REDUCING INDOOR AIR CONTAMINANTS INSIDE A CAMPUS BUS
PASSENGER COMPARTMENT
NOOR EMILIA BINTI AHMAD SHAFIE
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Doctor of Philosophy (Mechanical Engineering)
Faculty of Mechanical Engineering
Universiti Teknologi Malaysia
MARCH 2016
iii
This is dedicated to my beloved husband (Dr. Iqbal Bin Mokhtar) and
parents (Ahmad Shafie Bin Ariffin, Noor Lida Binti Raffie, Mokhtar Bin Ahmad
& Sabariah Binti Md. Kassim).
iv
ACKNOWLEDGEMENT
I would like to express my deepest gratitude to Dr. Haslinda Binti Mohamed
Kamar the main supervisor, for her guidance, support, understanding and
encouragement over the past years. I would like to thank to Assoc. Prof. Dr. Nazri
Bin Kamsah the co-supervisor, for his encouragement, advice, understanding and
support.
I am indebted to University Teknologi Malaysia (UTM) and the Ministry of
Higher Education for providing the funding on this PHD study (Vot No. 06H75).
Thanks are extended to Mr. Khairul Nizam Bin Mod Noh for providing
professional technical assistance in UTM bus service. Thanks are also given to MD
Interactive and Exact Analytical for supplying and providing professional technical
assistance in instrumentation and measurement.
Finally, I want to express my gratitude to my husband, parents and friends for
their fully support and love.
v
ABSTRACT
Good ventilation system is important to provide fresh air and comfortable
environment for passengers. Lack of fresh air inside a bus compartment may cause
various illnesses such as headache, asthma, cardiovascular and lung cancer. Two
factors influence the ventilation system effectiveness namely the arrangement of air
supply diffusers and the air return grilles. This thesis presents a study on air
contaminants inside a university’s bus passenger compartment. The goal is to find a
suitable ventilation arrangement that can reduce the concentration of the air
contaminants. First a field measurement was carried out on a selected bus to measure
the air contaminants at the front section, middle section and rear section. The
contaminants include carbon monoxide, carbon dioxide, formaldehyde and
particulate matter. Then computational fluid dynamics flow analyses were carried out
on simplified model of the bus compartment employing renormalization group k-ε
model for air flow, species transport for gases and discrete phase for particles. Five
cases of ventilation arrangements were considered namely displacement ventilation
with two air return grilles, underfloor air distribution with two air return grilles,
mixing ventilation with four air return grilles, displacement ventilation with four air
return grilles and underfloor air distribution with four air return grilles. It was found
from the field measurements that the concentration of carbon monoxide, carbon
dioxide, formaldehyde, particulate matter 1, particulate matter 2.5 and particulate
matter 10 were 7 ppm, 1102 ppm, 0.18 ppm, 52 µg/m3, 52 µg/m3 and 51 µg/m3,
respectively. Results of flow simulations show that the underfloor air distribution
with four air return grilles is able to reduce the contaminants concentration inside the
passenger compartment. On average, the concentrations of carbon monoxide, carbon
dioxide, formaldehyde, particulate matter 1, particulate matter 2.5 and particulate
matter 10 were reduced by about 40%, 10%, 38%, 37%, 33% and 30%, respectively.
vi
ABSTRAK
Sistem pengudaraan yang baik adalah penting untuk memberikan udara segar
dan persekitaran yang selesa kepada penumpang. Kekurangan udara di dalam
ruangan bas akan menyebabkan pelbagai penyakit seperti sakit kepala, asma, sakit
jantung dan kanser paru-paru. Dua faktor mempengaruhi keberkesanan sistem
pengudaraan iaitu susun atur sistem bekalan udara peresap dan jeriji udara pulangan.
Tesis ini membentangkan kajian terhadap bahan cemar udara di dalam ruangan
penumpang bas universiti. Matlamat kajian ini adalah untuk mencari susun atur
sistem pengudaraan yang sesuai bagi mengurangkan kepekatan bahan cemar udara.
Pertama pengukuran lapangan telah dijalankan pada bas yang telah dipilih untuk
mengukur bahan cemar pada bahagian depan, bahagian tengah dan bahagian
belakang. Bahan cemar ini terdiri daripada karbon monoksida, karbon dioksida,
formaldehid dan zarah. Kemudian analisis aliran pengkomputeraan dinamik bendalir
telah dijalankan pada model ruangan bas dengan menggunakan model
renormalization group k-ε untuk aliran udara, species transport untuk gas dan
discrete phase untuk zarah. Lima kes susun atur pengudaraan iaitu pengudaraan
anjakan dengan dua jeriji udara pulangan, pengudaraan udara bawah lantai dengan
dua jeriji udara pulangan, pengudaraan percampuran dengan empat jeriji udara
pulangan, pengudaraan anjakan dengan empat jeriji udara pulangan dan pengudaraan
udara bawah lantai dengan empat jeriji udara pulangan telah dikaji. Didapati
daripada data pengukuran dimana tahap kepekatan karbon monoksida, karbon
dioksida, formaldehid, zarah 1, zarah 2.5 dan zarah 10 adalah 7 ppm, 1102 ppm,
0.18 ppm, 52 µg/m3, 52 µg/m3 dan 51 µg/m3, masing-masing. Keputusan simulasi
aliran menunjukkan pengudaraan udara bawah lantai dengan empat jeriji udara
pulangan mampu mengurangkan tahap kepekatan bahan cemar di dalam ruangan
penumpang. Secara purata, tahap kepekatan karbon monoksida, karbon dioksida,
formaldehid, zarah 1, zarah 2.5 dan zarah 10 telah berkurang sebanyak 40%, 10%,
38%, 37%, 33% dan 30%, masing-masing.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xiv
LIST OF ABBREVIATIONS xvii
LIST OF SYMBOLS xix
LIST OF APPENDICES xx
1 INTRODUCTION 1
1.1 Introduction 1
1.2 Problem Statement 4
1.3 Objectives of the Research 4
1.4 Scopes of the Research 5
1.5 Important of the research 6
1.6 Thesis Outline 6
viii
2 LITERATURE REVIEW 8
2.1 Introduction 8
2.2 Indoor Air Contaminant 9
2.2.1 Gas Contaminants 10
2.2.2 Particle Contaminants 12
2.2.3 Standards and Guidelines of Indoor Air
Contaminant 13
2.2.4 Effects of Indoor Air Contaminants on
Human Health 14
2.2.5 Contaminant Concentration Level in a Bus
Passenger Compartment 17
2.3 Bus Transportation System 26
2.3.1 Types of Bus Service 26
2.3.2 Air-Conditioning System 28
2.4 Ventilation 30
2.4.1 Types of Ventilation System 31
2.4.1.1 Mixing Ventilation 31
2.4.1.2 Displacement Ventilation 33
2.4.1.3 Underfloor Air Distribution 34
2.5 Computational Fluid Dynamics Analysis on
Ventilation System 36
2.6 Summary and Research Gap 51
3 METHODOLOGY 53
3.1 Introduction 53
3.2 Field Measurement 55
3.2.1 Test Instrument 58
3.2.2 Test Procedure of Gas and Particle
Detections 61
ix
3.3 Computational Fluid Dynamics Analysis 63
3.3.1 Geometry (Baseline Model) 64
3.3.2 Mesh and Grid Verification 67
3.3.3 Boundary Conditions and Properties 72
3.3.4 Governing Equation and Finite Volume
Method 80
3.3.5 Solver, Solution Methods and
Convergence 83
3.3.6 Verification 85
3.3.6.1 Air Flow Model 85
3.3.6.2 Discretization Scheme 87
3.3.6.3 Numerical Iteration 87
3.3.6.4 Uncertainty and Error in CFD
Simulation 89
3.3.6.5 Uncertainty and Error in Field
Measurement 90
3.3.7 Validation 95
3.3.7.1 Validation on Air Contaminants
Concentration 95
3.4 Parametric Analysis (Cases) 98
3.5 Summary 103
4 RESULTS AND DISCUSSION 104
4.1 Indoor Air Contaminants Concentration 104
4.1.1 Field Measurements on Gas and
Particle Concentrations 105
4.1.2 Statistical Analysis 111
4.1.3 Summary 113
4.2 CFD Simulation on Ventilation System Design 114
4.2.1 Effects of Ventilation System Design
on Air Contaminants Concentration
(Baseline Case) 114
x
4.2.1.1 Spatial Distribution of Gases
Concentration in the Entire
Bus Compartment 117
4.2.1.2 Spatial Distribution of Particles
Concentration in the Entire
Bus Compartment 121
4.2.2 Effects of Ventilation System Design
on Air Contaminants Concentration
(Parametric Analysis) 126
4.2.2.1 Case 1 (MV+DV&2RG) 127
4.2.2.2 Case 2 (MV+UFAD&2RG) 131
4.2.2.3 Case 3 (MV&4RG) 135
4.2.2.4 Case 4 (MV+DV&4RG) 139
4.2.2.5 Case 5 (MV+UFAD&4RG) 143
4.3 New Ventilation System Design 148
4.3.1 Spatial Distribution of Gases
Concentration in the Entire Bus
Compartment 150
4.3.2 Spatial Distribution of Particles
Concentration in the Entire Bus
Compartment 154
4.3.3 Air Flow Pattern 159
4.3.4 Particulate Matter Transport Pattern 161
4.3.5 Summary 163
5 CONCLUSIONS AND RECOMMENDATIONS 164
5.1 Conclusions 164
5.2 Recommendations of Future Work 166
REFERENCES 167
Appendices A-I 177-219
xi
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Sources of gas contaminants 11
2.2 Sources of particle contaminants 12
2.3 Comparison of indoor environment standards and
guidelines 13
2.4 Formaldehyde exposure levels in relation
to human health 14
2.5 Types of indoor air quality instruments 24
2.6 Summary of literature review related to indoor
air contaminant inside a bus passenger compartment 25
2.7 Summary of literature review related to ventilation
strategy 48
2.8 Summary of literature review related to CFD
method in analysing ventilation strategy 49
3.1 Description of the bus used for the field
measurements 56
3.2 The range and accuracy of the anemometer, indoor
environmental quality and particle counter
instruments 60
3.3 Dimensions of the CFD model 64
3.4 Types of grid, grid element and grid size 71
3.5 Boundary conditions used in CFD model 76
3.6 Properties of air 79
3.7 Properties of solid 79
xii
3.8 Properties of gas 79
3.9 Properties of particle 79
3.10 Discretizations versus CO and PM1 concentrations 87
3.11 Number of iterations versus gases concentration 88
3.12 Number of iterations versus particles concentration 88
3.13 Uncertainty calculation of CO, CO2 and CH2O
concentrations 89
3.14 Uncertainty calculation of PM1, PM2.5 and PM10
Concentrations
3.15 Uncertainty calculation of CO concentration 91
3.16 Uncertainty calculation of CO2 concentration 92
3.17 Uncertainty calculation of CH2O concentration 92
3.18 Uncertainty calculation of PM1 concentration 93
3.19 Uncertainty calculation of PM2.5 concentration 93
3.20 Uncertainty calculation of PM10 concentration 94
3.21 Validation of CO concentration with field
measurement data 96
3.22 Validation of CO2 concentration with field
measurement data 96
3.23 Validation of CH2O concentration with field
measurement data 96
3.24 Validation of PM1 concentration with field
measurement data 97
3.25 Validation of PM2.5 concentration with field
measurement data 97
3.26 Validation of PM10 concentration with field
measurement data 97
3.27 Detail description of parametric analysis 98
4.1 Statistical results of CO, CO2 and CH2O
concentrations 112
4.2 Statistical results of PM1, PM2.5 and PM10
concentrations 113
4.3 Air contaminants concentration level at the
breathing level 125
xiii
4.4 Comparison of air contaminants concentration
level on each case 147
4.5 Dimensions of a new ventilation system design 149
4.6 Air contaminants concentration level at the
breathing level 158
xiv
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Source of complaints in an enclosed environment 9
2.2 Percentage of passengers on different modes
transportation 10
2.3 Air contaminants from the vehicles exhaust 11
2.4 Types of particle sizes 12
2.5 Different types of particle sizes 15
2.6 The accumulated particles in a human lung 16
2.7 CO2 concentration level versus time 18
2.8 CO concentration level versus time 18
2.9 PM2.5 concentration level versus time 18
2.10 The total vehicles travel in every year in Malaysia 26
2.11 Types of bus services in Malaysia (a) Public
(b) School (c) Highway (d) Factory
(e) University 27
2.12 The schematic layout of air-conditioning system 28
2.13 The schematic of a roof package unit 29
2.14 The schematic of a floor package unit 30
2.15 Mixing ventilation systems 32
2.16 Short circuiting flow phenomenon 32
2.17 Displacement ventilation systems 33
2.18 The schematic diagram of displacement
ventilation systems 34
2.19 Underfloor air distribution systems 35
xv
2.20 The schematic diagram of underfloor air
distribution systems 35
3.1 Methodology flow chart 54
3.2 Field measurement flow chart 55
3.3 Bus passenger compartment 56
3.4 Location measurements of gases and particles
inside the bus passenger compartment 57
3.5 The in-campus route followed during the field
measurement 58
3.6 Indoor environmental quality instrument (gas) 59
3.7 Indoor environmental quality instrument (particle) 59
3.8 Anemometer instrument 60
3.9 CFD analysis steps flow chart
(baseline case analysis) 63
3.10 Orthogonal diagram of the bus passenger
compartment (a) Top view (b) Side view
(c) Front view 65
3.11 Perspective diagram of the bus passenger
compartment (a) Front view (b) Side view 66
3.12 The concentration level of CO via the number
of elements 67
3.13 The concentration level of PM1 via the number
of elements 68
3.14 The meshed of CFD model 69
3.15 Rectangular Cartesian grids 81
3.16 Rectangular Cartesian grids 82
3.17 Convergence of the gas contaminant (CO) 84
3.18 Convergence of the particle contaminant (PM1) 84
3.19 The comparison of viscous models via CO
concentration 85
3.20 The comparison of viscous models via PM1
concentration 86
3.21 CFD analysis steps flow chart
(parametric analysis) 99
xv
3.22 Case 1 (MV+DV&2RG) 100
3.23 Case 2 (MV+UFAD&2RG) 100
3.24 Case 3 (MV&4RG) 101
3.25 Case 4 (MV+DV&4RG) 101
3.26 Case 5 (MV+UFAD&4RG) 102
4.1 Locations of data collection of gas and
particle concentrations (front section,
middle section and rear section) 104
4.2 CO concentration versus time 105
4.3 CO₂ concentration versus time 106
4.4 CH₂O concentration versus time 107
4.5 PM1 concentration versus time 108
4.6 PM2.5 concentration versus time 109
4.7 PM10 concentration versus time 110
4.8 The components of air contaminants through
the bus door 115
4.9 Top view of the bus passenger compartment
(locations of cross sections A-A’
(front section), B-B’ (middle section)
and C-C’ (rear section) 115
4.10 Sampling locations of air contaminants
concentration level (front section,
middle section and rear section) 116
4.11 Sampling height of air contaminants
concentration level 116
4.12 Spatial distribution of CO concentration
(a) In cross section A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 118
4.13 Spatial distribution of CO2 concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 119
xvi
4.14 Spatial distribution of CH2O concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 120
4.15 Spatial distribution of PM1 concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 122
4.16 Spatial distribution of PM2.5 concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 123
4.17 Spatial distribution of PM10 concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 124
4.18 Top view of the bus passenger compartment
(locations of cross sections A-A’ (plane a-a)
and B-B’ (plane b-b) 126
4.19 Comparison of gas concentrations over the
length of the bus (a) CO (b) CO₂ (c) CH₂O 128
4.20 Comparison of particulate concentrations
over the length of the bus (a) PM1 (b) PM2.5
(c) PM10 130
4.21 Comparison of gas concentrations over the
length of the bus (a) CO (b) CO₂ (c) CH₂O 132
4.22 Comparison of particulate concentrations
over the length of the bus (a) PM1 (b) PM2.5
(c) PM10 134
4.23 Comparison of gas concentrations over the
length of the bus (a) CO (b) CO₂ (c) CH₂O 136
4.24 Comparison of particulate concentrations
over the length of the bus (a) PM1 (b) PM2.5
(c) PM10 138
xvii
4.25 Comparison of gas concentrations over the
length of the bus (a) CO (b) CO₂ (c) CH₂O 140
4.26 Comparison of particulate concentrations
Over the length of the bus (a) PM1 (b) PM2.5
(c) PM10 142
4.27 Comparison of gas concentrations over the
length of the bus (a) CO (b) CO₂ (c) CH₂O 144
4.28 Comparison of particulate concentrations
over the length of the bus (a) PM1 (b) PM2.5
(c) PM10 146
4.29 Orthogonal diagram of the bus passenger
compartment (a) Top view (b) Side view
(c) Front view 149
4.30 Spatial distribution of CO concentration
(a) In cross section A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 151
4.31 Spatial distribution of CO2 concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 152
4.32 Spatial distribution of CH2O concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 153
4.33 Spatial distribution of PM1 concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 155
4.34 Spatial distribution of PM2.5 concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 156
xviii
4.35 Spatial distribution of PM10 concentration
(a) In cross sections A-A’ (front section)
(b) In cross section B-B’ (middle section)
(c) In cross section C-C’ (rear section) 157
4.36 Velocity vectors of a new ventilation system
design (a) CO (b) CO2 (c) CH2O (d) PM1
(e) PM2.5 (f) PM10 160
4.37 Particle trajectories of PM1 concentration 161
4.38 Particle trajectories of PM2.5 concentration 162
4.39 Particle trajectories of PM10 concentration 162
xvii
LIST OF ABBREVIATIONS
ACH - Air exchange rate
ASHRAE - American Society of Heating, Refrigerating and
Air-Conditioning Engineers
BC - Baseline case
BTEX - Benzene, toluene, ethyl-benzene and xylenes
CFD - Computational fluid dynamics
C1 - Case 1
C2 - Case 2
C3 - Case 3
C4 - Case 4
C5 - Case 5
CO - Carbon monoxide
CO₂ - Carbon dioxide
CH₂O - Formaldehyde
DPM - Discrete phase model
DV - Displacement ventilation
EPA - Environment Protection Agency
GCI - Grid convergence index
HPC - Handheld particle counter
H1N1 - Avian influenza and swine influenza
IAQ - Indoor air quality
IAC - Indoor air contaminant
IEQ - Indoor environmental quality
MAHCS - Monoaromatic hydrocarbons
MV - Mixing ventilation
xviii
NIOSH - National Institute for Occupational Safety and Health
NO₂ - Nitrogen dioxide
OSHA - Occupational Safety and Health Administration
O₂ - Oxygen
PPM - Part per million
PMs - Particulate matters
PM1 - Particulate matter 1
PM2.5 - Particulate matter 2.5
PM10 - Particulate matter 10
RANS - Reynolds-averaged Navier-Stokes
RG - Return grille
RNG - Renormalization-group
SARS - Severe acute respiratory syndrome
SD - Standard deviation
SIMPLE - Semi-implicit method for pressure linked equations
SO₂ - Sulphur dioxide
SF6 - Sulphur hexafluoride
TLVs - Threshold limit values
UTM - Universiti Teknologi Malaysia
UFPS - Ultrafine particles
UFAD - Underfloor air distribution
WHO - World Health Organization
xix
LIST OF SYMBOLS
� - Cross sectional area
� - Contaminant
�� - Maximum measured
�� - Maximum predicted
� - Diameter
�� - Hydraulic diameter
� - Gravity
hr - Hour
- Length
m - Meter
m3 - Cubic meter
� � - Mass flowrate of particle
� - Wetted perimeter
Re - Reynold number
ST - Rate of thermal energy production
T - Diffusion coefficient
� - Friction velocity
�, , � - Velocity
� - Distance to the wall
y+ - Non-dimensional number
� - Density
� - Kinematic viscosity
μg - Microgram
xx
LIST OF APPENDICES
APPENDIX TITLE PAGE
A1 University shuttle bus 177
A2 Description of the bus 177
A3 Front section of the bus passenger compartment 178
A4 Rear section of the bus passenger compartment 178
B1 CFD model of Baseline Case 179
B2 CFD model of Case 1 179
B3 CFD model of Case 2 180
B4 CFD model of Case 3 180
B5 CFD model of Case 4 181
B6 CFD model of Case 5 181
C1 CFD Fluent simulation settings 182
D1 CFD Fluent steps flow chart 192
E1 Contour x-z plane of air contaminants (Case 5) 193
E2 Contour y-z plane of air contaminants (Case 5) 196
F1 Ansys license server (ce.utmgrid.utm.my) 199
G1 Samples data 200
G2 Statistical calculations 212
H1 Instrument calibration certificate of gas
contaminants 216
H2 Instrument calibration certificate of particle
contaminants 217
I1 List of publications 218
I2 List of conferences 219
1
CHAPTER 1
INTRODUCTION
1.1 Introduction
Indoor air quality is one of the major environmental concerns since people
spend about 90% of their time indoors and about 7% of their daily time commuting,
mostly between their workplace and their residence [1]. At present, many people use
public transport buses for workplace, shopping, recreation and others [2]. Apart from
public transport buses, university shuttle buses have attracted extensive attention
since many students use this transportation in a university campus to travel to class,
extracurricular activities and others [3].
Indoor air contaminants are typically found inside the bus passenger
compartment namely gases (CO, CO₂, and CH₂O) and particles (PM1, PM2.5 and
PM10) [4]. The air contaminants such as CO, CO₂, CH₂O, PM1, PM2.5 and PM10
originate from mobile sources (exhaust gas) [5]. Based on the previous studies, the
concentration levels of CO, CO₂, CH₂O, PM1, PM2.5 and PM10 were exceeded the
threshold limit values by the World Health Organization guideline [2]. Peak hours,
passenger’s board and unboard, weather condition, ventilation setting, ventilation
system, bus engine and bus age are the factors that influences the air contaminants
2
concentration level inside the bus passenger compartment [6]. The exposure of CO,
CO₂, CH₂O, PM1, PM2.5 and PM10 concentrations could threaten the passenger’s
health in both the short and long term. Air contaminants concentration is responsible
for a wide range of health consequences such as headache, eye irritation, lung cancer,
cardiovascular, tuberculosis, asthma and airborne transmission (Severe Acute
Respiratory Syndrome and Avian Influenza and Swine Influenza) [7]. The particulate
matters such as PM1, PM2.5 and PM10 can penetrate into the thoracic part of the
airway and accumulate in the respiratory system [8]. Particles less than 10 µm in
diameter can be inhaled and 80% of them will be deposited in the human respiratory
system, possibly leading to fatal outcomes [9].
In bus passenger compartments require good ventilation system to provide
fresh air and comfortable environment for passenger. In engineering approach, the
efficiency of ventilation system is evaluated by the indoor air quality. Indoor Air
Quality (IAQ) refers to the effect, good or bad of the contents of the air inside an
enclosed environment [10]. Good IAQ is the quality of air which has no unwanted
contaminants. Poor IAQ occurs when contaminants are present in excessive
concentrations. Knowledge concerning the air contaminants concentration level is
very important to prevent the inhalation of harmful air contaminants by passengers
when commuting in a bus. The ventilation systems of buses must be improved as
bus travel is used for business, shopping, campus, school, recreation or others
activities. Several factors affect the performance of the ventilation system such as air
supply velocity, air supply temperature, layout of the air supply diffusers and layout
of the air return grilles [11].
3
Three types of ventilation system have been widely used in an enclosed
environment such as mixing ventilation, displacement ventilation and underfloor air
distribution [12]. Within the last few years, the mixing ventilation system has
become a popular design and has been used in bus transportation. A common
example of the mixing ventilation system is one equipped with ceiling-based air
supply diffusers and air return grilles [12]. In bus transportation, the air is supplied
via air supply diffusers (placed on the ceiling mounted ducting above passenger
seats) and released through the air return grilles (placed on the roof). Based on the
previous studies, this system is not capable of removing the indoor air contaminants
when the door is opened for boarding and unboarding passengers [2]. This is because
much of the supply air leaves the compartment without mixing with compartment air
due to improper layout of air supply diffusers and air return grilles. When this
situation occurs, the air contaminants will accumulate at the tight space of the
compartment such as on the floor and passenger seats [13]. Therefore, as an
alternative, modification of the present ventilation system is needed to reduce the air
contaminants concentration inside the bus passenger compartment.
The bus ventilation system is very important in order to reduce the air
contaminants concentration level. At present, research works on reducing indoor air
contaminants inside the bus passenger compartment is limited especially using
computational fluid dynamics (CFD) software [14]. CFD software offers an
alternative platform which is more convenient than experimental practice to predict
the indoor air contaminant in various applications [15]. Hence, an investigation of
indoor air contaminants using CFD method is necessity to find a suitable ventilation
system design that would lower the level of air contaminants inside the bus passenger
compartment.
4
1.2 Problem Statement
The present ventilation system in bus is not capable of reducing the air
contaminants. This is due to improper arrangements of the ventilation system such as
the air supply diffusers and the air return grilles. In bus compartment the
concentration level of air contaminants such as gases and particles exceeded the
threshold limit set by the World Health Organization guideline due to improper
arrangements of the ventilation system. The excessive concentration of gas and
particle contaminants could affect passenger’s health when commuting in a bus.
Therefore, the bus ventilation systems need to redesign to reduce the level of air
contaminants. Two methods were identified namely a field measurement and CFD
simulation. The field measurement was carried out to quantify the concentrations of
CO, CO₂, CH₂O, PM1, PM2.5 and PM10 inside the bus. The CFD simulation model
is to predict the air contaminants concentration level inside the bus. Five types of
ventilation system design were considered namely a displacement ventilation with
two air return grilles, underfloor air distribution with two air return grilles, mixing
ventilation with four air return grilles, displacement ventilation with four air return
grilles and underfloor air distribution with four air return grilles.
1.3 Objectives of the Research
Three objectives were developed to achieve the aim of this research. The
following objectives are as follows:
1. To quantify the indoor air contaminants concentration level inside a campus bus
passenger compartment.
2. To examine the effects of present ventilation system design (baseline case) on
contaminants concentration level through the use of CFD method.
3. To establish suitable ventilation system design for reducing the indoor air
contaminants concentration level inside the campus bus passenger compartment.
5
1.4 Scopes of the Research
The scope of this research is divided in two parts, i.e. field measurement and
CFD simulation. The field measurements were carried out in a university shuttle bus
passenger compartment. The distance of bus travelled within a university campus is
48 km. The measurements were conducted during the peak hour period to examine
the gas (CO, CO₂ and CH₂O) and particle (PM1, PM2.5 and PM10) concentration. In
this study, the door is opened during quantification of the air contaminants
concentration inside the bus passenger compartment. The weather condition was
clear and no rain fell while the field measurements were conducted.
Ansys CFD Fluent software (R-14) was used to develop a simplified
three-dimensional model of the bus passenger compartment. The CFD model was
meshed using the tetrahedron elements. In this study, the boundary condition of the
air contaminants was prescribed at the door only due to outside air contaminants
entering the bus. The passenger compartment is assumed clean and without air
contaminants. Three types of air flow analysis namely RNG k-ε turbulent model,
species transport model and discrete phase model were used to predict the
distribution of air flow and air contaminants. Five types of ventilation system design
were considered namely a displacement ventilation with two air return grilles,
underfloor air distribution with two air return grilles, mixing ventilation with four air
return grilles, displacement ventilation with four air return grilles and underfloor air
distribution with four air return grilles. The comparison between the field data and
CFD simulation on various ventilation system designs were discussed.
6
1.5 Important of the Research
Understanding the link between ventilation system design and air
contaminants concentration can help to reduce the level of air contaminants
concentration inside a bus passenger compartment. The reducing of air contaminants
concentration enhances the air quality inside the bus compartment and to prevent the
indoor air diseases to passengers.
1.6 Thesis Outline
Chapter 1 presents an introduction, problem statement, objectives and scopes
of this research.
In Chapter 2, a review on field measurement and CFD simulation on air
contaminants concentration in an enclosed environment are presented. Different
types of air contaminants namely gases and particles are investigated. The effects of
air contaminants on passenger health are also presented in this chapter. The
methodology on a field measurement and CFD analysis on air contaminants
concentration in an enclosed environment has been reviewed. In addition, different
types of ventilation systems such as mixing ventilation, displacement ventilation and
underfloor air distribution in an enclosed environment are presented.
Chapter 3 presents the methodology of this study. The methodology is
divided into two parts, i.e. field measurement and CFD simulation analysis. The field
measurements are conducted to quantify the air contaminants concentration level
inside the bus passenger compartment. The measured data is used for boundary
condition in the CFD model and validation. A three-dimensional CFD model has
7
been developed in order to simulate and predict the air contaminants concentration
inside the bus environment. Five cases of parametric analysis are presented in this
chapter.
In Chapter 4, the results of field measurements and CFD simulation on air
contaminants concentration are presented. The levels of air contaminants
concentration are discussed at the front section, middle section and rear section of the
passenger compartment. The CFD simulations results are discussed based on the
whole bus compartment, passenger seats and breathing level to examine the air
contaminants concentration level. A parametric analysis on the various cases of
ventilation system design is presented in this chapter. A new ventilation system
design that was obtained from the parametric analysis was discussed based on the air
flow and air contaminants.
Chapter 5 presents a conclusion on air contaminant concentration levels
inside the bus passenger compartment. The levels of air contaminants concentration
levels and the effects of present ventilation system are concluded in this chapter. As
well, the best ventilation system designs that reduce the indoor air contaminants
inside the passenger compartment are presented. Several recommendations for future
work have been proposed in this chapter.
167
REFERENCES
1. Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M.,
Switzer, P., & Engelmann, W. H. (2001). The National Human Activity Pattern
Survey (NHAPS): a resource for assessing exposure to environmental
pollutants. Journal of Exposure Analysis and Environmental
Epidemiology, 11(3), 231-252.
2. Zhu, S., Demokritou, P., & Spengler, J. (2010). Experimental and numerical
investigation of micro-environmental conditions in public transportation buses.
Building and Environment, 45(10), 2077-2088.
3. Zhang, Q., Fischer, H. J., Weiss, R. E., & Zhu, Y. (2013). Ultrafine particle
concentrations in and around idling school buses. Atmospheric Environment,
69, 65-75.
4. Shek Ka Wing (2010). Thermal & indoor air quality environment on air
conditioned buses. Doctor Philosophy, Hong Kong Polytechnic University.
5. Hess-Kosa, K. (2011). Indoor air quality: the latest sampling and analytical
methods. CRC Press.
6. Chan, A. T. (2003). Commuter exposure and indoor–outdoor relationships of
carbon oxides in buses in Hong Kong. Atmospheric Environment, 37(27),
3809-3815.
7. Lau, W. L., & Chan, L. Y. (2003). Commuter exposure to aromatic VOCs in
public transportation modes in Hong Kong. Science of the Total Environment,
308(1), 143-155.
8. Kadiyala, A., & Kumar, A. (2011). Study of in-vehicle pollutant variation in
public transport buses operating on alternative fuels in the city of Toledo, Ohio.
Open Environmental & Biological Monitoring Journal, 4, 1-20.
168
9. Vijayan, A., & Kumar, A. (2010). Experimental and statistical analyses to
characterize in-vehicle fine particulate matter behavior inside public transit
buses operating on B20-grade biodiesel fuel. Atmospheric
Environment, 44(34), 4209-4218.
10. Chan, M. Y. (2005). Commuters' exposure to carbon monoxide and carbon
dioxide in air-conditioned buses in Hong Kong. Indoor and Built Environment,
14(5), 397-403.
11. Hsu, D. J., & Huang, H. L. (2009). Concentrations of volatile organic
compounds, carbon monoxide, carbon dioxide and particulate matter in buses
on highways in Taiwan. Atmospheric Environment, 43(36), 5723-5730.
12. Awbi, H. B. (2003). Ventilation of buildings. Taylor & Francis.
13. Rim, D., Siegel, J., Spinhirne, J., Webb, A., & McDonald-Buller, E. (2008).
Characteristics of cabin air quality in school buses in Central Texas.
Atmospheric Environment, 42(26), 6453-6464.
14. Lin, Z., Jiang, F., Chow, T. T., Tsang, C. F., & Lu, W. Z. (2006). CFD analysis
of ventilation effectiveness in a public transport interchange. Building and
Environment, 41(3), 254-261.
15. Tian, Z. F., Tu, J. Y., Yeoh, G. H., & Yuen, R. K. K. (2007). Numerical studies
of indoor airflow and particle dispersion by large eddy simulation. Building
and environment, 42(10), 3483-3492.
16. Maroni, M., Seifert, B., & Lindvall, T. (Eds.). (1995). Indoor air quality: a
comprehensive reference book. Elsevier.
17. Land Public Transport Commission. (2007). National highway development
plan in Malaysia. Technical Report.
18. Faiz, A., Weaver, C. S., & Walsh, M. P. (1996). Air pollution from motor
vehicles: standards and technologies for controlling emissions. World Bank
Publications.
19. ASHRAE Standard Committee. (2013). ASHRAE HANDBOOK:
Fundamentals 2013.
20. NIOSH. 2004. NIOSH Pocket Guide to Chemical Hazards (NPG). National
Institute for Occupational Safety and Health, February.
21. U.S. Department of Labor, Occupational Safety and Health Administration.
Code of Federal Regulations, Title 29, Part 1910.1000-1910.1450.
169
22. U.S. Environmental Protection Agency. 2008. Code of Federal Regulations,
Title 40, Part 50. National Ambient Air Quality Standards.
23. World Health Organization. 2000. Air Quality Guidelines for Europe, 2nd
Edition. World Health Organization Regional Publications, European Series
No. 91.World Health Organization, Regional Office for Europe, Copenhagen.
24. Proctor, N. H., & Hughes, J. P. (1980). Chemical Hazards of the Workplace.
The American Journal of Forensic Medicine and Pathology, 1(4), 369.
25. Koskela, R. S., Mutanen, P., Sorsa, J. A., & Klockars, M. (2005). Respiratory
disease and cardiovascular morbidity. Occupational and Environmental
Medicine, 62(9), 650-655.
26. Adar, S. D., Davey, M., Sullivan, J. R., Compher, M., Szpiro, A., & Liu, L. J.
S. (2008). Predicting airborne particle levels aboard Washington State school
buses. Atmospheric Environment, 42(33), 7590-7599.
27. Gomez-Perales, J. E., Colvile, R. N., Fernandez-Bremauntz, A. A., Gutierrez-
Avedoy, V., Paramo-Figueroa, V. H., Blanco-Jimenez, S., & Nieuwenhuijsen,
M. J. (2007). Bus, minibus, metro inter-comparison of commuters’ exposure to
air pollution in Mexico City. Atmospheric Environment, 41(4), 890-901.
28. Li, S., Chen, S., Zhu, L., Chen, X., Yao, C., & Shen, X. (2009). Concentrations
and risk assessment of selected monoaromatic hydrocarbons in buses and bus
stations of Hangzhou, China. Science of the Total Environment, 407(6), 2004-
2011.
29. Wong, L. T., Mui, K. W., Cheung, C. T., Chan, W. Y., Lee, Y. H., & Cheung,
C. L. (2011). In-cabin exposure levels of carbon monoxide, carbon dioxide and
airborne particulate matter in air-conditioned buses of Hong Kong. Indoor and
Built Environment, 20(4), 464-470.
30. Mohammadyan, M., Alizadeh, A., & Mohammadpour, R. A. (2009). Personal
exposure to PM10 among bus drivers in Sari, Iran. Indoor and Built
Environment, 18(1), 83-89.
31. Chen, X., Zhang, G., Zhang, Q., & Chen, H. (2011). Mass concentrations of
BTEX inside air environment of buses in Changsha, China. Building and
Environment, 46(2), 421-427.
32. Zhang, Q., & Zhu, Y. (2010). Measurements of ultrafine particles and other
vehicular pollutants inside school buses in South Texas. Atmospheric
Environment, 44(2), 253-261.
170
33. Song, W. W., Ashmore, M. R., & Terry, A. C. (2009). The influence of
passenger activities on exposure to particles inside buses. Atmospheric
Environment, 43(39), 6271-6278.
34. Asmi, E., Antola, M., Yli-Tuomi, T., Jantunen, M., Aarnio, P., Makela, T., &
Hameri, K. (2009). Driver and passenger exposure to aerosol particles in buses
and trams in Helsinki, Finland. Science of the Total Environment, 407(8),
2860-2867.
35. Chan, L. Y., Lau, W. L., Lee, S. C., & Chan, C. Y. (2002). Commuter exposure
to particulate matter in public transportation modes in Hong Kong.
Atmospheric Environment, 36(21), 3363-3373.
36. Lu, H., & Zhu, L. (2007). Pollution survey of carbonyl compounds in train air.
Frontiers of Environmental Science & Engineering in China, 1(1), 125-128.
37. Behrentz, E., Fitz, D. R., Pankratz, D. V., Sabin, L. D., Colome, S. D., Fruin, S.
A., & Winer, A. M. (2004). Measuring self-pollution in school buses using a
tracer gas technique. Atmospheric Environment, 38(23), 3735-3746.
38. Borak, J., & Sirianni, G. (2007). Studies of self-pollution in diesel school
buses: methodological issues. Journal of Occupational and Environmental
Hygiene, 4(9), 660-668.
39. Wohrnschimmel, H., Zuk, M., Martinez-Villa, G., Ceron, J., Cardenas, B.,
Rojas-Bracho, L., & Fernandez-Bremauntz, A. (2008). The impact of a Bus
Rapid Transit system on commuters' exposure to Benzene, CO, PM 2.5 and
PM 10 in Mexico City. Atmospheric Environment, 42(35), 8194-8203.
40. Chio, C. P., Cheng, Y. H., Ling, M. P., Chen, S. C., & Liao, C. M. (2012).
Quantitative estimation of excess mortality for drivers and passengers exposed
to particulate matters in long-distance buses. Atmospheric Environment, 51,
260-267.
41. Molle, R., Mazoue, S., Gehin, E., & Ionescu, A. (2013). Indoor–outdoor
relationships of airborne particles and nitrogen dioxide inside Parisian buses.
Atmospheric Environment, 69, 240-248.
42. Tartakovsky, L., Baibikov, V., Czerwinski, J., Gutman, M., Kasper, M.,
Popescu, D., & Zvirin, Y. (2013). In-vehicle particle air pollution and its
mitigation. Atmospheric Environment, 64, 320-328.
43. Chandra, A. R. (2010). Refrigeration and air conditioning. PHI Learning Pvt.
Ltd.
171
44. Ashrae Fundamentals Handbook (2013) Ch. 16: Ventilation and infiltration,
American Society of Heating Refrigeration and Air-Conditioning Engineers,
Atlanta, GA.
45. Davidson, L. (1989). Ventilation by displacement in a three-dimensional room
a numerical study. Building and Environment, 24(4), 363-372.
46. Zhong, K., Yang, X., Feng, W., & Kang, Y. (2012). Pollutant dilution in
displacement natural ventilation rooms with inner sources. Building and
Environment, 56, 108-117.
47. Lin, Z., Chow, T. T., Tsang, C. F., Fong, K. F., & Chan, L. S. (2005). CFD
study on effect of the air supply location on the performance of the
displacement ventilation system. Building and environment, 40(8), 1051-1067.
48. Qiu-Wang, W., & Zhen, Z. (2006). Performance comparison between mixing
ventilation and displacement ventilation with and without cooled ceiling.
Engineering computations, 23(3), 218-237.
49. Webster, T., Bauman, F., & Ring, E., (2002). Supply fan energy use in
pressurized underfloor air distribution system. In: CBE Summary Report,
Berkeley.
50. Yu, B. F., Hu, Z. B., Liu, M., Yang, H. L., Kong, Q. X., & Liu, Y. H. (2009).
Review of research on air-conditioning systems and indoor air quality control
for human health. International Journal of Refrigeration, 32(1), 3-20.
51. Zhang, K., Zhang, X., Li, S., & Jin, X. (2014). Review of underfloor air
distribution technology. Energy and Buildings, 85, 180-186.
52. Khosrow. B (2012). Numerical simulation of turbulent airflow, tracer gas
diffusion and particle dispersion in a mock up aircraft cabin. Doctor
Philosophy, University of Technology, Tehran, Iran.
53. Zhang, Z., & Chen, Q. (2006). Experimental measurements and numerical
simulations of particle transport and distribution in ventilated rooms.
Atmospheric environment, 40(18), 3396-3408.
54. Ho, S. H., Rosario, L., & Rahman, M. M. (2011). Comparison of underfloor
and overhead air distribution systems in an office environment. Building and
Environment, 46(7), 1415-1427.
55. Kim, G., Schaefer, L., Lim, T. S., & Kim, J. T. (2013). Thermal comfort
prediction of an underfloor air distribution system in a large indoor
environment. Energy and Buildings, 64, 323-331.
172
56. Serra, N., & Semiao, V. (2009). Comparing displacement ventilation and
mixing ventilation as HVAC strategies through CFD. Engineering
Computations, 26(8), 950-971.
57. Zhong, K., Yang, X., & Kang, Y. (2010). Effects of ventilation strategies and
source locations on indoor particle deposition. Building and
Environment, 45(3), 655-662.
58. Zhang, T., & Chen, Q. Y. (2007). Novel air distribution systems for
commercial aircraft cabins. Building and Environment, 42(4), 1675-1684.
59. Zhu, S., Srebric, J., Spengler, J. D., & Demokritou, P. (2012). An advanced
numerical model for the assessment of airborne transmission of influenza in
bus microenvironments. Building and Environment, 47, 67-75.
60. Cheong, K. W. D., & Phua, S. Y. (2006). Development of ventilation design
strategy for effective removal of pollutant in the isolation room of a hospital.
Building and Environment, 41(9), 1161-1170.
61. Xiang, L., & Wang, H. (2011). Numerical simulation of pollutant transport
within an vehicle cabin. In Advances in Computer Science, Environment, Eco
informatics, and Education (pp. 575-579). Springer Berlin Heidelberg.
62. Gao, Z., & Zhang, J. S. (2010). Numerical analysis for evaluating the exposure
reduction effectiveness of room air cleaners. Building and Environment, 45(9),
1984-1992.
63. Zhang, Z., & Chen, Q. (2007). Comparison of the Eulerian and Lagrangian
methods for predicting particle transport in enclosed spaces. Atmospheric
Environment, 41(25), 5236-5248.
64. Daithankar, N., Udawant, K. D., & Karanth, N. V. (2015). Prediction of
thermal comfort inside a midibus passenger cabin using CFD and its
experimental validation (No. 2015-26-0210). SAE Technical Paper.
65. Vignesh, S., & Sachin, P. (2013). Evaluation of bus ventilation methods using
CFD (No. 2013-26-0043). SAE Technical Paper.
66. Pawar, S., Gade, U. R., Dixit, A., Tadigadapa, S. B., & Jaybhay, S. (2014).
Evaluation of cabin comfort in air conditioned buses using CFD (No. 2014-01-
0699). SAE Technical Paper.
67. Lin, B., Wang, X. T., & Hu, X. (2012, January). Research on the Effect of
Natural Ventilation on Buses in Summer Based on CFD Numerical Simulation
Method. In Advanced Materials Research (Vol. 361, pp. 1056-1060).
173
68. John, P., Sriram, B., Kumar, S., Kumar, S. V., Ramasamy, P., & Ram, C. V.
(2013). Ventilation improvement in a non-ac bus (No. 2013-01-2457). SAE
Technical Paper.
69. Riachi, Y., & Clodic, D. (2014). A numerical model for simulating thermal
comfort prediction in public transportation buses. International Journal of
Environmental Protection and Policy, 2(1), 1-8.
70. Petrone, G., Fichera, G., & Scionti, M. (2008). Thermal and fluid-dynamical
Optimisation of Passengers Comfort in a Touring Bus Cabin. In COMSOL
Conference.
71. Yan, W., Zhang, Y., Sun, Y., & Li, D. (2009). Experimental and CFD study of
unsteady airborne pollutant transport within an aircraft cabin mock-up.
Building and Environment, 44(1), 34-43.
72. Bulinska, A., Popiolek, Z., & Bulinski, Z. (2014). Experimentally validated
CFD analysis on sampling region determination of average indoor carbon
dioxide concentration in occupied space. Building and Environment, 72,
319-331.
73. Hou, X. T., Li, A. G., Wang, Z. H., & Zhao, Y. J. (2012). Numerical study on
indoor air quality of commercial kitchen in china. Advanced Materials
Research, 374, 1100-1105.
74. Tung, Y. C., Shih, Y. C., & Hu, S. C. (2009). Numerical study on the
dispersion of airborne contaminants from an isolation room in the case of door
opening. Applied Thermal Engineering, 29(8), 1544-1551.
75. Bari, S., & Naser, J. (2010). Simulation of airflow and pollution levels caused
by severe traffic jam in a road tunnel. Tunnelling and Underground Space
Technology, 25(1), 70-77.
76. Jiang, J., & Wang, X. (2012). On the numerical study of indoor particle
dispersion and spatial distribution. Air, Soil & Water Research, (5), 23-40.
77. Chang, T. J., Kao, H. M., & Hsieh, Y. F. (2007). Numerical study of the effect
of ventilation pattern on coarse, fine, and very fine particulate matter removal
in partitioned indoor environment. Journal of the Air & Waste Management
Association, 57(2), 179-189.
78. Chang, T. J., & Hu, T. S. (2008). Transport mechanisms of airborne particulate
matters in partitioned indoor environment. Building and Environment, 43(5),
886-895.
174
79. Beghein, C., Jiang, Y., & Chen, Q. Y. (2005). Using large eddy simulation to
study particle motions in a room. Indoor Air, 15(4), 281-290.
80. Akbari, K., & Mahmoudi, J. (2012). Numerical simulation of radon transport
and indoor air conditions effects. International Journal of Scientific and
Engineering Research, 3(6), 1-10.
81. Duci, A., Papakonstantinou, K., Chaloulakou, A., & Markatos, N. (2004).
Numerical approach of carbon monoxide concentration dispersion in an
enclosed garage. Building and Environment, 39(9), 1043-1048.
82. Chao, C. Y. H., & Wan, M. P. (2004). Airflow and air temperature distribution
in the occupied region of an underfloor ventilation system. Building and
Environment, 39(7), 749-762.
83. Cai, H., Long, W., Li, X., & Barker, D. (2010). Evaluating emergency
ventilation strategies under different contaminant source locations and
evacuation modes by efficiency factor of contaminant source. Building and
Environment, 45(2), 485-497.
84. Rim, D., & Novoselac, A. (2010). Ventilation effectiveness as an indicator of
occupant exposure to particles from indoor sources. Building and Environment,
45(5), 1214-1224.
85. Yam, R., Yuen, P. L., Yung, R., & Choy, T. (2011). Rethinking hospital
general ward ventilation design using computational fluid dynamics. Journal of
Hospital Infection, 77(1), 31-36.
86. Kang, Y., Wang, Y., & Zhong, K. (2011). Effects of supply air temperature and
inlet location on particle dispersion in displacement ventilation rooms.
Particuology, 9(6), 619-625.
87. Causone, F., Olesen, B. W., & Corgnati, S. P. (2010). Floor heating with
displacement ventilation: an experimental and numerical analysis. HVAC&R
Research, 16(2), 139-160.
88. Villafruela, J. M., Olmedo, I., de Adana, M. R., Méndez, C., & Nielsen, P. V.
(2013). CFD analysis of the human exhalation flow using different boundary
conditions and ventilation strategies. Building and Environment, 62, 191-200.
89. Fiser, J., & Jicha, M. (2013). Impact of air distribution system on quality of
ventilation in small aircraft cabin. Building and Environment, 69, 171-182.
175
90. Cheong, K. W. D., Djunaedy, E., Chua, Y. L., Tham, K. W., Sekhar, S. C.,
Wong, N. H., & Ullah, M. B. (2003). Thermal comfort study of an air-
conditioned lecture theatre in the tropics. Building and Environment, 38(1), 63-
73.
91. Posner, J. D., Buchanan, C. R., & Dunn-Rankin, D. (2003). Measurement and
prediction of indoor air flow in a model room. Energy and Buildings, 35(5),
515-526.
92. Srebric, J., & Chen, Q. (2002). An example of verification, validation, and
reporting of indoor environment CFD analyses (RP-1133). Transactions-
American Society of Heating Refrigerating and Air Conditioning
Engineers, 108(2), 185-194.
93. Gadgil, A. J., Lobscheid, C., Abadie, M. O., & Finlayson, E. U. (2003). Indoor
pollutant mixing time in an isothermal closed room: an investigation using
CFD. Atmospheric Environment, 37(39), 5577-5586.
94. Zhao, B., Zhang, Y., Li, X., Yang, X., & Huang, D. (2004). Comparison of
indoor aerosol particle concentration and deposition in different ventilated
rooms by numerical method. Building and Environment, 39(1), 1-8.
95. Tian, Z. F., Tu, J. Y., Yeoh, G. H., & Yuen, R. K. K. (2006). On the numerical
study of contaminant particle concentration in indoor airflow. Building and
Environment, 41(11), 1504-1514.
96. Chang, T. J., & Hu, T. S. (2008). Transport mechanisms of airborne particulate
matters in partitioned indoor environment. Building and Environment, 43(5),
886-895.
97. Liu, D., Zhao, F. Y., & Tang, G. F. (2008). Numerical analysis of two
contaminants removal from a three-dimensional cavity. International Journal
of Heat and Mass Transfer, 51(1), 378-382.
98. Rim, D., & Novoselac, A. (2008). Transient simulation of airflow and pollutant
dispersion under mixing flow and buoyancy driven flow regimes in residential
buildings. ASHRAE Transactions, 114(2), 130-142.
99. Zhang, Z., Chen, X., Mazumdar, S., Zhang, T., & Chen, Q. (2009).
Experimental and numerical investigation of airflow and contaminant transport
in an airliner cabin mockup. Building and Environment, 44(1), 85-94.
176
100. Kao, H. M., Chang, T. J., Hsieh, Y. F., Wang, C. H., & Hsieh, C. I. (2009).
Comparison of airflow and particulate matter transport in multi-room buildings
for different natural ventilation patterns. Energy and Buildings, 41(9), 966-974.
101. Ali, A. A., Elsafty, A. F., & Elsayed, A. A. (2012). CFD investigation of
indoor air distribution in marine applications. European Journal of Scientific
Research, 88(2), 196-208.
102. De Lieto Vollaro, R. (2013). Indoor climate analysis for urban mobility buses:
a CFD model for the evaluation of thermal comfort. International Journal of
Environmental Protection and Policy, 1(1), 1.
103. Li, F., Liu, J., Pei, J., Lin, C. H., & Chen, Q. (2014). Experimental study of
gaseous and particulate contaminants distribution in an aircraft cabin.
Atmospheric Environment, 85, 223-233.
104. Fluent, ANSYS. (2011). 14.0 User's Manual. ANSYS Inc., Canonsburg, PA.
105. Richardson, L. F., & Gaunt, J. A. (1927). The deferred approach to the limit.
Part I. Single lattice. Part II. Interpenetrating lattices. Philosophical
Transactions of the Royal Society of London. Series A, containing papers of a
mathematical or physical character, 299-361.
106. AIAA G-077-1998e, Guide for the Verification and Validation of
Computational Fluid Dynamics Simulations, American Institute of Aeronautics
and Astronautics, Reston.
107. Cengel, Y. A., & Cimbala, J. M. (2006). Fluid mechanics. McGraw-Hill
Education.
108. Bergman, T. L., & Incropera, F. P. (2011). Introduction to heat transfer. John
Wiley & Sons.
109. Zhang, Z., Zhang, W., Zhai, Z. J., & Chen, Q. Y. (2007). Evaluation of various
turbulence models in predicting airflow and turbulence in enclosed
environments by CFD: Comparison with experimental data from
literature. HVAC&R Research, 13(6), 871-886.
110. Hughes, I., & Hase, T. (2010). Measurements and their uncertainties: a
practical guide to modern error analysis. Oxford University Press.
111. ASTM E2655-14, Standard Guide for Reporting Uncertainty of Test Results
and Use of the Term Measurement Uncertainty in ASTM Test Methods,
ASTM International, West Conshohocken.
Recommended