Proceedings of International Forum for Green Asia 2019
5th International Exchange and Innovation Conference on Engineering & Sciences (IEICES 2019)

GA07 / P19
Comparative analysis of inhalation exposure to e-cigarette vapors
with different puffing behaviors using PBPK-CFD approach1

Kazuki Kuga1, Kazuhide Ito2
Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Japan
2
Faculty of Engineering Sciences, Kyushu University, Japan
Email: kuga@kudai.jp

Abstract: The purpose of this study is to develop a numerical prediction method for assessment of human health risks
caused from the inhalation exposure of various contaminants generated by electronic cigarettes (e-cigarettes). In thisstudy, the inhalation exposure to contaminants, generated from e-cigarette smoking under two types of puffing behaviors
was quantitatively estimated adapting a coupled physiologically based pharmacokinetic (PBPK)–computational fluid
dynamics (CFD) model to a numerical respiratory tract model. Heterogeneous contaminants concentration distributions
inside the respiratory tract were predicted through transient CFD simulations. For evaluating adsorption flux onto
respiratory tissue surface, tissue surface contaminants concentrations of respiratory tract were calculated by using two
concepts: partition coefficient between air-phase and tissue-phase and analogy of flux conservation. Contaminants
concentration distributions inside the tissue were analyzed by using PBPK model. Through the coupled PBPK-CFD
analyses, we indicated total fractions of contaminants inhaled from e-cigarette: (i) adsorbed onto tissue surface ofrespiratory tract, (ii) transported to deeper bronchial regions, and (iii) released into the indoor environment byexhalation.
Keywords: Inhalation exposure, CFD, Numerical respiratory tract model, PBPK model
1. INTRODUCTION
Electronic cigarettes (e-cigarette) have recently been
perceived as the safer alternatives to conventional
cigarettes in terms of first-, second-, and third-hand
exposure smoking throughout the world. Although ecigarette vapor is generally generated through a noncombustion process, it has been confirmed to include
some contaminants such as aldehydes, which affecthuman health and indoor environment. In addition,
puffing behaviours (short and long puffing) havepotential impact on the human health in terms ofinhalation exposure. Thus, it is essential to develop a
predictive framework to analyse the comparative healthimpact caused by the e-cigarette smoking with different
puffing behaviours. Correspondingly, this study focuses
on a qualitative and quantitative assessment of e-cigarette
vapor exposure in the human respiratory tract using
PBPK–CSP–CFD analyses.

addition to the inhalation period, an elliptical outflow
opening having an area of 1.2cm2 was set to reproduce
the shape of mouth opening in the exhalation period.

2. METHODS
2.1 Numerical respiratory tract model
As shown in Figure 1, the numerical respiratory tract
model considered in this study includes the oral cavity,pharynx, larynx, trachea, and bronchial tubes reproduced
around fourth bifurcation, and have 38 outlets, andexcludes nasal cavity region for reducing thecomputational load. We confirmed that the flow patterns
in the respiratory tract is independent on the existence ofnasal cavity region before the inhalation exposure
analysis using the respiratory tract excluding nasal cavityregion. The airway model has already been developedbased on CT data extracted healthy human male. Thismodel consists of approximately 2×106 polyhedral
elements and very fine prism layers in the near-wallregion. Moreover, for simulation of the inhalation of ecigarette vapor, a circular inlet opening having a diameterof 8.0mm was set to simulate a stream of vapor comingfrom the mouth directly into the pharynx region. In

2.2 Transient puffing profiles
To investigate the effect of puffing behaviours in termsof total respiratory uptake, two types of puffing profiles
based on the measurement data from Vansickel et al.
were prepared in this study. The exhalation duration of
two profiles was set a same value of 1.8s due to the factthat no difference was observed in exhalation duration
between cigarette smokers and e-cigarette users(Farslinos et al., 2013). The exhalation profile wasdescribed by sine function based on the exhalation
duration and inhaled volume. In this study, two types oftransient puffing profiles shown in Figure 2 were set in
the inlet/outflow opening (see Figure 1) as inflow
boundary condition.

Fig. 1. Numerical respiratory tract model.

- 17 -where Ca,0 and Ct,0 are wall surface contaminantconcentrations of the air and tissue phases, and Ca,1 andCt,1 are defined as the concentrations of the closest
elements to the wall surface of the air and tissue zones.
The wall surface contaminant concentrations arecalculated using a sufficient number of iterations.
The numerical and boundary conditions used for theinhalation exposure analyses are summarized in Table 1.
The chemically relevant parameters shown in Table 2 andused in the coupled PBPK–CFD analyses conducted inthis study.

(a)Fig. 3. PBPK–CFD model.
3. RESULTS
Figure 4 shows the time series of the formaldehyde
concentration distributions in the respiratory tract model
under short puffing condition, and Figure 5 shows thetime series of the formaldehyde adsorption flux
distributions on the mucus surface of the respiratory tract
model. The mass rate of formaldehyde transported to the4th generations of the bronchial tube and exhaled to theindoor environment was very high compared to the othercontaminants because the most of inhaled formaldehyde
was adsorbed onto the mucus surface of tongue region
and upper palate. The distributions and contributions ofcontaminants generated from e-cigarette puffing under
the short puffing and long puffing conditions are
summarized in Table 3. The long puffing behaviour leads
to elevated contaminants uptakes in the respiratory tract
model compared to the short puffing behaviour.
Therefore, if the total puff volume is almost same, thelong puffing behaviour with a lower puff intensity will
induce higher total adsorption than the short puffing
behaviour with a higher puff intensity. When we drew acomparison between contaminants in terms of theadsorption rate onto the mucus surface, the adsorption
rate of formaldehyde, glycerol, and nicotine, having
significant higher partition coefficient, were very high.
Furthermore, the diffusion coefficient and metabolic ratecause the differences of the adsorption rate between thesethree contaminants. On the other hand, the adsorption
rate of benzene and toluene, which have very low
partition coefficient, is very small. It leads to high rate ofexhalation by way of mouth opening and remaining
airway without adsorption.

(b)
Fig. 2. Transient puffing profiles. (a) short puff (b) longpuff
2.3. PBPK–CSP–CFD analyses
In this study, we focused on formaldehyde, acetaldehyde,acrolein, benzene, toluene, glycerol, and nicotine as
representative contaminants of e-cigarette vapors, and weapplied the PBPK–CFD model constructed by Tian andLongest (2010) to the numerical respiratory tract model.
In the PBPK–CFD model, the convection and diffusionof the contaminants in the airway are analysed using CFD,
while their absorption/diffusive transport, metabolic
clearance, and elimination in the respiratory tissue which
composes three layers: mucus, epithelium, and subepithelium, are predicted by the PBPK model. The
concept and governing equations of the PBPK–CFD
model are shown in Figure 3.
In this simulation, in order to analyse theadsorption/desorption fluxes with numerical stability, weapplied the double boundary film theory based on thepartition coefficient and the flux conservation between
the air and tissue phases as the Dirichlet boundary
condition. Based on equations of partition coefficient andflux conservation, the wall surface concentrations of theair and tissue phases can be represented by equations (1)and (2), respectively:æö æ D DxöD Dx
Ca ,0 = ç Ca ,1 + t a Ct ,1 ÷ ç1 + t a Pt:air ÷DDxDDxatatèø èø(1)æö æ D Dxt 1 ö
D DxtCt ,0 = ç Ct ,1 + a
Ca ,1 ÷ ç1 + a÷DDx
Dt Dxa Pt:air øtaèø è(2)- 18 -

Table 1. Numerical and boundary conditions for inhalation exposure analyses.
Turbulence ModelMeshAlgorithmScheme

Low Re number type k–ε model (Abe–Kondoh–Nagano Model)
9.0M mesh elements (unstructured and prism)
SIMPLE (unsteady)Convection term: Second upwind scheme

Inflow boundary

Qin = transient cigarette smoking profiles (see Figure 2)
kin = 3/2 (Uin ´ 0.1)2, e in = Cµ 3/4kin3/2lin
Tin = 45 °C
Cin = 0.7 µg/ml (formaldehyde), 0.22 µg/ml (acetaldehyde)

Outflow boundary
Wall treatment

Boundary type: Pressure boundary
Velocity: no slip
Temperature: Twall surface =36.4 °C
Contaminant: calculated by equations (3) and (4)Table 2. Physical properties of target chemicals
Formaldehyde
Diffusivity [cm2/s]
Air (Da)
0.18
Mucus (Dm)
2.00×10-5
Tissue (Dt)
6.67×10-6
Blood (Db)
3.03×10-6
Partition coefficient (P)Mucus:Air (Pma)
3.65×104
Tissue:Mucus (Ptm)
0.831
Blood:Tissue (Pbt)
1.0
Octanol:water (Pow)
2.24
Blood:air (Pta)
3.03×104
Metabolism
Km [µg/m3]
2.01×105
3Vmax [µg/m /s]
1.96×107
Kf [1/s]
1.8×10-2

Acetaldehyde

Acrolein

0.124
1.23×10-5
4.10×10-6
2.67×10-60.105
1.12×10-5
3.73×10-6
2.46×10-6

165
0.824
1.00.457
136

101
0.826
1.00.977
83.1

1.1×109
3.93×107
3.573×10-2

5.0×102
1.12×105
2.0×10-2

BenzeneTolueneGlycerolNicotine

Diffusivity [cm /s]
Air (Da)
Mucus (Dm)Tissue (Dt)Blood (Db)

0.088
9.8×10-6
3.23×10-6
2.20×10-6

0.087
8.6×10-6
2.87×10-6
2.08×10-6

0.08779.3×10-6
3.1×10-6
2.08×10-6

0.065
9.38×10-6
3.13×10-6
1.73×10-6

Partition coefficient (P)Mucus:Air (Pma)
Tissue:Mucus (Ptm)
Blood:Tissue (Pbt)
Octanol:water (Pow)
Blood:air (Pta)

2.65
1.36
1135
3.61

2.31
2.98
1537
6.88

3.25×109
0.826
10.9772.68×109

8.18×106
0.881
1
14.8
7.21×106

2Fig. 4. The time series of the formaldehyde concentration distributions in the respiratory tract model under shortpuffing condition- 19 -Fig. 5. The time series of the formaldehyde adsorption flux distributions on the mucus surface of the respiratory
tract model
Table 3. Distributions/contributions of contaminants generated from e-cigarette puffing.

Shortpuff

Long
puff

Formaldehyde
Acetaldehyde
Acrolein
BenzeneTolueneGlycerolNicotine
Formaldehyde
Acetaldehyde
Acrolein
BenzeneTolueneGlycerolNicotine

Total mass of
inhalation (µg)
45.82 (100%)
14.40 (100%)
1.31 (100%)
0.108 (100%)
0.205 (100%)
2173 (100%)17.67 (100%)
47.92 (100%)15.06 (100%)
1.37 (100%)0.113 (100%)0.215 (100%)
2273 (100%)
18.48 (100%)

Total mass of
exhalation by wayof mouth opening(µg)
2.02 (4.4%)
2.95 (20.5%)
0.35 (27.2%)
0.066 (61.7%)
0.126 (61.4%)
204.8 (9.4%)
2.18 (12.4%)
2.18 (4.6%)
2.53 (16.8%)
0.30 (22.2%)
0.065 (58.1%)
0.124 (57.6%)
198 (8.7%)
2.02 (10.9%)

4. DISCUSSIONS AND CONCULUSIONS
We have been tackling the development of numericalrespiratory tract model for coupled PBPK–CFD analyses
and have reported preliminary analysis results. In ourprevious study, we analyzed the formaldehyde
adsorption flux distribution using the normalized
partition coefficient concept in which the reactiondiffusion resistance in the tissue was reduced to a surface
reaction and PBPK analysis was omitted (Kuga, et al.,2017). Moreover, we analyzed formaldehyde andacetaldehyde adsorption flux distribution using coupledPBPK–CFD model with two compartments; mucus +epithelium and sub-epithelium which can simultaneously
analyze the diffusive transport in respiratory tissues(Kuga, et al., 2018). In this study, for improvement ofprediction accuracy of inhalation exposure, we modified
the model separating mucus and epithelium
compartments and elaborately estimated the physicalparameters of target chemicals. The modification has
theoretically improved the prediction accuracy of oursimulation. Based on our understanding, the numericalsimulation of the respiratory tract using coupled PBPK–
CFD analyses might be the only effective, quantitativeand reasonable way to conduct inhalation exposure
assessments. Furthermore, the differences of theadsorption rates of different chemical contaminants are

Total mass ofadsorption on airmucus interface(µg)
43.79 (95.6%)
10.96 (76.1%)
0.86 (65.8%)
0.002 (1.8%)
0.005 (2.5%)
1956 (90.0%)
15.26 (86.3%)
45.71 (95.3%)
12.20 (81.0%)
0.99 (72.3%)
0.0024 (2.1%)
0.006 (3.1%)
2061 (90.7%)
16.20 (87.6%)

Total mass ofcontaminantsremaining airway
without
absorption (µg)
0.006 (0.0%)
0.50 (3.4%)
0.09 (7.0%)
0.039 (36.5%)
0.074 (36.1%)
12 (0.6%)
0.22 (1.3%)
0.02 (0.1%)
0.34 (2.2%)
0.07 (5.5%)
0.045 (39.7%)
0.084 (39.3%)
14 (0.6%)
0.26 (1.5%)

considerable. Hence, coupled PBPK–CFD analyses
which can predict the dynamics of various chemicals in
the air and tissue phases of the respiratory tract should bedeveloped. In this study, we modified coupled PBPK–
CFD analyses and estimated the effect of different
puffing behavior against the first- and second-handhuman health risks of inhalation exposure of e-cigarettesmoke.
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