Assessing the Filtration Effectiveness of a Portable Ultraviolet Air Cleaner on Airborne SARS-CoV-2 Laden Droplets in a Patient Room: A Numerical Study

It has been confirmed that SARS-CoV-2 can infect humans via airborne transmission from person to person. Accordingly, there are two concerns about the exhaled airborne SARS-CoV-2 aerosol emitted from the coughs or sneezes of COVID-19 patients: (1) It might infect healthcare providers in the same confined patient rooms; (2) It might enter the main ventilation system and transmit to other patient room as a health threat. Therefore, a portable ultraviolet (UV) air cleaner is designed to mitigate the exposure risks to SARS-CoV-2 laden droplets in the patient room. Using the experimentally validated computational fluid-particle dynamics (CFPD) model, this study simulated the airborne transmission, deposition, and clearance of the COVID-19 virusladen droplets emitted from a virtual patient in a virtual patient room with realistic ventilation conditions and various operating conditions of the portable UV air cleaner. Parameter analysis was performed to investigate how the ventilation conditions and the operation conditions of the sanitizer can influence the effectiveness of the filtration, which are quantified by the reduction of the concentration of virus-laden droplets suspended in the room and escaped from the room into the main ventilation system. Results indicate that the air cleaner is effective in virus-laden droplets clearance if placed in appropriate locations. In addition, maximizing the ventilation flow rate of the air cleaner will provide the best mitigation effect, with the highest filtration efficiency.


INTRODUCTION
In the context of coronavirus disease 2019 , which is highly contagious and can spread among humans via airborne transmission, it is in urgent need for hospitals and other health care facilities to take care of COVID-19 patients. Since it has been confirmed by multiple research teams that SARS-CoV-2 RNA was found in the aerosol samples collected in COVID-19 patient rooms (Morawska et al., 2020), infection controls for the airborne transmission of the SARS-CoV-2 in those patients room are crucial to minimizing the two potential health risks: (1) It might infect healthcare providers in the same confined patient room; (2) It might enter the main ventilation system and transmit to other patient rooms as a health threat.
According to the National Institute for Occupational Safety and Health (NIOSH) (NIOSH, 2015), the most effective way to control exposure to occupational hazards is the elimination, rather than social distancing and wearing personal protective equipment (PPE). Thus, removing and reducing airborne SARS-CoV-2 is the most effective way to protect clinicians and other healthy healthcare workers who will be close to COVID-19 patients. According to such a priority, a novel designed indoor portable ultraviolet (UV) air cleaner has been achieved (see Fig. 1). However, the optimized operation parameters and the filtration efficiency of the sanitizer in the patient room are not known yet, especially under the current ventilation conditions in the hospital.
Therefore, the objective of this study is to evaluate how the novelly designed indoor UV air cleaner can positively affect the air quality by reducing the airborne SARS-CoV-2 laden aerosol droplets emitted from a patient's cough. Employing a well-validated computational fluid-particle dynamics (CFPD) model (Feng et al., 2020), this paper simulated the airborne transmission, deposition, and clearance of the COVID-19 virus-laden droplets emitted from a virtual patient in a virtual patient room with realistic ventilation conditions (Thatiparti et al., 2016). Parameter analysis was performed to investigate how the ventilation conditions and the operation conditions of the sanitizer can influence the effectiveness of the filtration, which are quantified by the reduction of the concentration of virus-laden droplets suspended in the room and escaped from the room into the main ventilation system.

Geometry and Computational Mesh
The proposed computational domain is shown in Fig. 1, including the patient room, main ventilation setup, patient bed, virtual patient, and the UV air cleaner. Specifically, the size of the patient room is 4.8 m (Length) × 4.3 m (Width) × 2.4 m (Height). The dimensions of the patient room are identical to the patient room documented in an existing paper (Thatiparti et al., 2016). The virtual patient has a mouth opening with a hydraulic diameter equal to 20 mm. More details of the virtual patient geometry can be found in previous publications Feng et al., 2020). The UV air cleaner design, the portable UV air cleaner, uses ultraviolet (UV) light to kill the virus in a fully light sealed chamber (see Fig. 1), with 2 axial fans to suck the airborne virusladen droplets in and generate clean air outflow from the bottom.
Mesh independence test has been performed by comparing the localized velocity magnitudes at 100 randomly selected locations in the patient room. Four polyhedron-based meshes were generated with the average mesh element size refined by a factor of 3.375. The second most refined mesh was selected as the final mesh, which contains 9,427,248 elements, 33,834,226 nodes. Compared with the simulation results using the most refined mesh, the average relative velocity magnitude difference percentage is less than 5%.

Governing Equations
An Euler-Lagrange based multiphase flow method has been employed to track the cough  droplet size changes simultaneously with their trajectories individually in the patient room due to the condensation/evaporation induced phase changes (Feng et al., 2020). The governing equations can be found in existing publications Feng et al., 2020).

Boundary Conditions 2.3.1 Transient cough jet waveform
A cough-jet airflow waveform based on the cough characterization data of 25 subjects (Yang et al., 2018;Kuga et al., 2020) is employed in this study (see Fig. 2(a)). Fourier series is used to generate the correlations of the transient airflow waveform applied as the boundary condition at the mouth opening of the coughing patient (see Fig. 1).

Initial size distribution of cough droplets
The widest span of the cough droplet size distribution measurements was used (Duguid, 1946) in this study (see Fig. 2(b)), which ranges from 2 to 2000 µm. The composition of the droplets is 10.4% NaCl and 89.6% water, which is used in previous studies (Haghnegahdar et al., 2019;Feng et al., 2020).

Main ventilation flow conditions
With the two ventilation openings at the ceiling (see Fig. 1), three main ventilation conditions were employed in this study to test the generality of the mitigation effectiveness using the portable UV air cleaner. As shown in Table 1, Cases A1-A3 use ventilation opening A as the exhaust and ventilation opening B as the airflow inlet, Cases B1-B3 use ventilation opening B as the exhaust and ventilation opening A as the airflow inlet, and Cases C1-C3 simulate the condition when the main ventilation is shut down. The main air exhaust Qmain = 225 CFM (382.27 m 3 per hour).

UV air cleaner flow rate and filtration conditions
Three UV air cleaner flow rates are employed in this study to quantify the effect of the operational condition of the UV air cleaner on filtration efficiency. The flow rates are Q as = 0, 210 m 3 h -1 (1X), and 1050 m 3 h -1 (5x) (see Table 1). Cough droplets were assumed to be completely trapped when entering the UV air cleaner.

Numerical Setup
Numerical simulations were performed using ANSYS Fluent 2019 R1 (ANSYS Inc., Canonsburg,   . Convergence is defined for continuity, momentum, and supplementary equations when residuals are lower than 1.0e-4. The residuals for scalar transport equations are set as 1.0e-6 for convergence. Since a single cough releases within less than 0.55 s, a small time step Δtf = 0.005 s was used when time t < 0.6 s, while Δt f = 0.01 s when t ≥ 0.6 s. The total physical time duration simulated is 100 s.

Model Validations
The credibility of the CFPD model has been extensively validated by the comparisons against benchmark experimental data on (1) laminar-to-turbulence airflow patterns, (2) droplet size change dynamics induced by condensation/evaporation, (3) particle depositions in complex computational domains. Details of the model validations can be found in previous publications Haghnegahdar et al., 2018;Haghnegahdar et al., 2019;Zhao et al., 2019;Feng et al., 2020).

Airflow Fields in the Patient Room
Using the normalized velocity magnitude V * , the quasi steady-state indoor airflow fields with different ventilation conditions and operational conditions of the UV air cleaner are visualized at selected planes in Figs. 3(a)-3(f). Specifically, V * is defined as: vortices are due to the combined effect of the stronger convection induced by the sanitizer inflow and the outflow, which are not influenced too much by the main ventilation conditions (see Figs. 3(c) and 3(f)). The double-vortex pattern is not observed when Q AS is low (see Figs. 3(a), 3(b), 3(d), and 3(e)). The above-mentioned comparisons indicate that increased UV air cleaner flow rate Q AS can potentially filter more cough droplets in a unit time. However, the enhanced convection and airflow recirculation may also carry small droplets further away from their generation sources and create a more scattered virus suspension pattern in the patient room. Figs. 3(a)-3(e) also demonstrate that the main ventilation condition will also significantly alter the airflow patterns in the indoor room, which is dominant for the airflow field at locations not close to the UV air cleaner. It is worth mentioning that the localized airflow patterns can significantly influence the cough droplet trajectories, especially for droplets with small aerodynamic diameters. The underlying connections between droplet transmission patterns and the airflow field characteristics are discussed in the following sections, associated with different ventilation and operational conditions.

Droplet Transmission and Deposition Patterns in the Patient Room
Transient droplet suspension patterns associated with different main ventilation conditions and UV air cleaner flow rates are shown in Figs. 4-7. It can be found that the droplet cloud shapes are not influenced significantly by the different main ventilation conditions or UV air cleaner flow rates at t = 0.5 s (see Figs. 4 (a), 5(a), 6(a), and 7(a)), which is close to the end of the cough. Indeed, during the cough, the initial momentum and energy of droplets obtained from the cough are still relatively high, so that the viscous dissipation effect given by the local airflows are negligible. However, the influence of the main ventilation and UV air cleaner flow rate becomes noticeable , and 8(f)). Such scattered deposition patterns are due to the convection direction of the main ventilation system, which is from the opening above the virtual patient head to the right opening. The main ventilation flow enhances the convection of the droplets from , and 8(f)) will shift the droplet cloud deposited on the patient body towards the UV air cleaner, and reduce the number of droplets deposited in the patient room. Such observations are due to the enhanced filtration effect with the higher Q AS . It is interesting to notice that the deposition on the floor downstream to the UV air cleaner in Fig. 8(f) is more intense than what is shown in Fig. 8(e). This is because the high outlet flow from the UV air cleaner in Case C3 can create flow circulation and bring more droplets from the cough to the floor than in Case C1 with the UV air cleaner shut down. In summary, depositions of small SARS-CoV-2 laden cough droplets can be controlled with the optimization of main ventilation conditions, UV air cleaner locations, and the UV air cleaner flow rate.

Main Ventilation Influence on Cough Droplet Transmission and Filtration
To investigate the main ventilation influence on droplet transmission and filtration, Figs. 4 and 5 visualize the airborne droplet transmissions in Case A1 and Case B1 with the UV air cleaner shut down. It can be found in Figs. 4(a)-4(d) that with the exhaust located near the patient bed, more droplets are carried by the suction flow into the main ventilation system (see the red circle in Fig. 4(c)), which may introduce potential exposure risks to other rooms. In contrast, with the , the droplet distributions are more scattered in the room (see Fig. 5(d)) than what is shown in Fig. 4(d). Integrating the observations provided in the previous section, it can be concluded that the main ventilation conditions have a noticeable influence on the transmission of small droplets, but a negligible influence on large droplets. Considering the longer residence time of the small droplets, the main ventilation system needs to be designed well to create preferable airflow distributions in the indoor environment to minimize the escaped droplet number through the exhaust and retain a smaller zone for droplet suspension. The same conclusions are also to be drawn based on the deposition patterns shown in Figs. 8(a)-8(f).

Influence of the UV Air Cleaner Flow Rate on Cough Droplet Transmission and Filtration
Using Cases A1 to A3 as an example to show the influence of the UV air cleaner flow rate on cough droplet transmission, Figs. 4, 6, and 7 visualize the droplet transmission pattern shifts associated with different Q AS . It can be found that with the increase in Q AS , the cough droplet cloud shifts faster to the UV air cleaner at t = 2.0 s and t = 10.0 s. Also, increasing the suction power of the UV air cleaner prevents droplets from being drawn into the main exhaust, as shown at t = 10.0 s. It can also be observed that higher Q AS can remove the virus-laden droplets more effectively, supported by the droplet suspension pattern comparisons between Figs. 4(d), 6(d), and 7(d). The observation can also be supported by the deposition pattern comparisons shown in Figs. 8(a)-8(f). Figs. 8(a)-8(f) also indicate that the UV air cleaner is efficient in filtering small droplets with diameters less than 10 µm. Large droplets with diameters larger than 20 µm deposit before they can be drawn into the UV air cleaner. Increasing QAS further may improve the filtration effectiveness for larger droplets.

Evaluation of the Mitigation Effectiveness
Three variables need to be quantified as the direct indicators of the exposure risk and the effectiveness of the mitigation strategy using the UV air cleaner. The mitigation effectiveness is considered high if: (1) Droplets number fraction filtered by the UV air cleaner is high; (2) Droplets number fraction escaped into the main ventilation system is low; and (3) Droplets number suspended in the patient room is low. Accordingly, three number fractions are defined as follows to facilitate the analysis of mitigation effectiveness, i.e.,

Number of Droplets Filtered Number of Droplets Generated
where η F is the UV air cleaner filtration efficiency, η E is the escaped droplet number fraction, and η S is the suspended droplet number fraction. Figs. 9(a)-9(c) show η F , η E , and η S with different main ventilation conditions and UV air cleaner flow rates. As shown in Fig. 9(a), the filtration efficiency η F is enhanced with the increase in UV air cleaner flow rate in all three main ventilation conditions (see Table 1 for the three main ventilation conditions employed in this study). The improvement in filtration efficiency is due to the increased droplet number flow rate entering the UV air cleaner induced by the stronger suction power. The underlying mechanisms can also be observed from the comparisons of droplet cloud locations and number concentrations in Figs. 4, 6, and 7 (Cases A1, A2, and A3). Specifically, with a higher UV air cleaner flow rate (see Case A3 in Fig. 7), more droplets traveled faster and entered the UV air cleaner, and fewer droplets suspended in the air compared with  cases with low UV air cleaner flow rates (see Case A1 and A2 in Figs. 4 and 6). However, the influence of the UV air cleaner flow rate on the suspended droplet number is dependent on the main ventilation conditions (see Fig. 9(b)). Specifically, increasing the UV air cleaner flow rate will decrease the concentration of the suspended droplets for main ventilation conditions A and C. In contrast, with main ventilation condition B, the suspended droplet number in Case B2 is higher than Case B1. Indeed, the enhanced convection caused by the UV air cleaner in Case B2 can increase the droplet suspension and distribution in the room, which is more dominant than the improved filtration effectiveness. In addition, with the main ventilation system on (Cases A1-A3 and Cases B1-B3), increasing the UV air cleaner flow rate will reduce the escaped droplet number fraction ηE into the main ventilation system (see Fig. 9(c)), indicating a reduced risk of the transmission of the virus from the patient room to other rooms via the ventilation system. Therefore, it can be concluded the highest UV air cleaner flow rate (Q AS = 1050 m 3 h -1 (5X)) can provide the optimized mitigation effectiveness by providing the highest filtration efficiency, lowers suspended and escaped droplet number fractions. In addition, it can also be observed that the filtration efficiencies are the highest with main ventilation condition A than conditions B and C (see Fig. 9(a)). Specifically, η F can be up to 82% approximately. This is because the exhaust in the main ventilation condition A is located above the patient bed, which creates airflow fields that can dissipate the momentum of the cough droplets that move away from the UV air cleaner (see Figs. 3(a)-3(c)). Because the exhaust in main ventilation condition A is the closest to the cough droplet generation site, the escaped droplet number fractions ηE are the highest in condition A compared with conditions B and C (see Fig. 9(c)). Thus, although main ventilation condition A can most effectively reduce the exposure risk in the patient room, it will also create the highest risk with the highest transmission capability of the virus-laden droplets into other rooms via the ventilation system.
In summary, the relative positions among the UV air cleaner, patient, and the main exhaust play a key role in the mitigation effectiveness of the exposure risks to airborne SARS-CoV-2 laden cough droplets.

CONCLUSIONS
In this study, an experimentally validated CFPD model has been employed as a noninvasive assessment tool to quantify the exposure risk and the effectiveness of the mitigation strategy using a UV air cleaner in the patient room to the airborne SARS-CoV-2 laden droplets. The generation, transmission, deposition, and clearance of airborne SARS-CoV-2 laden droplets have been simulated and compared with different main ventilation conditions and UV air cleaner operational conditions in a COVID-19 positive patient room. Major conclusions are listed as follows: • Transmission of cough droplets less than 20 µm in diameter are sensitive to the ventilation conditions and can be filtered by the UV air cleaner more effectively. In contrast, larger droplets will be dominated by the gravitational effect and will be insensitive to the ventilation conditions and UV air cleaner flow rate conditions. • Although increasing UV air cleaner flow rate can enhance the filtration efficiency of SARS-CoV-2 laden droplets, it can also improve the convection in the indoor environment and potentially carry virus-laden droplets further and create a more scattered virus distribution in the patient room. • The relative positions among the UV air cleaner, patient, and the main exhaust play a key role in the mitigation effectiveness of the exposure risks to airborne SARS-CoV-2 laden cough droplets.
The CFPD model employed in this study can provide guidance for the minimization of aerosol residence time as well as provide insight into mitigation strategies via non-pharmacological methods. It will be able to provide quantitative evidence on how to effectively prevent or even eliminate SARS-CoV-2 laden aerosols by introducing appropriate disinfection devices, optimizing the ventilation system, and modulating protocols of human interactions.
For future work, multiple UV air cleaners and room designs will be considered for a more generalized mitigation effectiveness evaluation. The CFPD modeling framework will be further developed and serve as a rapid health risk assessment tool for operational directives when airborne infectious disease outbreaks occur in various indoor environments, such as classroom, office, restaurant, and auditorium (Fig. 10).