Mitigation of airborne transmission of COVID virus between occupants in a confined room with an air purifier

Abstract

This study evaluated the effect of ventilation rate and air supply angle of an air purifier using computational fluid dynamics to determine the dispersion of airborne COVID virus exhaled by an infected person. The risk of infection for an occupant was determined based on the virus concentration in the active area and accumulated particle dose within the breathing zone by varying the ventilation parameters. The air purifier was found to provide a local dilution and would block the development of an expiratory jet for a short time to reduce transmission risk. Compared to the case without an air purifier, the maximum reductions were 94.27% in the accumulated dose and 53.2% in the particle count concentration. In the breathing area, the larger air supply angle (90° > 60° > 30°) is better when the ventilation rate was 27.0 m³/h and 40.5 m³/h. Otherwise, 60° air supply angle is preferable where the ventilation rate was 54.0 m³/h. Assessing the results with the grey relational analysis revealed that the relational degree for particle count concentration was greater by varying the ventilation rate than by varying the air supply angle. However, the relational degree according to the accumulated dose was greater by varying the air supply angle than by increasing the ventilation rate. These findings may provide an important control strategy to effectively mitigate the risk of infection in a confined room by using an air purifier.

Keywords

Airborne transmission Ventilation parameters Air purifier Risk mitigation Computational fluid dynamics COVID virus

Introduction

In recent years, respiratory diseases such as SARS, H1N1 and COVID-19 have greatly threatened human health, productivity and life.^1,2 The influenza A (H1N1) subtype caused 17,000 deaths worldwide in 2009–2010.³ The 2019 coronavirus disease (COVID-19) has caused 504,807,758 infected cases in 228 countries, of which 6,223,267 million have died as recorded on 18 April 2022,⁴ and there is still a large number of new cases every day. Research has found that in indoor environments with poor ventilation, airborne particles exhaled when an infected person coughs, talks and breathes,⁵ may accumulate in the air over time and could cause infection to people nearby.⁶ Mixtures of accumulated particles and microorganisms in the air form aerosols that are transported by airflows and could cause infection when inhaled.^7–9 Oliveira et al.¹⁰ found that if two people talk normally in a poorly ventilated room, then particles could be dispersed by indoor air activity from the infected person to remote areas of the room within a few seconds, with an infection risk greater than that of coughing. Alsved et al.¹¹ also found that when a person talks normally, they exhale more particles than when they are breathing, which indicates the risk of infection is higher in talking than when breathing. Additionally, the long-distance transmission risk depends on the background concentration of the airborne particle, while at a short distance, both relatively large droplets and relatively small droplet nuclei may become transmission medium, resulting in a high risk of infection.^12–14

To reduce the short-distance transmission risk, Heating, Ventilation and Air-Conditioning (HVAC) system can be important control against the spread of the disease.¹⁵ Personalized ventilation (PV) was reported to provide clean air for occupants and reduce the risk of infection.¹⁶ Bolashikov and Melikov¹⁷ reviewed different ventilation strategies used for the control of airborne disease and suggested that PV may provide a better protection than total volume room air replacement. In addition to the protective effect of PV, it may facilitate the transport of exhaled particles,¹⁸ and its efficiency depends to a large extent on the relative orientation between the design and occupants.^19,20 In general, ventilation is an effective method to reduce the risk of infection. In this regard, the published guidelines documented by ASHRAE, REHVA and WHO also point out that better ventilation is a major way to control the spread of airborne disease.²¹ Similarly, Thatiparti et al.²² found that increasing the ventilation to provide a sufficient level of dilution could prevent the dispersion of viral contamination in confined spaces. The target of ventilation is to replace polluted air with clean air.²³ However, present ventilation systems designed for normal operating conditions may not have the capacity to significantly increase ventilation rates to meet the dilution requirements during a pandemic,²⁴ and the minimum ventilation rate to control respiratory diseases is unclear.²⁵ Additionally, in most public buildings, such as offices, schools and public transportation, the ventilation rate is usually not sufficient for economic purposes.^26,27 More importantly, ventilation via dilution cannot effectively control the short-distance transmission dominated by airborne particles exhaled by infected humans.²⁸

Air purifiers, as an active air purification technology, are used to provide clean air and could effectively reduce indoor infection risk in confined spaces.^29,30 Some studies have shown that air purifiers can remove viral particles from the airflow in indoor environments.^31,32 In the COVID-19 pandemic, the published guidelines documented by ASHRAE recommend the use of high-efficiency filter (HEPA) air purifiers.³³ REHVA considers air purifiers that achieve high-efficiency filter levels to be effective.³⁴ The guidelines for office buildings to deal with emergency operational measures for the new coronavirus also advise that air purifiers should be put into operation during work hours.³⁵ However, there are various types of air purifiers that can impact the distribution of indoor pollutants due to their different ventilation parameters, such as ventilation rates and air supply angles,³⁶ and might increase the transmission risk. Dbouk et al.³⁷ found that the air inlet of an air purifier induces airflow circulation, leading to an increase in the spread of airborne particles in the indoor environment. Although some studies have reported associations between the use of air purifiers and reducing the risk of infection, these are focused more on the effects of air purifiers in confined spaces for longer periods of time such as hours³² and days.³⁶ Indeed, the application of air purifiers to reduce the risk of short-time infection is very important due to the infection that could occur within a few seconds. Thus, an understanding of the role of air purifiers is necessary to protect occupants against the risk of airborne transmission in a short time.

Considering the above, we evaluated the effect of ventilation parameters of air purifiers on the dispersion of airborne particles exhaled by infected persons within a few seconds, and we considered two key factors, namely, ventilation rate and air supply angle. In these cases, the particle accumulated dose in the breathing area and particle count concentration in the activity area were used to investigate and assess the risk of infection when people were indoors. The benefits, ventilation design and possible negative effects of using air purifiers were explored. Meanwhile, the relational grades of ventilation rate and air supply angle on infection risk were determined using grey relational analysis.

Model and computational method

Case description and geometric model

A confined room with an air purifier was modelled in this study. The overall computational dimensions, length (L), width (W) and height (H) of the confined room were 4.0 m (L) × 3.8 m (W) × 3.0 m (H). There was a table measuring 0.6 m (L) × 0.3 m (W) × 0.5 m (H) in the confined room. The dimensions of the air purifier for removing particles were 0.2 m (L) × 0.2 m (W) × 0.25 m (H). The HEPA (high-efficiency particulate air) filter was installed inside the air purifier to ensure a clean air supply (removal efficiency of PM_1.0 is 99.99%), and the air purifier can be adjusted for different air supply rates (27m³/h, 40.5 m³/h and 54.0 m³/h) and different air supply angles. Two occupants facing each other were placed in the confined room (Figure 1). The infected human with a contagious virus is shown in red, and the exposed human is shown in grey in Figure 1. The distance between their mouths was approximately 1.1 m.

Figure 1.

(a) Geometric model of a confined room with an air purifier. (b) The air supply angle of the air purifier.

To determine the effect of the ventilation rate of the air purifier on short-distance transmission and the risk of infection, the ventilation rate of 27.0 m³/h for acceptable indoor air quality for one person (ASHRAE 62.1-2019)³⁸ was considered the basic design condition. Various ventilation rates of the air purifier were set at 27.0 m³/h, 40.5 m³/h and 54.0 m³/h for comparison. The air supply angles of the air purifier were set at 30°, 60° and 90°.³⁹ Details of the ventilation parameters are shown in Table 1. To compare the effect of the ventilation parameters of the air purifier on the exposure of the uninfected occupant to particles, the particle count concentration in the activity area and accumulated particle dose in the breathing zone were used to provide a quantitative evaluation of the risk of infection in the confined room. Case 1, without an air purifier, was set as the reference case. Cases 2, 5 and 8 were set with an air supply angle of 30°. Cases 3, 6 and 9 were set with an air supply angle of 60°. Cases 4, 7 and 10 were set with an air supply of 90°. These were designed to study the effects of different ventilation rates on particle exposure. In addition, Cases 2–4 were set with a ventilation rate of 27 m³/h. Cases 5–7 were set with a ventilation rate of 40.5 m³/h. Cases 8–10 were set with a ventilation rate of 54.0 m³/h. These were designed to study the effect of different ventilation parameters on particle exposure.

Table 1.

The ventilation parameters of the air purifier for the cases studied.

Case	Ventilation rate (m³/h)	Air supply angle (°)
1	—	—
2	27.0	30
3	27.0	60
4	27.0	90
5	40.5	30
6	40.5	60
7	40.5	90
8	54.0	30
9	54.0	60
10	54.0	90

Numerical procedure

Computational fluid dynamics (CFD) modelling was conducted with ANSYS Fluent version 2021R (ANSYS, Inc) in the present study. The standard $k - ε$ turbulence model was used to solve the airflow and temperature fields in the confined room.⁴⁰ The enhanced wall function was employed to simulate the turbulent flow near room walls and human surfaces. An inflation layer was formed around the thermal and fluid boundary layers, and the nondimensional walls at a distance y+ were below five.⁴¹ The governing transport equations were solved by the finite volume method. The numerical scheme used the SIMPLE algorithm to couple the pressure and velocity coupling iterative solution method. A second-order upwind scheme was used to solve the basic discrete governing equations of momentum, energy and turbulence. The indoor temperature difference in the confined room was used in the Boussinesq model.⁴²

The discrete particle model (DPM) of the Lagrangian method was used to simulate particle dispersion in the confined room. According to a previous study,⁴³ due to the high evaporation rate of small droplets, the evaporation effect of small droplets considered in this study could be neglected. The research found that the pressure gradient force, virtual mass force and basset force could be neglected due to being two orders of magnitude less than the drag force.⁴⁴ A previous investigation found that the thermophoretic force and Saffman force play important roles in the turbulent boundary layer.⁴⁵ Therefore, gravity, drag, thermophoresis and Saffman forces were considered in this study to simplify the orbital particle model. The motion of particles was governed by Newton’s second law, which was determined by equation (1):

\frac{d u_{p}}{d t} = F_{D} (u - u_{p}) + \frac{g_{i} (ρ_{p} - ρ)}{ρ_{p}} + F_{i}

(1)

where

u_{p}

is the velocity of the particle,

g_{i}

is the gravitational acceleration,

ρ_{p}

is the particle density,

F_{D}

is the drag force of the particle and

F_{i}

are various forces, such as the mass force, thermophoretic force and Saffman force.

The gravity F₁ of the particle was determined by equation (2):

F_{1} = \frac{π}{6} ρ_{p} d_{p}^{3} g

(2)

where g is the gravitational acceleration and

d_{p}

is the particle diameter.

The drag force F₂ of the particle was determined by equation (3):

F_{2} = \frac{π d_{p}^{2} ρ (u - u_{p}) C_{d}}{8 C_{c}}

(3)

where

ρ

is the air density,

u

is the air velocity,

u_{p}

is the particle velocity and

C_{c}

is the Cunningham correction factor.

When calculating the drag force of fine particles, especially for submicron particles. The actual resistance of particulate matter is less than the value calculated by Stokes formula due to the phenomenon of molecular sliding, and it must be modified. The correction of the Canning correction coefficient can be obtained by equations (4) and (5):

C_{c} = 1 + K n [1.257 + 0.4 \exp (- 1.1 / K n)]

(4)

K n = 2 λ / d

(5)

where

K n

is the Knudsen number,

λ

is the average free path of air molecules,

d

is the particle diameter. In this study, the particle size was 16 μm and the mean molecular free path of air was 0.069 μm. The correction of the Canning correction coefficient was 1.01.

The thermophoretic force F₃ of the particle was determined by equation (6):

F_{3} = - \frac{3 π μ^{2} d H_{2}}{ρ_{a} T} \frac{d T}{d y}

(6)

where dT/dy is the temperature gradient in the y direction and H_th is the thermophoretic coefficient.

The Saffman force F₄ of the particle was determined by equation (7):

F_{4} = \frac{1.62 μ d p^{2} (d u / d y)}{\sqrt{γ / (d u / d y)}} (u - v_{p x})

(7)

where du/dy is the airflow velocity gradient and v_px is the moving force of the particle in the axial direction.

In this study, the discrete random walk (DRW) model was used to calculate the turbulent dispersion of the particles,⁴⁶ which was one of the main mechanisms of particle dispersion due to its association with transient flow fluctuations. In addition, a particle was considered to be trapped once it reached the wall. When the dispersion of aerosol particles is in contact with the boundary of physical model (such as wall surface, air supply vent and exhaust vent), Fluent software applies discrete boundary conditions to determine the conditions of the particle motion, which are respectively set as reflect, trap, interior and escape. Reflect represents the bounce of particles when they collided with the surface of the physical model. Escape represents the particles leaving the calculation domain when they collided with the surface of the physical model. Trap represents the particles colliding with the surface of the physical model. When the particles stopped moving, Fluent would terminate the particle trajectory calculation.

Boundary conditions and grid independence

In this modelling, all the walls of the room were set as adiabatic, and the occupants were set as the solid boundary condition with a constant surface temperature of 304 K and an air exhalation temperature of 306 K.⁴⁷ For the air purifier, the specific velocities and temperatures were adopted for inlet boundary conditions, and the pressure-out was used for the outlet of the air purifier. The breathing flow rate from the mouth of the exposed person was set according to the measured data from Gao et al.⁴⁸ through a user-defined function (UDF), which is shown in Figure 2(a). From the Gupta et al. study,⁴⁹ the flow rate was set as a constant of 0.015 m³/s for talking, as shown in Figure 2(b). The boundary conditions and parameters used in the CFD model are shown in Table 2.

Figure 2.

Boundary conditions for breathing and talking: (a) Flow generated over time for the exposed person. (b) Flow generated over time for the index person.

Table 2.

Boundary conditions and parameters.

Boundary surface	Velocity	Temperature	Particle
Air supply (air purifier)	27.0, 40.5, 54.0 m³/h	297 K	Reflect
Air return (air purifier)	Pressure-out	—	Escape
Wall	No slip	—	Trap
Floor	No slip	—	Trap
Occupant	No slip	304 K	Trap
Mouth of index occupant	UDF	306 K	Escape
Mouth of exposed occupant	UDF	306 K	Escape

In this study, particles were exhaled by an index person through talking. The average particle size exhaled by an index person was given as 16.0 $μ m$ by Chao et al.⁵⁰ According to the Gupta et al. study,⁴⁹ the mouth breathing jet produced by talking was set as 30°. In addition, the duration of talking was set to 5 s. The other person kept breathing while the infected person was talking. For breathing, each breathing cycle was 4 s, and the breathing angle of the nose was 30°.⁵¹

For the grid-independent tests, a CFD model with 929,028 (coarse), 2,976,428 (moderate) and 4,920,099 (fine) poly-hexacore grids was selected for comparison. To achieve high resolution, grid refinement was performed around the air supply inlet and the heat source due to the high velocity and temperature gradients (see Figure 3). The velocity distributions at lines of X = 0.5 m and y = 3 m of the three grids were compared under identical conditions. The results are displayed in Figure 4, show sufficient accuracy was ensured when the number of grids was 2,976,428.

Figure 3.

Mesh of the computational domain (X = 0 m).

Figure 4.

Comparison of the velocity distributions for the three grids (X = 0.5 m, y = 3 m).

Evaluation of particle exposure risk

To evaluate the risk of exposure to particles for occupants in the confined room, the accumulated particle dose was determined by equation (8).⁴⁷ In this study, the particles were suspended in the breathing zone of the occupant and were inhaled by the occupant. Thus, the accumulated dose was increased with time even when the number of particles in the breathing zone was the same.

Q_{i} = \int_{0}^{t} C_{i, t} p d t

(8)

where

Q

is the accumulated particle dose,

p

is the occupant’s breathing flow rate and

C_{i, t}

is the particle concentration in the breathing zone of the occupant.

The particle concentration in the breathing zone was determined by equation (9):

C_{i, t} = \frac{n u m_{t}}{V_{i}}

(9)

where

n u m_{t}

is the total particle number in the breathing zone of the occupant and V_i is the breathing zone of the occupant. The breathing zone was a spherical space centred on the nose of each person with a radius of 0.2 m.⁵²

Grey relational analysis

Grey relational analysis is a method proposed by Deng⁵³ in 1982 to analyze the correlation between reference parameters and comparison parameters. By establishing a grey relational grade through a small amount of data, the importance of the influencing factors can be determined. Grey relational analysis does not lead to discrepancies between quantitative and qualitative results, which can compensate for problems caused by regression analysis, principal component analysis and statistical methods for systematic analysis.⁵⁴ Thus, grey correlation analysis is a good comprehensive evaluation method to determine the importance amongst various parameters.

Grey relational analysis has a wide range of applications in many domains, such as industry, agriculture, economics and energy.⁴¹ In this study, grey relational analysis was used to obtain the importance of each parameter in reducing the risk of infection and particle count concentration. Grey relational analysis proceeds as follows:

The results were normalized to eliminate the dimension of each parameter, that is, to obtain the correct conclusion when making comparisons. equations (10) and (11) show the normalizing method.

{\tilde{X}}_{0} (j) = \frac{X_{0} (j)}{\frac{1}{n} \sum_{j = 1}^{n} X_{0} (j)}, j = 1,2, . . ., n

(10)

{\tilde{X}}_{i} (j) = \frac{X_{i} (j)}{\frac{1}{n} \sum_{j = 1}^{n} X_{i} (j)}, j = 1,2, . . ., n

(11)

where

X_{0} (j)

and

X_{i} (j)

represent the original reference sequence and comparison sequence, respectively.

{\tilde{X}}_{0} (j)

represents the normalized reference sequence and

{\tilde{X}}_{i} (j)

represents the normalized comparison sequence.

The grey relational coefficient $ξ_{0 i} (j)$ was determined by equation (12).

ξ_{0 i} (j) = \frac{\min_{i} \min_{j} | {\tilde{X}}_{0} (j) - {\tilde{X}}_{i} (j) | + ρ \max_{i} \max_{j} | {\tilde{X}}_{0} (j) - {\tilde{X}}_{i} (j) |}{| {\tilde{X}}_{0} (j) - {\tilde{X}}_{i} (j) | + ρ \max_{i} \max_{j} | {\tilde{X}}_{0} (j) - {\tilde{X}}_{i} (j) |}

(12)

Then, the grey relational degree $γ_{0 i}$ was obtained by equation (13).

γ_{0 i} = \frac{1}{n} \sum_{i = 1}^{n} ξ_{0 i} (j)

(13)

The grey relational degree indicates the degree of correlation between the reference sequence and comparison sequence. The grey relational degree ranges between zero and one, and the closer the relationship is between the reference sequence and comparison sequence, the higher the grey relational degree. Thus, the grey relational degree is a good index to determine which ventilation parameters of the air purifier are more important to reduce the risk of infection for the occupant.

Results and discussion

CFD validation

To verify the accuracy of the CFD model for calculating particle dispersion, we validated the CFD simulation by comparing our simulation data with experimental results for a room where displacement ventilation had been installed,⁵⁵ as shown in Figure 5. The comparisons of the velocity, temperature and particle concentration from the simulated and measured results are shown in Figure 6. The results are in reasonable agreement with the experimental data, thus validating the simulation technique presented in this study. As for the validation of velocity and temperature fields, we adopted the same turbulence model, and the Boussinesq assumption was also adopted for air density. The numerical scheme used the SIMPLE algorithm to couple the pressure and velocity coupling iterative solution method. A second-order upwind scheme was used to solve the basic discrete governing equations of momentum. As for the validation of particle dispersion, we used the DPM of the Lagrangian method, and the DRW model was used to calculate the turbulent dispersion of particles.

Figure 5.

The diagram of the environmental chamber and the measurement point layout used by Zhang and Chen.⁵⁵

Figure 6.

Comparison of simulated and measured data at different locations. Note: dimensionless temperature (T − T_supply)/(T_exhaust − T_supply); dimensionless particle concentration (C − C_supply)/(C_exhaust − C_supply).

Particle dispersion in a confined room without an air purifier (reference case)

To research the effect of an air purifier on particle dispersion, the particle distribution exhaled by an infected person in a confined room, without an air purifier, which was used as a reference case, is shown in Figure 7. In a confined room without an air purifier, the airborne particles exhaled by the infected occupant flowed directly to the uninfected occupant within 6.0 s, and then the particles moved upwards due to the thermal plume of the occupant, resulting in the dispersion of particles to the upper part of the room.

Figure 7.

Particle distribution in a confined room without an air purifier (red person is infected person, black person is exposure person).

Figure 7(a) shows the airborne particles were exhaled by infected person, and rapidly dispersed to the susceptible person. As shown in Figure 7(b), the particles exhaled by the infected person flowed to the centre of the area between two occupants 1.0 s after the infected occupant spoke, and the particles did not reach the breathing zone of the occupant. Figure 7(c) shows the particles exhaled by the infected occupant directly reached the head and shoulders of the uninfected occupant 6.0 s after the occupant spoke, and the risk of infection risk was very high over time. Therefore, the uninfected occupant was exposed to particles when the room was confined without the air purifier and the infection risk was increased over time. Figures 7(d) to (f) show most of the particles moved upwards due to the thermal plume of the occupant. However, there was still a large number of particles around the uninfected occupant. Therefore, the uninfected occupant was exposed to the particles when the room was confined without the air purifier, and the infection risk was increased over time.

The effect of the ventilation parameters of the air purifier on particle dispersion

Ventilation parameters have an important impact on the particle distribution.⁵⁶ Thus, Figure 8(a) displays the distributions of particles in a confined room without an air purifier, while Figure 8(b) to (j) show the distributions of particles in a confined room with an air purifier at different ventilation rates and air supply angles.

Figure 8.

Airborne particle distribution in a confined room with an air purifier; (a) without an air purifier (b) ventilation rate 27.0 m³/h, air supply angle 30° (c) ventilation rate 40.5 m³/h, air supply angle 30° (d) 54.0 m³/h, air supply angle 30° (e) ventilation rate 27.0 m³/h, air supply angle 60° (f) ventilation rate 40.5 m³/h, air supply angle 60° (g) ventilation rate 54.0 m³/h, air supply angle 60° (h) ventilation rate 27.0 m³/h, air supply angle 90° (i) ventilation rate 40.5 m³/h, air supply angle 90° and (i) ventilation rate 54.0 m³/h, air supply angle 90°.

To research the effect of the ventilation rate from an air purifier on the dispersion of particles, Figure 8 illustrates the dispersion of particles exhaled by an infected occupant in the confined room under different ventilation rates of 27 m³/h, 40.5 m³/h and 54 m³/h with different angles (30°, 60° and 90°). Figures 8(h) to (j) shows the exhaled particles and airflow under different ventilation rates with an air supply angle of 90°. The particles dispersed towards the uninfected occupant, as shown in Figure 5(h), indicating that the particles exhaled by the infected occupant were not suppressed by the airflow of the air purifier, and the particles could easily penetrate the breathing area of the uninfected occupant. This was attributed to the limitation of the height of the air supply from the air purifier, which was lower when the ventilation rate was insufficient, thus failing to separate the particles exhaled by the infected occupant. As the ventilation rate was increased, the jet flow of the air purifier was stronger, pushing the airflow and particles exhaled by the infected occupant upwards and reducing the number of particles around the breathing zone of the uninfected occupant.

Figure 8(e) to (g) indicates the particles exhaled by the infected occupant at different ventilation rates for an air supply angle of 60°. The particles exhaled by the infected occupant were suppressed by the airflow of the air purifier and moved upwards and backward. When the ventilation rate was increased from 27 m³/h to 54 m³/h, the particles were blocked due to the increasing strength of the jet flow of the air purifier, resulting in a reduction in the number of particles around the breathing zone of the occupant. In Figure 8(b) to (d), the particles dispersed towards the uninfected occupant when the ventilation rate was increased from 27 m³/h to 54 m³/h at an air supply angle of 30°, showing that the particles could easily penetrate the breathing zone of the occupant. Research showed that the airflow of the air purifier was increased with the increased ventilation rate, which blocked the particles and kept them away from the breathing zone of the uninfected occupant.

We also investigated the effect of the air supply angle on the dispersion of particles from the infected occupant. Figure 8(b), (e) and (h) show the particles exhaled by the infected occupant under different air supply angles with a ventilation rate of 27 m³/h. These figures show the number of particles was reduced when the air supply angle was increased from 30° to 90°. However, at any angle, the particles exhaled by the infected occupant flowed towards the uninfected occupant over the airflow of an air purifier, and the uninfected occupant was exposed to the particles, which increased their risk of infection in the confined room. Figure 8(c), (f) and (i) depict the dispersion of particles exhaled by the infected occupant under different angles with a ventilation rate of 40.5 m³/h. The particles were dispersed and flowed forward to the breathing zone of the uninfected occupant with an air supply angle of 30°, as shown in Figure 8(c). When the air supply angle was increased from 30° to 90°, the particles exhaled by the infected occupant were partly suppressed when the air supply angle was increased from 30° to 90°, which moved particles away from the breathing zone of the uninfected occupant.

Figure 8(d), (g) and (j) depict the particle dispersion under different air supply angles with a ventilation rate of 54.0 m³/h. The air supply angles of 60° and 90° controlled the particles exhaled by the infected occupant and kept them away from the breathing area of the uninfected occupant. When the air supply angle was 30°, the particles exhaled by the infected occupant penetrated the airflow of the air purifier towards the uninfected occupant, who was directly exposed to the particles, which increased their risk of infection in the confined room.

The effect of the ventilation parameters of the air purifier on the particle count concentration in the active area

Figure 9 shows the effect of the ventilation parameters of the air purifier on the particle count concentration in the indoor activity area. According to BS EN 12792:2003,⁵⁷ the activity area of a person was defined by a plane from the wall to 0.5 m as the boundary with a height from 0.1 m to 1.3 m above the ground. In the research cases, the infected person continuously exhaled airborne particles for 4–9 s, and the particle count concentration in the active area was increased with time. When the infected person stopped talking after 9 s, the particle count concentration in the active area was reduced. In the reference case, the particle count concentration was 79 #/m³ in the activity area when the time was 34 s. Compared with the reference case, the particle count concentration in the active area with an air purifier was significantly reduced (8.8%–53.2%), and the particle count concentrations in the activity area were dependent on different ventilation rates and air supply angles. The particle count concentration was reduced with an increase in the ventilation rate and the air supply angle. The lowest particle count concentration was 37 #/m³ when the ventilation rate was 54.0 m³/h with an air supply angle of 90°. When the ventilation rate was increased from 27.0 m³/h to 54.0 m³/h, the particle count concentration was reduced with the air supply angle of 30° (from 72 #/m³ to 55 #/m³), 60° (from 62 #/m³ to 45 #/m³) and 90° (from 48 #/m³ to 37 #/m³).

Figure 9.

Comparison of the particle count concentration in the activity area under different ventilation parameters of the air purifier.

The effect of the ventilation parameters of the air purifier on the accumulated particle dose in the breathing zone

To evaluate the overall performance of the air purifier with respect to the risk of exposure, the particulate matter accumulation in the personnel breathing area was analyzed according to the simulation results. Figure 10 displays the effect of the ventilation parameters of the air purifier on the accumulated dose in the breathing zone. Compared with the reference case, the accumulated dose in the breathing zone with an air purifier was significantly reduced (7.64–94.27%). In the confined room without an air purifier, the accumulated dose of the uninfected occupant was 11.00. Notably, the accumulated dose was reduced to 0.63–11.00, depending upon different ventilation rates and air supply angles. The lowest accumulated dose was 0.63 when the ventilation rate was 54.0 m³/h with an air supply angle of 60°. A larger ventilation rate could produce a better performance in terms of the accumulated dose in the breathing zone. With the increase in ventilation rate from 27 m³/h to 54 m³/h, the air supply angle 30°, 60° and 90°, the accumulated dose of the uninfected occupant was reduced from 10.16 to 5.01 (30° angle), from 9.02 to 0.63 (60° angle) and from 8.01 to 0.90 (90° angle), respectively. However, the optimal air supply angle was linked to the ventilation rate. For example, when ventilation rates were 27.0 m³/h and 40.5 m³/h, the accumulated dose was reduced with the increase in the air supply angle. For a ventilation rate of 54.0 m³/h, the accumulated dose was first reduced and then increased.

Figure 10.

Comparison of the accumulated particle dose in the breathing zone under different ventilation parameters of the air purifier.

Grey relational analysis

Using CFD results, the grey relational grades between ventilation parameters and particle count concentration and accumulated dose were determined by grey relational analysis, as shown in Figure 11. For the accumulated particle dose, the grey relational grades for the air supply angle were slightly higher than for the ventilation rate (grey relational grade values of 0.57 and 0.54). This means that the air supply angle of the air purifier had a greater impact than the ventilation rate on the accumulated particle dose in the breathing zone of the occupant. However, the grey relational grade of the ventilation rate was higher than the air supply angle (grey relational grade values of 0.60 and 0.52), which indicates that the ventilation rate had a greater effect on the particle count concentration in the active area. The comparison of these cases indicates that the air supply angle could significantly affect the accumulated dose in the breathing zone of the occupant, and the ventilation rate had an impact on the particle count concentration in the active area.

Figure 11.

Grey relational grades of ventilation parameter of the air purifier on particle count concentration and accumulated dose.

Discussion

Effect of air purifier on infection control

This study demonstrates that the air purifier was effective in mitigating exposure to airborne particles exhaled by infected people, which mainly provides local dilution and air blocking for a short time, avoiding short-distance exposure, therefore, it could help to reduce the risk of infection between people sitting opposite to each other at the same desk. Furthermore, the ventilation rate and air supply angle can have important roles in the dispersion of particles exhaled by an infected occupant, and they caused different impacts on the particle count concentration of the active area and accumulated particle dose in the breathing zone. When considering the link between a higher risk of infection and poor ventilation in indoor environments, we believe that air purifiers may provide a solution for reducing the risk of airborne transmission. In this study, we found that the air purifier provides a local dilution and blocks the development of an expiratory jet for a short time, which may reduce transmission risk. An air purifier is relatively inexpensive, easy to obtain, targets problem areas within a confined room and is easy to operate without resorting to detailed engineering controls and expensive ventilation upgrades.

Although this study focused on airborne transmission for infection, the air purifier had little effect on contact transmission due to contact transmission occurring via direct contact with infected people or indirect contact with contaminated surfaces. This can be avoided by measures such as social distancing, frequent surface disinfection and hand washing.³² In addition, the use of an air purifier provides more surfaces for the attachment of virus-carrying particles. They can contact the deposited particle through physical contact and resuspension and inhalation. Increasing the disinfection frequency is recommended. The filter element of the air purifier should also be replaced regularly to prevent secondary pollution.⁵⁸

Limitations of this study

The findings of this study could serve as a guideline for the improved design and application of air purifiers to formulate effective mitigation control strategies for reducing indoor infection risk. However, this investigation has limitations. In most office (and other) buildings, ventilation systems are installed to ensure air mixing in the space. Therefore, the design of room ventilation may have a great impact on the effect of air purifiers placed face to face between two occupants. In further research, more consideration should be given to the dispersion of particles under the combined effect of ventilation systems and air purifiers. In some cases, the exposure to particles exhaled by the infected occupant was almost zero; in reality, this may not always be true. Since the numerical simulation of the whole process was very time-consuming, in this study, we simulated the dispersion of particles for 30 s after the infected person spoke for 5 s. The settings for the cases in this study was not general, and more cases should be considered in further studies.

This study considered the position of the air purifier at the centre of a desk shared between two people, but in general, the location and configurations of the air purifier may have an effect on indoor pollutant distribution,⁴⁰ and more cases should be considered in further studies. In addition, since anyone may be infected, in some cases, there is no guarantee that placing an air cleaner between two people can protect either of these people.

This study did not consider the body movements, especially the head movements, of occupants, which may have a significant effect on the dispersion of exhaled airflow. Besides, the numerical calculation method may have introduced errors and ignored less influential parameters. Therefore, the simulation results may be different from an actual situation. Generally, the significant factors were controlled during the simulation. Under most conditions, the actual situation of infection risk among people staying indoors is much worse than that was considered in this study due to people talking for longer and staying in the room for longer, which proves the necessity of using air purifiers.

Conclusions

This study investigated the effects of air purifier ventilation parameters on the dispersion of airborne particles exhaled by an infected occupant in a confined room. The results showed that the air purifier provides a local dilution and blocks the development of an expiratory jet for a short time, which may reduce transmission risk. Compared with using no air purifier, the maximum reduction was 94.27% in the accumulated dose, and the maximum reduction was 53.2% in the particle count concentration.

In the activity area, the higher ventilation rate and larger air supply angle could better reduce the particle count concentration. For the breathing area, when the ventilation rate was 27.0 m³/h and 40.5 m³/h, the order of risk of infection according to the air supply angle used, was as follows: 90° > 60° > 30°. When a higher ventilation rate (54.0 m³/h) was used, the order of risk of infection according to the air supply angle used, was as follows: 60° > 90° > 30°.

Based on the grey relational analysis, the relational degree for the particle count concentration was higher for the ventilation rate than for the air supply angle. However, the relational degree for the accumulated dose was higher for the air supply angle than for the ventilation rate. The results of this research should provide guidance for developing a control strategy to mitigate the risk of infection in a confined room by using an air purifier.

Footnotes

Authors’ contribution

All authors contributed equally to the preparation, drafting, review and execution of this paper.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the National Natural Science Foundation of China (Grant U1867221).

ORCID iDs

Chenhua Wang

Chengjun Li

References

Luo

Cao

. The 2019-nCoV epidemic control strategies and future challenges of building healthy smart cities. Indoor Built Environ 2020; 29(5): 639–644.

CWF

. Prevention and Control of COVID-19 transmission in the indoor environment. Indoor Built Environ 2022; 31(5): 1159–1160.

Tellier

. Aerosol transmission of influenza a virus: a review of new studies. J R Soc Interface 2009; 6(Suppl 6): S783–S790.

WorldoMeter . COVID-19 coronavirus pandemic statistics for the world. 2022, https://www.worldmeters.info/coronavirus/ (Accessed 19 April 2022).

Pan

CWF

Liu

. Characterization and size distribution of initial droplet concentration discharged from human breathing and speaking. Indoor Built Environ. Online publication ahead of print 27 June 2022. DOI: 10.1177/1420326X221110975

Nielsen

Wang

Liu

. Measuring interpersonal transmission of expiratory droplet nuclei in close proximity. Indoor Built Environ 2022; 31(5): 1306–1318.

Chen

Cui

. Large-eddy simulation of droplet-laden cough jets with a realistic manikin model. Indoor Built Environ 2022; 31(5): 1271–1286.

Burridge

Fan

Jones

Noakes

Linden

. Predictive and retrospective modelling of airborne infection risk using monitored carbon dioxide. Indoor Built Environ 2022; 31(5): 1363–1380.

Park

Choi

Song

Kim

E K

. Natural ventilation strategy and related issues to prevent coronavirus disease 2019 (COVID-19) airborne transmission in a school building. Sci Total Environ 2021; 789: 147764.

10.

de Oliveira

Mesquita

LCC

Gkantonas

Giusti

Mastorakos

. Evolution of spray and aerosol from respiratory releases: theoretical estimates for insight on viral transmission. Proc Math Phys Eng Sci 2021; 477(2245): 1471–2946, DOI: 10.1098/rspa.2020.0584

11.

Alsved

Matamis

Bohlin

Richter

Bengtsson

Fraenkel

Medstrand

Löndahl

. Exhaled respiratory particles during singing and talking. Aerosol Sci Technol 2020; 54(11): 1245–1248.

12.

Zhai

. Distributed probability of infection risk of airborne respiratory diseases. Indoor Built Environ 2022; 31(5): 1262–1270.

13.

Pallares

Fabregat

. A model to predict the short-term turbulent indoor dispersion of small droplets and droplet nuclei released from coughs and sneezes. Indoor Built Environ 2022; 31(5): 1393–1404.

14.

Hashimoto

Melikov

. Airborne transmission between room occupants during short-term events: measurement and evaluation. Indoor Air 2019; 29(4): 563–576.

15.

Lin

Liu

Zhang

. Systematic summary and analysis of Chinese HVAC guidelines coping with COVID-19. Indoor Built Environ 2022; 31(5): 1176–1192.

16.

Wei

Liu

W B

Wang

Nielsen

. Effects of personalized ventilation interventions on airborne infection risk and transmission between occupants. Build Environ 2020; 180: 107008.

17.

Bolashikov

Melikov

. Methods for air cleaning and protection of building occupants from airborne pathogens. Build Environ 2009; 44: 1378–1385.

18.

Liu

Nielsen

Jensen

Wei

. Short-range airborne transmission of expiratory droplets between two people. Indoor Air 2016; 27: 452–462.

19.

Cermak

Melikov

. Protection of occupants from exhaled infectious agents and floor material emissions in rooms with personalized and underfloor ventilation. HVAC R Res 2007; 13(1): 23–38.

20.

Melikov

. Advanced air distribution: improving health and comfort while reducing energy use. Indoor Air 2016; 26(1): 112–124.

21.

Guo

Xiao

Dai

. Comparison of existing HVAC operation guidelines for COVID-19 epidemic. HV AV 2020; 50(11): 13–19.

22.

Thatiparti

Ghia

Mead

. Computational fluid dynamics study on the influence of an alternate ventilation configuration on the possible flow path of infectious cough aerosols in a mock airborne infection isolation room. Sci Technol Built Environ 2016; 23: 355–366.

23.

Xie

Wang

Tian

Wang

. A study on the three-dimensional unsteady state of indoor radon diffusion under different ventilation conditions. Sustain Cities Society 2021; 66: 102599.

24.

Mak

Gao

Niu

. Tracer gas is a suitable surrogate of exhaled droplet nuclei for studying airborne transmission in the built environment. Build Simul 2020; 13: 489–496.

25.

Leung

Tang

Yang

Chao

CYH

Lin

Nielsen

Niu

J L

Qian

Sleigh

HJJ

Sundell

Wong

Yuen

. Role of ventilation in airborne transmission of infectious agents in the built environment -a multidisciplinary systematic review. Indoor Air 2007; 17(1): 2–18.

26.

Zhao

Liu

Yin

Pei

Xiao

Zhang

Wei

. Field investigation of pollutant characteristics and targeted ventilation control strategies in high-ceiling aircraft spraying workshop. Process Safety Environ Protect 2022; 159: 627–639.

27.

Xie

Wang

Ding

Wang

. Modelling dispersion of radioactive aerosols and occupational dose assessment of workers in a large nuclear plant industrial workshop with a stratified air conditioning system. Environ Technol Innovation 2020; 19(1): 100828.

28.

Zhang

. A new possible route of airborne transmission caused by the use of a physical partition. J Build Eng 2021; 44: 103420.

29.

Barnewall

Bischoff

. Removal of sars-cov-2 bioaerosols using ultraviolet air filtration. Infect Control Hosp Epidemiol 2021; 42(8): 1014–1015. DOI: 10.1017/ice.2021.103

30.

Conway Morris

Sharrocks

Bousfield

Kermack

Maes

Higginson

Forrest

Pereira-Dias

Cormie

Old

Brooks

Hamed

Koenig

Turner

White

Floto

Dougan

Gkrania-Klotsas

Gouliouris

Baker

Navapurkar

. The removal of airborne severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other microbial bioaerosols by air filtration on coronavirus disease 2019 (covid-19) surge units. Clin Infect Dis 2022; 75: e97–e101. DOI: 10.1093/cid/ciab933

31.

Zhao

Chen

. Using an air purifier as a supplementary protective measure in dental clinics during the coronavirus disease 2019 (COVID-19) pandemic. Infect Control Hosp Epidemiol 2020; 42(4): 1–2.

32.

Zuraimi

Nilsson

Magee

. Removing indoor particles using portable air cleaners: implications for residential infection transmission. Build Environ 2011; 46: 2512–2519.

33.

Simmons

. ASHRAE issues statements on relationship between COVID-19 and HVAC in buildings. https://www.ashrae.org/about/news/2020/ashrae-issues-statements-on-relationship-between-covid-19-and-hvac-in-buildings (Accessed 30 June 2022).

34.

REHVA . How to operate HVAC and other building service systems to prevent the spread of the coronavirus (SARS-CoV-2) disease (COVID-19) in workplaces, https://www.rehva.eu/fileadmin/user_upload/REHVA_COVID19_guidance_document_V4_23112020_V2.pdf (Accessed 25 May 2022).

35.

The Architectural Society of China . The guidelines for office buildings to deal with emergency operational measures for the new coronavirus. (T/ASC 08- 2020). (2020-05-06]), http://www.chinaasc.org/news/127036 (Accessed 15 June 2022).

36.

Zhang

Liu

Hou

Yin

Pei

Wang

. Experimental study on the performance of the air purifier in removing the particulate pollutants. J Safety Environ 2016; 16(5): 303–307.

37.

Dbouk

Drikakis

. Air purifiers may do more harm than good in confined spaces with airborne viruses. Phys Fluids 2021; 33: 011905.

38.

Qian

Zhou

Zheng

. Using air curtains to reduce short-range infection risk in consulting ward: a numerical investigation. Build Simul 2021; 14(2): 325–335.

39.

DuBois

Murphy

Kramer

Quam

Fox

Oberlin

Logan

. Use of portable air purifiers as local exhaust ventilation during COVID-19. J Occup Environ Hyg 2022; 19(5): 310–317.

40.

ANSI/ASHRAE 62.1-2019 . Ventilation for acceptable indoor air quality. Atlanta, GA: ASHRAE, 2019.

41.

Xie

Wang

. Numerical investigation of radon dispersion and dose assessment for typical ventilation schemes with an air purifier. Indoor Built Environ 2020; 30(1): 114–128.

42.

Wang

Liu

Xie

. Numerical analysis for the optimization of multi-parameters stratum ventilation and the effect on radon dispersion. J Build Eng 2022; 62: 105375.

43.

Lyu

Feng

Cheng

Liao

. Experimental and numerical analysis of air temperature uniformity in occupied zone under stratum ventilation for heating mode. J Build Eng 2021; 43: 103016.

44.

Baskaya

Eken

. Numerical investigation of air flow inside an office room under various ventilation conditions. Pamukkale Univ J Eng Sci 2011; 12: 87–95.

45.

Chen

Zhao

. Some questions on dispersion of human exhaled droplets in ventilation room: answers from numerical investigation. Indoor Air 2010; 20(2): 95–111.

46.

Ahmadi

. Dispersion and deposition of spherical particles from point sources in a turbulent channel flow. Aerosol Sci Technol 1992; 16(4): 209–226.

47.

Nazaroff WW . Indoor particle dynamics. Indoor Air 2004; 14: 175–183.

48.

Ansys Fluent . User’s and theory guide, 14.5. Canonsburg, PA, USA: ANSYS, Inc, 2014.

49.

Liu

Zhao

Nichols

Bonilha

Derwinski

Auxier

Chen

. Evaluation of airborne particle exposure for riding elevators. Build Environ 2022; 207: 108543.

50.

Gao

Niu

. Transient CFD simulation of the respiration process and inter-person exposure assessment. Build Environ 2006; 41: 1214–1222.

51.

Gupta

Lin

Chen

. Characterizing exhaled airflow from breathing and talking. Indoor Air 2010; 20: 31–39.

52.

Chao

CYH

Wan

Morawska

Johnson

Ristovski

Hargreaves

Mengersen

Corbett

Xie

Katoshevski

. Characterization of expiration air jets and droplet size distributions immediately at the mouth opening. J Aerosol Sci 2009; 40(2): 122–133.

53.

Niu

Gao

Zhu

. CFD study of exhaled droplet transmission between occupants under different ventilation strategies in a typical office room. Build Environ 2011; 46: 397–408.

54.

You

Lin

Wei

Chen

. Evaluating the commercial airliner cabin environment with different air distribution systems. Indoor Air 2019; 29(5): 840–853.

55.

Ju-Long

. Control problems of grey systems. Systems Control Lett 1982; 1(5): 288–294.

56.

Wang

. A new approach to performance analysis of ejector refrigeration system using grey system theory. Appl Thermal Eng 2009; 29(8): 1592–1597.

57.

Zhang

Chen

. Experimental measurements and numerical simulations of particle transport and distribution in ventilated rooms. Atmospheric Environ 2006; 40(18): 3396–3408.

58.

Bai

Chen

. Experimental approaches to heightening the removal effect of the air purifiers on the particles of various sizes. J Safety Environ 2018; 18(6): 2321–2327.