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Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 44, Issue 1 (2018) 12-23
12
Journal of Advanced Research in Fluid
Mechanics and Thermal Sciences
Journal homepage: www.akademiabaru.com/arfmts.html
ISSN: 2289-7879
I
mpacts of Temperature on Airborne Particles in A Hospital
Operating Room
Nazri Kamsah
1
, Haslinda Mohamed Kamar
1,
∗
, Muhammad Idrus Alhamid
2
, Wong Keng Yinn
1
1
Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Johor, Malaysia
2
Departemen Teknik Mesin, Fakultas Teknik, Universitas Indonesia, Kampus Baru
–
UI, Depok, 16424, Indonesia
ARTICLE INFO ABSTRACT
Article history:
Received 23 February 2018
Received in revised form 15 March 2018
Accepted 6 April 2018
Available online 15 April 2018
A proper ventilation system is necessary for isolating and reducing airborne particles
in a hospital operating room. Most healthcare uses a downward unidirectional
(laminar) flow in the area of the operating table to give a sterile environment to the
patient. However, the unidirectional downward airflow can easily be deviated due to
a buoyancy force induced by heated surfaces such as a person's and medical lamp's
surfaces. Therefore, the goal of this study is to investigate the effects of lamps and
human body surface temperatures on particles distribution in the vicinity of the
operating table inside an operating room. A simplified computational fluid dynamics
(CFD) model of the operating room was developed using commercial software. An
RNG k-epsilon turbulent flow model was used to simulate the airflow while a discrete
phase model (DPM) was used to simulate the movement of the airborne particle of
size 5 μm. Results of CFD simulations show that when the surgical lamp and staff
surface temperatures were prescribed at 45°C and 37°C, respectively, a more
significant amount of particles appear to be on the floor of the adjacent area of the
operating table head section. On average, the particle concentration in the vicinity of
the operating table increases by 16%.
Keywords:
Operating room, inlet air diffuser,
airborne particle, computational fluid
dynamics Copyright © 2018 PENERBIT AKADEMIA BARU - All rights reserved
1. Introduction
Hospital operating room (OR) is a facility inside a hospital where surgical operations are carried
out in a hygienic environment. The environment should hold a free pathological microorganism
atmosphere, and it depends on the quality of air [21]. The air quality inside the operating room is
affected by various types of chemicals such as waste anaesthetic, sterilizing substance and airborne
particles. Usually, these chemicals and particles are referred as contaminants, and most of them are
infectious to the patient and medical staffs. Through a proper distribution of ultraclean air,
infectious particles can be isolated efficiently and diluted, and surgical site infection (SSI) rate could
be controlled. An SSI is defined as any disease that follows an operative procedure and occurs at or
∗
Corresponding author.
E-mail address: haslinda@mail.fkm.utm.my (Haslinda Mohamed Kamar)
Penerbit
Akademia Baru
Open
Access
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Volume 44, Issue 1 (2018) 12-23
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near the surgical incision within 30 days of the process [7, 10]. SSIs are ranked third amongst the
most common Healthcare-Associated Infections (HAI). Nearly 13 - 17% [1-2] and 10 - 40% [22] of
the total HAI cases reported in Europe and the US, respectively, are associated with SSI. Singh et al.
[22] discovered that in over 27 million operations performed annually in the US, approximately
300,000 cases were caused by SSI, of which 8,000 ended up in fatalities. SSIs also contribute to
additional treatment costs and prolong hospital stays. Extra costs of 3 to 29 thousand US dollars has
been wasted on the hospital charges [9].
SSI is originated from microbial contamination of the air, and it depends on the type of surgery,
and the behaviour of staff in the operating room [2, 3, 13]. Various types of microorganisms such as
Staphylococcus aureus, Sphingomonas paucimobilis, Pseudomonas aeruginosa, Stenotrophomonas
maltophilia, Clostridium difficile, Legionella spp. and Pseudomonas aeruginosa commonly exist in
healthcare facilities. However, Staphylococcus aureus and Coagulase-negative staphylococcus
(CoNS) are the main bacterial species found in the operating room, and they are the most common
cause of SSI [11, 17]. SSI cannot be treated by ordinarily used antibiotics due to the increasing
resistance of Staphylococcus aureus to conventional drugs, in which it is known as Methicillin-
resistant Staphylococcus aureus (MRSA). MRSA fits to survive under dry conditions for a more
extended period, especially in a less cleaned area [16]. Several studies suggested that the microbial
level in operating rooms can be evaluated by assessing the number of particulate matters (PMs).
The airborne particles with an aerodynamic diameter ranging from 5 μm to 10 μm are widely
considered as the bacteria-carrying particles [8, 15].
To provide proper distribution of ultraclean air inside an OR requires an exclusive ventilation
system that capable of producing a free particle sediments environment. To fulfil this requirement,
the ventilation system must perform as a dual-functioning machine that could filter the unwanted
residues and remove the existing particles to the adjacent area. The direction of the airflow and the
rate of air-change (ACH) in the OR are the main factors in determining the amount of airborne
particle settlement [12]. Most of operating rooms use laminar airflow (LAF) air-supply systems
which equipped with a high-efficiency particulate air (HEPA) filters or ultra-low penetration air
(ULPA) filters. The HEPA filters are designed to filter 99.97% of particles of a diameter size above
0.3 μm, and the ULPA filters are for filtering 99.999% of particles with 0.12-μm diameter size.
Operating rooms in many developed countries use the ultraclean ventilation systems with the ULPA
filters because they are capable of supplying clean air and provide excellent comfort conditions to
the medical staffs and patient [4, 17].
However, in Malaysia, due to high construction and maintenance costs of the latter system,
only the LAF air-supply systems with the HEPA filters are widely used. The LAF provides
unidirectional airflow ventilation in the OR where the air supply diffuser is located at the ceiling
directly above the operation area, with the low-level exhaust outlets at the room edge. Such the
unidirectional laminar flow pattern is achievable with an air velocity at 0.46 m/s or below [5].
However, sufficient amount of clean air with such magnitude of air velocity does not assure the LAF
system to provide the desired unidirectional airflow pattern. Obstacles found along the airflow path
such as a surgical lamp and person could also affect the airflow streamline. The effects could be
remarkable if these barriers are also dissipating heat and cause a rise in their surface temperatures.
The hot surface objects could form a buoyancy force due to density difference in the adjacent air.
The buoyance-driven airflow around the human body and lamps capable of deviating the airflow
streamline and increasing bacteria-carrying particles toward the surgical wound [5].
Therefore, in this study, a steady-state numerical analysis was carried out to investigate the
effects of surface temperatures of surgical lamps and medical staffs on particles distribution inside
an operating room. The OR was equipped with a LAF system, and the analysis was concentrated in
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the operation area which is in the vicinity of the operating table. Particles with the size of 5 μm
diameter were assumed to discharge from exposed faces of the surgical staffs at a given mass flow
rate. A simplified model of the operating room was developed using Computational Fluid Dynamics
(CFD) software. An RNG k-epsilon turbulent model was employed to simulate the airflow while a
discrete phase model (DPM) was used to simulate the transport of the particles.
2. Methodology
2.1 Simulations of Air Flow and Particles
A simplified three-dimensional CFD model of the operating room was developed based on the
literature. The operating room was modelled with a vertical air supply system and four horizontal
outlet grilles. The model consists of an operating table, an air inlet diffuser, four air exhaust grilles,
four medical staffs and two surgical lamps as shown in Figure 1. The air inlet diffuser was placed at
the ceiling of the operating room, directly above the operating table. Figure 2 illustrates the
dimensions of the operating room CFD model.
Fig. 1. Features of operating room CFD model
Fig. 2. Dimension of CFD model of the
operating room (all dimensions are in
meter)
Nurse 2
Outlet 3
Outlet 4
Doctor 2
Patient
Surgery Table
Outlet 2
Outlet 1
Nurse 1
Inlet
Surgery Lamp 2 Surgery Lamp 1
Doctor 1
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The dimensions of the staffs, operating table and surgical lamps are given in Table 1.
Table 1
Dimensions of the medical staff, operating table and surgical lamp
Model Dimensions
(Length (m)
Width (m)
Height (m))
Medical staff
- Body
-
Head
- Hand
- Leg
Patient
- Body
- Head
- Hand
- Leg
Surgical lamp
Operating table
2.2 Meshing of the Computational Domain
The CFD computational domain was meshed using an unstructured grid of tetrahedral elements
as shown in Figure 3. A volume meshing option with a skewness of 0.67 was chosen to enable
automatic meshing process. Mesh refinement was performed in the areas where a significant
variation of airflow field occurred, precisely, close to the supply air diffusers, exhaust grilles, and
surgical lamps.
Fig. 3. Meshing of the operating room CFD model
2.3 Baseline Case Model Boundary Conditions
A baseline case model was developed to evaluate particles distribution in the vicinity of the
operating table in the operating room when the effects of surgical lamps and medical staffs surface
temperatures were not taken into account. The inlet air velocity of 0.32 m/s was prescribed at the
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ceiling mounted air supply diffusers. Also, at such location, the air temperature and turbulence
intensity were fixed at 19°C and 20%, respectively. A zero-gage pressure boundary condition was
specified at each exhaust grille, which serves as the air outlets. The turbulence intensity of 10% and
the air temperature of 21°C were also defined at the outlets. The prescriptions of the air
temperature, inlet air velocity, and turbulent intensities were based on the work of Liu et al. [8]. All
airflow boundary conditions were specified in the direction normal to the respective surfaces. The
air flow inside the operating room was assumed as incompressible.
For the particle boundary conditions, an escape option was specified at the supply air
diffusers and medical staff faces while a trap condition was set on the walls, patient and exhaust
grilles. The trap boundary condition indicates that once a particle touches the solid surface, it
remains, and the particle tracking process would stop. The escape boundary condition signifies that
when the particle reaches the solid surface, the trajectory calculations end Liu et al. [8]. The particle
size of 5 μm diameter or equivalent to 2 g/cm3 was considered as the released particles by each
medical staff. The particles were assumed to be released from the face of the staffs, at a rate of 600
particles/minute which is equivalent to 1.31 × 10−12 kg/s. This value was chosen based on the work
of Liu et al. [8]. The wall, medical staffs, patient, operating table, floor, and ceiling were specified as
wall boundary conditions with a no-slip and stationary features. With this state, the fluid sticks to
the wall, and its flow velocity gradually increases away from the walls. Table 2 summarizes the
baseline case prescribed boundary conditions.
Table 2
Baseline Case Boundary Conditions
Zone Type Boundary conditions
Air diffuser Velocity inlet Velocity magnitude: 0.32 m/s
Temperature: 292 K
Turbulent intensity: 20%
DPM*: escape
Exhaust grille Pressure outlet Gauge pressure: 0 Pa
Temperature: 294 K
Turbulent intensity: 10%
DPM*: escape
Surgical lamps Wall Wall condition: stationary
Shear condition: no-slip
DPM*: trap
Medical staff Wall Wall condition: stationary
Shear condition: no-slip
DPM*: escape
Walls Wall Wall condition: stationary
Shear condition: no-slip
Temperature: 294 K
DPM*: trap
* Discrete phase model specification
2.4 Mesh Sensitivity Test
A mesh sensitivity test was carried out on the CFD model to ensure that the meshing has
negligible effects on the results of the analysis through a grid independent test (GIT) analysis.
Several sets of element numbers were tested under steady-state conditions, and the variation of
airflow velocity at a selected location in the model versus a number of elements was plotted, as
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shown in Figure 4. It can be seen that the airflow velocity was nearly unchanged when 1,174,520
elements were used to mesh the computational domain. Increasing the number of elements more
than 1.2 million gives negligible effects on the airflow velocity of 0.238 m/s. Thus, 1,174,520
tetrahedral elements with non-structured meshing were considered adequate for the airflow and
particle flow simulations and were adopted for all the proceeding simulations.
Fig. 4. Variation of airflow velocity with a number of elements
2.5 Selection of Airflow and Particle Flow Models
The governing equations that describe the fluid flow and particles concentration within an
enclosure are all based on the conservation of mass, momentum, energy and species
concentration. Several flow models are available in the CFD software to simulate the airflow inside
a computational domain [6, 20]. However, according to Liu et al., [8], the RNG k-epsilon model is
adequate to give sufficiently reliable results for assessing a steady-state airflow and particles flow
since it is capable of responding appropriately to the effects of rapid strain and streamline
curvature. The governing equations that describe the fluid flow within an enclosure are all based on
the conservation of mass, momentum and thermal energy [14]. The conservation of mass under
steady state condition is given by Equation (1),
(1)
where u, v and w are the components of velocity in x, y and z directions, respectively. The
momentum equations in x, y and z directions are expressed by Equations (2), (3) and (4),
respectively,
!
!
!
" #
(2)
$
$
$
" #
(3)
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$
$
$
"#
(4)
where g is the gravity acceleration, is the effective viscosity, p is the pressure, R
i
is the source
term for distributed resistance (suffix i is x, y and z) and # is the viscous stress. The energy equation
is given in the following form:
%&
'
(
)
%&
'
(
)
%&
'
(
)
%&
'
(
)
*
+
,
"
*
+
,
"
*
+
,
" -
.
/
0
1
.
2
(5)
where C
p
is the specific heat, '
(
is the total temperature, K is the thermal conductivity of air, -
.
is
the viscous work term, 1
.
is the volumetric heat source, 2 is the viscous heat generation term, and
/
0
is the kinetic energy.
A semi-implicit method for pressure linked equations (SIMPLE) scheme was used in solving the
pressure-velocity coupling calculation. The simulation was performed in a steady-state condition
with the second-order upwind discretization scheme. The discretization scheme was selected as
second-order upwind to reduce the effects of numerical diffusion on the solution as it would help
improve the accuracy. Absolute residual value for all conservation equations was set to 1 × 10
-4
except for the energy equation, where it was set to 1 × 10
-6
. The convergence of the airflow velocity
is shown in Figure 5.
Fig. 5. Convergence of baseline case in steady-state
The discrete phase model (DPM) was used for simulating the particles flow in the OR CFD
model. The DPM is based on the Euler-Lagrange approach, and it is appropriate for particles that
occupy a volume fraction of less than 10% regardless of its mass fraction Rui et al. [15]. Many
studies have shown that this model is reliable to be used in modelling particles movement [17-
19].The governing force balance equation for the discrete phase model (DPM) is given in Equation
(6) below,
3!
4
5
3
6
7
%
8
8
)
9
4
%:
5
;:)
:
5
<
4
:
5
(6)
where the first term on the right represents a drag force with a function of the relative velocity, the
second term represents a gravity force, and the third term describes the Staffman lift and Brownian
forces. Also,
8
is the fluid velocity,
8
is the particle velocity, ρ is the fluid density,
is the particle
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density, the g
i
is the gravitational acceleration and t is time. The Brownian and Staffman lift forces
are used to model the movement of small particles having sizes ranging from 1 μm to 10 μm.
2.6 Effects of Surgical Lamps and Staffs Surface Temperatures
A parametric analysis was conducted to evaluate the effects of surgical lamps and staffs surface
temperatures on the particles distribution in the vicinity of the operating table. The same baseline
CFD model of the OR was used for such analysis. However, each lamp and medical staff exposed
surfaces were prescribed as wall boundary conditions with uniform temperatures of 45°C and 37°C,
respectively. The exposed surface of the personnel was assumed to be at the body section as
described in Table 1.
3. Results and Discussion
The results of the two different cases are compared to assess the effects of surgical lamps and
medical staffs surface temperatures on the particles distribution in the vicinity of the operating
table in steady-state conditions. Case (a) designates the baseline case, where the effects of surface
temperatures of such bodies are neglected. Case (b) denotes the modified case, where the effects
of surface temperatures of both groups are introduced into the analysis.
Figures 6 (a) and (b) show the airflow patterns inside the operating room in three-dimensional
views for case (a) and case (b), respectively. As can be observed from Figure 6 (b), it was found that
by introducing the surface temperatures of the surgical lamps and medical staffs in the analysis has
developed more vortex currents in the vicinity of the operating table as compared to the baseline
case in Figure 6 (a). A buoyance-driven airflow could cause such phenomena due to the difference
in the air density between the hot surfaces and the adjacent air, which yields in the airflow
deviation from the intended unidirectional airflow. Furthermore, since the lamps and medical staffs
dwell within the operation zone and close to the operating table, further stimulate the unusual
behaviour of the airflow in the vicinity of the operating table.
Fig.
6.
Airflow streamline inside the operating room for (a) Case (a)
–
Baseline case; (b) Case (b)
–
Modified
case
Figures 7 (a) & (b) show the airflow velocity contour in a vertical plane that passes through two
medical staffs who are standing at the head-end of the operating table. It can be seen from the
figures, when the personnel's body surface temperatures are considered in the analysis as shown in
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Figure 7 (b), an outspread contour of the airflow velocity is growing significantly on both left and
right sections of the surgical zone. However, when compared to the baseline case as illustrated in
Figure 7 (a), such effects are lesser. It can also be observed from the two figures that the airflow
velocity gradient on the left section of the surgical zone for case (b) is much higher than for case (a).
The maximum magnitude of the airflow velocities in such area for both cases (a) and (b) are noticed
to be approximately 0.34 m/s and 0.29 m/s, respectively, which is about 17% variation. On average,
the magnitude difference of airflow velocity on the left sections of the operating zone between
cases (a) and (b) is around 23%. In summary, the temperature difference of about 10°C between
the staff body surfaces and the adjacent air could significantly influence the airflow velocity and
interfere the unidirectional flow of the LAF diffuser. Similar conditions can be observed on the right
side of both figures.
Fig.
7.
Airflow velocity contour inside the operating room in a Y
-
Z plane for (a) Case (a)
–
Baseline cas
e; (b)
Case (b) – Modified case
Figures 8 (a) and (b) show the results of particle concentrations inside the operating room in a
vertical plane that intersects through the medical lamps which are located directly below the inlet
air diffuser and above the operating table for cases (a) and (b), respectively. It can be observed in
both cases that a more significant number of particles appear to be on the floor of the adjacent
area of the operating table head section. However, the thickness of the particles layer for case (b) is
more prominent than for case (a), indicates that the effects of medical lamp temperatures on the
particles amount in the vicinity of the operation zone are significant. It can also be seen from both
figures that the highest particle concentration of 5.365 × 10
-4
mg/m
3
accumulates in the region
close to the head section of the operating table. Also, for case (b) the highest particle concentration
of the same magnitude occurs on the operating table. These findings indicate that the accumulation
of particles on the operating table is affected by the temperature difference between the medical
lamps and the next air causes buoyancy effects in the vicinity air. High accumulation of airborne
particles in such area is unfavourable as this would increase the possibility of the particles to settle
on the patient. In the actual surgical procedure, this would enhance the risk of the patient to be
infected by the bacteria carried by the falling particles. In summary, when the surgical lamp and
staff surface temperatures were prescribed at 45°C and 37°C, respectively, on average, the particle
concentration in the vicinity of the operating table increases by 16%.
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Fig.
8.
Particles distribution inside the operating room in an X
-
Y plane for (a) Case (a)
–
Baseline cas
e; (b)
Case (b) – Modified case
Figures 9 (a) and (b) show the results of particle concentrations inside the operating room in a
horizontal plane that passes through the medical staffs who are standing close to the operating
table for cases (a) and (b), respectively. As the particles being released by the medical teams, the
most significant number of particles can be observed in the vicinity of the staff bodies in both cases
(a) and (b) as shown in Figure 9. It can be noticed from Figure 8 (b) that the particles merely
dismissed from the personnel's body and moving toward the exhaust grille which is located at the
left corner of the operating room. It can also be noticed from Figures 9 (a) and (b) that the number
of particles nearby the medical staffs is more substantial for case (b) than the baseline case (a). A
significant deviation of particles movement is undesirable as this would induce the particles to
travel in arbitrary directions which in turn would sink onto the patient wound.
Fig.
9.
Particles distribution inside the operating room in an X
-
Z plane for (a) Case (a)
–
Baseline cas
e; (b)
Case (b) – Modified case
(a)
(b)
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4. Conclusion
A steady-state numerical analysis was carried out to investigate the effects of surface
temperatures of surgical lamps and medical staffs on particles distribution inside an operating room
equipped with a LAF ventilation system. A simplified model of the operating room was developed
using Computational Fluid Dynamics (CFD) software. Particles with the size of 5 μm diameter were
assumed to discharge from exposed faces of the surgical staff at a given mass flow rate. Results of
the CFD simulations show that when the surgical lamp and staff surface temperatures were
prescribed at 45°C and 37°C, respectively, on average, the particle concentration in the vicinity of
the operating table increases by 16%. These findings indicate that the accumulation of particles on
the operating table is affected by the surgical lamps and medical staffs’ temperatures due to
buoyance-driven force effects in the vicinity air.
Acknowledgement
The authors are grateful to the Universiti Teknologi Malaysia for providing the funding for this
study, under the vote numbers of 14H64 and 20H44. Also, by the Ministry of Higher Education
(MOHE) Malaysia under the Research University Grant. The grant was managed by the Research
Management Centre, Universiti Teknologi Malaysia: FRGS Fund with the Vote No. 4F645.
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