Ozone Initiated Chemistry and Its 

Impact on Cabin Air Quality 

*

Qingyan “Yan” Chen, Ph.D and Aakash C. Rai, Ph.D 

National Air Transportation Center of Excellence for 

Research in the Intermodal Transport Environment (RITE) 

Airliner Cabin Environment Research Program 

Purdue University 

May 29, 2014

Report No. RITE-ACER-CoE-2014-Purdue
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-2014-Purdue 

4. Title and Subtitle 5. Report Date 

Ozone Initiated Chemistry and Its Impact on Cabin Air Quality May 29, 2014 

6. Performing Organization Code 

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

Qingyan “Yan” Chen and Aakash C. Rai 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Purdue University West Lafayette, Indiana 

11. Contract or Grant No. 

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 

2/1/2010 to 8/31/2014 

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 

Ozone is a major indoor air pollutant, and exposure to it is associated with increased morbidity and 

mortality risks for humans. This study first measured the ozone consumption by, and ozoneinitiated VOC emissions from, reactions with a T-shirt soiled with human skin-oils. The results 

show that the rate of ozone removal by the T-shirt increased with increasing soiling level and 

ventilation rate, decreased at high ozone concentrations, and was relatively unaffected by the 

humidity level. The ozone-initiated VOC emissions were generally higher at higher ozone 

concentrations, humidity level, soiling degree of the T-shirt, and ventilation rate. Our measurements 

also show that the ozone/T-shirt reactions generated sub-micron particles, and this generation was 

enhanced by the soiling of the T-shirt with skin-oils. We used computational fluid dynamics (CFD) 

to study ozone consumption and developed new empirical models for computing the emissions of 

several major VOCs generated from ozone/clothing reactions. Good agreement was generally 

observed between the CFD results and the available experimental data. This investigation 

developed a numerical model to probe particle generation from ozone reactions with human-worn 

clothing under realistic indoor conditions. The model can predict ultra-fine particles generated. 

17. Key Words 18. Distribution Statement 

Ozone, VOC, ultrafine particles, experimental 

measurements, computational fluid dynamics, models, 

chambers, mockup cabin, aircraft cabin 

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

Unclassified Unclassified 143

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 
TABLE OF CONTENTS

Page

LIST OF TABLES 

LIST OF FIGURES 

SUMMARY

CHAPTER 1. INTRODUCTION ................................................................................. 1

CHAPTER 2. LITERATURE REVIEW ...................................................................... 6

2.1 Experimental Studies................................................................................. 6

2.1.1 Ozone Consumption and Ozone-initiated VOC Emissions ................6

2.1.2 Ozone-initiated Particle Generation ....................................................9

2.2 Modeling Studies ..................................................................................... 10

2.2.1 Ozone Consumption and Ozone-initiated VOC Emissions ..............10

2.2.2 Ozone-initiated Particle Generation ..................................................12

2.3 Literature Summary, Research Objectives, and Thesis Outline .............. 12

CHAPTER 3. OZONE REACTION WITH CLOTHING AND ITS INITIATED 

VOC EMISSIONS IN AN ENVIRONMENTAL CHAMBER ....................................... 14

3.1 Experimental Setup ................................................................................. 14

3.2 Experimental Procedure .......................................................................... 17

3.3 Evaluation Parameters ............................................................................. 19

3.3.1 Ozone Removal (α) ...........................................................................19

3.3.2 Ozone Removal Rate (βtotal) ..............................................................19

3.3.3 Contribution to the Ozone Removal Rate (βs) ..................................20

3.3.4 Deposition Velocity (vd) ...................................................................20

3.3.5 Molar Yield (Y) ................................................................................21

3.4 Results ..................................................................................................... 21

3.4.1 Effect of Different Surfaces on Ozone Reactions .............................22

3.4.2 Effect of Different Factors on Ozone-initiated Chemistry ................24

3.4.3 Ozone Reaction Chemistry under Typical Building Conditions ......31

3.5 Discussion ............................................................................................... 33
3.6 Conclusions ............................................................................................. 35

CHAPTER 4. OZONE REACTION WITH CLOTHING AND ITS INITIATED 

PARTICLE GENERATION IN AN ENVIRONMENTAL CHAMBER ........................ 36

4.1 Method .................................................................................................... 36

4.2 Results ..................................................................................................... 38

4.2.1

4.2.2

Effect of Different Surfaces on the Ozone-initiated Particle 

Generation 39

Effect of Different Factors on the Ozone-initiated Particle 

4.2.3

Generation 44

Ozone-initiated Particle Generation under Typical Building

Conditions 52

4.3 Discussion ............................................................................................... 53

4.4 Conclusions ............................................................................................. 54

CHAPTER 5. SIMULATIONS OF OZONE DISTRIBUTIONS IN AN AIRCRAFT 

CABIN USING COMPUTATIONAL FLUID DYNAMICS .......................................... 55

5.1 Method .................................................................................................... 55

5.1.1 Surface Deposition ............................................................................56

5.1.2 Mass Accommodation Coefficient (γ) ..............................................57

5.2 Case Setup ............................................................................................... 58

5.3 Evaluation Parameters ............................................................................. 64

5.3.1 Ozone Removal Rate (βtotal) ..............................................................64

5.3.2 Contribution to the Ozone Removal Rate (βs) ..................................65

5.3.3 Ozone Deposition Velocity (vd) ........................................................66

5.3.4 Retention Ratio (θ) ............................................................................66

5.3.5 Ozone Ratio (rozone) ...........................................................................67

5.4 Results ..................................................................................................... 67

5.4.1 Ozone Removal by Carpet and Seats (Cases 1 and 2) ......................67

5.4.2 Ozone Removal by T-shirts Soiled with Human Skin-oil (Case 3) ..70

5.4.3 Ozone Removal by Passengers (Cases 4 and 5) ...............................71

5.5 Discussion ............................................................................................... 75
5.6 Conclusions ............................................................................................. 75

CHAPTER 6. NUMERICAL MODELING OF VOC EMISSIONS FROM OZONE 

REACTIONS WITH HUMAN-WORN CLOTHING IN AN AIRCRAFT CABIN ....... 77

6.1 Method .................................................................................................... 77

6.1.1 Selection of Empirical Equations ......................................................78

6.1.2 Mass Balance Model .........................................................................81

6.1.3 CFD Model .......................................................................................81

6.2 Case Setup ............................................................................................... 82

6.3 Results ..................................................................................................... 84

6.3.1 VOC Emissions .................................................................................84

6.3.2 Distributions of Different VOCs in the Cabin Mockup ....................88

6.4 Discussion ............................................................................................... 91

6.5 Conclusions ............................................................................................. 92

CHAPTER 7. NUMERICAL MODELING OF PARTICLE GENERATION FROM 

OZONE REACTIONS WITH HUMAN-WORN CLOTHING IN INDOOR 

ENVIRONMENTS ........................................................................................................... 94

7.1 Method .................................................................................................... 94

7.1.1 Model Formulation ...........................................................................96

7.1.2 Estimating Physical Constants and Model Parameters ...................103

7.2 Results and Discussions ........................................................................ 104

7.2.1 Apparent Nucleation Rate Formulation ..........................................105

7.2.2 Nucleation Models ..........................................................................111

7.2.3 Ozone-initiated Particles under Different Indoor Environments ....119

7.3 Conclusions ........................................................................................... 123

CHAPTER 8. CONCLUSIONS AND FUTURE WORK ........................................ 125

8.1 Conclusions ........................................................................................... 125

8.2 Future Work .......................................................................................... 127

Appendix A. Experimental Data for the VOC measurements .................................... 140

Appendix B. Particle Generation and Growth ............................................................ 141
LIST OF TABLES

Table .............................................................................................................................. Page

Table 3.1. Details of the experimental conditions in the chamber used for studying ........... 

the ozone reactions with clothing. .................................................................................... 18

Table 3.2. Key results from the study of VOC generation from ozone reactions with 

human clothing.................................................................................................................. 22

Table 4.1. Details of the experimental conditions in the chamber used for studying 

particle generation from ozone reactions with human clothing. ....................................... 38

Table 4.2. Key results from the study of particle generation from ozone reactions ............. 

with human clothing. ........................................................................................................ 39

Table 5.1. Description of the five cases used in studying the ozone reaction in a ............... 

cabin mockup. ................................................................................................................... 62

Table 5.2. The thermal, ozone, and turbulence boundary conditions used for the five 

cases. ................................................................................................................................. 62

Table 6.1. Description of the three cases used for studying ozone-initiated VOC 

emissions in a cabin mockup. ........................................................................................... 83

Table 6.2. The ozone and VOC boundary conditions used for this study. ....................... 84

Table 7.1. Various physical constants and model parameters used in this ........................... 

investigation. ................................................................................................................... 103

Table 7.2 Details of the experimental conditions in the chamber used for simulating 

particle generation from ozone reactions with human clothing ...................................... 105

Appendix Table 

Table A.1. VOC concentrations measured during the ozone reactions with clothing .......... 

in Chapter 3. .................................................................................................................... 140
LIST OF FIGURES

Figure ............................................................................................................................. Page

Figure 1.1. Health effects of ozone (www.epa.gov/apti/ozonehealth/population.html). .... 2

Figure 3.1. Setup of the environmental chamber used to study the ozone reactions ............ 

with a T-shirt on a human simulator. ................................................................................ 15

Figure 3.2. Schematic of the experimental setup used to study the ozone reactions ............ 

with a T-shirt on a human simulator. ................................................................................ 16

Figure 3.3. Effect of the chamber, laundered T-shirt, and soiled T-shirt surfaces on .......... 

the ozone-initiated VOC emissions. Error bars indicate plus one standard deviation. ..... 24

Figure 3.4. Effect of ozone concentration on the VOC emissions from its reactions ........... 

with the soiled T-shirt at an air change rate of 0.5 h−1. Error bars indicate plus one 

standard deviation. ............................................................................................................ 26

Figure 3.5. Effect of ozone concentration on the VOC emissions from its reactions ........... 

with the soiled T-shirt at an air change rate of 2.7 h−1

. Error bars indicate plus one 

standard deviation. ............................................................................................................ 27

Figure 3.6. Effect of relative humidity on the ozone-initiated VOC emissions with ........... 

the soiled T-shirt. Error bars indicate plus one standard deviation. .................................. 28

Figure 3.7. Effect of T-shirt soiling by skin-oils on the ozone-initiated VOC ..................... 

emissions. Error bars indicate plus one standard deviation. ............................................. 29

Figure 3.8. Effect of air change rate on the ozone-initiated VOC emissions with the ......... 

soiled T-shirt. Error bars indicate plus one standard deviation. ....................................... 31

Figure 3.9. Ozone-initiated VOC emissions with soiled T-shirt under typical .................... 

building conditions with different relative humidity levels. Error bars indicate plus .......... 

one standard deviation. ..................................................................................................... 32
Figure ............................................................................................................................. Page

Figure 3.10. The ozone-initiated emissions of VOCs in duplicate experimental 

measurements, (a) for Case 3 (b) for Case 10. Error bars indicate plus one standard 

deviation. ........................................................................................................................... 34

Figure 4.1. Effect of chamber, laundered T-shirt, and soiled T-shirt surfaces on the 

ozone-initiated particles measured at the exhaust. ............................................................ 40

Figure 4.2. Temporal variations of particle number and mass concentrations at the ........... 

inlet and exhaust for the ozone reactions with the soiled T-shirt in Case 3. ..................... 41

Figure 4.3. Evolution of particle size distributions at the exhaust for the ozone .................. 

reactions with the soiled T-shirt in Case 3. ....................................................................... 42

Figure 4.4. Effect of ozone concentration on (a) particle number generation and (b) 

particle mass generation when the ozone reacted with the soiled T-shirt. ........................ 45

Figure 4.5. Effect of relative humidity on (a) ozone-initiated particle number .................... 

generation and (b) ozone-initiated particle mass generation with the soiled T-shirt. ....... 47

Figure 4.6. Effect of the soiling level of the T-shirt by skin-oils on (a) ozone-initiated 

particle number generation and (b) ozone-initiated particle mass generation. ................. 49

Figure 4.7. Effect of T-shirt wearing by different human subjects on (a) ............................ 

ozone-initiated particle number generation and (b) ozone-initiated particle mass 

generation. ......................................................................................................................... 51

Figure 4.8. Ozone-initiated particle number generation under typical building ................... 

conditions. ......................................................................................................................... 53

Figure 5.1. The occupied cabin setup for Case 5, (a) the schematic of the case and (b) 

boundary surfaces in the cabin mockup. ........................................................................... 60

Figure 5.2. Mesh distribution in the longitudinal section through the cabin center for ........ 

Case 5. ............................................................................................................................... 63

Figure 5.3. Occupant geometries and breathing zones for Cases 4 and 5. ....................... 64

Figure 5.4. Comparison of the computed ozone removal rate with the corresponding 

experimental data from Tamas et al. (2006) for various cabin and human related .............. 

surfaces. ............................................................................................................................ 68
Figure ............................................................................................................................. Page

Figure 5.5. Comparison of the computed deposition velocity with the corresponding 

experimental data from Tamas et al. (2006) for various cabin and human related .............. 

surfaces. ............................................................................................................................ 69

Figure 5.6. Ozone distribution in the longitudinal section through the cabin center. ....... 73

Figure 5.7. Comparison of ozone ratio (rozone) for the passengers in the breathing zone 

between Cases 4 and 5. Seats 1D, 2F, 3A, and 3D were unoccupied. .............................. 74

Figure 6.1. Model selections for the different VOCs based on the R2

adj criterion. The 

circles identify the final model selections. ........................................................................ 80

Figure 6.2. Ozone-initiated VOCs from reactions with soiled T-shirts: comparisons .......... 

of the predicted and measured data for Case 1. The measurement errors are unknown, ...... 

and the model error bars represent the 95% confidence intervals. ................................... 86

Figure 6.3. Ozone-initiated VOCs from reactions with human surfaces: comparisons ........ 

of the predicted and measured data for Case 2. The measurement error bars represent ....... 

the range, and the model error bars represent the 95% confidence intervals. .................. 87

Figure 6.4. Ozone-initiated VOCs from reactions with human surfaces: comparisons ........ 

of the predicted and measured data for Case 3. The measurement error bars represent ....... 

the range, and the model error bars represent the 95% confidence intervals. .................. 88

Figure 6.5. The 4-OPA distribution in the longitudinal section through the center of ......... 

the occupied cabin mockup with an outdoor air change rate of 8.8 h−1 for Case 2. ......... 89

Figure 6.6. The 4-OPA distribution in the longitudinal section through center of the 

occupied cabin mockup with an outdoor air change rate of 4.4 h−1 for Case 3. ............... 90

Figure 6.7. VOC ratio for different passengers seated in the middle row of the .................. 

occupied cabin mockup under an outdoor air change rate of (a) 8.8 h−1 (Case 2) and ......... 

(b) 4.4 h−1 (Case 3). ........................................................................................................... 91

Figure 7.1. An illustration of the particle-generation mechanism. .................................. 95

Figure 7.2. Comparison of particle number concentrations computed by the ...................... 

“excess skin-oils” model with the corresponding measurements at different ozone 

concentrations. ................................................................................................................ 106
Figure ............................................................................................................................. Page

Figure 7.3. Comparisons of particle mass concentrations computed by the “excess ........... 

skin-oils” and “skin-oils depletion” models with the corresponding measurements at ........ 

(a) 40 ppb and 148 ppb ozone with 6 hours of T-shirt soiling, and (b) 57 ppb ozone .......... 

with 12 hours of T-shirt soiling. ..................................................................................... 108

Figure 7.4. Comparisons of particle size distributions computed by the “skin-oils 

depletion” model with the corresponding measurements at an ozone concentration of ....... 

40 ppb for (a) t = 4.2 h and 6.2 h, and (b) t = 8.5 h and 12.5 h. ...................................... 110

Figure 7.5. Comparisons of particle number concentrations computed by the different 

nucleation models with the corresponding measurements at an air change rate of 0.5 ........ 

h−1 with (a) 40 ppb ozone and (b) 148 ppb ozone. .......................................................... 113

Figure 7.6. Comparisons of particle number concentrations computed by the different 

nucleation models with the corresponding measurements at an air change rate of 2.7 ........ 

h−1

 with (a) 51 ppb ozone and (b) 133 ppb ozone. .......................................................... 115

Figure 7.7. Comparisons of particle mass concentrations computed by the different 

nucleation models with the corresponding measurements at (a) 40 ppb ozone and (b) ....... 

148 ppb ozone. ................................................................................................................ 117

Figure 7.8. Comparisons of particle size distributions computed by the different 

nucleation models with the corresponding measurements at 40 ppb concentration for ....... 

(a) t = 4.2 h, (b) t = 6.2 h, (c) t = 8.5 h, and (d) t = 12.5 h. ............................................. 118

Figure 7.9. Four-hour averaged ultrafine particle number concentrations computed ........... 

for a typical classroom under various air change rates for 50 and 100 ppb outdoor ............ 

ozone conditions with and without background particles. .............................................. 120

Figure 7.10. Four-hour averaged ultrafine particle number concentrations computed ......... 

for different buildings under various air change rates for 100 ppb without background 

particles. .......................................................................................................................... 122

Figure 7.11. Measurements of the ozone-initiated UFPs in a Boeing 737-700 flight .......... 

and the corresponding contributions by ozone reactions with passengers’ clothing as 

computed by the model. .................................................................................................. 123
Figure ............................................................................................................................. Page

Appendix Figures .................................................................................................................. 

Figure B.1. Particle number and surface area concentrations in Case 3. ........................ 141

Figure B.2. Effect of relative humidity on the particle surface area concentration. ....... 142
SUMMARY

Ozone is a major indoor air pollutant, and exposure to it is associated with increased 

morbidity and mortality risks for humans. Ozone is also a major driver of indoor air 

chemistry, and it reacts with various indoor materials such as fragrances, cleaners, paints, 

carpets, etc. to generate a myriad of byproducts. These ozone-initiated byproducts include 

numerous volatile and semi-volatile organic compounds as well as particles, which can 

be even more harmful to human health than ozone itself. Therefore, it is imperative to 

characterize the indoor exposure of humans to ozone and ozone-initiated byproducts.

A large number of investigations have measured ozone reactions with terpene-based 

consumer products and found these reactions to be sources of volatile organic compounds 

(VOCs) and particles in buildings. Ozone chemistry in airliner cabins has also received 

considerable attention because the ozone concentration is typically much higher in 

aircraft cabins than in buildings, which translates to a high risk of exposure to ozone and 

its byproducts for passengers and crew. These investigations have shown that the 

occupants themselves provide significant sites for ozone consumption and VOC 

emissions through ozone reactions with their skin, hair, and clothing. In contrast to the 

large number of experimental studies devoted to ozone reaction chemistry, only a few 

modeling studies can be found in the literature. It is crucial to develop modeling tools 

because they can provide cheap and quick estimates of human exposure to ozone and the 

byproducts of its reactive chemistry and can also aid in the development of possible 

mitigation strategies. Therefore, the aim of this investigation was to develop modeling 

tools for studying exposure to ozone and its initiated byproducts in indoor environments 
with an emphasis on airliner cabins, which have been identified as high-risk 

environments. 

To generate the experimental data needed for developing the models, we first measured 

the ozone consumption by, and ozone-initiated VOC emissions from, reactions with a Tshirt soiled with human skin-oils. The measurements were conducted in an environmental 

chamber, and they were designed to identify the impact of various factors on ozone 

reactions. These factors included ozone concentration, relative humidity, the degree to 

which the T-shirt was soiled by human skin-oils, and ventilation rate. It was found that 

ozone reactions with the soiled T-shirt consumed ozone and generated VOCs. The ozone 

deposition velocity, which was used to quantify the ozone removal rate by the soiled Tshirt, ranged from 0.15 to 0.29 cm/s. The rate of ozone removal by the T-shirt increased 

with increasing soiling level and ventilation rate, decreased at high ozone concentrations, 

and was relatively unaffected by the humidity level. The ozone-initiated VOC emissions 

included C6–C10 straight-chain saturated aldehydes, acetone, and 4-oxopentanal (4-

OPA). The VOC emissions were generally higher at higher ozone concentrations, 

humidity level, soiling degree of the T-shirt, and ventilation rate. The total molar yield 

was approximately 0.5 in most of the experiments, which means that for every two moles 

of ozone removed by the T-shirt surface, one mole of VOCs was produced. 

In addition to measurements of ozone consumption and ozone-initiated VOC emissions, 

we explored the possibility of particle generation from ozone reactions with the soiled Tshirt by using the same environmental chamber setup. Our measurements showed that the 

ozone/T-shirt reactions did indeed generate sub-micron particles, and this generation was

enhanced by the soiling of the T-shirt with skin-oils. In these reactions, a burst of 

ultrafine particles (UFPs) was observed approximately one hour after the injection of 

ozone into the chamber, and then the particles grew to larger sizes. Particle generation 

from the ozone/T-shirt reactions was also significantly affected by the various factors that 

were studied, and these reactions were identified as another potential source of indoor 

UFPs. 
On the basis of the aforementioned measurements, we subsequently developed modeling 

techniques for studying ozone reactions in realistic indoor settings. We used 

computational fluid dynamics (CFD) to study ozone consumption by, and ozone-initiated 

VOC emissions from, different surfaces in an aircraft cabin. The CFD model for ozone 

was based on existing models in the literature, and we developed new empirical models 

for computing the emissions of several major VOCs generated from ozone/clothing 

reactions, such as acetone, 4-OPA, nonanal, and decanal. Good agreement was generally 

observed between the CFD results and the available experimental data for ozone 

consumption by different surfaces in the aircraft cabin. For the VOC concentrations, the 

model predictions showed accurate trends qualitatively, but there were quantitative 

discrepancies between the predictions and the corresponding measurements, which were 

mainly due to the small experimental dataset used for model development. Nevertheless, 

the models are promising and can be improved with the use of additional data. Our CFD 

analysis also obtained detailed concentration distributions of ozone and its initiated 

VOCs in the aircraft cabin. The analysis predicted that in the breathing zone of the 

passengers, the concentration of ozone would typically be 10% lower, and that of ozoneinitiated VOCs 10–50% higher, than their respective bulk air concentrations. Therefore, 

to accurately assess passengers’ exposure to ozone and its initiated VOCs, their breathing 

zone concentrations should be used. 

Finally, this investigation developed a numerical model to probe particle generation from 

ozone reactions with human-worn clothing under realistic indoor conditions. The model 

was based on the particle generation measured in our environmental chamber as well as 

physical models of particle nucleation, condensational growth, and deposition. In five out 

of the six test cases, the model was able to predict particle size distributions reasonably 

well. The failure in the remaining case demonstrated the fundamental limitations of the 

nucleation models that were employed. The numerical model that had been developed 

was then used to predict particle generation under various building and airliner-cabin 

conditions. These predictions indicate that ozone/clothing reactions could be an important 

source of UFPs in densely occupied indoor spaces such as classrooms and airliner cabins. 

Such reactions could account for about 40% of the total UFPs measured on a Boeing 737-
700 flight. However, the model predictions at this stage are indicative and should be 

improved further. 

In summary, this investigation studied ozone reactions with human-worn clothing 

through chamber measurements and numerical simulations and came to the following 

conclusions: 

• Our measurements showed that the ozone/clothing reactions consumed ozone and

generated several VOCs such as C6–C10 straight-chain saturated aldehydes, acetone,

and 4-OPA, together with sub-micron particles.

• Ozone consumption and ozone-initiated VOCs and particle generation were generally

found to be significantly affected by the different factors that were studied, such as

ozone concentration, relative humidity, soiling degree of clothing, and ventilation rate.

• Our CFD simulations showed that the concentration of ozone decreased, and that of

ozone-initiated VOCs increased, in the breathing zones of aircraft cabin passengers as

compared with the corresponding bulk air concentrations. Thus, it is recommended

that the breathing zone concentrations be used for estimating the exposure of

passengers to ozone and ozone-initiated VOCs.

• The numerical model for particle generation extended our chamber measurements to

realistic indoor environments. The model results showed that ozone/clothing reactions

could be an important source of UFPs in densely occupied indoor spaces such as

classrooms and airliner cabins.
1 

CHAPTER 1. INTRODUCTION

Ozone is a major air pollutant that poses significant health concerns for humans (EPA, 

2006). Ozone exposure has been found to be associated with respiratory problems such as 

asthma, bronchoconstriction, airway hyperresponsiveness, and inflammation (EPA, 2006). 

There is also evidence that links ozone to cardiovascular morbidity (EPA, 2006). Several 

investigations have even associated ozone exposure with premature mortality (Bell et al., 

2005, 2004; Ito et al., 2005; Levy et al., 2005). For example, a study based on 40% of the 

total U.S. population identified an increase of 0.52% in daily mortality per 10 ppb 

increase in the previous week’s ground-level ozone concentration (Bell et al., 2004). 

Another nationwide study (Bell et al., 2006) showed that increased ground-level ozone 

was associated with increased mortality even at very low concentrations (10–25 ppb). 

The severity of ozone effects in relation to the proportion of the population affected is 

qualitatively presented in Figure 1.1. 
2

Figure 1.1. Health effects of ozone (www.epa.gov/apti/ozonehealth/population.html).

Thus, to protect public health, a number of regulations have been established that limit 

the ozone concentration in the atmosphere. For example, in 1997 the US Environmental 

Protection Agency (EPA) set the ground-level ozone limit to 80 ppb (calculated as the 

annual fourth-highest daily maximum 8-hr concentration, averaged over 3 years). In spite 

of this regulation, in the 2000–2002 time period 36–56% of ozone monitors determined 

that the standard had not been met. The consequences were an estimated 800 premature 

deaths, 4,500 hospital and emergency department admissions, 900,000 school absences, 

and over 1 million minor restricted activity days (Hubbell et al., 2005). In 2008, because 

of the growing scientific evidence of the ill effects of ozone, the EPA further reduced the 

ground-level ozone concentration limit to 75 ppb. Observance of the new ozone 

regulation could lead to health-related benefits that are valued between $2 billion and $17 

billion annually (EPA, 2008).

The median value of the ground-level ozone (mean daily maximum 8-hour averaged 

concentration) across the United States is 49 ppb (EPA, 2006). However, the ozone level 
3

can be as high as 500–800 ppb at typical airplane cruising altitudes (NRC, 2002). This 

ambient ozone is transported to indoor spaces such as homes, offices, and airliner cabins, 

mainly through their ventilation systems. The indoor ozone level is generally lower than 

the outdoor level because of ozone removal by indoor surfaces and gas phase reactions in 

buildings and aircraft, and by the ozone converters used in many modern aircraft to 

remove ozone from the cabin air supply. Thus, the typical ozone concentration is about 

10 ppb in buildings and about 50 ppb in airliners. The high ozone concentrations inside 

airliner cabins constitute a substantial health risk to occupants.

The issue of ozone exposure in airliners has received considerable attention in the past 

two decades with the rapid increase in air travel. In 2012, 736.7 million passengers 

travelled on US flights, and this number is expected to exceed one billion by 2027 (FAA 

(Federal Aviation Administration), 2013). Furthermore, there has been an increase in the

number of young and elderly passengers, including children, infants, and people with 

medical conditions (NRC, 2002). The health consequences of ozone exposure are 

particularly significant for individuals with pre-existing cardiopulmonary conditions, 

frequent fliers, and cabin crew members (Nazaroff and Weschler, 2010). Passengers and 

cabin crew often complain about issues related to air quality such as fatigue; irritation and 

dryness of lips, eyes, and skin; headaches; dizziness; sore throat; and sinus irritation.

These conditions have been associated with exposure to ozone (Strøm-Tejsen et al., 

2008).

The issue of ozone exposure is further exacerbated by the fact that its reaction byproducts 

in indoor settings (both buildings and airliners) can be even more harm harmful than 

ozone itself (Weschler, 2004). Volatile organic compounds (VOCs) are one class of such 

harmful byproducts. They are usually emitted as gases from a variety of indoor materials 

such as paints, furniture, and building supplies, and these emissions are called primary 

VOC emissions (Brown et al., 1994; Kostiainen, 1995). However, another important 

source of indoor VOCs are the gaseous and surface reactions of ozone with various

indoor materials, which produce secondary VOC emissions. For example, Reiss et al. 

(1995) studied the reaction of ozone with interior latex paint in a tube flow reactor and 
4

observed secondary carbonyl emissions. Morrison and Nazaroff (2000, 2002)

investigated ozone reactions with carpets in an environmental chamber and found 

secondary emissions of saturated and unsaturated aldehydes. These ozone-initiated VOC 

emissions are particularly important in aircraft cabins because of the typically high ozone 

levels encountered during flights (Bhangar et al., 2008; Spengler et al., 2004; Weisel et 

al., 2013). Weschler’s group (Tamás et al., 2006; Weschler et al., 2007; Wisthaler et al., 

2005) studied the ozone-initiated reactive chemistry in an aircraft cabin mockup through 

a series of experiments. They concluded that humans themselves constitute the primary

site for ozone-initiated chemistry, and occupants were found to be responsible for the 

generation of most of the VOCs identified in the cabin.

The health consequences of ozone-generated VOCs are not well studied (Weisel et al., 

2013), and only a few of them are recognized as health hazards. One such VOC is 

formaldehyde, a known carcinogen to humans (Cogliano et al., 2005) and also a reaction 

byproduct of ozone with many indoor materials such as carpets (Morrison and Nazaroff, 

2002), paints (Reiss et al., 1995), cleaners, and fragrances (Destaillats et al., 2006; Singer 

et al., 2006). Another such VOC is 4-oxopentanal (4-OPA), which is generated from the 

ozonolysis of squalene (a major component of human skin oils). Exposure to 4-OPA has 

recently been identified as a cause of irritant and allergic responses, which are symptoms 

generally associated with sick building syndrome (Anderson et al., 2012).

Another class of ozone-generated byproducts is particulate matter, which is also a serious 

health concern for humans. Several studies have associated exposure to outdoor particles 

with increased morbidity and mortality risks (Dominici et al., 2006; Pope and Dockery, 

2006; Pope, 2000). However, outdoor exposure to particles is usually less significant than 

indoor exposure because people spend the majority of their time indoors. Therefore, it is 

presumable that many of the adverse health effects that are apparently due to outdoor 

particles are actually caused by exposure indoors (Jones, 1999), where ozone/terpene

reactions are an important source of particles (Sarwar and Corsi, 2007; Singer et al., 2006; 

Weschler and Shields, 1999). For example, Long et al. (2000) found that ozone/terpene 

reactions drastically increased (with a peak increase of 7–100 times) the particle number 
5

concentrations in Boston-area homes. They also found that more than 50% of those 

particles (by volume) were ultrafine in nature. Such ultrafine particles (UFPs) can be 

deposited deep in the lungs and presumably are responsible for many of the adverse 

health effects associated with particles (Donaldson et al., 2001, 1998; Seaton et al., 1995; 

Sioutas et al., 2005). Hence, several researchers focused on measuring UFP generation 

from ozone/terpene reactions (Coleman et al., 2008b; Destaillats et al., 2006; Ito and 

Harashima, 2008). They found that such reactions were indeed a major source of indoor 

UFPs. Thus, ozone-initiated particles are also a potential health risk, but this risk is 

tentative because the composition and toxicities of these particles are unknown (Carslaw 

et al., 2009; Weschler, 2006).

Therefore, it is well established that ozone and its reaction byproducts are significant 

health hazards for humans. Moreover, because people spend 90% of their time indoors 

(Klepeis et al., 2001) and ozone reaction byproducts are present indoors in much larger 

concentrations than outdoors (Weschler, 2006), it is crucial to assess the extent of indoor 

exposure to ozone and its byproducts. Although the ozone concentration is generally 

lower in the indoor air than outdoors because of its reactions with various indoor 

materials, indoor ozone inhalation is still estimated to be between 25% and 60% of total 

ozone intake (Weschler, 2006). Therefore, this study was undertaken in order to evaluate 

the exposure risk to humans from ozone and ozone-initiated reactive chemistry in the 

indoor air with an emphasis on airliner cabins, which have been identified as high-risk 

environments.
6

CHAPTER 2. LITERATURE REVIEW

This chapter presents an analysis of the available literature on ozone-initiated reactive 

chemistry in indoor environments. We first review experimental investigations of ozone

consumption and ozone-initiated VOC and particle generation. Next, we discuss the 

available modeling tools for simulating indoor ozone chemistry. Finally, we summarize 

the state of the art and identify gaps in the existing knowledge, which were used to 

formulate our research objectives.

2.1 Experimental Studies

This section reviews the available literature on ozone consumption and the generation of 

ozone-initiated byproducts. This literature consists primarily of experimental 

investigations conducted in environmental chambers and/or realistic indoor environments. 

We first describe measurements of ozone consumption and ozone-initiated VOC 

emissions from reactions with different indoor materials. Next, we discuss measurements 

of ozone-initiated particle generation from terpene-based consumer products, which have 

been identified as an important source of indoor particles.

2.1.1 Ozone Consumption and Ozone-initiated VOC Emissions

The indoor environment can be thought of as a large chemical vessel in which reactions 

take place continuously (Weschler and Shields, 1997). Ozone is a major driver of this 

indoor chemistry and reacts with various compounds in the gas phase and on indoor 

surfaces to produce oxidation byproducts (Weschler, 2011). These byproducts are called 

ozone-derived secondary emissions, and they may be more harmful than the primary 

emissions from various indoor surfaces (Morrison, 2008; Weschler, 2006, 2000). 

Therefore, many investigations have studied ozone reactions with commonly used indoor 

products such as fragrances and cleaning agents. Gas-phase reactions of ozone with 
7

terpene-containing cleaners and air fresheners were found to consume ozone and generate

harmful VOCs such as formaldehyde and acetaldehyde (Destaillats et al., 2006 and 

Singer et al., 2006). Likewise, surface applications of terpene-containing cleaning agents 

and their subsequent reactions with ozone were also found to produce hazardous VOCs 

(Destaillats et al., 2006).

In addition to reactions with terpene-based consumer products, ozone reactions on 

various indoor surfaces also generate secondary VOC emissions. For example, Reiss et al. 

(1995) studied ozone reactions with interior latex paint in a tube flow reactor and 

observed secondary carbonyl emissions. Morrison and Nazaroff (2000, 2002)

investigated ozone reactions with carpets in an environmental chamber and found 

secondary emissions of saturated and unsaturated aldehydes. Kagi et al. (2009) showed 

that ozone reacted with flooring material surfaces and increased the formaldehyde and 

acetaldehyde emissions from them. Coleman et al. (2008a) studied ozone reactions with 

various samples of aircraft surfaces and clothing fabrics in an environmental chamber. 

They concluded that the occupants’ clothing was the dominant contributor to ozone 

removal and VOC emissions in an aircraft cabin environment.

Besides the above studies conducted in environmental chambers, several investigations 

have measured ozone reactions in buildings. For example, Lee et al. (1999) measured 

ozone decay rates in 43 residences in southern California and found that the decay rate 

was correlated with the type of house, number of bedrooms, and surface-area-to-volume 

ratio. Another investigation by Wang and Morrison (2006, 2010) measured ozone 

reactions inside homes on walls, floors, carpets, and kitchen counter surfaces. They found 

that the secondary VOC emissions from indoor surfaces can persist for decades because 

of the slow destruction rate of surface reactants by ozone and their replenishment by 

human activities.

The ozone-initiated chemistry in airliner cabins has also received considerable attention 

because of the typically high ozone levels encountered in flights as compared with those 

in buildings. Spengler et al. (2004) measured the average ozone concentration in 106 

flights. They found that the ozone concentration in 20% of the flights exceeded the 100 
8

ppb limit set by the U.S. Federal Aviation Administration (FAA). Bhangar et al. 

(2008)(Bhangar et al., 2008) collected real-time ozone data from 76 flights and found that 

ozone levels varied strongly with the season and the presence or absence of an ozone 

converter (employed in the air-supply system to reduce the ozone level). Weisel et al. 

(2013) measured VOCs on 52 U.S. transcontinental or international flights and developed 

linear regression models that are based on the log transformed mean ozone concentration, 

percent occupancy, and plane type.

In-flight measurements of ozone and ozone-initiated VOCs provide valuable information 

about cabin air quality, but they are very expensive and tedious. It is also difficult to 

identify the various factors affecting ozone removal and byproduct formation through inflight measurements. To overcome these difficulties, Weschler’s group (Tamás et al., 

2006; Weschler et al., 2007; Wisthaler et al., 2005) studied the ozone-initiated reactive 

chemistry through a series of experiments in an aircraft cabin mockup. They concluded 

that cabin occupants constitute a major site for ozone-initiated chemistry. Occupants and 

their clothing were found to be responsible for more than 55% of the ozone removal and

the generation of most of the VOCs identified in the cabin.

As the above review clearly shows, ozone consumption and ozone-initiated VOC 

emissions have been extensively studied through measurements in environmental 

chambers as well as real indoor spaces. However, very few investigations have identified 

the impact of environmental factors on VOC emissions. A study by Coleman et al. 

(2008a) demonstrated that environmental factors (such as ozone and humidity) can 

significantly affect VOC emissions from indoor ozone reactions. For example, they found 

that ozone-initiated VOC emissions from laundered cotton fabric approximately doubled 

when the relative humidity increased from 10% to 50%. However, because their 

experiments used a small-scale chamber and likely had well-mixed conditions, their 

results might not be suitable for many indoor settings that typically do not have wellmixed conditions (Sørensen and Weschler, 2002). Moreover, they did not study the 

effects of ventilation rate and the degree of T-shirt soiling by human skin-oils on VOC 

emissions, which should be explored further.
9

2.1.2 Ozone-initiated Particle Generation

Evidently, indoor ozone chemistry can generate a myriad of byproducts. Those 

byproducts with low vapor pressures can also increase the indoor particle concentrations 

by partitioning to particulate phase (Sarwar et al., 2003; Weschler and Shields, 1999). 

Hence, fine and ultrafine particles constitute another important class of ozone-initiated 

byproducts, a potential health concern.

Weschler and Shields (1999) observed particle generation from gaseous ozone/terpene 

reactions in experiments conducted in an office environment. Long et al. (2000) found 

that ozone/terpene reactions drastically increased (with a peak increase of 7–100 times) 

the particle number concentrations in Boston area homes. They also found that more than 

50% of these particles (by volume) were ultrafine in nature. Morawska et al. (2009)

measured ultrafine particles (UFPs) in three classrooms and found that art and cleaning 

activities led to a significant increase in indoor particle concentrations. They also 

conducted controlled experiments which showed that d-limonene (a monoterpene) 

emitted from the detergent reacted with ozone to generate particles.

A remarkably large number of investigations have studied ozone-initiated particle 

generation from gas-phase reactions of ozone with various terpenes and terpene-based 

consumer products under controlled conditions in environmental chambers (Coleman et 

al., 2008b; Sarwar and Corsi, 2007; Sarwar et al., 2004; Singer et al., 2006). These 

investigations have revealed details about the chemical and physical mechanisms of 

particle generation. It is now understood that ozone-initiated particles are generated 

through a burst of nucleation, which is followed by condensational growth towards larger 

sizes.

Several investigations have also studied particle generation from ozone reactions with 

terpenes applied to surfaces. For example, Destaillats et al. (2006) measured particle 

generation from ozone reactions with dried residues of terpene-based cleaners. Recently, 

Waring and co-workers measured ozone-initiated particle generation from reactions with 

surface-sorbed d-limonene and squalene (Wang and Waring, 2014; Waring and Siegel, 
10

2013). These investigations have suggested that ozone reactions with surface-sorbed 

terpenes could potentially generate submicron particles in indoor environments.

The above review of literature on ozone-initiated particles showed that ozone/terpene

reactions are an important source of submicron particles in indoor air. It is also well 

known that humans themselves constitute an important site for ozone reactions with their 

skin (Wisthaler and Weschler, 2010), hair (Pandrangi and Morrison, 2008), and clothing

(Coleman et al., 2008a). These reactions are attributed primarily to squalene, which is a 

triterpene found in human skin oils(Wisthaler and Weschler, 2010). Hence, we can also 

expect ozone reactions with clothing soiled with skin oils to generate submicron particles, 

which was hypothesized by Fadeyi et al. (2013). However, to our knowledge there has 

been no investigation of particle generation from soiled clothing, which should be studied 

further.

2.2 Modeling Studies

The experimental studies discussed above have provided reliable results, but they are

typically expensive and cumbersome. For this reason, a number of modeling studies have 

been attempted in order to provide a fast and convenient way to evaluate ozone’s impact 

on indoor air quality. We have categorized these studies into two groups, according to 

whether they primarily address ozone-initiated VOC emissions or particle generation, and 

we discuss them one by one in the following subsections.

2.2.1 Ozone Consumption and Ozone-initiated VOC Emissions

Cano-Ruiz et al. (1993) developed an analytical model to determine the consumption of 

ozone at various indoor surfaces. They obtained an algebraic expression for computing 

the ozone removal rate under three different airflow conditions and also performed a 

numerical simulation to further analyze their results. Weschler and Shields (2000)

developed a mass balance model to study the influence of ventilation rate on 

unimolecular and bimolecular chemical reactions occurring indoors under the assumption

of well-mixed conditions. The results indicated that adequate ventilation is necessary, not 

only to remove pollutants generated indoors but also to limit chemical reactions in indoor 
11

air. These analytical models provide a quick and simple way to estimate ozone 

contamination in indoor environments. However, it is difficult to analytically solve the 

model equations for complex indoor geometries and flow conditions without using the 

well-mixed assumption.

Hence, several computational fluid dynamics (CFD) studies on ozone have been 

performed. Some researchers have used CFD to analyze the volumetric and surface 

reactions of ozone in indoor settings (Russo and Khalifa, 2011, 2010; Sørensen and 

Weschler, 2002). They found significant spatial variations in the concentrations of 

reactants and products within a room and concluded that a well-mixed assumption might 

not be appropriate for many situations. Another CFD investigation by Rim et al. (2009)

studied ozone reactions with an occupant under different indoor conditions and found that 

in the occupant’s breathing zone, the concentration of ozone was 10–40% lower, and the 

concentration of ozone-initiated byproducts 1.2–6.0 times higher, than their 

corresponding bulk air concentrations. Thus, they concluded that these ozone reactions 

may reduce an occupant’s exposure to ozone but would substantially increase exposure to 

ozone-initiated byproducts.

These CFD studies provide a method for extending analytical models to realistic indoor 

settings without using the well-mixed assumption. However, the CFD studies were 

performed for indoor environments with a simple geometry that required minimum 

validation. Recent advances in computing power have certainly made CFD methods 

attractive for studying the distributions of indoor contaminants. However, CFD requires 

thorough validation with experimental data because of numerical errors (discretization 

error, computer round-off error, etc.) and modeling errors (turbulence model errors, 

unknown boundary conditions, etc.).The limited availability of such experimental data in 

the existing research literature for indoor ozone reactions has hindered these modeling 

efforts.
12

2.2.2 Ozone-initiated Particle Generation

As discussed previously, ozone reactions are also an important source of indoor particles. 

Particle generation from ozone reactions is a complex phenomenon, and it involves the 

following steps: (1) production of ozone-initiated semi volatile organic compounds 

(SVOCs) in the vapor phase, which act as particle-forming precursors, and (2) the 

conversion of these vapor-phase compounds to the liquid phase through nucleation of 

new particles or condensation on pre-existing particles.

Therefore, in order to model particle generation, details of the ozone reaction chemistry 

need to be coupled with physical formulations of particle dynamics such as nucleation, 

condensation, etc. Several investigations have focused on modeling the chemical 

mechanisms without explicitly dealing with particle dynamics (Leungsakul et al., 2005; 

Sarwar et al., 2003). These models can provide reasonably good predictions of particle 

mass generation, but they cannot compute particle number concentrations and size 

distributions, which are essential indices for characterizing exposure to particles

(Donaldson et al., 1998; Peters et al., 1997). A recent study by Ito and Harashima (2011)

developed a sectional model for simulating particle generation from ozone/terpene

reactions. Their model was based on physical formulations of particle dynamics, while 

omitting the detailed reaction chemistry. The model was able to predict particle 

generation and size distributions during the initial stages of generation. However, the 

model predictions were unsatisfactory in the later stages because they did not include the 

condensation growth of particles.

2.3 Literature Summary, Research Objectives, and Thesis Outline

A number of researchers have studied ozone consumption and ozone-initiated VOC 

emissions in different indoor environments, such as buildings and airliner cabins, and 

have identified human skin, hair, and clothing as significant sites for these reactions. 

However, very few researchers have studied the impact of environmental factors such as 

ozone concentration, relative humidity, and ventilation rate on ozone reactions. Therefore, 

our investigation first characterized the influence of these factors on ozone reactions with 
13

clothing soiled by human skin oils through measurements in an environmental chamber, 

which is described in Chapter 3.

In addition to VOC emissions, we conjectured that ozone reactions with soiled clothing 

could generate submicron particles. Our hypothesis was based on the fact that 

ozone/terpene reactions were found to generate submicron particles under typical indoor 

conditions and the fact that squalene (a terpene) is a major component of human skin-oils. 

However, our literature review did not find any studies of particle generation from 

ozone/clothing reactions. Therefore, we also utilized the environmental chamber setup to 

examine the possibility of particle generation from ozone reactions with clothing, as 

discussed in Chapter 4.

The literature review also showed that in contrast to the large number of experimental 

investigations, only a few modeling studies have been performed. These studies indicate

that CFD is a good method for studying ozone reaction chemistry and its impact on 

indoor air quality, but experimental data is needed to validate the CFD results. We 

therefore developed a CFD model for simulating the ozone consumption and ozoneinitiated VOC emissions in an aircraft cabin. The CFD model was based on existing 

models in the literature for studying ozone consumption, together with new empirical 

models developed for computing VOC emissions on the basis of our chamber 

measurements. The performance of the CFD model was critically analyzed by comparing 

its results with experimental measurements available in the literature. The CFD modeling 

technique for simulating ozone consumption is explained in Chapter 5, and for VOC 

emissions in Chapter 6.

Finally, we developed a model to study particle generation from ozone reactions with 

soiled clothing on the basis of our chamber measurements. Unlike the modeling 

approaches typically found in the literature, which focus on either the chemical 

mechanism of particle generation or the details of particle dynamics, we combined the 

two. The resulting model was useful for further exploring the potential of ozone/clothing 

reactions to generate particles under realistic indoor conditions, as discussed in Chapter 7.
14

CHAPTER 3. OZONE REACTION WITH CLOTHING AND ITS INITIATED VOC 

EMISSIONS IN AN ENVIRONMENTAL CHAMBER

The introductory chapters discussed that the ozone reactions with various indoor 

materials can produce secondary emissions of VOCs such as formaldehyde, acetaldehyde, 

4-oxopentanal (4-OPA), etc. Human exposure to these VOCs was found to be a greater 

health risk than the exposure to ozone itself. It was also identified that human surfaces 

(skin, hair, etc.) and their clothing are important sites for such ozone reactions and the

associated VOC emissions. Out of these, human clothing is predominant because of its 

largest surface area. Hence, this investigation studied the ozone reactions with human 

clothing under different indoor conditions by conducting experiments in an 

environmental chamber. These experiments measured the ozone consumption and its 

initiated VOC emissions from the ozone reactions with a T-shirt with/without human 

skin-oils. The experiments were designed to characterize the effect of some important 

factors such as ozone concentration, humidity, soiling degree of clothing by skin-oils, and 

air change rate on the ozone reaction chemistry.

3.1 Experimental Setup

This investigation used a stainless-steel chamber with dimensions of 1.8 m × 1.7 m × 1.7 

m to conduct experiments as shown in Figure 3.1. A steel box of 0.2 m × 0.4 m × 1.2 m 

with an internal heat source to maintain a surface temperature at 31±1°C was placed at 

the center of the chamber as a human simulator. The temperature was determined from a

previous experimental study by Rim et al. (2009), which seems reasonable since the 

comfortable skin temperature for humans under sedentary conditions is about 33–34°C

(ASHRAE, 2009). A cotton T-shirt (area ≈ 0.9 m

2

) with/without human skin-oils was 

stretched over the human simulator and was the primary site for the ozone reactions. The 

T-shirt was washed in a fragrance- and dye-free detergent and its soiling by skin-oils was
15

achieved by a male subject’s (26 years old Indian) sleeping in it unless otherwise 

mentioned. Note that the cotton T-shirt came from a set of identical T-shirts and we used 

the same method of laundering in all experiments. The T-shirt was stored in a clean 

metal-foil bag (storage time was usually less than three hours) and handled with gloves 

during laundering, storage, and placement inside the environmental chamber. The

chamber was supplied with outdoor air with an air change rate of either 0.5 h−1 or 2.7 h−1, 

and was maintained with an exhaust air temperature of approximately 24oC.

Figure 3.1. Setup of the environmental chamber used to study the ozone reactions with a 

T-shirt on a human simulator.

The detailed schematic of the experimental setup is shown in Figure 3.2. As shown in 

that figure, ozone was generated outside the chamber using UV irradiation and injected

together with the supply air. A dynamic dilution calibrator (Model 700, Teledyne 

Instruments) was used for ozone generation in most cases, and a high capacity ozone 

generator (Model 610, Jelight Company, Inc.) was used in cases (Cases 4, 9, 10, 10D, and 

11) that required high ozone levels. The ozone depleted inside the chamber due to its 

reactions with the T-shirt, human simulator, and chamber walls and produced VOCs as 

reaction byproducts. We measured the concentration of ozone at the inlet, near the T-shirt, 

and exhaust by using a three-point solenoid switching system that changed the sampling 

location (from inlet, to near T-shirt, to exhaust) every five minutes. The VOC
16

concentrations were measured before and after the ozone injection. The schematic also 

shows a Scanning Mobility Particle Sizer (SMPS), which was used for particle 

measurements discussed in Chapter 4.

Figure 3.2. Schematic of the experimental setup used to study the ozone reactions with a 

T-shirt on a human simulator.

The ozone concentration was monitored with a photometric ozone analyzer (Model 202, 

2B Technology), which has a precision equal to the greater of 1.5 ppb or 2% of the 

reading. The air samples were collected on Tenax® TA filled tubes and DNPH (2,4-

Dinitrophenylhydrazine) cartridges using a sampling pump at the exhaust. The Tenax®

TA samples were collected in duplicates at a sampling airflow rate of 100 ml/min for 2 

hours (i.e., with a sampling volume of 12 L). These duplicate samples were analyzed by 

thermal desorption gas chromatography-mass spectrometry (TD-GC/MS) for C6–C10 

straight-chain saturated aldehydes, 4-oxopentanal (4-OPA), 6-methyl-5-hepten-2-one (6-
17

MHO), and geranyl acetone according to ASTM-D6196 (ASHRAE, 2009b). The DNPH 

samples were collected at an airflow rate of 500 ml/min for 2.5 hours (i.e., with a 

sampling volume of 75 L), extracted with acetonitrile, and analyzed by high performance 

liquid chromatography (HPLC) for formaldehyde, acetaldehyde, and acetone as specified 

in ASTM-D5197 (ASHRAE, 2009c). Note that the sampling durations were different for 

Tenax® TA and DNPH samples to satisfy the sampling volume requirements under 

recommended sampling flow rates.

3.2 Experimental Procedure

We designed 13 test cases, as shown in Table 3.1 to systematically study ozone 

consumption and VOC production from human-worn clothing. The first three cases 

(Cases 1–3) were designed to identify individual contributions of the chamber, laundered 

T-shirt, and soiled T-shirt surfaces on the ozone removal and VOC emissions. The next 

eight cases (Cases 4–11) studied the effects of ozone concentration, relative humidity, 

soiling degree of the T-shirt, and air change rate. The last two cases replicated building 

conditions (i.e. lower ozone and higher relative humidity than the other test cases).

Additionally, we also conducted duplicate Cases 3D and 10D, which had chamber 

conditions almost identical to those of Cases 3 and 10, respectively, to study the 

repeatability of the results. Note that most cases were conducted at low air change rate 

(0.5 h−1

) and moderate ozone (~ 50 ppb) conditions to produce sufficiently high VOC 

concentrations in the chamber, which reduced uncertainties in their measurements.
18

Table 3.1. Details of the experimental conditions in the chamber used for studying the 

ozone reactions with clothing.

Case

Ozone 

concentration (ppb) Relative 

humidity 

(%)

Hours the Tshirt was 

worn (h)

Air 

change 

rate

(h−1) Inlet Exhaust

1 74 42 15 No T-shirt 0.5

2 158 46 17 Laundered Tshirt 0.5

3 191 40 12 6 0.5

4 530 148 20 6 0.5

5 274 54 44 6 0.5

6 247 70 23 2 0.5

7 254 57 24 12 0.5

8 244 53 27

6 hours by a 

different 

human subject

0.5

9 86 75 12 No T-shirt 2.7

10 87 48 10 6 2.7

11 224 133 11 6 2.7

12 97 22 28 6 0.5

13 105 24 49 6 0.5

3D 191 44 11 6 0.5

10D 87 51 11 6 2.7

In all the above cases, the chamber was ventilated overnight with outdoor air to remove 

any leftover VOCs from previous experiments. Then, a T-shirt was placed inside the 

chamber and it was further ventilated for 1.5 hours (about 3 air changes). Next, we set the 

air change rate to the desired value (0.5 h−1 or 2.7 h−1) and switched on the heating 

system of the manikin, and waited half an hour for the chamber airflow to stabilize. 

Subsequently, the background levels of ozone and VOCs were measured for 2.5 hours. 

The background ozone concentration was typically less than 5 ppb and the VOC 

concentrations are given in Table A.1. After background measurements, ozone was 

constantly injected through the inlet and the system was allowed to reach a steady state, 

which was assumed to prevail after 3 air changes (6 hours at 0.5 h−1 air change rate or 
19

1.25 hours at 2.7 h−1 air change rate). Finally, the VOC concentrations were measured at 

the steady state and the experiment was completed.

3.3 Evaluation Parameters

This study defined several parameters to evaluate the ozone removal and VOC emissions 

for the cases shown in Table 3.1. The parameters were ozone removal, ozone removal 

rate, contribution to the ozone removal rate, deposition velocity, and molar yield.

3.3.1 Ozone Removal (α)

The ozone removal (α) quantifies the total ozone loss in the chamber due to surface and 

gas phase reactions. Under steady state, α is given by:

α=λ − total inlet exhaust (C C ), (3.1)

where λtotal is the total air change rate and Cinlet and Cexhaust, the ozone concentrations at 

the chamber inlet and exhaust, respectively.

3.3.2 Ozone Removal Rate (βtotal)

The total ozone removal rate (βtotal) quantifies the total ozone loss in the chamber due to 

surface and gas phase reactions per unit chamber ozone concentration. At steady state, 

the βtotal can be approximated by:

inlet exhaust

total total

exhaust

(C C )

C

− β =λ . (3.2)

Note that the above equation is strictly valid only when the chamber and exhaust 

concentrations are same, i.e., for well-mixed conditions.
20

3.3.3 Contribution to the Ozone Removal Rate (βs)

The contribution to the ozone removal rate (βs) identifies the ozone removal by a surface. 

It can be estimated by adding one reacting surface at a time and measuring the βtotal in 

each case. For example, Case 1 had only chamber surfaces , Case 2 was added with a 

laundered T-shirt (clean T-shirt), and Case 3 had a soiled T-shirt (with skin-oil on it). The 

βs, chamber surfaces was approximated as:

β =β s, chamber surfaces total, Case1 . (3.3)

The βs for the laundered T-shirt and the soiled T-shirt with the skin-oils were calculated, 

respectively, as:

β =β −β s, laundered T-shirt total, Case2 total, Case1 , (3.4)

β =β −β s, soiled T-shirt total, Case3 total, Case1 . (3.5)

Note that Eqs. (3.3)–(3.5) assume that the gas phase reactions are negligible and that the 

ozone deposition on one surface does not affect the deposition on other surfaces.

3.3.4 Deposition Velocity (vd)

We also computed the deposition velocity for the soiled/laundered T-shirt, which

quantifies the ozone mass transfer coefficient and can be compared to those reported in 

the literature for similar airflow conditions. It is defined as:

s, T-shirt chamber

d

T-shirt

V

v

A

β × = , (3.6)

where AT-shirt is the area of the T-shirt surface and Vchamber the chamber volume.
21

3.3.5 Molar Yield (Y)

The molar yield of VOCi (Yi) is defined as the moles of VOCi generated per mole of 

ozone consumed by a reacting surface (Weschler et al., 2007). Hence, it characterizes the 

conversion efficiency of ozone to VOCi.

i

i

inlet exhaust

VOC Y

(C C )

∆ = − , (3.7)

where ΔVOCi is the increase in concentration of the VOCi after ozone injection. Note 

that the above equation was used in this study to compute molar yields for the T-shirt 

surface by assuming that the VOC generation by the chamber surfaces was negligible.

We defined Yfabric as the sum of molar yields of C6–C10 straight-chain saturated 

aldehydes. This parameter quantifies the efficiency of ozone conversation to C6–C10 

aldehydes from its reactions with the T-shirt fabric because C6–C10 aldehydes were 

predominantly generated by the fabric (as shown in Section 3.4.1). We also computed 

Yskin-oil by summing the molar yields of acetone and 4-OPA. It quantifies the efficiency of 

ozone conversion to acetone and 4-OPA due to its reactions with the skin-oils on the Tshirt, since these compounds were generated by the skin-oils (as shown in Section 3.4.1). 

Additionally, the molar yield for total VOCs (Ytotal) was also computed, which indicates 

the overall conversion efficiency of ozone to VOCs.

3.4 Results

This section first presents the results of Cases 1, 2, and 3, which were to identify the 

contributions of different surfaces to the ozone removal and its initiated VOC emissions. 

Then, the results of Cases 4–11 are presented, which analyze the influence of various 

factors on the ozone reaction chemistry. Finally, the ozone-initiated chemistry in indoor 

conditions similar to those in buildings is presented. The key results have been 

summarized in Table 3.2 and will be discussed in the following subsections. Detailed 

results for the individual VOC concentrations can be found in the Table A.1.
22

Table 3.2. Key results from the study of VOC generation from ozone reactions with 

human clothing.

Case α

(ppb/h)

βtotal

(h−1)

vd

(cm/s) Yfabric Yskin-oil Ytotal

1 16±1 0.38±0.03 - - - -

2 55±2 1.18±0.05 0.13±0.01 0.19±0.03 - 0.21±0.03

3 75±2 1.86±0.09 0.23±0.02 0.12±0.02 0.12±0.01 0.28±0.03

4 195±5 1.31±0.04 0.15±0.01 0.18±0.01 0.09±0.01 0.30±0.01

5 107±2 1.98±0.07 0.25±0.02 0.39±0.02 0.16±0.01 0.57±0.02

6 91±2 1.31±0.04 0.15±0.01 0.35±0.02 0.13±0.01 0.52±0.02

7 99±2 1.74±0.06 0.21±0.02 0.28±0.02 0.16±0.01 0.46±0.02

8 97±2 1.82±0.07 0.23±0.02 0.34±0.02 0.18±0.01 0.54±0.02

9 30±7 0.40±0.09 - - - -

10 107±7 2.3±0.2 0.29±0.03 0.15±0.08 0.27±0.05 0.5±0.1

11 250±10 1.9±0.1 0.23±0.02 0.22±0.04 0.18±0.02 0.44±0.05

12 40±1 1.9±0.1 0.23±0.03 0.23±0.04 0.23±0.03 0.49±0.06

13 40±1 1.6±0.1 0.20±0.02 0.29±0.04 0.22±0.03 0.54±0.05

3D 75±2 1.72±0.08 0.21±0.02 0.11±0.02 0.11±0.01 0.23±0.03

10D 100±7 2.0±0.1 0.25±0.03 0.11±0.09 0.20±0.06 0.4±0.1

α is the ozone removal, βtotal the total ozone removal rate, vd the deposition 

velocity for the T-shirt, Yfabric the molar yield of C6–C10 straight-chain saturated 

aldehydes, Yskin-oil the molar yield of squalene oxidation products (acetone and 4-

OPA), and Ytotal the total molar yield of VOCs. The measurement uncertainties 

were estimated from an error propagation analysis and reported as plus/minus one 

standard deviation (±σ). Case numbers followed by 'D' are duplicate experiments.

3.4.1 Effect of Different Surfaces on Ozone Reactions

Cases 1, 2, and 3 were designed to identify the contributions of individual surfaces (the 

chamber, laundered T-shirt, and soiled T-shirt) on the ozone removal and VOC emissions 

by adding reacting surfaces one at a time.

Table 3.2 shows that α in Case 1 was the smallest since it only had the chamber surfaces. 

The α increased in Case 2 because of additional ozone removal by the T-shirt. The α 

further increased in Case 3, which was due to the extra ozone removed by the skin-oils in

the soiled T-shirt. According to Eqs. (3.3)–(3.5) and the data shown in Table 3.2, βs from 

the chamber surfaces was 0.38 h−1, from the laundered T-shirt was 0.80 h−1, and from the 
23

soiled T-shirt was 1.48 h−1. We also calculated the deposition velocities (vd) for the 

laundered and soiled T-shirts as shown in Table 3.2. The vd for the T-shirt compared well 

with those measured by Tamas et al. (2006), who reported values between 0.19 and 0.27 

for soiled T-shirts stretched over seat backs in a Boeing-767 aircraft cabin mockup.

Figure 3.3 shows the ozone-initiated VOCs emissions for Cases 1 to 3. Without the Tshirt (Case 1), the concentrations of VOCs did not increase much with ozone, but with 

the T-shirt (Cases 2 and 3) they did. Without the T-shirt, only formaldehyde and 

acetaldehyde concentrations increased slightly with ozone. With the laundered T-shirt 

(Case 2), the ozone led to a slight increase in formaldehyde concentration as in the 

previous case, but not acetaldehyde. A small amount of increase in C6–C8 straight-chain 

saturated aldehydes (~ 2 ppb) and a significant increase in nonanal and decanal 

concentrations (> 5 ppb) were observed with ozone injection, when compared to Case 1. 

This indicates that these VOCs were ozone reaction byproducts with the laundered Tshirt fabric. With the T-shirt soiled with human skin-oils (Case 3), the increase in 

aldehyde concentrations was about the same as in Case 2. The concentrations of acetone 

and 4-OPA increased considerably (> 5 ppb) with ozone when compared to Case 2, 

which means that these are the ozone reaction byproducts with the human skin-oils. 

Wisthaler and Weschler (2010) pointed out that acetone and 4-OPA were the major

oxidation products of the unsaturated compound squalene present in the human skin 

lipids. The other squalene oxidation products such as 6-MHO and geranyl acetone were 

not detected, probably due to their low concentrations. Our results are in qualitative 

agreement with the study by Coleman et al. (2008a), which measured the ozone-initiated 

VOC emissions from soiled and laundered clothing fabrics. They reported emissions of 

straight-chain saturated aldehydes from ozone reactions with both laundered and soiled 

fabrics, but the squalene oxidation products (acetone and 6-MHO) were mainly produced 

with soiled fabrics.
24

Figure 3.3. Effect of the chamber, laundered T-shirt, and soiled T-shirt surfaces on the 

ozone-initiated VOC emissions. Error bars indicate plus one standard deviation.

Table 3.2 shows that the Yfabric was significantly higher in Case 2 than in Case 3 because 

of the lower ozone consumption in Case 2, although the generation of C6–C10 aldehydes 

was approximately the same in both the cases. The Yskin-oil was approximately zero in 

Case 2 since no squalene oxidation products (acetone and 4-OPA) were generated from 

the ozone reactions with the laundered T-shirt surface. The Ytotal was higher in Case 3 

than in Case 2, indicating that the overall efficiency of the ozone conversion to VOCs 

was higher with the soiled T-shirt.

3.4.2 Effect of Different Factors on Ozone-initiated Chemistry

This investigation designed Cases 4 to 11 to identify the influence of several key factors 

on the ozone reaction chemistry by using Case 3 as a reference case. This required that 

only one factor would vary in Cases 4–11 and that the other factors would be identical to 

the reference case. However, it was difficult to achieve this with the current experimental 

setup, which made comparisons with the reference case unsuitable while analyzing the 

effect of T-shirt soiling. Hence, to isolate the influence of T-shirt soiling, we compared 
25

Cases 6, 7, and 8 which had different soiling levels, but otherwise similar conditions 

(ozone and relative humidity).

3.4.2.1 Effect of Ozone Concentration

In order to identify the effect of ozone concentration, this investigation compared Case 4 

(148 ppb ozone) with Case 3 (40 ppb ozone), given in Table 3.1. These cases had roughly 

identical chamber conditions except for the large difference in the ozone concentrations.

Table 3.2 shows that the βtotal and vd were about 30% lower in Case 4 than in Case 3. 

However, the βtotal (and vd) should be constant for different ozone levels since the ozone 

reactions with surfaces that have large amounts of reactant species generally follow a 

first-order mechanism (Tamás et al., 2006). Hence, the decrease in βtotal (and vd) at high 

ozone concentrations shows that the reaction was probably saturated (ozone was in 

excess) and limited by the availability of skin-oils on the T-shirt surface. The α was 

higher in Case 4 than in Case 3, due to the high ozone concentration in Case 4 (α = 

Cexhaust×βtotal).

Figure 3.4 shows that total VOC emissions more than doubled when the soiled T-shirt 

was exposed to higher ozone (148 ppb in Case 4 versus 40 ppb in Case 3). However, 

Coleman et al. (2008a) found that ozone-initiated VOC emissions from cotton fabric only 

increased ~ 30% when the chamber ozone concentration was increased from 45 ppb to 

213 ppb ozone (Figure 5 in Coleman et al. (2008a)). Compared with their experiment, our 

chamber was 50 times larger in volume and the air change rate was 40 times lower, 

which created a spatial ozone gradient in our experiments. For example, the ozone 

concentrations near the T-shirt surface were about 15 ppb and 66 ppb in Cases 3 and 4, 

respectively, where the exhaust concentrations were 40 ppb and 148 ppb. The chamber 

used by Coleman et al. (2008a) likely did not have such a gradient, which means that the 

ozone concentrations near the soiled fabric were close to the chamber concentrations (45–

213 ppb). Hence, it is possible that the high ozone concentrations near the fabric already 

saturated the reaction in their study, and there was insignificant influence of ozone 

concentration on the VOC emissions.
26

Figure 3.4. Effect of ozone concentration on the VOC emissions from its reactions with 

the soiled T-shirt at an air change rate of 0.5 h−1

. Error bars indicate plus one standard 

deviation.

Table 3.2 shows that the Yfabric was significantly higher in Case 4 than that in Case 3, 

which indicates that the high ozone concentration in Case 4 enhanced the ozone 

conversion efficiency to aldehydes from the T-shirt fabric. However, the Yskin-oil was 

lower in Case 4, probably because the reaction was already saturated due to the high 

ozone concentration. The Ytotal was approximately equal in both cases.

Table 3.2 further shows the impact of ozone concentration on Yfabric, Yskin-oil, and Ytotal at 

a higher air change rate of 2.7 h−1 (Cases 10 vs. 11), which was similar to that observed at 

0.5 h−1 air change rate. Under the high air change rate, Figure 3.5 shows much lower 

VOC concentrations when compared with Figure 3.4, which is discussed in the Section

3.4.2.4. To avoid the VOC concentrations becoming lower than our equipment detection 

limit or causing a high measuring error, most of our measurements used a lower air 

change rate at 0.5 h−1.
27

Figure 3.5. Effect of ozone concentration on the VOC emissions from its reactions with 

the soiled T-shirt at an air change rate of 2.7 h−1

. Error bars indicate plus one standard 

deviation.

3.4.2.2 Effect of Relative Humidity

This investigation used Cases 3 and 5 to study the impact of different humidity conditions

on the ozone reactions. Case 5 had a relative humidity of 44% while Case 3 had 12%. 

The ozone concentration was slightly higher in Case 5 as it was difficult to control it 

during our experiment, but this should not play a major role in this comparison.

Table 3.2 shows that βtotal and vd were about the same in both cases, and the humidity did 

not have much effect. The α was significantly higher in Case 5 than that in Case 3 since it 

varies linearly with ozone concentration for a constant βtotal (α = Cexhaust×βtotal). Ozoneinitiated VOC emissions except for formaldehyde and acetaldehyde were much higher in 

Case 5 than in Case 3, as shown in Figure 3.6. The most significant increase was in the 

emissions of C6–C8 aldehydes, nonanal, and decanal, which were 3–6 times higher at the 

higher humidity level in Case 5. These results agreed qualitatively with those by Coleman 

et al. (2008a), who found that ozone-initiated VOC emissions with a laundered cotton 
28

fabric increased by raising the relative humidity from 10% to 50%. The Yfabric in Case 5 

was about three times higher than in Case 3, and the Yskin-oil and Ytotal also increased in 

Case 5, as shown in Table 3.2. This indicates that the high relative humidity increased the 

conversion efficiency of ozone (to VOCs) from the soiled T-shirt, especially from the 

cotton fabric.

Figure 3.6. Effect of relative humidity on the ozone-initiated VOC emissions with the 

soiled T-shirt. Error bars indicate plus one standard deviation.

3.4.2.3 Effect of T-shirt Soiling

This study compared Cases 6 and 7 to characterize the effect of soiling amount of T-shirt 

by human skin oils on ozone removal and its initiated VOC emissions. The different 

soiling levels in these cases were achieved by having a person sleep in the T-shirt for 2 h 

in Case 6 and 12 h in Case 7. Note that the ozone concentration in the two cases also 

differed slightly.

Table 3.2 shows that βtotal and vd were significantly lower in Case 6 compared to that in 

Case 7, probably due to incomplete soiling of the T-shirt surface by human skin-oils. The 
29

α was also low in Case 6 despite the higher ozone concentration since the βtotal was low (α 

= Cexhaust×βtotal). Figure 3.7 shows the effect of the soiling amount of the T-shirt on the 

ozone-initiated VOC emissions. The C6–C10 aldehyde emissions were the same between 

Cases 6 and 7 since these were primarily reaction byproducts of the ozone with the cotton 

T-shirt and were not affected by the soiling of the T-shirt. The emissions of squalene 

oxidation products (acetone and 4-OPA) were slightly lower in Case 6 than in Case 7 

because the T-shirt had not become completely soiled by the skin-oils in 2 hours. Table 

3.2 shows that the Yfabric and Ytotal were higher in Case 6 than in Case 7 due to the low 

ozone consumption in Case 6. The Yskin-oil was smaller in Case 6 because of the lower 

emissions of squalene oxidation products.

Figure 3.7. Effect of T-shirt soiling by skin-oils on the ozone-initiated VOC emissions.

Error bars indicate plus one standard deviation.

To understand the effect of the skin-oils from the human subject on the ozone reactions 

with the T-shirt, this study used a T-shirt in Case 8 worn by a female subject (middleaged American). The results were compared with those from Case 7 since both cases had 

similar chamber conditions (i.e., exhaust ozone and humidity levels). 
30

The α, βtotal, and vd were nearly the same between Cases 7 and 8, as shown in Table 3.2.

Figure 3.7 shows that the VOC emissions were slightly higher in Case 8, which made the 

Yfabric, Yskin-oils, and Ytotal also slightly higher. Hence, it seems that the ozone reaction 

chemistry with the soiled T-shirt was not affected by different human subjects sleeping in 

the T-shirt.

As stated previously, this investigation did not used the reference case (Case 3) to 

analyze the effect of T-shirt soiling (studied in Cases 6, 7, and 8) since the ozone and

relative humidity levels were quite low in Case 3 compared with those in Cases 6, 7, and 

8. However, it is noteworthy that the ozone-initiated VOC emissions more than doubled 

in Cases 6, 7, and 8 compared to Case 3. Such high VOC emissions were likely due to the 

combined effects of high ozone and relative humidity levels in Cases 6, 7, and 8.

3.4.2.4 Effect of Air Change Rate

Cases 9 and 10 were performed with an air change rate of 2.7 h−1. Case 9 did not have a 

T-shirt in the chamber, while Case 10 had a soiled T-shirt. Cases 9 and 10 can be 

compared to Cases 1 and 3, which had the lower air change rate of 0.5 h−1.

Table 3.2 shows that α was higher in Cases 9 and 10 than in Cases 1 and 3, respectively, 

because of higher βtotal and ozone concentrations (α = Cexhaust×βtotal). Ozone removal rate 

by the chamber surfaces (βs, chamber surfaces) under different air change rates was the same 

(0.40 h−1 for Case 9 and 0.38 h−1 for Case 1). However, ozone removal rate by the T-shirt 

(βs, T-shirt) increased with the air change rate (2.3 h−1 – 0.4 h−1 = 1.9 h−1 for Case 10, and 

1.86 h−1 – 0.38 h−1 = 1.48 h−1 for Case 3.), and also its deposition velocity (vd) given in

Table 3.2. Ozone removal rate (and deposition velocity) by a surface depends on (1) the 

fluid motion and ozone diffusion that transport ozone molecules to the surface and (2) the 

ozone chemical reactions on the surface (Cano-Ruiz et al., 1993). For relatively lowreactivity surfaces (such as the chamber walls), ozone removal is limited by ozone 

reactions at the surface. For highly reactive surfaces (soiled T-shirt surfaces), ozone 

uptake by the surface is limited by the rate at which ozone can be transferred to the 

surface (and hence air change rate).
31

The ozone-initiated VOC emissions were very low in Case 9 without the T-shirt, and the 

total concentration of VOCs only increased by about 4 ppb after ozone injection (results 

not shown). Figure 3.8 compares the VOC emissions in Case 10 with those in Case 3. In 

Case 10, the air change rate was 5 times higher, but the VOC concentrations only reduced 

by a factor of 2, which means that the VOC emission rate was approximately 2.5 times 

higher than Case 10 compared with Case 3. The higher emission rate was probably 

because Case 10 had a thin boundary layer compared with Case 3 (due to the higher air 

change rate), which could have enhanced the convective transport of VOCs from the Tshirt surface to the chamber air. Hence, Yfabric, Yskin-oil, and Ytotal in Case 10 were also 

higher than those in Case 3, as shown in Table 3.2. The most significant increase was in 

Yskin-oil, which more than doubled in Case 10.

Figure 3.8. Effect of air change rate on the ozone-initiated VOC emissions with the soiled 

T-shirt. Error bars indicate plus one standard deviation.

3.4.3 Ozone Reaction Chemistry under Typical Building Conditions

Since this investigation used 0.5 h−1 air change rate for most of the cases, the ventilation 

rate corresponded to that in buildings. Of course, the ozone concentration in buildings is 
32

normally not very high. Cases 12 and 13 simulated environmental conditions that could 

be found in buildings on a poor air-quality day. The ozone concentration was about 22 

ppb. This investigation studied the ozone reactions under two different relative humidity 

conditions, 28% for Case 12 and 49% for Case 13.

The βtotal and vd in Case 13 was about 10% lower than in Case 12. However, the α in both 

cases was almost the same because the ozone concentration was higher in Case 13 (α = 

Cexhaust×βtotal). Figure 3.9 shows that both the cases had significant ozone-initiated VOC 

emissions despite the low ozone concentrations. The VOC emissions were slightly higher 

in Case 13 compared to those in Case 12 probably due to the higher humidity level, 

which is also consistent with the results when comparing Case 3 with Case 5. The Yfabric

and Ytotal were also higher in Case 13 compared with those in Case 12, but the Yskin-oil

was approximately equal.

Figure 3.9. Ozone-initiated VOC emissions with soiled T-shirt under typical building 

conditions with different relative humidity levels. Error bars indicate plus one standard 

deviation.
33

3.5 Discussion

There were several sources of uncertainties in the experiment such as measurement errors 

of ozone monitors, GC-MS, and HPLC; uncertainties in soiling of T-shirts; random errors; 

etc. For example, this study did not quantify the amount of skin-oils on the T-shirt 

surface, which means there would be no assurance that the amount of soiling would be 

the same even if the same human subject slept in the T-shirt for the same amount of time. 

Hence, this investigation conducted duplicate experiments 3D and 10D for chamber 

conditions almost identical to Cases 3 and 10, respectively, to analyze the repeatability of 

the results. The βtotal was 1.72 h−1 in Case 3D, which was about 8% lower than in Case 3. 

The βtotal was 2.0 h−1 in Case 10D, which was about 12% lower than in Case 10. The 

production of the ozone-initiated VOCs was similar between the duplicate pairs of 

experiments as shown in Figure 3.10. Hence, there was reasonable repeatability between 

the duplicate pairs of experiments despite the experimental uncertainty.
34

Figure 3.10. The ozone-initiated emissions of VOCs in duplicate experimental 

measurements, (a) for Case 3 (b) for Case 10. Error bars indicate plus one standard 

deviation.
35

3.6 Conclusions

This chapter experimentally studied ozone reactions with a cotton T-shirt in a middlescale environmental chamber under different factors. The factors considered were ozone 

concentration, relative humidity, soiling degree of the T-shirt by human skin-oils, and air 

change rate. The study measured the ozone consumption and its initiated VOC emissions 

and came to the following conclusions:

The ozone reactions with the T-shirt consumed ozone and generated VOCs. The C6–C10 

straight-chain saturated aldehydes were the major byproducts of the ozone reaction with 

the cotton T-shirt fabric. Acetone and 4-OPA were mainly produced due to the ozone 

reaction with the human skin-oils in the T-shirt.

These reactions were significantly affected by the chamber conditions. The ozone 

removal rate by the T-shirt and its deposition velocity increased with the increasing 

soiling level and air change rate, decreased at high ozone concentrations, and was 

relatively unaffected by the humidity.

The ozone-initiated VOC emissions were generally higher at higher ozone, humidity, 

soiling of T-shirt, and air change rates. The total molar yield for most cases was 0.5 as 

shown in Table 3.2, which means that for every two moles of ozone removed by the Tshirt surface, one mole of VOCs were produced.
36

CHAPTER 4. OZONE REACTION WITH CLOTHING AND ITS INITIATED 

PARTICLE GENERATION IN AN ENVIRONMENTAL CHAMBER

The previous chapter studied the ozone-initiated VOC emissions from its reactions with a 

T-shirt. Some of those VOC emissions were attributed to the ozone reactions with 

squalene, which is a triterpene found in the human skin-oils. Several researchers have 

also shown that the ozone reactions with terpene-based consumer products are important 

sources of submicron particles in the indoor air (Coleman et al., 2008b; Destaillats et al., 

2006; Sarwar et al., 2004; Singer et al., 2006). Hence, it is conceivable that the ozone 

reactions with human skin-oils (which contains a triterpene) could also generate 

submicron particles. However, to our knowledge there was no investigation of particle 

generation from skin-oils. Therefore, we conducted measurements to investigate particle 

generation from ozone reactions with clothing with/without human skin-oils under 

different indoor conditions as discussed in this chapter. We studied the effect of various 

factors such as ozone concentration, relative humidity, degree of soiling of a T-shirt with 

skin-oils, and air change rate on the particle generation.

4.1 Method

This investigation conducted experiments in the environmental chamber (see Figures 3.1

and 3.2) that was described in the previous chapter. In addition to measurements of ozone

and VOCs (reported in Chapter 3), we also measured the time-varying particle 

concentrations at the inlet, near the T-shirt, and exhaust. Particle concentrations were 

monitored by a Scanning Mobility Particle Sizer (SMPS) (Model 3936 by TSI), which 

measured the number concentrations of particles between 9.65 and 421.7 nm in diameter 

and separated them into 105 bins (or 64 bins per decade of particle diameter). The SMPS 

software also calculated the particle mass concentrations by:
37

3 M Dp N p m 6

π

= ρ , (4.1)

where ρp is the particle density; Dpm the geometric midpoint diameter of the particle size 

bin; N the particle number concentration; and M the particle mass concentration. The 

particle density was taken as 1.2 g/cm3 as suggested by Turpin and Lim (2001), who 

derived their results by using the data of Rogge et al. (1993) for atmospheric organic 

aerosols. Note that the particle number concentrations are approximate values since the 

SMPS was not calibrated before making the measurements, and the existing factory 

settings were used. Hence, the mass concentrations also bear large uncertainties since it 

was derived from the number concentrations by using Eq. (4.1).

To examine the particle generations due to ozone reactions with clothing and identify the 

effect of different factors on these generations, we used the same cases that were

described in Table 3.1. However, some of those cases could not be used for studying the 

particle generations due to malfunctioning of the SMPS. Hence, Table 4.1 lists those 

cases (from Table 3.1) that measured the particle generations. Note that these cases have 

been renumbered in Table 4.1 (from Case 1–12) to carry out an orderly discussion of the 

results for this chapter.
38

Table 4.1. Details of the experimental conditions in the chamber used for studying 

particle generation from ozone reactions with human clothing.

Case

Corresponding 

case in Table 

3.1

Ozone 

concentration 

(ppb)

Relative 

humidity 

(%)

Hours the T-shirt was 

worn (h)

Air 

change 

rate (h−1)

1 1 42 15 No T-shirt 0.5

2 2 46 17 Laundered T-shirt 0.5

3 3 40 12 6 0.5

4 4 148 20 6 0.5

5 5 54 44 6 0.5

6 6 70 23 2 0.5

7 7 57 24 12 0.5

8 8 53 27 6 hours by a different 

human subject 0.5

9 9 75 12 No T-shirt 2.7

10 10D 51 11 6 2.7

11 11 133 11 6 2.7

12 12 22 28 6 0.5

4.2 Results

This section first presents the results from Cases 1, 2, and 3, which were used to identify 

the effects of different surfaces on ozone-initiated particle generation. Next, the results of 

Cases 4–12 were used to analyze the influence of different factors on these particle 

generations and also to study them for typical building conditions. Table 4.2 summarizes 

the key results.
39

Table 4.2. Key results from the study of particle generation from ozone reactions with 

human clothing.

Case

Nmax, 

exhasut

(#/cm3)

Time at 

Nmax, 

exhaust

(hours)

GMD at 

Nmax, 

exhaust

(nm)

GSD at 

Nmax, 

exhaust

Particle number 

concentrations at 

the end of 

experiment

(#/cm3)

Particle mass 

concentrations 

at the end of 

experiment 

(μg/m3)

Inlet Exhaust Inlet Exhaust

1 1212 8.2 20 2.2 271 509 0.15 0.30

2 2180 5.2 28 1.8 512 1235 0.28 0.49

3 8034 4.2 31 1.4 138 1408 0.08 0.79

4 23326 3.7 31 1.4 134 2212 0.07 1.10

5 8639 4.0 35 1.5 102 1819 0.07 1.86

6 3762 4.7 34 2.3 878 1404 0.62 2.09

7 8929 4.2 32 1.5 240 1886 0.14 1.29

8 5527 4.5 36 1.6 200 1293 0.15 1.32

9 323 6.0 19 2.3 217 208 0.17 0.16

10 781 1.7 19 2.2 159 192 0.05 0.17

11 714 0.5 20 2.2 140 171 0.08 0.11

12 1185 6.5 36 1.7 62 591 0.04 0.18

Nmax, exhaust is the peak particle number concentration at exhaust; GMD and GSD are the 

geometric mean diameter and standard deviation of particle size distributions, 

respectively.

4.2.1 Effect of Different Surfaces on the Ozone-initiated Particle Generation

This subsection discusses particle generation due to ozone reactions with the steel 

chamber, laundered T-shirt, and soiled T-shirt surfaces. Figure 4.1 shows that the exhaust 

particle concentrations were very low in Cases 2 and 3 without ozone injection (t < 2.5 h). 

The particle concentrations were not measured without ozone in Case 1 because of a 

malfunction of the SMPS. After ozone injection (t > 2.5 h), the exhaust particle 

concentration did not show a significant increase without the T-shirt (Case 1) if it is

assumed that the conditions for Cases 2 and 3 for t < 2.5 h applied to Case 1. However, 

the particle concentration increased significantly when the T-shirt was present (Cases 2 

and 3). Compared with the laundered T-shirt (Case 2), the particle concentration was 

much higher with the soiled T-shirt (Case 3) after ozone injection. These results show 
40

that the ozone reactions with the T-shirt generated particles, which were enhanced by the 

soiling of the T-shirt with human skin-oils, probably due to the ozone reactions with 

human skin-oils as suggested by Fadeyi et al. (2013).

Figure 4.1. Effect of chamber, laundered T-shirt, and soiled T-shirt surfaces on the 

ozone-initiated particles measured at the exhaust.

We further analyzed particle generation due to ozone reactions with the soiled T-shirt 

(Case 3) since it was the most significant. Figure 4.2 shows the temporal variations of 

particle number and mass concentrations at the inlet and exhaust for Case 3. The 

corresponding particle size distributions at the exhaust are shown in Figure 4.3 for some 

important points in time. The exhaust particle concentrations were low from t = 0–2.5 h 

as shown in Figures 4.2 and 4.3(a), due to the low inlet concentrations. From t = 2.5 h, 

ozone was continuously injected in the chamber, but the particle concentrations were still 

low even after 40 minutes (t = 3.2 h) as shown in Figure 4.3(b). At t = 3.5 h, a primary 

burst of ultrafine particles (UFPs) with a geometric mean diameter of approximately 18 

nm was measured, as shown in Figure 4.3(c). This one-hour delay after the ozone 

injection was probably due to the time required for the generation of sufficient precursors 
41

from the ozone reactions (low-vapor-pressure oxidation products), which can form 

particles.

Figure 4.2. Temporal variations of particle number and mass concentrations at the inlet 

and exhaust for the ozone reactions with the soiled T-shirt in Case 3.
42

Figure 4.3. Evolution of particle size distributions at the exhaust for the ozone reactions 

with the soiled T-shirt in Case 3.

The primary burst could be attributed to nucleation (which forms new particles) or 

condensation (which leads to particle growth) on smaller existing particles, which grew 
43

to sizes measurable by SMPS. However, nucleation seems more likely, since 

condensation on existing particles should also produce particle bursts in higher sizes as 

suggested by Sarwar et al. (2003). After the primary burst, the number and mass 

concentrations increased for about 45 minutes (until t = 4.2 h), and the particles sizes also 

grew as shown in Figures 4.2 and 4.3(d). Hence, nucleation, condensation, and 

coagulation mechanisms seemed to prevail. The number concentrations then decayed 

(until t = 7.0 h), which suggests that new particles formed at a slower rate as compared to 

the removal of existing particles by ventilation and deposition. However, the mass 

concentrations and particle sizes continued to increase, indicating that particle generation 

by condensation was dominant, as shown in Figures 4.2, 4.3(e), and 4.3(f). Coagulation 

of particles could also be responsible for some of the particle growth, but it cannot 

account for the increasing mass concentrations, suggesting that growth by condensation 

was more significant.

A secondary burst of UFPs was observed at t = 7.2 h as shown in Figures 4.2 and 4.3(g). 

Hence, it seems that new particles were formed by nucleation, since insufficient particles 

were left in the chamber which reduced the surface area available for condensation as 

shown in Figure B.1. A similar phenomenon was also observed by Coleman et al. (2008b)

during ozone-initiated particle generations from terpene-rich household products. The 

particle concentrations and sizes continued to increase until t = 8.5 h, probably as a result 

of nucleation, condensation, and coagulation mechanisms as shown in Figures 4.2 and

4.3(h). Finally, the particle number concentrations decayed until the end of the 

experiment because of ventilation and deposition. The mass concentrations also decayed, 

probably because of a decrease in particle generation rate or particle growth beyond the 

measuring range. Figures 4.3(i) and (j) illustrate the particle size distributions towards the 

end of the experiment. We did not observe another burst of particles toward the end of 

the experiment despite the number concentrations (and surface area) being lower than 

that at which the secondary burst was observed. Hence, it seems that the particle 

generation rate declined towards the end, probably as a result of depletion of skin-oils 

from the T-shirt surface.
44

4.2.2 Effect of Different Factors on the Ozone-initiated Particle Generation

This study used Cases 4 to 11, given in Table 4.1 to identify the effect of different factors 

on particle generation resulting from ozone reactions with the soiled T-shirt. These cases 

were originally designed by keeping Case 3 as the reference and varying one factor at a 

time to isolate its effect on particle generation. However, it was difficult to control the 

chamber conditions precisely, and sometimes more than one factor varied from the 

reference case, which made the reference case unsuitable for comparison. Hence, we 

compared those cases which had roughly identical conditions except for the factor to be 

examined. The factors examined were ozone concentration, relative humidity, soiling 

degree of the T-shirt, and air change rate.

4.2.2.1 Effect of Ozone Concentration

This investigation compared Cases 3 and 4, which had 40 ppb and 148 ppb ozone, 

respectively, in order to identify the effect of ozone on particle generation. The other

conditions were similar between the two cases.

Figures 4.4(a) and (b) compare the particle number and mass generation, respectively, 

from the ozone reaction with the soiled T-shirt in Cases 3 and 4. In both the cases, the 

inlet particle concentrations were very low throughout the experiment. A primary and 

secondary burst of UFPs was measured at exhaust after ozone was injected at t = 2.5 h, 

and the particle generation mechanisms were also similar. However, the primary burst of 

particles occurred earlier in Case 4 (148 ppb ozone), and the particle number and mass 

concentrations were also generally higher. Hence, it seems that the high ozone 

concentration in Case 4 increased the production of particle-forming precursors from its 

reactions with the soiled T-shirt. The secondary burst of particles happened later in Case 

4 than that in Case 3, since the high particle concentrations (and high surface areas) in 

Case 4 probably sustained condensation for a longer period of time.
45

Figure 4.4. Effect of ozone concentration on (a) particle number generation and (b) 

particle mass generation when the ozone reacted with the soiled T-shirt.
46

4.2.2.2 Effect of Relative Humidity

This study used Case 5 with a relative humidity of 44% to study the impact of relative 

humidity on particle formation, by comparing it with Case 3 with a relative humidity of 

12%. The other conditions were roughly similar for the two cases.

The inlet and exhaust particle number concentrations over time were approximately the 

same in Cases 3 and 5 as shown in Figure 4.5(a), except that the secondary burst was less 

pronounced in Case 5. The secondary burst was less significant in Case 5 because 

condensation was dominant over nucleation due to the large surface area of existing 

particles as shown in Figure B.2. The particle mass concentrations were also significantly 

higher in Case 5 (44% RH), as shown in Figure 4.5(b), because of larger particle sizes. 

Such an increase in sizes was reported by Dua and Hopke (1996) for some indoor 

particles at high relative humidity conditions, and was attributed to hygroscopic growth. 

Hence, it seems that the particles generated by ozone reactions with the soiled T-shirt 

were hygroscopic in nature, and their sizes increased at high relative humidity, which 

also increased their masses.
47

Figure 4.5. Effect of relative humidity on (a) ozone-initiated particle number generation 

and (b) ozone-initiated particle mass generation with the soiled T-shirt.

4.2.2.3 Effect of T-shirt Soiling

This investigation used Cases 6 and 7 given in Table 4.1, which had different soiling 

levels of the T-shirt but otherwise similar conditions, to identify the effect of soiling level 
48

on particle generation. The different soiling levels of the T-shirt were achieved by a 

human subject’s sleeping in it for 2 hours and 12 hours in Cases 6 and 7, respectively.

Initially, the exhaust particle number and mass concentrations with the 2-hour soiled Tshirt in Case 6 were higher than those with the 12-hour soiled T-shirt in Case 7 because 

of the high inlet concentrations as shown in Figures 4.6(a) and (b). From t = 2.0 h to t = 

3.5 h, the exhaust concentrations in Case 6 further increased because of an increase in the 

inlet particle concentrations. For t > 3.5 h the exhaust concentrations continued to 

increase, but not the inlet concentrations. This indicates that the particles were generated 

from the ozone reactions in Case 6 because the ozone was injected at t = 2.5 h. Since the 

inlet particle concentrations for Case 7 were relatively stable and small, it is much easier 

to see particle generation from the ozone reaction after the ozone was injected at t > 2.5 h.
49

Figure 4.6. Effect of the soiling level of the T-shirt by skin-oils on (a) ozone-initiated 

particle number generation and (b) ozone-initiated particle mass generation.
50

After ozone injection, the particle number concentration in Case 7 was significantly 

higher than that in Case 6. The lower particle concentration in Case 7 before ozone 

injection could have promoted much higher nucleation of new particles compared to that 

in Case 6. Such a phenomenon was also reported by Sarwar et al. (2003), who found that 

low initial particle concentrations (and low surface areas) supported nucleation of new 

particles, whereas high initial concentrations (and high surface areas) favored 

condensation on existing ones. The particle mass generation in Case 7 was lower than 

that in Case 6 as shown in Figure 4.6(b), probably because the 2-hour soiling level was 

sufficiently high for the reaction and the reaction was already saturated.

In order to characterize the effect of skin-oils from different human subjects on particle 

generation, this investigation used a T-shirt soiled by a female subject (middle-aged 

American) in Case 8. The results were compared with those of Case 7 under similar 

conditions.

Figure 4.7(a) shows the particle number concentrations at the inlet and exhaust for the 

two cases. Before the ozone injection (t < 2.5 h), the exhaust particle concentration in 

Case 8 was greater than that in Case 7 because of high inlet concentration. However, after 

the ozone injection, the exhaust number concentration was higher in Case 7, probably 

because of higher nucleation of new particles resulting from low initial concentrations, 

which was discussed when comparing Cases 6 and 7. The particle mass generation was 

similar for both cases as shown in Figure 4.7(b), which indicates that it was unaffected by 

the different human subjects in these cases.
51

Figure 4.7. Effect of T-shirt wearing by different human subjects on (a) ozone-initiated 

particle number generation and (b) ozone-initiated particle mass generation.
52

4.2.2.4 Effect of Air Change Rate

Cases 9 to 11, given in Table 4.1, were at a higher air change rate of 2.7 h−1. Case 9 was 

without a T-shirt; Case 10 with a soiled T-shirt at 51 ppb ozone; and Case 11 with a 

soiled T-shirt at 133 ppb ozone. In all of these cases, particle generation due to ozone 

reactions was not observed because the exhaust and inlet particle concentrations were 

approximately the same, as illustrated in Table 4.2. It seems that there was insufficient 

time for particle formation at the high air change rate, since the high ventilation could 

quickly remove the particle-forming precursors from the chamber. These results are in 

qualitative agreement with a previous study by Weschler and Shields (2003), who 

measured particle generation from gas phase ozone reactions with d-limonene (a terpene) 

under different air change rates. They found that ozone-initiated particle generations 

decreased when the air change rate was increased, and finally that no excess particles 

were detected at air change rates exceeding 12.0 h−1

.

4.2.3 Ozone-initiated Particle Generation under Typical Building Conditions

Most of the cases studied (Cases 1 to 8) were with an air change rate of 0.5 h−1

, which 

corresponded to typical ventilation conditions in buildings. However, these cases had 

high ozone concentrations when compared with about 10 ppb as typically found in 

buildings. The high concentration, in fact, helps us accurately characterize the effect of 

different factors on the ozone reactions, since a low ozone concentration would 

considerably increase the experimental uncertainties. Nevertheless, we conducted Case 

12 as a low-ozone case with only 22 ppb concentration, which can be found in buildings 

on a poor air quality day.

Figure 4.8 shows the ozone-initiated particle number generation for Case 12. The particle 

mass generation is not shown here; it was very low because of the small number 

concentrations, which led to high fluctuations in the estimation of mass concentrations 

from Eq. (4.1) Nevertheless, the particle generation mechanism seemed similar to 

previous cases. A primary burst of UFPs was observed at about one hour after ozone 

injection, which was followed by particle growth. Finally, the particle number 
53

concentration attained a steady value of about 600 #/cm3 with most particles lying in the 

ultrafine region (diameters < 100 nm). Hence, it seems that the ozone reactions with 

human clothing can become a possible source for indoor UFPs, which is further discussed 

in the next section.

Figure 4.8. Ozone-initiated particle number generation under typical building conditions.

4.3 Discussion

UFPs are presumably responsible for the association between the exposure to particulate 

matter and morbidity and mortality (Donaldson et al., 1998; Seaton et al., 1995; Sioutas 

et al., 2005). Wallace and Ott (2011) estimated the indoors exposure to UFPs as 2373 

#/cm3 when no sources were present, and the total daily intake was estimated to be 

172000 #-h/cm3 for typical suburban locations. We calculated that the ozone reactions

with human surfaces can contribute between 80–160 #/cm3 UFPs for typical building 

conditions on a poor air quality day (See Appendix B for details), which corresponds to a 

daily intake of 1760–3520 #-h/cm3 UFPs for 22 hours spend indoors. Hence, such ozone 

reactions can account for approximately 5% of the indoor UFPs and 2% of the total daily

intake. Note that the above calculations had some simplifying assumptions and only 

provide a very rough estimate of exposure to ozone-initiated UFPs from human surfaces.
54

The ozone-initiated particle generation from human surfaces seems quite small when 

compared with that from ozone reactions with common consumer products such as 

fragrances and cleaners. For example, Long et al. (2000) observed a peak increase of 7–

100 times in particle number concentrations in Boston-area homes due to the ozone 

reactions with a commercial cleaning agent. However, other human surfaces such as skin 

and hair could potentially react with ozone and generate particles at much higher rates 

compared to that by clothing, since human skin-oil seems to be an important ingredient 

for the particle generation. Such particle generation could become significant in indoor 

settings with high occupancy and ozone levels such as airliner cabins, which is further 

analyzed in Chapter 7.

4.4 Conclusions

This chapter studied the particle generation due to ozone reactions with clothing (a Tshirt) in an environmental chamber under typical indoor conditions. The study identified 

the effect of some important factors such as ozone concentration, relative humidity, 

degree of soiling of the T-shirt with human skin-oils, and air change rate on the particle 

generation. The study led to the following conclusions:

The ozone reactions with T-shirt with/without human skin-oils led to generation of submicron particles. Particle generation was significantly higher when the T-shirt was soiled 

with skin-oils. Those particles were initially generated in the ultrafine region and then 

grew to larger sizes.

The particle generation increased with increasing ozone concentrations. A higher relative 

humidity did not increase the particle number generation, but led to hygroscopic growth 

of particles to larger sizes, which increased their masses. The particle generation was 

relatively unaffected by an increase in soiling level of T-shirt from 2 hours to 12 hours or 

by different human subjects. At a higher air change rate, few particles were formed due to 

short residence time of the air. The ozone reactions with clothing were identified as 

another potential source of UFPs in the indoor environment.
55

CHAPTER 5. SIMULATIONS OF OZONE DISTRIBUTIONS IN AN AIRCRAFT 

CABIN USING COMPUTATIONAL FLUID DYNAMICS

It was reviewed in Section 2.2.1 that a few studies have utilized computational fluid 

dynamics (CFD) to show that significant gradients can exist in the indoor ozone 

concentration due to its gas-phase and surface reactions occurring in the indoor 

environment. These CFD studies also found that the bulk air measurements of ozone 

were insufficient to accurate quantify its exposure to humans. However, these studies had 

limited validation. Therefore, to better assess the exposure to ozone we developed a CFD 

model to simulate the ozone distributions in an aircraft cabin and also conducted detailed 

analysis for validating the CFD model, which is discussed in this chapter.

5.1 Method

This investigation used CFD to model the ozone transport and consumption by different 

surfaces in an aircraft cabin since it is inexpensive and informative. The CFD model 

solved the Reynolds-averaged Navier-Stokes equations with the re-normalization group 

(RNG) k-ε turbulence model (Yakhot and Orszag, 1986). Zhang et al. (2009)

recommended using the RNG k-ε turbulence model since it can effectively predict the 

turbulent features of the airflow in an aircraft cabin. The ozone concentration distribution 

was solved by the following species transport equation:

t

ozone

t

.( uC) .(( D ) C) Sc

μ ∇ ρ =∇ ρ + ∇  , (5.1)

where ρ is air density, u

 the air velocity vector, C the ozone concentration, Dozone the 

binary diffusion coefficient of ozone in air, μt the turbulent viscosity, and Sct the 

turbulent Schmidt number.
56

This investigation used the second-order upwind discretization scheme for solving all the 

variables except pressure. Pressure discretization was based on the PRESTO! (Pressure 

staggering option) scheme (FLUENT, 2009). The Boussinesq approximation was used to 

account for the buoyancy effects. The governing equations were solved using the 

SIMPLE algorithm (Patankar, 1980) in the commercial CFD software FLUENT

(FLUENT, 2009).

5.1.1 Surface Deposition

In order to solve the ozone distribution in an aircraft cabin by using Eq. (5.1), it is 

necessary to have an appropriate model to compute the ozone deposition (or consumption) 

at cabin and human related surfaces. The surface ozone deposition depends on (1) fluid 

motion and ozone diffusion that transport ozone molecules to the surfaces and (2) the 

ozone chemical reactions on the surfaces. The ozone deposition flux at surface Jozone is 

given by (Cano-Ruiz et al., 1993):

2 ozone y l 3

v J C

4 =

< >

= −γ , (5.2)

where γ is the mass accommodation coefficient (or reaction probability) between the 

ozone and the deposition surface and is defined as the fraction of all ozone molecules 

collision with the surface that results in deposition, <v> Boltzmann velocity for ozone 

(360 m/s at 293 K), C ozone concentration, and l mean molecular free path (6.5×10−8 m 

at 293 K and 1 atm). Eq. (5.2) can be used to calculate the ozone flux at cabin and human 

related surfaces. However, Eq. (5.2) requires CFD to use an extremely fine grid size near 

the deposition surface (comparable to l). To increase the grid size near the surface, this 

study used the following flux model (Sørensen and Weschler, 2002):

1

ozone

1 y y

ozone

v . 4 J .C

v y 1. .

4 D

=∆

< >

−γ

= < > ∆

+ γ

, (5.3)
57

where ∆y1 is the distance of the first cell center from the surface. Note that Eq. (5.3) is 

valid only when the first grid point is very close to the surface (ideally y+ < 1).

This study used Eq. (5.3) to determine the ozone flux at cabin surfaces such as the carpet 

and seats. Since ozone reacts significantly with human related surfaces such as skin, hair, 

and clothing (Pandrangi and Morrison, 2008; Weschler et al., 2007; Wisthaler et al., 

2005), the ozone concentration is expected to be very low at those human related surfaces 

(Pandrangi and Morrison, 2008). Hence, this study set zero ozone concentration at human 

related surfaces, as suggested by Rim et al. (2009).

5.1.2 Mass Accommodation Coefficient (γ)

The γ for different surfaces is a necessary input for the CFD model to compute the ozone 

deposition using Eq. (5.3). The γ was calculated by using the two-resistor model 

developed by Cano-Ruiz et al. (1993):

1

d t

v 11

4vv

−

  < >  

γ= −        

, (5.4)

where vd is the ozone deposition velocity and defined as the ozone flux normalized by a 

characteristic ozone concentration; vt the transport limited deposition velocity and 

defined as the deposition velocity when γ equals one.

The vd for the different surfaces was available from the experimental data of Tamas et al. 

(2006). The vt was estimated using CFD as follows:

• The vd is equal to vt, when the surface resistance to the ozone deposition becomes 

zero; i.e., the surface becomes a perfect sink.

• Hence, in order to estimate vt for a surface, we performed CFD simulations by setting 

the ozone concentration equal to zero at that surface.

• The vd (which equals to vt) was calculated by the following equation:
58

inlet exhaust

d

avg

Q (C C )

A C

− ν = , (5.5)

where Q is the supply airflow rate to the cabin; A the area of the ozone deposition surface; 

and Cinlet, Cexhaust, and Cavg the inlet, exhaust, and volume averaged cabin ozone 

concentrations, respectively. Note that the above equation is valid for a cabin with only 

one deposition surface, one inlet, and one exhaust, under steady state.

The γ for the carpet surface is 8.4×10−6 by using the above-mentioned method. The value 

is lower than that of some previous measurements made for carpet surfaces (Coleman et 

al., 2008a; Morrison and Nazaroff, 2000), where γ was found to be between 10−4 and 10−5. 

The study by Morrison and Nazaroff (2000) also found that all carpet specimens 

exhibited the phenomenon of “aging” since the γ decreased after a long period of ozone 

exposure. The γ obtained in this investigation is comparable to that of carpet surfaces 

obtained after 48-hour ozone exposure (Morrison and Nazaroff, 2000). A direct 

comparison between these different γ values should be avoided as different studies used 

different carpet specimens that had a wide variety of storage and usage history.

The γ for seat surfaces was determined to be 1.9×10−5

, which is lower than that obtained 

experimentally by Coleman et al. (2008a) for a soiled seat fabric (γ = 1.4×10−4

). Again, 

the differences could be attributed to the differences in seat fabric and usage history. 

Nevertheless, the γ for seat surfaces is higher than that for carpet since the seat fabric is 

soiled with human skin-oils to some extent. These γ values for carpet and seat surfaces 

have been used in this study to compute the ozone deposition by using Eq. (5.3).

5.2 Case Setup

This investigation used CFD to simulate the ozone distributions in an aircraft cabin 

mockup for which detailed experimental data were available (Tamás et al., 2006). The 

cabin mockup was a section of Boeing-767 (3 rows, 21 seats) as shown in Figure 5.1, 

which was 4.9 m wide, 3.2 m long, and 2.0 m high in the center with a total volume of 

28.5m3. The experimental setup injected the air containing ozone to the cabin from the 

two overhead air-supply slots along the longitudinal direction (12 mm × 3200 mm each)
59

with a velocity of 2.6 m/s and a flow rate of 200 L/s. The ozone concentration in the 

cabin mockup was measured at its center and varied from 41–341 ppb depending on the 

experimental conditions and objectives, but this investigation used only a constant ozone 

concentration of 100 ppb at the inlets. Note that the species transport equation (Eq. (5.1)) 

and the CFD boundary conditions used in this investigation were homogeneous (if ‘C’ is 

a solution, then all its multiples will also be solutions) with respect to the ozone 

concentration except the inlet condition. Hence, the absolute level of ozone would be 

determined by the inlet concentration and all the results can be normalized with the 

volume averaged cabin ozone concentration for comparison against the experimental data.
60

Figure 5.1. The occupied cabin setup for Case 5, (a) the schematic of the case and (b) 

boundary surfaces in the cabin mockup.

In the experiment, the air containing ozone entered the cabin mockup through the air 

supply system. The ozone in the cabin depleted due to its reaction with various surfaces 

(carpet, seats, human skin, and clothing) and gas phase compounds. The ozone removal 

by surface reaction versus gas phase reactions was governed by the outdoor air change

rate. The high outdoor air change rate (between 3.0 and 8.8 h−1

) in the cabin reduced the 
61

time available for gas phase reactions because the residence time of the gases in the cabin 

was low. At such high outdoor air change rates, only unsaturated organic compounds can 

undergo gas phase reactions with ozone. Weschler et al. (2007) measured the level of 

unsaturated organic compounds in the cabin mockup at outdoor air change rates of 4.4 

and 8.8 h−1 in the absence of ozone (ozone concentration less than 2 ppb). They found 

that the concentration of unsaturated organic compounds was very low (less than 2 ppb) 

for any significant loss of ozone through gas phase reactions. Thus, the high air change

rate in the cabin coupled with the low concentration of unsaturated organic compounds 

prevented the ozone removal by gas phase reactions. Hence, the present investigation 

only modeled the ozone removal by surface reactions.

In order to separate the influence of each surface on the ozone concentration, this 

investigation designed five different cases as illustrated in Table 5.1. The cabin setup in 

the design varied systematically, such as the presence or absence of seats and people and 

soiled T-shirts. The gradual changes in the complexity of the boundary conditions in 

these cases enabled us to make a step-by-step comparison with the experimental data for 

validating the CFD model. The occupied cabin cases (Cases 4 and 5) were designed to 

gain an understanding of the exposure to ozone of passengers seated at different locations 

in the cabin as well as the overall ozone distribution in the cabin environment. The 

boundary conditions in the CFD model are presented in Table 5.2. The enhanced wall 

treatment model (FLUENT, 2009) was used to solve the airflow near the walls. The inlet 

temperature was 24°C for Cases 1, 2, and 3 and 21.2°C for Cases 4 and 5. The lower 

temperature in Cases 4 and 5 was to maintain the same cabin air temperature by 

offsetting the heat generated by the passengers. Figure 5.1 shows the schematic and its 

boundary surfaces for Case 5, which represented the most complex scenario. Figure 5.2

shows the grid used for Case 5. The grid consisted of 2.43 million elements where 

tetrahedral elements were used for the bulk volume, and layers of extruded triangular 

prisms were created on ozone reactive surfaces. The prism elements were used near the 

ozone reactive surfaces to accurately capture the boundary layer flow and ozone 

deposition. The initial height of the prism layer was kept very small (~ 2 mm) to ensure 

that the y+ was small (~ 5) near the ozone reactive surfaces, and the deposition model (Eq. 
62

(5.3)) was valid. The average y+ for the other cabin surfaces was around 15 and the 

maximum value was less than 100 in all the cases. This meshing strategy was used for all 

the cases.

Table 5.1. Description of the five cases used in studying the ozone reaction in a cabin 

mockup.

Case Description Ozone reaction surfaces

1 Empty cabin Carpet

2 Cabin with seats Carpet and seats

3 Cabin with seats and T-shirts Carpet, seats, and T-shirts

4 Occupied cabin with simple human geometry 

(block model)

Carpet, seats, and 

passengers

5 Occupied cabin with detailed human geometry Carpet, seats, and 

passengers

Table 5.2. The thermal, ozone, and turbulence boundary conditions used for the five 

cases.

Surfaces Temperature Ozone Turbulence

Inlet Case 

specific 100 ppb

k = 3/2(0.1Uin

2

), ε = (Cμk3/2)/Lin; 

Cμ = 0.09, Lin = (Air supply slot 

width)/7

Cabin walls 18°C Zero flux ∂k/∂y = 0, ε: local equilibrium 

hypothesis

Exhausts Outflow Outflow Outflow

Carpet 18°C Flux calculated with 

Eq. (5.3)

∂k/∂y = 0, ε: local equilibrium 

hypothesis

Seats Adiabatic Flux calculated with 

Eq. (5.3)

∂k/∂y = 0, ε: local equilibrium 

hypothesis

T-shirts Adiabatic Zero concentration ∂k/∂y = 0, ε: local equilibrium 

hypothesis

Passengers 31°C Zero concentration ∂k/∂y = 0, ε: local equilibrium 

hypothesis

where k is the turbulent kinetic energy, ε the turbulence dissipation rate, Cμ a constant 

used in the k- ε turbulence model, Uin the air velocity at the inlet, and y the local 

coordinate normal to the surface.
63

Figure 5.2. Mesh distribution in the longitudinal section through the cabin center for Case 

5.

Our study simulated the passengers by two different human geometry models for the 

occupied cabin cases. Case 4 used a simple block model, while Case 5 used a more 

detailed representation of human shape, as shown in Figure 5.3. The two geometric 

models were designed to identify whether the simple block model was sufficient for CFD 

modeling. Figure 5.3 also depicts a breathing zone of 500 cm3 volume below the nose 

since this investigation assessed the ozone dose inhaled by the passengers by calculating 

the volume-averaged concentration in the breathing zone as suggested by Rim et al. 

(2009). The volume of breathing zone was chosen larger than the hemispherical volume 

suggested by Brohus (1997) to account for the face movements of passengers.
64

Figure 5.3. Occupant geometries and breathing zones for Cases 4 and 5.

5.3 Evaluation Parameters

This section defines some important parameters for evaluating the cabin air quality for 

the cases designed in the previous section. These parameters help evaluate the CFD 

results against the available experimental data.

5.3.1 Ozone Removal Rate (βtotal)

The ozone removal rate (βtotal) was defined in Section 3.3.2 and can be used to quantify 

the total ozone loss in the cabin environment due to surface and gas phase reactions by 

using Eq. (3.2). The βtotal can also be obtained by measuring the first order decay of ozone 

inside the cabin. In this method, ozone is injected into the cabin until a reasonable ozone 

concentration (roughly around 50–100 ppb) is reached. The ozone injection is then 

stopped and the cabin ozone concentration is measured with respect to time. The ozone 

decay in the cabin is quantified by a best fit to an exponential decay equation given by:

outdoor total ( )t C C .e t t0

− λ +β = = , (5.6)
65

where Ct is the ozone concentration at time t; Ct=0 is the concentration at the time when 

the ozone injection was stopped; λoutdoor is the outdoor air change rate. The decay constant 

(λoutdoor + βtotal) in the above equation can be used to determine βtotal when the λoutdoor is 

known. The experimental study by Tamas et al. (2006) obtained the βtotal primarily by 

using Eq. (5.6), and Eq. (3.2) was used when steady state conditions were achieved.

5.3.2 Contribution to the Ozone Removal Rate (βs)

The contribution to the ozone removal rate (βs) quantifies the ozone removal by an 

individual surface. It is defined as:

ozone

s

avg cabin

J dA

C V β = ∫ , (5.7)

where Vcabin is the volume of the air inside the cabin. The βs definition implies that it can 

be calculated from CFD but cannot be directly measured, since the surface ozone 

deposition (the numerator in Eq. (5.7)) is difficult to quantify. Therefore, to determine βs, 

a reacting surface should be added one at a time as discussed in Section 3.3.3. This is 

why the investigation designed five cases. In Case 1, the βs was approximated as follows 

if the gas phase reactions of ozone are neglected:

β =β s, carpet total, Case1 . (5.8)

By adding the reacting surfaces one at a time in Cases 2, 3, and 4, the βs for seats, T-shirt, 

and passengers was estimated as:

β =β −β s, seats total, Case2 s, carpet , (5.9)

β =β −β −β s, T-shirts total, Case3 s, carpet s, seats , (5.10)

β =β −β −β s, passengers total, Case4 s, carpet s, seats . (5.11)

Hence, the experimental βs values can be compared with those obtained from CFD (Eq. 

(5.7)). 
66

5.3.3 Ozone Deposition Velocity (vd)

The deposition velocity (vd) characterizes the intensity of ozone surface reactions and can 

be compared to those reported in the literature. It is analogous to the heat transfer 

coefficient as:

ozone

d

avg

J

v

C = , (5.12)

where Jozone and Cavg are analogous to the heat flux and temperature difference. 

According to Eq. (5.12), vd will vary across a surface as the ozone flux (Jozone) will vary 

depending on the position. Hence, it is convenient to define vd by using the average 

ozone flux over a surface (or multiple surfaces) as:

( ozone )

d

avg

J dA / A

v

C = ∫ . (5.13)

Similar to βs, the vd can also be calculated from CFD, but cannot be measured directly. 

Hence, by combining Eqs. (5.7) and (5.13), vd can be determined as:

s cabin

d

V

v

A

β = . (5.14)

which is similar to the expression used for vd in Section 3.3.4.

5.3.4 Retention Ratio (θ)

The retention ratio (θ) is a parameter that indicates the ozone loss in the aircraft due to 

reactions in the cabin and the air supply system in the absence of ozone converters. It is 

defined as:

avg

ambient

C

C

θ = , (5.15)
67

where Cambient is the ambient ozone concentration. If the ozone reactions in the air supply 

system are neglected, then λ =λ +λ total inlet outdoor ambient recirculated exha CC C ust . Eq. (5.15) can be

rearranged to give:

outdoor avg

total inlet reciculated exhaust

C

C C

λ

θ =

λ −λ , (5.16)

where λoutdoor is the outdoor air change rate; λrecirculated the recirculated air change rate. 

The experiment used Eq. (5.15) to determine θ from the measured Ccabin and Cambient. 

However, this investigation used Eq. (5.16) to calculate θ, since the air supply system 

was not modeled.

5.3.5 Ozone Ratio (rozone)

In order to quantify the ozone dose for different passengers, it is essential to calculate the 

ozone concentration in the breathing zone. This study used ozone ratio (rozone) to compare 

the inhaled ozone concentration with the average ozone concentration in the cabin:

BZ

ozone

avg

C

r

C = , (5.17)

where CBZ is the ozone concentration in the breathing zone. The rozone can be used for 

assessing the ozone exposure of the passengers based on the average ozone concentration.

5.4 Results

The following section reports how the CFD was used to obtain the evaluation parameters 

defined in Section 5.3 and shows the comparison with the experimental data from Tamas 

et al. (2006).

5.4.1 Ozone Removal by Carpet and Seats (Cases 1 and 2)

Cases 1 and 2 were designed for identifying the ozone removal by the cabin surfaces 

(carpet and seats) by adding them one by one. 
68

In Case 1, the carpet was assumed to be the only ozone reactive surface to determine its 

βs and vd. Hence, the measured βtotal and the βs calculated by Eq. (5.7) should be equal. 

This investigation calculated that the βs for the carpet was 1.07 h−1. The βs calculated and 

the βtotal measured were indeed nearly the same as shown in Figure 5.4. The vd for the 

carpet was calculated by using Eq. (5.13) as 0.06 cm/s, which also agreed with the 

measurements as shown in Figure 5.5.

Figure 5.4. Comparison of the computed ozone removal rate with the corresponding 

experimental data from Tamas et al. (2006) for various cabin and human related surfaces.
69

Figure 5.5. Comparison of the computed deposition velocity with the corresponding 

experimental data from Tamas et al. (2006) for various cabin and human related surfaces.

In Case 2, the seats were also placed in the cabin together with the carpet. This was done 

in the experiment to determine the βs for the seats by using Eq. (5.9) since the βs for the 

carpet was assumed to be known from the previous case. This investigation calculated the 

βs for the carpet and seats as 1.19 h−1 and 2.97 h−1

, respectively, by using Eq. (5.7). The 

seats had a higher βs than the carpet because they had a larger surface area for reaction 

and also a higher reactivity. The βtotal in Case 2 was greater than that in Case 1 because of 

the additional ozone removal by the seats. The computed βs and βtotal agreed with the 

measured data as shown in Figure 5.4. The vd for the carpet and seats was calculated by 

using Eq. (5.13) as 0.06 cm/s, and 0.10 cm/s, respectively, which also agreed with the 

measurements as shown in Figure 5.5. Hence, the “measured” βs and vd for the seats seem 

correct, and the CFD results are also reliable.

The βtotal and vd for Case 2 (which represents a typical unoccupied cabin setup) can also 

be compared to those in buildings to better understand the ozone depletion in the cabin 

and the reactivity of the cabin surfaces. Lee et al. (1999) measured the average βtotal as 

2.80±1.30 h−1

, and vd as 0.049±0.017 cm/s in the living rooms of 43 Southern California 
70

homes. The vd measured by Lee et al. (1999) included all indoor surfaces (including both 

ozone reactive and inert surfaces). If the same method is applied to this cabin, the vd for 

both ozone reactive and inert surfaces is 0.04 cm/s. Note that although the vd for the cabin 

was almost the same as that for the homes, the βtotal for the cabin was 1.5 times higher 

than that for the homes. This is because the V/A in Eq. (5.14) for the cabin was lower 

than that for the homes.

5.4.2 Ozone Removal by T-shirts Soiled with Human Skin-oil (Case 3)

This case was designed for identifying the ozone removal by clothing soiled with human 

skin-oil. The cabin in Case 3 was identical to the one in Case 2, except that the seat backs 

were covered with T-shirts. The T-shirts were soiled with human skin-oil as male 

subjects had slept in them overnight.

The βs for the carpet, seats, and T-shirts were 1.23 h−1

, 1.34 h−1, and 4.29 h−1, respectively, 

calculated by Eq. (5.7). The area of the T-shirts was approximately 40% of all the surface 

areas, but it removed about 60% ozone due to the high reactivity of ozone with squalene 

in human skin-oil. The βtotal was higher than that in previous cases because of the addition 

of the T-shirts. The experiment used Eq. (5.10) to determine βs, T-shirts, with βs, carpet and βs, 

seats from Cases 1 and 2. However, it is not appropriate to use the βs, seats obtained in Case 

2 to calculate βs, T-shirts, since a large area of the seats was covered with the T-shirts and 

was not part of the ozone reaction in Case 3. Thus, this investigation used the following 

procedure to determine the “measured” βs, T-shirts:

• The βs, seats for Case 3 was assumed to be proportional to the exposed area, which was 

available for ozone reactions.

• Since the exposed area of the seats was unknown, it was assumed to be equal to that 

used in the CFD investigation, which was about 45% of the total area.

• The “measured” βs, seats was determined by using

s, seats, Case3 s, seats, Case2 exposed total β =β ×(A / A ) , where Aexposed/Atotal is the ratio of the

exposed area to the total area of the seats.
71

• The “measured” βs, T-shirts was then determined from the βs, seats obtained in the 

previous step by using Eq. (5.10).

The comparisons between the “measured” βs obtained using the above procedure and the 

CFD results are shown in Figure 5.4. The computed βs, carpet and βs, seats agreed well with 

the measurements but the computed βs, T-shirts and βtotal were underestimated by CFD. A 

possible reason for these discrepancies could be that when ozone reacted with the human 

skin-oil present in T-shirts, some of the volatile byproducts that entered the gas phase 

reacted further with the ozone (Weschler et al., 2007). This gas phase chemistry could 

have contributed to the additional ozone removal in the experiment, but was not 

considered in the CFD analysis.

The computed vd for the T-shirts was 0.21 cm/s by using Eq. (5.13), and the computed vd

for the carpet and seats remained the same as in the previous cases (0.06 cm/s and 0.1 

cm/s, respectively). The high value of vd for the T-shirts shows that the ozone reaction 

intensity was very high at the surfaces. In order to compare the CFD results with the 

measurements, this investigation used the “corrected” βs, T-shirts to calculate the “measured” 

vd, T-shirts by using Eq. (5.14). Since the CFD underpredicted the βs, T-shirts, the vd, T-shirts was 

also lower than the measured one, as shown in Figure 5.5.

5.4.3 Ozone Removal by Passengers (Cases 4 and 5)

Cases 4 and 5 were designed for identifying the ozone removal in an occupied cabin 

mockup. The only difference between them was: Case 4 represented passengers by 

simple block models, whereas Case 5 had a more detailed representation of human 

geometry, as shown in Figure 5.3. Despite the differences in the human geometric 

presentation, the area available for ozone reaction remained approximately the same.

The βs for the carpet, seats, and passengers calculated from Eq. (5.7) was 1.23 h−1

, 2.25 

h−1, and 8.03 h−1, respectively, in Case 4; and it was 1.18 h−1, 2.12 h−1, and 8.18 h−1, 

respectively, in Case 5. The small differences in βs between Cases 4 and 5 show that the 

detailed representation of occupant geometry for the CFD studies was not important for 

evaluating the ozone removal rate as long as the reactive surface area was the same. The 
72

βtotal for the two cases was significantly greater than those in all the previous cases due to 

the large contribution of the passengers to ozone removal.

In order to compare the βs with the experimental data, this investigation used the 

procedure described in Case 3 to correct the measured βs, seats and determine the 

“measured” βs, passengers by using Eq. (5.11). Figure 5.4 compares βs and βtotal obtained by 

CFD and the measurements. The computed βs, carpet and βs, seats agreed well with the 

measurements, but the CFD overpredicted the βs, passengers and βtotal by 10%.

The vd for the passengers was computed by using Eq. (5.13) to be 0.29 cm/s and 0.33cm/s 

for Cases 4 and 5, respectively. The computed vd for the carpet and seats was the same as 

those in the previous cases (0.06 cm/s and 0.1 cm/s, respectively). Similar to vd, T-shirts, the 

high vd for the passengers showed that the ozone removal by the passengers was most 

important or the reactivity of the passengers was the highest. The “measured” vd, passengers 

was determined from the corrected βs, passengers, based on the exposed surface area of the 

passengers (estimated at about 1.2 m2 per passenger) and the volume of the cabin air 

(estimated at about 27 m3

) by using Eq. (5.14). Since the CFD results overpredicted the βs, 

passengers, Figure 5.5 shows that the vd, passengers was also overpredicted.

This investigation computed θ as 0.42 for an outdoor airflow rate (8.8 h−1

) in Case 5 by 

using Eq. (5.16). The θ for Case 4 was approximately equal to Case 5. The calculated θ 

was less than that reported in the experiment (0.21) because the ozone removal in the air 

supply system was not modeled. The θ was smaller than the default value of 0.7 used by 

the Federal Aviation Administration (FAA) for determining the cabin ozone 

concentration and showing compliance with regulations. The low θ indicated that the 

cabin ozone concentration should be lower than that specified by the FAA. But this 

reduction in the cabin ozone levels was accompanied by the formation of even more 

harmful volatile byproducts (Weschler, 2004; Wisthaler et al., 2005). Hence, a low θ may 

reduce health risks from ozone inhalation, but would increase them from its byproducts.

Figure 5.6 shows the ozone distribution along the longitudinal plane through the center of 

the cabin in Case 5. It shows that ozone depleted near the carpet, seats, and breathing 
73

zone of the passenger because of the surface chemical reactions. This investigation used 

rozone to quantify the breathing zone ozone concentration for the passengers as compared 

to in the average cabin concentration. The rozone varied between 0.77–0.93 in Case 4 and 

0.77–0.99 in Case 5. The averaged rozone for all the passengers was 0.85 and 0.90 for 

Cases 4 and 5, respectively, which is in qualitative agreement with previous studies (Liu 

et al., 1994; Rim et al., 2009).The breathing zone concentration is generally lower than 

the average ozone concentration in the indoor environment (rozone < 1) because of 

reactions at the human surfaces. The rozone varied among the aircraft passengers due to the 

differences in the ozone transport and surface reactions at different locations. Figure 5.7

compares the rozone for different passengers between the two cases, which shows rozone

being quite sensitive to human geometrical representations. Since most of the passengers 

inhaled an ozone concentration lower than the average one, it is better to use the local

ozone concentration if one wants to accurately assess the health risks associated with the 

ozone exposure.

Figure 5.6. Ozone distribution in the longitudinal section through the cabin center.
74

Figure 5.7. Comparison of ozone ratio (rozone) for the passengers in the breathing zone 

between Cases 4 and 5. Seats 1D, 2F, 3A, and 3D were unoccupied.
75

5.5 Discussion

The primary difficulty in computing the ozone distribution was that the γ for the ozone 

reactive surfaces was unknown. Hence, this investigation obtained the γ from the 

“measured” vd and computed vt by using Eq. (5.4). The flux model (Eq. (5.3)) was then 

used to compute the ozone removal by cabin surfaces. However, in the cases with the Tshirts and passengers, the flux model (Eq. (5.3)) was not used to compute the surface 

deposition since the vd was found to be very close to vt, and Eq. (5.4) was not suitable. 

Instead, a zero ozone concentration was assumed on the human related surface. Since the 

computed βs and vd agreed with the measured data, our method seems acceptable.

This study also performed grid independence analysis for the CFD simulations. For 

example, in Cases 2 and 3 where the cabin geometry was identical, this investigation used 

a coarse grid of 2.31 million elements and a fine grid of 4.96 million elements for the two 

cases. The initial prism layer height was 2 mm and 1 mm, for the coarse and fine grid, 

respectively. The velocity and ozone distributions obtained with the two grid sizes were 

similar and the difference between the computed ozone removal rates (βtotal) was less than 

5%. Thus, the coarse grid was selected for performing all CFD simulations reported in 

this chapter.

5.6 Conclusions

The investigation developed a CFD model to study the ozone reactions at different cabin 

and human related surfaces and simulate the ozone distributions in the cabin. The 

investigation led to the following conclusions:

• The study identified the individual contributions of cabin and human related surfaces 

to ozone removal and their deposition velocities. The results concluded that the 

human related surfaces (T-shirts and passengers) removed much more ozone than the 

cabin surfaces (carpet and seats). 

• The ozone removal rate and deposition velocities calculated by the model were in 

good agreement with those measured by Tamas et al. (2006). 
76

• The retention ratio predicted by the model was higher than the measured one (Tamás 

et al., 2006) since the air supply system was not modeled. The retention ratios were 

lower than the FAA recommended value, indicating a reduced risk directly from 

ozone inhalation but an increased risk from associated by-products.

• Ozone depleted more in the breathing zone compared to the average cabin 

concentration due to reaction at the human surfaces. To accurately assess the personal 

exposure to ozone, its concentration in the breathing zone should be used.
77

CHAPTER 6. NUMERICAL MODELING OF VOC EMISSIONS FROM OZONE 

REACTIONS WITH HUMAN-WORN CLOTHING IN AN AIRCRAFT CABIN

The previous chapter described CFD analysis for computing ozone consumption in an 

airliner cabin mockup from its reactions with different cabin surfaces and passengers. It 

was also discussed previously that such ozone consumption leads to secondary emissions 

of VOCs, which can be even more harmful to human health than ozone itself. 

Additionally, it was found that these secondary emissions were mainly attributed to 

ozone reactions with human-worn clothing under typical airliner cabin conditions.

Therefore, this investigation developed models for computing secondary VOC emissions 

from ozone reactions with human-worn clothing based on the prevailing environmental 

conditions. Our objective was to compute common byproduct concentrations due to 

ozone/clothing reactions in an aircraft cabin mockup, such as acetone, 4-oxopentanal (4-

OPA), nonanal, and decanal. Our study also integrated the models in CFD for computing 

the ozone-initiated VOC concentrations in breathing zones of passengers in an occupied 

airliner cabin mockup.

6.1 Method

To compute the secondary VOC emissions from surface reactions of ozone, we used the 

molar yield equation (Weschler et al., 2007), which calculates VOC emissions directly 

from ozone consumption as follows:

VOCi i ozone J YJ = , (6.1)

where JVOCi and Jozone are the molar fluxes of VOCi and ozone, respectively, on a 

particular surface; and Yi is the molar yield of VOCi for that surface.
78

In the above equation, Jozone can be computed as the product of the bulk air ozone 

concentration (C) and the measured/estimated ozone deposition velocity (vd) for the 

surface (see Eq. (5.12)) , under the assumption of well-mixed conditions (Cano-Ruiz et 

al., 1993). Alternatively, Jozone can be obtained by CFD analysis, which does not require

the well-mixed assumption as described in Chapter 5. However, Yi is generally unknown, 

and it depends on the environmental conditions under which ozone reacts with soiled 

clothing as shown in Chapter 3. Thus, it was reasonable to search for empirical equations 

connecting VOC yields to the environmental conditions for reactions with clothing. The 

equations can then be used to compute VOC yields and eventually their secondary 

emissions from Eq. (6.1). The procedure for determining these equations is discussed in 

the following subsection.

6.1.1 Selection of Empirical Equations

This investigation developed empirical equations for computing the yields of several

major VOCs generated from ozone reactions with human-worn clothing based on the 

prevailing environmental conditions. The equations were developed from the chamber 

experiments discussed in Chapter 3. From the experimental cases, we choose the ones 

that had been conducted with soiled T-shirts (a total of 12 cases) because human-worn 

clothing would normally be soiled with skin-oils (see Table 3.1).

By analyzing the cases given in Table 3.1, it was found in Chapter 3 that air change rate 

(ACR), relative humidity (RH), and ozone concentration (C) were the major factors that 

influenced VOC yields. Therefore, we chose those factors as predictor variables for 

developing empirical equations connecting the VOC yields to the environmental 

conditions.

Among the predictors, humidity and ozone were used as continuous variables with linear 

(RH and C) and quadratic (RH2 and C2

) terms since we had a range of conditions for 

these variables. ACR was used a dummy variable, and its value was set to zero or one for

low or high ventilation conditions, respectively. We used ACR as a dummy variable 

because (1) in the measurements it had only two levels, an insufficient number for it to be 
79

treated as a continuous variable, and (2) it was conjectured that ACR indirectly affected 

the yields of some VOCs by changing the airflow features and their resulting convective 

mass transport. For the cases given in Table 3.1, 0.5 h−1 and 2.7 h−1

were used as low and 

high air change rates, respectively. Another possible approach to account for the 

influence of airflow features on VOC yields could be to use the friction velocity (u*) as a 

predictor variable, which would account for the effects of indoor airflow intensity at the 

reaction surface. Such an approach was used by Lai and Nazaroff (2000) to characterize 

the influence of airflow features on particle deposition. However, this approach requires

the value of the u*, which is very difficult to determine by either measurements or 

computations.

After the predictor variables had been selected, the yields of the major ozone-initiated

VOCs were regressed on all the different linear combinations of these five variables 

(ACR, RH, RH2

, C, and C2) for the 12 cases by using the SAS (Statistical Analysis 

System) statistical package. The resulting regression models (31 in total) were then 

categorized according to their complexity (i.e., the number of predictor variables). From 

each of these categories, the best model was identified as the one with the largest value of 

R2

adj (adjusted R2

). Figure 6.1 shows the relationship between R2

adj and the number of 

predictors for each VOC, along with the models that were finally selected. The finally 

selected models were the ones having a relatively large value of R2

adj together with least 

complexity.
80

Figure 6.1. Model selections for the different VOCs based on the R2

adj criterion. The 

circles identify the final model selections.

The selected regressions models are as follows:

1 1 3 6 2 Y 1.85 10 1.35 10 ACR 2.45 10 C 9.72 10 C ace

−− − − =×+× − × + × , (6.2)

22 2 4

4-OPA

4 2

Y = 3.71? 0 3.94 10 ACR 1.14 10 RH 3.49 10 C...

... 1.61 10 RH

−− − −

−

− +× +× −×

− × , (6.3)

2 3 3 5 2 Y 4.32 10 2.52 10 RH 2.25 10 C 1.16 10 C non−− − − =− × + × + × − × , (6.4)

1 2 3 4 2 5 2 Y ? .48 10 1.53 10 RH 5.18 10 C 1.80 10 RH 2.70 10 C dec−− − − − = ×+× +× −× − × , (6.5)

where Yace, Y4-OPA, Ynon, and Ydec are the molar yield of acetone, 4-OPA, nonanal, and 

decanal, respectively. Eqs. (6.1) to (6.5) are the models for estimating secondary VOC

emissions of acetone, 4-OPA, nonanal, and decanal, respectively, from ozone reactions 

with human-worn clothing. To use the above equations for computing VOC yields, the 

ozone concentration (C) must be inputted in ppb (on a molar basis). RH must be set to the 

value of the relative humidity without percentage. For example, if the relative humidity is 

20%, the RH value must be set to 20. Finally, ACR value must be either zero or one 
81

depending on low or high ventilation conditions. It is recommended to use an ACR value 

of zero for buildings since they usually have low ventilation rates, and a value of one for 

high ventilation settings such as airliner cabins.

The computed VOC emissions can then be incorporated in a simple mass balance 

analysis or used to provide boundary conditions for CFD analysis. The mass balance 

analysis can be used to estimate the contributions of human-worn clothing to bulk air 

concentrations of those VOCs, whereas the CFD analysis can provide a wealth of 

information about the concentration distributions. The next two subsections describe the 

mass balance and CFD modeling techniques, respectively.

6.1.2 Mass Balance Model

A simple mass balance can be used to compute the contributions of human clothing to 

ozone-initiated VOCs under indoor settings by assuming well mixed conditions, as 

follows:

VOCi clothing

VOCi, clothing

outdoor

J A

C

V = λ , (6.6)

where CVOCi, clothing is the contribution of clothing to the concentration of VOCi, Aclothing

the clothing area, V the volume of the air in the indoor space, and λoutdoor the outdoor air 

change rate.

6.1.3 CFD Model

If the air distribution in a room cannot be assumed to be well-mixed, CFD should be used 

to compute the velocity, temperature, and ozone distributions as described in Chapter 5. 

The ozone and VOC distributions were obtained by solving the corresponding species 

transport equations. For example, the transport equation for VOCi takes the following 

form:
82

t

VOCi VOCi VOCi

t

.( uC ) .(( D ) C ) Sc

μ ∇ ρ =∇ ρ + ∇  , (6.7)

where ρ is the air density, u

 the air velocity vector, CVOCi the concentration of VOCi,

DVOCi the binary diffusion coefficient of VOCi in air, μt the turbulent viscosity, and Sct

the turbulent Schmidt number. These equations were solved by the use of commercial 

CFD software FLUENT (FLUENT, 2009).

6.2 Case Setup

We used the same Boeing-767 mockup that was used for validating our model for ozone 

consumption. The details of the cabin mockup were given in Section 5.2. In addition to 

the measurements of ozone consumption, this mockup was also used by Weschler’s 

group for quantifying the ozone-initiated VOC emissions from different surfaces by 

adding them one at a time. For example, they first measured ozone-initiated VOC 

emissions in an empty cabin with carpet and seats only. Next, they stretched soiled Tshirts on the seat backs and repeated the VOC measurements. Finally, they conducted 

experiments with passengers in the cabin mockup. These arrangements enabled them to 

quantify the contributions to ozone-initiated VOC emissions from individual cabin 

surfaces, such as carpet and seats, soiled T-shirts, and passengers.

Because one of our aims was to evaluate our model performance for computing the 

secondary VOC emissions from human-surfaces, we studied those cases which contained 

human-worn clothing or passengers as given in Table 6.1. The environmental conditions 

for these cases are also summarized in Table 6.1. Case 1 was conducted with soiled Tshits stretched on seat backs as surrogates for humans, whereas Cases 2 and 3 were 

conducted with real human subjects.
83

Table 6.1. Description of the three cases used for studying ozone-initiated VOC 

emissions in a cabin mockup.

Case Description Ozone reaction 

surfaces

Average 

cabin 

ozone 

(ppb)

Relative 

humidity 

(%)

Air change rate (h−1)

Outdoor Recirculated

1

Cabin with 

seats and 

T-shirts

Carpet, seats, 

and T-shirts 66 6.8 3.0 20.0

2 Occupied 

cabin

Carpet, seats, 

and passengers 75 10 8.8 12.2

3 Occupied 

cabin 

Carpet, seats, 

and passengers 62 20 4.4 16.6

We have previously reported and validated detailed CFD modeling techniques for 

computing airflow, temperature, and ozone distributions for the cabin mockup in Chapter 

5, but with a slightly higher air change rate. Therefore, we used similar meshing and 

solution techniques for computing the ozone-initiated VOCs.

Table 6.2 presents the boundary conditions for ozone and different VOCs in the CFD 

model for the occupied cabin cases. The inlet ozone concentration was unknown, and a 

value for this concentration was assumed in order to obtain the correct cabin ozone 

concentrations as measured in Cases 2 and 3. Correct concentrations in the cabin were 

essential for computing the flux of ozone (Jozone) at reaction surfaces. The inlet VOC 

concentrations were calculated from those in the recirculated air because a portion of the 

exhaust air was recirculated. The outdoor air was assumed to have no VOCs. The VOC 

emissions from the cabin walls were assumed to be zero, and those from the carpet and 

seats were estimated from the measurements of Wisthaler et al. (2005), as the sum of 

both primary and secondary emissions. The primary VOC emissions from the human 

surfaces (clothing, skin, and hair) were neglected in accordance with the experimental 

measurements of Weschler et al. (2007). It was further assumed that all human surfaces 

had the same secondary emissions as the soiled cotton T-shirts, which was estimated 

from Eqs. (6.2)–(6.5). Because acetone is also present in human breath as a result of
84

metabolic processes (Fenske and Paulson, 1999), this study set its flux at the nose of 

occupants on the basis of experimental estimates of Weschler et al. (2007).

Table 6.2. The ozone and VOC boundary conditions used for this study.

Surfaces Ozone 4-OPA, nonanal, and 

decanal Acetone

Inlet Case specific Case specific Case specific

Cabin walls Zero flux Zero flux Zero flux

Carpet and Seats Flux computed 

from Eq. (5.3)

Flux estimated from 

measurements

Flux estimated 

from measurements

Human surfaces 

(skin, hair, and 

clothing)

Zero 

concentration

Flux calculated with 

Eqs. (6.3), (6.4), and 

(6.5), respectively

Flux calculated 

with Eq. (6.2)

Passenger’s nose Zero 

concentration

Flux calculated with 

Eqs. (6.3), (6.4), and 

(6.5), respectively

Flux estimated 

from measurements

Exhausts Outflow Outflow Outflow

6.3 Results

The following subsections present (1) the comparisons between our model predictions 

and the corresponding experimental data for the three cases shown in Table 6.1, and (2) 

the results obtained by conducting CFD analysis for the occupied cabin cases.

6.3.1 VOC Emissions

Case 1 was designed to study the contributions of the soiled T-shirts to ozone-initiated 

VOC emissions. In this case the VOC emissions mainly composed of primary and 

secondary emissions from the carpet and seats plus secondary emissions from the soiled 

T-shirts (Wisthaler et al., 2005). The contributions of cabin surfaces (carpet and seats) to 

VOCs (both primary and secondary) were isolated by means of another experiment 

performed in the same cabin mockup without T-shirts. It should then have been possible 

to estimate the contribution of the T-shirts to the overall concentration of VOCi in the 

cabin by simply subtracting the contribution of the cabin surfaces from the total VOCi 

concentration measured in Case 1. It should be noted, however, that because the T-shirts 
85

were stretched on the seat backs in Case 1, about 55% of the seat surface was unavailable 

for VOC emissions. As a result, the VOCi emissions from the seats in Case 1 were 

probably 55% lower than in the similar case conducted without T-shirts. The above 

correction was incorporated in order to obtain the “measured” contributions of soiled Tshirts to the different VOCs in Case 1.

Figure 6.2 shows the contributions of the soiled T-shirts to the concentrations of different 

VOCs as computed by the mass balance model and their corresponding “measured” data 

for Case 1. The computed concentration of 4-OPA agreed well with the measurement. 

However, acetone, nonanal and decanal concentrations were considerably over-predicted 

by the model. The discrepancies between the model predictions and the measurements 

were caused primarily by the following factors. The regression models used to compute 

yields were developed on the basis of cotton T-shirts soiled by the same human subject in 

all but one of the cases, which means that the effects of fabric and of soiling by different 

human subjects could not be captured. The use of cotton T-shirts meant that VOC 

emissions were overestimated because soiled cotton fabric seems to have higher 

secondary emissions than the soiled wool or polyester fabrics, as demonstrated by 

Coleman et al. (2008a). The measured relative humidity in the mockup was 6.8%, which 

was outside the humidity range (10%–49% RH) used to develop the model. When the 

above factors are taken into account, the model’s performance does not seem to be poor.
86

Figure 6.2. Ozone-initiated VOCs from reactions with soiled T-shirts: comparisons of the 

predicted and measured data for Case 1. The measurement errors are unknown, and the 

model error bars represent the 95% confidence intervals.

We also used the mass balance model to compute secondary VOC emissions from 

ozone/human-surface reactions in the occupied cabin mockup under high and low 

outdoor air change rate conditions (Cases 2 and 3, respectively). As in Case 1, the 

contributions of human surfaces were estimated from measurements by taking into 

account the portion of the seat backs that was covered by the passengers and unavailable 

for VOC emissions.

Figure 6.3 compares the computed contributions of ozone/human-surface reactions to 

VOC concentrations in the mockup with their corresponding experimental estimates at an 

air change rate of 8.8 h−1

. The computed VOC concentrations were higher than the 

measured data, especially for decanal. These discrepancies can be attributed to modeling 

deficiencies, as discussed above for Case 1. In addition, different passengers in the cabin 

can be expected to have different secondary VOC emissions because of differences in 

clothing, skin-oil composition, and usage of terpene-containing personal care products 

(such as perfumes and other fragrances). However, incorporating those details was 

beyond the scope of the current investigation.
87

Figure 6.3. Ozone-initiated VOCs from reactions with human surfaces: comparisons of 

the predicted and measured data for Case 2. The measurement error bars represent the 

range, and the model error bars represent the 95% confidence intervals.

Figure 6.4 compares the contributions of human surfaces to the different VOCs in the 

cabin for Case 3. The “measured” and predicted contributions of human surfaces to 

different VOCs in the cabin were higher in Case 3 than in Case 2. Case 2 was conducted 

at 10% relative humidity, whereas in Case 3 the relative humidity was 20%. The VOC 

emission models (Eqs. (6.2)–(6.5)) are sensitive to changes in humidity, and they 

predicted a large increase in VOC emissions with increased humidity. A similar 

conclusion was obtained by Coleman et al. (2008a) for laundered cotton fabrics. In reality, 

an increase in relative humidity may not have a significant influence on the secondary 

emissions from other clothing fabrics. Unfortunately, there is no information available in 

the literature that could be used to verify our hypothesis.
88

Figure 6.4. Ozone-initiated VOCs from reactions with human surfaces: comparisons of 

the predicted and measured data for Case 3. The measurement error bars represent the 

range, and the model error bars represent the 95% confidence intervals.

6.3.2 Distributions of Different VOCs in the Cabin Mockup

The previous subsections described modeling results for the concentrations of ozoneinitiated VOCs from reactions with human surfaces under the assumption of well-mixed 

conditions. However, the well-mixed assumption might not be suitable because 

ozone/clothing reactions occur in rising thermal plumes, and the VOCs are expected to be 

enriched in the breathing zone. Hence, the actual exposure of passengers to VOCs could 

be higher than that calculated from a simple mass balance analysis under the well-mixed 

assumption. By using CFD modeling of VOC distributions, we can determine their 

concentrations in the breathing zone and make better risk assessments.

Figure 6.5 shows the distribution of 4-OPA normalized by its exhaust concentration 

along the longitudinal plane through the center of the cabin in Case 2. It can be seen that 

4-OPA was enriched in the breathing zone of the occupant. The normalized distributions 

of nonanal and decanal were similar to the distribution of 4-OPA, and these compounds 

were also enhanced in the breathing zone. The distribution of acetone was qualitatively 
89

similar to that of OPA, but its concentration was much higher near the breathing zone 

since it was also emitted in human breath.

Figure 6.5. The 4-OPA distribution in the longitudinal section through the center of the 

occupied cabin mockup with an outdoor air change rate of 8.8 h−1 for Case 2.

The normalized 4-OPA distribution in Case 3 was qualitatively similar to that in Case 2, 

as shown in Figure 6.6. The normalized concentration in Case 3 was significantly lower 

than that in Case 2. This difference occurred because the exhaust concentration in Case 3 

was much higher than that in Case 2 as a result of the lower outdoor air change rate, 

which was used for normalization.
90

Figure 6.6. The 4-OPA distribution in the longitudinal section through center of the 

occupied cabin mockup with an outdoor air change rate of 4.4 h−1 for Case 3.

To quantify the enhancement of VOC concentrations in the breathing zone, this 

investigation computed the VOC ratio (rVOCi), which was defined as the VOC 

concentration in the breathing zone divided by its exhaust concentration. Figure 6.7

shows the rVOCi for the different VOCs under the two air change rates for the middle row 

in the cabin mockup. The racetone, r4-OPA, rnonanal, and rdecanal were 1.51±0.18, 1.22±0.05, 

1.22±0.06, and 1.21±0.05, respectively, in Case 2; and 1.26±0.10, 1.11±0.03, 1.11±0.03, 

and 1.10±0.03, respectively, in Case 3. The rVOCi values were higher in Case 2 than in 

Case 3 because the exhaust concentrations were much lower in Case 2 as a result of the 

higher air change rate. The racetone was much higher than r4-OPA, rnonanal, and rdecanal because 

acetone is also present in human breath and was consequently enriched in the breathing 

zone, as discussed previously.
91

Figure 6.7. VOC ratio for different passengers seated in the middle row of the occupied 

cabin mockup under an outdoor air change rate of (a) 8.8 h−1 (Case 2) and (b) 4.4 h−1

(Case 3).

Overall, the ozone-initiated VOCs were found to be significantly enhanced in the 

breathing zones as compared with their corresponding exhaust concentrations, and thus 

the exhaust air measurements would have underestimated the inhalation dosage of the 

passengers. Hence, to accurately estimate passengers’ exposure to such VOCs, their 

concentrations in the breathing zones should be used instead of those at the exhausts.

6.4 Discussion

This chapter demonstrated a method for estimating the contributions of human surfaces to 

the concentrations of several ozone-initiated VOCs in an aircraft cabin mockup. The 

method developed is a general one, and it can be extended to compute secondary 
92

emissions from a variety of materials under given indoor conditions. The model 

development shows that experimental data obtained through chamber measurements 

constitutes the most important component of the model. The experimental dataset used in 

our investigation was rather small (only 12 cases), and it did not address the effects of 

clothing fabric or variations in soiling from different human subjects. These limitations 

were the major reasons for the discrepancies that were observed between the model 

predictions and experimental data. The model’s performance may be improved by 

conducting additional experiments under a wide range of conditions. Nevertheless, the 

model is useful for estimating concentrations of ozone-initiated VOCs in indoor spaces.

6.5 Conclusions

This chapter studied ozone-initiated secondary emissions of acetone, 4-oxopentanal (4-

OPA), nonanal, and decanal from reactions with human-worn clothing in an aircraft cabin 

mockup. The study was conducted by developing empirical models to compute the 

secondary VOC emissions from the prevailing environmental conditions (such as ozone 

concentration, humidity, and air change rate) based on experiments conducted in an 

environmental chamber.

These models were then used to compute the contributions of human surfaces (skin, hair, 

and clothing) to the ozone-initiated VOCs in an aircraft cabin mockup. The model 

predictions showed very good trends qualitatively, but with quantitative discrepancies 

when compared with the corresponding experimental data obtained in the mockup. A 

major reason for the discrepancies is that a limited dataset from the chamber was used for 

developing the model. Nevertheless, the models are promising and can be improved with 

more experimental data.

When the VOC emission models were integrated into a CFD model, much higher 

concentrations of the various VOCs were identified in the breathing zone of the 

passengers than in the mockup exhausts. The reason for this difference is that the VOC 

concentrations are not uniform, and the ozone/human-surface reactions occurring in the 

thermal plumes from passengers can enhance their concentrations. Therefore, it is more 
93

accurate to assess the passengers’ exposure risk by using the concentrations of the ozoneinitiated VOCs in the breathing zone than those in the exhausts.
94

CHAPTER 7. NUMERICAL MODELING OF PARTICLE GENERATION FROM 

OZONE REACTIONS WITH HUMAN-WORN CLOTHING IN INDOOR 

ENVIRONMENTS

We observed sub-micron particle generation from the ozone reactions with clothing 

soiled by human skin-oils in Chapter 4. These reactions were identified as another 

potential source of indoor ultrafine particles (UFPs), a potential health hazard for humans. 

Therefore, to better understand particle generation from ozone reactions with humanworn clothing and human exposure to these particles, we developed a numerical model 

for computing size-resolved particle concentrations. The model was then used to compute

concentrations of such ozone-initiated particles in various indoor environments such as 

buildings and airliner cabins.

7.1 Method

We simulated the generation of ozone-initiated particles from reactions with human-worn 

clothing measured in an environmental chamber. The measurements details were 

presented in Chapter 4. These measurements presented a well-controlled and challenging 

case study for developing models to study ozone-initiated particle generation from the 

reaction of ozone with clothing.

To model particle generation, this investigation assumed that ozone reacted with skin-oils 

on the T-shirt to produce a hypothetical semi-volatile organic compound (SVOC) in the 

vapor phase, as shown in Figure 7.1. The concentration of the SVOC vapor then 

increased in the chamber because its production rate from ozone/skin-oil reactions was 

higher than its removal rate by ventilation and deposition. The SVOC vapor 

concentration subsequently crossed its nucleation threshold, and new particles (liquid 

droplets of SVOC) were generated. Note that if ventilation and deposition rates were 
95

sufficiently high, particle generation would not take place because the SVOC vapor 

concentration would never cross its nucleation threshold.

Figure 7.1. An illustration of the particle-generation mechanism.

These freshly nucleated particles then served as condensation sites for the SVOC vapor 

and subsequently reduced its concentration below the nucleation threshold, preventing

further nucleation. From this time onward, condensation was predominant, which led to 

growth in particle size and consumption of the SVOC vapor. Furthermore, the number of 

particles in the chamber was also decreasing because nucleation had stopped and particles 

were continuously removed by ventilation and deposition, as illustrated in Figure 7.1. 

This in turn reduced the number of available condensation sites for the SVOC vapor, and 

its concentration started to increase again, producing another nucleation burst of particles. 

This cycle of particle generation, growth, and removal continued until the ozone/skin-oil 
96

reactions could not generate sufficient SVOC vapor in the chamber. Note that 

coagulation of particles was ignored in the above description because the particle 

concentrations in most cases were too low for coagulation to have a significant influence 

(Hussein et al., 2009).

7.1.1 Model Formulation

As described above, the SVOC vapor was the primary driver of the particle generation 

cycle. The SVOC concentration in the chamber was assumed to be under well-mixed 

conditions because of the small particle size. The mass conservation equation for the 

SVOC is as follows:

SVOC 3

gen outdoor SVOC d,SVOC SVOC SVOC p nuc nuc

dC S C C CS C Dp J dt 6

π = −λ −λ − ⋅ −ρ , (7.1)

where CSVOC is the concentration of SVOC vapor, t the time, Sgen the SVOC production 

rate, λoutdoor the outdoor air change rate, λd,SVOC the SVOC deposition rate coefficient on 

the chamber walls, CS the condensational sink of SVOC vapor on the existing particles, 

ρp the particle density, Dpnuc the diameter of nucleating particles, and Jnuc the particle 

nucleation rate. The first term in the above equation is the rate of change of SVOC 

concentration in the chamber. The second term describes the production of SVOC from 

ozone/skin-oil reactions. The third and fourth terms describe SVOC removal by 

ventilation and deposition, respectively. The fifth and sixth terms are the SVOC 

conversion rates of particles from vapor to liquid droplets as a result of condensation and 

nucleation, respectively. Clearly, the above equation can be used to determine the timevarying SVOC vapor concentration in the chamber if the various terms on the right-hand 

side are computed. However, the equation is intrinsically coupled with the particle 

concentrations through the condensation and nucleation sink terms.

Therefore, it is necessary to introduce governing equations for particles. Just as the mass 

balance equation was used for the SVOC vapor, this investigation used the number 

balance equation to compute the particle number concentrations in the chamber. The 
97

particles were first classified into different groups on the basis of diameter, and their 

number concentrations were computed by the following equations:

( )

( )

i

outdoor i d,p i i nuc

i

outdoor i d,p i i

dN(Dp , t) N(Dp , t) (Dp )N(Dp , t) J for i 1 dt

dN(Dp , t) N(Dp , t) (Dp )N(Dp , t) for i 2, 3, ... dt

= −λ − λ + =

= −λ − λ =

, (7.2)

where i is the group number, N the number concentration of particles with diameters Dpi

at time t, λd,p their deposition rate coefficient, and Dpnuc the diameter of nucleating 

particles. The first term in each of the above equations is the rate of change of particle 

number concentration with diameter Dpi. The second and third terms describe the 

removal of particles by ventilation and deposition, respectively. The fourth term 

expresses particle generation by nucleation of SVOC vapor, which is a source of

nucleating particles (i = 1 and Dp1 = Dpnuc) only. Note that the above equations do not 

include the incoming particles from ventilation and that we chose those experimental 

cases for which the inlet air contained negligible particles for comparison with the 

numerical results. Although inlet particles do not pose any theoretical problems and can 

be easily incorporated into Eq. (7.2), they increase computational costs drastically by 

increasing the number of groups in the model, and therefore they were not considered. 

We also did not account for coagulation in Eq. (7.2) because it was usually insignificant 

as a result of low particle concentrations.

To solve Eq. (7.2) for computing size-resolved particle concentrations, it is also 

necessary to account for particle size growth as described by Kumar and Ramkrishna 

(1997). The growth of particle diameters was governed by the condensation of SVOC 

vapor, which was computed by the following expression (Kulmala, 1988):

i c SVOC SVOC

p i

dDp 4D C

dt Dp

β = ρ , (7.3)

where DSVOC is the diffusion coefficient of SVOC and βc the transition correction factor 

(Fuchs and Sutugin, 1971).
98

Overall, Eqs. (7.1), (7.2), and (7.3) can be used for computing the time-dependent SVOC 

vapor concentration, particle number concentrations, and particle diameters, respectively. 

However, the various terms on the right-hand side of these equations needed to be 

determined, as described in the following subsections.

7.1.1.1 Modeling the SVOC Generation (Sgen)

The ozone-initiated SVOC generation (Sgen in Eq. (7.1)) was estimated by assuming a 

first-order reaction between ozone and skin-oils on the T-shirt as follows:

ozone k Ozone Skin-oils SVOC + → vapor .

Thus, the generation rate of SVOC was expressed as:

ozone T-shirt skin-oils

gen

chamber

kA C C S

V = , (7.4)

where kozone is the ozone/skin-oils reaction rate constant; AT-shirt the area of the T-shirt; 

Vchamber the chamber volume; and C and Cskin-oils the concentrations of ozone in the air 

and skin-oils on the T-shirt, respectively. The determination of ozone/skin-oils-reactiongenerated SVOC was based on the assumption that the T-shirt was soiled with skin-oils 

because it was worn by a human subject. We calculated the time-varying concentration of 

skin-oils by solving the following equation:

skin-oils

ozone skin-oils

dC kC C

dt = − , (7.5)

Clearly, the above equation can be easily solved if the initial concentration of skin-oils on 

the T-shirt (C0,skin-oils), C, and kozone are known. C was measured during the experiments; 

however, C0,skin-oils and kozone were not known beforehand. Therefore, this investigation 

fitted those two parameters by comparing the model predictions for ozone-initiated 

particles with their corresponding measurements, as described in Section 7.1.2. Finally, 

Eqs. (7.4) and (7.5) can be solved for computing Sgen.
99

The above calculation method for Sgen can be simplified if it is assumed that the T-shirt 

contained a large reservoir of skin-oils and that the reactions were controlled only by the 

ozone concentration in the chamber. Then the expression for Sgen becomes:

ozone-ex T-shirt

gen

chamber

k AC S

V = , (7.6)

where kozone-ex is the ozone/skin-oils reaction rate constant with the assumption of excess 

skin-oils. Again, kozone-ex can be estimated by comparing the model predictions with 

measurements as described in Section 7.1.2.

This study used both of the methods described above for computing Sgen and also 

compared their performance in simulating ozone-initiated particles, as analyzed in the 

results section. The calculation of Sgen from Eqs. (7.4) and (7.5) was termed the “skin-oils 

depletion” model, and the calculation of Sgen from Eq. (7.6) was termed the “excess skinoils” model.

7.1.1.2 Modeling the Nucleation Rate (Jnuc)

Nucleation governs the generation rate of new particles from ozone/skin-oil reactions. 

Hence, the aim of any nucleation model is to predict the generation rate of new particles 

given the physical properties of the nucleating species (SVOC vapor in this study), its 

super-saturation, and other environmental conditions. Various nucleation theories are 

described in detail in many texts (Seinfeld and Pandis, 2006), so our discussion here will 

be brief.

The nucleation rate can be determined experimentally if the generation of freshly 

nucleated particles is measured. However, such particles have extremely small sizes (on 

the order of a few nanometers) and usually cannot be detected by conventional 

measurement instruments such as a scanning mobility particle sizer (SMPS). Therefore, 

particles are measured only when they grow beyond the detection size limit, which is 

usually about ten nanometers. For example, the minimum detection limit for the SMPS 

used in our ozone/T-shirt experiments was 9.65 nm, which means that the true nucleation 
100

rate was unknown. Nevertheless, these SMPS measurements can be used to compute the 

generation rate of the smallest detectable particles, also called the apparent nucleation 

rate (Kerminen and Kulmala, 2002).

To calculate the apparent nucleation rate, we summed Eq. (7.2) over all particle sizes and 

performed some algebraic manipulations as follows:

i nuc i nuc i nuc

i SMPS i SMPS

i nuc i SMPS i nuc i SMPS

i SMPS

i nuc i SMP

outdoor d,p nuc

Dp Dp Dp Dp Dp Dp

Dp Dp Dp Dp

outdoor outdoor

Dp Dp Dp Dp Dp Dp Dp Dp

Dp Dp

d,p d,p

Dp Dp Dp D

dN N NJ

dt

dN dN N N...

dt dt

... N N

∞ ∞ ∞

= = =

= ∞ ∞ =

= = = =

=

= =

= −λ − λ +

⇒ + = −λ − λ

− λ− λ

∑ ∑∑

∑∑ ∑ ∑

∑ S

i SMPS i SMPS i SMPS

i SMPS i SMPS i SMPS

i nuc i nuc i nuc

i SMPS i SM

nuc

outdoor d,p

Dp Dp Dp Dp Dp Dp

Dp Dp Dp Dp Dp Dp

nuc outdoor d,p

Dp Dp Dp Dp Dp Dp

outdoor

Dp Dp Dp Dp

J

dN N N...

dt

dN ... J N N

dt

dN N

dt

∞

∞ ∞ ∞

= = =

= = =

= = =

∞

= =

+

⇒ = −λ − λ

 

+ −λ   − λ −

 

⇒ = −λ

∑

∑ ∑∑

∑∑ ∑

∑ PS i SMPS

i SMPS i SMPS i SMPS

i SMPS

d,p nuc,app

Dp Dp

nuc,app outdoor d,p

Dp Dp Dp Dp Dp Dp

total,SMPS

nuc,app outdoor total,SMPS d,p

Dp Dp

N J

dN J N N

dt

dN (t) J N (t) N

dt

∞ ∞

=

∞ ∞ ∞

= = =

∞

=

− λ+

⇒ = +λ + λ

⇒ ≈ + λ + λ

∑ ∑

∑ ∑∑

∑ , (7.7)

where Ntotal,SMPS(t) is the total particle number concentration measured by SMPS at time t, 

and DpSMPS is the smallest particle diameter detected by the SMPS (9.65 nm). The terms 

in the square brackets above were defined as the apparent nucleation rate (Jnuc,app) because

they act together as an effective source term for particles with diameters greater then 

DpSMPS. In deriving the above equation, we used the shorthand notation N and λd,p for 

N(Dpi, t) and λd,p(Dpi), respectively. In addition, the derivation assumed that there were a 

negligible number of particles with sizes larger than the upper detection limit of the 

SMPS.
101

Finally, to compute Jnuc,app, Eq. (7.7) can be solved jointly with Eqs. (7.1) to (7.3) by 

using Ntotal,SMPS(t) from the measured data. However, we first need to replace Dpnuc with 

DpSMPS and Jnuc with Jnuc,app in Eqs. (7.1) and (7.2) because those particles with diameters 

below DpSMPS were eliminated from the model formulation and were accounted for 

indirectly through the Jnuc,app term in Eq. (7.7). Such an approach was extremely useful 

for studying the evolution of particle sizes after they were detected by the SMPS. It was 

also useful to compare the model results with measurements of particle size distributions 

for validating Eqs. (7.1) to (7.3).

The primary limitation of the above approach was that Jnuc,app was computed from the 

measured Ntotal,SMPS(t) in an environmental chamber in which ozone/T-shirt reactions 

were the only particle source. A similar technique would not be possible in real indoor 

settings because the measured Ntotal,SMPS(t) would also contain contributions from other 

particle-generating sources. Therefore, it was necessary to explore a number of 

theoretical models for nucleation that did not require Ntotal,SMPS(t) as an input. However, 

the nucleation precursors and mechanism of particle generation from ozone/clothing 

reactions is itself unknown. Therefore, we chose several popular nucleation models from 

the literature and compared their performance in order to identify the most suitable model. 

The models studied were the thermodynamic, kinetic, and activation nucleation models.

The thermodynamic model is based on the classical homogenous nucleation theory,

which is derived from the thermodynamic theory of fluctuations. A widely used 

expression for nucleation rate is the following (Seinfeld and Pandis, 2006):

1/2 2 2 3

SVOC

nuc 5 33 2

B

2 vC 16 v J exp m S 3k T (lnS)

  ω   −π ω =       π  , (7.8)

where ω is the surface tension of the SVOC, m the molecular mass, v the molecular

volume, S the super-saturation ratio, kB the Boltzmann constant, and T the air 

temperature. The super-saturation ratio of the SVOC is defined as S=CSVOC/CSVOC,sat, 

where CSVOC,sat is the SVOC concentration in a saturated vapor at equilibrium.
102

Another popular theory of nucleation is the kinetic theory, which assumes that nucleation 

is determined by the collision rate of molecules rather than by the thermodynamic 

criterion (Lushnikov, 2010). The nucleation rate is given by:

2

nuc kin SVOC J kC = , (7.9)

where kkin is the kinetic nucleation constant.

Recently, the activation nucleation theory was proposed by Kulmala et al. (2006) for 

explaining atmospheric nucleation events. It was also used by Vartiainen et al. (2006) to 

compute nucleation of particles from ozone/d-limonene reactions in an indoor 

environment. Therefore, it was chosen as another possible candidate for computing 

nucleation. This theory assumes that nucleation occurs through the activation of existing 

thermodynamically stable clusters by SVOCs, and the nucleation rate is given by:

nuc act SVOC J kC = , (7.10)

where kact is the activation nucleation constant.

7.1.1.3 Modeling the Condensational Sink (CS) and Deposition Rate Coefficients 

(λd,SVOC and λd,p)

For the condensational growth governed by Eq. (7.3), CS is given by the following 

expression (Kulmala et al., 2001):

SVOC c i i

i

CS 2 D .Dp .N(Dp ) =π β ∑ . (7.11)

The SVOC deposition on the chamber walls was computed from Lai and Nazaroff’s 

(2000) deposition model, as suggested by Weschler and Nazaroff (2008). The particle 

deposition coefficient (λd,p) on the chamber walls was taken as 0.25 h−1 for all diameters.
103

7.1.2 Estimating Physical Constants and Model Parameters

The solutions of the model equations (Eqs. (7.1)–(7.11)) require appropriate values for 

the physical properties of the hypothetical SVOC and particles as well as the various 

modeling parameters. Table 7.1 shows the values used in this investigation. However, 

because the ozone/skin-oils reaction byproducts responsible for particle generations are 

themselves unknown, those physical properties and modeling constants could only be 

estimated.

Table 7.1. Various physical constants and model parameters used in this investigation.

Variable Estimated value Variable Estimated value

ρp 1.2 g cm−3 DSVOC 5×10−6 m

2

s

−1

kozone-ex 2×10−10 kgm−2 h−1 ppb−1 CSVOC,sat 1×10−13 kg m−3

kozone 1×10−3 h−1 ppb−1 ω .025 kg s−2

C0,skin-oils 2×10−7 kg m−2 kkin 8.4×1025 m3 kg−2s

−1

m 250 g mole−1 kact 1.6×1016 kg−1s

−1

A recent investigation (Wang and Waring, 2014) found SVOCs such as geranyl acetone, 

4-methyl-4-octene-1,8-dial (4-MOD), and 4-methyl-8-oxo-4-nonenal (4-MON) 

responsible for condensational mass transfer to particulate phase from ozone reactions 

with surface-sorbed squalene (a major component of human skin-oils). The authors also 

conjectured that SVOCs such as 5,9,13-trimethyltetradeca-4,8,12-trienal (C17-trienal), 

4,9,13,17-tetramethyl-octadeca-4,8,12,16-tetraenal (C22-tetraenal), and 4,8,13,17,21-

pentamethyl-docosa-4,8,12,16,20-pentaenal (C27-pentaenal) could be responsible for 

nucleation. Therefore, we considered the hypothetical SVOC as a surrogate for these

ozone/squalene generated SVOCs. Its property values for m and DSVOC were estimated by 

computing the mean property values for the SVOCs mentioned above. 

A similar method (i.e., computing the average property values for ozone/squalenegenerated SVOCs) was attempted for determining the values of CSVOC,sat and ω used in 

the thermodynamic nucleation models. However, reasonable results could not be obtained 

because the nucleation rate expression is extremely sensitive to these parameters. We 
104

therefore fitted the values of CSVOC,sat and ω such that the peak particle number 

concentration was predicted well for a reference case (a case with intermediate 

conditions). We chose such a criterion because nucleation primarily influenced the 

number concentrations. Similarly, we fitted the values of kkin and kact used in the kinetic 

and activation models, respectively, such that peak number concentrations were best 

predicted for the same reference case.

Likewise, the values of λd,p and kozone-ex in the “excess skin-oils” model were fitted such 

that the particle size distributions were predicted reasonably well for the reference case. 

In the case of the “skin-oils depletion” model, we retained the value of λd,p obtained 

previously and then fitted kozone and C0,skin-oils such that the particle mass concentrations 

for the case with the highest ozone concentration were predicted well. We chose such an 

approach because the T-shirt in that case was expected to be highly depleted of skin-oils 

as a result of the high ozone reactions, which means that the “skin-oils depletion” was 

most suitable. The particle density was taken from Turpin and Lim (2001), who derived 

their results by using the data of Rogge et al. (1993) for atmospheric organic aerosols.

7.2 Results and Discussions

This section first reports the simulation results for ozone-initiated particle generations 

from reactions with soiled clothing obtained by using the apparent nucleation formulation 

(Eq. (7.7)) and their comparison with the measurements described in Chapter 4. These 

comparisons were useful for evaluating the performance of the various model 

formulations used in this investigation, except for the nucleation model, because Eq. (7.7)

computed the nucleation from the measurements themselves (i.e., the measurements of 

Ntotal,SMPS(t)). However, as discussed previously, it is not practical to use such an 

approach for computing ozone/clothing-generated particles under realistic indoor 

conditions, since their individual contributions to particles cannot be isolated from other 

particle sources. Therefore, we subsequently replaced the apparent nucleation model with 

different theoretical nucleation models that can compute ozone-initiated particles without 

the measurement of Ntotal,SMPS(t). We then evaluated the performance of the 

thermodynamic, kinetic, and activation nucleation models by comparing their predictions 
105

with the corresponding measurements. Finally, we used the thermodynamic model to

estimate the ozone-initiated particle generation from human clothing under typical 

building and airliner cabin conditions, because performance of this model was found to 

be superior to that of the other two models.

7.2.1 Apparent Nucleation Rate Formulation

Because nucleation modeling was the most difficult aspect of this research, we computed 

the apparent nucleation rates from Eq. (7.7). To reduce computational expenses, our 

model assumed that the inlet air contained a negligible number of particles. Therefore, to 

test the model formulations, we chose those cases from the measurements given in Table 

4.1 which had low inlet particle concentrations. The experimental conditions for these

cases are summarized in Table 7.2.

Table 7.2 Details of the experimental conditions in the chamber used for simulating 

particle generation from ozone reactions with human clothing

Case

Corresponding 

case in Table 

4.1

Ozone 

concentration 

(ppb)

Hours the 

T-shirt was 

worn (h)

Air change 

rate (h−1

)

1 12 22 6 0.5

2 3 40 6 0.5

3 7 57 12 0.5

4 4 148 6 0.5

5 10 51 6 2.7

6 11 133 6 2.7

Among these cases, we studied Cases 1–4 with the apparent nucleation formulation. We 

could not study Cases 5 and 6 with this approach because particle generation was not 

detected in these cases, which meant that Eq. (7.7) could not be used for computing the 

nucleation rate (Jnuc,app). These two cases were used for studying the performance of the 

various nucleation models, which is discussed in the next subsection.

Figure 7.2 compares the computed and measured ozone-initiated particle number 

concentrations for Cases 1, 3, and 4. The measurements showed that the ozone-initiated 
106

particle numbers were negligible from t = 0 to t = 2.5 h because the ozone concentration 

was very low (~ 5 ppb). Particle generation was measured about one hour after ozone 

injection through a burst of nucleation, and the total number concentrations reached a 

maximum. The particle numbers then started to decay because of the lack of nucleation 

and because of particle removal by ventilation and deposition. A secondary burst of 

nucleation was clearly observed in cases with high ozone concentrations, and the particle 

numbers showed secondary maxima. The secondary burst was not detected under low 

ozone conditions, mainly because of the large measurement uncertainties. Finally, the 

particle numbers decayed further, and the experiments were terminated at t = 12.5 h.

Figure 7.2. Comparison of particle number concentrations computed by the “excess skinoils” model with the corresponding measurements at different ozone concentrations.

As shown in Figure 7.2 for the “excess skin-oils” model, the computed particle number 

concentrations were in excellent agreement with the measurements at different ozone 

levels, primarily because the apparent nucleation rate was estimated from the 

measurements themselves. Note that in the case of 148 ppb ozone, the model slightly 

over-predicted the number concentrations at certain times. This case had particle 

concentrations in excess of 10,000 #/cm3

, meaning that coagulation could be responsible 
107

for a reduction in particle number (Hussein et al., 2009). The fact that coagulation was 

not included in our model probably led to slight over-prediction. We also obtained 

excellent agreement in the 40 ppb ozone case (results not shown). The results with the 

“skin-oils depletion” model were almost identical to the “excess skin-oils” model and 

therefore are not shown here.

Figure 7.3(a) shows the particle mass concentrations obtained by the two SVOC 

generation models for the 40 ppb and 148 ppb ozone cases (Cases 2 and 4) and their 

comparisons with measurements. In both cases, the measured mass concentrations first 

increased as a result of particle generation after ozone injection, and then decayed toward 

the end. In the case of 40 ppb ozone, both models predicted the particle mass 

concentrations reasonably well when compared with the measurements, but the “excess 

skin-oils” model seemed to provide slightly better predictions than the “skin-oils 

depletion” model. However, with 148 ppb ozone, the “excess skin-oils” model hugely 

over-predicted the particle masses, while the “skin-oils depletion” model performed 

much better, although it still over-predicted the mass concentration by about 30%. Thus, 

it seems that the soiled T-shirt contained a limited quantity of skin-oils available for 

ozone reactions. These were consumed faster at the higher ozone concentration, and the 

T-shirt was significantly depleted of skin-oils, especially at later times.
108

Figure 7.3. Comparisons of particle mass concentrations computed by the “excess skinoils” and “skin-oils depletion” models with the corresponding measurements at (a) 40 

ppb and 148 ppb ozone with 6 hours of T-shirt soiling, and (b) 57 ppb ozone with 12 

hours of T-shirt soiling.

In the case of 57 ppb ozone (Case 3), the trends of the results for particle mass 

concentrations from both models were similar to those in the previous cases, as shown in 
109

Figure 7.3(b). In this case, the T-shirt was soiled for 12 hours, as compared with 6 hours 

in the previous cases, which means that the quantity of skin-oils in the T-shirt was 

probably higher. Thus, the performance of the “excess skin-oils model” was somewhat

better than that of the skin-oils depletion model, although it still slightly over-predicted 

the mass concentrations towards the end. We did not make comparisons for mass 

concentrations in the 22 ppb ozone (Case 1) because there were large uncertainties in the 

measurements of particle masses at this low ozone level.

Overall, the “skin-oils depletion” model seemed more suitable for simulating ozoneinitiated particle generations from the soiled T-shirt over the human simulator because (1) 

its predictions of particle mass concentration agreed reasonably well with the 

measurements for all three cases analyzed, and (2) it correctly captured the decreasing 

trend of particle masses towards the end of the experiments, particularly in the case of 

148 ppb ozone. However, it seems that the “excess skin-oils” model would be more 

appropriate for simulating particle generation with clothing worn by human beings,

because the skin-oils would be replenished by human activities. The various 

discrepancies between the computed and measured values of mass concentration arose

because (1) the T-shirt most likely did not contain the same amount of skin-oils in all 

cases, while the “skin-oils depletion” model assumed otherwise; and (2) the model 

assumed a first-order reaction between ozone and skin-oils, whereas the actual reaction is 

very complex.

Figure 7.4 compares the particle size distributions computed by the “skin-oils depletion” 

model with the corresponding measurements at an ozone concentration of 40 ppb (Case 

2). The details of the particle generation, growth, and removal phenomena were provided

in Chapter 4, and we briefly describe those results here for the purpose of comparison 

with model predictions.
110

Figure 7.4. Comparisons of particle size distributions computed by the “skin-oils 

depletion” model with the corresponding measurements at an ozone concentration of 40 

ppb for (a) t = 4.2 h and 6.2 h, and (b) t = 8.5 h and 12.5 h.

As shown in Figure 7.4(a), ozone-initiated particles were generated in the ultrafine region 

with a mode diameter of approximately 40 nm at t = 4.2 h, and this was predicted 

reasonably well by the model. At t = 6.2 h, the particle mode diameter shifted to 90 nm as 
111

a result of condensational growth, and the number of particles was also depleted by

ventilation and deposition losses; these phenomena were also well captured by the model. 

At t = 8.5 h, the particle size distribution was predominantly bimodal, with modes at 40 

nm and 136 nm as shown in Figure 7.4(b). The former mode (40 nm) resulted from a 

secondary nucleation burst, and the latter (136 nm) was due to condensational growth of 

the original mode. The model predictions also showed two dominant modes and an 

overall good match with the measurements. From t = 8.5 h to 12.5 h, the particle 

concentrations decayed because of ventilation and deposition, and there were no obvious 

modes in the size distribution at t = 12.5 h. The model prediction also showed decay in 

particle concentrations, and the overall size distribution was again predicted reasonably

well.

Evidently, the model captured the overall trend of particle generation, growth, and 

removal with good accuracy for the 40 ppb ozone case described above. The model 

performed slightly worse for the other cases (Cases 1, 3, and 4), but those predictions still 

compared well with their corresponding measurements. It should be emphasized that the 

model contained physical formulations for SVOC generation, condensation, and SVOC 

and particle deposition with few fitting parameters. Hence, these model formulations 

seemed reasonable, given the good agreement between model predictions and 

experimental measurements. However, the apparent nucleation rate was computed from 

the experimental data itself (i.e, the measured Ntotal,SMPS(t)), which was a big drawback in

applying the model under new indoor conditions. Moreover, even if the Ntotal,SMPS(t) were 

obtained through measurement, there would be no way to separate the contributions of 

particles generated from ozone/clothing reactions from those from other indoor sources. 

Therefore, it was desirable to incorporate a suitable nucleation model for simulating 

ozone-initiated particles without aid from experimental measurements, as discussed in the 

next subsection.

7.2.2 Nucleation Models

This section discusses the results obtained by using the different theoretical nucleation 

models described in Section 7.1.1.2: the activation, kinetic, and thermodynamic 
112

nucleation models. We again used the “skin-oils depletion” model to compute the results 

for this section because its performance was found to be superior to that of the “excess 

skin-oils” model.

As discussed in 7.1.2, the modeling constants in the various nucleation models were 

chosen such that the peak of the particle number concentration (at t = 4.2 h) in the 

reference case (40 ppb ozone) was captured reasonably well. Thus, all the nucleation 

models provided a good prediction of the maximum particle concentration in the case of 

40 ppb ozone, as shown in Figure 7.5(a). However, none of the nucleation models could 

effectively capture the steep decline in particle number from t = 4.2 h to t = 7.0 h and the 

subsequent secondary nucleation burst. In the case of 148 ppb ozone, the thermodynamic 

model over-predicted the peak particle concentration by about 30%, whereas the kinetic 

and activation models under-predicted it by about 10% and 40%, respectively, as shown 

Figure 7.5(b). Subsequently, all the models over-predicted the number concentrations, but 

the overall trend was better captured by the thermodynamic model than by the other two 

models.
113

Figure 7.5. Comparisons of particle number concentrations computed by the different 

nucleation models with the corresponding measurements at an air change rate of 0.5 h−1

with (a) 40 ppb ozone and (b) 148 ppb ozone.

Figure 7.6 compares the particle number concentrations obtained by different nucleation 

models with their corresponding measurements for Cases 5 and 6 conducted at high air 

change rates with different ozone concentrations. In both cases, particle generation was 
114

not measured. However, the different nucleation models generally predicted particle 

generation, as shown in Figures 7.6(a) and (b). In the case of 51 ppb ozone, the 

predictions of the thermodynamic model were the best, and those of the activation model 

were the worst. However, the opposite was true in the case of 133 ppb ozone. All the 

nucleation models failed overall in these cases, especially at the higher ozone 

concentration.
115

Figure 7.6. Comparisons of particle number concentrations computed by the different 

nucleation models with the corresponding measurements at an air change rate of 2.7 h−1

with (a) 51 ppb ozone and (b) 133 ppb ozone.

For Cases 1 and 3, all the nucleation models generally over-predicted the particle number 

concentrations. For example, the activation, kinetic, and thermodynamic models overpredicted the peak particle concentrations by about 400%, 260%, and 60%, respectively,
116

in Case 1; and by about 10%, 30%, and 40% in Case 3. By and large, the thermodynamic 

model performed the best, and the activation model performed the worst in these cases.

In contrast to the significantly different predictions obtained for number concentrations 

by the various nucleation models, the predicted mass concentrations were similar among

the three models, as shown in Figure 7.7 for the 40 ppb and 148 ppb ozone cases. In all 

the other cases that were analyzed, the particle mass concentrations generally were not 

very sensitive to the choice of nucleation model. This result was reasonable because mass 

transfer from vapor to liquid particles is determined primarily by condensation, and 

nucleation plays a secondary role. The reasons for the discrepancies between the model 

predictions and measurements (as seen in Figure 7.7) were discussed previously and can 

be attributed to the assumptions used in the “skin-oils depletion” model.
117

Figure 7.7. Comparisons of particle mass concentrations computed by the different 

nucleation models with the corresponding measurements at (a) 40 ppb ozone and (b) 148 

ppb ozone.

Figure 7.8 compares the particle size distributions calculated by the different nucleation 

models with their corresponding measurements at several instants of time in the case of 

40 ppb ozone. At t = 4.2 h, all the models predicted the particle size distributions 
118

reasonably well, as shown in Figure 7.8(a). At t = 6.2 h, the predictions by the 

thermodynamic and kinetic model seem reasonable, but those by the activation model are

quite poor when compared with the measurements. The model predictions become much 

worse at t = 8.5 h and 12.5 h, as shown in Figures 7.8(c) and (d), respectively, and none 

of the models could reasonably capture the secondary nucleation burst.

Figure 7.8. Comparisons of particle size distributions computed by the different 

nucleation models with the corresponding measurements at 40 ppb concentration for (a) t

= 4.2 h, (b) t = 6.2 h, (c) t = 8.5 h, and (d) t = 12.5 h.

It was also illustrative to compare Figures 7.4 and 7.8 for identifying the differences 

between the results obtained from the apparent nucleation formulation and the various 

theoretical nucleation models. After the theoretical nucleation models were introduced in 

place of the apparent nucleation approach, the computed results showed significant 

deterioration when compared to the measurements. Therefore, an accurate nucleation 
119

model is vital for correctly computing the size-resolved particle concentrations generated 

from ozone reactions with human-worn clothing.

In Cases 1, 3, and 4, the comparisons between particle size distributions predicted by the 

different nucleation models and their corresponding measurements were similar to the 

comparisons obtained in Case 2 (40 ppb ozone). In Cases 5 and 6, all the models 

predicted particle generation, but no generation was observed in the experiments. Hence, 

all the nucleation models failed in these two cases. The discrepancies can be attributed 

primarily to the unknown nucleation precursors and mechanism as well as the limitations 

of the different nucleation theories (Anisimov, 2003; Seinfeld and Pandis, 2006).

7.2.3 Ozone-initiated Particles under Different Indoor Environments

In spite of the disagreement between the model predictions and measurements, it was 

seen that the thermodynamic nucleation model worked reasonably well in five out of six 

cases. Hence, we used this model to simulate particle generation from ozone reactions 

with occupant’s clothing in the following indoor settings: a classroom, office, and home

as building environments, and Boeing 737-700 and Boeing 777 airliner cabins as airplane 

environments.

To compute ozone-initiated particle generation due to reactions with human clothing, we 

first studied a typical classroom with a volume of 182 m3 occupied by 25 pupils and one 

teacher, as described in Fischer et al. (2013), under different air change rates. We 

assumed outdoor ozone concentrations of 50 ppb and 100 ppb, which represent clean and 

polluted environments, respectively. The outdoor-to-indoor ozone transport was 

calculated according to the mass balance model from Weschler (2000). We also analyzed 

the influence of background particles on the generation of new ozone-initiated particles 

by simulating conditions with and without background particles. It should be noted that 

background particles can significantly decrease the generation of new particles because

the condensation of SVOC on background particles is more favorable than its nucleation. 

To generate a typical background particle profile, we utilized the study of Franck et al. 

(2003), which computed the average size distribution for indoor particles of outdoor 
120

origin. That distribution was approximately lognormal with a total concentration of 

around 1450 #/cm3 and mode diameter of about 100 nm. We utilized this representative 

distribution to compute an additional condensational sink for the SVOC vapor from Eq. 

(7.11) of 6.4 h−1 by assuming that the background particles remained unchanged.

Figure 7.9 shows the four-hour averaged UFP number concentrations computed for the 

classroom under two different outdoor ozone levels with and without background 

particles. Under all the conditions, the particle concentrations were quite low at the low 

air change rates because there was insufficient outdoor-to-indoor ozone transport to 

generate particles. The particle concentrations then increased with increasing ventilation 

rates due to higher indoor ozone concentrations, which led to higher particle generations. 

A further increase in ventilation rate led to a decrease in particle concentrations because

the particle-forming precursors were quickly removed from the classroom.

Figure 7.9. Four-hour averaged ultrafine particle number concentrations computed for a 

typical classroom under various air change rates for 50 and 100 ppb outdoor ozone 

conditions with and without background particles.
121

Figure 7.9 also shows that the particle concentrations were lowest in the presence of 

background particles at the 50 ppb outdoor ozone concentration under different air 

change rates. Under the same level of background particles, the concentrations of ozoneinitiated particles increased as the outdoor ozone was increased from 50 to 100 ppb, as a 

result of the higher reaction rate. The ozone-initiated particle concentrations were much 

higher in the cases without background particles than in those with background particles 

under the same outdoor ozone conditions. Again, in those cases (without background 

particles) the particle concentrations increased with increasing ozone levels. Overall, the 

maximum particle generation was computed under high ozone concentrations and 

without pre-existing background particles.

In addition to classroom conditions, we also studied particle generation for typical office 

and home environments. These cases assumed the same conditions as in the classroom, 

except that there were four occupants in the office and two in the home. UFP generation 

in the office and home was much lower because the reduced occupancy resulted in lower 

ozone reactions, as shown in Figure 7.10 for 100 ppb outdoor ozone without background 

particles. Even under conditions that were favorable for new particle generation, the 

maximum UFP concentration was only 400 #/cm3 for the office and 40 #/cm3 for the 

home, obtained at an air change rate of 1 h−1. Hence, it seems that such ozone-initiated 

particles would not be significant under low occupancy conditions.
122

Figure 7.10. Four-hour averaged ultrafine particle number concentrations computed for 

different buildings under various air change rates for 100 ppb without background 

particles.

Since the ozone/clothing generated UFPs were found to be significant under high ozone 

and occupancy conditions without background particles, we also studied those particle 

generations in airliner cabins that usually have such conditions. This investigation

simulated UFP generation from ozone/clothing reactions in a fully occupied Boeing-777 

flight for a duration of 10 hours without background particles. The flight is typically 

trans-continental. The cabin ozone concentration was taken to be 50 ppb. It was predicted 

that the flight would have significant UFP generation, and the time-averaged particle 

concentration was computed as 6800 #/cm3

.

Next, we conducted a case study for a typical domestic flight of 1.5 hours. The flight was 

made by a partially occupied B737-700 airliner for which ozone-initiated UFP generation 

was recently measured by Spengler et al. (2012). The measured ozone and particle 

concentrations very quite low initially, as shown in Figure 7.11. After 15 minutes, the 

ozone concentration started to increase, and after another five minutes the particle 

concentrations also rose steeply. The particle concentration then remained steady 
123

throughout the cruising portion of the flight. Finally, the ozone and particles decayed

sharply during the descent. Figure 7.11 also shows the contributions of UFPs generated 

from ozone reactions with the passengers’ clothing as computed by our model. These 

reactions can account for about 40% of the total UFPs measured. Obviously, other ozoneinitiated mechanisms of particle generation must have prevailed, such as the reaction of 

ozone with various terpene-containing consumer products, but taking these reactions into 

account was beyond the scope of this investigation. Nevertheless, the model results 

indicate that ozone reaction with passengers’ clothing could be a significant contributor 

of UFPs under typical airliner cabin conditions.

Figure 7.11. Measurements of the ozone-initiated UFPs in a Boeing 737-700 flight and 

the corresponding contributions by ozone reactions with passengers’ clothing as 

computed by the model.

7.3 Conclusions

This chapter described numerical models to study particle generation from the reaction of 

ozone with human-worn clothing. The models were developed with the use of

experimental measurements of particle generation in an environmental chamber and 

physical formulations for particle nucleation, growth, and removal mechanisms. The 
124

models can predict size-resolved particle number concentrations, and they have provided 

insights into the overall particle-generation mechanism.

The particle size distributions predicted with the apparent nucleation formulation were in 

good agreement with the experimental data, in which the “nucleation rate” was directly 

estimated from the measured value of the total particle number concentration. However, 

the numerical models in combination with different theoretical nucleation models 

displayed significant discrepancies in predicting the particle size distributions because of 

some fundamental limitations of the nucleation models. Overall, the thermodynamic 

model based on the classical homogeneous nucleation theory provided the most 

reasonable predictions for five out of the six experimental cases.

Finally, the thermodynamic model was used to compute particle generation from ozone 

reactions with human-worn clothing in different building and airplane environments. The 

results show that ozone reactions could significantly contribute to UFP generation in

high-occupancy spaces such as classrooms and airliner cabins. Ozone/clothing reactions 

may have accounted for about 40% of the UFPs measured in a Boeing 737-700 flight. 

However, their contributions to UFPs in office and home environments did not seem to 

be important because of the low occupant density.
125

CHAPTER 8. CONCLUSIONS AND FUTURE WORK

8.1 Conclusions

This research developed numerical models for computing the concentrations of ozone 

and its reaction byproducts (VOCs and particles) in the indoor environment and 

estimating the resulting exposure of occupants.

To develop these models, we first measured ozone consumption and ozone-initiated VOC 

emissions from ozone reactions with a T-shirt soiled with human skin oils in an 

environmental chamber. It was found that these ozone/T-shirt reactions consumed ozone 

and generated VOCs such as C6–C10 straight-chain saturated aldehydes, acetone, and 4-

oxopentanal. The ozone reactions were significantly affected by the different factors that 

were studied (such as ozone concentration, humidity, soiling degree of the T-shirt, and air 

change rate). The total molar yield for most cases was 0.5, which means that for every 

two moles of ozone removed by the T-shirt surface, one mole of VOCs was produced.

We also examined the possibility of particle generation from the ozone/T-shirt reactions 

through the same chamber experiments. Particle generation was indeed observed from 

these reactions, and it was enhanced by the soiling of the T-shirt with human skin oils. 

Initially, the ozone reactions generated a burst of ultrafine particles (UFPs), and then the 

particles grew to larger sizes. Particle generation was also affected by the various factors 

that were studied, and ozone reactions with human clothing were identified as another 

potential source of indoor UFPs.

Next, this research developed modeling techniques for extending the experimental data to 

realistic indoor conditions. We first developed a CFD model for simulating the ozone 

distribution in an airliner cabin. The CFD model provided a reasonably good prediction 
126

of the rate of ozone removal by the different reaction surfaces in the airliner cabin when 

compared with their corresponding measurements. The CFD results also indicated that 

the ozone is depleted by about 10% in the breathing zone of passengers as compared to 

its concentration in the bulk cabin air because of its reactions with human skin and 

clothing. Hence, the ozone concentration in the breathing zone should be used for 

assessing the exposure of airliner passengers.

We subsequently developed models for computing the ozone-initiated secondary 

emissions of acetone, 4-oxopentanal (4-OPA), nonanal, and decanal from reactions with 

human-worn clothing in an aircraft cabin. We developed empirical equations to compute 

secondary VOC emissions from the prevailing environmental conditions (such as ozone 

concentration, humidity, and air change rate) on the basis of our chamber measurements. 

These models were then used in a mass balance analysis to compute the contributions of 

human surfaces (skin, hair, and clothing) to ozone-initiated VOCs in an aircraft cabin 

mockup for three different cases. The model predictions showed some discrepancies 

when compared with the corresponding measurements because of the limited dataset used 

to develop the model as well as the many complexities of the cabin mockup. Nevertheless, 

the models were useful for computing rough estimates of ozone-initiated VOCs from the 

given indoor conditions. The VOC emission models were also integrated into a CFD 

model, and the breathing zone concentrations of different VOCs were computed for 

passengers seated in various locations in an occupied cabin mockup. It was found that the

various VOCs were enriched in the breathing zone by about 10%–50% when compared 

with their corresponding exhaust concentrations. Therefore, it was recommended that the 

concentrations of ozone-initiated VOCs in the breathing zone be used for assessing the

exposure of airliner passengers.

This investigation also developed a numerical model to study particle generation from

ozone reactions with human-worn clothing. The model was developed on the basis of our 

chamber measurements of particle generation and contained physical formulations for 

particle nucleation, growth, and removal mechanisms. The model was capable of 

predicting size-resolved particle number concentrations and provided insights into the 
127

overall particle generation mechanism. Finally, the model was used to compute particle 

generation from ozone reactions with human-worn clothing in different indoor 

environments, keeping in mind that these results need to be further validated because of 

the limitations of the nucleation model that was used. It was predicted that such ozone 

reactions could significantly contribute to UFP generation in high-occupancy settings 

such as classrooms and airliner cabins. It was also shown that ozone/clothing reactions 

may have accounted for about 40% of the UFPs measured in a Boeing 737-700 flight. 

However, their contributions to UFPs in office and home environments did not seem to 

be significant because of the lower occupant densities of these environments.

8.2 Future Work

This investigation provided a number of insights into ozone reactions with human-worn 

clothing occurring in the indoor environment. However, many research questions remain 

unanswered and should be explored in future work.

We conducted chamber experiments to quantify the ozone consumption and ozoneinitiated VOC emissions from reactions with a cotton T-shirt under various conditions. 

However, we did not characterize the influence of clothing fabric (e.g. wool, polyester, 

etc.), garment type (e.g. trousers, outerwear, etc.), or the human subjects themselves on 

the ozone reactions. It is hypothesized that these factors would significantly alter the 

ozone-initiated VOC and particle generation, which should be further explored though 

additional chamber experiments. Such experiments would also generate more data for 

improving the empirical models developed for computing ozone-initiated VOC emissions.

Our chamber experiments measured particle generation from ozone/clothing reactions, 

and we also developed numerical models to compute the concentrations ozone-initiated 

particles under realistic indoor conditions. The results of our model indicated that these 

ozone reactions are potential sources of UFPs in densely occupied indoor environments 

such as classrooms and aircraft cabins. However, our results need to be substantiated by 

measuring UFPs in real or simulated indoor settings with and without occupants.
128

A fundamental difficulty in modeling particle generation from ozone/clothing reactions 

was that the nucleation mechanism was unknown, and none of the tested nucleation 

models could predict the nucleation rates with reasonably good accuracy. Future research 

into the nucleation mechanism may help to better understand and predict particle 

generation in the indoor environment.
129

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APPENDICES
140

Appendix A. Experimental Data for the VOC measurements

Table A.1. VOC concentrations measured during the ozone reactions with clothing in

Chapter 3.

Case Chamber 

condition

Formaldehyde 

(ppb)

Acetaldehyde 

(ppb)

C6–C8 

aldehydes 

(ppb)

Nonanal 

(ppb)

Decanal 

(ppb)

Acetone 

(ppb)

4-OPA 

(ppb)

1 background 0.0 0.7 0.6 1.1 2.3 4.9 0.1

with ozone 2.7 2.1 0.9 1.9 2.8 2.4 0.3

2 background 2.5 2.7 0.3 1.1 2.0 9.2 0.0

with ozone 4.5 2.1 2.6 6.8 15.7 6.8 1.5

3 background 1.1 0.4 0.4 1.3 1.5 4.0 0.0

with ozone 4.8 2.5 2.8 9.0 9.6 15.2 7.2

4 background 1.7 1.7 0.4 0.9 1.5 7.7 0.1

with ozone 15.9 3.1 11.6 19.9 38.4 20.9 19.3

5 background 2.6 0.9 1.3 2.4 4.0 3.2 0.3

with ozone 4.6 2.3 14.4 25.6 54.1 19.2 19.6

6 background 3.0 1.3 0.9 2.1 4.6 5.0 0.0

with ozone 8.2 3.5 12.0 19.9 37.5 13.5 14.7

7 background 0.8 4.0 1.6 3.2 7.9 6.5 0.6

with ozone 6.1 2.5 11.7 18.3 38.1 17.4 21.1

8 background 1.8 2.0 1.6 2.6 5.7 6.3 0.4

with ozone 4.4 4.6 13.0 21.7 40.3 19.8 20.6

9 background 0.7 0.2 1.0 1.2 2.0 6.6 0.0

with ozone 1.2 1.1 1.0 2.7 3.7 5.4 0.8

10 background 0.0 1.0 0.1 0.3 0.4 5.5 0.0

with ozone 2.4 2.0 1.2 2.2 3.2 13.0 3.0

11 background 0.7 2.1 0.5 0.9 1.8 2.9 0.1

with ozone 3.5 3.0 3.7 7.9 12.1 13.9 5.6

12 background 2.2 1.8 0.6 2.0 4.5 4.6 0.0

with ozone 2.5 2.8 3.9 6.6 14.3 12.7 9.6

13 background 4.4 1.6 2.1 3.5 7.6 4.2 0.4

with ozone 6.2 2.4 6.8 12.0 18.2 12.8 9.3

3D background 2.5 2.3 0.6 1.8 2.5 3.3 0.0

with ozone 4.7 1.9 2.7 7.4 10.6 13.1 6.1

10D background 0.0 1.3 0.5 1.0 2.2 3.0 1.3

with ozone 2.3 1.8 1.4 2.5 4.0 9.8 1.8
141

Appendix B. Particle Generation and Growth

As discussed in Chapter 4, the generation of new particles by nucleation versus particle 

growth by condensation depends on the surface area of existing particles. A low particle 

surface area concentration in the chamber favored generation of new particles (by 

nucleation), whereas a high surface area concentration favored particle growth (by 

condensation). To illustrate this, the variations of particle number and surface area 

concentrations with time are shown in Figure B.1 for Case 3. As shown in the figure, 

initially the particle surface area concentration was very low and facilitated a huge 

primary burst (t = 3.4 h) of ultrafine particles from the ozone reactions. The surface area 

concentration then increased until about t = 6.0 h due to particle generation and growth. 

Then, from t = 6.0 h to t = 7.5 h, the surface area concentration decreased, which 

probably led to the secondary burst of particles by nucleation at t = 7.2 h since 

insufficient surface area was available for particle growth by condensation. Finally the 

particle number and area concentrations decayed till the end of the experiment, probably 

due to a reduction in ozone-initiated particle generations because of depletion of skin-oils 

from the T-shirt.

Figure B.1. Particle number and surface area concentrations in Case 3.
142

Figure B.2 compares the surface area concentrations in Cases 3 (12% RH) with that in 

Case 5 (44% RH). The particle surface areas were much higher in Case 5 probably due to 

hygroscopic growth of particles at high humidity conditions. The high surface areas in 

Case 5 favored condensation over nucleation and reduced the secondary burst of particles 

in Case 5 compared to that in Case 3 as shown in Figure 4.5.

Figure B.2. Effect of relative humidity on the particle surface area concentration.

Exposure to Ozone-initiated Ultrafine Particles under Building Conditions

We analyzed the contributions of ozone-initiated particle generation in realistic indoor 

conditions. It was assumed that the particle number concentration reaches steady state (as 

seen in Figure 4.8), the chamber air was well mixed, and deposition was negligible, then:

SA Q N N p T shirt outdoor exhaust inlet ×=× − − ( ) , (B.1)

where Sp is the particle number generation per unit time per unit area of the human 

surface; AT-shirt the area of the T-shirt; Qoutdoor the outdoor airflow rate to the chamber;

and Ninlet and Nexhaust the particle number concentrations at inlet and exhaust, respectively.
143

For Case 12 (in Table 4.1) that corresponded to a typical building condition on a poor air 

quality day (22 ppb ozone and 0.5 h−1 outdoor air change rate):

AT-shirt = 0.9 m2

, Qoutdoor = 2.55 m3/h, Ninlet ≈ 0, and Nexhaust ≈ 600 #/cm3

⇒ Sp = 472222 #/m2-s (from Eq. (B.1))

Next, we estimated the particle generation from the ozone reaction with human surfaces

for a full-scale building environment by assuming that conditions for Case 12 were 

applicable. It was further assumed that the building is occupied by Pnum occupants and 

each occupant contributes an area of 1.7 m2 (total area = 1.7×Pnum m

2

) for the ozone 

reactions. From Eq. (B.1):

SA Q N N p T shirt outdoor exhaust inlet ×=× − − ( )

⇒× × = × × − S 1.7 P P R N N p num num p exhaust inlet ( )

⇒ − =∆ = × (N N N S 1.7 / R exhaust inlet ) ( p ) p , (B.2)

where ΔN is the increase in particle number concentrations due to the ozone reactions 

and Rp is the outdoor airflow rate per person. We used the above equation to calculate ΔN 

= 80–160 #/cm3 for Rp between 5–10 L/s.