key: cord-0898217-bucqn2tt
authors: Zhang, Sheng; Niu, Dun; Lu, Yalin; Lin, Zhang
title: Contaminant removal and contaminant dispersion of air distribution for overall and local airborne infection risk controls
date: 2022-04-11
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2022.155173
sha: 5cfc4f6a0138b5ff175709634902db0aca8d0cf0
doc_id: 898217
cord_uid: bucqn2tt

Proper air distribution is crucial for airborne infection risk control of infectious respiratory diseases like COVID-19. Existing studies evaluate and compare the performances of different air distributions for airborne infection risk control, but the mechanisms of air distribution for airborne infection risk control remain unclear. This study investigates the mechanisms of air distribution for both overall and local airborne infection risk controls. The experimentally validated CFD models simulate the contaminant concentration fields in a hospital ward based on which the airborne infection risks of COVID-19 are evaluated with the dilution-based expansion of the Wells-Riley model. Different air distributions, i.e., stratum ventilation, displacement ventilation, and mixing ventilation, with various supply airflow rates are tested. The results show that the variations of the overall and local airborne infection risks under different air distributions and different supply airflow rates are complicated and non-linear. The contaminant removal and the contaminant dispersion are proposed as the mechanisms for the overall and local airborne infection risk controls, respectively, regardless of airflow distributions and supply airflow rates. A large contaminant removal ability benefits the overall airborne infection risk control, with the coefficient of determination of 0.96 between the contaminant removal index and the reciprocal of the overall airborne infection risk. A large contaminant dispersion ability benefits the local airborne infection risk control, with the coefficient of determination of 0.99 between the contaminant dispersion index and the local airborne infection risk.

considerable portion (e.g., 49%) of asymptomatic infections, which may cause further cross infections [1] . There is growing evidence supporting the airborne transmission of COVID-19 [2] . The SARS-CoV-2 RNA has been found in the samples from ventilation openings, central ducts, and HEPA filters in a hospital [3] . The SARS-CoV-2 RNA aerosols have also been detected in hospital wards [4] . The above facts make ventilation become one of the most crucial engineering measures in preventing the infection of COVID-19 [5, 6] .

Air distribution controls the airborne infection risk by diluting the contaminant in the room with clean supply air and extracting the contaminant from the room with the exhausted air [7] . Air distribution of ventilation significantly affects the transmission of airborne pathogens [8] . Berlanga et al. [9] compared the peak value and mean value of inhaled contaminant concentration of the healthcare worker under four different configurations of mixing ventilation and displacement ventilation and highlighted the good performance of displacement ventilation for hospital rooms. The intake fraction (as the exposure index calculated from the aerosol contaminant concentration) of displacement ventilation was maintained at 0.2, while that of mixing ventilation could exceed 0.6 [9] . Liu et al. [10] found that under unilateral downward ventilation, the removal efficiency of aerosol contaminant concentration in the breathing zone was 50% higher than that under bilateral downward ventilation. Cho [11] proposed a new ventilation strategy of "low-level extraction" for isolation rooms which employed two exhaust air grilles on the wall behind the bed at the low floor level and effectively reduced the aerosol contaminant concentration in the breathing zone by up to 66.4%. Zhang et al. [12] found that compared with the ceiling air supply or the upper sidewall air supply, adaptive wall-based attachment ventilation reduced the aerosol contaminant concentration by 15% -47%. Lu et al. [13] found that the exposure risk (indicated by the exhaled contaminant concentration) in the hospital ward was lowest under stratum ventilation compared with that under mixing ventilation, downward ventilation, and J o u r n a l P r e -p r o o f Journal Pre-proof displacement ventilation. Lu and Lin [14] further compared the coughed droplet dispersion patterns under different air distributions and found that compared with mixing ventilation and displacement ventilation, stratum ventilation had better control over the 50 μm diameter droplet by increasing the deposition of the 50 μm diameter droplet. Dao and Kim [15] compared the behaviors of cough droplets by COVID- 19 Existing studies focus on comparing airborne infection risk control performances of different air distributions, but few studies directly look into the airborne infection risk [18] .

The above studies [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] directly concern the aerosol contaminant concentration or the exposure indices based on the aerosol contaminant concentration rather than the airborne infection risk. The aerosol contaminant concentration cannot quantitatively evaluate the airborne infection risk since the airborne infection risk is not linearly related to the aerosol contaminant concentration [20] . The airborne infection risk can be calculated by the Wells-Riley model which relates the airborne infection risk to the exponential function of inhaled quanta by the susceptible (i.e., the synthesis of the infectivity and the quantity of inhaled infectious pathogen) [21] [22] [23] . The Wells-Riley model has been revised to account for the effects of temporal variation [24] , physical distance [25] , close contact [26] , relative humidity experimentally validated to conveniently and robustly account for the temporal and spatial variations of aerosol contaminant concentration. Based on the dilution-based expansion of the Wells-Riley model, Su et al. [30] evaluated and compared the local airborne infection risks of nine students in a classroom served by different air distributions. However, the evaluation and comparison are straightforward, with the mechanisms of air distribution for airborne infection risk control remaining unclear [30] .

The contribution of this study is to reveal the mechanisms of air distribution for controlling both overall and local airborne infection risks, which provides a better understanding of how air distribution controls airborne infection risk and helps to guide the design and operation of air distribution for better airborne infection risk control. This study investigates the mechanisms of air distribution for airborne infection risk control. The contaminant removal and the contaminant dispersion are proposed as the mechanisms which explain the overall airborne infection risk control performances and local airborne infection risk control performances respectively under different air distributions (i.e., stratum ventilation, displacement ventilation, and mixing ventilation) with various supply airflow rates. The remaining part of this paper is organized as follows. In Section 2, the computational fluid dynamics models (CFD) are established, and experiments are conducted

As shown in Figure 1 , the hospital ward is 5.5 m in length, 3.0 m in width, and 2.4 m in height. There are two beds, two nightstands, three ceiling lamps, one ordinary patient (left) (i.e., the susceptible), and one asymptomatic infector (right) in the hospital ward. The height of the bed is 0.5 m. The patients are in supine position (lying with face facing up). Stratum ventilation, displacement ventilation, and mixing ventilation are studied. Stratum ventilation (supply momentum dominated) and displacement ventilation (thermal buoyance dominated) are two representatives of advanced air distributions, and mixing ventilation is a conventional air distribution [31] . For stratum ventilation, the supply air grilles (S1 -S4) are arranged at the wall near the bed and are 1.5 m above the floor level to supply the conditioned air directly into the breathing zone of healthcare workers [32] , and the exhaust air grilles (E1 -E4) are at the wall near the bed and 0.3 m above the floor level. For displacement ventilation, the supply air diffusers (S8 -S13) are at the wall opposite to the beds and 0.3 m above the floor level, and the exhaust air grilles (S5 -S7) are at the ceiling [33] . For mixing ventilation, the four-way supply air diffusers with the discharge angle of 20° (S5 -S7) are at the ceiling, and the exhaust air grilles are the same as those of stratum ventilation. All the supply/exhaust terminals are of the same size (0. where healthcare workers could be. The sampling point P7 is close to the nose of the susceptible. Since the real-time monitoring of airborne pathogen concentration is unrealized [34] , CO 2 exhaled by the infector is used as a biomarker for airborne infection risk evaluation [35] . CO 2 as a biomarker has been validated to be effective for the study of airborne infection J o u r n a l P r e -p r o o f Journal Pre-proof of respiratory diseases [24] . The clothing insulation of patients is 1.38 clo and the metabolic rate is 0.8 met (reclining) [13] . The supply air temperature and supply airflow rate are set to maintain the predicted mean vote in the occupied zone within ±0.5 for thermal comfort [36] .

The detailed boundary conditions are listed in Table 1 .

The Reynolds-averaged-Navier-Stokes (RANS) turbulence model is applied to solve the airflow field. The RNG k-ε model is applied as the turbulence model because it performs well in room ventilation [13] . The governing equations for mass, energy, and momentum are described in Equation 1. Table 2 shows the explanations of the variables in Equation 1.

Where  is the fluid density, kg • m -3 ;  represents the variables; t is the time, s; U is the velocity vector, m•s -1 ;   represents the effective diffusion coefficient for each variable;

S is the source term.

The species transport model is used to solve the dispersion of the exhaled contaminant

Where i Y is the local mass fraction of the exhaled contaminant; i J is the diffusion flux of the exhaled contaminant, kg⋅m -2 ⋅s -1 ; i R is the net rate of production, which is 0; i S is the contaminant generation rate of the source, kg⋅m -3 ⋅s -1 .

The numerical model is solved by the FLUENT software. The standard wall function is applied for the near-wall treatment. The discrete ordinates radiation model is applied to simulate the radiative heat transfer among indoor surfaces. The Boussinesq approximation is J o u r n a l P r e -p r o o f Journal Pre-proof applied for the buoyancy of the airflow. The SIMPLE algorithm is used to couple the pressure and velocity. The second order scheme is used for pressure interpolation. The second order upwind scheme is used for convection terms. Firstly, the steady state of the airflow field is obtained as a reasonable initial condition. Then, the transient evolutions of the air velocity, air temperature, and CO 2 concentration are simulated.

The structured grids for the hospital ward with different spatial resolutions are Compared with the time step of 0.1 s, the relative differences for the air velocity, air temperature, and exhaled contaminant concentration are less than 1.8%, 1.1%, and 2.0% respectively.

The The sampling rate is 30 s. The sampled SF 6 concentration is measured by the photoacoustic gas monitor (INNOVA 1412i) with a measurement accuracy of ±0.06 ppm.

Compared with the studied hospital ward described in Section 2.1, the ward in the environmental chamber is of a larger area and with one more bed and two more manikins.

Although the dimensions of the hospital ward in Figure 2 are different from those of the hospital ward in Figure 1 , the two hospital wards share similar inner configurations and layouts of air distributions. Thus, the experiments can be used for the validation of the CFD

To validate the CFD model, the grid generation strategy and temporal resolution described in Section 2.3 are used. Under stratum ventilation, the air velocity reaches up to 0.58 m/s. The air velocity is particularly high at sampling points P2 and P5 since these two sampling points are within the supply air jets and close to the supply air grilles. Under displacement and mixing ventilation, the air velocity is below 0.25 m/s, and generally lower than 0.15 m/s.

As Then, the inhaled quanta ∫ during the exposure time is calculated. The quantum generation rate (q) of 48 quanta/h of COVID-19 is adopted for the airborne infection risk evaluation [22] . According to reference [43] , 

This study proposes to investigate the airborne infection control mechanisms from 

The airborne infection risks in a hospital ward under stratum ventilation, displacement ventilation, and mixing ventilation with various supply airflow rates are computed using experimentally validated CFD simulations and the airborne infection risk control mechanisms are discussed. The main findings are summarized as follows.

(1) The effects of air distributions and supply airflow rates on both overall and local airborne infection risks are complicated and non-linear. a Four-way diffuser with the discharge angle of 20° is used for mixing ventilation. It is simplified as a square, and the square is divided into four parts with four supply air directions. 

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Plan view of hospital ward

Sheng Zhang: Conceptualization, Formal analysis, Methodology, Writing -original draft, Funding acquisition

Formal analysis, Methodology, Writing -review & editing

Formal analysis, Methodology, Validation, Investigation, Supervision, Writing -Original Draft

Writing -review & editing, Funding acquisition

This study is financially supported by the Top Young Talent