for
Suvajyoti Guha*
1
, Prasanna Hariharan
1
, Matthew R. Myers
1
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 (s tot
), considering the standard deviations in length and diameter to be σ l
and σ d
, respectively, is given by:
(

6 s tot l d p p
2
)

((
 l p l )
2 
2(
 d d p
) )
1
(S1)
The volume of the non-spherical bioaerosols V e
is calculated using V e


6 l d p
2
and the equivalent diameter for non-spherical bioaerosols is determined using d e

( l d 2 p p
)
1
3 . 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 s tot
V e
<<1 and using a Taylor’s series expansion, the standard deviation (expressed in µm) can be estimated using:
  d e s tot
3 V e
(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 normalized and 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 σ norm
as a function of d e by using a functional form of σ norm which is similar to the functional form of σ g
used by ICRP and is represented by the red solid line in Figure S1:
 norm
    cd e f
1

1
) (S3)

Here
 norm

 d e
. 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 obtain the minimum root mean square errors for the experimentally determined σ norm
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.

Protein, Viruses and
Bacteria
Prediction
5
1
4
0.001
2
6
0.08
0.07
0.06
0.05
8
7
0.04
0.03
3
0.02
0.01
0
0.01
0.1
1 10
100
Figure S1. Normalized standard deviation (σ norm
) of monomeric bioaerosol size distribution as a function of the equivalent spherical diameter (d e
). The solid red line indicates our prediction and the blue rhombuses are the normalized standard deviations obtained for different bioaerosols.
The d e
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 σ norm 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, ET
1
and
ET
2
. Of these the flow through ET
1
is relatively straightforward. If a person breathes completely through the nose at V
.
n mL/s, then the flow through ET
1 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 ET
1
region sees no flow and V
.
total
= V
.
m
, while if a person breathes through the nose and the mouth then flow through ET
1 equals V
.
n while V
.
total
= V
.
n
+ V
.
m
.
The above analogy is not applicable for ET
2
region. For clarity in discussion let us divide
ET
2
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 ET
2
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
ET
2
region (ICRP 1994):
 th



.
( ET ) m
  
D V SF m t
1 1

) ) 2



(S4)
Here D is the diffusion coefficient. As explained before, the flow through different sub-regions of the ET
2
can vary and thus using V
.
m
may not be accurate. Thus we recommend the equation be changed to:

 th



.
( ET m
  
D V total
SF t

1 1
) ) 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 ET
2
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 50 th (or 5 th or 95 th ) 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.

2
1
2
3
1
.
V
V
.
m n
V
.
total
Figure S2. ET
1
and ET
2
regions are demarcated as recommended by ICRP. We further divide the
ET
2
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 (50 th
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 (σ g
) for describing polydisperse aerosols. Thus, we define the probability distribution function, pdf as (Raabe 1978) : pdf
 exp(
 d
 d e
))
2
2 log(
 g
)
2
) d log(
 g
) 2

(S6)
Following LUDEP’s approach for polydisperse distributions, the lower d min
and the upper bin size d max
were determined: d min
 d
 e g
5 (S7) d max
 d
 e g

5 (S8) resulting in n logarithmic spaced intervals on either side of d e
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 intervals but for BAIL, we use 1001 intervals.
0.85
0.84
0.83
0.82
0.81
0.8
0.79
10
100 1000 10000
Number of intervals
4
2
0
100000
12
10
8
6
0.46
0.45
0.44
0.43
0.42
0.41
0.4
0.39
0.38
10
100 1000 10000
Number of intervals
3
2
1
0
100000
6
5
4
8
7
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 (ET
2
), 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/cm
3
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 Tables S1, S2 and S3 for the ET
1
, ET
2
, 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 to 6 %. 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 d ae
, 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 5-
6 % 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 = BB fast
+ BB slow
from Annex F, and bb-ICRP = bb fast
+ bb slow
from
Annex F, ICRP, 1994 so as to minimize the differences in the compared models.
(
AMTD
µ m) ET1 - ICRP ET1
0.001 4.20E-01 4.00E-01
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
% ET2- ICRP ET2
4.7 4.20E-01 4.05E-01
% BB - ICRP BB
3.5 9.60E-02 1.07E-01
% bb - ICRP bb
-11.6 4.60E-02 6.45E-02
% AI - ICRP AI %
Total -
ICRP Total
-40.2 9.90E-05 3.19E-04 -222.6 9.82E-01 9.77E-01
0.002 3.20E-01 3.26E-01
0.01 9.70E-02 9.90E-02
-1.9 3.40E-01 3.41E-01
-2.1 1.10E-01 1.12E-01
-0.3 1.24E-01 1.25E-01
-1.7 5.00E-02 5.16E-02
-1.0 1.58E-01 1.54E-01
-3.2 2.60E-01 2.55E-01
2.3 6.30E-03 5.80E-03
1.8 3.20E-01 3.15E-01
7.9 9.48E-01 9.54E-01
1.6 8.37E-01 8.33E-01
0.05 3.40E-02
0.1 2.60E-02
0.2 3.10E-02
AMAD
(µm)
3.45E-02
2.56E-02
2.98E-02
0.5 4.70E-02 4.66E-02
0.7 6.60E-02 6.76E-02
-1.5 3.60E-02 3.71E-02
1.5 2.60E-02 2.61E-02
3.9 3.10E-02 2.99E-02
-3.1 1.70E-02 1.75E-02
-0.4 1.18E-02 1.20E-02
3.5 8.40E-03 8.40E-03
-2.9 1.06E-01 1.08E-01
-1.7 7.00E-02 7.12E-02
0.0 4.60E-02 4.66E-02
-2.2 2.90E-01 3.02E-01
-1.7 2.00E-01 2.09E-01
-1.3 1.50E-01 1.54E-01
-4.2 4.83E-01 5.00E-01
-4.3 3.34E-01 3.44E-01
-2.5 2.66E-01 2.69E-01
1 9.50E-02
5 2.70E-01
10 3.00E-01
20 3.00E-01
9.81E-02
2.81E-01
3.18E-01
3.12E-01
0.9 5.10E-02 5.01E-02
-2.4 7.60E-02 7.67E-02
-3.3 1.10E-01 1.16E-01
-4.0 3.20E-01 3.37E-01
-5.9 3.50E-01 3.63E-01
-4.1 3.20E-01 3.38E-01
1.8 7.30E-03 7.30E-03
-0.9 7.30E-03 7.40E-03
-5.5 8.20E-03 8.40E-03
-5.2 1.60E-02 1.64E-02
-3.7 1.43E-02 1.46E-02
-5.6 9.09E-03 9.50E-03
0.0 3.60E-02 3.67E-02
-1.4 3.30E-02 3.35E-02
-2.4 3.10E-02 3.29E-02
-2.5 2.80E-02 3.06E-02
-2.1 1.83E-02 1.96E-02
-4.5 8.30E-03 8.80E-03
-1.9 1.40E-01 1.43E-01
-1.5 1.40E-01 1.45E-01
-6.1 1.40E-01 1.53E-01
-9.3 1.10E-01 1.14E-01
-7.1 5.60E-02 5.90E-02
-6.0 2.00E-02 2.07E-02
-1.9 2.81E-01 2.83E-01
-3.8 3.22E-01 3.31E-01
-9.5 3.84E-01 4.09E-01
-3.6 7.44E-01 7.78E-01
-5.4 7.39E-01 7.74E-01
-3.5 6.57E-01 6.89E-01
%
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 infant (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 = BB fast
+ BB slow
from Annex F, and bb-ICRP
= bb fast
+ bb slow
from Annex F, ICRP, 1994 so as to minimize the differences in the compared models.
AMTD
(µm) ET1 - ICRP ET1
0.001 4.30E-01 4.10E-01
0.002 3.40E-01 3.37E-01
0.01 1.00E-01 1.04E-01
0.05 3.40E-02 3.43E-02
0.1 3.50E-02 3.35E-02
0.2 6.20E-02 6.07E-02
AMAD
(µm)
0.5 1.00E-01 1.03E-01
0.7 1.40E-01 1.43E-01
1 1.80E-01 1.91E-01
5 3.50E-01 3.72E-01
10 3.60E-01 3.74E-01
20 3.20E-01 3.38E-01
% ET2- ICRP ET2
4.7 4.30E-01 4.13E-01
0.9 3.50E-01 3.53E-01
-4.1 1.20E-01 1.18E-01
-0.9 3.70E-02 3.66E-02
4.3 3.70E-02 3.43E-02
2.1 7.10E-02 6.87E-02
-2.7 1.20E-01
-1.8 1.70E-01
-6.1 2.40E-01
-6.3 4.10E-01
-3.8 3.90E-01
-5.6 3.40E-01
1.26E-01
1.80E-01
2.45E-01
4.33E-01
4.08E-01
3.52E-01
% BB - ICRP BB
4.1 1.16E-01 1.41E-01
-0.9 1.92E-01 1.93E-01
2.0 1.02E-01 1.02E-01
1.1 3.20E-02 3.32E-02
7.3 2.30E-02 2.31E-02
3.2 1.60E-02 1.60E-02
-4.8 1.26E-02
-6.0 1.12E-02
-2.1 1.04E-02
-5.6 9.10E-03
-4.7 6.50E-03
-3.6 3.52E-03
1.27E-02
1.13E-02
1.06E-02
9.50E-03
6.80E-03
3.70E-03
% bb - ICRP bb
-21.5 1.16E-02 2.16E-02
-0.3 8.00E-02 8.15E-02
0.2 2.80E-01 2.84E-01
-3.8 1.28E-01 1.28E-01
-0.4 8.60E-02 8.65E-02
0.0 5.60E-02 5.55E-02
% AI - ICRP AI %
Total -
ICRP Total
-86.2 1.20E-06 9.43E-06 -686.1 9.88E-01 9.85E-01
-1.9 5.70E-04 6.12E-04
-1.4 1.80E-01 1.82E-01
-0.2 2.50E-01 2.78E-01
-0.6 1.90E-01 2.06E-01
0.9 1.40E-01 1.49E-01
-7.3 9.63E-01
-0.8 7.82E-01
-11.4 4.81E-01
-8.3 3.71E-01
-6.1 3.45E-01
9.65E-01
7.89E-01
5.11E-01
3.83E-01
3.50E-01
%
0.3
-0.2
-0.9
-6.2
-3.3
-1.3
-0.8 4.00E-02
-0.9 3.20E-02
-1.9 2.40E-02
-4.4 8.60E-03
-4.6 4.30E-03
-5.1 1.54E-03
3.90E-02
3.04E-02
2.35E-02
8.20E-03
4.20E-03
1.60E-03
2.5 1.10E-01
5.0 9.80E-02
2.1 8.70E-02
4.7 3.50E-02
2.3 1.50E-02
-3.9 4.30E-03
1.21E-01
1.08E-01
9.71E-02
3.87E-02
1.61E-02
4.60E-03
-10.0 3.83E-01 4.01E-01
-10.1 4.51E-01 4.72E-01
-11.6 5.41E-01 5.67E-01
-10.6 8.13E-01 8.62E-01
-7.3 7.76E-01
-7.0 6.69E-01
8.09E-01
7.00E-01
-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 = BB fast
+ BB slow
from Annex F, and bb-ICRP = bb fast
+ bb slow
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) ET1 - ICRP ET1
0.001 2.00E-01 1.88E-01
% ET2- ICRP ET2
6.3 4.60E-01 4.28E-01
% BB - ICRP BB
7.0 1.00E-01 9.74E-02
% bb - ICRP bb
2.6 2.00E-01 2.28E-01
% AI - ICRP AI
-14.0 2.30E-02 3.65E-02
%
Total -
ICRP Total
-58.7 9.83E-01 9.77E-01
0.002 1.50E-01 1.49E-01
0.01 4.30E-02 4.41E-02
0.5 3.30E-01 3.30E-01
-2.6 9.10E-02 9.27E-02
0.0 8.00E-02 8.16E-02
-1.9 2.00E-02 2.09E-02
-2.0 2.80E-01 2.86E-01
-4.5 1.48E-01 1.51E-01
-2.1 1.20E-01 1.18E-01
-2.0 5.90E-01 5.90E-01
1.9 9.60E-01 9.64E-01
0.0 8.92E-01 8.99E-01
0.05 1.60E-02
0.1 1.70E-02
0.2 2.90E-02
AMAD
(µm)
1.66E-02
1.60E-02
2.88E-02
0.5 4.80E-02 4.86E-02
0.7 6.50E-02 6.78E-02
-3.8 3.30E-02 3.33E-02
5.9 2.70E-02 2.65E-02
0.7 4.20E-02 4.04E-02
-0.9 6.80E-03 7.10E-03
1.9 6.00E-03 5.70E-03
3.8 1.08E-02 1.03E-02
-4.4 5.40E-02 5.44E-02
5.0 3.40E-02 3.34E-02
4.6 2.20E-02 2.16E-02
-0.7 3.10E-01 3.27E-01
1.8 2.00E-01 2.08E-01
1.8 1.40E-01 1.41E-01
-5.4 4.20E-01 4.38E-01
-4.0 2.84E-01 2.90E-01
-0.4 2.44E-01 2.42E-01
1 8.80E-02
5 1.70E-01
10 1.80E-01
20 1.60E-01
9.14E-02
1.82E-01
1.85E-01
1.68E-01
-1.2 7.00E-02 7.05E-02
-4.3 1.00E-01 1.04E-01
-3.9 1.40E-01 1.49E-01
-7.2 3.90E-01 4.06E-01
-2.7 4.40E-01 4.65E-01
-5.1 4.40E-01 4.65E-01
-0.7 2.10E-02 2.09E-02
-3.8 3.30E-02 3.36E-02
-6.7 5.00E-02 5.16E-02
-4.0 1.11E-01 1.16E-01
-5.6 8.50E-02 8.92E-02
-5.6 4.41E-02 4.65E-02
0.5 1.78E-02 1.76E-02
-1.8 1.76E-02 1.75E-02
-3.2 1.91E-02 1.95E-02
-4.6 2.43E-02 2.48E-02
-4.9 1.46E-02 1.49E-02
-5.4 5.60E-03 5.70E-03
1.1 1.20E-01 1.23E-01
0.6 1.20E-01 1.20E-01
-2.1 1.20E-01 1.22E-01
-2.1 7.30E-02 7.78E-02
-2.1 3.40E-02 3.61E-02
-1.8 1.00E-02 1.09E-02
-2.2 2.77E-01 2.80E-01
0.1 3.36E-01 3.43E-01
-1.9 4.17E-01 4.34E-01
-6.6 7.68E-01 8.07E-01
-6.2 7.54E-01 7.90E-01
-9.0 6.60E-01 6.96E-01
%
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
When using BAIL an user can either select a bioaerosol from the database (that constitutes BTX, influenza virus and B. anthracis as discussed in the main article) or use the customizable bioaerosol option and choose either spherical or non-spherical particles and provide the dimensions (diameter if spherical, length and diameter if non-spherial) 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. These plots are shown for a sleeping nose breathing infant who inhaled B. anthracis : 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 5 th percentile, 50 th
percentile and 95 th
percentile as a function of size for B. anthracis and the 5 th percentile, 50 th
percentile, 95 th
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 5 th
, 50 th
and 95 th
percentile are saved in an
EXCEL spreadsheet “FDA_ICRP_Lung_Depostion.xlsx” (not shown) for post-processing.

(A)
(C)
(B)
(D)
(E) (F)
Figure S4. Probability density function (A), total deposition fraction (B), extra-thoracic deposition (C), tracheobronchial deposition fraction (D), alveolar deposition fraction (E) for a sleeping nose breathing 60 cm tall infant. (F) Location specific fractional deposition of monodispersed anthax in the 5 th
, 50 th
and 95 th
percentile of population of subjects with height 60 cm.
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 but 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) and equal to those discussed in section 3 of the main article, which expectedly should have 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. 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 .
Comparing column 6 with column 8 shows us that the ratio is closer to 1 in column 6 and varies significantly from 1 in column 8 if the particle size is bigger, irrespective of the height of the subject. This difference between BAIL and LUDEP in column 8 is 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 to completely override the necessity to guess or determine aerosol parameters by using BAIL. In addition the readers are reminded that LUDEP has the following disadvantages over BAIL: it can provide predictions only at discrete age groups, the shape factor of the bioaerosol has to be calculated by the user, it cannot provide 5 th
percentile and 95 th
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.
BTX
Anthrax
60 cm subject
ET
TB
AI
TD
176 cm subject
ET
TB
AI
TD
ET
60 cm subject
TB
AI
TD
176 cm subject
ET
TB
AI
TD
BAIL (%)
LUDEP
(bioaerosol parameters as guesses)
2.15
7.18
39.86
7.35
2.31
11.65
21.31
24.26
41.22
16.35
81.82
23.06
33.28
26.46
82.80
30.54
2.19
7.98
38.67
8.16
2.05
11.26
21.46
24.10
40.41
15.40
79.91
22.53
32.05
28.96
83.53
28.51
Ratio
1.02
1.11
0.97
1.11
0.89
0.97
1.01
0.99
0.98
0.94
0.98
0.98
0.96
1.09
1.01
0.93
LUDEP
(workplace aerosol parameters as guesses)
3.26
8.30
58.05
24.09
4.05
14.75
42.89
24.20
40.27
15.45
79.91
22.63
32.02
28.88
83.52
46.49
Ratio
1.52
1.16
1.46
3.28
1.75
1.27
2.01
1.00
0.98
0.94
0.98
0.98
0.96
1.09
1.01
1.52

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