key: cord-0829031-1zn9qcjl authors: Dai, Hui; Zhao, Bin title: Reducing airborne infection risk of COVID-19 by locating air cleaners at proper positions indoor: Analysis with a simple model date: 2022-02-04 journal: Build Environ DOI: 10.1016/j.buildenv.2022.108864 sha: eef0931aec3c8aefab747bc950bffbea5c146263 doc_id: 829031 cord_uid: 1zn9qcjl Portable air cleaners (PACs) can remove airborne SARS-CoV-2 exhaled by COVID-19 infectors indoor. However, effectively locating PAC to reduce the infection risk is still poorly understood. Here, we propose a simple model by regressing an equation of seven similarity criteria based on CFD-modeled results of a scenario matrix of 128 cases for office rooms. The model can calculate the mean droplet nucleus concentration with very low computing costs. Combining this model with the Wells–Riley equation, we estimate the airborne infection risk when a PAC is located in different positions. The two similarity criteria, B(p)(+) and G(p)(+), are critical for characterizing the effect of the position and airflow rate of PAC on the infection risk. An infection probability of less than 10% requires B(p) (+) to be larger than 144 and G(p) (+) to be larger than 0.001. These criteria imply that locating PAC in the center of the room is optimal under the premise that the airflow rate of PAC is greater than a certain level. The model provides an easy-to-use approach for real-time risk control strategy decisions. Furthermore, the placement strategies offer timely guidelines for precautions against the prolonged COVID-19 pandemic and common infectious respiratory diseases. than 10 % requires Bp + to be larger than 144 and Gp + to be larger than 0.001. These 28 criteria imply that locating PAC in the center of the room is optimal under the premise 29 that the airflow rate of PAC is greater than a certain level. The model provides an easy-30 to-use approach for real-time risk control strategy decisions. Furthermore, the 31 placement strategies offer timely guidelines for precautions against the prolonged 32 COVID-19 pandemic and common infectious respiratory diseases. Convincing evidence has shown that human exhaled droplets are closely related to the 38 transmission of infectious respiratory diseases in indoor environments (Chen et The parameters for the dimensional similarity analysis and their units are listed in Table 132 1. Seven similarity criteria were deduced using similarity analysis approach (Table 2) . (Table 2) . To perform MLR, each similarity criterion affecting the droplet nucleus concentration where , ⃗ and are the air density, velocity vector, and concentration of the particles, 176 respectively. is the turbulent diffusivity of . is the sum of the molecular and 177 turbulent dynamic viscosities. ⃗ is the settling velocity of particles. The field measurements indicated that the size of the human exhaled droplet nucleus always has the same direction as gravitation, that is, perpendicularly downward. The above equations were discretized into algebraic equations by the finite volume is the shear stress at the walls. The particle size of the droplet nucleus was divided into four segments between 0. where ̅ + is the normalized total mean droplet nucleus concentration in the office Where , is defined as: Where u is the velocity magnitude. We set three groups of grid numbers: 782217,1532423, and 3910099. The GCI values 264 of the tested group grids were all less than 10 %. Then, CFD cases were simulated based 265 on a grid number of 1500000 to balance sufficient accuracy and calculation speed. The 266 grid diagram at three most representative locations is shown in Fig.2 (d) . The results were checked for multicollinearity by examining the predictor variables' 295 We established an association between the airborne infection probability and droplet where is the infection probability, is the quantum generation rate produced by a 303 source (h -1 ), set as 14 h -1 based on our previous study (Dai and Zhao, 2020) . This value Table 4 . Relative error ( is closely related to Gp + as the airflow mainly determines the droplet nucleus motion, 336 and Gp + represents the degree of effect of the PAC air flow rate relative to the room 337 volume. When Gp + is small, PAC has relatively little effect on the airflow field and 338 vortices near the wall and human body, regardless of location. Hence, the droplet nuclei 339 accumulate in the breathing zone, resulting in a high risk of infection, as in cases 1-4. The infection probabilities of cases 1-4 were larger than 20 %, even as high as 44 % in 341 case 1. By contrast, the Gp + in case 5-8 is larger than 0.001, the infection probability is 342 reduced to less than 20 %, and the Gp + in case 9 is as high as 0.0024, corresponding to 343 a reduction in the infection probability to less than 10 %. This is because the operation It should be noted that blindly increasing Gp + does not always reduce the probability of 349 infection. For example, the Gp + of case 10 was higher than 0.001, but the risk was still 350 as high as 24 %. This implies that the airflow rate of PAC does not entirely characterize 351 its effectiveness in controlling droplet nucleus exposure. We found that increasing Bp + can reduce infection risk by comparing cases 1 and 11, to less than 10 %. In addition, these two criteria are significantly affected by the room 384 size. We suggest that the room area be at least 500 times larger than the PAC outlet area. 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