contaminants to 7 rows or more and could be the major reason 

behind the transmission of SARS contaminants from the infected passenger to fellow 

passengers seated as far as 7 rows away on the Air China Flight from Hong Kong to 

Beijing. 

9.2 Recommendation for Future Studies 

The exhalation/inhalation flow measurements in the current study were performed 

on healthy human subjects. The subjects suffering from infectious disease may have 

different airflow for these exhalations/inhalations. It is required to conduct these airflow 

measurements on infected human subjects to characterize the differences. Moreover the 

current study did not measure the airflow of sneezing as it was difficult to generate a 

sneeze. Measurements on sneezing should be performed in future as it could be an 

important source of infectious droplets. 

The developed CFD models assumed negligible droplet deposition as the modeled 

expiratory droplet sizes were small. It is required to quantify the deposition of the 

expiratory droplets on cabin surfaces as it can help in investigating the infection spread 

through contact. The current study developed the methods to predict the spatial and 

temporal distribution of expiratory droplets. The observed airflow agreed qualitatively 

with other studies and the transport of expiratory droplets was in agreement with the 

airflow. But it is required to conduct controlled measurements in the cabin to validate the 

results, as it can aid in improving the CFD models. 

The deterministic and probabilistic approaches adopted to quantify the risk can 

further be developed if the information on the tolerance doze for the infectious diseases is 

available. 

Effectiveness of N-95 mask in reducing the risk of infection was evaluated using 

the available literature. The available literature quantified the protection level for the 

occupants wearing the N-95 masks if the ambient air is contaminated, but did not 

quantify the ambient contamination if an infected occupant wears the mask. It is required 

to conduct controlled experiments to quantify the filtration of these masks if an infected 

occupant wears it. If it can effectively reduce the level of contaminants in the ambient air, 

just the infected passenger needs to wear the mask and it can save cost to the airliners and 

discomfort for the other occupants. 

The effect of jet airflow from personalized supply systems in airliner cabins on 

contaminant transmission has not been addressed in detail. Personalized airflow on a 
‐ 123 ‐ 

contagious passenger may have a major impact on contaminant transmission near the 

location of release due to the jet momentum. Such a fresh air supply can reduce the risk 

of transmission to other passengers inside the cabin. Optimization of such personalized 

systems to reduce risk of transmission can also be addressed in a future study. 

This investigation analyzed the influence of movement on contaminant 

transmission inside airliner cabins using a simplified human model and uniform walking 

speeds. More realistic human body geometry and walking motions may change the 

contaminant transmission characteristics due to the change in the structure of the local 

wake formation behind the moving body. Furthermore, movement patterns of humans 

have an inherently high level of uncertainty due to the random behavioral nature of 

people and, hence, a large number of movement scenarios need to be studied to draw 

general conclusions on the effects of movement on the risk of contaminant transmission. 
‐ 124 ‐ 

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Part 2: 

Measurement of Particle Deposition Behavior 

in an Aircraft Cabin 

Prepared by 

Byron W. Jones, P.E., Ph.D. 

Institute for Environmental Research 

Kansas State University 

Manhattan, Kansas 

February 22, 2012 
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11. INTRODUCTION 

As stated in the original work statement for the project, the purpose of this portion 

of the study is to use the KSU B-767 cabin mockup to experimentally measure particle 

deposition on aircraft surfaces to supplement the modeling studies. The B-767 mockup is 

shown in Figure 11.1 and contains 11 rows of seats in a 2-3-2 configuration. It has a 

fully functioning air supply system using B-767 supply diffusers and associated ducting 

to faithfully represent air distribution in the actual aircraft cabin. The cabin is supplied 

with HEPA filtered, temperature controlled air. A simple thermal manikin is placed in 

each seat to provide a representative thermal load for the cabin. A picture of the interior 

of the cabin is provided in Figure 11.1. 

Figure 11.1 Eleven-row mockup of the interior of a Boeing 767 cabin. 

The original plan was to use a liquid aerosol generator with recovery of chemical 

tracers from test areas to measure particle deposition rates. However, a number of 

methods of measuring particle deposition were considered. In the end, it was decided to 

use microscopic photographic methods for measuring deposition. There were two key 

reasons for this approach. First, it is a direct measurement. It is possible to see exactly 

what is being deposited. All other methods resulted in some indirect indication of the 

number of particles being deposited. Second, this approach allowed us to use the 

identical particle dispersion method as used in previous work in the Contaminant 

Transport Project where extensive airborne data were collected (Beneke 2010, Beneke et 

al, 2011). Having both airborne concentrations and surface deposition data for the same 

conditions for the same locations will allow us to relate these two data sets to each other. 
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12. EXPERIMENTAL METHODS 

In this work, as in the previous project, a talcum powder dust was use for the 

particulate measurements. A small amount of talcum powder was loaded into a plastic 

cup. This cup was placed under a copper tube. A puff of air from the tube aerosolized 

the talcum powder without disrupting the overall airflows in the cabin. A picture of the 

apparatus is shown in Figure 12.1. Such a cup was placed at each seat in the second row 

of the cabin and the release was made for all seven seats at the same point in time. This 

approach allowed us to focus on the longitudinal transport in the cabin. A point source 

release tends to have a chaotic result due to the chaotic nature of local airflow patterns. 

Using seven simultaneous sources provides a much stronger signal at distances several 

rows from the release, which are of most use, without overwhelming measurements near 

the release. 

Figure 12.1 Dust injection apparatus 

The particle deposition measurements were made using a traversing digital 

microscope to scan surfaces of interest. The digital microscope, shown in Figure 12.2, is 

then used to look at a number of surface locations using the traversing mechanism shown 

in Figure 12.3. The traversing mechanism spanned the three center seats of each row. A 

clear plastic tape was used as the deposition surface. This tape slides over the 

microscope platform as the microscope traversed to allow the microscope focus to be 

maintained. At the resolution necessary to see particles of a few microns in diameter, the 

depth of field is very small and, thus, the test surface must be held at precisely the correct 

depth. A traverse was made prior to and after particle release and the number of new 
‐ 138 ‐ 

particles showing up on the image were counted manually at each test location. This 

process is very tedious and the data reported here took several months to collect. 

Figure 12.2 Digital microscope used to inspect surface.

Figure 12.3 Microscope traversing system. 

However, by using manual counting, we were able to ensure quality results and 

caught some measurement problems that would have resulted in substantial, likely 
‐ 139 ‐ 

uncorrected, errors if automated methods had been used. Figures 12.4 and 12.5 show 

camera images before and after particle release. 

Figure 12.4 Image before dust release. 

Figure 12.5 Image after dust release (red circles denote new particles on the surface). 

Figure 12.6 shows the cabin layout and nomenclature for the data reporting. The 

release was in the second row of seats. This row is referred to as “row 0” and data are 

reported in terms of the number of rows from the release point. That is, “row 1” is the 

third seat row of the cabin but is one seat row from the release. Seats are numbered 1-3 

with 1 being the left-most of the three center seats, 2 being center seat, and 3 being the 

right-most seat of the three center seats. 
‐ 140 ‐ 

Figure 12.6 Cabin layout and nomenclature for experiments. 
‐ 141 ‐ 

13. RESULTS 

The data reported here were collected in the armrest area of the seat, as can be 

seen by the location of the clear tape in Figure 12.3. Five locations were measured for 

each seat and the experiments were all conducted at least twice. Thus, there are at least 

10 measurements for each seat for the reported data. Figure 13.1 summarizes the data for 

these locations. At this point, data are just reported in terms of counts as they will 

ultimately be related to the airborne concentrations. Each bar shown is the average 

number of counts for the five locations for that seat. For reference, the count area is 0.7 

mm2. Figures 13.2-13.4 present the same data but include standard deviation 

information. As can be seen, the standard deviation is large but that is typical of transport 

phenomena for aircraft cabins. The flows are quite chaotic and the transport from a given 

release point to any location in the cabin can depend significantly on the instant the 

release happens to occur. 

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

12345678

Rows from Release

Particle Counts

Seat 1

Seat 2

Seat 3

Figure 13.1 Summary of the particle deposition data collected. 
‐ 142 ‐ 

0.00

3.00

6.00

9.00

12.00

15.00

012345678

Row Number

Average Particle Count

Seat 1 Average Deposition

Figure 13.2 Deposition data for seat 1. 

0.00

3.00

6.00

9.00

12.00

15.00

012345678

Row Number

Average Particle Count

Seat 2 Average Deposition

Figure 13.3 Deposition data for seat 2. 
‐ 143 ‐ 

0.00

3.00

6.00

9.00

12.00

15.00

012345678

Row Number

Average Particle Count

Seat 3 Average Deposition

Figure 13.4 Deposition data for seat 3. 
‐ 144 ‐ 

14. CONCLUSIONS AND NEXT STEPS 

At this point in time, there are no specific conclusions to draw from the data. The 

deposition rates drop off with distance from the release in a pattern that is similar to what 

was seen with the airborne concentration measurements, a result that is not particularly 

surprising but is reassuring. Also, there is a tendency for the deposition rates to be higher 

on the right side of the cabin than on the left, even though the particles are dispersed 

uniformly across the width of the cabin. This result, again, is consistent with the airborne 

measurements and also the nature of the secondary flow patterns seen in the cabin. 

Currently a similar data set is being collected for vertical surfaces. Since this set is not 

complete, it is not reported here. Once the vertical data set is complete, we will compare 

the deposition data to the airborne concentration data to determine deposition rates as a 

function of airborne concentration. 
‐ 145 ‐ 

15. REFERENCES 

J. M. Beneke, B. W. Jones and M.H. Hosni, “Fine Particle Dispersion in a Commercial 

Aircraft Cabin,” HVAC&R Research, Volume 17, Issue 1, February 2011. 

J.M. Beneke,. 2010. Small Diameter Particle Dispersion in a Commercial Aircraft Cabin. 

MS Thesis. Manhattan, KS: Kansas State University. 
‐ 146 ‐ 

Part 3: 

Role of Relative Humidity in the Inactivation of 

Influenza Viruses on Steel Surfaces and Microbial 

Surface Sampling in the Air Cabin 

Prepared by 

James J. McDevitt, Ph.D., CIH and John D. Spengler, Ph.D. 

Division of Exposure, Epidemiology and Risk 

Department of Environmental Health 

Harvard School of Public Health 

Cambridge, Massachusetts 

February 10, 2012 
‐ 147 ‐ 

16. INTRODUCTION 

The transmission of bacterial and viral pathogens from inanimate surfaces to 

susceptible persons is a concern with regard to infectious disease transmission. For 

infection to occur, the pathogenic organisms must be present and survive on the 

environmental surface until such a time that the pathogen is transferred to a susceptible 

host at the appropriate route of entry. Bacteria and viruses have been shown to survive on 

surfaces for hours to months (Kramer, Schwebke et al. 2006; Boone and Gerba 2007). A 

summary of selected clinically relevant bacteria and virus duration of persistence is 

shown in Table 16.1. 

Table 16.1 Bacteria and Virus Duration of Persistance. 

Adapted from Kramer et. al. BMC Infectious Diseases 2006: 6:130(Kramer, Schwebke et al. 

2006).

Bacterium Type 

Duration of 

Persistence Virus Type 

Duration of 

Persistence 

Acinetobacter spp. 3 days to 5 

months 

Adenovirus 7 days – 3 mos 

Bordetella pertussis 3 – 5 days Coronavirus 3 hours 

Campylobacter jejuni up to 6 days SARS associated virus 72 – 96 hours 

Clostridium difficile 

(spores) 

5 months Echovirus 7 days 

Corynebacterium 

diphtheriae 

7 days – 6 mos Influenza virus 1 – 2 days 

Mycobacterium 

tuberculosis 

1 day – 4 mos Norovirus 8 hours – 7 

days 

Pseudomonas aeruginosa 6 hours – 16 mos Respiratory syncytial 

virus 

up to 6 hours 

Salmonella typhi 6 hours – 4 

weeks 

Rhinovirus 2 hours – 7 

days 

Staphylococcus aureus, 

including MRSA 

7 days – 7 mos Rotavirus 6 – 60 days 

The longer pathogens survive once deposited on inanimate surfaces, called 

fomites, the higher the probability they will be transmitted to susceptible individuals. 

This is especially true in the air cabin environment which is a unique environment for 

disease transmission. The air cabin has a high occupation density with high through-put 

of passengers. While seated for hours at a time, “sick” individuals have ample 

opportunity to contaminate their surrounding area (i.e. arm rests, tray tables, lavatories, 

etc). Once surfaces in the air cabin are contaminated, susceptible persons in the 

immediate area may come into contact with the contaminated surfaces. Contamination 

may result from coughing out large particles which deposit on these surfaces or by 

touching surfaces with contaminated hands. Since many pathogens may survive for hours 

or days, when new susceptible individuals board the plane they are at risk of coming into 

contact with these surfaces. Likely exposure to the susceptible individual will be via the 
‐ 148 ‐ 

hands which then can transfer the pathogens to mucous membranes of the eyes, nose, and 

mouth. 

Environments such as daycare centers and hospitals have been characterized with 

respect to the presence of bacteria and viruses on contact surfaces and provide a starting 

point for assessing the risk of infection and interventions for transmission(Boone and 

Gerba 2005; Kramer, Schwebke et al. 2006; Boone and Gerba 2007). The air cabin 

environment has been characterized, to some degree, for bioaerosol exposures(Moser, 

Bender et al. 1979; McKernan, Burge et al. 2007; McKernan, Hein et al. 2008; 

McKernan, Wallingford et al. 2008; Gerba 2009). However, surface pathogen 

characterization has not been done in the in the air cabin environment. Pathogens such as 

influenza virus, norovirus, methacilin resistant staphylococcus (MRSA), measles, rubella, 

hemorrhagic fevers, mycobacterium tuberculosis(TB) are of concern in the air cabin 

environment and are under study by the Transportation Reseach Board Airport 

Cooperative Research Program (ACRP) under project ACRP 02-20A(TRB 2011). With 

the exception of TB, all of these organisms may be spread from surface contamination. 

Intuitively, tray tables, arm rests, and other high touch areas are likely contaminated 

during flights when sick people fly. Furthermore, the air cabin is typically not cleaned 

(with the exception of lavatories and galleys), between flight segments. However, 

whether these organisms are commonly found in the aircraft environment is unknown. 

 The goal of this investigation was to develop a better understanding of surface 

contamination in the air cabin through laboratory investigation and a pilot field study. 

The laboratory investigation focused on evaluating survival of influenza virus on surfaces 

and developing a field, microbial, surface sampling protocol for use in the air cabin 

environment. While the pilot field study focused on developing a rational method for 

evaluating microbial contamination on air cabin surfaces. 
‐ 149 ‐ 

17. BACKGROUND 

17.1 Relative Humidity and Influenza Surface Survival 

 Due to the threat of highly lethal bird flu and the recent pandamic associated with 

seasonal flu, much attention has been focused influenza virus transmission. There is 

much debate about whether influenza virus is transmitted directly through large droplet, 

by fine aerosols, or by contact with fomites(Tellier 2006; Brankston, Gitterman et al. 

2007). Based on the available data, transmission via fomites cannot be ruled out as a 

route of exposure and should not be disregarded in a exposure control plan. Although 

there is no specific data on influenza on air cabin surfaces, influenza nucleic acid has 

been found on surfaces in daycare centers and hospitals (Boone and Gerba 2005; Bright, 

Boone et al. 2010). Furthermore, influenza has been was found to survive on various 

surfaces for considerable time on non-pourous surfaces such as stainless steel (Rudnick, 

McDevitt et al. 2009). In these studies survival times were quite variable and ranged from 

several hours to 2 days.(Bean, Moore et al. 1982; Thomas, Vogel et al. 2008; Greatorex, 

Digard et al. 2011). 

A significant amount of past research has shown that influenza aerosol survival is 

dependant upon relative humidity with influenza survival favored at low RH (Myatt, 

Kaufman et al. 2010). Whereas, influenza surface survival research is limited. Buckland 

et al, evaluated how relative humidity effects influenza survival at only two RH extremes 

and evaluations by Bean et al. and Greaterox et al. did not vary relative humidity at 

all(Buckland and Tyrrell 1962; Bean, Moore et al. 1982; Greatorex, Digard et al. 2011). 

McDevitt et al. measured the effects of RH on influenza surface survival, but experiments 

were done at temperatures well above those found in the air cabin (>55oC)(McDevitt, 

Rudnick et al. 2010). Based on the existing research the air cabin may provide an 

optimum environment for influenza surface survival, which in turn, could increase the 

probability for disease transmission. 

17.2 Surface Sampling Protocol Development 

The goal of surface sampling in the air cabin was to detect the presence of 

bacteria and viruses on non-porous surfaces. Bacteria would be analyzed by culture 

methods to detect and quantify bacteria. Using culture methods, samples are suspended in 

liquid and inoculated onto agar petri dishes. The agar provides nutrients that allow the 

bacteria to grow when incubated. The bacterial growth results in the formation of distinct 

colonies which can be grouped based on morphology and counted. Each colony with a 

unique morphology can then be identified using standard microbiology techniques. 

Culture analysis of bacteria provides a means of detecting a large variety of bacterial 

types in a single sample and in a cost effective manner. Virus analysis is more 

challenging since viruses require animal cells to infect and replicate. Different viruses 

require specific, and different, types of cells in which to grow. Thus, it is practically 
‐ 150 ‐ 

impossible to grow samples containing unknown viruses. For this reason PCR is used to 

amplify and detect sequences of nucleic acid which are unique to individual viruses. PCR 

is also limited since the sequence for each type of virus must be screened. For this reason, 

methods have been developed to test groups of viruses, called panels, in a single sample. 

Such a test, the Respiratory MultiCode Assay (RMA)(Lee, Grindle et al. 2007), has been 

developed for respiratory viruses and was used in this study. The respiratory viruses 

detected by the RMA are the following: HumanRrhinoviruses (HRV), Enteroviruses, 

Coronaviruses (CoV), Respiratory Syncitial Virus (RSV), Metapneumovirus (MPV), 

Parainfluenza viruses (PIV), Influenza (InfV), Adenoviruses (AdV), and Bocavirus. 

Enteric viruses, those found in the intestinal tract, are also of interest for disease 

transmission. We considered that respiratory viruses shed by coughing, sneezing, or hand 

transfer would be more likely to be present on surfaces in the air cabin, than enteric 

viruses spread by fecal oral or water routes. Thus, for the pilot study we limited our 

investigation to respiratory viruses. 

Surface sampling is commonly done in a dry fashion in which the surface is 

vacuumed and particulate matter is collected onto a filter. The material on the filter is 

then subsequently removed and analyzed accordingly. This method is useful for 

collecting biological materials that are associated with dust which settles or collects on 

surfaces. However, in the air cabin environment, due in part to high amounts of traffic 

and air movement, dust does not accumulate on common touch surfaces such as tray 

tables, arm rests, etc. Furthermore, respiratory secretions or deposits associated with 

contaminated hands would not be of a dusty nature, but rather “sticky”. Swab sampling 

methods, such as those, commonly used in hospital and food service evaluations are more 

suited to surface sampling for pathogens in the air cabin. Generally, swab sampling 

consists of wetting the swab, moving the swab over the sample surface, extracting the 

swab, and analyzing the extract. However, specific details are lacking for the best type of 

swab, method of extraction, etc. Thus, methods require characterization to ensure 

efficient collection and recovery of organisms from surfaces. To that end, laboratory 

testing using surrogate organisms to optimize methods and maximize collection and 

recovery of organisms from surfaces are needed. In our laboratory Bacillus subtillus was 

used as a surrogate for spore forming bacteria and Serratia Marcescens for vegetative 

bacteria. Protocol optimization was focused on bacteria since molecular methods, which 

detect nucleic acid rather than rely on growing organisms, will be used for virus analysis. 

17.3 Field Study 

Our initial sampling concept was to develop a biological, surface sampling kit for 

use on aircraft. The original plan was to distribute the sampling kits to volunteers who 

would be taking a future flight. The volunteer would collect samples during their flight 

and return the samples to the laboratory for analysis. This strategy would benefit from 

sampling a large variety of aircraft and numerous air carriers. However, preliminary 

discussions of regarding this strategy highlighted significant limitations questioning 

whether this style of sample collection would be feasible or scientifically rigorous. 

Relying on volunteers to collect field samples using only printed instructions and 

no actual training could easily compromise quality of the samples collected. Surface 
‐ 151 ‐ 

sampling requires using a swab to remove the pathogens from the surface. Variables such 

as swab wetting, swab pressure, area of sample would not be controlled from one 

individual to another and potentially increase variability. Secondly, the potential for cross 

contamination between samples or contamination of the samples by the person sampling 

would be much higher with unskilled persons. Post sampling handling would also vary 

between individuals based on flight times, number of segments, and time to get the 

samples shipped to the laboratory. Another limitation was that samples would have to be 

returned to a central lab, processed for analysis and then sent to the analyzing laboratory. 

Furthermore, samples would also need to be stored for group analysis (for cost effective 

analysis) and would result in varying storage time between samples. There were also nonscientific concerns associated with the initial protocol. An informal survey of colleagues 

showed that most were concerned about transporting sampling kits through airport 

security and issues about the perception of the sampling from other passengers or flight 

crew. The prospect of coordinating sampling for each individual with FAA, TSA, and 

individual airlines posed serious concerns. 

Instead, our sampling strategy focused on partnering with an airline which serves 

Logan airport. Logan airport is convenient to our processing laboratory at HSPH and 

allows for immediate sample processing and shipment to the analytical laboratory in a 

single batch within 24 hours. Aircraft surfaces were sampled from numerous aircraft at 

the completion of the flight day and prior to being serviced for the following flight day. 

This method provided access to multiple types of aircraft servicing routes from across the 

country. Since sampling was done at the end of the day it provided a composite of the 

days’ contamination by passengers. Also, this strategy allowed samples to be collected in 

several different areas within the air cabin and subsequent comparison. A limitation of 

this design is that sampling was restricted to single airline. To overcome this limitation, 

the number or partner airlines and airports could be expanded in future studies. 

 
‐ 152 ‐ 

18. METHODS 

18.1 Test Organisms 

18.1.1 Influenza Virus 

Suspensions of influenza viruses (A/PR/8/34 H1N1), were purchased from Advanced 

Biotechnologies Inc. (Columbia, MD), thawed, divided into single-use, working stock 

portions, and stored at -80C until needed. 

A fluorescent focus reduction assay, which labels nucleoproteins expressed by 

infected MDCK cells with fluorescent antibodies, was used to enumerate the number of 

infective viruses and has been described previously(Hartshorn, White et al. 2007; 

Rudnick, McDevitt et al. 2009). Briefly, triplicate wells on a 96-well plate containing 

monolayers of Madin Darby Canine Kidney (MDCK) cells (ATTC # CCL-34) were 

infected with 50-ul rinsate from each sample and allowed to incubate for approximately 8 

hours. Then infected cells containing influenza A nucleoproteins were labeled with 

influenza A virus nucleoprotein antibody (Abcam, Cambridge, MA) and subsequently 

labeled with rhodamine-labeled goat anti-mouse IgG (Jackson ImmunoResearch 

Laboratories, West Grove PA). The number of cells having a resulting fluorescent foci, 

which are referred to as fluorescent focus units (FFU), were then counted at 200x total 

power using an Olympus CKX-41 inverted fluorescent microscope (Olympus, Center 

Valley, PA). Each well was scanned in a standard pattern with 10 fields chosen at 

random for counting (about 30% of the well). For samples with less than 2 FFU per 

viewing field, the entire well was counted. We summed the counts for the 9 wells 

associated with each test. The number of FFUs per sample was then computed based on 

dilution factors and the fraction of the well counted. 

18.1.2 Bacteria 

Serratia marcescens 

S. marcescens was obtained from the American Type Culture Collection (ATCC 

8195) and stored at -70°C. Cells used in the experiments were prepared by inoculating a 

100 ul of S. Marcescens (approximately 1x107

 cfu/ml) into 100 ml of nutrient broth 

(Difco Laboratories) and culturing the mixture at 25°C for 24 h with 200 rpm of agitation 

on a shaker table. The cell suspension was transferred to 50 ml centrifuge tubes and 

pelleted at 250xg. The supernatant was removed and the pellet re-suspended in PBS. This 

procedure was repeated 3 times with the pellet being suspended in a final volume of 50 

ml of PBS. One ml portions of the cell suspension were stored at 70°C for later use. 

Representative portions were thawed and titered to determine the cell concentration. 

All sample extracts containing S. Marcescens were plated in duplicate with 100 ul 

volumes on 100 mm petri dishes containing nutrient agar (BD, Franklin, NJ). Cultures 

were left at room temperature to grow and were counted after incubating for 24 hours. 
‐ 153 ‐ 

Bacillus atrophaeus 

Spores of Bacillus atrophaeus (ATCC 9372) were provided by the Baker 

Company (Sanford, Maine). Spores were prepared using the protocols contained in 

NSF/ANSI 49, Biosafety Cabinetry: Design, Construction, Performance, and Field 

Certification. Concentrated stocks were thawed, diluted 10-fold in cell culture grade 

water, portioned into 1 ml volumes, frozen, and stored at -70 until use. Representative 

portions were thawed and tittered to determine the cell concentration. 

All sample extracts containing B. atrophaeus were plated in duplicate with 100 ul 

volumes on 100 mm petri dishes containing tryptic soy agar (BD, Franklin, NJ). Cultures 

were left at room temperature to grow and were counted after incubating for 24 hours. 

18.2 Relative Humidity and Influenza Surface Survival 

18.2.1 RH Control Chamber 

Stainless-steel coupons measuring 7.6 x 2.5 x 0.4 cm were used as test surfaces. 

Prior to use the coupons were cleaned with soap and water, rinsed with 70% ethanol, 

allowed to dry, and then autoclaved. Fifty microliters of virus suspension was pipetted 

onto a clearly marked section of each coupon. Coupons were placed into drying chamber 

maintained at 50% RH for 30 minutes until dry. After drying the coupons were 

transferred to a RH and temperature controlled test chamber. The test chamber consisted 

of an insulated box with interior volume of dimensions of 0.12m3. A mix of dry air and 

water saturated air were pumped into the chamber to yield the desired relative humidity. 

Room air was saturated by pumping the air through 2 liters of water via a fritted bubbler. 

Dry air was produced by pumping room air through 3 Drierite laboratory gas drying units 

(Xenia, OH) which were connected in series. The total air flow through the chamber was 

approximately 6 lpm. Test coupons were loaded onto a thin tray which was able to be slid 

into and out of the chambers through narrow slot. This feature allowed for loading and 

unloading of test coupons from the chamber over the course of the experiments (up to 

120 hrs) without impacting the RH level being maintained in the chamber. The RH and 

temperatures within the chambers were continuously monitored with Omega RH32 

temperature and RH meters (Stamford, CT). 

18.2.2 RH Survival Tests 

In preparation for an experiment, influenza working stocks were thawed and 

separated into single use 55 ul portions and then refrozen at -80C. At the start of an 

experiment, three of portions were thawed and 50ul of virus from each portion was 

applied onto separate coupons. Triplicate coupons were used for each test condition. The 

coupons were put into the drying chamber for 30 minutes and subsequently transferred to 

the RH control chamber. After a predetermined time, another three, single use portions 

were thawed, placed onto the coupons, dried, and transferred to the RH control chamber. 

This procedure was repeated multiple times and at the end of the experiment all of the 
‐ 154 ‐ 

coupons were rinsed and analyzed at the same time. Exposure times were generally for 

120, 48, 24, 12, and 0 hours of exposure. RH levels were set at 25, 50, or 75%. All RH 

conditions were tested in triplicate, separate experimental runs. Zero hours of exposure 

corresponded to coupons which were seeded, dried in the drying chamber, and then 

immediately rinsed without being placed into the RH control chamber. 

18.2.3 Data Analysis 

The log reduction, n, for each exposure time interval was calculated according to 

the equation: 

n = -log10 (Cti / Ct0) (18.1) 

where n is the log10 reduction of virus, concentration Cti is the concentration of viruses 

after i hours of exposure, Ct0 is the concentration of viruses at time=0. Where n=3 would 

correspond to 3 log10 reductions or 99.9 percent inactivation of the virus. Linear 

regression through the origin was used to estimate the decay constant for each level of 

humidity. The log-reduction was our outcome variable, and the dose level was our 

response. The estimated coefficient of the regression line then was our estimate of the 

decay constant. All statistical analysis was performed using Excel (Redman, CA). 

18.3 Surface Sampling Protocol Development 

18.3.1 Swab Selection 

A complete evaluation of commercially available swabs was not within the scope 

of this research. For example one of the major swab manufacturers had over 30 types of 

swabs available, another manufacture had over 10 varieties of single type of swab. 

Instead, swabs samples were solicited from various suppliers to provide a variety of tip 

materials and sizes that would be suited for environmental studies. These swabs were 

then evaluated qualitatively for performance based on factors such as: swab size, swab 

durability, swab shaft flexibility, liquid holding capacity, hydrophobicity, liquid holding 

capacity (Table 18.1). These qualitative tests consisted of wetting the swab with buffer, 

moving them across a non-porous surface, and squeezing the liquid out of the swab. 

Those swabs which met the qualitative requirements were then subjected to replicate 

surface recovery experiments using B. Subtilis and S. Marcescens. 
‐ 155 ‐ 

Table 18.1. Swab selection criteria. 

Swab Factor Explanation 

Size  Large size swabs will require large amounts of liquid for 

extraction and result in sample dilution 

 Swabs which are too small require tedious swabbing of the 

surface 

Durability  Swab must not degrade during swabbing operations or 

during vortexing 

Flexibility  The swab shaft must be stiff enough to exert “adequate” 

pressure on the surface while swabbing 

Hydrophobicity  Swab must be wettable 

Liquid holding capacity  The swab must be able to hold enough liquid to wet the 

surface during swabbing without excessive rewetting of the 

swab 

 The swab must not retain excessive amounts of liquid 

during recovery 

18.3.2 Swab Surface Testing 

The general swab testing protocol consisted of supending 50ul portion of bacteria 

suspended in PBS/0.1% BSA onto stainless steel coupons. The coupons were dried in the 

drying chamber at 50%RH for approximately 30 minutes. The test swab was wetted with 

100ul of PBS and moved across the horizontal surface with a zigzag pattern vertically 

and horizontally (Fig. 18.1). The swab tip was then broken off into a 1.5ml 

microcentrifuge tube containing 1ml of PBS. The microcentrifuge tube was vortexed on 

maximum for 30 seconds. The tip was then “dewatered” by pressing and rolling against 

the tube wall. Serial dilutions were made of the extract, plated, and incubated as 

described previously. 
‐ 156 ‐ 

18.3.3 Surface Testing Data Analysis 

Swab performance was compared in a relative manner. Results were normalized, 

as a percentage, against a reference swab: 

Recovery % = CT /CR (18.2)

where CT is the concentration of bacteria recovered from the test swab and CR is the 

concentration of bacteria recovered from the reference swab. By convention, the 

reference swab was defined as the cotton swab in any series of given tests. 

18.4 Field Study 

18.4.1 Field Study Sampling Plan 

Our intent was to perform a preliminary characterization of bacteria and viruses 

present on “high touch” surfaces within the air cabin. Surface sampling was performed 

in the air cabin of 5 aircraft. Individual sample kits were used for each sample. Each kit 

contained a nitrile surgical glove, a sterile swab, and 1.5 ml microcentrifuge tube 

containing 1.0ml of PBS. All materials for each kit were contained in sealed Chex-All 

Sterilization Pouches (Propper Manufacturing Laboratory Consumables). 

Each sample consisted of swabbing surfaces with a moistened swab over a 

predetermined surface. The swab was pre-moistened with PBS buffer contained in the 

1.5ml microcentrifuge tube. The swab was moved along the sampling surface in an up 

and down pattern vertically and horizontally (Fig. 18.1).The swab was re-moistened as 

needed using the PBS buffer in the 1.5 ml tube. At the completion of sampling the swab 

tip was placed into the 1.5ml microcentrifuge tube and the swab handle snapped off. The 

microcentrifuge tube was then closed. Three seating areas were sampled in the front, 

Fig 18.1 Swab sampling diagram 
‐ 157 ‐ 

middle, and rear of the aircraft while randomly alternating sides. For each seating area the 

following was sampled: tray table top, tray table bottom, arm rests. In addition, the rear 

lavatory in each aircraft was sampled. The lavatory surfaces sampled were the sink 

handles, counter, and trash receptacle cover. 

18.4.2 Field Study Sample Processing 

All samples consisted of the swab tip contained in a 1.5 ml centrifuge tube with 

approximately 0.75ml of PBS. All sample processing took place in a class II-A biological 

safety cabinet (BSC) while wearing gloves. Gloves were replaced frequently in any case 

of contact with the tube contents. All tubes were kept on ice while processing. Each 1.5 

ml centrifuge tube was vortexed at max setting for 15 seconds. The tube was opened and 

forceps were used to grab the end of the swab and press against the side of the tube to 

remove liquid absorbed onto the swab. PBS was added to the 1ml line on the 

microcentrifuge tube using a P-1000 Pipetteman equipped with a new 1 ml filter tip. The 

tube was vortexed for 5 seconds and 500 ul was removed and put into a new 1.5 ml 

microcentrifuge tube. Half of the tubes were packed on “blue ice” and sent via overnight 

courier for bacterial analysis by EMSL Analytical, Inc. Cinnaminson, NJ. The remaining 

samples were stored at -70 awaiting RMA analysis. The RMA samples were 

subsequently thawed and split into two 250ul portions. One set of samples was packed on 

dry ice and sent via overnight courier for RMA analysis to the laboratory of Dr. James 

Gern at the University of Wisconsin, Madison, WI. The set was archived at -70oC. 
‐ 158 ‐ 

19. RESULTS 

19.1 Relative Humidity and Influenza Surface Survival 

Figure 19.1 shows the average number of log reductions of influenza viruses 

measured for the four exposure times (120, 48, 24, 12 hrs) and three RH levels (25%, 

50%, and 75%). Generally, surface inactivation of influenza virus increased with 

increasing exposure time and RH. The number of viruses detected after 120 hours was 

typically very low (<10 per coupon) and at times none were detected. Thus, only data for 

0, 12, 24, and 48 hours was included in our analysis. A greater than 2.5 log reduction of 

influenza virus on surfaces was achieved after 48 hrs at 75% RH compared to less than 

1.5 log reduction at either 25 or 50%RH in the same time period. After 24 hours of 

exposure, greater than 1.5 log reduction of influenza virus on surfaces was achieved at 

75% RH compared to less than 1.5 log reduction at either 25 or 50%RH in the same time 

period. The number of log10 reductions versus exposure time was approximated with 

linear regression. The results are summarized in Table 19.1. Accordingly, the decay rates 

of influenza were estimated at 0.025, 0.027, and 0.062 log10 reductions per hour at 25, 50, 

and 75% RH, respectively. This would correspond to half-lives of 12.2, 10.9, and 4.8 

hours at 25, 50, and 75%, respectively. There was no statistical difference between 

influenza inactivation at 25 and 50% RH, while influenza inactivation at 75% RH was 

statistically different from 25 and 50% RH conditions. 

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

0 10 20 30 40 50 60

Time Elapsed (Hours)

Number of Log Reductions

RH 25

RH 50

RH 75

Figure 19.1 Log reduction of influenza A virus at 25, 50, and 75% relative humidity. 
‐ 159 ‐ 

Table 19.1 Decay constant and half-life for influenza virus survival on stainless steel surfaces at 

varying relative humidities. 

RH Level 

% 

Decay Constant 

(Log10 Reductions/hr) 

(95% CI) 

R2 Half-Life 

(hrs) 

25 0.025 (0.020-0.030) 0.93 12.2 

50 .027 (.023-0.031) 0.97 10.8 

75 0.062 (.051-.074) 0.95 4.8 

The median concentration of viruses measured on the control coupons (time=0) 

after drying was 3.9x105

 FFU per slide and ranged from 2.9x105 to 3.6x106 FFU/slide. 

Previous research has shown using these procedures have shown an average loss of 63% 

of viruses during the drying process(McDevitt, Rudnick et al. 2010). These drying losses 

were assumed to be the same for the control and test coupons during this investigation. 

19.2 Surface Sampling Protocol Development 

Ten different swabs were evaluated from 4 different suppliers. The original 

selection was primarily based on availability and swab tip composition. Based on the 

results of the qualitative testing 4 of the 10 original swabs were selected for recovery, 

comparison testing: cotton, polyester, rayon, and foam. A summary of the testing results 

are shown in Figs. 19.2 and 19.3. Four replicate tests were carried out for B. atrophaeus

and 3 replicate tests for S. Marcescens. The results changed remarkably between tests and 

organisms. As can be seen in Fig. 19.1, there is no clear trend in recovery between swabs 

among the replicate experiments for B. atrophaeus. When viewed as an average the 

performance from best to worst was: polyester, rayon, foam, and cotton. However, based 

on the large amount of variability there were no statistical differences in swab 

performance. Conversely, the data is more consistent for tests with S. marcescens as 

shown in Fig. 19.3. The cotton swab had the best performance, followed by the polyester 

swab. Despite the small number of samples, there was a significant difference between 

cotton when compared to rayon or foam and between polyester and rayon swabs (Paired, 

One-tail, T-test, 90% Confidence Level). Field tests, in which the surfaces of office desks 

were swabbed and cultured, showed polyester recovering 6-tmes more environmental 

bacteria as cotton swabs. 
‐ 160 ‐ 

Figure 19.2 Summary of swab testing with Bacillus atrophaeus.

0.00E+00

2.00E-01

4.00E-01

6.00E-01

8.00E-01

1.00E+00

1.20E+00

1.40E+00

1.60E+00

1.80E+00

2.00E+00

1 23 4

Replicate

Recovery Normalized to Cotton

Cotton

Polyester

Rayon

Foam

0.00E+00

2.00E-01

4.00E-01

6.00E-01

8.00E-01

1.00E+00

1.20E+00

123

Replicate #

% Recovery Normalized to Cotton

Cotton

Polyester

Rayon

Foam

Figure 19.3 Summary of swab testing with Serratia Marcescens. 
‐ 161 ‐ 

19.3 Field Study 

Surface sampling was conducted at Logan Airport during the evening when 

aircraft were finished flying for the day. The aircraft were basically left “as is” after the 

last passengers left and were sampled prior to end of day cleaning. Five different aircraft 

were sampled (Table 19.2) which represent 3 “long haul” aircraft and 2 regional jets. The 

results of surface sampling are shown in Table 19.3 for bacterial and respiratory viruses. 

Table 19.2 Description of aircraft sampled 

Tail 

Number 

Aircraft Total 

Passengers

672 Boeing 757-200 180 

3725 Boeing 737-800 160 

N316 Airbus 320SR 148 

753 Embraer CRJ700-EV 65 

641 Embraer 170 69 

Table 19.4 provides a composite summary of all aircraft. A total of 24 different 

bacteria were identified from the 50 surface samples collected. Of these 50 samples, 74% 

were positive for bacterial growth and an average of 1.5 species were identified in each 

sample. The most frequently identified species of bacteria was Staphylococcus 

epidermidis, followed by Micrococcus luteus, and Staphylococcus (not aureus). Each of 

the remaining bacteria listed in Table 19.4 were not predominant and were identified in 

less than 3 of the 50 samples collected. The median number of colonies on each surface 

was 46 colonies/100cm2 with a range of 0-7450 colonies. 

Table 19.5 provides a summary of surface sampling stratified by tail number. 

There were no obvious trends noted. Perhaps the two regional jets had generally lower 

types and numbers of bacteria than the long haul aircraft. However, limited sample size 

precludes statistical analysis. Table 19.6 provides a summary of surface sampling 

stratified by the sampling location (arm rest, tray table, or lavatory). A review of Table 

19.7 suggests that tray tables had fewer varieties and number of bacteria than the arm 

rests and the lavatory. The tray table bottom (exposed when in the upright position) had 

few varieties and number of bacteria than the tray table top (exposed when in the down 

position). The lavatory was the only location to have all positive samples. There was a 

higher average number of species identified in the lavatory (3.8) versus other areas (1.8, 

1.5, and 0.6). The mean and median number of colonies/100cm2

 was higher in the 

laboratory compared to the other areas samples. 

Only Bocaviruses tested positive of the 9 respiratory viruses in the RMA 

screening. Bocaviruses were detected on two aircraft. One sample was positive for the 

lavatory sample, while the other sample was positive for a single tray bottom. We do note 

that there were numerous positive samples for influenza A virus. However, these results 

were not considered valid since a field blank was also positive for influenza A virus. 
‐ 162 ‐ 

Table 19.3 The results of surface sampling for bacterial and respiratory viruses. 

Tail # Location Item # Species

Total 

Colonies

Bacillus cereusBacillus circulansBacillus pumilusBacillus sp.Cornybacterium coyleaCornyebacterium sp.Flavobacterium aquatileKocuria rhizophilaLactococcus lactusMicrococcus luteusMoraxelaa sp.PaenibacillusPaenibacillus alveiPsuedomonas aeruginosaRothia dentocariosaStaphylococcus auricularisStaphylococcus epidemridisStaphylococcus haemolyticusStaphylococcus hominusStaphylococcus sp. (not aureus)Staphylococcus pasteuriStaphylococcus saprophyticusStaphylococcus warneriStreptococcus oralisBocavirus

641 13D Arm Rest 2 130 104 26

641 13D Tray Bottom 0 0 X

641 13D Tray Top 0 0

641 15A Arm Rest 1 78 78

641 15A Tray Bottom 1 23 23

641 15A Tray Top 1 23 23

641 6D Arm Rest 1 26 26

641 6D Tray Bottom 0 0

641 6D Tray Top 1 23 23

641 Lav Lav 4 1258 37 74 555 592

672 43E Arm Rest 1 7540 7540

672 43E Tray Bottom 1 46 46

672 43E Tray Top 2 345 276 69

672 B22 Arm Rest 2 130 26 104

672 B22 Tray Bottom 2 46 23 23

672 B22 Tray Top 1 92 92

672 F28 Arm Rest 1 26 26

672 F28 Tray Bottom 1 69 69

672 F28 Tray Top 3 161 69 69 23

672 Lav Lav 5 1776 37 740 296 148 555

753 13C Arm Rest 2 52 26 26

753 13C Tray Bottom 1 23 23

753 13C Tray Top 2 69 23 46

753 15A Arm Rest 4 104 26 26 26 26

753 15A Tray Bottom 0 0

753 15A Tray Top 1 23 23

753 6C Arm Rest 0 0

753 6C Tray Bottom 0 0

753 6C Tray Top 1 23 23

753 Lav Lav 2 148 74 74

3725 31D Arm Rest 1 26 26

3725 31D Tray Bottom 2 69 46 23

3725 31D Tray Top 2 575 46 529

3725 B12 Arm Rest 5 2210 26 156 208 416 1404

3725 B12 Tray Bottom 0 0

3725 B12 Tray Top 3 598 69 345 184

3725 E19 Arm Rest 3 78 26 26 26

3725 E19 Tray Bottom 0 0

3725 E19 Tray Top 4 138 23 23 46 46

3725 Lav Lav 3 1406 444 333 629

N316 12E Arm Rest 1 780 780

N316 12E Tray Bottom 0 0

N316 12E Tray Top 0 0

N316 24B Arm Rest 0 0

N316 24B Tray Bottom 0 0

N316 24B Tray Top 1 23 23

N316 7D Arm Rest 3 230 23 92 115

N316 7D Tray Bottom 1 23 23

N316 7D Tray Top 0 0

N316 Lav Lav 5 555 148 37 74 37 259 X

N316 Blank Blank 0 0

672 Blank Blank 0 0

Lab Blank Blank 0 0

Lab Blank Blank 0 0
‐ 163 ‐ 

There were no other positive results for any blanks for other viruses or bacteria. 

Laboratory contamination is suspected since high titers of influenza A virus was 

commonly used in the laboratory in which the sample kits were prepared and were the 

field samples were processed on two separate occasions. No other respiratory viruses are 

used in the processing laboratory. The sequence of positive of influenza samples does not 

suggest cross contamination in the field. Furthermore, flu activity was at low to minimal 

levels during the time of the study. As such, few people would be expected to be infected 

with influenza while on the aircraft. Considering these factors, the positive results for 

influenza A were suspect and not considered valid. 

Table 19.4. Composite summary of all air cabin samples 

Total 

Number of Species Identified 24 

% Positive Samples (n=56) 74 

% Negative Samples (n=50) 26 

Average Number of Species/Sample 1.5 

1st

 Most Frequently Identified Species Staphylococcus epidermidis 

2nd Most Frequently Identified Species Micrococcus luteus 

2nd

 Most Frequently Identified Species Staphylococcus (not aureus) 

Average # Colonies/100cm2 379 

Median # Colonies/100cm2

 46 

Range of Colonies/100 cm2 0-7450 
‐ 164 ‐ 

Table 19.5 Summary of air cabin surface sampling results stratified by tail number  Tail Number  641 672 753 3725 NNumber of Species Identified 7 12 7 13 9 % Positive Samples (n=10) 70 100 70 80 5% Negative Samples (n=10) 30 0 30 20 5Average Number of Species 1.1 1.9 1.3 2.3 11st Most Frequently Identified 

Species 

Staphylococcus epidermidis 

Staphylococcus epidermidis 

Staphylococcus sp. (not aureus) 

Staphylococcus epidermidis 

Sh

2nd Most Frequently Identified Species 

Micrococcus luteus 

Micrococcus luteus 

* Staphylococcus 

sp. (not aureus) 

Cs

Average # Colonies/100cm2 156 1023 44 510 1Median # Colonies/100cm2 23 111 23 108 1Range of Colonies/100 cm2 0-1258 0-7450 0-148 0-2210 0
‐ 165 ‐ 

Table 19.6. Summary of air cabin surface sampling stratified by surface type 

 Surface Type  Tray Table 

Top 

Tray Table Bottom 

Arm Rest Lavatory 

Number of Species Identified 11 5 12 12 % Positive Samples (n=10) 80 47 87 100 % Negative Samples (n=10) 20 53 13 0 Average Number of Species 1.5 0.6 1.8 3.8 1st Most Frequently Identified Species 

Staphylococcus sp. (not aureus) 

Micrococcus luteus 

Staphylococcus epidermidis 

Staphylocepidermid

2nd Most Frequently Identified 

Species 

Staphylococcus epidermidis 

Staphylococcus epidermidis 

Micrococcus luteus 

Staphylocsp. (not a

Average # Colonies/100cm2 140 20 761 1028 Median # Colonies/100cm2 23 0 78 1258 Range of Colonies/100 cm2 0-598 0-96 0-7450 148-1776 
‐ 166 ‐ 

20. DISCUSSION 

20.1 Relative Humidity and Influenza Surface Survival 

The results of our investigation systematically show that RH survival on surfaces 

is a function of relative humidity. There was a trend of increasing influenza inactivation 

with increasing relative humidity. However, the inactivation rates for influenza virus 

were statistically higher at 75% compared to 25 and 50% RH. We have previously 

reported that influenza inactivation at elevated temperatures (55-65oC) was a function of 

RH, temperature and exposure time(McDevitt, Rudnick et al. 2010). In these experiments 

inactivation rates increased with temperature and RH. 

Buckland and Tyrrel also deposited influenza virus on slides and reported a 1.7-

log reduction after 2.5 h at room temperature at 20% RH and up to a 3.5-log reduction 

when RH was increased to 84%. These trends of increasing inactivation with increasing 

RH, determined from a single exposure time and widely varying RHs, are consistent with 

our findings. However, much more pronounced log reductions were reported by 

Buckland and Tyrell over shorter time periods compared to the current investigation. 

Bean et al. investigated influenza survival for up to 72 hours on stainless steel surfaces at 

35-40% relative humidity(Bean, Moore et al. 1982). Log reductions based on the 

published figure were approximately 1.0, 1.5, and 2.2. These reductions were higher than 

our values for 25 and 50% RH, but somewhat lower than our data at 75%RH. 

Interestingly, Bean et al. reported in their study that log reductions of influenza B on 

stainless steel were higher than Influenza A. This difference was attributed to the type of 

virus. However, they note that the RH levels when testing influenza B were higher than 

those when testing influenza A. Perhaps this difference may have been due to RH. 

Similarly, Greatorex et al. evaluated influenza inactivation on stainless steel surfaces. 

Experiments were completed at 23-24% RH and they reported log reductions of 1.5 and 

2.2 after 4 and 9 hrs, respectively. These reductions are similar in magnitude to those 

found in the current study, but occur much more quickly. 

Knowing the log reduction of virus yields is important information for 

decontamination, but determination of the decay rate or half life is more useful for risk 

assessment purposes. In our experiments, influenza virus survived on stainless steel 

surfaces for hours to days. However, the survival time in our experiments, and of other 

investigators, is a function of the very high initial inoculums seeded onto the surfaces. 

Since the decay appears to be a first order function, the more viruses that are deposited on 

a surface the longer that infective viruses will be detected. Researchers often use 

extremely high influenza virus inoculums to allow measurement of large reductions of 

viruses and the associated lengthy survival times are likely not reflective of inoculums 

deposited on surfaces by persons infected with influenza virus. 

It is potentially more useful to look at the data as a rate function in terms of halflives to determine how quickly initial inoculums deposited on a surface will decrease. 

Our data show half-lives of greater than 10 hrs when humidity is 50% or lower. If we 

assume the RH is 50% or less (a reasonable assumption for the air cabin considering the 

low moisture content of air when flying at altitude), only half of the dried, infective 

influenza virus initially deposited on a surface would become inactivated during the 
‐ 167 ‐ 

length of most regional flights. Our data showed inactivation rates for influenza virus 

decreased by more than two-fold when relative humidity was maintained at 75% versus 

either 25 or 50%. Thus, increasing relative humidity could facilitate virus inactivation. 

Maintaining elevated RH in the air cabin environment during flight, or in most 

ground operations, would be impractical. Perhaps a control strategy incorporating 

elevated relative humidity in the air cabin during evenings, when aircraft are not in use, 

warrant further investigation. However, such a strategy should be well thought out 

considering potential problems with molds associated with elevated RH. 

Although estimated half-lives for influenza virus inactivation on surfaces have 

been computed, more information is needed for sound risk assessment. For example, little 

is known about the amount of virus shed to the environment by infected persons. Thus, 

we have no estimate of the concentration of influenza virus actual on surfaces. 

Furthermore, information is also limited on influenza inactivation during the drying 

process or the efficiency of virus transfer from the surface. Alhough we note 63% 

reductions during drying in a controlled laboratory environment, reductions may vary 

when virus is expelled from an actual person infected with influenza. These additional 

characterizations are needed to before meaningful models of influenza infection risks 

from contaminated surfaces are developed. 

Although there have been differences noted between the current study and 

previously mentioned studies in magnitude of inactivation or half-live of influenza virus, 

the associations of increasing RH and increasing influenza surface inactivation is 

consistent. The difference associated with the magnitude of log reductions or decay rates 

in the present study compared to other investigations may be a function of experimental 

methodologies or conditions. There are no widely accepted methods for surface testing 

and laboratories use varying methods of virus deposition, recovery from surfaces, and 

culture methods. Influenza virus survival may also depend on the type of influenza virus 

or how it is cultured(Schaffer, Soergel et al. 1976). Survival differences based on the type 

of virus have been reported previously for influenza aerosols. However, due to laboratory 

safety restrictions only one virus was tested as part of this investigation. 

20.2 Surface Sampling Protocol Development and Field Study 

There was no clear trend which indicated a superior swab type for sampling 

bacteria from surfaces. Both cotton and polyester swabs appeared to outperform foam 

and rayon swabs. Field tests also showed polyester swabs have increased recovery over 

cotton. Based on our analysis and concern about degradation of the cotton swab after 

extended immersion and vortexing, the polyester swab was chosen for the pilot field 

study. Furthermore, clinical research also suggested superior recoveries from polyester 

swabs(Esposito, Molteni et al. 2010). 

Initially our plan for the pilot study was to recruit persons of opportunity to 

perform surface sampling while taking planned flights. This method appeared to provide 

a means of obtaining air cabin samples from a wide variety of aircraft and flight routes. 

However, this strategy was changed due to study design weaknesses associated with data 

collection reliability, sample processing, and security/perception issues. Past research 

experience has shown that placing a trained researcher on actual flight segments is costly 

and time consuming. Therefore, a new strategy was developed that would allow a trained 
‐ 168 ‐ 

technician to collect samples from a variety of aircraft, which had flown a variety of 

routes, in a single evening. The strategy consisted of partnering with a local airline to 

allow access to aircraft on the ramp at the airport after completion of the days flying, but 

before janitorial crew serviced the aircraft. Using this strategy two technicians were able 

to collect 50 samples from 5 aircraft within a 3 hour time period. Samples could be 

processed locally and avoid multiple days of sample shipping. The single sampling kits 

provided were small in size and easily transported on the ramp from aircraft to aircraft. 

Due to the pilot scale of the study, data analysis was limited to descriptive 

statistics. Increasing sample size in future studies would certainly be feasible by 

extending the sampling campaign over several nights. Despite the limited sample size, 

multiple trends were suggested by the data. Far and above, Staphylococcus epidermidis

and Micrococcus luteus were the most commonly identified bacteria. This is not 

unexpected considering that these bacteria are part of the normal, human skin flora and 

are continually sloughed off from the skin. The presence of these bacteria does not 

indicate an increased risk of infection, but does show the method was capable of 

consistently recovering bacteria from the environment. Potentially pathogenic species of 

skin flora, such as Staphylococcus aureus or methicilin resistant Staphylococcus aureus

(MRSA) were not found. The data trends appeared logical. The variety and number of 

bacteria was generally lower on tray table tops than tray table bottoms and is consistent 

with tray table bottoms being contacted less frequently than tray table tops. Likewise, 

high touch surfaces such as arm rests had a generally higher variety and number of 

bacteria than the tray tables. Not surprising, the lavatories had the greatest variety and 

numbers of bacteria. In addition, the lavatories were the only location where clinically 

relevant bacteria, Pseudomonas auriginosa (and potentially Moraxella sp., depending 

upon the specific species), was found. 

There are no standards or limits available to compare the surface levels of bacteria 

found during the study. The risk of infection associated with the absolute number of 

bacteria present on the surfaces is difficult to determine. Many factors play a role in 

determining whether a susceptible person becomes infected such as: surface to hand 

transfer rate, hand to entry point (mouth, eyes, nose, etc.) transfer rate, health of the 

individual, or pathogenicity of the organism. Thus, it is not possible to determine if a 

number of bacteria is “low or high” or “acceptable or unacceptable”. Rather, these data 

are useful in a relative manner and can be used to evaluate cleaning methods, cleaning 

frequency, cleaning materials, other decontamination methods, or risk compared to other 

modes of transportation. However, the presence of highly pathogenic bacteria, in and of 

itself, may trigger concern regardless of the amount present. For example the fact that 

Psuedomonas auriginosa was found in 2 out of 5 lavatories tested may be justification for 

concern and increased lavatory cleaning. 

Bocavirus was the only type of respiratory virus detected. These positive virus 

samples were found on a tray table bottom in one aircraft and in the lavatory of another. 

Bocavirus is found in respiratory samples of patients with respiratory complaints, is 

associated with mild disease, and its etiology not fully understood. The relevance of 

bocavirus is somewhat limited, but its detection demonstrates the potential usefulness of 

the RMA assay. However, it is difficult to determine if a lack of positive detections of 

respiratory viruses was due to a lack of these viruses in the environment, rapid 

degradation of respiratory viruses in the environment, low virus recovery, or isues 
‐ 169 ‐ 

associated with the RMA. The RMA relies on using polymerase chain reaction, based 

molecular methods for amplifying and detecting nucleic acid associated with viruses. 

These techniques can be highly sensitive and specific, but are also sensitive to inhibitory 

substances which are commonly found in environmental samples. Further laboratory 

research is needed to evaluate the usefulness of the RMA for environmental surface 

sampling. 

The introduction of influenza A virus into many of the samples was likely the 

result of laboratory contamination at the HSPH Environmental Microbiology Lab (EML). 

Very high titers (109

/ml) of influenza A virus, such as those used in surface survival 

experiments, are handled in the EML. Sample kits were assembled and samples were 

processed for bacterial and viral analysis at the EML. Procedures were used in the lab to 

prevent cross contamination such as: working in a Class IIA BSC, using barrier pipet tips, 

and changing gloves frequently. However, the RMA assay is highly sensitive and can 

detect down to a few copies of nucleic acid. In the future, all sample kits and sample 

processing will be performed in laboratories which do not handle influenza or other 

respiratory viruses. 
‐ 170 ‐ 

21. CONCLUSIONS 

The results of our investigation show that RH survival on stainless steel surfaces 

is impacted by relative humidity. There was a trend of increasing influenza inactivation 

with increasing relative humidity. However, the inactivation rates for influenza virus 

were statistically higher at 75% compared to 25 and 50% RH. The low RH in the air 

cabin may be conducive to influenza survival and increase infection risk. Surface 

recovery of bacteria from surfaces varied and was inconsistent among a variety of swab 

types. The sampling kit used I the pilot field study recovered a wide variety of bacteria, 

but only a single type of virus. The strategy of sampling aircraft at the end of the flight 

day, and prior to cleaning, provided an efficient means of evaluating microbial surface 

contamination of a variety of aircraft. 
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