Supplemental Information
for
Enhancement of ICRP’s lung deposition model for
pathogenic bioaerosols
Suvajyoti Guha*1, Prasanna Hariharan1, Matthew R. Myers1
1
Center for Devices and Radiological Health, Food and Drug Administration (FDA), Silver
Spring, MD 20993
*Corresponding author:
Address:
10903 New Hampshire Avenue,
WO62, Room 2233, Division of Applied Mechanics,
Center for Devices and Radiological Health,
Food and Drug Administration, Silver Spring, MD, 20993
Phone: 301-796-9573
Email: Suvajyoti.Guha@fda.hhs.gov
S1. Normalized standard deviation of monodisperse bioaerosol particle size distribution as
a function of size
In order to describe the aerosol distribution function completely we need two parameters:
the mean size and the width of the distribution. The mean size can often be easily determined by
using the equivalent volume diameter. The determination of the width of bioaerosols is slightly
more complicated. As the width of bioaerosols is almost often different from engineered
nanoparticles (You et al, 2014) and workplace aerosols (ICRP, 1994), thus in this section, we
assimilate different examples of bioaerosol distributions from literature to come up with a
general equation for determining width of bioaerosols as a function of size.
While bioaerosols that are spherical will have a single value for the width of the
distribution corresponding to their spherical diameter, for bioaerosols that have two dimensions,
each dimension will be associated with a width of distribution. Thus determination of a single
standard deviation for a non-spherical bioaerosol is slightly more complicated and is determined
as follows. The standard deviation in measuring volume (stot), considering the standard
deviations in length and diameter to be σl and σd , respectively, is given by:

stot
 ((
2
p
( lpd )
6
l
lp
)  2(
2
d
dp
1
2 2
) )
(S1)
The volume of the non-spherical bioaerosols Ve is calculated using Ve 

6
l p d p2
and the
1
2 3
p
equivalent diameter for non-spherical bioaerosols is determined using d e  (l p d ) . It should be
pointed out that the equation for calculating the volume can vary depending on the shape of the
non-spherical bioaerosol. In our code, we assume non-spherical bioaerosols to be shaped like
ellipsoids (Carrera et al. 2007). Further, assuming
stot
<<1 and using a Taylor’s series expansion,
Ve
the standard deviation (expressed in µm) can be estimated using:
  de
stot
3Ve
(S2)
The above approach was used by us to extract a single standard deviation for B. ames and B.
anthracis for which the standard deviation for length and width were separately provided in
Carrera et al. 2007. These values were subsequently used in Figure S1 (blue diamond, 7 and 8).
After combining different experimentally determined width of size distributions for
spherical and non-spherical bioaerosols as shown with blue diamonds in Figure S1 (1 - insulin,
measured by DMA (Pease, III et al. 2010), 2 – bovine serum albumin, measured by DMA (Guha
et al. 2012), 3 – immunoglobulins, measured by DMA (Guha et al. 2012), 4 – bacteriophage pp7,
measured by DMA (Guha et al. 2011), 5 – bacteriophage PR772, measured by DMA (Guha et al.
2011), 6 – influenza virus, measured by TEM (Booy et al. 1985), 7, 8 – pathogenic Bacillus ames
and anthracis, respectively, measured by TEM, (Carrera et al. 2007). (DMA – Differential
mobility analyzer, TEM – Transmission electron microscopy) we plotted σnorm as a function of de
by using a functional form of σnorm which is similar to the functional form of σg used by ICRP
and is represented by the red solid line in Figure S1:
 norm  a  b  (1 
Here  norm 

de
1
)
cd e f  1
(S3)
. By choosing reasonable bounds for a (0.011 to 0.02), b (0.005 to 0.01), c (100
to 1000) and f (0.1 to 2), we find a= 0.02, b = 0.045, c = 100 and f = 1.2 to give the minimum
root mean square errors for the experimentally determined σnorm values. A separate MATLAB
code was developed for achieving this minimization. It should also be pointed out that in order to
capture the standard deviation of the size distribution of a bioaerosol it is important that the
measurement device used have a resolution that is significantly better than standard deviation of
the size distribution the instrument is measuring. This was the case for all measurements in
Figure S1, except for insulin (blue diamond, 1). For insulin, because of its very small size of
around 2.9 nm (Pease, III et al. 2010) the resolution of the differential mobility analyzer (DMA)
used was at best comparable to that of the standard deviation it was measuring. We therefore
forced Equation S3 to deviate significantly from 1 and close to data 2 and 3 in Figure S1.
It should be pointed out that there are other relevant articles that discuss the size and
distribution of bioaerosols but because such articles do not provide sufficient information about
the equivalent spherical diameter (Aizenberg et al, 2000), or the uncertainties in the dimensional
measurements (Reponen et al, 2001), or are for polydisperse distributions (Cho et al, 2005,
Schafer et al, 1998, Weis et al, 2002) they were not used in developing Equation S3.
0.08
Protein, Viruses and
Bacteria
Prediction
5 6
1
0.07
8
0.06
7
4
σnorm
0.05
0.04
0.03
2 3
0.02
0.01
0
0.001
0.01
0.1
1
10
Equivalent spherical diameter (µm)
100
Figure S1. Normalized standard deviation (σnorm) of monomeric bioaerosol size distribution as a
function of the equivalent spherical diameter (de). The solid red line indicates our prediction and
the blue rhombuses are the normalized standard deviations obtained for different bioaerosols.
The de values are 2.95 nm, 6.7 nm, 8.7 nm, 23.4 nm, 67.8 nm, 127 nm, 1012 nm and 1064 nm for
data points 1, 2, 3, 4, 5, 6, 7 and 8, respectively; the corresponding σnorm values are 0.05, 0.022,
0.023, 0.047, 0.055, 0.055, 0.061 and 0.068, respectively.
S2. Flow distribution through the nose and mouth
The nose and mouth flow passages are extremely complex. Figure S2 shows how the
ICRP committee chose to divide the extra-thoracic region into two sub-compartments, ET1 and
ET2. Of these the flow through ET1 is relatively straightforward. If a person breathes completely
.
.
.
through the nose at V n mL/s, then the flow through ET1 is equal to the total flow and V total = V n .
.
For a person breathing completely through the mouth at V m mL/s, the ET1 region sees no flow
.
.
and V total = V m , while if a person breathes through the nose and the mouth then flow through ET1
.
.
.
.
equals V n while V total = V n + V m .
The above analogy is not applicable for ET2 region. For clarity in discussion let us divide
ET2 into 3 sub-regions (marked as 1, 2 and 3 in green, Figure S2). When a person breathes
.
.
completely through the nose, then flow through sub-region 1 and 3 equals V n  V total while most
of region 2 sees no flow. However, if a person breathes through the mouth, then there is no flow
.
.
through region 1, while flow through regions 2 and 3 equal V m  V total . If a person breathes
.
through both mouth and nose, then flow through region equals V n , flow through region 2 equals
.
.
V m and flow through region 3 equals V total . As the ICRP model does not subdivide ET2 into 3
sub-regions, the deposition equations cannot capture the different flows in these sub-regions.
In the case of mouth breathing in the presence of aerosols driven by thermodynamic
diffusion, the following expression is used for determining the efficiency of deposition in the
ET2 region (ICRP 1994):

.

1 1

th ( ET )m  1  exp  9( D(Vm SFt ) 4 ) 2 


(S4)
Here D is the diffusion coefficient. As explained before, the flow through different sub-regions
.
of the ET2 can vary and thus using V m may not be accurate. Thus we recommend the equation be
changed to:

.

1 1

th ( ETm )  1  exp  9( D(V total SFt ) 4 ) 2 


(S5)
This modification is not required for the empirical correlation used for aerodynamic deposition
.
in the mouth region, as that equation (sec, ICRP 1994) already considers V total in the correlation
.
instead of V m . This modification implies that the deposition efficiency predicted by Equation S5
is less than Equation S4, or in other words, this change results in lower predicted aerosol
deposition in the ET2 region. Physically, this is because of lower transition times for the aerosols.
This change only impacts calculations in the section labeled “Use predefined empirical
correlations for deposition to calculate ET, TB, Al, Total deposition for 50th (or 5th or 95th)
percentile in each size interval” in Figure 1. As this is only a minor change in one of the several
empirical equations recommended by ICRP, it is not shown by a different color coding in Figure
1. To our knowledge, this modification is already accounted for in current versions of LUDEP as
well.
ET2
1
Extrathoracic
region
ET1
2
3
.
V. n
Vm
.
V total
Tracheobronchial
region
Alveolar region
Figure S2. ET1 and ET2 regions are demarcated as recommended by ICRP. We further divide the
ET2 region into three sub-regions (marked as 1, 2 and 3 in green) to demonstrate that the flow
across these sub-regions can vary depending on the subject’s breathing patterns. Figure adapted
from (NRC 2008).
S3. Comparisons with ICRP predictions
In order to validate our implementation of the ICRP model equations we compare our
predictions with those provided in Annex F, (ICRP 1994). As our code is written with
bioaerosols in mind and has some modifications compared to the ICRP model, we had to revert
back to the main ICRP code and develop a new set of six in-house codes, each dedicated for the
thermodynamic (AMTD) and aerodynamic diameter (AMAD) for three different cases (sleeping
adult, adult involved in heavy exercise and sleeping infant). First, like the ICRP model, the
AMTD or the AMAD was directly used instead of the equivalent spherical diameter or the length
and diameter of the aerosol discussed in section 3. Second, instead of representing the lung
geometries as a continuous function of height, discrete values from Table 15 of ICRP, 1994 were
used. Third, Annex F in ICRP model only provides the average (50th percentile) deposition in
different compartments. Thus unlike BAIL discussed in the main article, these programs only
calculate the average deposition. Lastly, Annex F, ICRP, 1994 is catered towards mono-modal
log-normal distributions. Consistent with that approach, we use geometric a log-normal standard
deviation (σg) for describing polydisperse aerosols. Thus, we define the probability distribution
function, pdf as (Raabe 1978) :
exp(
pdf 
(log(d )  log(de )) 2
)
2 log( g ) 2
d log( g ) 2
(S6)
Following LUDEP’s approach for polydisperse distributions, the lower dmin and the upper bin
size dmax were determined:
dmin  de g 5
(S7)
dmax  de g 5
(S8)
resulting in n logarithmic spaced intervals on either side of de for a total 2n+1 intervals. Using a
large number of intervals enables more accurate integration when determining deposition.
LUDEP fixes n=50 i.e. the total size range is binned into 101 intervals. However, we
investigated the effect of interval sizes more systematically. By comparing the total deposition
(diamond blue, primary Y axis, Figure S3) and the computational time (square red, secondary Y
axis, Figure S3) for thermodynamic (Figure S3A) and aerodynamic diameters (Figure S3B) as a
function of number of intervals in different size regimes, we found that choosing 1001 intervals
rather than 10001 intervals only nominally changes the total deposition fraction and increases the
computation time almost threefold. Thus for comparing against ICRP predictions we choose 101
0.84
10
0.83
8
0.82
6
0.81
4
0.8
2
(A)
0.79
10
100
1000
10000
Number of intervals
0
100000
0.46
8
0.45
7
0.44
6
0.43
5
0.42
4
0.41
3
0.4
2
(B)
0.39
1
0.38
10
Time (s)
12
Total deposition
0.85
Time (s)
Total deposition
intervals but for BAIL, we use 1001 intervals.
100
1000
10000
Number of intervals
0
100000
Figure S3. (A) Plot of the total deposition in the lung (blue diamond, primary Y-axis) and total
time required for obtaining the results (red square, secondary Y-axis) for a particle with AMTD
of 0.01 µm. (B) Plot of the total deposition in the lung (blue diamond, primary Y-axis) and total
time required for obtaining the results (red square, secondary Y-axis) for a particle with AMAD
of 1 µm.
We compared our values against multiple aerosol sizes, age groups and breathing patterns
provided in Annex F of ICRP 1994. In this regard, Annexe F, ICRP 1994 results were probably
determined using older versions of LUDEP (we presume this as the ICRP committee members
who wrote Annexe F, ICRP 1994 also played an instrumental role in developing LUDEP). The
fractional deposition at different locations represented by the extrathoracic region 1 (ET 1),
extrathoracic region 2 (ET2), bronchi region (BB), bronchiolar region (bb), alveolar region (Al)
and the total deposition (TD) as obtained by the ICRP committee are shown in columns 2, 5, 8,
11, 14 and 17 of Table S1, respectively, for sleeping male adults exposed to aerosols of different
size and with density = 3 g/cm3 and shape factor = 1.5. The results provided in Annex F of ICRP,
1994 also take into account mucociliary clearance in the bronchi (BB) and bronchiolar (bb)
region. A part of this clearance is slow while the rest is fast. As our program does not consider
any clearance mechanism, we add up the slow and fast clearance fractions in Annex F, which
then account for ~ 100 % of the deposition fractions in the BB and bb regions. The results
predicted by our model are provided in columns 3, 6, 9, 12, 15 and 18 of Table S1, S2 and S3 for
the ET1, ET2, bronchi, bronchiolar, alveolar region and the total deposition, respectively.
The percentage difference between Annex F values and our values were obtained by
determining the difference between the two and then normalizing that data with respective values
from Annex F, and are shown in columns 4, 7, 10, 13, 16 and 19 of Tables S1, S2 and S3. To
examine the consistency of our model across different age groups and breathing patterns, two
other comparisons were made. Table S2 shows a comparison of our predictions with Annex F for
sleeping 3 month old infants. Table S3 shows comparisons of our predictions with Annex F for
adult males involved in heavy exercise and thus breathing through nose and mouth. Consistent
with the comparisons in Table S1, the difference between our predictions and those obtained by
ICRP are small, typically less than 5 %. For the smallest particle size of 1 nm, the alveolar
fraction depositions predicted by us and ICRP appear to be substantially different. As the
alveolar fraction at this size is low, it is possible that this discrepancy may be because of
differing rounding off errors schemes used by the LUDEP developers and by us. Other factors
such as property values, convergence tolerances for determining dae, switching from single
precision to double precision and different interval sizes (101 and 1001) were also explored but
they did not reduce the differences with the ICRP predictions. We are uncertain as to what could
have contributed to these differences. Nevertheless, the experimental variability even amongst
similar subjects can often be very high, (ICRP 1994;NCRP 1997) far exceeding the maximum 56 % variability that we find with our predictions when compared to ICRP model. And as our
predictions are in good agreement with ICRP predictions, we have confidence that our enhanced
model can satisfactorily capture deposition of bioaerosols in lungs of human subjects.
Table S1: Comparison of the ICRP predictions and predictions from our unmodified ICRP code developed in-house in a sleeping adult
(breathing rate = 0.45 m3/hr, breathing frequency = 12 min-1) and breathing 100 % through the nose. The results from our unmodified
ICRP code are presented in bold letters. Fractional regional deposition of aerosols with density = 3 gm/cc and shape factor = 1.5. Our
model does not take into account clearance, so for us BB-ICRP = BBfast + BBslow from Annex F, and bb-ICRP = bbfast + bbslow from
Annex F, ICRP, 1994 so as to minimize the differences in the compared models.
Adult Male, Particle density = 3000 kg/m3, shape factor = 1.5, sleeping, breathing rate = 0.45 m3/hr, proportion of breathing through nose = 100 %, no wind
AMTD
(µm)
0.001
0.002
0.01
0.05
0.1
0.2
AMAD
(µm)
0.5
0.7
1
5
10
20
4.20E-01
3.20E-01
9.70E-02
3.40E-02
2.60E-02
3.10E-02
ET1
4.00E-01
3.26E-01
9.90E-02
3.45E-02
2.56E-02
2.98E-02
4.70E-02
6.60E-02
9.50E-02
2.70E-01
3.00E-01
3.00E-01
4.66E-02
6.76E-02
9.81E-02
2.81E-01
3.18E-01
3.12E-01
ET1 - ICRP
%
4.7
-1.9
-2.1
-1.5
1.5
3.9
4.20E-01
3.40E-01
1.10E-01
3.60E-02
2.60E-02
3.10E-02
ET2
4.05E-01
3.41E-01
1.12E-01
3.71E-02
2.61E-02
2.99E-02
0.9
-2.4
-3.3
-4.0
-5.9
-4.1
5.10E-02
7.60E-02
1.10E-01
3.20E-01
3.50E-01
3.20E-01
5.01E-02
7.67E-02
1.16E-01
3.37E-01
3.63E-01
3.38E-01
ET2- ICRP
%
3.5
-0.3
-1.7
-3.1
-0.4
3.5
9.60E-02
1.24E-01
5.00E-02
1.70E-02
1.18E-02
8.40E-03
BB
1.07E-01
1.25E-01
5.16E-02
1.75E-02
1.20E-02
8.40E-03
1.8
-0.9
-5.5
-5.2
-3.7
-5.6
7.30E-03
7.30E-03
8.20E-03
1.60E-02
1.43E-02
9.09E-03
7.30E-03
7.40E-03
8.40E-03
1.64E-02
1.46E-02
9.50E-03
BB - ICRP
%
-11.6
-1.0
-3.2
-2.9
-1.7
0.0
0.0
-1.4
-2.4
-2.5
-2.1
-4.5
4.60E-02
1.58E-01
2.60E-01
1.06E-01
7.00E-02
4.60E-02
bb
6.45E-02
1.54E-01
2.55E-01
1.08E-01
7.12E-02
4.66E-02
%
-40.2
2.3
1.8
-2.2
-1.7
-1.3
3.60E-02
3.30E-02
3.10E-02
2.80E-02
1.83E-02
8.30E-03
3.67E-02
3.35E-02
3.29E-02
3.06E-02
1.96E-02
8.80E-03
-1.9
-1.5
-6.1
-9.3
-7.1
-6.0
bb - ICRP
Total ICRP
3.19E-04
5.80E-03
3.15E-01
3.02E-01
2.09E-01
1.54E-01
%
-222.6
7.9
1.6
-4.2
-4.3
-2.5
9.82E-01
9.48E-01
8.37E-01
4.83E-01
3.34E-01
2.66E-01
Total
9.77E-01
9.54E-01
8.33E-01
5.00E-01
3.44E-01
2.69E-01
1.43E-01
1.45E-01
1.53E-01
1.14E-01
5.90E-02
2.07E-02
-1.9
-3.8
-9.5
-3.6
-5.4
-3.5
2.81E-01
3.22E-01
3.84E-01
7.44E-01
7.39E-01
6.57E-01
2.83E-01
3.31E-01
4.09E-01
7.78E-01
7.74E-01
6.89E-01
AI - ICRP
AI
9.90E-05
6.30E-03
3.20E-01
2.90E-01
2.00E-01
1.50E-01
1.40E-01
1.40E-01
1.40E-01
1.10E-01
5.60E-02
2.00E-02
%
0.6
-0.6
0.5
-3.5
-2.9
-1.1
-0.7
-2.5
-6.4
-4.6
-4.8
-4.9
Table S2: Comparison of the ICRP predictions and predictions from our unmodified ICRP code developed in-house in a sleeping 3
month old infants (breathing rate = 0.09 m3/hr, breathing frequency = 38 min -1) and breathing 100 % through the nose. The results
from our unmodified ICRP code are presented in bold letters. Fractional regional deposition of aerosols with density = 3 gm/cc and
shape factor = 1.5. Our model does not take into account clearance, so for us BB-ICRP = BBfast + BBslow from Annex F, and bb-ICRP
= bbfast + bbslow from Annex F, ICRP, 1994 so as to minimize the differences in the compared models.
AMTD
(µm)
0.001
0.002
0.01
0.05
0.1
0.2
AMAD
(µm)
0.5
0.7
1
5
10
20
4.30E-01
3.40E-01
1.00E-01
3.40E-02
3.50E-02
6.20E-02
ET1
4.10E-01
3.37E-01
1.04E-01
3.43E-02
3.35E-02
6.07E-02
1.00E-01
1.40E-01
1.80E-01
3.50E-01
3.60E-01
3.20E-01
1.03E-01
1.43E-01
1.91E-01
3.72E-01
3.74E-01
3.38E-01
ET1 - ICRP
%
4.7
0.9
-4.1
-0.9
4.3
2.1
4.30E-01
3.50E-01
1.20E-01
3.70E-02
3.70E-02
7.10E-02
ET2
4.13E-01
3.53E-01
1.18E-01
3.66E-02
3.43E-02
6.87E-02
-2.7
-1.8
-6.1
-6.3
-3.8
-5.6
1.20E-01
1.70E-01
2.40E-01
4.10E-01
3.90E-01
3.40E-01
1.26E-01
1.80E-01
2.45E-01
4.33E-01
4.08E-01
3.52E-01
ET2- ICRP
%
4.1
-0.9
2.0
1.1
7.3
3.2
1.16E-01
1.92E-01
1.02E-01
3.20E-02
2.30E-02
1.60E-02
BB
1.41E-01
1.93E-01
1.02E-01
3.32E-02
2.31E-02
1.60E-02
-4.8
-6.0
-2.1
-5.6
-4.7
-3.6
1.26E-02
1.12E-02
1.04E-02
9.10E-03
6.50E-03
3.52E-03
1.27E-02
1.13E-02
1.06E-02
9.50E-03
6.80E-03
3.70E-03
BB - ICRP
%
-21.5
-0.3
0.2
-3.8
-0.4
0.0
-0.8
-0.9
-1.9
-4.4
-4.6
-5.1
1.16E-02
8.00E-02
2.80E-01
1.28E-01
8.60E-02
5.60E-02
bb
2.16E-02
8.15E-02
2.84E-01
1.28E-01
8.65E-02
5.55E-02
%
-86.2
-1.9
-1.4
-0.2
-0.6
0.9
4.00E-02
3.20E-02
2.40E-02
8.60E-03
4.30E-03
1.54E-03
3.90E-02
3.04E-02
2.35E-02
8.20E-03
4.20E-03
1.60E-03
2.5
5.0
2.1
4.7
2.3
-3.9
bb - ICRP
Total ICRP
9.43E-06
6.12E-04
1.82E-01
2.78E-01
2.06E-01
1.49E-01
%
-686.1
-7.3
-0.8
-11.4
-8.3
-6.1
9.88E-01
9.63E-01
7.82E-01
4.81E-01
3.71E-01
3.45E-01
Total
9.85E-01
9.65E-01
7.89E-01
5.11E-01
3.83E-01
3.50E-01
1.21E-01
1.08E-01
9.71E-02
3.87E-02
1.61E-02
4.60E-03
-10.0
-10.1
-11.6
-10.6
-7.3
-7.0
3.83E-01
4.51E-01
5.41E-01
8.13E-01
7.76E-01
6.69E-01
4.01E-01
4.72E-01
5.67E-01
8.62E-01
8.09E-01
7.00E-01
AI - ICRP
AI
1.20E-06
5.70E-04
1.80E-01
2.50E-01
1.90E-01
1.40E-01
1.10E-01
9.80E-02
8.70E-02
3.50E-02
1.50E-02
4.30E-03
%
0.3
-0.2
-0.9
-6.2
-3.3
-1.3
-4.8
-4.7
-4.8
-6.0
-4.3
-4.6
Table S3: Comparison of the ICRP predictions and predictions from our unmodified ICRP code developed in-house in an adult
exercising heavily (breathing rate = 3.0 m3/hr, breathing frequency = 26 min -1) and breathing 50 % through the nose. The results from
our unmodified ICRP code are presented in bold letters. Fractional regional deposition of aerosols with density = 3 gm/cc and shape
factor = 1.5. Our model does not take into account clearance, so for us BB-ICRP = BBfast + BBslow from Annex F, and bb-ICRP = bbfast
+ bbslow from Annex F, ICRP, 1994 so as to minimize the differences in the compared models.
Adult Male, Particle density = 3000 kg/m3, shape factor = 1.5, heavy exercise, breathing rate = 3 m3/hr, proportion of breathing through nose = 50 %, no wind
AMTD
(µm)
0.001
0.002
0.01
0.05
0.1
0.2
AMAD
(µm)
0.5
0.7
1
5
10
20
2.00E-01
1.50E-01
4.30E-02
1.60E-02
1.70E-02
2.90E-02
ET1
1.88E-01
1.49E-01
4.41E-02
1.66E-02
1.60E-02
2.88E-02
4.80E-02
6.50E-02
8.80E-02
1.70E-01
1.80E-01
1.60E-01
4.86E-02
6.78E-02
9.14E-02
1.82E-01
1.85E-01
1.68E-01
ET1 - ICRP
%
6.3
0.5
-2.6
-3.8
5.9
0.7
4.60E-01
3.30E-01
9.10E-02
3.30E-02
2.70E-02
4.20E-02
ET2
4.28E-01
3.30E-01
9.27E-02
3.33E-02
2.65E-02
4.04E-02
-1.2
-4.3
-3.9
-7.2
-2.7
-5.1
7.00E-02
1.00E-01
1.40E-01
3.90E-01
4.40E-01
4.40E-01
7.05E-02
1.04E-01
1.49E-01
4.06E-01
4.65E-01
4.65E-01
ET2- ICRP
%
7.0
0.0
-1.9
-0.9
1.9
3.8
1.00E-01
8.00E-02
2.00E-02
6.80E-03
6.00E-03
1.08E-02
BB
9.74E-02
8.16E-02
2.09E-02
7.10E-03
5.70E-03
1.03E-02
-0.7
-3.8
-6.7
-4.0
-5.6
-5.6
2.10E-02
3.30E-02
5.00E-02
1.11E-01
8.50E-02
4.41E-02
2.09E-02
3.36E-02
5.16E-02
1.16E-01
8.92E-02
4.65E-02
BB - ICRP
%
2.6
-2.0
-4.5
-4.4
5.0
4.6
2.00E-01
2.80E-01
1.48E-01
5.40E-02
3.40E-02
2.20E-02
bb
2.28E-01
2.86E-01
1.51E-01
5.44E-02
3.34E-02
2.16E-02
0.5
-1.8
-3.2
-4.6
-4.9
-5.4
1.78E-02
1.76E-02
1.91E-02
2.43E-02
1.46E-02
5.60E-03
1.76E-02
1.75E-02
1.95E-02
2.48E-02
1.49E-02
5.70E-03
bb - ICRP
%
-14.0
-2.1
-2.0
-0.7
1.8
1.8
1.1
0.6
-2.1
-2.1
-2.1
-1.8
Total ICRP
2.30E-02
1.20E-01
5.90E-01
3.10E-01
2.00E-01
1.40E-01
AI
3.65E-02
1.18E-01
5.90E-01
3.27E-01
2.08E-01
1.41E-01
%
-58.7
1.9
0.0
-5.4
-4.0
-0.4
9.83E-01
9.60E-01
8.92E-01
4.20E-01
2.84E-01
2.44E-01
Total
9.77E-01
9.64E-01
8.99E-01
4.38E-01
2.90E-01
2.42E-01
1.20E-01
1.20E-01
1.20E-01
7.30E-02
3.40E-02
1.00E-02
1.23E-01
1.20E-01
1.22E-01
7.78E-02
3.61E-02
1.09E-02
-2.2
0.1
-1.9
-6.6
-6.2
-9.0
2.77E-01
3.36E-01
4.17E-01
7.68E-01
7.54E-01
6.60E-01
2.80E-01
3.43E-01
4.34E-01
8.07E-01
7.90E-01
6.96E-01
AI - ICRP
%
0.7
-0.4
-0.8
-4.4
-2.2
0.7
-1.0
-2.1
-4.1
-5.0
-4.8
-5.5
S4. Brief instructions for running BAIL
BAIL when executed either asks the user to select a bioaerosol from the database (that
constitutes BTX, influenza virus and B. anthracis as discussed in the main article) or a
customizable bioaerosol for which the user can choose the dimensions (length and diameter) and
density. Upon furnishing the above information the user is asked for the standing height in
centimeters followed by the activity for which the user can choose either “sleeping”, “sitting” or
“light activity”. Finally the user needs to choose the breathing pattern (either nose or mouth
breathing, or a customizable value between 0 and 1). The code determines the deposition
fractions and doses in different compartments of the lungs in the next 5 to 10 seconds and plots
the following six figures: the probability distribution function of monodisperse B. anthracis as a
function size (Figure S4A), four plots (Figures S4B – S4E) showing the deposition fraction in the
different compartments of the lungs for 5th percentile, 50th percentile and 95th percentile as a
function of size for B. anthracis and the 5th percentile, 50th percentile, 95th percentile deposition
fraction in different components of the lungs for the subject for B. anthracis (Figure S4F).
Additionally, the location-specific deposition fractions and the doses in each compartment for
the
5th,
50th
and
95th
percentile
are
saved
in
an
“FDA_ICRP_Lung_Depostion.xlsx” (not shown) for post-processing.
EXCEL
spreadsheet
(A)
(B)
(C)
(E)
(D)
(F)
Figure S4. Probability density function (A), total deposition fraction (B), extra-thoracic
deposition (C), tracheobronchial deposition fraction (D), alveolar deposition fraction (E) as a
function of height. (F) Location specific fractional deposition of monodispersed anthax in the 5th,
50th and 95th percentile of population of subjects with height 60 cm (corresponds to 3 month
infant).
S5. Differences between BAIL and LUDEP for some specific bioaerosols
One may naturally wonder, how different would the results be if instead of BAIL, ICRP
was used for determining the deposition of bioaerosols in the lungs. As the ICRP 1994 report did
not contain any specific bioaerosol examples that this manuscript discusses, LUDEP was used
for the case studies in this section, i.e. we assume that LUDEP accurately predicts ICRP’s lungdeposition model. In order to make this comparison the average deposition values in different
compartments of the lungs obtained for two different bioaerosols (BTX and B. anthracis) and
two different subjects (3 month old and adult) were compared with ICRP predictions. In order to
conveniently compare the differences here we expressed the results in the form of ratio whereby
we divided LUDEP % deposition prediction by BAIL % deposition prediction. When both
predictions are close to each other this ratio nears 1 and would deviate from 1 otherwise. For
comparisons we created two sets of cases in Table S4: one in which the input parameters used in
BAIL (Column 4) were exactly the same as LUDEP (Column 5) which expectedly had the least
difference between BAIL and LUDEP (Column 6 in bold ink), and then another case where
BAIL was compared with LUDEP but the property values were not guessed as well as the
previous case (Column 7). In this later case the aerosol parameters with the exception of the
aerosol size were assumed to be the same as workplace aerosols as recommended by ICRP,
1994, i.e. the density was assumed to be 3 g/cc and shape factors were assumed to be 1.5 for both
BTX and B. anthracis and the geometric standard deviations were assumed to be around 1 for
BTX and 2.48 for B. anthracis. Compared to these values BAIL in column 4 and LUDEP in
column 5 assumed values discussed in section 3. The difference between BAIL and LUDEP as
evident from column 8 was because of the higher standard deviation that was assumed in column
7. LUDEP with aerosol parameter guesses similar to workplace aerosols over predicted the
deposition of B. anthracis by a factor of 1.5 in 3 month old subjects and by a factor of 2 in adult
subjects (Column 8). Most of this additional deposition occurred in the ET region. Thus this
demonstration example emphasizes the need for either using appropriate values for bioaerosols
when using ICRP based LUDEP or otherwise completely override the necessity to guess or
determine aerosol parameters by using BAIL. In addition the readers are reminded then even
when accurate guess values are used in LUDEP, it can provide predictions only at specific age
groups, the shape factor of the bioaerosol has to be calculated by the user, it cannot provide 5th
percentile and 95th percentile deposition data, and is lastly not an open source code.
Table S4. Differences in between BAIL and LUDEP depositional predictions for different input
parameters.
LUDEP
(bioaerosol
BAIL (%)
parameters
as guesses)
ET
TB
60 cm subject
AI
TD
BTX
ET
176 cm
TB
subject
AI
TD
ET
TB
60 cm subject
AI
TD
Anthrax
ET
176 cm
TB
subject
AI
TD
24.26
41.22
16.35
81.82
23.06
33.28
26.46
82.80
30.54
2.15
7.18
39.86
7.35
2.31
11.65
21.31
24.10
40.41
15.40
79.91
22.53
32.05
28.96
83.53
28.51
2.19
7.98
38.67
8.16
2.05
11.26
21.46
Ratio
0.99
0.98
0.94
0.98
0.98
0.96
1.09
1.01
0.93
1.02
1.11
0.97
1.11
0.89
0.97
1.01
LUDEP
(workplace
aerosol
parameters
as guesses)
24.20
40.27
15.45
79.91
22.63
32.02
28.88
83.52
46.49
3.26
8.30
58.05
24.09
4.05
14.75
42.89
Ratio
1.00
0.98
0.94
0.98
0.98
0.96
1.09
1.01
1.52
1.52
1.16
1.46
3.28
1.75
1.27
2.01
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