Building Services Engineering Research and Technology. ...... particles instead of outdoor field measurement in the previous aerobiology studies for a small ...
An empirical drag coefficient model for simulating the dispersion and deposition of bioaerosol particles in ventilated environments
YU HO CHING
Ph.D
The Hong Kong Polytechnic University
2016
1
The Hong Kong Polytechnic University Department of Building Services Engineering
An empirical drag coefficient model for simulating the dispersion and deposition of bioaerosol particles in ventilated environments
Yu Ho Ching
A thesis submitted in partial fulfilment of the requirements
for the degree of Doctor of Philosophy
June 2016
2
Certificate of Originality
I hereby declare that this thesis is my own work and that, to the best of my knowledge and belief, it reproduces no material previously published or written, nor material which has been accepted for the award of any other degree or diploma, except where due acknowledgement has been made in the text.
_______________________________ Yu Ho Ching
Department of Building Services Engineering The Hong Kong Polytechnic University Hong Kong, China June 2016 i
Abstract
Bioaerosol particles in indoor air are related to airborne transmission infections and some pandemic outbreaks such as Severe Acute Respiratory Syndrome (SARS) in 2003 and Middle East Respiratory Syndrome (MERS) in 2015. Several environmental control strategies and parameters for a ventilation system have been suggested to prevent infections in building environments. To design an appropriate ventilation system, the infection risks of the proposed ventilation system were evaluated in the thesis in order to achieve effective infection control. Computational fluid dynamics (CFD) simulation is often used to predict the dispersions and depositions of bioaerosol particles to evaluate the infection risks of ventilation systems. However, there are differences between bioaerosol and aerosol particles in terms of shape, diameter, surface texture and elasticity. In this study, the transport mechanism of a bioaerosol particle was investigated to formulate a bioaerosol particle transport model for CFD simulation.
The empirical bioaerosol drag coefficient model was developed in this study to investigate the transport mechanism of bioaerosol particles. A chamber study was used to collate the empirical data from 13 common indoor bioaerosol species with the three common ventilation rates (1.7, 10.3 and 18.8 ACH). By comparing the experimental and numerical data, the empirical drag constants and coefficients were determined for each bioaerosol species.
ii
The model (i.e. Kdrag,bp=dbp2/2) was developed by correlating the drag constants Kdrag,bp with the equivalent bioaerosol diameters dbp in a range between 0.054 and 6.9 µm. Several validations were done for the generalization of the model for various bioaerosol species, ventilation rates, enclosures and literature. The model simplifies the transport mechanism of bioaerosol particles, for example, dispersion and deposition, in terms of the equivalent bioaerosol diameter dbp and drag coefficient Cdrag,bp. This is beneficial in that only a single morphological characteristic (i.e. dbp) is required to predict the movement of any bioaerosol species.
A numerical bioaerosol transport framework has been extended based on the proposed model to simulate the bioaerosol distribution to enhance the applicability of the model and impact on the ventilation system design for infection control in terms of ventilation rate and other design factors. The impacts of the proposed model and framework were demonstrated by simulating three practical scenarios such as healthcare centre, sanitation and office. The over-predictions of the drag force and ventilation performance by the Stokes drag was recognized, especially in environments with a unidirectional airflow pattern. The ventilation strategies for infection control need to be reviewed urgently because of the over-prediction of the carrying power of the airflow by the Stokes drag coefficient model.
In this study, the correlation between the drag constant Kdrag,bp and the equivalent bioaerosol diameter dbp has been investigated. This study provides iii
a useful source of reference for ventilation system engineers to minimize the infection risk of airborne transmission diseases, and to mitigate the risk of outbreaks. However, some improvements are suggested to enhance the reliability of the model. Furthermore, the development of the atomistic drag model (i.e. kinetic theory) may provide a solid theoretical base to support the model.
iv
Publications SCI and EI Journal papers *
Yu. H. C., Mui, K. W. Wong, L. T. (2017). Numerical simulation of bioaerosol
particle exposure assessment in office environment from MVAC systems, The Journal of Computational Multiphase Flows. [Submitted] *
Yu, H. C., Mui, K. W., Wong, L. T., & Chu, H. S. (2016). Ventilation of general
hospital wards for mitigating infection risks of three kinds of viruses including Middle East Respiratory Syndrome Coronavirus (MERS-CoV). Indoor and Built Environment. doi: 10.1177/1420326X16631596 *
Wong, L. T., Yu, H. C., Mui, K. W., & Chan, W. Y. (2015). Drag constants of
common indoor bioaerosols. Indoor and Built Environment, 24(3), 401-413. doi: 10.1177/1420326X13515897 *
Mui, K. W., Wong, L. T., Yu, H. C., Cheung, C. T., & Li, N. (2016). Exhaust
ventilation performance in residential washroom for bioaerosol particle removal after water closet flushing. Building Services Engineering Research and Technology. doi: 10.1177/0143624416660597
Mui, K. W., Wong, L. T., Cheung, C. T. & Yu, H. C. (2016). Cooling energy for indoor environmental quality (IEQ) acceptance in demand-controlled ventilated and adaptive comfort temperature-controlled air-conditioned offices, HKIE transactions, 24(2), 78-87. doi: 10.1080/1023697X.2017.1312561 *
Lai, A. C. K., Wong, L. T., Mui, K. W., Chan, W. Y., & Yu, H. C. (2012). An
experimental study of bioaerosol (1-10µm) deposition in a ventilated chamber. Building and Environment, 56, 118-126. doi: 10.1016/j.buildenv.2012.02.027.
*
Publication related to the thesis v
International and local conference papers *
Yu, H.C., Mui, K.W., Wong, L.T., & Chan, W.Y. Numerical study of 10μm
bioaerosols (Rhizopus) deposition in a forced-ventilated chamber. Paper presented at the Healthy buildings 2012, The 10th International Conference, 8-12 Jul, Brisbane, Queensland. *
Mui, K. W., Yu, H. C., & Wong, L. T. (2014b). Validation of the bioaerosol
deposition model in ventilated chamber (Paper HP0585). Paper presented at the 13th International Conference on Indoor Air Quality and Climate, Indoor Air 2014, 7-12 July, Hong Kong. *
Mui, K. W., Wong, L. T., & Yu, H. C. (2014a). Determine the aerodynamic
properties of Legionella pneumophila for a drag force expression Paper presented at the 40th International Symposium on Water Supply and Drainage for Buildings, CIBW062 Symposium 2014, 8-10 September, São Paulo. Brazil.
Cheung, C. T., Mui, K. W., Wong, L. T. & Yu, H. C. (2014). Mobile application of indoor environmental quality (IEQ) calculator in air-conditioned offices and university classrooms. Joint Symposium 2014, Change in Building Services Engineering for Future, 25-November: 2-1 to 2-5.
Mui, K. W., Wong, L. T., Xiao, F., Cheung, C. T., & Yu, H. C. (2015). Use of sustainable building environmental model (SBEM) in Hong Kong air-conditioned buildings. Paper presented at the 13th Asia Pacific Conference on the Built Environment, 19-20 November, Hong Kong, China, pp. 555-563
Mui, K. W., Wong, L. T., Xiao, F., Cheung, C. T., & Yu, H. C. (2015). Development of sustainable building environmental model (SBEM) in Hong Kong. Paper presented at the ICEEE 2015: 17th International Conference on Energy and Environmental Engineering, London, United Kingdom.
*
Publication related to the thesis vi
Acknowledgements
I am heartily thankful to my chief supervisor Dr. Mui Kwok-wai, and my cosupervisor, Dr. Wong Ling-tim, for their valuable guidance and suggestions throughout my research work, as well as their indispensable support and encouragement for my personal development from the initial to the final stage of my study.
Thanks are also extended to whom have assisted with this research study, and my friends who have been supportive both mentally and technically, which helped me to pass through all hurdles.
Lastly, I would like to express my most sincere appreciation to my family especially to, my mother. This thesis would not be successfully completed with their unconditional support and endless love.
vii
Table of Content Certificate of Originality Abstract Publications Acknowledgements Table of Content List of Figures List of Tables List of Abbreviations List of Symbols
i ii v vii viii xii xix xxi xxiv
Chapter 1 Introduction 1.1 Background of bioaerosol particle simulation in buildings 1.1.1 Impacts of Infectious disease outbreaks 1.1.2 Spread of airborne infectious diseases 1.1.3 Infection in indoor environments 1.1.4 Infection risk assessment for ventilation system design 1.1.5 Prediction of bioaerosol movement for infection risk 1.2 Research Objectives 1.3 Research scope 1.4 Organization of the thesis Chapter 2 Literature review of Bioaerosol transport model 2.1 Bioaerosol transport models for ventilaton system 2.2 Bioaerosols 2.3 Bioareosol infection risk models for ventilation system 2.3.1 Epidemiological model for infection risk 2.3.2 Wells-Riley quantum model for infection risk 2.3.3 Dose-Response model for infection risk 2.4 Prediction of bioaerosol particle movement 2.5 CFD simulation for bioaerosol particle transport models 2.5.1 Drift-flux model (DFM) for bioaerosol transport 2.5.2 Discrete phase model (DPM) for bioaerosol transport 2.6 Drag coefficient for transport model 2.6.1 Analytical model of drag coefficient of a rigid sphere 2.6.2 Morphology and rheological factors of drag coefficients 2.6.3 Gas-kinetic theory for drag coefficient in rarefied gas 2.7 The uncertainties of drag coefficient for bioaerosol viii
1 3 3 5 8 21 21 22 23 29 33 33 36 39 39 43 48 55 59 60 63 69 70 76 95 97
Chapter 3 Development of bioaerosol drag coefficient model 3.1 Introduction of the research methodology 3.1.1 Empirical approach for bioaerosol drag coefficient model 3.1.2 Chamber study for a bioaerosol particle trajectory 3.2 Experimental study for Chamber A 3.2.1 Experimental setup and procedures in Chamber A 3.3 Numerical study for Chamber A 3.3.1 Eulerian framework for airflow field simulation in Chamber A 3.3.2 Lagrangian framework for bioaerosol particles simulation in Chamber A 3.3.3 User defined function (UDF) for the empricial bioaerosol drag constant simulation 3.4 Summary of the development of the chamber study Chapter 4 Empirical bioaerosol drag coefficient model 4.1 Introduction of the empirical drag coefficient model 4.2 Determination of empirical drag constants from experiment and simulation results of Chamber A 4.2.1 Bioaerosol fractional count of the deposition patterns 4.2.2 Experimental fractional counts 4.2.3 Simulated fractional count of numerical Chamber A 4.2.4 Empiricald drag constants from Chamber A study 4.3 Bioaerosol drag coefficient model 4.3.1 Correlation of the bioaerosol drag coefficient and morphological characteristic of the bioaerosol 4.3.2 Error analysis for the bioaerosol drag coefficient model 4.4 Implementation of the empirical bioaerosol drag coefficient model 4.4.1 Drag coefficient and Reynolds number of the bioaerosol 4.4.2 Comparison of the bioaerosol drag coefficient model and Stokes-Cunningham drag coefficient model 4.5 Validations for the bioaerosol drag coefficient model 4.5.1 Validation for ventilation rate (Validation A) 4.5.2 Validation for bioaerosol species (Validation B) 4.5.3 Validation for viral group (Validation C) 4.5.4 Validation for Chamber B (Validation D) 4.5.5 Validation summary 4.6 Summary of the empirical bioaerosol drag coefficient model
ix
98 98 100 106 109 109 123 124 129 133 136 138 138 140 140 141 147 154 156 156 157 165 165 168 170 171 173 175 178 180 184
Chapter 5
185
5.1
185
5.1.1 5.1.2 5.1.3 5.2 5.2.1 5.2.2 5.2.3 5.2.4 5.2.5 5.3 5.3.1 5.3.2 5.3.3 5.3.4 5.3.5 5.3.6 5.3.7 5.4 5.4.1 5.4.2 5.4.3 5.4.4
Development of numerical bioaerosol transport framework with the empirical bioaerosol drag coefficient model Introduction of the numerical bioareosol transport framework Development of the numerical bioaerosol transport framework Validations for the numerical transport framework by practical environment from the open literature The applicability of the proposed numerical bioaerosol transport framework Transport characteristics in the hospital ward for human expiratory activites Background of a hospital ward CFD simulation of a general ward (i.e. Ward A) Bioaerosol removal performance of ventilation systems Results and discussion of hospital ward simulations Impact of the bioaerosol drag coefficient model in Ward A simulation Exhaust ventilation performance of residential washroom for bioaerosol removal after water closet (WC) flush Introduction of a residential washroom application (i.e. Washroom A) Exhaust ventilation design for washrooms Scenario study for design factors Modification of the framework for water closet flushing emission Results and discussion of the exhaust ventilation performance in residential washrooms Capacity and design procedure for local exhaust system Impact of the bioaerosol drag coefficient model in Washroom A simulation Potential continuous bioaerosol emission in office environment from MVAC systems Introduction of an office application (i.e. Office A)Introduction of an office application Development of the numerical bioaerosol transport framework for continuous emission source Bioaerosol exposure level of an office cubicle of Hong Kong Impact of the bioaerosol drag coefficient model in Office A simulation x
188 189 194 196 198 200 206 208 218 220 220 224 226 229 233 241 243 246 246 248 252 263
Summary of the numerical bioaerosol transport framework with the bioaerosol drag coefficient model Chapter 6 Conclusions and recommendations 6.1 Findings to address the research objectives and question 6.2 Significance of the study 6.3 Limitations, recommendations and future works Appendix A Matlab script program for the calculation of simulated fractional count Appendix B Fluent UDF program for simulating the drag constant Appendix C Fluent Scheme program for batch automation Appendix D Fluent UDF program for the numerical transport framework Appendix E Fluent Scheme program for the continuous emission source Reference 5.5
xi
266 268 270 274 275
278 281 282 285 286 288
List of Figures
Figure 1.1
The structure diagram of Chapter 1
Figure 1.2
Airborne infection environments
Figure 1.3
Bioaerosol systems
Figure 1.4
The outline of the thesis
32
Figure 2.1
Structure diagram of bioaerosol transport model for infection risk models
35
Figure 2.2
Size range of bioaerosol (in µm)
36
Figure 2.3
Illustration of bioaerosol particles in droplet nucleus and droplet forms
37
Figure 2.4
The Wells evaporation-falling airborne and droplets
38
Figure 2.5
The cycle of infection transmission model
40
Figure 2.6
Infection cycle of SIR models
41
Figure 2.7
An example of outbreak for airborne infection prediction using SIR model
42
Figure 2.8
Infection cycle of SEIR model
43
Figure 2.9
Infection probability of the intake dose and ID50
49
Figure 2.10
Exposure and dose-response assessments for risk infection framework
52
Figure 2.11
Creeping flow over a sphere
71
Figure 2.12
Drag coefficient with various Reynolds number Rep
77
factors
transmission
xii
with
2
in
indoor
9
ventilation
18
curve
of
Figure 2.13
Drag coefficient with sphericity ratios
81
Figure 2.14
Drag coefficients for bubbles
84
Figure 2.15
Structure of a bacterial cell
87
Figure 2.16
Surface roughness on drag coefficient
88
Figure 2.17
Mechanism of flagella movement. (a) Relating the local viscous force to the local filament velocity relative to the fluid, (b) a planar sinusoidal flagellar wave and (c) a helical flagellar wave
90
Figure 2.18
Drag coefficient with Knudsen numbers Kn
92
Figure 2.19
Regimes of gas flow and bioaerosol species range
94
Figure 3.1
Structure diagram for the investigation of an empirical drag coefficient model for bioaerosols
99
Figure 3.2
The morphological and rheological characteristics for bioaerosol drag constants
101
Figure 3.3
Reference SEM photos of 13 common indoor bioaerosol species
103
Figure 3.4
Determine the projected image area, length and width by ImageJ
105
Figure 3.5
Trajectory of a bioaerosol particle under ventilation rates in the field of drag and gravity forces
106
Figure 3.6
The chamber study to determine the empirical drag coefficients and constants for the 13 bioaerosol species
108
Figure 3.7
Procedures of the experimental chamber for bacterial group
110
Figure 3.8
Double-vial package for microorganism strain. (a) photo of the package, and (b) illustration of the package
111
Figure 3.9
Streaked dish for the isolation of single colony of the microorganism
112
xiii
Figure 3.10
Setup of Chamber A
114
Figure 3.11
Six-jet collision nebulizer for the experiment. (a) Empty nebulizer, and (b) Nebulizer with the Ringer’solution
116
Figure 3.12
Generation of the bioaerosol by nebulizer
116
Figure 3.13
Formation of bioaerosol droplet nuclei by diffusion dryer
117
Figure 3.14
Photo of the aerosolization box for bioaerosols with equivalent bioareosol diameter dbp> 3m
118
Figure 3.15
Photo of the matured aerosolization box
the
119
Figure 3.16
Colonies of P. citrinum in the chamber with VRchamber of 10.3 ACH
121
Figure 3.17
Eulerian-Lagrangian framework for numerical Chamber A
124
Figure 3.18
Geometry of the numerical Chamber A
126
Figure 3.19
Flowchart of bioaerosol particle procedures in DPM
130
Figure 3.20
Flowchart of the automation process for CFD simulation
135
Figure 4.1
Structure diagram of the empirical bioaerosol drag coefficient
139
Figure 4.2
Experimental fractional counts on the chamber floor
146
Figure 4.3
Air velocity distribution in Chamber A. a) VRchamber=1.7 ACH, (b) VRchamber=10.3 ACH and (c) VRchamber=18.8 ACH
148
Figure 4.4
Simulated bioaerosol fractional counts by Stokes drag with experimental fractional counts
153
Figure 4.5
Bioaerosol drag constants Kdrag,bp and absolute errors for the 13 tested bioaerosol species
154
Figure 4.6
Bioaerosol drag constant Kdrag,bp against equivalent bioaerosol diameter dbp
156
xiv
spores
for
Figure 4.7
Simulated bioaerosol fractional counts by bioaerosol drag coefficient model with experimental fractional counts
163
Figure 4.8
Errors between simulated and measured bioaerosol deposition patterns. (a) єfb and (b) єnmse
164
Figure 4.9
Bioaerosol drag constant against the Reynolds number of the equivalent bioaerosol particles
167
Figure 4.10
Bioaerosol drag constants against Cunningham slip effect
169
Figure 4.11
Bioaerosol fractional count for Validation A
171
Figure 4.12
Absolutes errors with other ventilation rate settings
172
Figure 4.13
Referenced micrograph of bioaerosol for Validation B
173
Figure 4.14
Bioaerosol drag constants and absolute errors for Validation B
174
Figure 4.15
Electron micrograph of Escherichia coli Phage Phi x 174 (ATCC 13706-B1)
176
Figure 4.16
Absolute errors of Escherichia coli Phage Phi x174
177
Figure 4.17
Chamber B and agar dishes setup.
178
Figure 4.18
Experiment setup for Chamber B
179
Figure 4.19
Absolute errors of E. coli (ATCC 13706) in Chamber B
180
Figure 4.20
Bioaerosol drag constant Kdrag,bp against equivalent bioaerosol diameter dbp for validations
181
Figure 4.21
Reynolds number and drag coefficient for: (a) Validation A, (b) Validation B, (c) Validation C and, (d) Validation D
182
Figure 5.1
Structure diagram of the development of numerical bioaerosol transport framework for ventilation system design
187
xv
Figure 5.2
Framework framework
for
the
Figure 5.3
The washroom of Validation E
191
Figure 5.4
The washroom of Validation F
193
Figure 5.5
Structure diagram of bioaerosol simulation in a hospital general ward
197
Figure 5.6
A 6-bedded general ward cubicle (dimensions in mm)
200
Figure 5.7
CFD configurations of a 6-bedded general ward cubicle
201
Figure 5.8
Electron micrograph of reference viruses. (a) MERS-CoV (b) SARS-CoV and (c) H1N1 influenza virus
202
Figure 5.9
Time-varying fractional counts of bioaerosol particle
207
Figure 5.10
Simulation results of the cubicle with VRward = 6 ACH. (a) Temperature distribution, (b) Air velocity distribution, (c) Flow pathlines from air supply inlets
208
Figure 5.11
MERS-CoV pathways for 6 source locations with VRward = 6 ACH. (a) Man 1, (b) Man 2, (c) Man 3, (d) Man 4, (e) Man 5 and (f) Man 6
210
Figure 5.12
MERS-CoV pathways for 6 source locations with VRward=6 ACH. (a) Man 1, (b) Man 2, (c) Man 3, (d) Man 4, (e) Man 5 and (f) Man 6
212
Figure 5.13
MERS-CoV removal processes for different source locations and VRward
214
Figure 5.14
SARS-CoV removal processes for different source locations and VRward
215
Figure 5.15
H1N1 influenza virus removal processes for different source locations and VRward
216
Figure 5.16
Average max. elapsed time tmax,air with design standards
217
xvi
proposed
transport
188
Figure 5.17
Average max. elapsed time tmax,air between the bioaerosol drag and the Stokes drag coefficient models
219
Figure 5.18
Structure diagram for an effective exhaust system design for washroom
223
Figure 5.19
Illustration washroom
inside
225
Figure 5.20
Overall design factors for an effective exhaust system
226
Figure 5.21
Ventilation arrangement of Washroom A
228
Figure 5.22
Modified framework for the Washroom A simulation
230
Figure 5.23
Exhausted fractional count FCexh against ventilation rate VRwashroom in Washroom A
235
Figure 5.24
Max. elapsed time tmax,air against VRwashroom in Washroom A
236
Figure 5.25
Bioaerosol particle fractional counts FCdepos, FCexh and FCair against time t.
237
Figure 5.26
Simulated air velocity distribution
238
Figure 5.27
Bioaerosol exhausted fractional count FCexh against distance from WC to exhaust fan lfan
239
Figure 5.28
Bioaerosol removal rate in Washroom A
240
Figure 5.29
Flow diagram to improve washroom exhaust system
242
Figure 5.30
Difference between the bioaerosol and the Stokes drag in the local exhaust ventilation system for Washroom A
243
Figure 5.31
Difference of bioaerosol removal rate between the bioaerosol and the Stokes drag coefficient models
245
Figure 5.32
Structure diagram for a continuous emission source in an office cubicle
247
Figure 5.33
Typical mixing ventilation for an office
248
of
design
xvii
variables
Figure 5.34
Description of the bioaerosol removal process.
251
Figure 5.35
Floorplan of Office A (in m)
253
Figure 5.36
CFD layout of Office A.
254
Figure 5.37
S. aureus concentration for ventilation rates and emission concentrations
257
Figure 5.38
C. cladosporides concentration for ventilation rates and emission concentrations
258
Figure 5.39
Fractional count of S. aureus for ventilation rates and emission concentrations
259
Figure 5.40
Fractional count of C. cladosporides concentration for ventilation rates and emission concentrations
260
Figure 5.41
Fractional count of bioaerosol particles for single-shot emission at 1 ACH of VRoffice. (a) S. aureus and (b) C. cladosporides
261
Figure 5.42
Bioaerosol exposure level with different emission concentrations. (a) S. aureus and (b) C. cladosporides
262
Figure 5.43
Average exposure level of the empirical bioaerosol and the Stokes drag in the office cubicle simulation
264
Figure 5.44
Flow pathlines in the office cubicle simulation
265
Fgure 6.1
Structure diagram for the investigation in the thesis
269
Figure 6.2
Roadmap of future works
276
xviii
List of Tables
Table 1.1
Some airborne pathogens for communicable, non-communicable opportunistic disease groups
the and
6
Table 1.2
Selected measurement methods of droplet size distribution of expiratory activities
12
Table 1.3
Summary of selected experiments of toilet seeding
14
Table 1.4
Selected literature on building type related to bioaerosol infections
17
Table 1.5
Selected literature on ventilation systems related to bioaerosol infections
19
Table 2.1
Microorganism morphologies
68
Table 2.2
Stokes correction factors for Reynolds numbers in flow regions
78
Table 2.3
Stokes correction factors for spheroid shaped particles
80
Table 2.4
Aerodynamic diameters of some bioaerosol species
83
Table 2.5
Cunningham slip correction parameters from selected experiments
93
Table 3.1
Common indoor bioaerosol species
102
Table 4.1
Bioaerosol drag constant for the 13 common indoor bioaerosol
155
Table 4.2
Particle tracking results by the bioaerosol drag coefficient model
158
Table 4.3
Reynolds numbers of bioaerosol particle and relative velocities of the 13 bioaerosol species
166
xix
Table 4.4
Validation cases for the empirical bioaerosol drag coefficient model
170
Table 4.5
Validation A results for VRchamber
172
Table 4.6
Validation B results for other bioaerosol species
175
Table 4.7
Validation C results for Escherichia coli Phage Phi x 174
178
Table 5.1
Validations E and F results for practical environments
192
Table 5.2
Application cases for the numerical simulation framework
195
Table 5.3
Virus information of general ward simulation
203
Table 5.4
CFD simulation and boundary condition settings for the general inpatient ward cubicle
205
Table 5.5
Selected exhaust fan locations for simulations in Washrooms A
227
Table 5.6
Information of the emitted bioaerosols in flushing
229
Table 5.7
Configurations and boundary settings for Washroom A simulation
233
Table 5.8
Information of the bioaerosol species
252
Table 5.9
CFD simulation conditions.
settings
xx
and
boundary
255
List of Abbreviations
ACGIH
American Conference of Governmental Industrial Hygienists
ACH
Air change rate per hour
AHAM
Association of Home Appliance Manufacturers
AHU
Air handling unit
AIIR
Airborne infection isolation room
ASHRAE
American Society of Heating, Refrigerating and Air-Conditioning Engineers
ASTM
American Society for Testing and Materials
ATCC
American Type Culture Collection
ATD
Air terminal device
BBO
Basset-Boussinesq-Oseen
CAV
Constant air volume
CDC
Centers for Disease Control and Prevention
CDF
Cumulative distribution function
CFD
Computational fluid dynamics
CFU
Colony forming unit
CIBSE
Chartered Institution of Building Services Engineers
CO
Carbon monoxide
CO2
Carbon dioxide
COPD
Chronic obstructive pulmonary disease
CoV
Coronavirus
DES
Detached eddy simulation
DFM
Drift-flux model
DNA
Deoxyribonucleic acid
DPM
Discrete phase model
DRW
Discrete random walk
ELISA
Enzyme-linked immunosorbent assay
FB
Fractional bias
FCU
Fan-coil unit
FVM
Finite volume method
GCI
Grid convergence index xxi
HAI
Hospital acquired infection
HCW
Healthcare worker
HEPA
High efficiency particulate air
HFMD
Hand, foot and mouth disease
HKHA
Hong Kong Hospital Authority
HVAC
Heating, ventilating, and air conditioning
IAQ
Indoor air quality
ICU
Intensive care unit
ID50
Infectious dose
IMI
Interferometric Mie imaging
LES
Large eddy simulation
MEA
Malt extract agar
MEB
Malt extract broth
MERS
Middle East Respiratory Syndrome
MERV
Minimum efficiency reporting value
MRSA
Methicillin-resistant Staphylococcus aureus
MV
Mixing ventilation
MVAC
Mechanical ventilation and air conditioning
MVOC
Microbial volatile organic compounds
NCRP
National Council on Radiation Protection, and Measurements
NIH
National Institute of Health
NIOSH
National Institute for Occupational Safety and Health
NIST
National Institute of Standards and Technology
NMSE
Normalized mean standard error
OPC
Optical particle count
PAU
Primary air unit
PCA
Plate count agar
PCR
Polymerase chain reaction
PDE
Partial differential equation
PFU
Plaque forming unit
PISO
Pressure implicit with split operator
PIV
Particle image velocimetry
PV
Personalized ventilation
qPCR
Quantitative polymerase chain reaction xxii
RA
Relative abundances
RANS
Reynolds-averaged Navier-Stokes
RH
Relative Humidity (%)
RNA
Ribonucleic acid
RNG
Renormalization group
RT-PCR
Reverse transcriptase polymerase chain reaction
SARS
Severe Acute Respiratory Syndrome
SD
Standard deviation
SEIR
Susceptible-exposed-infected-recovered
SEIS
Susceptible-exposed-infected-susceptible
SEM
Scanning electron microscopy
SIMLPE
Semi-implicit method for pressure linked equation
SIS
Susceptible-infected-susceptible
SIR
Susceptible-infected-recovered
SIRS
Susceptible-infected-recovered-susceptible
TB
Tuberculosis
TCID
Tissue culture infectious dose
TEM
Transmission electron microscopy
TLV
Threshold level value
TSA
Tryptone soya agar
TSB
Tryptone soya broth
T-RFLP
Terminal restriction fragment length polymorphism
UA
Ultrasonic anemometry
UDF
User defined function
UV
Ultraviolet
UVGI
Ultraviolet germicidal irradiation
VAV
Variable air volume
VBNC
Viable but non-culturable
VR
Ventilation rate
WC
Water closet
WHO
World Health Organization
xxiii
List of Symbols
A
Area (µm2)
Aproj
Projected image area of a particle (µm2)
Bo
Bond number
C
Coefficient
Cdrag
Drag coefficient
Ccorr
Coefficient of correlation
Cdetm
Coefficient of determination
Conc
Concentration (kg m-3, CFU m-3)
d
Diameter (µm)
daero
Aerodynamic diameter (µm)
dinit
Microbe-laden droplet diameter (µm)
dbp
Equivalent bioaerosol diameter (µm)
Expo
Exposure level (CFU min L-1)
F
Force (N)
Fdrag
Drag force (N s kg-1)
Fadd
Additional acceleration force per unit particle mass (N kg-1)
FC
Fractional count
FCair
Fractional count of bioaerosol particles suspended in the air
FCexh
Fractional count of bioaerosol particles removed through exhaust
FCdepos
Fractional count of bioaerosol particles deposited onto surfaces
Fr
Froude number
f
Fraction
fasymp
Asymptotic range of convergence
favg
Average volume fraction of room air
fbp
Volumetric fraction of bioaerosol particles (mL) over air (L)
fmolecule
Number of molecules per unit volume (m-3)
fp
Fraction of particle density to continuous-fluid density
freflection
Reflection fraction of the molecules from the surface of the rigid sphere
fsafe
Safety factor
fslip
Cunningham slip correction factor xxiv
GCI
Grid convergence index
g
Gravitational acceleration (m s-2)
ID50
Human infectious dose (ID50)
KBoltzmann
Boltzmann constant (J K-1)
Kconv
Theoretical order of convergence
Kdrag
Drag constant
Kgas
Ideal gas constant (i.e. 8.314 J mol-1 K-1)
KSL
Lift force constant
Kn
Knudsen number
Keff,therm_condy
Effective values of thermal conductivity (W m-1 K-1)
lfan
Distance from WC to exhaust (m)
lmean
Molecular mean free path (µm)
lp
Radius of a solid sphere (µm)
l1
Length of a bioaerosol particle (µm)
l2
Width of a bioaerosol particle (µm)
Mach
Mach number
Mo
Morton number
m
Mass (kg, g)
N
Number
Nair
Number of bioaerosol particles suspended in the air
Nexh
Number of bioaerosol particles removed through exhaust
Ndepos
Number of bioaerosol particles deposited onto surfaces
Ninit,quanta
Number of initial quanta value (quanta)
Nsrc
Number of bioaerosol particles emitted from source
Nwc
Number of microorganisms in the WC seal
Prob
Probability
Prz
Pressure (pa)
p
p-value
Q
Flow rate (m3 s-1, L s-1, m3 min-1)
R
Rate (s-1)
Rcontact
Contact rate (person day-1)
Rdepos
Rate deposition loss of the bioaerosol particles
Routbreak
Basic reproduction number
Rquanta
Quanta generation rate (quanta min-1) xxv
Rrecover
Recovery rate (person day-1)
Rdepos
Rate deposition loss of the bioaerosol particles (min-1)
Reair
Reynolds number for air
Rebp
Reynolds number for bioaerosol particles
r
Ratio
raspect
Aspect ratio of bioaerosol particles
rrefine
Refinement ratio
Schturbulent
Turbulent Schmidt number
Src
Source term
T
Temperature (°C, K)
TCID50
Tissue culture infectious dose (TCID)
t
Time (s, min, hr)
tinit
Initial time (s)
texpo
Exposure time interval (s)
tmax,air
Maximum elapsed time (s)
V
Volume (m3)
Vroom
Room volume (m3)
VR
Ventilation rate (ACH)
VRchamber
Ventilatoin rate of a chamber (ACH)
VRoffice
Ventilatoin rate of an office (ACH)
VRward
Ventilatoin rate of a ward (ACH)
VRwashroom
Ventilatoin rate of a washroom (ACH)
v
Velocity (m s-1)
vair
Air velocity (m s-1)
vbp
Bioaerosol particle velocity (m s-1)
vsettle
Settling velocity (m s-1)
vrel
Relative velocity (m s-1)
vwc
Air velocity at a height of 0.2 m above the WC seal (m s-1)
viab
Viability function
Web
Weber number
є
Error
єrms
Relative error of computed average mass flow rate
єfb
Fractional bias xxvi
єnmse
Normalized mean standard error
θ
Inclined angle (rad)
ρ
Density (kg m-3)
ρair
Air density (kg m-3)
ρbp
Density of the bioaerosol particles emitted (kg m-3)
σ
Diffusivity (m2 s-1)
σbin
Dispersed phase diffusivity (m2 s-1)
ξ
Surface tension (N m-1)
Γ
Surface concentration of surfactant (mol m-2)
τ
Shear stress (N m-2),
μ
Molecular viscosity(kg m-1 s-1)
μair
Molecular viscosity of air (kg m-1 s-1)
μkin
Kinematic viscosity (kg m-1 s-1)
μeff
Effective viscosity of the air (kg m-1 s-1)
Ψ
deformation rate tensors
𝜓
Stream function
ω
Vorticity
∆
Difference
∇
Gradient of a scalar function
Subscripts 0, 1, 2…
of conditions 0, 1, 2…
AM
of added mass
add
of additional
aero
of aerodynamic
air
of air
alveolar
of alveolar
aspect
of aspect
avg
of average
axis
of axis
BH
of Basset history
Boltzmann
of Boltzmann xxvii
Brown
of Brownian
bin
of bin number
bp
of bioaerosol particle
Cunningham
of Cunningham
chamber
of chamber
coarse
of coarse grid
crit
of critical
data-pair
of data pair
depos
of deposition
dose
of dose
drag
of drag
Epstein
of Epstein
eff
of effective
emass
of equivalent mass
emission
of emission
emob
of mobility diameter
epa
of equivalent projected area
esurf
of equivalent surface area
exh
of exhausted
exp
of experiment
expo
of exposure
evol
of equivalent volume
FK
of Froude-Krylov
Feret
of Feret
fb
of fractional bias
fine
of fine gird
floor
of floor
grav
of gravity
infect
of infection
inhale
of inhale
init
of initial
intake
of intake
interface
of interface
Martin
of Martin xxviii
max
of maximum
mean
of mean free path
min
of minimum
molecule
of molecule
mvac
of mechanical ventilation and air conditioning (MVAC)
new
of new infection
nmse
of normalized mean standard error
Oseen
of an Oseen approximation
ocp
of occupant
office
of office
outbreak
of outbreak
p
of particle
pop
of population
prz
of pressure
proj
of projected
pulmonary
of pulmonary
quanta
of quanta
random
of random
recover
of recover
refine
of refinement
reflection
of reflection
rel
of relative
relax
of relaxation
removal
of removal
resp
of respiratory
respirator
of respirator
room
of room
SL
of Saffman’s lift
Stokes
of Stokes
scale
of scale
settle
of settling
shape
of shape factor
sim
of simulation
site
of site xxix
slip
of Cunningham slip correction
sph
of sphericity
sphere
of sphere
src
of source
stress
of stress
surface
of surface
suscept
of susceptible
term
of terminal
turbulent
of turbulent
vaero
of vacuum aerodynamic
vol
of volume
wall
of wall
ward
of ward
washroom
of washroom
wc
of water closet
wf
of wall friction
χ
of dynamic shape factor
∞
of infinite
xxx
1
Introduction
In this chapter, the background and rationale are presented to highlight the significance of this thesis. The goal is to develop an empirical drag coefficient model and transport framework for bioaerosol (i.e. biological aerosol) in order to understand the fundamental transport mechanism of bioaerosol particles, to determine effective strategies of infection control and to prevent airborne infectious diseases as illustrated in Figure 1.1. The relation of the public health and ventilation system design is presented regarding the outbreaks of infectious diseases, infection control and prevention and the indoor environmental conditions in Section 1.1. The spatial and temporal predictions of the bioaerosol distribution are recognized to be critical factors for infection control by ventilation systems. The scope and objectives of the thesis are identified with respect to the findings in this study. The outline of the thesis is summarized in Section 1.4.
1
Public health
Outbreaks of infectious disease
Communi -cable disease
Noncommunicable disease
Opportunistic disease
Ventilation system design
Bioaerosol infection risk
Ventilation system
Source emission
Human expiratory activities
Sanitary system operations
Ventilation system operations
Building environments
Distribution type
Ventilation rate
Healthcases
Transport -ations
Temperature
Relative humidity
Sanitary facilities
Offices
Airflow pattern
Recirculation ratio
Disinfection
Filtration
factories
Residences
Figure 1.1 The structure diagram of Chapter 1 2
Schools
Infection risk assessment Spatial and temporal distribution of bioaerosol particles
Prediction of bioaerosol particle movement CFD simulation
1.1 Background of bioaerosol particle simulation in buildings 1.1.1 Impacts of Infectious disease outbreaks Public health has been threatened by the outbreaks of Severe Acute Respiratory Syndrome (SARS) in 2003, avian A/H5N1 in 2003, pandemic influenza A/H1N1 in 2009, Middle East Respiratory Syndrome (MERS) in 2012, avian A/H7N9 in 2013, Ebola virus in 2014 and MERS in 2015 (Braden et al., 2013; Tang et al., 2015). These outbreaks spread rapidly and globally to the human population due to globalization (Heymann, 2003; Smith, 2006). For example, the cumulative number of probable SARS cases was 8,098 with 774 reported deaths (WHO, 2003b). According to the World Health Organization (WHO), within weeks, SARS spread from Hong Kong to infect individuals in 37 countries (Smith, 2006). The emergence from time to time of pandemic outbreaks of these infectious diseases remains an ongoing threat to the human population.
For example, in 2003, several hundred cases of SARS were reported within 10 days in Amoy Gardens, one large housing estate in Hong Kong (Lee et al., 2003). Four out of seven tower blocks reported SARS cases on different storeys. The spread of SARS was suggested to be by way of airborne transmission of the SARS virus from the faeces of a single index case with diarrhoea. It was possible the virus–contaminated aerosols leaked out from the sewer pipes when travelling down from the apartment (Cheng et al., 2008). The viral particles might have been transmitted to other tower blocks by the wind and thermal plume, thereafter entering other apartments in the adjacent 3
towers through window openings if the kitchen or bathroom exhaust fans were operating, creating a negative pressure sink within.
In May 2015, the first case of MERS in South Korea was identified. Totally, 150 infection cases were reported including 18 deaths in June 2015 (Hui et al., 2015). Some confirmed cases were related to healthcare facilities and a remarkable proportion were associated with healthcare workers (HCWs) (17%). The outbreak in South Korea grew to be the second-largest MERS outbreak since the Saudi Arabia outbreak.
Both examples of outbreaks caused considerable morbidity and mortality. Mortality is undesirable to the individual and to communities. Morbidity causes large social and economic impact due to productivity, absenteeism and medical treatment costs. The cost of outbreaks is rarely quantified; however, the benefits of infection control are visible in terms of health and economic benefits (Zimlichman et al., 2013).
The potential health hazards of infectious diseases to the public are significant, not only from outbreaks (Burger, 1990; Douwes et al., 2003). For example, 18.8 billion cases of upper respiratory infections and 1.5 million cases of lower respiratory infection were reported in the Global Burden of Disease Study 2013 (Vos et al., 2015). For chronic respiratory disease (38.6 million cases), 26.1 million, 10.6 million and 0.5 million cases of chronic obstructive pulmonary disease (COPD), asthma and pneumoconiosis were recorded. 4
COPD is associated with a high dose of endotoxin, fungal spores or mycotoxins inhalation within several hours (Srikanth et al., 2008). The relation of bioaerosol (i.e. biological aerosol) to asthma has also been confirmed as exposure agents (Arya & Kaushik, 2013). Furthermore, attention to indoor air quality (IAQ) has increasingly had a large effect on public health, impacting health and productivity (Wong et al., 2009).
Inhalation of a bioaerosol through breathing can result in a respiratory infection if the inhaled microorganism cannot be removed by lung clearance, phagocytosis or dissolution and adsorption by a primary defensive mechanism. The microorganisms may stay in the lungs and begin either replicating or reactions. The results of such growth might be respiratory infection, allergic reaction or even toxic reaction (Cox & Wathes, 1995). However, respiratory irritation may be caused by microbial volatile organic compounds (MVOCs) produced from bacteria and fungi. Only infectious diseases caused by the transmission of pathogens to others (i.e. bioaerosol particles), are focused on in this thesis.
1.1.2 Spread of airborne infectious diseases Most outbreaks are caused by infectious diseases, especially airborne diseases, because they can be transmitted person to person over a short distance through the air (Wells & Stone, 1934). Inhalation and surface contact are two major routes of airborne transmission: 1) Respiratory droplets (i.e. bioaerosol particles) produced when an infected person coughs or sneezes are inhaled 5
into a respiratory tract of persons who are nearby; and 2) the bioaerosol particles can also be spread when a person touches a surface or object contaminated with infectious droplets and then touches his or her mouth, nose or eye(s) (Morawska, 2006).
The viability, infectivity, allergenicity, toxicity or pharmacological properties of diseases are related to their pathogen (i.e. bioaerosol) species (Fiegel et al., 2006). Airborne diseases can be categorized as communicable, noncommunicable and opportunistic diseases groups according to their epidemiological aspects as shown in Table 1.1. Table 1.1 Some airborne pathogens for the communicable, noncommunicable and opportunistic disease groups (Kowalski, 2006) Noncommunicable disease
Communicable diseases
Respiratory infection
SARS-CoV, MERS-CoV, Tuberculosis bacilli, Mycobacterium
Nonrespiratory infection
E. coli, Norwalk virus
Legionella, H5N1, Aspergillus, Bacillus anthracis,
Cladosporium,
Opportunistic disease Pseudomonas aeruginosa, Rhizopus spp., Staphylococcus aureus, Serratia marcescens, Streptococcus pyogenes Enterococcus faecalis, Staphylococcus epideris
Airborne communicable diseases are those transmitted between humans. All airborne human respiratory viruses are communicable except Hantavirus (Roy & Milton, 2004). For communicable respiratory diseases, airborne transmission may occur by means other than direct inhalation into a 6
respiratory tract, including direct and indirect contact with fomites left on a surface (i.e. surface contact). Secondary transmission (i.e. vertical transmission) may occur by respiratory pathogens liberated in a sneeze or a cough from an infected person (Nicas et al., 2005). For example, the SARS and MERS outbreaks in 2003 and 2013 were caused by airborne communicable respiratory disease pathogens (Joshi, 2013). This group of diseases rapidly spread as outbreaks without physical contact with infected people.
Airborne communicable non-respiratory diseases, including infections of the eyes, ears and skin, can be transmitted by direct or indirect contact with bioaerosol particles deposited on a surface, such as pathogenic infections of Escherichia coli via surface contact on infantile enteritis and diarrhoea (Neter & Shumway, 1950). Other common diseases, such as norovirus illness and hand, foot and mouth disease (HFMD) are kinds of airborne communicable non-respiratory diseases (Klausner et al., 2015; Liu et al., 2013).
A non-communicable pathogen is not transmissible from person to person, but it can be transmitted to humans from animals (i.e. zoonotic) or the environment (i.e. water or soil). Secondary infections among humans rarely occur (Cox & Wathes, 1995). For instance, Legionnaires’ disease is not transmitted between humans, but Legionella is common in soil and aquatic environments. The aerosolized Legionella particles can travel at least 6 km from a water system or cooling tower in air. Some zoonotic diseases
7
transmitted from infected animals to humans such as the H5N1 influenza virus, which can cause avian influenza or bird flu, are commonly found in birds. Human-to-human transmission of the virus has not yet been observed (Nikitin et al., 2014), but the chance of mutation of the virus is not guaranteed (Kawaoka, 2012).
Some opportunistic fungal diseases can be transmitted by taking advantage of a host (i.e. human) with a weakened immune system (i.e. immunodeficient) (Mims, 2001), but they cause no disease in a healthy host (i.e. human) with a normal immune system. An opportunistic infection, therefore, often happens in healthcare facilities as a nosocomial infection. Infection may be through inhalation, via deposited airborne pathogens on wounds or burns, or as a result of intrusive procedures being contaminated. For example, outbreaks of methicillin-resistant Staphylococcus aureus (MRSA) are often reported due to its resistance to antibiotics such as penicillins and cephalosporins (WHO, 2002).
1.1.3 Infection in indoor environments Airborne infections occur both outdoors and indoors. Indoor airborne transmissions, however, are of higher concern, since people spend over 90 percent of their time indoors (Jenkins et al., 1992; Spengler & Sexton, 1983). Research on bioaerosol infections, which are caused by bioaerosol particles, has focused on indoor environments, especially buildings. The indoor environment in buildings plays a key role in the wide range of bioaerosol 8
infections, not only the longer occupancy time (Burger, 1990). Investigations have intensively studied bioaerosol emission in building environments and ventilation systems as shown in Figure 1.2 (Lai & Nazaroff, 2000).
Human (i.e. host)
Bioaerosol emission
Bioaerosol particles (i.e. agent)
Infection
Ventilation system
Building environment
Indoor air (i.e. enviro -nment)
Figure 1.2 Airborne infection factors in indoor environments
Bioaerosol emission for infection Microorganisms are easily aerosolized in ambient air by air movement caused by wind, coughing or sneezing due to their submicron size range (Kowalski, 2006). Aerosolization or atomization is a process of producing bioaerosol particles (i.e. airborne or droplets form) from breaking up the liquid surface by a high-velocity air jet (Sirignano, 2010). If microorganisms are contained in the liquid, they may become microbe-laden droplets and dry to become a droplet nucleus due to air jets or evaporation. For example, respiratory droplets are aerosolized from respiratory fluid in the respiratory tract by the high-velocity exhaled air jet (i.e. 50~100 m s-1) in the sneezing process (Mui 9
et al., 2009; Tang et al., 2012a). The aerosolization process also occurs in toilet flushing in addition to the expiratory activities of humans. Sometimes, water may not be involved in the process, for example, resuspension of deposited bioaerosol particles from a floor by humans walking or from a bedsheet by bed-making (Hathway et al., 2011; Kubota & Higuchi, 2012; Leung et al., 2013; You & Wan, 2014a). Many aerosolization processes occur in nature, such as waterfalls and wind that cause about 20~30% of bioaerosol particles in atmospheric particles in outdoor environments. Some potential bioaerosol particle emission sources in indoor environments have been identified in relation to infection control and are listed as follows (Morawska, 2006): 1) Human expiratory activities – breathing, speaking, coughing and sneezing; 2) Sanitary system operations – drainage systems, toilet flushing, showering, water taps; and 3) Ventilation system operations – cooling tower, filter, heat exchange Each of these processes generates bioaerosol particles of different characteristics in terms of their species, size, shape, emission rate, viability loss and initial speed.
Humans and their activities are linked to a number of processes resulting in the introduction of droplets containing bioaerosol particles into indoor air, including coughing and sneezing. Besides expiratory activities, bioaerosol particles can be emitted from skin or by walking (Bhangar et al., 2015). Sneezing and coughing are the two most commonly studied activities 10
regarding the expiratory activities in terms of emission species, amounts and velocities. The high frequency of breathing, talking and laughing is also significant in infection research. Most of the expiratory studies are associated with bacterial and viral groups that are communicable respiratory disease groups which were discussed in Section 1.1.2. The formation of expiratory bioaerosol particles inside the respiratory tract is less interesting than the development of bioaerosol particles in the air (Morawska et al., 2009).
High-speed photography was first used to investigate the velocities and sizes of the bioaerosol droplets from breathing and sneezing (Jennison, 1942; Jennison & Edgerton, 1940). By the use of an optical particle count (OPC) and a transmission electron microscope (TEM), about 80~90% of the droplets were found to be less than 1 µm in diameter (Papineni & Rosenthal, 1997). Besides expiratory bioaerosol particle studies, the dynamic airflow generated by expiratory activities is also important for infection control. Cough peak flow rate (L s-1), peak velocity time (s) and cough expiratory volume (L) have been investigated regarding the general steady airflow characteristics caused by coughing (Leiner et al., 1966; Smina et al., 2003). Selected literature on research techniques of expiratory activities is summarized in Table 1.2.
11
Table 1.2 Selected measurement methods of droplet size distribution of expiratory activities Measurement method or technique
Description
High-speed photography
The advantages of high-speed, stroboscopic-light photography for the subjects are intense illumination, by which extremely minute particles are mode visible, and short exposure-time, by which their motion is “stopped”.
Solid impaction
Using a celluloid-surface, glass slider, bond paper and chamber for collecting the respiratory particles, the samples are measured under a light microscope.
>0.1µm
Radioactive spores
Using labelled spores with a radioactive isotope, the distribution of the spores can be traced after the injection.
-
Optical particle counter
By counting the pulses of scattered light reaching the detector, particle number can be determined through a beam of light.
>1µm
Aerodynamic particle sizer spectrometer
Scanning mobility particle sizer Interferometric Mie imaging technique
Schlieren and Shadow-graph imaging method
Particle time-of-flight methods rapidly determine the number-weighted aerodynamic particle size distribution by accelerating the incoming particles through a well-defined flow field in the measurement zone of the analyzer. The transit time of the particle between two well-defined locations in the measurement zone is a monotonic function of aerodynamic diameter. The travelling time is measured by light scattering signals to associate the aerodynamic particle size. The technique determines particle size by scanning the electrical mobility of particles when traversing an electrical field. By the out-of-focus imaging of particles illuminated by a laser light sheet, the farfield scattering can be calculated by the Mie theory from the overlapping region of the interference fringe pattern. The method is based on high-resolution imaging with pulsed backlight illumination. The measurement volume is defined by the focal plane and the depth of field of the imaging system.
12
Measurement size
Reference
>1 µm
Jennison (1942), Jennison and Edgerton (1940) Duguid (1946), Papineni and Rosenthal (1997) Harper and Morton (1953), NCRP (1997) Fennelly et al. (2004), Xie et al. (2009), Lieberman (2006)
0.1~20µm
Morawska et al. (2009), Johnson et al. (2011)
2~1000nm
Yang et al. (2007)
1~100 µm
Chao et al. (2009)
-
Settles et al. (1995), Tang et al. (2012b)
The aerosolization process is also found in relation to vomiting wherein 107 virus particles per mL of vomit fluid have been reported in infected individuals (Barker et al., 2001). For example, a series of infections are suspected to have come from an SARS-infected person who vomited in the Metropol Hotel in the 2003 SARS outbreak in Hong Kong (Lee et al., 2003). In addition, the Norwalk-like virus outbreak in a school was found to have taken place after a student vomited in a classroom (Marks et al., 2003).
The virus content (i.e. 1012 virus particles per gram) was reported in human faeces (Barker et al., 2001). The mechanisms for aerosolization in sanitary facilities have been reported in toilet flushing and sewage transport in building drainage systems (Johnson et al., 2013a; Morawska, 2006). In general, little quantitative research has looked at the mechanism of sewage aerosolization through the above processes in terms of the size of the bioaerosol particles generated, and thus their fate in the air and the potential for spreading in sanitations (Cheng et al., 2010; Morawska, 2006; Wise & Swaffield, 2002).
Aerosolization in sanitary facilities may have a high potential for airborne infection with each toilet flush or use of water. The bioaerosol particles are inhaled or ingested by hand contact with deposited surfaces indirectly (Johnson et al., 2013b). For instance, bacteria samples have been reported in water, air and surfaces of hospital toilets (Newsom, 1972). Table 1.3 shows the selected seeding experiments of the aerosolization process after flushing.
13
Table 1.3 Summary of selected experiments of toilet seeding Toilet Type Cistern-fed, gravity-flow and mains-fed pressure value Wash-down Wash-down and siphonic
Conducted by
Year
Microorganism
Jessen (1955)
1955
Serratia Marcescens
1959
Serratia Marcescens
1966
Escherichia coli
Darlow and Bale (1959) Bound and Atkinson (1966)
Wash-down and double-trap siphonic
Newsom (1972)
1972
Escherichia coli, Salmonella typhimurium, Shigella sonnei, Proteus mirabilis, Serratia marcescens, Klebsiella aerogenes, Pseudomonas aeruginosa and Achromobacter spp.
Siphonic gravity-flow
Gerba et al. (1975)
1975
Escherichia coli and MS2 bacteriophage
Siphonic gravity-flow
Barker and Bloomfield (2000)
2000
Salmonella enteritidis and PT4
Siphonic gravity-flow
Barker and Jones (2005)
2005
Serratia marcescens and MS2 bacteriophage
Siphonic gravity-flow
Best et al. (2012)
2012
Clostridium difficile
In a drainage system, the leakage of bioaerosol particles outside the system should be prevented if the system is maintained properly (Wise & Swaffield, 2002). However, the leakage of bioaerosol particles from a drainage system may cause the outbreak of a disease such as the spread of SARS-CoV in Amoy Garden, which was described in Section 1.1 (Sobsey & NMeschke, 2003; WHO, 2003b). Dry floor traps and bathroom fans were identified as the major contributory causes of this outbreak by numerical simulation studies (Cheng et al., 2013; Gormley et al., 2012; Jack et al., 2006). Some drainage designs 14
have been recommended for the avoidance of bioaerosol particles leakage within the network and sewer pipework; especially, predictions of airflow, transient network pressures and trap seal retention level are suggested to be incorporated in the design stage (Jack, 2006). The infection risk analysis for drainage systems has been investigated further in high-rise residential buildings through failure mode effects analysis (Cheng et al., 2008). The reuse of greywater has been quantified by experiments (Benami et al., 2016).
The aerosolization processes of bioaerosol particles have been observed in other sanitary activities, such as pavement cleaning (Seidl et al., 2015), showering (Dennis et al., 1984), water taps (Carson, 1996), water systems (Kool et al., 1999) and hot spa springs (Armstrong & Haas, 2007b), especially the Legionella group in these systems (Ellis, 1993).
The aerosolization process of bioaerosol particles found in ventilation systems such as cooling towers is suspected to be a source of many Legionella outbreaks (Buse et al., 2012; Fraser et al., 1977), although Legionella is found less in cooling towers nowadays due to legislation. Other microorganism species, however, are still found in ventilation systems such as Micrococcus spp. Staphylococcus spp., and Aspergillus spp. Growths of microorganisms inside ventilation systems have been found in many places such as mixing chambers, cooling coils, air filters, air ducts and heat exchangers (Bluyssen et al., 2003; Chow et al., 2005; Hugenholtz & Fuerst, 1992; Schmidt et al., 2012; Zuraimi, 2010). The high potential for aerosolization of bioaerosol
15
particles is correlated with the high airflow rate in ventilation systems (Zuraimi, 2010). The aerosolized particles are then dispersed through the system, and eventually arrive in occupied rooms through the air distribution system. Such bioaerosol emissions from ventilation systems may create an impact on infection control strategy, especially if the ventilation system is a major control mechanism of infection control. No study is available to assess quantitatively bioaerosol emissions from ventilation systems and other relevant factors, like ventilation rate (VR) and numbers of occupants. The maintenance plan and disinfection devices in the installation of ventilation systems should be included in the effect of this potential bioaerosol emission in ventilation system design.
Building environments for infection For building environments, potential emission source, occupant density and occupant activity pattern differ among building types. To optimize the performance of infection control, a ventilation system design has to be specified based on the characteristics of the building type. Table 1.4 summarizes some key studies on building types. In particular, some building types with high potential risk have been intensively studied. Standards and guidelines have been developed to specify the ventilation system design in the building type such as healthcare and transportation facilities (ASHRAE, 2013c; CDC, 2003, 2004).
16
Table 1.4 Selected literature on building type related to bioaerosol infections
Healthcare facilities
Offices and factories
Transportation facilities
Washrooms, water systems, and drainage systems
Aircraft, trains, buses, vehicles and ships
Others
Isolation rooms, and elderly centres
Sanitary facilities
Area
Workplace
Building types
Educational and residential facilities
Literature Nosocomial infection or hospital acquired infection (HAI) is a special type of infection which is acquired in healthcare facilities such as hospitals, clinics and specialized care centres. The facilities have a very high infection risk since some patients could be emission sources of the bioaerosol particles and have weak immune systems, so they are infected easily by opportunistic diseases. For example, the high infection rates of HCW of SARS (20.5%) and MERS (17%) outbreaks were recorded in Hong Kong and South Korea (Jack, 2015; Lau et al., 2004). Healthcare facilities consist of various types of function areas such as operating suites, AIIR, intensive care units (ICUs), burns wards, general patient wards, emergency rooms, laboratories and offices. These areas have different ventilation and infection control requirements (Tang et al., 2015; Tang et al., 2006). Some areas are identified as high-risk areas such as operating suites, AIIR, ICUs and burns wards due to the high infection risk for both patients and HCWs (Noakes et al., 2015). Some ventilation suggestions, such as high VR (i.e. >12 ACH), pressurization control, local exhaust system, and the use of HEPA filters and UVGI devices are recommended for these areas (ASHRAE, 2013c; CDC, 2007). A warm and humid environment promotes microorganism breeding in sanitary facilities such as toilets, showers and bathrooms (Wells, 1943). Microorganisms have been highly reported in washrooms, especially on seats and under the flushing rim of water closets (WC) (Flores et al., 2011; Irvine & Robertson, 1964; Mendes & Lynch, 1976; Newsom, 1972). These microorganisms can survive up to six hours in washrooms (Newsom, 1972). Potential infection paths of related diseases have been identified via ingestion, splashing during defecation, surface contact and direct inhalation (Barker & Jones, 2005; Burgess, 1963; Darlow & Bale, 1959). Bioaerosol particles in washrooms sourced from toilet flushing through splashing and frothing were identified as the key medium of infection (Burgess, 1963). Formation of bioaerosol particles by flushing has been investigated experimentally (Jessen, 1955). To reduce the infection risk by flushing, some research of the reduction of microorganisms in water seals and bioaerosol emission has been conducted, such as using disinfectants, closing the lid and using siphonic gravity flow WC (Barker & Bloomfield, 2000; Johnson et al., 2013b). However, these studies only focused on source control rather than a source removal strategy by an exhaust ventilation system which is used to remove odour and humidity in the facilities. The existing exhaust ventilation design practices only focus on the required VR for odour dilution and fresh air intake in a washroom (ASHRAE, 2013b; Chung et al., 1997; CIBSE, 2005; Rock & Zhu, 2002; Tung et al., 2009; Tung et al., 2010). Long working hours and high occupant densities in workplace environments should not be neglected for infection research. Because of the daily occupancy and interaction of occupants within the environments, many infections are regularly transmitted inside these building types (Morawska, 2006). Occupational safety is also associated with the studies (Corzine et al., 2003; HSE, 2003). Offices and factories, except healthcare facilities for HCWs, are two common types of workplace environments for airborne infection research. Various respiratory symptoms and health outcomes of biocontaminated office occupants have been reported, often implicated by elevated bioaerosol exposure (Wu et al., 2005). Microorganism growths inside ventilation systems have been reported, such as cooling coils, mixing chambers, humidifiers and heat exchangers (Bluyssen et al., 2003; Chow et al., 2005; Hugenholtz & Fuerst, 1992; Schmidt et al., 2012; Zuraimi, 2010). The existence of these microorganisms inside ventilation systems can endanger office occupants. The high potential of the microorganisms to be aerosolized to form bioaerosol particles is due to the high airflow rates inside the ventilation systems (Zuraimi, 2010). The bioaerosol particles are then dispersed in the system, and eventually into the occupied offices through the air distribution system. Such emissions from ventilation systems may create an impact for infection control in offices, especially since over 90% of commercial buildings in Hong Kong are equipped with ventilation systems (Chow et al., 2005). Two typical bioaerosol sources in factories are found in hazardous environments, such as work in landfills or farms, and working processes that will generate bioaerosols such as the water spray in wastewater treatment plant. For poultry houses, zoonotic diseases (i.e. transmissible from animals to humans), such as avian influenza and swine disease, have to be considered for infection control. In addition, foodborne and waterborne diseases may be aerosolized during the manufacturing process such as in wastewater treatment (Gormley et al., 2014; Sanchez-Monedero et al., 2008) and textile plants (Eduarda & Heederik, 1998; Su et al., 2002). Local exhaust systems are mostly recommended for factories. However, the ventilation system should be specified for each industry in terms of bioaerosol emission species, loading and locations. Transportation systems are a hub connecting people in different places, and they can also be a hub for spreading diseases over a large region or even on a global scale (Xu et al., 2013). The infection control research on transportation systems, therefore, has been intensively studied, especially aircraft cabins (Gupta et al., 2011; Liu et al., 2012; Sze To et al., 2009; Wan et al., 2009; Yin et al., 2012). Aircraft, trains, buses and other cabin environments can pose infection risks similar to those of other indoor environments except the typically smaller total volumes often increase the risk due to proximity and make air distribution and air cleanliness a more critical factor. Several infection control challenges in the cabin environment of transport vehicles have been addressed (Liu & Zeng, 2012) regarding the necessity for ventilation, comfort and health in commercial aircraft, cars, trains and buses. These challenges can be summarized as involving three issues. Due to the small spaces of the vehicles, a cabin is often highly packed with passengers, the high density of the cabin environment makes the air distributions more complex than in a building, the weight of the air ventilation system is very sensitive to the consumption of energy, and the thermo-fluid boundary condition of the occupied zone is much nearer to that in a building due to the small spaces in cabin environments (Liu & Zeng, 2012). Educational and residential buildings are the two common building types to be investigated in addition to the above building types. For educational facilities, universities and kindergartens are the two most investigated facilities. (Yamamoto et al., 2015). Residential buildings, especially high-rise residential buildings, have been studied under naturally ventilated conditions (Gao et al., 2008, 2009; Mao & Gao, 2015). However, a ventilation system design for infection control has still to be noted in other building types regarding potential bioaerosol emission sources and exhaust mechanisms.
17
Ventilation systems for infection Ventilation systems are important for the control of bioaerosol infection (Kowalski, 2006). These systems provide a significant engineering control measure in indoor air as well as providing fresh air and a comfortable IAQ and thermal environment for the occupants as illustrated in Figure 1.3 (Azimi & Stephens, 2013). Fresh air
Filtration Ventilation
Occupant (i.e. susceptible)
Recirculation
Infected person Dispersion Deposition Figure 1.3 Bioaerosol transmission with ventilation systems (Azimi & Stephens, 2013)
Some guidelines specify certain ventilation configurations and parameters for minimizing the infection for various building types such as healthcare facilities and air transportation (ASHRAE, 2013c; CDC, 2003, 2004). These design parameters include the selection of air distribution system types, VRs and the location of air intakes. Table 1.5 summarizes the key research on how these ventilation parameters affect bioaerosol infection.
18
Table 1.5 Selected literature on ventilation systems related to bioaerosol infections System
Area
Air distribution system type
Mixing Displace -ment
Natural Pressuri -zation Local exhaust Personal ized
Ventilation parameters
Ventilation rate Temperature & RH Recircul -ation ratio
Others
Air flow pattern Filtration Disinfec -tion devices
Literature Mixing ventilation (MV) systems are the most used commercial ventilation systems. The design and operation of the systems are simple and cost-effective if well designed due to the uniform distribution of fresh air concentration and temperature by the mixing of the supply and room air. The dilution effect of the mixing ventilation for the widespread TB outbreaks in the 1960s was investigated (Nardell et al., 1991; Riley et al., 1959; Rudnick & Milton, 2003). Some guidelines and standards recommended minimum ventilaton rates to reduce infection risks for mixing systems, especially in healthcare settings (ASHRAE, 2013b, 2013c; CDC, 2003). Displacement ventilation systems supply air at floor level and plume the pollutants to the upper levels of a room. Compared with mixing ventilation, the system is more efficient in removing gaseous pollutants than the mixing systems (ASHRAE, 2013a; Chen & Glicksman, 2003). However, a longer elapsed time and more depositions for bioaerosol particles have been reported due to the buoyancy force not being sufficient to lift the bioaerosols up to exhaust level (Mui et al., 2009). Bioaerosol particles in the breathing zone are critical for infection control instead of diluting the total air volume (Bolashikov & Melikov, 2009; Pantelic et al., 2009b). Natural ventilation is commonly found in residential buildings. The meteorological conditions are critical for designing naturally ventilated buildings rather than the building envelope, window opening ratio, cross-ventilation or stack effect that is driven by the wind or a buoyant force (Liu & Cheung, 2009). Outdoor infiltration of bioaerosol to indoor space is not controllable by natural ventilation (Gao et al., 2009; Mao & Gao, 2015). The Amoy Gardens outbreak of SARS in 2003, for example, was dispersed by natural ventilation from one block to the others via the single-sided window design (Gao et al., 2009). A trickle vent design was suggested to reduce the infection risk for naturally ventilated buildings (Mao & Gao, 2015; Yang & Gao, 2015). Pressurization control is frequently found in some building types such as healthcare facilities and biological research laboratories. Positive pressure reduces infiltration of bioaerosols into an indoor space and is recommended in operating rooms, while negative pressure is suggested in AIIR to avoid bioaerosols leakage (ASHRAE, 2013c). Local exhaust ventilation is the most effective contaminant removal strategy if it is located near the emission source. Adding a hood sometimes improves the removal performance of exhaust regardless of the distance (Goodfellow & Tahti, 2001). Sterilization and autopsy rooms are recommended to use the system to control the infection risk (ASHRAE, 2013c; Dygert & Dang, 2012; Leung et al., 2006). The system is also used in laboratories, kitchens and industrial factories for removing chemical pollutants as well. Personalized ventilation (PV) supplies clean air to occupants individually. The gaseous pollutant concentration in a breathing zone could be reduced by 2 to 50 times compared with mixing ventilation (Cermak & Melikov, 2006; Melikov, 2004). Improvement of occupant protection for bioaerosol infection has also been reported as a supplemental method for infection control. Also, the system has been suggested in hospitals and aircraft cabins (Qian et al., 2006; Wan et al., 2009). However, the applications of this suggestion are limited to theatres, cinemas, lecture halls and aircraft cabins where the chairs are connected to fresh air pipes (Pantelic et al., 2009b). Ventilation with outdoor air is intended to provide fresh air to occupants and remove pollutants emitted from indoor sources. The association between VR and occupant health has been reported (Sundell et al., 2011). The importance of the VR has also been reviewed in regard to bioaerosol infection risk (Carrer et al., 2015; Rudnick & Milton, 2003; Sundell et al., 2011). In practice, the actual percentage of air which is exchanged inside a room as the ventilation effectiveness of that air is not perfectly mixed (Li et al., 2008; Wang et al., 2008). However, the VR can still be a surrogate indicator to understand the overall room ventilation performance regarding IAQ and infection control. Environmental conditions such as air temperature and relative humidity are strongly associated with the indoor bioaerosol concentration level (i.e. bacteria and fungi level) (Chan, 2012; Tang, 2009; Wong et al., 2008). These thermal and humidity conditions may accelerate the germination, growth and survival rate of bioaerosol particles. For example, lipid-enveloped human coronavirus 299 E remains alive with a half-life of 67 hours at a RH of 50% and air temperature of 20°C The long survival time of bioaerosol particles increases the infection risk due to the chance of re-entry to the room or entry to other rooms by the recirculation process. For energy saving, room air is usually recirculated and mixed with a certain amount of fresh air (i.e. fresh and recirculation rate) in an air handling unit (AHU) room that reduces the energy consumption in cooling and dehumidifying the fresh air. However, bioaerosol particles may re-enter the room with the recirculated air. Bioaerosol particles could be looped in the room until the end of their lifetime by the recirculation process, which enhances the overall infection risk. On the other hand, the infectious risk could be further spread to other rooms that connect with the same AHU (Kupferschmidt, 2015). Reducing the fresh air recirculation ratio might encourage viruses spreading in indoor space (Mendell et al., 2002). Some suggestions of airflow patterns should also be of concern in the design of ventilation systems for infection control and prevention (Morawska, 2006; Sundell et al., 2011). In general, a downward airflow pattern was found to be the best for controlling the lateral dispersion of bioaerosol particles, especially for particles with a diameter of less than 45 µm (Chao & Wan, 2006). The spatial location of the emission source (i.e. coughing or sneezing) and the subjects was identified as a key factor in the dispersion and deposition of bioaerosol particles (Sze To et al., 2008). Regarding the difficulties in quantifying the airflow pattern for a ventilation system, CFD simulation has been introduced to simulate the airflow in bioaerosol infection studies (Sze To, 2010). HEPA filters are commonly recommended to be used in a bioaerosol infection control environment, although they are expensive and have a higher energy consumption due to the pressure drop from the filter. The HEPA filters are effective at capturing most bacterial and fungal species (1, then the outbreak or epidemic is propagating. If Routbreak1 Routbreak 3 µm (Douglas, 1975), 𝐼𝐷50 = 223.5 × 𝑇𝐶𝐼𝐷50
[2.14]
The relationship between the intake dose in the alveolar region (i.e. target infection site) and the inhaled dose from the surrounding air via nose was suggested in the estimation of the airborne infection risk of tuberculosis bacilli (Nicas, 1996). 𝑁𝑖𝑛𝑡𝑎𝑘𝑒 = 𝑁𝑖𝑛ℎ𝑎𝑙𝑒 × 𝑓𝑎𝑙𝑣𝑒𝑜𝑙𝑎𝑟 =
𝑁𝑖𝑛𝑓𝑒𝑐𝑡 × 𝑅𝑠𝑟𝑐 × 𝑄𝑝𝑢𝑙𝑚𝑜𝑛𝑎𝑟𝑦 × 𝑡𝑒𝑥𝑝𝑜 𝑄𝑟𝑜𝑜𝑚
[2.15]
× 𝑓𝑎𝑙𝑣𝑒𝑜𝑙𝑎𝑟
Where Rsrc is the airborne tuberculosis bacilli emission source rate (min-1), falveolar is the deposition fraction of infectious particles in the alveolar region. fsite is the fraction of the intake dose that reaches the target infection site. For example, the alveolar region may be the target infection site for respiratory tract infections or airborne diseases mostly. Equation (2.15) predicts the intake dose Nintake of tuberculosis bacilli by multiplying the falveolar and inhale dose Ninhale, where the inhale dose Ninhale could be estimated from Rsrc, Ninfect, Qroom and texpo. Combining Equations (2.12) and (2.15), the infection probability for airborne is derived in Equation (2.16). 𝑃𝑟𝑜𝑏𝑖𝑛𝑓𝑒𝑐𝑡 = 1 − exp (−𝑓𝑑𝑜𝑠𝑒
[2.16] 𝑁𝑖𝑛𝑓𝑒𝑐𝑡 × 𝑅𝑠𝑟𝑐 × 𝑓𝑎𝑙𝑣𝑒𝑜𝑙𝑎𝑟 × 𝑄𝑝𝑢𝑙𝑚𝑜𝑛𝑎𝑟𝑦 × 𝑡𝑒𝑥𝑝𝑜 × ) 𝑄𝑟𝑜𝑜𝑚
50
Equation (2.16) has demonstrated the application potential of the doseresponse model in assessing the infection risk for airborne transmission. The equation is similar to the Wells-Riley model (i.e. Equation (2.10)) with quanta generation rate Rquanta replaced by Rsrc× falveolar. Sometimes, falveolar and fdose will be combined to falveolar to simplify the equation.
In addition, the adequacy of using dose-response models in assessing airborne infection risk for another disease was demonstrated in a Legionnaires’ disease study (Armstrong & Haas, 2007a; Armstrong & Haas, 2007b; Armstrong & Haas, 2008). The study performed animal tests for extrapolating the interspecies of the dose-response curve of Legionnaires’ disease, especially in low dose conditions (Armstrong & Haas, 2007a). The spatial variation of the exposure level at a steady state was estimated by a near field model for hot spring spa outbreaks (Armstrong & Haas, 2007b). Then the risk assessments were predicted by the dose-response curve and the exposure level and validated by comparing the estimated risk from the reported attack rate in the outbreaks (Armstrong & Haas, 2007b; Armstrong & Haas, 2008).
These studies also highlighted the potential of the dose-response model in assessing the infection risk of exposure to bioaerosol particles instead of the particles generated by an infector since the model separates the exposure and dose-response assessments for the infection risk assessment framework as shown in Figure 2.10 (Nicas, 1996; Sze To, 2010).
51
Bioaerosol Emission
Bioaerosol dispersion
Exposure assessment
Bioaerosol concentration
Exposure time
Bioaerosol exposure
Doseresponse assessment
Dosimetry factors
Intake dose Dose-response curve
Probability of infection
Risk Infection assessment
Risk characterization
INFECTION RISK Figure 2.10 Exposure and dose-response assessments for risk infection framework (Nicas, 1996; Sze To, 2010)
Under this framework, various emission mechanisms, such as a multiple of coughs, could be expressed as an infectious source strength Rsrc in terms of 52
cough frequency, bioaerosol particle concentration in respiratory fluid and the amount of bioaerosol particles generated in coughs (Nicas et al., 2005). Furthermore, by this risk assessment, a spatial and temporal distribution of the exposure assessment model for virus has been proposed to evaluate the infection risk of a non-uniform distribution ventilated space in Equation (2.17) (Sze To et al., 2008; Sze To et al., 2007) 𝑁𝑖𝑛ℎ𝑎𝑙𝑒 (𝑥, 𝑡𝑒𝑥𝑝𝑜 ) = 𝐶𝑜𝑛𝑐𝑏𝑝
[2.17] 𝑡𝑒𝑥𝑝𝑜
𝑓𝑏𝑝 (𝑥, 𝑡)𝑣𝑖𝑎𝑏(𝑡)𝑑𝑡
× 𝑄𝑝𝑢𝑙𝑚𝑜𝑛𝑎𝑟𝑦 ∫ 0
Where Ninhale(x, texpo) is the exposure level of the bioaerosol particles at location x during the exposure time interval (Plaque forming unit (PFU)), Concbp is the bioaerosol particle concentration in the respiratory fluid (PFU mL-1), viabbp(t) is the viability (i.e. survival) rate function of a bioaerosol particle (%), fbp(x, t) is the volumetric fraction of bioaerosol particles (mL) over air (L) at the location (mL L-1 of air). fbp(x, t) can be determined by measurements or CFD simulation. Generally, Equation (2.17) will provide more practical exposure estimations, but it is more timeconsuming than obtaining the exposure level based on the well-mixed assumption or other simple models. The equation could be further incorporated with particle size bins to form an infection risk model for polydispersed bioaerosol particles regarding their species. The modification is especially suitable for parametric studies on the effect of environmental 53
control from multiple emission sources, such as ventilation strategy or airflow pattern on the infection risk for non-uniform distribution environments (Sze To et al., 2008). Furthermore, the equation was examined by crossreferencing the most probable quanta generation rate of the Wells-Riley model with maximum likelihood estimation in a Varicella (i.e. chickenpox) outbreak (Chao, 2011).
The dose-response model provides the bioaerosol infection risk assessment for indirect contact (i.e. surface contact) transmission (Atkinson & Wein, 2008; Nicas & Jones, 2009; Sze-To et al., 2014). A relative contribution between the direct and indirect contract transmission of influenza virus exposure routes was estimated by the inhaled (i.e. airborne) and ingested (i.e. finger contact from virus-contaminated surfaces, then finger contact to the eyes, nostrils or lips) doses. A significant contribution was reported by hand contact with facial membranes (i.e. 31% contribution) (Nicas & Jones, 2009). On the other hand, the effect of surface material was examined in aircraft and healthcare environments. The results showed that the indirect contact risk of non-fabric surfaces may be higher than that contacting on fabric surface by a thousand times. In addition, reducing the contact rate of the surfaces is relatively more effective instead of increasing the VR for the infection control.
Furthermore, the dose-response model also provides an alternative infection risk assessment for airborne transmission rather than the Wells-Riley quantum model. Various types of the emission source, such as coughing, hot 54
spring spa, toilet flushing or drainage system, could be assessed by the doseresponse model due to the separation of the exposure and dose-response assessments in the infection risk framework (Nicas, 1996). An emission source could be expressed more flexibly in terms of emission species, amount, rate, size, shape and velocity. The estimation of the input parameters for the emission, therefore, is an important factor for the exposure assessment. The prediction of the spatial and temporal distributions of bioaerosol particles are critical for both infection risk models, especially for low dose conditions.
2.4 Prediction of bioaerosol particle movement For the transport model, the bioaerosol particle diameter and falling time were empirically correlated to estimate the possible travel distance from the infector (Wells et al., 1948; Wells et al., 1942). The gravity force, settling velocity and evaporation time have been associated to understand the dispersion and deposition of bioaerosol particles as the Wells evaporationfalling curve in Figure 2.4. Only particles less than 100 µm was concluded to be significance for airborne transmission in stagnant air, although the critical diameter of the 100 µm is questioned (Xie et al., 2007). The study led the empirical transport model to study the dispersion and deposition of bioaerosol particles instead of outdoor field measurement in the previous aerobiology studies for a small travel distance. (Buller, 1909; Burge, 1995; Marshall, 1904). In addition, the study suggests the physical properties (i.e. diameter) of bioaerosol particles are critical factors on particles spreading and the ventilation system design for infection control. Various empirical models 55
have investigated the particle dynamics from an infected person to a new host. For example, a study of air duct shapes (i.e. a long straight, an ‘S’ shape and a ‘U’-shape) suggested the bends of the duct could isolate the larger bioaerosol particles instead of the smaller particles (Andrewes & Glover, 1941).
The understanding of size distribution of bioaerosol particles related to various release mechanisms and their subsequent transport was still limited. Modeling of droplet transport based on the Gaussian plume model was developed for bioaerosol particles transport for dispersion in atmospheric conditions (Gregory, 1961). The bioaerosol emission, evaporation, dispersion and survival functions were included in the model. The findings provided a 30 m accuracy from the source (Lighthart & Kim, 1989). Advection and advection-diffusion models were proposed to improve the accuracy of the mathematical analytical transport model (Aylor, 1986). However, these above models could not provide accurate results for indoor environments. A mass balance model was used to predict the deposition, resuspension and penetration of bioaerosol particles (i.e. 1 to 5 µm) for a residence (Thatcher & Layton, 1995). A multi-zone mass balance model was further developed to solve the indoor bioaerosol concentration level with the Wells-Riley quantum model for infection risk assessment (Noakes & Sleigh, 2008). However, a simple geometric structure and no spatial variation of a zone limit the applicability of the multi-zone mass balance model.
56
Since the SARS outbreak in 2003, CFD approach has been used and popularized to simulate the indoor bioaerosol particle transports and recognized the possible transmission routes in the SARS outbreak, especially in healthcare facilities and transportations, as discussed as in Section 1.1.3 (Chao & Wan, 2006; Lee et al., 2003; Li et al., 2005). A two-phase flow (i.e. continuum-dispersed) simulation resolves spatial and temporal solutions for airflow and bioaerosol particle dynamics to evaluate the infection risk assessment by the Wells-Riley quantum or dose-response models (Ishii & Hibiki, 2006). This framework is a hybrid approach utilizing a reference description (i.e. Eulerian or Lagrangian) for continuum airflow fields and another reference description (i.e. Eulerian or Lagrangian) for a bioaerosol particle (Subramaniam, 2013; Zhang & Chen, 2007). In the framework, the airflow field of the ventilated space was solved by continuum phase simulation in terms of air velocities, pressures and turbulences. Then bioaerosol particles were predicted in dispersed phase simulation since the droplet nuclei form of bioaerosol particles were supposed to be dried residue. Prediction movement of individual particles is important for understanding the spatial risks associated with an identified airborne pathogen.
For the continuum phase simulation, DNS and LES approaches provide very detailed simulation results in airflow and particle movements. However, both approaches require heavy computing resources and are very time-consuming. The RANS model is commonly used due to reasonable computational demand, especially in RNG k-ε turbulence model (Chen et al., 2013), 57
although other simulation approaches, such as the lattice Boltzmann method, DES model and population balance model, were proposed for predicting the bioaerosol particle movements (Chen et al., 2013; Fu et al., 2015).
The RANS model is popular for bioaerosol particle simulation in ventilated enclosures since the better performance of near-surface turbulence has been demonstrated for indoor surfaces (Lai & Nazaroff, 2000). Standard and RNG k-ε turbulence have been suggested for use in ventilated environments (Chen, 1995). Various air distributions systems for infection control were compared based on the results of CFD simulation such as displacement and personal ventilation systems (Tham & Pantelic, 2011; Wan & Chao, 2007). The CFD simulation was also used in ventilation system design for healthcare facilities and transportations (Tang et al., 2015; Wan et al., 2009). Various air distribution systems and building types could be predicted and recognize the ventilation system design problem for infection control and prevention by CFD simulation (Pantelic & Tham, 2011; Wan & Lin, 2015).
The interaction between occupants and room environment for the bioaerosol particle transport has been investigated by CFD simulation recently. Due to the dynamic airflow generated by humans or objects (i.e. door) movements, the potential bioaerosol emission or transient breakdown of infection control was reported (Han et al., 2014; Tang et al., 2005) The interaction between human and bioaerosol particles movement have been investigated by dynamic mesh technique to simulate the human walking and door opening in an 58
isolation room (Shih et al., 2007). Due to the heavy loading of the method, an interface-tracking technique was suggested by setting up some interfaces between the human and space (Brohus et al., 2006; Mazumdar & Chen, 2007). The results were more accurate than an interface-capturing method since the fixed mesh reduces the computation power (Tezduyar, 2006). By adding sources of momentum to some zones for human walking, a simple steady state model has been proposed to simulate the human movement in an isolation room (Brohus et al., 2006; Hathway & Papakonstantis, 2015). The results provide a relatively simple method to simulate the influence of movements for a transient condition in an acceptable approximate way. In addition, the re-suspension by human movements was also simulated (Kubota & Higuchi, 2012; Leung et al., 2013; You & Wan, 2014b). The effect of the interaction between occupants and indoor air on the bioaerosol particle transport is a key issue in the future development of CFD simulation.
2.5 CFD simulation for bioaerosol particle transport models For bioaerosol particle movement simulation, the drift-flux model (DFM) and discrete phase model (DPM) are two commonly used approaches in dispersed phase CFD simulation (Holmberg & Li, 1998). Since the size of the bioaerosol particles less than 100 µm, it was assumed to be airborne (Wells, 1934), the gas-gas two-phase CFD simulation was used to estimate bioaerosol particle dispersion by a DFM under a Eulerian-Eulerian framework (Hibiki & Ishii, 2003). However, the behaviors of bioaerosol particles in air differ from gas molecules for the same boundary condition. For example, bioaerosol 59
particles maintain higher momentum (i.e. velocity) along an airstream when compared with the rapid momentum decay of gas molecules (Wan, 2006).
In addition, molecular diffusion is an important transport mechanism for the gas, but the diffusion could be neglected for bioaerosol (Nicas et al., 2005; Pantelic et al., 2009a; Wan, 2006). DPM have been proposed to predict bioaerosol particle movement by the force balance of the interactions with the continuum phase. This approach is sometimes regarded as the EulerianLagrangian framework. Compared with the Eulerian-Eulerian approach, the Eulerian-Lagrangian framework provides a more natural description of the actual physical phenomena since each particle is considered individually. Stochastic fluctuations are complemented with statistical turbulent dispersions by discrete random walk (DRW), although the instantaneous turbulence quantities of the dispersed phase cannot be solved by the RANS model (Wan, 2006). In this section, both models are discussed to understand the fundamental mechanisms of bioaerosol particle transport model.
2.5.1 Drift-flux model (DFM) for bioaerosol transport The drift-flux model (DFM) is a mixed-flow model in which the focus is on the relative motion between the phases rather than the motion of the individual phase motion (Hibiki & Ishii, 2003; Zuber & Findlay, 1965). The dynamics between two phases, therefore, can be expressed by the mixture momentum equation. Four field equations (i.e. mixture continuity, mixture 60
momentum, mixture energy and gas continuity) are only involved in DFM instead of six equations in a two-fluid model (Hibiki & Ishii, 2003).The simplicity of the model makes it widely used in two-phase flow applications such as bubble, slug, droplet, annular and fluidized bed simulations (Ishii, 1977). Furthermore, the model provides an inter-species simulation for various bioaerosol particle sizes.
The void propagation function of the DFM avoided the non-continuum distribution of bioaerosol particles where low particle volume fraction was found in CFD simulation of the particle transport for an indoor environment (Holmberg & Li, 1998). Further, the application of DFM was used for SARS-CoV simulation for the nosocomial outbreak in Hong Kong (Li et al., 2005). Modelling of the dispersed phase can be solved by adding an equation of mixture momentum in Equation (2.18). 𝜕(𝜌𝑎𝑖𝑟 𝑓𝑏𝑝,𝑏𝑖𝑛 ) + ∇ ∙ (𝜌𝑎𝑖𝑟 (𝑣𝑏𝑝,𝑏𝑖𝑛 + 𝑣𝑠𝑒𝑡𝑡𝑙𝑒,𝑏𝑖𝑛 )𝑓𝑏𝑝,𝑏𝑖𝑛 ) 𝜕𝑡 𝜇𝑒𝑓𝑓 =∇∙( ∇𝑓 ) + 𝑆𝑟𝑐𝑏𝑖𝑛 𝜎𝑏𝑖𝑛 𝑏𝑖𝑛
[2.18]
Where fbp,bin is the volumetric fraction of bioaerosol particles (mL) over air (L) of the dispersed phase at the binth size bin, vbp,bin is the velocity of bioaerosol particles at the binth size bin, σbin is the dispersed phase diffusivity (m2 s-1) at the binth size bin, Srcbin is the source term at the binth size bin, µ eff is the effective viscosity of the air (kg m-1 s-1), vsettle,bin is the settling velocity at the binth size bin, which could be obtained by Equation (2.19): 61
0.5
𝑣𝑠𝑒𝑡𝑡𝑙𝑒,𝑏𝑖𝑛
4 𝑔𝑑𝑝 𝜌𝑝 − 𝜌𝑎𝑖𝑟 =[ ] 3 𝐶𝑑𝑟𝑎𝑔,𝑏𝑖𝑛 𝜌𝑎𝑖𝑟
[2.19]
Where dbp,bin is the diameter of the particle in the binth size bin (µm) and ρbp is the particle density in the binth size bin (kg m-3), Cdrag,bin is the drag coefficient in the binth size bin, ρair is the air density (kg m-3). The turbulent effect has been added to improve the equation for near surfaces diffusion for the indoor environment by modifying the dispersed phase diffusivity σbin (m2 s-1) (Shimada et al., 1996). Furthermore, the Stokes-Einstein diffusivity was included into the diffusivity σbin for improving the deposition of human airways simulation (Wang & Lai, 2005). The effects were combined into Equation (2.20) (Wan, 2006).
𝜎𝑏𝑖𝑛 =
𝐾𝐵𝑜𝑙𝑡𝑧𝑚𝑎𝑛𝑛 𝑇𝑎𝑖𝑟 𝑓𝑠𝑙𝑖𝑝 𝜇𝑡𝑢𝑟𝑏𝑢𝑙𝑒𝑛𝑡 + 𝜌𝑎𝑖𝑟 𝑆𝑐ℎ𝑡𝑢𝑟𝑏𝑢𝑙𝑒𝑛𝑡 3𝜋𝜇𝑎𝑖𝑟 𝑑𝑏𝑝
[2.20]
Where µ turbulent is the turbulent viscosity of the air (kg m-1 s-1), Schturbulent is the turbulent Schmidt number, KBoltzmann is the Boltzmann constant (i.e. 1.38×10-23 J K-1), Tair is the temperature (K), fslip is the Cunningham slip correction factor. However, the gaseous mixture of the DFM simulation could not reflect the behaviours of bioaerosol particles in the air, such as the higher velocity of particles (i.e. momentum) maintain better along the pathline of airflow than gas molecules. Furthermore, the diffusion mechanism is important for the gas, but diffusion for bioaerosol particles is rather negligible (Pantelic et al., 2009a; Wan, 2006). 62
2.5.2 Discrete phase model (DPM) for bioaerosol transport The discrete phase model (DPM) is a gas-solid two-phase transport model that simulates the continuum (i.e. air) phase in the Eulerian framework and the dispersed (i.e. bioaerosol particles) phase in the Lagrangian framework (Hutchinson et al., 1971). The trajectory of a bioaerosol particle is described by the balance of acting forces on the particle as point-volume descriptions for interphase transfer of momentum in Equation (2.21) (Loth, 2000). 𝐹𝑝 = 𝐹𝑑𝑟𝑎𝑔 + 𝐹𝑔𝑟𝑎𝑣 + 𝐹𝐹𝐾 + 𝐹𝐴𝑀 + 𝐹𝐵𝐻 + 𝐹𝑎𝑑𝑑
[2.21]
Where Fp is the force acted on a particle (N), Fdrag is the drag force (N), Fgrav is the gravity force, FFK is the Froude-Krylov force due to the fluid stress gradients arising from the continuous-phase acceleration (N), FAM is the added mass (N), FBH is the Basset history terms (N) and Fadd is the additional forces acting on the particle (N).
The Maxey and Riley equation was used to simplify the motion of a small spherical shaped particle in unsteady, non-uniform flow at the low Reynolds numbers (i.e. Rep
