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Modality of human expired aerosol size
distributions
G.R. Johnson1, L. Morawska1*, Z.D. Ristovski1, M. Hargreaves1, K. Mengersen1,
C.Y.H. Chao2, M.P.Wan2, Y. Li3, Xiaojian Xie3,6, D. Katoshevski4, S. Corbett5
1Queensland University of Technology, Brisbane, QLD, Australia
2Department of Mechanical Engineering, The Hong Kong University of Science and
Technology, Hong Kong SAR, China
3Department of Mechanical Engineering, The University of Hong Kong, Hong Kong
SAR, China
4Department of Biotechnology and Environmental Engineering, Ben-Gurion
University of the Negev, Beer-Sheva, Israel
5Centre for Public Health, Western Sydney Area Health Service, Sydney, NSW,
Australia
6Faculty of Power Engineering, Nanjing Normal University, No. 78 Bancang Road,
Nanjing, China
*Corresponding email: l.morawska@qut.edu.au
Abstract
An essential starting point when investigating the potential role of human expired
aerosols in the transmission of disease is to gain a comprehensive knowledge of the
expired aerosol generation process, including the aerosol size distribution, the various
droplet production mechanisms involved and the corresponding sites of production
within the respiratory tract. In order to approach this level of understanding we have
integrated the results of two different investigative techniques spanning 3 decades of
particle size from 700 nm to 1 mm, presenting a single composite size distribution,
and identifying the most prominent modes in that distribution. We link these modes to
specific sites of origin and mechanisms of production. The data for this were obtained
using the Aerodynamic Particle Sizer (APS) covering the range 0.7 ≤ d ≤ 20 µm and
Droplet Deposition Analysis (DDA) covering the range d ≥ 20 µm.
In the case of speech three distinct droplet size distribution modes were identified
with count median diameters at 1.3, 2.0 and 145 µm. In the case of voluntary
coughing the modes were located at 1.3, 1.4 and 123 µm. The modes are associated
with three distinct processes; one occurring deep in the lower respiratory tract, another
in the region of the larynx and a third in the upper respiratory tract including the oral
cavity. The first of these, the Bronchiolar Fluid Film Burst (BFFB or B) mode
contains droplets produced during normal breathing. The second, the Laryngeal (L)
mode is most active during voicing and coughing. The third, the Oral cavity (O) mode
is active during speech and coughing. The number of droplets and the volume of
aerosol material associated with each mode of aerosol production during speech and
coughing is presented. The size distribution is modeled as a tri-modal lognormal
distribution dubbed the Bronchiolar/Laryngeal/Oral tri-modal (B.L.O.) model.
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Keywords: human expired aerosol, B.L.O. tri-modal model, size distribution,
modality, bronchiolar, laryngeal
Introduction
The need to obtain a comprehensive understanding of human expired aerosols across
the entire range of droplet sizes has become an increasingly urgent issue over the past
decade. Much of the focus in infection control in the past has been on maintaining a
safe distance from infected subjects. This was based on an assumption that infection
would require exposure to droplet transmission in which pathogen laden respiratory
droplets are deposited directly on mucosal surfaces of the respiratory tract. Although a
maximum distance for droplet transmission cannot readily be defined, a safe distance
of 1m was often assumed, based on simulations with specific organisms and
epidemiological studies (Dick et al., 1987; Feigin et al., 1982). But even droplets as
large as 30μm can remain suspended in the air for extended periods (Cole & Cook,
1998), and airborne transmission has been unambiguously documented for Varicella
(Leclair et al., 1980; Sawyer et al., 1994) and Measles (Chen et al., 1989; Ehresmann
et al., 1995).
There is also mounting evidence that the 1 m rule should be questioned for a range of
other diseases. Wong et al.(Wong et al., 2004) found that proximity to an infected
patient was associated with SARS transmission, with transmission appearing to occur
over distances up to and well beyond 1 m so that transmission through small aerosols
could not be ruled out. Airborne transmission can result from the dissemination of
airborne droplets within the respirable size range (D50=4 µm) containing respiratory
pathogens that remain viable and potentially infectious over time and distance (Siegel
et al., 2007). Such droplets can be dispersed by air currents and may infect susceptible
individuals who have had no direct contact with an infected person. Fabien et
al(Fabian et al., 2008) detected viral RNA in aerosols emitted from subjects infected
with influenza A and B during tidal breathing suggesting that the fine particles
emitted during tidal breathing may be an infection risk. Fenelly et al (Fennelly et al.,
2004) identified Mycobacterium tuberculosis colonies on plates collecting respiratory
aerosol droplets from TB subjects in the droplet size range 0.65-0.1 µm implying that
breath aerosol could be capable of transporting this organism in viable form from
infected subjects. Atkinson and Wein(Atkinson & Wein, 2008) stated that “the rarity
of close, unprotected and horizontally-directed sneezes—coupled with the evidence of
significant aerosol and contact transmission for rhinovirus and our comparison of
hazard rates for rhinovirus and influenza” lead them to suspect that aerosol
transmission is the dominant mode of transmission for influenza”.
With this increasing emphasis on the question of airborne transmission, the need to
understand the mechanisms of aerosol generation as well as the sites of origin within
the respiratory tract and the proximity of those sites to regions of active infection is
very evident. Previous studies which have looked at this question arrived at a variety
of conclusions. Nicas et al (Nicas et al., 2005) reviewed and compared the results of
the particle size studies for coughing and sneezing by Duguid (Duguid, 1946),
Loudon and Roberts (Robert G. Loudon & Rena Marie Roberts, 1967) and Papineni
and Rosethal (Papineni & Rosenthal, 1997) and found substantial differences. It
appears that the results of Papineni and Rosenthal suffered from insufficient
measurement size range leading to an underestimate of the numbers of larger droplets.
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The work of Duguid applied a potentially incorrect evaporation correction and was
not reported in enough detail for an appropriate adjustment to be retrospectively
applied to the reported data. Duguid combined the data produced by different
techniques without explaining or justifying the approach used to do so.
A further difficulty common to virtually all reports of expired droplet size
distributions is the lack of a consistent rigorous approach to size distribution data
presentation. Instead the data may be presented in tabulated form using arbitrary size
classifications which do not facilitate rigorous analysis and comparison. The need for
a comprehensive understanding of human expired aerosol size distributions requires
the adoption of a more rigorous approach to data collection and reporting. Such
standards have already been established over many years by the aerosol research
community.
In an effort to address each of the issues discussed above, investigations of expired
droplets were conducted using the Expired Droplet Investigation System (EDIS)
(Morawska et al., 2009), applying two separate measurement techniques to cover the
entire size range from 0.5 µm -1 mm; the Aerodynamic Particle Sizer (APS, 0.5 ≤ d ≤
20 µm and Droplet Deposition Analysis (DDA, 20 ≤ d ≤ 2000 µm).
Results obtained using the above methods are being published by the authors in
separate manuscripts (Johnson & Morawska, 2009; Li et al., Submitted; Morawska, et
al., 2009), however the relationship between the measurements has not been examined
in detail and no attempt has yet been made to combine the measurements to form a
coherent view of the overall expired aerosol size distribution. The current paper
integrates the APS and DDA based measurement results for speech and coughing
aerosols to produce comprehensive size distributions for both types of expired
aerosol. The modality of the size distributions is examined and its significance is
discussed in terms of human expired aerosol research, epidemiological modeling,
infection control and breath condensate analysis research.
1 Methods
Aerosol size distribution measurements were conducted using an Aerodynamic
Particle Sizer APS and Droplet Deposition Analysis (DDA) in the EDIS. The EDIS is
described in detail in a previous publication by the authors (Morawska, et al., 2009),
however the schematic diagram of the system from that publication is reproduced in
Figure 1. It is a small wind tunnel 0.5 m in diameter, into which a subject can
comfortably insert their head. HEPA filtered air is propelled past the subject at a very
low, controlled velocity. This particle free air carries the aerosol droplets emitted by
the subject to instrument sampling inlets positioned at a set distance downwind. The
EDIS operates at slightly higher than ambient pressure, ensuring that no ambient
aerosol contaminates the sample.
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APS
transparent
modules
flow straightener
flexible ducting
air speed
sensor
HEPA filter
speed controlled fan
butterfly
valve
RH
flow direction
inflow
for overpressure
The APS measures the aerodynamic diameter of particles in the diameter range 0.5-
20µm, and detects particles as small as 0.3 µm. The total inlet flow rate drawn by the
instrument is 5 L.min-1 which includes a 4 L.min-1 sheath flow and a 1 L.min-1sample
flow. The detection and sizing process in the APS takes less than 5 µs, however the
time required for delivering the particles to the detection area is limited by the air
velocity in, and length of the sample probe and delivery tube. The sample probe used
with the APS in the EDIS consists of a 0.28 m length of copper tubing with an
internal diameter of 0.0163 m. The probe tube enters the EDIS perpendicular to the
direction of airflow, and is curved at its end through an angle of 90° (radius of
curvature 0.08 m), so that the probes mouth faces upwind toward the volunteer. The
delay between the sample entering the probe mouth and particle detection and sizing
is around 0.7 s which was sufficient for most droplets in the instrument size range to
dry to their equilibrium size before measurement (Morawska, et al., 2009).
The DDA measurements involved conducting stain size and droplet distribution
measurements using discrete sampling points occupied by glass slides. The DDA
measurements were conducted in an isolated section of the EDIS ducting without the
use of the EDIS airflow system. The slides were laid out in a sampling grid
encompassing the lower inner surface of a section of the sampling duct. The droplet
stains remaining on the glass slides after droplets settled there, were measured and
classified according to size and the number of droplets of each size per unit slide
surface area was calculated at each slide location.
The resulting droplet-deposition-density data points were interpolated radially and
longitudinally over the interior cylindrical duct surface within the sampling grid. The
resulting continuous droplet deposition field was then integrated over the grid area to
obtain the total droplet concentration for each size class. The droplet number size
distribution values were divided by the log of the droplet size class interval to obtain
the number size distribution as dN/dLogD. This was then divided by the total volume
of air exhaled to obtain the number concentration size distribution dCn/dLogD. The
total volume of air exhaled was estimated using the sampling duration and the average
Figure
1: Schematic diagram of the Expired Droplet Investigation System (EDIS).
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adult tidal volume ventilation rate (Sidebotham et al., 2007) (“minute ventilation”) of
7.5 Lpm.
The experimental protocols used for the APS (Johnson & Morawska, 2009;
Morawska, et al., 2009) and DDA (Li, et al., Submitted) measurements are described
in detail in the relevant publications however an important difference in protocol
should be noted. During the course of the campaign, slightly different respiratory
maneuver protocols were adopted for the DDA measurements and the APS
measurements. This difference was necessary because the DDA measurements focus
on a region of the size distribution where although droplet mass is large, the numbers
of droplets may be extremely small, necessitating long sampling times in order to
acquire a statistically significant number of droplets in each size class. In contrast to
the situation for DDA, droplets in the APS range are relatively plentiful. For cough
emission sampling using DDA, the volunteers were asked to cough 50 times in each
test. This large number of coughs necessitated that the volunteer be permitted to drink
water whenever they wished during the test to prevent drying out of the upper
respiratory tract and to maintain comfort. This is thought to have had little effect for
the larger droplet sizes targeted by DDA because large droplets exhibit much slower
evaporative diameter shrinkage. However dilution of the natural respiratory tract
lining fluid by water will certainly reduce the potential size of the droplet nuclei
measured by the APS, so no such fluid intake could be permitted during the APS
measurements. The larger droplet number concentrations in the APS droplet size
range readily accommodated a reduced sampling time, so to maintain volunteer
comfort and a productive cough, the test duration for coughing was reduced to 30
seconds in the APS measurements. The volunteers were asked to cough naturally by
their own estimation, and as many times as they could without significant discomfort,
within the 30 second period.
All volunteers were recruited via a broadcast email invitation with a small financial
reward. The volunteers were university students and postgraduate research students,
all of whom were under 35 years of age. People who were experiencing illness, who
had recently experienced respiratory problems, or who felt they were likely to
experience discomfort in confined spaces were excluded. The pool of volunteers
consisted of fifteen individuals. The APS measurements included all fifteen
volunteers (nine females and six males). The DDA group included eight volunteers;
(six females and two males). This variation in the size and makeup of the groups
tested is not ideal but the combination of these two data sets was considered suitable
for the purposes of exploring the modality of the size distribution and for deriving a
basic model of the size distribution and generation process.
1.1 Combining the size distributions
Composite size distributions were produced by combining the APS and DDA droplet
number size distribution data sets after transformation onto a common scaling
dCn/dLogD. Here Cn denotes the concentration expressed in cm-3 and D is the
particle diameter expressed in µm.
In constructing the size distribution segments for the two different measurement
techniques, average particle detection frequencies for each diameter class were
calculated using all available measurements across all volunteers. The number count
data for individual measurements was typically very low, so that a zero particle count
was frequently recorded in many of the larger size classes. Therefore in order to
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obtain a more nearly normal probability distribution, a square root transformation was
applied to the data prior to calculating means and determining confidence intervals.
Hence, except where otherwise stated, all count data manipulations including
averaging and calculation of 95% confidence intervals have been performed using
square root transformed data. All results are presented on the original scale through
the subsequent application of an inverse transformation, (squaring the result).
The resulting size distributions are considered to be representative for this group of
healthy volunteers. They are not intended to be predictive of emissions for a single
healthy volunteer because inter-volunteer and within-volunteer variability is very
large, typically of the order of measured concentration itself or greater.
1.2 Overview of corrections to the measurements
In order to correctly represent the size distribution at the point of origin (the
volunteer’s mouth) the size distribution data obtained with both measurement
techniques require corrections. These corrections are described below.
1.2.1 APS data corrections
Due to their small size and the time delay between emission and measurment, droplets
measured by the APS evaporate to equilibrium before sizing (Morawska, et al., 2009).
To estimate the initial size of the aerosol at the mouth, the aerosol detected by the
APS was assumed to have evaporated to an equilibrium diameter of Deq = EF x D0
where D0 is the initial droplet size, and EF is the diameter evaporative shrinkage
factor. A value of 0.61 has been estimated for EF (Nicas, et al., 2005).
The APS data also requires correction of the aerosol number concentration in order to
account for sample dilution by entrained air. Average APS sample dilution factors
(DF) relating the concentration in the sample to that at the source (which was taken to
be the volunteers’ upper respiratory tract), were calculated for speech and coughing.
These were based on continuous measurements of the water vapor concentration in
the aerosol sample and in the EDIS airflow, taking into account the fixed water vapor
concentration in the respiratory tract according to the method described by Morawska
et al. (Morawska, et al., 2009).
1.2.2 DDA data corrections
In the case of the DDA measurements, the aerosol size distribution was determined
from stains left after the droplets settled onto the glass slides. The settling times
depend strongly on the initial droplet size. The largest droplets have the greatest
settling velocity but also undergo the slowest rates of relative diameter change due to
evaporation. The settling time for droplets with diameters of 20 µm or smaller
exceeds the time taken to dry to the equilibrium diameter and this equilibration time
decreases rapidly with droplet size. Droplets smaller than 20 µm therefore remain
airborne long enough to be dispersed by ambient air currents such that large numbers
leave the deposition sampling area before settling. Therefore 20 µm was considered to
be the lower limit for DDA sampling.
The process of droplet spreading results in stains which are larger in diameter than the
airborne droplets which produce them. When aqueous solution droplets settle onto a
surface, they spread to an extent which depends on the impaction velocity, the surface
tension of the droplet liquid and the hydrophilic/phobic properties of the surface onto
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which they settle. This spreading can be represented by a spread factor (β) defined as
the ratio of the resulting stain diameter to that of the original droplet during flight.
Liu et al.(B. Y. H. Liu et al., 1982) conducted an investigation of the spreading of di-
octyl phthalate (DOP) and oleic acid aerosol droplets in the 2-50 µm size range on
surfactant coated and uncoated glass slides and found that the spreading was strongly
dependant on droplet composition and the composition of the deposition surface, but
did not depend strongly on droplet size at smaller sizes where gravitational influence
on spreading is negligible. Liu et al. did not examine the behavior of aerosols with
composition similar to that of respiratory fluid however. According to measurements
conducted by Duguid (Duguid, 1946), 1-3 mm droplets of saliva falling onto a glass
slide, exhibit a spread factor of 2. Most droplets detected by DDA were considerably
smaller than 1 mm and spread factors are also known to depend on droplet diameter.
For example water sensitive paper supplied by Quantifoil Instruments (Quantifoil-
Instruments, www.qinstruments.com) yields a spread factor of 2.1 for larger water
droplets but this decreases with the droplet stain diameter (Ds) according to β
=0.24ln(Ds)+0.56 and the spread factor is 1.7 for 59 µm droplets. Water sensitive
papers produced by Ciba-Geigy are said to give spread factors of 1.9 and 1.5 for the
same respective droplet diameters (Chapple et al., 2007). Based on Duguid’s
measured spread factor of 2 for larger droplets and the trend toward smaller spread
factors for smaller droplets seen for water sensitive paper, respiratory tract lining fluid
and saliva droplets settling on glass as examined in the current study, should exhibit
spread factors in the range 1-2.
1.3 Calculation of volume size distributions
The volume and mass size distributions can be calculated from the number size
distribution provided the geometry and density of the particles is known. For the cases
considered here it is assumed that the particles are spherical and have unit density.
The first of these assumptions is reasonable for respiratory aerosol particles of all
sizes, whether measured as dry residue or liquid droplets, because each begins as a
fluid droplet in which the geometry is determined by surface tension forces. The
second assumption is also a good approximation because the composition of the dry
residue particles and of the larger droplets is dominated by water and/or organic
solutes of similar density, with only minor contributions from higher density
components such as inorganic salts.
2 Results and Discussion
The dependence of the expired aerosol size distribution within the APS range on the
type of expiratory maneuver is illustrated in Figure 2. We have restricted the size
distribution to the APS range in order to focus on important aspects of the modality in
that range. The APS measurement method is less labor intensive than the DDA
approach and this facilitated the examination of a wider range of activities to highlight
somewhat subtle but important effects of vocalization and coughing on the size
distribution modality. As will be discussed later, these effects are important because
of their implications concerning the source regions involved.
The figure shows the mean measured size distributions in the APS size range for a)
Breathing, b) Speech, c) Sustained vocalization and d) Coughing. These respiratory
maneuvers are defined in Table 1. Note that many young volunteers do not produce
significant breath aerosol during tidal breathing, so for the purpose of illustration here,
8
the breathing maneuver was purposely designed to enhance breath aerosol production
by including deep exhalation breathing. It is also important to note that the data have
not yet been corrected for dilution which affects the overall concentration, or for
evaporation which affects the droplet size.
In order to indicate the level of the background, each graph includes the size
distribution obtained for the bypass maneuver. This size distribution was obtained
with the volunteers’ heads positioned to one side of the sample inlet so that no aerosol
from the subjects’ mouths could directly enter the inlet.
Also shown are the upper and lower 95% confidence intervals for the size distribution
and a smoothed representation obtained by performing a 5 point adjacent average
smoothing. No confidence interval is shown for the bypass maneuvers because in
those tests, few channels returned a non-zero count, and those that did, produced very
low counts.The graphs also include a number of fitted lognormal curves which will be
discussed in detail in the subsequent section on modality.
Table 1: Respiratory maneuvers
Maneuver
Label
Description
(a) Breathing
b-3-3
Inhaling a normal breath volume via the mouth over
a 3 s period, followed immediately by a 3 second
full, deep exhalation via the mouth over a 3 s period.
Repeated for 2 minutes.
(b) Speech
c-v
Alternately 10s of voiced counting and 10s of
naturally paced breathing (2 min sample).
(a) Sustained
vocalisation
aah-v
Alternately 10s of un-modulated vocalization
(voiced “aah”) and 10s of naturally paced breathing.
(2 min sample). Mouth open throughout.
(b) Coughing
cough
Coughing at an intensity and frequency which the
volunteer felt comfortable with. In practice, for most
volunteers, the resulting cough intensity can be best
described as a mild throat clearing cough. (30s
sample).
(c) Bypass
bp
The volunteer positioned their head to one side and
slightly forward of the sample probe so that expired
air was not directly sampled.
Figure 3 again presents the size distributions for the speech and cough aerosols, but
this time the range has been extended to include the data obtained using the DDA
method. The figure includes four graphs, a-d; where a and b respectively show the
size distribution for speech before and after applying a series of corrections to the
data; while c and d show the same for cough. The corrections account for dilution and
evaporation in the APS data and droplet spreading in the DDA data. These will be
discussed later.
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Figure 2: Uncorrected size distributions for breathing (b-3-3), speaking (c-v), sustained
vocalization (aah-v) and voluntary cough (cough). R2 values are for the multimodal lognormal fit
to the smoothed APS data.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.1 110 100
Diamet er ( µm)
d. voluntary coughing (cough)
R
2
=0.9992
0
0.1
0.2
APS No Corr ections
Lower Conf 95%
Upper Conf 9 5%
Smoothed
B mod e
L mo de
Model (experimental range)
bpp (background)
a. breathing (b-3-3)
R
2
=0.9991
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
c. sustained vocalisation (aah-v)
R
2
=0.9995
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
b. speaking (c-v)
R
2
=0.9992
dCn dLog(D) (cm
-3
)
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Figure 3: Composite size distribution and the fitted BLO model for speaking and voluntary
coughing before and after applying the corrections in Table 3. R2 values are for the fitting of the
lognormal O mode to the smoothed APS data.
1E-05
0.0001
0.001
0.01
0.1
1
0.1 110 100 1000 10000
Diamet er ( µm)
d. voluntary coughing (cough), corrected
1E-05
0.0001
0.001
0.01
0.1
1
APS No Corr ections
Lower Conf 95%
Upper Conf 9 5%
Smoothed
B mod e
L mo de
DDA No Corrections
Lower Conf 95%
Upper Conf 9 5%
Smoothed
O mo de
BLO Model (experimental range)
a. sp eakin g ( c-v), uncorrected
R
2
=0.9992, O-mode f it to smoothe d DDA data
1E-05
0.0001
0.001
0.01
0.1
1
b. speaking (c-v), corre cted
1E-05
0.0001
0.001
0.01
0.1
1
c. voluntary coughing (cough), uncorre cte d
R
2
=0.9995, O-mode f it to smoothe d DDA data
dCn dLog(D) (cm
-3
)
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2.1 Modality of the Composite Size Distributions and it’s Physical Significance
2.1.1 Modality in the APS range – The B and L modes
The aerosol number size distribution shown in Figure 2a is an example of a breath or
breathing aerosol. Breath aerosols have been investigated in detail by co-authors
Johnson and Morawska and shown to be dominated by a single mode in the APS size
range as can also be seen in Figure 2a. This aerosol is produced in the respiratory
bronchioles in the early stages of inhalation. The resulting aerosol is drawn into the
alveoli and held before exhalation. This mechanism was dubbed the bronchiolar fluid
film burst (BFFB) mechanism (Johnson & Morawska, 2009) and the corresponding
size distribution mode will be referred to as the BFFB mode or simply the B mode.
These findings concerning the mechanism and modality have been subsequently
confirmed by others (Almstrand et al., 2010), although there is some disagreement on
the count median diameter (CMD) of the B mode.
The intensity of the B mode increases strongly with the depth of exhalation because
deeper exhalation results in the closure of greater numbers of respiratory bronchioles.
As discussed in the aforementioned publications, it is the opening of these fluid
closures on the subsequent inhalation phase of breathing that produces the B mode
aerosol. Furthermore, because B mode particles are generated during the inhalation
phase of breathing the CMD of the mode displays an inverse relationship to the
duration of breath holding, because particles are lost from the large diameter side of
the mode through gravitational settling in the alveoli while the aerosol remains in the
alveoli. Hence the exhaled concentration in the B mode typically increases by a factor
of 12 for healthy volunteers when the breathing pattern is changed from tidal
breathing to deep exhalation breathing. When the breath holding period is increased to
10 seconds the CMD of the B mode decreases by 20-30%. A large variation is
therefore to be expected in the B mode concentration and CMD in different
respiratory maneuvers. The shift to smaller diameters also has the effect of reducing
the apparent GSD of the mode when measured by the APS, because the detection
efficiency of the APS begins to decline below 0.9 µm which is approaching the lower
limit of the instrument range (Armendariz & Leith, 2002).
We have represented the B mode aerosol by a single lognormal mode. The mode
represented by the dashed curve, was fitted to the smoothed b-3-3 breathing aerosol
size distribution in Figure 2a. The fitting algorithm was allowed to converge freely
without fixing the count median diameter (CMD), geometric standard deviation
(GSD) or concentration (Cn) associated with the mode and the single lognormal mode
fit achieved an R2 value of 0.9991 with respect to the smoothed curve. The portion of
the mode lying within the measurement range is indicated by the continuous dark line.
The aerosol size distribution for speaking shown in Figure 2b, has additional modal
structure beyond the B mode due to the vocalization process. We have represented
this by another lognormal mode. To generate the overall bimodal lognormal fitting the
fitting algorithm was allowed to converge freely to the smoothed APS data without
fixing the CMD, GSD or the Cn values of either of the two modes. The resulting
bimodal lognormal mode fit achieved an R2 value of 0.9992 with respect to the
smoothed APS data. The portion of the bimodal fit lying within the measurement
range is indicated by the continuous dark line.
12
The source of the extra lognormal mode was examined further by simplifying the
vocalization to remove any effect due to the mouth movements associated with speech
articulation, while emphasizing the vibrations of the vocal folds in the larynx. The
maneuver chosen for this was a repeating, monotone, sustained, vocalization without
any mouth closures. This is denoted as aah-v in Table 1. The size distribution for aah-
v is shown in Figure 2c. The additional mode is clearly much more pronounced in this
case clearly linking the appearance of the mode to the vocal fold vibrations associated
with voicing. As in the previous case we have represented the vocalization aerosol by
an additional lognormal mode which we have called the laryngeal or L-mode. We
have avoided calling this a voice mode because as will be seen a second mode in the
APS range is also produced during coughing, a process which also involves energetic
activity at the larynx and this mode has a similar GSD to the L mode in vocalized
maneuvers, although the CMD is smaller.
Once again, to generate the overall bimodal lognormal fitting to the aah-v data, the
fitting algorithm was allowed to converge freely with the smoothed APS data without
fixing the CMD, GSD or the Cn values of either of the two modes. The resulting
bimodal lognormal mode fit in this case achieved an R2 value of 0.9995 with respect
to the smoothed APS data. The portion of the bimodal fit lying within the
measurement range is again indicated by the continuous dark line.
The size distribution of the voluntary-cough maneuver shown in Figure 2d again
shows broadening which we attribute to an L-mode but at reduced CMD. The same
free fitting procedure was again used, in this case resulting in an R2 value of 0.9992.
2.1.2 Modality in the DDA range – The O mode
Inclusion of the uncorrected DDA data in Figure 3a and Figure 3c, shows that the size
distribution for speech and coughing in the DDA size range, is well represented by a
third lognormal mode. The single lognormal mode fitted to the smoothed version of
the DDA data, is represented by the dot-dash curve. The fitting algorithm was again
allowed to converge freely without fixing the CMD, GSD or Cn associated with the
mode and the single lognormal mode fit achieved an R2 value of 0.9992 with respect
to the smoothed data.
The third mode contains all aerosol detected in the DDA range. In a separate
experiment, droplets of this aerosol collected on glass slides and examined using a
microscope, always showed evidence of the food dye introduced to the test
volunteers’ saliva in an oral rinse. Hence it is clear that these larger droplets were
produced exclusively in the region of the respiratory tract where saliva is present (Li,
et al., Submitted) and hence between the lips and the epiglottis and is therefore
referred to as the Oral or O Mode.
2.1.3 The B.L.O. model for speaking and coughing in HVs
The BLO tri-modal models of the aerosol concentration size distributions for speaking
and coughing are summarized by Equation 1 in conjunction with Table 2 and the
correction factors listed in Table 3.
The DF values for speech and coughing, determined by the method discussed earlier,
are listed in Table 3. An evaporative diameter shrinkage factor (EF) for the APS
samples is also included in the table. This is based on the publication by Nicas et al. as
described earlier. A diameter spread factor (SF) value of 1.5 was chosen to recover
13
the original droplet sizes from the DDA stain diameters. This value was chosen to fall
midway within the expected range discussed in section 1.2.2. The fully corrected
measurements and the corresponding BLO models are presented in Figure 3b and
Figure 3d.
Naturally, given that only healthy adult volunteers were tested in these studies, the
size distribution of the emitted aerosol and the sites of origin and mechanisms
described cannot be assumed to hold for those suffering from respiratory disease.
Equation 1: BLO tri-modal model.
dCn
dLogD =ln(10)× Cn
2 ln(GSD)exp (ln Dln CMD)
2(ln GSD)
, 0.8mD1000m
Table 2: Model parameters for aerosols produced by healthy volunteers during speaking and
coughing. DF = APS sample dilution factor. EF = APS sample evaporative diameter shrinkage
factor. SF = DDA droplet spread factor.
i =
1
2
3
(B mode)
(L mode)
(O mode)
Mean
SE
Mean
SE
Mean
SE
Speaking
Cni (cm
-3
)
0.015
x
DF
16%
0.019
x
DF
15%
0.00126
0.8%
CMDi (µm)
0.807
/
EF
0.45%
1.2
/
EF
8.1%
217
/
SF
0.5%
GSDi
1.30
1.3%
1.66
3.1%
1.795
0.5%
Coughing
Cni (cm
-3
)
0.021
x
DF
9%
0.033
x
DF
8%
0.01596
0.6%
CMDi (µm)
0.784
/
EF
0.61%
0.8
/
EF
2.9%
185
/
SF
0.4%
GSDi
1.25
0.8%
1.68
1.5%
1.837
0.4%
Table 3: Parameter correction factors: DF = APS sample dilution. EF = APS sample evaporative
diameter shrinkage. SF = DDA droplet diameter spreading on slide surface.
Correction
Speaking
Coughing
DF (APS)
3.6
4.3
EF (APS)
0.61
0.61
SF (DDI)
1.5
1.5
The three modes discussed above are also reflected in the volume size distributions
and these can be readily calculated using the BLO model. Figure 4 shows the
cumulative number and volume concentration size distributions for speaking and
voluntary coughing. These can be used to estimate concentrations within any sub-
range of the distributions.
14
Figure 4: Cumulative number and volume concentration size distributions for speaking and
coughing according to the BLO model.
The total numbers and volume or mass of particles within the individual modes can be
resolved by integrating the B, L, and O modes individually. The number and mass
concentrations, for the three modes, corrected according to Table 3, and determined
from the area under the number and volume size distribution modes are summarised
in Table 4. The droplets have been assumed to be spherical and to have a density of 1
g.cm-3 for the reasons discussed earlier.
In determining the volume of droplet material associated with each mode, the likely
existence of larger droplets outside the measurement range should be considered. The
existence of such droplets can be inferred by extrapolation of the fitted mode beyond
the measured range. Nevertheless droplet production at sizes exceeding 1 mm is likely
to be a rare event and there are physical limitations on the amount of fluid which can
be expelled from the mouth in individual drops. Physically the lognormal mode must
be truncated at a limit not much larger than a few millimetres because although larger
drops of fluid or catarrh can be expelled from the throat, the process of producing
these large globules differs somewhat from a normal cough. Nevertheless the
experimental range and the extrapolated values are both included in the table for
comparison.
0
0.05
0.1
0.15
0.2
0.25
Numbe r Cummulative
Concentration (cm
-3
)
Speaking BLO Model (Cumulative)
Coughing BLO Model (Cumulative)
0.0001
0.001
0.01
0.1
1
10
100
1000
10000
100000
1000000
0.1 110 100 1000
Volum e Cum mulative
Concentration (µm
3
.cm
-3
)
Diamet er ( µm)
15
Table 4: Number and mass concentrations associated with the three modes. The first value in
each cell is the concentration within the measurement range. The second value in italics is the
concentration obtained if the mode is extrapolated beyond the measured range in both directions.
The corrections in Table 3 have been applied to the parameters in Table 2 to produce these
values.
i =
1
2
3
SUM
(B mode)
(L mode)
(O mode)
(B+L+O)
Speaking
Cni (cm
-3
)
0.067
/
0.069
0.083
/
0.086
0.001
/
0.001
0.15
/
0.16
CMDi (µm)
1.3
2.0
145
N/A
Cmi (µg.m
-3
)
0.11
/
0.11
1.2
/
1.2
7500
/
9300
7500
/
9300
MMDi (µm)
1.6
4.4
404
N/A
Coughing
Cni (cm
-3
)
0.085
/
0.087
0.11
/
0.13
0.016
/
0.016
0.21
/
0.24
CMDi (µm)
1.3
1.4
123
N/A
Cmi (µg.m
-3
)
0.12
/
0.12
0.60
/
0.60
69000
/
83000
69000
/
83000
MMDi (µm)
1.5
3.1
374
N/A
*Diameters assume spherical droplets with the density of water.
2.2 Comparison with other published data
Figure 5a shows the number size distributions obtained in the current study for
speaking compared with those based on the results of studies by Duguid (Duguid,
1946), Loudon and Roberts (Robert G. Loudon & Rena Marie Roberts, 1967) and
Papineni and Rosenthal (Papineni & Rosenthal, 1997). Also shown are recent results
obtained for tidal breathing in a study by Amstrand et al. (Almstrand, et al., 2010)
using an optical particle counter. That study confirmed our earlier finding that the
BFFB (Johnson & Morawska, 2009) mechanism is responsible for aerosol formed
during breathing.
Papineni and Rosenthal reported detailed concentration data for speech for only one
volunteer (in graphical form) and this volunteer was the lowest emitter for speech
(though highest for cough) of 5 volunteers tested, emitting less than one quarter of the
concentration reported for the other volunteers.
Note that the data of Papineni and Rosetnhal, and those of Almstrand are not
corrected for evaporation and such a correction would shift them to larger values. Ote
also that both Amstrand et al. and Papineni and Rosenthal obtained their results using
optical particle counters (OPCs). The accuracy of this method depends strongly on the
optical properties of the aerosol droplets and these instruments are typically calibrated
using standard polystyrene latex spheres which have different optical properties and
structures to respiratory aerosol where the particles may be multiphase mixture of
different species. It has previously been shown that the sizing accuracy of the OPC
technique can be in error by up to a factor of 2 if the device is not calibrated for the
specific aerosol being measured (Y. Liu & Daum, 2000; Pinnick et al., 2000).
Duguid reported size distribution data (presumably for a single volunteer) as average
numbers of droplets registered in each size class when speaking 100 words by
counting loudly. For the purposes of the current comparison a conversion of Duguid’s
16
particle count data to concentration was achieved by assuming that counting occurred
at a rate of 2 words per second and the total volume of air exhaled was then estimated
as 625 mL based on an average adult tidal volume ventilation rate (Sidebotham, et al.,
2007) of 7.5 Lpm.
Louden and Roberts reported the overall total number of droplets in size classes for a
total of three volunteers during talking where each volunteer counted loudly from 1 to
100 twice. For the purposes of the current comparison a conversion of Louden and
Roberts droplet count data to concentration was performed by again assuming that
speech occurred at a rate of two words per second so that counting occurred at a rate
of approximately one number per second (eg: the number “twenty seven” is counted
as two words). The total time was therefore assumed to be 600 seconds and the total
volume of air exhaled was then estimated as 75 L based on an average adult tidal
volume ventilation rate (Sidebotham, et al., 2007) of 7.5 Lpm.
As was also explained by Nicas, Duguid greatly overestimated the adjustment
required to allow for evaporation of the smaller droplets which are most affected by
evaporation in his measurements but Duguid didn't say which of his data points had
been so adjusted. A correction factor of 4 was used by Duguid but a more realistic
factor would be 2 according to Nicas so the mode near 10 µm in Duguid’s result
might be shifted considerably toward smaller diameters independently of the larger
droplet component of the size distribution although the exact diameter below which
this should occur cannot be discerned from the information provided in Duguid’s
manuscript.
Figure 5b shows the number size distributions obtained in the current study for
coughing compared with those based on the results of other studies. The data of
Duguid, of Louden and Roberts and of Papineni and Rosenthal have been scaled using
the average cough exhalation volume of 1400 mL reported by Zhu et al. (Zhu et al.,
2006). As was pointed out by Nicas et al. (Nicas, et al., 2005), Duguids (Duguid,
1946) results for coughing are an order of magnitude higher than several other studies
including that by Louden and Roberts. Again, the mode at 11 µm in Duguid’s size
distribution should be shifted to much smaller diameters to correct for Duguid’s
overcompensation for evaporation making the size distribution more obviously
bimodal and bringing it more into line with that obtained in the current study and
those of Louden and Roberts (Robert G. Loudon & Rena Marie Roberts, 1967; R. G.
Loudon & R. M. Roberts, 1967) and Papineni and Rosenthal (Papineni & Rosenthal,
1997).
Again, Papineni and Rosenthal used an OPC to obtain their data and their results are
therefore subject to a very significant sizing inaccuracy but the concentrations and
approximate form of the size distribution is expected to be representative allowing for
this unknown shift in particle size. There results are also uncorrected for evaporation
and such a correction would further increase the diameters.
17
Figure 5: Comparison of size distributions derived from the results of the current study (as
represented by the BLO model) with those based on the results of studies published by Duguid,
Louden and Roberts, Papineni and Rosenthal as well as tidal breathing published by Amstrand
et al.
Implications and conclusions
The modality of the aerosols and the association of the modes with specific source
regions, together show that the collection of aerosol samples for the purpose of
assessing the concentration of substances originating from specific regions of the
respiratory tract can be designed to target specific source regions. This could be
achieved by using size specific collection methods and by choosing respiratory
maneuvers designed to emphasize specific modes. Such measurements might assess
viral loadings for the purpose of investigating or modeling modes of infection
transmission such as droplet spray and airborne droplet transmission. Measurements
might also be performed to more effectively assess the presence and concentration of
materials produced through gene expression associated with pathological changes in
the lung.
Epidemiological modeling studies utilizing detailed size distribution data such as that
presented here can be designed to make full use of the entire human expired aerosol
volume size distribution and its associated modality for the cough and speech activity
aerosols presented here. If specific viral loadings for each droplet size distribution
mode (ie B, L and O) can be determined, these too can be included in such modeling
in combination with existing models of particle deposition efficiency versus particle
size in the respiratory tract.
0.00001
0.0001
0.001
0.01
0.1
1
10
0.1 110 100 1000
Diamet er (µm )
b. Coughing
0.00001
0.0001
0.001
0.01
0.1
1
10
Duguid
Loudon & Roberts
Papineni & Rosenthal -OPC DATA
BLO Model (experimental range)
Almstrand et al. -OPC DATA (for tidal breathing)
a. Speaking
dCn dLog(D) (cm-3 )
18
Significant variation of viral loading with the aerosol source region is likely, because
pathogens tend to colonize specific regions of the respiratory tract and because the
ratio of tissue surface to respiratory tract lining fluid volume varies throughout the
respiratory tract. Another important parameter to be included is the viral strain
specific infectivity of different regions of the respiratory tract based on emerging
knowledge of the distribution of key proteins in the cell surface required for viral
attachment(Shinya et al., 2006; van Riel et al., 2007).
In principle the model parameters given in Table 2 and the corrections in Table 3
allow particle number and volume emission rate size distributions and total emission
fluxes for healthy volunteers to be estimated on a mode by mode basis or across all
sizes. The particle number (or mass) concentration size distributions for each mode
can be reproduced by inserting the corresponding parameters for the modes into
separate lognormal size distribution functions. The complete composite distribution
function is then the sum of these functions. A factor equal to the average exhalation
rate during the maneuver will convert such a concentration size distribution function
to an emission rate size distribution function. The particle number or volume emission
rate size distribution function can in turn be integrated across particle size to obtain
the total number and volume production rates (fluxes) within any size range.
Hence the B.L.O. model provides a basis for estimating parameters needed for
improving our epidemiological modeling of influenza epidemics. Droplet number and
droplet volume production rates can be estimated for each aerosol size distribution
mode during speech and coughing. The distributions will also provide a basis for
devising experiments to measure parameters for those models such as pathogen
concentrations in the different respiratory fluids comprising each mode, and the
inactivation rates of those pathogens in the aerosol phase.
Acknowledgments
This work was supported by the Australian Research Council under grant
DP0558410.
19
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