key: cord-1048999-7nn4380q authors: Liu, Wenbing; Liu, Li; Xu, Chunwen; Fu, Linzhi; Wang, Yi; Nielsen, Peter V.; Zhang, Chen title: Exploring the potentials of personalized ventilation in mitigating airborne infection risk for two closely ranged occupants with different risk assessment models date: 2021-10-01 journal: Energy Build DOI: 10.1016/j.enbuild.2021.111531 sha: b213162d5127ee2cf68dd19d85b94ae8676f0b45 doc_id: 1048999 cord_uid: 7nn4380q In the context of COVID-19, new requirements are occurring in ventilation systems to mitigate airborne transmission risk in indoor environment. Personalized ventilation (PV) which directly delivers clean air to the occupant’s breathing zone is considered as a promising solution. To explore the potentials of PV in preventing the spread of infectious aerosols between closely ranged occupants, experiments were conducted with two breathing thermal manikins with three different relative orientations. Nebulized aerosols were used to mimic exhaled droplets transmitted between the occupants. Four risk assessment models were applied to evaluate the exposure or infection risk affected by PV with different operation modes. Results show that PV was effective in reducing the user’s infection risk compared with mixing ventilation alone. Relative orientations and operation modes of PV significantly affected its performance in airborne risk control. The infection risk of SARS-CoV-2 was reduced by 65% with PV of 9 L/s after an exposure duration of 2 h back-to-back as assessed by the dose-response model, indicating effective protection effect of PV against airborne transmission. While the side-by-side orientation was found to be the most critical condition for PV in airborne risk control as it would accelerate diffusion of infectious droplets in lateral diffusion to occupants by side. Optimal design of PV for closely ranged occupants were hereby discussed. The four risk assessment models were compared and validated by experiments with PV, implying basically consistent rules of the predicted risk with PV among the four models. The relevance and applicability of these models were discussed to provide a basis for risk assessment with non-uniformly distributed pathogens indoor. (c) (d) The dimensions are in millimeters (mm). 7 Two square diffusers (570 0.57 mm × 570 0.57 mm) were located on the right wall ( Fig. 1(a) ). 8 The conditioned air was supplied to the room from the upper diffuser with a temperature of 9 16.5±1 °C and an airflow rate of 24.3 L/s (corresponding to an air change rate of 2 h -1 ), and was 10 exhausted from the lower diffuser. The settings of temperature and airflow rate were basically 11 complied with the bus regulation GB9673-1996 [4445] . Two high-efficiency particulate air 12 filters (HEPA, filtration efficiency of 99.5%) were respectively installed behind the inlet and 13 exhaust diffusers to maintain the cleanness of the chamber, and the supplied air can bewas 14 considered as clean air free of particlesdropletsparticles. The room air was fully mixed with a 15 measured average temperature of 25±1 °C and relative humidity of 37±3%, which was in line 16 with the standard of a coach bus in cooling condition [4545] . 17 PV was used to provide clean air to the sedentary manikins in the test room with an adjustable 18 flow rate of 3 L/s, 6 L/s, and 9 L/s, respectively. The setup of the PV system is shown in Fig. 19 2. Recirculated room air was filtered by a customized HEPA (filtration efficiency of 99.5%) to 20 maintain the cleanness and then supplied to the breathing zone of the thermal manikin with a 21 downward direction of 23.6° and a relative distance of 0.36 m between the nozzle exit to the 22 nose tip. The supplied air from PV was in isothermal condition with the ambient air. The 23 configuration of PV was designed according to the optimal relative positioning to achieve a 24 better thermal sensation and a higher ventilation efficiency [46] . Previous studies found that the 1 flow pattern of the personalized PV nozzle in aircraft cabins was similar to that of the an 2 ordinary circular nozzle [47, 48] . A circular nozzle with an exit diameter of 0.05080.8 mm was 3 applied to mimic the flow shape of the a jet from the nozzles used in aircraft cabins or coaches. 4 Two breathing thermal manikins were placed in the room to represent two adjacent passengers 5 with different orientations, as shown in Fig. 1 . PVs and PVt were installed in front of manikin 6 Athe source manikin and Bthe target manikin, respectively. Different combinations of the use 7 of PVs and PVt were are considered and listed in Table 1 . 12 1 a PVs is PV applied to the source manikin only; PVt is PV applied to the target manikin only; PVs-t is PV 2 applied to both the source and target manikin with the same flow rate; MV is the mixing ventilation used as 3 the background ventilation alone without using PV. 5 Two breathing thermal manikins (BTMs) placed in the middle of the chamber were used to 6 represent the infected and exposed occupants in close proximity (, as shown in Fig. 1 ). Manikin 7 A represented the infected source, and manikin B was the exposed occupant. Three different 8 relative orientations: side-by-side, back-to-back, and face-to-back were considered in the 9 experiments, which were designed according to the realistic relative positioning between 10 adjacent passengers in vehicles. The distance between the two manikins (0. 65 0 mm, side-by-11 side; 0.850 mm, face-to-back; 0.860 mm, back-to-back.) corresponded to the normal distance 12 for seat arrangements in coach buses or airplanes [45, 49, 50] . 13 The heat release from the manikin's body was approximately equivalent to a metabolic rate of 2 which means the exposed manikin inhales while the source manikin exhales at the same time. 13 Two measuring points ( Fig. 1(b) ) were placed at the mouth opening of the source manikin 's 14 mouth (Point 1) and 0.01 cm under below the nose of the exposed manikin (Point 2), 15 respectively, respectively. as shown in Fig. 1 (b). An aerodynamic particle sizer (APS TSI model 16 3321) with 52 channels was used to measure the number and aerodynamic diameters of the 17 particles with a sampling time interval of 1 s. The measurable aerodynamic particle size range 18 of APS is 0.5-20 μm, and the smallest optical particle size that can be detected by APS is 0.37 19 μm. The efficiency of APS varies with particle size and increases from 30% at 0.5 μm to 100% 20 at 0.9 μm [6263, 643]. In order to prevent the settling loss of particles during the sampling 21 process, a Teflon tube with good anti-stick properties was adopted to connect the APS. 22 Restricted by the atomization pressure, the concentration of droplets released by from the 23 manikin was supposed to be higher than that from real human breathing. The initial 24 concentration of aerosols droplets released from the manikin was thereby normalized with that , which reflects the efficiency of ventilation in certain points or 21 regions in the room. The ε bz in the breathing zone of the exposed occupant can be defined as: where C bz and C e are are droplet concentrations in the breathing zone of the target manikin and 2 the exhaust of the room, respectively. ; C PV was is measured obtained at the PV outlet with a 3 relatively low value as the PV air was is filtered by a HEPA and was is considered as clean air 4 free of droplets. The best inhaled air quality will be achieved when ε bz =0 with all the inhaled 5 air is directly drawn from clean PV air. And ε bz =1 indicates a fully mixing condition in the 6 breathing zone of the exposed occupant and other parts of the room. It also happens with ε bz >1, 7 when C bz is larger than C e , indicating even worse air quality at the breathing zone of the exposed 8 occupant than the background or at the exhaust. The ε bz depicts the mixing degree of exhaled 9 contaminant from the source in the ventilated context, and a lower value indicates better 10 performance of PV in exposure risk control. 12 The intake fraction model (IF) was is used to assess the ratio of the contaminant inhaled by the Where where C in is the inhaled concentration of the exposed person (here C in =C bz , which was 17 measured at P2); C ex is the exhaled concentration of the infected person (measured at P1); M in 18 and M ex are are the flow rates of inhaled flow of the exposed person and exhaled flow of the 19 infected person, respectively; C in (t) and C ex (t) are the inhaled concentration of the exposed 20 person and the exhaled concentration of the infected person at time t, respectively; t in and t ex 21 are the exposure time of the exposed person and the respiratory duration of the infected person, 22 respectively. The intake fraction normally varies between 0 and 1. The exposure level increases 1 with a higher intake fraction value. Where Here, P 1 (t 0 ) is the probability of infection of the susceptible person within an the Table 2 . 21 The volume density (v(t) j ) of the infector's expiratory droplets can be derived from the mass 22 concentration measured in the respiratory area of the exposed person occupant [7574]. Since 1 the initial concentration of the droplets released by from the source manikin in experiment was 2 higher than that released from real human subjects, the concentration of droplets measured at 7 a TCID 50 refers to the mean tissue-culture infectious dose, a unit to describe the quantity of virus. Table 3 .outbreak. 5 8 The q can also be estimated with Eq. (5) on a basis of the viral load in the mouth, the type of 9 respiratory activity (e.g. speaking, breathing), respiratory physiological parameters (e.g. 10 inhalation rate), and activity level (e.g. resting, standing, light exercise) as proposed by 11 Buonanno et al. (2020) [76]. The quanta emission rate of SARS-CoV-2 has been estimated by Buonanno 15 to assessment the infection risk of susceptible subjects exposed in indoor microenvironments 1 more accurately. The traditional model allowed quick assessment without considering the activity of virus and 3 particle characteristics. 4 The viral load emitted by the infected person is defined as quanta emission rate ER q (quanta h 5 −1 ), which is obtained from the epidemiological data from an outbreak. The quanta emission 6 rate of SARS-CoV-2 has been estimated by Buonanno et al. [77] through Eq. (4), and the 7 estimated ER q is equal to 142 quanta h −1 . Here, Where C v is the viral load in the sputum (RNA copies copies/mL −1 )., C i is defined as the 12 ratio between one infectious quantum and the infectious dose expressed in viral RNA copies. , Table 3 is applied to evaluate 20 thefor infection risk of SARS-CoV-2 affected by PV with the improved Wells-Riley model, 21 whichthe improved Wells-Riley model ascan be expressed by Eq. (6) and Eq. (7). Here, n(t) is defined as the quantum concentration (quanta/m 3 ) of the indoor environment at 3 time t, expressed by Eq. (56),. n 0 represents the initial concentration of quanta in the space. , I 4 is the number of infectors. , and V is the volume of exposed space. IVRR (h -1 ) is the infectious 5 virus removal rate in the exposed space, which is the sum of three factors: the air change rate 6 (ACR) via ventilation, the particledroplet deposition on surfaces (k) and the viral inactivation The infection risk R at the total exposure time T is estimated by Eq. (76). Eq. (76) considers the 10 not only the the ventilation removal effect rate, but also deposition and viability inactivation 11 losses of airborne pathogens, which shows would be more realistic compared with improvement Table 34 . 16 The infection risk of the three cases [77, 78] in Table 3 was predictedcalculated by the 17 improved Wells-Riley model (Eq. (7)). Fig.3 shows by substituting the input parameters in 18 Table 4 is shown in Fig. 3 . It shows that the predicted infection risk is good agreement basically 19 consistent with the actual riskvalue by using the improved model. The with a maximum 20 absolute deviation and relative deviation was 0.017 and 15%, respectivelyof merely 6%, 21 indicating the improved Wells-Riley model being reasonable for SARS-CoV-2 risk assessment 22 in enclosed indoor environment. 1 Table 4 . The result of k 16 was equal to 0.305 h -1 , which could be calculated from Table 3 .The deposition rate (k) was 1 evaluated as a ratio between the settling velocity of super-micrometric particle, roughly with 5 [53] . The inhalation rate (IR) of exposed occupants was affected by their activity level, which 6 was equal to 0.54 m 3 h -1 by just sitting or standing. 7 Table 4 obtained from actual cases. Fig.3 shows that the 5 predicted infection risk is roughly consistent with the actual data with a minimal maximum 4 Fig. 4 shows the exposure risk of droplets (ε bz ) for the exposed occupant with different PV 5 combinations and the three relative orientations. The ε bz of MV for all the three relative 6 orientations was close to 1, indicating a relatively uniform mixing condition in the ventilated 7 room with MV alone. It was found that the ε bz side-by-side was the highest among the three 8 orientations, and the ε bz of PVs was also the highest among the three different combinations of 9 PV usesupply. Especially the ε bz of PVs was all larger than 1 with a the side-by-side orientation, 9 The theoretical expression of free jet concentration deteriorationattenuation is shown in Eq. 10 78. C x , C e and C 0 are the centerline concentration at distance x from the nozzle, the 11 concentration at the exhaust of the room and in the outlet of PV nozzle, respectively. K c is a 12 characteristic constant for the PV nozzle, and a is its exit area of the nozzle. When PV supplies 1 clean air without infectious particledroplets, C 0 is then equal to C PV of zero. C bz is equivalent 2 to C x if the breathing zone of the occupant is aligned exactly right with the centerline of the PV 3 airflow, just as the experimental setup in this study. Then Eq. (78) can be converted into the 4 relationship between the contaminant concentration deteriorationattenuation with ε bz . As shown in Fig. 5 and Eq. (87), the ε bz is mainly determined by the characteristic constant K c 7 of the nozzle, the nozzle exit area a and the relative distance between the nozzle exit and the 8 breathing zone of the occupant. This explains why the ε bz was is approximately equal for all the 9 back-to-back or face-to-back cases with PVt or PVs-t. It can also be inferred that the clean air 10 delivery efficiency of the PV nozzle is about 0.47, which agrees well with the tested efficiency 11 of the same PV nozzle by our previous study [8381] . For occupants placed side-by-side, the jet 12 entrains polluted air from both the ambient air and lateral dispersed exhalation, the ε bz is hereby 13 increased and cannot be predicted by Eq. (78) which is used applicable tofor an approximate 14 free jet. 16 Fig. 6 showed shows the IF of the exposed occupant affected by PV with the three different 17 orientations. TIt can be seen that the IF under MV for all the three orientations was was basically 18 similar with a value around 0.01, with the a negligible effect of direct exposure to the exhaled 19 particledroplets from the source. The IF of the exposed occupant was significantly influenced 20 by both the use patterns of PV supply (e.g. PVs, PVt and PVs-t) and the relative orientations to 21 the infectious source. 22 For all tested cases with PVt and PVs-t, the use of PV would lower the IF of the exposed 1 occupant. The exposed IF with PVs strongly depends depended on the relative orientations to 2 the infectious source. The exposed IF under PVs was significantly elevated with the side-by- 15 The use of PVt or PVs-t would reduce the IF of the exposed occupant with similar effectiveness. 16 Clean air was delivered to the inhalation zone of the exposed occupant with the use of PVt for 17 all the experimenttested cases, which showed protective effect against exhaled droplets for the 18 PV user. But However, the IF was not significantly affected by the flow volume of PVt 3 The dose-response model was is used to estimate the infection risk of SARS-CoV-2 of the 4 exposed person with an exposure duration of 2 h, as shown in Fig. 7 . The existing 5 epidemiological data of SARS-CoV-2 in Table 2 was applied in this model to evaluate the effect 6 of PV on infectious pathogen transmission. 14 The back-to-back orientation performed the best in infection risk mitigation by applying PV to 15 the exposure occupant, then was followed by the face-to-back orientation. and tThe side-by- 3 The risk reduction ratio ε r was is defined as the ratio of reduced exposure risk or infection risk 4 caused by PV (I PV, on ) to the risk without PV (I PV, off ), as shown inwhich is given by Eq. (98) . 7 Fig. 9 . shows the ε r obtained from the four risk assessment models considering different PV 8 combinations and relative orientations between occupants. The ε r calculated by the averaged ε bz 9 and IF is equivalent and they are depicted with the same columns in Fig. 9 . The equivalence 12 It can be seen that from Fig. 9 that when PV was is supplied to the exposed occupant, it would 16 When the two occupants were placed back-to-back or face-to-back, both PVt and PVs-t were 17 robust in protecting the exposed occupant from airborne infection and the ε r was all mostly 21 PV performed the best for the back-to-back orientation in mitigating the airborne infection risk 22 between the two occupants compared with the other two orientations. Then was followed by 34 1 the face-to-back orientation. A risk reduction ratio of 0.62 65 was achieved with PVs-t 9 L/s by 2 using the dose-response model of a lasting exposure time of 2 h. PV used for the side-by-side 3 occupants was found with the lowest ε r . In the case of PVs 3 L/s used for the infector alone 4 side-by-side, the infection risk reduction ratio ε r was -0.82 81 as evaluated by the exposure risk 5 index, implying severe negative effect of PVs in airborne disease control side-by-side. The red column indicates a negative value of the reduction ratio. 4 Some previous studies have also noticed the impact of the relative orientations between the 5 occupants on the efficiency of PV in airborne transmission control. The problems and attentions 6 of PV in airborne risk control application wereare discussed in Table 5 [8785] also indicated that the highest exposure risk occurred when provided 2 high volume PV airflow to the infector setting in tandem (the infector being at front). To 3 summarize, for closely ranged occupants, the back-to-back and face-to-back (infector in the 4 back) orientations were are the most preferable to the application offor PV in mitigating 5 airborne transmission risk. 6 Table 5 Remarks on the application of PV in airborne transmission control for different orientation scenarios. Back-to-back • The back-to-back orientation is the best arrangement for the use of PV in reducing airborne infection. • The interventions of the exhalation flow from the infector are minimal compared with other orientations. • The risk reduction ratio for the exposed person is determined by the efficiency of PV itself. A small PV flow volume of >3 L/s can achieve protection effect to the exposed occupant. Face-to-back (the infector at back) The exhaled flow from the infector would not significantly affect the front person with this arrangement. • PV also shows a positive effect in mitigating airborne transmission. Side-by-side • The lateral diffusion of exhaled droplets should be noticed when PV is used for the infector alone. • The risk reduction ratio for the exposed person with PV is the lowest compared with the arrangement face-to-back or back-to-back. Face-to-face • PV provided tofor the exposed person may entrain the exhaled droplets from the infector with a short distance face-to-face (≤0.86 m) and hereby increase the infection risk [38]. • When PV is applied to persons face-to-face, a relative large distance between them is recommended. The critical distance should be further determined by experiments or CFD simulations. Face-to-back (the infector at front) Providing high volume PV flow (＞13 L/s) to the infector sitting in front may cause a higher exposure risk [846-868]. • PV with large flow volume may prohibit the forward spread of exhaled flow from the infector and even facilitate the dispersion of exhaled droplets backward to the exposed person. • PV flow volume should be optimized and relative large distance between the two persons should be recommended. 1 2 In order to achieve a robust PV efficiency in mitigating airborne disease transmission between 3 occupants, some recommendations for the application of PV in a highly occupied environment 4 were are discussed. The discussion was is based on the experimental results in this study. As 5 not all the seat arrangements in indoor environments or vehicles are the preferred orientation 6 like back-to-back or face-to-back, Fig. 10 takes the side-by-side case as an example to illustrate 7 some measures that may be useful to improve the potential efficiency of PV in airborne risk 8 control. 10 Fig. 10 . Sketch of the measures that may be used to improve the ability of PV in mitigating 11 airborne risk mitigationinfection: ① use highly efficient PV ATDs, ② use personalized 12 exhaust, and ③use partitions. 13 Highly efficient PV air terminal devices (ATDs) The optimal performance for most of the ATDs has not exceeded 50-60% of clean air in 15 inhalation [8987] . In this study, the delivery efficiency of clean air for the present PV 16 nozzle is about 47%. Some highly efficient ATDs providing over 90% of clean air should 1 be developed to maximize the protective function of PV in airborne disease control. 3 PV used only for by the infector may facilitate the transport of exhaled infection 4 particledroplets, especially for the side-by-side or face-to-back orientation as discussed in 5 Table 5 . PE used by occupants may directly eliminate exhaled pathogens and be effective between occupants with a sufficient height, which should be further determinedvalidated. 14 5.2 Comparisons of the risk assessment models 15 All the four models used in this study can predict the exposure risk of non-uniformly distributed 16 pathogens in the indoor environment due to the use of PV for the occupants. It can be seen from 17 Fig. 9 that the basic rules of the predicted risk affected by PV are similar among the four models. 18 This is because the core parameter used in the four models for risk prediction is the same, which 19 is the droplet concentration in the breathing zone of the exposed occupant. The relevance and 20 applicability of these four models are summarized in Table 6 . 21 The two indicators ε bz and IF can both predict the effect of PV on the exposure risk of exhaled 22 contaminants from the source, just with different baselines. The exposure risk index ε bz 23 demonstratesd the relative contamination level of exhaled droplets in the breathing zone of the 24 exposed occupant compared with the ambient environment. If the inhaled air of the exposed 1 occupant was is directly drawn from the initial core of the PV jet, ε bz reached will reach a 2 minimum value of zero. The intake fraction IF, which illustrated illustrates the dilution effect 3 of the exhaled droplets with respect to the source. It was, is considered as a better exposure risk 4 assessment model than ε bz to predict the exposure risk level affected by the overall ventilation 5 [9492]. As the increase of the air change rate may both dilute the contaminants in the inhalation 6 and in the exhausts with the same proportion and results in a constant ε bz . In this study, it was 7 is found that ε bz and IF were are both suitable for simple and fast predictions of the exposure 8 risk of pathogens by experiments or simulations with tracer gas or tracer 9 particledroparticlesplets, but the infectivity of a specific disease agent was is not considered. The quantity concentration measured in the breathing zone of exposed occupants and the exhaust of the room. The quantity concentration was measured in the source and the breathing zone of exposed occupants. The mass concentration measured in the breathing zone of the exposed occupants and the does-response data was obtained from human infection experiments. The quanta emission rate or measured quanta concentration in the breathing zone of the exposed occupants. 2 Experiments in this study were designed with a PV flow rate ranging from 3 L/s to 9 L/s with 3 three relative orientations. The face-to-face and face-to-back (the infector at front) orientations 4 discussed in Table 5 were not studied with present experiments. In practical applications, the 5 PV terminal device may provide adjustable flow rate varying from 3 L/s to even 20 L/s [87]. 6 The protective effect of PV in mitigating infection risk with higher flow rate was not validated. 7 The relative alignment of PV to the occupant may also vary with different angles or distances 8 compared with present experimental setup, which will affect the efficiency of PV in protecting 9 the occupant against airborne infection. The influences of the motion of human body, the type 10 of PV terminal devices, and the supplied air temperature from PV are still not clearly 11 demonstrated and need further investigation. In the context of COVID-19 pandemic, accurate 12 quantification of the activity and pathogenicity of SARS-CoV-2 is needed, which will be 13 helpful to evaluate the airborne infection risk of the virus by using the infection risk models, in 14 particular for the dose-response model. 15 16 This study explored the potentials of personalized ventilation (PV) in mitigating cross-infection 17 risk between two closely ranged occupants in the indoor environment. Three typical relative 18 orientations were studied, which were side-by-side, face-to-back, and back-to-back. The impact 19 of different combinations of PV used for the infected source and/or the exposed occupant was 20 also considered. To evaluate the effectiveness of PV in reduction reducing the exposure or 21 infection risk to the epidemic disease-COVID-19, four frequently-used risk assessment models 1 were employed and compared. Some meaningful findings of this study were addressed as 2 follows.: 3 (1) When PV airflow was directly supplied to the exposed occupant, it would always play a 10 (2) The protection protective effect of PV for the exposed occupant was primarily dominated be increased with higher flow volume, and a lower risk can would be achieved in a side-by-24 side orientation but the risk was still higher than using mixing ventilation alone. 1 (4) All the four risk assessment models in this study can be used to predict the exposure or Infection risk of SARS-CoV-2 assessed by the dose-response model over time in different relative 2 orientations: (a) side-by-side, (b) face-to-back and(c) back-to-back model for the three relative orientations under the effect of PV. The use of PVs 6 side-by-side increased the infection risk significantly, and the highest infection risk was caused 7 by PVs 3 L/s of 0.92 85 49 over 2 h. The infection risk of PVs side-by-side over 2 h was around 8 0.85 7640 for both 6 L/s and 9 L/s. This is significantly lower than the infection risk predicted 9 by the dose-response model The evaluated infection risk for the exposed occupant didn't vary 13 much with the PV flow rates or the use of PVs, when the exposed occupant applied PV in back-14 to-back or face-to-back orientation. These results were basically in consistent with the change 15 laws of exposure risk evaluated by the exposure risk index in Fig. 4 or intake fraction in Fig. 6. which was slightly lower than the. However, the infection risk of 18 SARS-CoV-2 predicted by the improved Wells-Riley model was higher than that by the dose-19 response model for the two orientations with PVt or PVs-tof 0.23. 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