
==== Front
PLoS One
PLoS One
plos
PLOS ONE
1932-6203
Public Library of Science San Francisco, CA USA

10.1371/journal.pone.0309730
PONE-D-24-00989
Research Article
Optimizing cabin air inlet velocities and personal risk assessment: Introducing the Personal Contamination Ratio (PCR) method for enhanced aircraft cabin infection risk evaluation
Optimizing cabin air speeds and PCR for infection risk assessment
Tu Renquan Conceptualization Data curation Investigation Methodology Software Validation Visualization Writing – original draft 1
https://orcid.org/0009-0005-4874-6666
Shang Yidan Conceptualization Funding acquisition Investigation Methodology Project administration Supervision Validation Writing – review & editing 2 *
https://orcid.org/0009-0004-8828-3726
Li Xueren Investigation Methodology Validation Writing – review & editing 3
He Fajiang Funding acquisition Investigation Validation 1 *
Tu Jiyuan Funding acquisition Writing – review & editing 3
1 College of Air Transportation, Shanghai University of Engineering Science, Shanghai, China
2 School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
3 School of Engineering, RMIT University, Bundoora, VIC, Australia
Pongpirul Krit Editor
Chulalongkorn University Faculty of Medicine, THAILAND
Competing Interests: NO authors have competing interests.

* E-mail: yidan_shang@163.com (YS); mikehfj@sues.edu.cn (FH)
6 9 2024
2024
19 9 e030973010 1 2024
18 8 2024
© 2024 Tu et al
2024
Tu et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Recurrent epidemics of respiratory infections have drawn attention from the academic community and the general public in recent years. Aircraft plays a pivotal role in facilitating the cross-regional transmission of pathogens. In this study, we initially utilized an Airbus A320 model for computational fluid dynamics (CFD) simulations, subsequently validating the model’s efficacy in characterizing cabin airflow patterns through comparison with empirical data. Building upon this validated framework, we investigate the transport dynamics of droplets of varying sizes under three air supply velocities. The Euler-Lagrangian method is employed to meticulously track key parameters associated with droplet transport, enabling a comprehensive analysis of particle behavior within the cabin environment. This study integrates acquired data into a novel PCR (Personal Contamination Rate) equation to assess individual contamination rates. Numerical simulations demonstrate that increasing air supply velocity leads to enhanced stability in the movement of larger particles compared to smaller ones. Results show that the number of potential infections in the cabin decreases by 51.8% at the highest air supply velocity compared to the base air supply velocity, and the total exposure risk rate reduced by 26.4%. Thus, optimizing air supply velocity within a specific range effectively reduces the potential infection area. In contrast to previous research, this study provides a more comprehensive analysis of droplet movement dynamics across various particle sizes. We introduce an improved method for calculating the breathing zone, thereby enhancing droplet counting accuracy. These findings have significant implications for improving non-pharmacological public health interventions and optimizing cabin ventilation system design.

http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 82370101 https://orcid.org/0009-0005-4874-6666
Shang Yidan http://dx.doi.org/10.13039/501100013285 Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning 0920000016 https://orcid.org/0009-0005-4874-6666
Shang Yidan This research was funded by the National Natural Science Foundation of China (Grant No. 82370101) and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (Project ID: 0920000016). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript. Data AvailabilityAll relevant data are within the paper and its Supporting Information files.
Data Availability

All relevant data are within the paper and its Supporting Information files.
==== Body
pmc1. Introduction

Airborne infectious diseases pose a significant threat to global public health, with the potential to spread rapidly and overwhelm healthcare systems during outbreaks. These respiratory-borne illnesses can lead to widespread infections, affecting millions of people worldwide each year. The transmission of such diseases has become a focal point of research, particularly in light of recent global health crises [1–3]. Among the various airborne infectious diseases, some stand out due to their impact and prevalence. For instance, Respiratory Syncytial Virus (RSV) spreads extensively during certain periods each year, with a median duration of 4.6 months, affecting millions globally [4]. More recently, the COVID-19 pandemic has had an unprecedented global impact, resulting in 775.69 million confirmed cases and 6.95 million deaths [5]. This pandemic has significantly raised awareness about the transmission of respiratory diseases and led to extensive research and vaccination campaigns [6–8]. The emergence of influenza during the later stages of the COVID-19 pandemic further highlights the threat posed by respiratory diseases. These outbreaks have intensified the focus on transmission studies, revealing the potential for rapid spread and severe strain on healthcare resources. In the current globalization period, air travel in particular has become an essential way of linking people worldwide. In this mode of transportation, air cabin plays an important role in spreading infectious diseases [9–12]. Hence, it is vital to create ways to understand how infectious diseases spread within aircraft cabins under different circumstances. Prioritizing research in this area is essential to protecting public health and preventing future outbreaks.

In previous indoor air quality studies, researchers have mostly focused on simulating how droplets spread in closed spaces with either natural airflow or mechanical ventilation systems [13]. These spaces include, but are not limited to, environments such as aircraft cabins, subway, and supermarkets [14–16]. Among these, the study of cabin ventilation systems has attracted considerable attention from scholars due to their unique characteristics and widespread application. This research focus has a notable example in Li et al. [17], who constructed an actual cabin environment to quantify airflow in three distinct directions. Employing large-scale 2D particle image velocimetry (PIV), they compared their experimental findings with computational fluid dynamics (CFD) numerical simulations. This endeavor offered insights into the airflow dynamics within cabin models. Building on this foundation, Cao et al. [18] investigated further into the domain of droplet transmission within cabin environments, considering droplets of varying diameters under different ventilation systems. This study reviewed existing literature data, highlighting the advantages and disadvantages of various parameters. Taking a substantial step forward, Yan et al. [19] built upon the experimental data from Li et al. [17] to validate CFD-simulated airflow patterns within cabin spaces. Notably, this pioneering study introduced the concept of a spherical breathing zone aimed to statistically analyze pollutant concentrations in a defined area, including critical infection probability data. This specific diameter choice aligns with guidelines from the World Health Organization and Safe Work Australia [20–22], which recommend a 30cm range for assessing the breathing zone.

However, Yan et al. [19]’s investigations predominantly focused on droplets of a representative size, which may not be widely applicable to all scenarios. Such an approach falls short in providing comprehensive insights into the diverse spread trajectories and characteristics of droplets of varying sizes. According to the existing measurement and analysis results [23–26], the droplets discharged by the human respiratory system are composed of droplets of different sizes, which are usually categorized into two scenarios. One is that the larger droplets quickly deposit on the ground under the effect of gravity, and another is that the smaller droplets spread with the airflow and gradually deposit at a distance from the discharge outlet. Thus, it is important to consider the influence of the different diameters on the spread trajectories.

The advent of 2020 ushered in a global upheaval with the onset of the COVID-19 pandemic, prompting a surge in research pertaining to the transmission of respiratory droplets within enclosed spaces, particularly aircraft cabins. Kong et al. [27] undertook a significant initiative by simulating the cabin of a commercial wide-body airliner. His groundbreaking work involved an enhancement of the Wells-Riley equation to discern the intricacies of droplet diffusion, thus establishing a direct correlation between droplet dispersion and the ventilation system. This study introduced innovative ventilation systems capable of mitigating infection risks, subsequently offering recommendations for cabin designers. Further, Zee et al. [28] engineered a simulated control group within cabin scenarios. By manipulating airflow rates and initial conditions and observing airflow patterns, droplet diffusion extents, and lifespans within the cabin environment, this method enabled the practical calculation of infection probabilities for passengers. It can be seen from the above that in the research on the spread of droplets in the cabin, the research focus is mainly on how droplets spread with the airflow in the cabin, while there is less research on the spread of droplets near the breathing area of the head of passengers in the cabin. Recognizing the need to address the gaps in research concerning droplet dispersion near passengers’ respiratory zones, Kuga et al. [29] proposed an innovative method to identify stable breathing zones in transient conditions. Their approach accounted for various factors, including passenger postures and air re-inhalation. Furthermore, Abouelhamd et al. [30] extended this method’s application to semi-outdoor settings, examining both steady and transient states. Their research involved simulations across eight wind directions and four speeds, culminating in a revised breathing zone range under different Scale for Ventilation Efficiency 5 (SVE5) values. Notably, this refined methodology offered a more accurate assessment of infection risk by narrowing the scope of the respiratory zone and enhancing breathing zone calculations for broader research purposes. While elucidating breathing zone dynamics is essential, quantifying infection risk remains equally pivotal in indoor pollutant analysis. In this context, Zhang et al. [31] presented a susceptible exposure index that quantified passenger infection risk at various locations but overlooked the critical influence of air supply. Cough droplets, acting as vectors for viral transmission, can be transported through airflow and inhalation. The omission of air supply in such analyses can lead to substantial inaccuracies. To address this, the Wells-Riley model was introduced as a simplified yet influential framework that predicted infection probabilities based on room ventilation and respiratory airflow. Sun et al. [32] not only considered factors that influence the result of the simulation but also proposed a perfect-mixing-based Wells-Riley model [33] was modified by introducing the social distancing index and ventilation effectiveness. They display infection probabilities in various scenarios but neglect the cabin environment’s infection risk. Then Shang et al. [34] took water vapor evaporation into consideration in the simulation, which improved the realism and accuracy of the simulation. Sun et al. [32] made further improvements to the Wells-Riley model. A model was constructed to predict infection risk in offices and suggest social distancing.

In these existing studies, most studies focus on exploring the spread of droplet in the indoor environment, without specific analysis of droplet dispersion in the breathing zone. However, the study on the droplet distribution in the breathing zone only compares a more accurate range of the breathing zone, without combining the outside of the breathing zone, Therefore, it is necessary to combine breathing zone with practical problems. In this study, we’ve developed an advanced computational fluid dynamics (CFD) model to predict how viral droplets spread in aircraft cabins. Our model includes a detailed 3D representation of the cabin, considering crucial factors such as air supply, exhaust locations, simplified manikins, and seating arrangements. We’ve used real measurements from aviation flight simulator data to account for various variables, including gravity, temperature dynamics, air supply patterns, and passenger breathing rates. Our simulations analyze cabin airflow, the path of cough-generated droplets, and how expelled droplets move in three different air supply scenarios. Improved accuracy by calibrating our model using a modified Wells-Riley model, providing more predictable Pathogen Index (PI) predictions in various situations. In addition, we innovatively proposed PCR(Personal comtamination ratio) to predict personal contamination ratios to more intuitively display infection risk and exposure risk distribution. We’ve also adopted a novel breathing zone definition proposed by Abouelhamd et al. [30]. Additionally, we’ve introduced a crucial metric: the ratio of droplets within the breathing zone to total expelled droplets, which indicates passenger infection risk in various cabin areas. Our study compares infection probabilities for passengers in different air supply scenarios and examines the impact of seat placement on droplet spread. Our primary aim is to prevent future disease outbreaks, minimize disease transmission risk, and offer valuable guidance to aircraft cabin designers and disease control agencies. Additionally, we aim to assist aircraft manufacturers in enhancing ventilation systems for a cleaner, more comfortable passenger environment.

2. Methods

2.1. Geometry, mesh and computational setups

This study draws on existing literature and field measurements to investigate droplet transmission within the confines of an Airbus A320 aircraft cabin model. Building upon previous literature [19,27,28,35,36], to optimize computational resources, we have chosen to focus our analysis on a representative subset of the cabin section with three rows. This approach, coupled with periodic boundary conditions, enables us to conduct CFD simulations efficiently. This approach, coupled with periodic boundary conditions, enables us to conduct CFD simulations efficiently. Fig 1 illustrates the computational model of this cabin section with passenger. Ventilation inlets are located at the upper portions of the cabin walls and ceilings, while outlets are placed along the lower sections of the cabin walls.

10.1371/journal.pone.0309730.g001 Fig 1 Computational model of cabin section and passengers.

a: Part of narrow-bodied 3×3 aircraft used to mimic the aircraft cabin environment, restoring the whole cabin through periodic face in post-processing; b: Details of the CSP model. It shows the location of droplet inlet and the geometry details of the model.

When considering computational simulated person (CSP) models, we have created a CSP model with the similar dimensions as the model in Yan et al. [19] (as depicted in Fig 1). Following the recommendations of Kong et al. [27], we have represented the CSP model’s particle injection, located on the face, as a simplified circular inlet with a diameter of 1.24 cm. In accordance with findings from Haselton et al. [37] and Kuga et al. [29], the exhalation zone is identified to span from 27° and 33°. This study set a central value of 30° as the particle injection angle. To optimize the release of droplets, we have positioned the injector at row 2, seat C, as this location aligns with existing literature suggesting it as an ideal point for droplet release, significantly enhancing the transmission dynamics of droplets [38].

As shown in Fig 1, passengers and seats are arranged on two sides of the cabin. There are three rows, each accommodating six persons, making a total number of 18 passengers. To achieve mesh independence, this study experimented with five sets of mesh configurations, utilizing total mesh elements of 2.60 million, 3.92 million, 5.20 million, 6.51 million, 7.84 million, respectively. Significant discrepancies in velocity were observed among the groups with grid numbers of 2.60, 3.92, and 5.20 million, while an increase in mesh elements from 6.51 million to 7.84 million revealed negligible deviation in the velocity field, as illustrated in Fig 2. This indicates that a mesh of 6.51 million elements is suitable for simulating airflow within the computer cabin. Therefore, in our simulations employed a mesh grid totaling 6.51 million polyhedral cells within the cross-section of the three-row cabin, achieving a maximum skewness of 0.81. Fig 3 illustrates the detailed local surface mesh and the periodic surface mesh. Specifically, the grid dimension near the head of the Computational simulated person (CSP) was 5 mm, with a mouth opening mesh size of 1 mm. This configuration is designed to effectively capture variations in the airflow patterns and droplet dispersion near the critical breathing zone. The mesh size around the air supply and exhaust areas is maintained at 18 mm to ensure full coverage of these key regions. In order to simulate the airflow within the cabin, this study set a boundary layer mesh with a first height of 0.002m, and a growth rate of 1.2 on the wall. It was ensued that the y+ values were kept below 5.

10.1371/journal.pone.0309730.g002 Fig 2 Mesh independence test based on velocity field of the minimum air supply.

10.1371/journal.pone.0309730.g003 Fig 3 The surface mesh of the computational model.

2.2. Boundary conditions setting

The previous literature has highlighted the key role of air flow velocity in the pathogenic dispersion [39]. Previous research has mainly concentrated on pathogenic dispersion in scenario with minimal air supply within cabin environments. It is noteworthy that actually cabin air supply volumes surpass this minimal threshold, as they are established and validated by aircraft designers. Therefore, there is a significant knowledge gap between the diffusion and dispersion of pathogenic under different inlet velocities. To narrow this gap and take account of air supply volume, this study introduced three different velocities to simulate the influence of air supply on droplet trajectories. We have designated the velocity of 0.51 m/s as the baseline inlet velocity at the minimum air supply [40], with 1.03 m/s representing nearly twice the baseline inlet velocity. Initial simulations at both baseline and double baseline velocities showed notable differences in droplet spread. Consequently, an intermediate velocity of 0.77m/s was introduced to address this disparity.

A droplet inlet was set on the head surface of seat C in row 2. This velocity represents the minimum required air supply and the droplet releasing location was selected to simulate the worst-case scenario. Additionally, we have established the inlet and outlet temperatures at 19°C, in accordance with guidelines in the IATA [41].

To simulate the cabin’s physical environment, we have set the floor, seats, walls, and ceiling as no-slip surfaces. Additionally, we employed translation as the periodic face type. In terms of the computational simulated person (CSP), we maintained a constant surface temperature of 32°C [34]. The temperature difference between the CSP and cabin environment induces a natural upward airflow along the CSP’s surface due to the buoyancy effect. The flow speed of normal-breath-induced droplets at this inlet is 1 m/s normal to the face, according to the research by Zhang et al. [31]. This study employs the SIMPLE scheme for pressure-velocity coupling and the second-order upwind scheme for momentum space discretization. This combination has been proven particularly suitable for indoor environment simulations, aligning well with the scope of our study [42,43]. Once the flow field stabilizes, we release a total of 100,000 droplets to simulate typical droplet transmission patterns during normal breathing.

2.3. Determination of droplets

In this research, we investigate the range of sizes of expelled normal breath droplets, which vary from submicron to several hundred microns in diameter. To simplify our analysis, we have converted the droplet count into a more convenient unit: ‘number of droplets per unit μm’. According to Atkinson [44], when a man sneeze, talk, or normal breath, they generate various droplets. These droplets can be categorized by size, with those larger than 5 μm rapidly falling to the ground due to gravity, on the contrary, the droplets with diameter less than 5 μm will float for a while in the air with the airflow. Based on that, 5 μm is considered as a cut-off size to distinguish the small and large contaminant. 1 μm droplet (small size) and 5 μm droplet (large size) was thereupon adopted in this study to investigate the key droplet aerodynamics within the cabin environment. Our primary objective is to investigate the spread characteristics of these two categories of droplets within the cabin environment.

To simulate the droplets based on equivalent aerodynamic principles, we defined them as inert particles, allowing us to focus on their aerodynamic behavior and physical characteristics without involving complex chemical reactions or biodegradation processes. Based on previous research, the droplets are modeled using water-liquid, with a density of 1000 kg/m3. This configuration ensures that we can accurately capture the droplets’ behavior in the airflow [45–47].

2.4. Governing equations

This study utilized the commercial computational fluid dynamics (CFD) software ANSYS Fluent 2021 R1 for conducting all numerical calculations. The Navier-Stokes(N-S) equations with the Boussinesq approximation were used to simulate airflow field in Eulerian method, ∇∙v→=0 (1)

(v→∙∇)v→=−∇Pρ+μρ∇2v→+g→ (2)

ρCpu→∙∇Τ=k∇2Τ+ρCpg→∙u→+Q (3)

Where the v→ is the air velocity vector, ρ is the density of air, P is the static pressure of air, g→ is the gravity, ρ and μ are the density and the viscosity of air. Cp is the Specific heat capacity at constant pressure. T is temperature. And k is the thermal conductivity coefficient. Q is the heat source term.

ρv¯i∂Φ¯∂xi−∂∂xi[Γ(φ,eff)∂Φ¯∂xi]=Sφ (4)

Where u¯i represents the components of fluid velocity, Γφ,eff is the effective diffusion coefficient, φ is the flow variables, and Sφ represents the source term. Cao et al. [18] has compared that the Realizable k-epsilon, Standard k-epsilon, RNG k-epsilon and SST k-omega. We employed the realizable k-epsilon model for simulating airflow turbulence. From the aspects of velocity, temperature, particle concentration and comprehensive quality, it is found that the Realizable k-epsilon has a better balance between accuracy and computational cost, as highlighted by Li et al. [17]. The particles were tracked by the Lagrangian method. In particles tracking accuracy control, the trapezoidal scheme and analytic discretization scheme were employed to track particle trajectories, with a control tolerance of 1e-05.

dup→dt=18μρpdp2(u→−up→)+g→(ρp−ρ)ρp (5)

Where the up→ is the droplet velocity (m/s), t is the time (s), μ is the air viscosity (Pa*s), ρp is the droplet density (kg/m3), dp is the droplet diameter (m), u→ is the air velocity (m/s), g→ is the acceleration of gravity (m/s2), ρ is the air density (kg/m3),

2.5. Modified Wells-Riley model and breathing zone

The Wells-Riley model represents a foundational framework for modeling infectious diseases, initially formulated by Willam F. Wells and Richard L. Riley [33], and widely adopted in the epidemic transmission assessment field [48]. The Probability of Infection (PI), defined as the ratio of infected individuals and susceptible individuals within a confined environment, can be approximated by a well-established expression [33], PI=1−exp(−IqptQ) (6)

In this equation, I represents the count of infectors, q denotes the quantum generation rate from a single infector, determined through reverse calculations derived from empirical data. p stands for the pulmonary ventilation rate, while t signifies the duration of exposure, and Q represents the room’s ventilation rate.

In the classical equation, however, the ventilation modes and social distance scenarios are not taken into account. Sun et al. [32] improved the classical equation through introducing the air distribution effectiveness Ez and social distance index (SDI). Among these, the Social Distance Index (SDI is also exemplified by Pd in Sun et al. [32]) quantifies the cumulative proportion of droplets expelled through breathing, which ultimately reach the respirable region at a specified distance d, posing the potential risk of inhalation by susceptible individuals.

InfectionRisk=1−exp(−SDIIqptQ∙Ez) (7)

In study of Sun et al. [32], the specific breathing zone is not taken into account, which led to the possibility of inaccurate infection risks. Shang et al. [34] modified the SDI using a simplistic approach known as the ’Distance-Reaching Method’. This method relied on the assumption that droplets consistently achieved their terminal velocity, balancing drag force, gravity, and buoyancy. Nevertheless, it overlooked droplet height, raising concerns about its accuracy. To address this limitation, Shang et al. [34] demonstrated that only the breathing zone within a radius of less than 30 mm significantly influences droplet inhalation. Thus, the ’Spherical Zone Method’ was introduced to calculate the SDI more accurately.

InfectionRisk(WithSphericalZoneMethod)=CS=1−exp(−SDIIqptQEZ)=1−exp[−SDIBqpt(Q/N)EZ] (8)

Afterwards, Kuga et al. [29] introduced a novel concept for the breathing zone, particularly in the context of SVE5. They conducted simulations of various ventilation systems under both steady-state and transient conditions, including displacement ventilation, microgravity ventilation, and mixing ventilation. Their analysis explored the range and characteristics of the breathing zone in diverse scenarios, the proposal of a refined breathing zone concept that offers enhanced accuracy. Compared with the previous sphere breathing zone Shang et al. [34], the later makes the range of breathing zone more improved.

In this study, we built on the previous model and method, adopting the innovative breathing zone concept introduced by Kuga et al. [29] and combining it with the principles of the PI to assess infection risk within an aircraft cabin employing a mixing ventilation system. Compared to previous methods used for predicting droplet spread, this study introduces a new exposure risk quantification method which named PCR (Personal Contamination Ratio). By comparing the PCR index within each breathing zone, the concentration of droplets in each breathing zone under macro conditions is displayed straightly, which could clearly contrast the distribution of droplet concentrations. This new method studies the individual exposure risk passengers in different areas rather than simply treating all contacts as infected by default. This is a breakthrough in replacing a global perspective with a local perspective. It is helpful to identify the specific person who is more susceptible to infection among contacts. In addition, this study combines the PCR method with Wells-Riley model into a new infection risk assessment model to accurately assess individual relative infection risk.

The defined breathing zone is a rectangular region measuring 4.5 cm in length, 5.4 cm in width, and 21 cm in height, originating from the lower nasal cavity’s cross-section, as recommended by Kuga et al. [29]. In view of the simplified human body in the CSP model employed in this study, the starting surface of the breathing zone is defined at the midpoint of the quarter of the semicircular frontal head section, as depicted in Fig 4.

PersonalContaminationRatio=Nvs(V)Nvr(V)

=∑i(singlerespirable)Vcuboid−i×CVcuboid−i×C=∑i(singlerespirable)Vcuboid−iVcuboid−i×C (9)

10.1371/journal.pone.0309730.g004 Fig 4 The scale and location of the breathing zone.

Among them, Nvs(V) indicates the cumulative number of droplets in a single breathing zone, and Nvr(V) represents the cumulative number of droplets particles in all breathing zones. C represents the droplets concentration in the droplets discharged at the moment of discharge, and di is the original diameter of the ith expelled droplet.

Inserting Eq (1) leads to a refined Wells-Riley model for the regional risk of infection, based on the local particle quantity [49].

PI=1−exp(−ln2θTCID50∙PCR∙Nparticles∙p∙t) (10)

Among them, θ denotes the ratio coefficient of HID50 (median infective dose in humans) to TCID50, specifically we utilize the influenza data for estimation [49]. Nparticles represents the number of the particles, p signifies the pulmonary ventilation rate, and t represents the duration of the flight, estimated as 6.5 hours according the data in Sun et al. [32].

3. Results and discussion

3.1. Model validation

In this study, the experimental measurement conducted by Li et al. [17] is adopted for the purpose of validation. They have significantly contributed by creating a physical air cabin model and employing large-scale 2D Particle Image Velocimetry (PIV) to meticulously measure airflow patterns across five different sections. The primary measurement areas included beneath the overhead luggage, above the seats, and within the aisle region. These data sets provide velocity information for Rows 3, 4, and 5 in the air cabin, contributing to the validation of the model in advance of simulation. To maintain consistency with experimental conditions, we try to replicate all parameters as closely as possible in order to align with the original experimental setup [17], including air inlet angle and speed, among other variables. We use these velocity measurement data as a benchmark for comparing and validating our simulation results, ensuring the accuracy of our cabin airflow simulations.

In Fig 5, we set six measurement lines in front of the CSP and one in the center of the cabin aisle to capture the velocity data, as indicated in the upper portion of Fig 5. The red line in the lower part of Fig 5 represents the simulation data generated in this study, while the black points represent the experimental data from Li et al. [17]. Lines A, B, E, F, and the Aisle show similar trends when compared to the experimental data. Although lines C and D show deviation within the height range of 1.25 meters to 1.5 meters, while the numerical results demonstrated a satisfactory level of agreement with the corresponding experimental data, especially for heights below 1.25 m, which corresponds to the breathing height for seated occupants. As depicted in Fig 6, in both experimental measurements and numerical simulations, there are similarities in the airflow patterns. After being injected through the air inlet, the airflow converges in the aisle, and experiences intersection and collision, leading to the creation of two symmetrical vortices. The vector raw experimental data is sourced from Li et al. [17].

10.1371/journal.pone.0309730.g005 Fig 5 The comparison of the specific lines in experiment and simulation situation.

10.1371/journal.pone.0309730.g006 Fig 6 Comparative analysis of velocity vectors: Simulation and experimental measurements [17] at row 4.

3.2. Airflow field prediction

This study explores the risk of infectious diseases within the aircraft cabin, focusing on the transmission paths of pathogenic. Consequently, this research begins with visualizing cabin airflow characteristics to better understanding of the particle distribution. Representative locations within the cabin would be further identified and sectional analysis points would be established to assess airflow velocity and direction in different cabin sections. This facilitates a thorough investigation into the characteristics of droplet propagation within aircraft cabins, enabling a more comprehensive exploration.

Fig 7 illustrates velocity contour sections in three different directions: cross-section, longitudinal section, and horizontal section. As depicted in the figure, the primary driving force of airflow within the cabin is the air released from the cabin’s inlet. The fastest airflow is mainly concentrated along the inner walls of the cabin, while other areas have relatively consistent airflow velocities. Notably, there is the slowest airflow near the aisle. It is evident that the longitudinal and horizontal sections demonstrate comparatively stable airflow velocities.

10.1371/journal.pone.0309730.g007 Fig 7 The velocity contour in all sections of air cabin (0.51 m/s).

The difference is the cross sections show significant variations in velocity, indicating a consistent trend across all three sections. The airflow from the inlet descends along the inner walls of the cabin, and upon reaching the floor, some of it rebounds and rises, while the rest escapes through the outlet. In each of the different sections, it is evident that the velocity is higher in the region including the CSP and seats compared to the aisle region. Furthermore, the cabin ceiling region shows relatively stable airflow velocities with minimal fluctuations.

The distribution of particle in the cabin influenced not only by inlet air velocity but also the direction of airflow. As depicted in Fig 8, we present three different perspectives of cabin row: a cross-section, a longitudinal section, and a horizontal section. It is evident that the initial inlet air from the cabin inlet propels forward for a distance before descending along the cabin’s sidewalls. Subsequently, the airflow from both the left and right sides meet near the aisle, ascending in opposite directions. Furthermore, the human thermal plume has an influence on the airflow direction as shown in Fig 9.

10.1371/journal.pone.0309730.g008 Fig 8 The velocity contour and vector in all sections.

10.1371/journal.pone.0309730.g009 Fig 9 The velocity contour and vector in all sections.

Consequently, as the airflow collides and ascends on either side, it rises to generate two vortices near to the CSP-A and CSP-F regions. Meanwhile, two eddies displayed on the cabin’s left and right sides, situated near the ceiling.

Fig 10 shows the velocity counter and vector of the cross section under three inlet speeds. It can be inferred that as the inlet speed increases, the angle between the jet and sidewall gradually increases; In fact, the angle between the sidewall and the ceiling also has a great impact on the path of jet. In short, the overall airflow trend in the cabin seems to have no great influence as the inlet speed increases.

10.1371/journal.pone.0309730.g010 Fig 10 The velocity contour and vector of three cases with different velocity.

3.3. Influence of the ventilation velocity on the transmission path

In this study, there are three cases designed with three different inlet velocity, 0.51 m/s (foundation case), 0.77 m/s, and 1.03 m/s, respectively. These additional cases are based on the foundational air supply case. It’s important to note that, due to concerns about comfortable and temperature, we did not simulate scenarios with higher velocities. Nevertheless, the results obtained from the selected velocities provide valuable insights into the relationship between pathogenic and air supply volume.

In the analysis of droplet diffusion paths, we illustrate the trajectories of droplets with sizes of 1 μm and 5 μm separately in order to demonstrate the impact of different inlet wind velocity on the spread paths. Fig 11 visually shows the trajectories of droplet within the cabin, with trajectory color transitioning as time. Notably, whether considering larger or smaller droplets, their residence times predominantly fall below 350 seconds. Moreover, the direction of their movement opposes the direction of exhalation. The reason of this phenomenon is the meeting of incoming air flows from cabin inlets along the sides, their collision at the aisle’s bottom, and subsequent upward movement with the influence of human thermal plumes and respiratory effects. Consequently, the airflow trend propels the droplets towards the rear of the cabin. Therefore, the spread trajectory of droplets is backward, and most of the droplets are distributed behind the injector.

10.1371/journal.pone.0309730.g011 Fig 11 The droplets trajectories in three different cases of inlet velocity.

From a detailed perspective, the trajectory of small droplets’ spread is directly influenced by the inlet velocity. At the minimum inlet velocity, droplets initially concentrate in two rows behind the injector and gradually disperse to greater distances, including the rear rows of seats in the opposite row. When the inlet velocity is in the mid-speed, droplets diffusion tends to extend to the last few rows of the column, but the spread range is noticeably smaller than at the minimum inlet velocity case. As the velocity is maximum, the droplets spread range shrinks significantly, with large and small droplets following similar trajectories, leading to the rapid escape of many droplets from the cabin.

Conversely, for larger droplets, regardless of whether the inlet air speed is high or low, their motion trajectories are relatively uniform. It’s evident that with increasing inlet velocity, the spread distance of larger droplets also increases, resulting in longer residence time compared to other two velocity cases.

In summary, as velocity gradually increases, the diffusion of droplets is somewhat constrained. Smaller, more easily dispersible droplets imply reduced dispersion ranges, while larger droplets do not experience a significant expansion in their dispersion range with increased air flow velocity. However, as the air flow velocity continues to increase, it can induce the movement of droplets of various sizes. While passengers near the injector may initially appear unaffected by droplets, the potential infection area could expand as airflow persists.

3.4. Exposure and infection probability in different ventilation velocity

The detailed information of the expelled droplets (e.g., location, residence time, etc.) in each passengers’ breathing region was extracted and being further used for exposure and infection quantification using the PCR and modified Wells-Riley model To illustrate potentially infected individuals, we create heatmaps and 3D bar chart, and transform the dataset into percentages, enhancing the clarity of infection probabilities. This visual representation shows a direct assessment of variations in infection risk among different passengers, offering insights into the potential extent of infections within the cabin.

Figs 12 and 13 clearly illustrates those individuals at a higher risk of exposure tend to be situated behind the row of injectors. The closer a passenger is to the source of contamination, the more likely they are to be included in the potential infection area. When the inlet velocity changes from its minimum to maximum values, we observe a concentration of high-risk individuals predominantly in rows positioned behind the injector. In an intermediate velocity case, high-risk individuals are more evenly distributed within the cabin. Furthermore, at this intermediate velocity, the potential infected persons appear to be surrounding the Source as a whole.

10.1371/journal.pone.0309730.g012 Fig 12 Variation maps in exposure risk within the passenger breathing zone across different inlet velocities.

10.1371/journal.pone.0309730.g013 Fig 13 Three-dimensional bar chart based on the relative probability of infection distribution.

While a superficial analysis of the three exposure maps and infection distribution bar charts might suggest a reduction in the potential infection area at maximum velocity, it is essential to note that higher airspeed causes droplets to travel greater distances. In reality, this does not lead to a decrease in potential infection risk instead.

Based on this comparative analysis, it is evident that at an inlet air velocity of 0.77 m/s, the potential infection area is comparatively narrower than under the other two velocity conditions. This implies that a much higher air velocity does not necessarily confer an advantage. Therefore, we recommend maintaining an optimal air inlet velocity of approximately 0.77 m/s.

In addition, it can be found that in the previous method evaluation, the virus amount of escape was also included actually, which may result the actual infection probability of the human to be lower than the actual infection probability. Meanwhile, most studies lack of analysis of exposure risk. However, the new method—PCR has the potential to shift the focus from a global perspective to a local one. This shift aims to avoid the influence of escape viruses and trap viruses by the cabin wall in the assessment process, and fill the gap in the supplementary exposure risk section. Additionally, the method integrates the modified Wells-Riley model to redistribute the collective infection risk, enabling a reasonable evaluation of the relative infection risk for a specific individual rather than a collective assessment for a group of people.

4. Conclusion

In an effort to support aircraft designers in optimizing cabin components such as ventilation systems and seat arrangements and to assist health authorities in developing more effective pandemic prevention strategies, particularly in cases of outbreaks such as COVID-19 and Influenza, this research utilizes computational fluid dynamics (CFD) techniques. The primary focus is to investigate the potential risks of airborne contamination within individual breathing zones under different inlet air velocities. To quantify these risks, we introduce and apply the Personal Contamination Ratio (PCR) method. Our findings reveal differences in potential infection risks in different areas of the cabin under different velocity conditions. Based on the results of CFD experiments, we draw the following conclusions:

The direction of airflow within the aircraft cabin is closely linked to the inlet velocity, a critical parameter in cabin ventilation systems. Our investigation reveals that air supplied through the ventilation systems converges near the lower section of the aisle before ascending, regardless of the inlet velocity. This airflow pattern is significantly influenced by thermal plumes generated by human occupants and their respiratory processes. Notably, as the inlet velocity increases, the rearward flow of air becomes more pronounced. These observations underscore the complex interplay between inlet velocity, thermal plumes, and the resultant airflow patterns within the cabin environment.

The trajectories of small-sized droplets exhibit high sensitivity to variations in inlet air velocity. As the inlet air supply incrementally increases from its minimum threshold, these droplets demonstrate a tendency to converge, resulting in a relative reduction of potential infection areas. In contrast, larger droplets display a slower response to changes in inlet air velocity. Their trajectories undergo significant alterations only in response to substantial increases in inlet air velocity.

Optimizing the inlet air velocity is crucial for effective infection control. Experimental results demonstrate that the minimum inlet velocity leads to the largest potential infection area. Conversely, maximum velocity reduces the potential infection area in the transverse direction but significantly increases it longitudinally. Notably, the number of potential infections within the cabin at the highest inlet air velocity decreases by 51.8% compared to the basic inlet air velocity, and the total exposure risk rate is reduced by 26%. Furthermore, the number of rows with infected passengers decreases to 4 rows under maximum inlet air velocity. The probability of infection for more than 50% of individuals is reduced by 65.2% at the highest inlet air velocity and by 21.74% at moderate inlet air velocity compared to the basic inlet air velocity. Additionally, the total number of individuals at risk of infection is nearly halved at the highest inlet air velocity. Considering passenger comfort alongside infection control, operating at a moderate velocity emerges as the optimal strategy. This balanced approach effectively mitigates infection risk while maintaining acceptable comfort levels for passengers. In this study, only one pathogen-releasing CSP, seated in position 5C, was considered.

In this study, we introduce a new approach, the Personal Contamination Ratio (PCR) method, to quantitatively assess individual infection risks. This method offers a more detailed assessment of specific personal risk areas, distinguishing it from previous approaches. The outcomes of this research can serve as a valuable reference for optimizing cabin air inlet velocities and implementing protective measures to safeguard potentially infected individuals. In this study, we did not explicitly account for the effects of humidity on droplet transport within the aircraft cabin environment. Our decision was informed by previous research indicating that droplets tend to evaporate rapidly in the extremely dry conditions of an aircraft cabin, thereby minimizing the impact of ambient humidity on droplet dynamics. This simplification was necessary to focus on the primary objective of our research: the development and validation of the Personal Contamination Ratio (PCR) method. While this approach allowed us to highlight the efficacy of the PCR method, we recognize that excluding humidity and evaporation effects may limit the generalizability of our findings to other environments. Future studies should consider more factors to provide a more comprehensive understanding of droplet behavior under varying humidity conditions.

Supporting information

S1 Data Docx for Fig 2.

(DOCX)

S2 Data Docx for Fig 5.

(DOCX)

S3 Data Docx for Fig 13.

(DOCX)

10.1371/journal.pone.0309730.r001
Decision Letter 0
Pongpirul Krit Academic Editor
© 2024 Krit Pongpirul
2024
Krit Pongpirul
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
5 Mar 2024

PONE-D-24-00989Optimizing cabin air inlet velocities and personal risk assessment: introducing the Personal Contamination Ratio (PCR) method for enhanced aircraft cabin infection risk evaluationPLOS ONE

Dear Dr. Shang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Apr 19 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Krit Pongpirul, MD, MPH, PhD.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating the following in the Acknowledgments Section of your manuscript: 

"This research was funded by the National Natural Science Foundation of China (Grant No. 82370101) and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (Project ID: 0920000016)."

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. 

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: 

"This research was funded by the National Natural Science Foundation of China (Grant No. 82370101) and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (Project ID: 0920000016). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript."

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

3. We note that your Data Availability Statement is currently as follows: All relevant data are within the manuscript and its Supporting Information files.

Please confirm at this time whether or not your submission contains all raw data required to replicate the results of your study. Authors must share the “minimal data set” for their submission. PLOS defines the minimal data set to consist of the data required to replicate all study findings reported in the article, as well as related metadata and methods (https://journals.plos.org/plosone/s/data-availability#loc-minimal-data-set-definition).

For example, authors should submit the following data:

- The values behind the means, standard deviations and other measures reported;

- The values used to build graphs;

- The points extracted from images for analysis.

Authors do not need to submit their entire data set if only a portion of the data was used in the reported study.

If your submission does not contain these data, please either upload them as Supporting Information files or deposit them to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of recommended repositories, please see https://journals.plos.org/plosone/s/recommended-repositories.

If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. If data are owned by a third party, please indicate how others may request data access.

4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: N/A

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper reports the results of a series of numerical analysis of airborne transmission in a enclosed space, assuming an aircraft cabin. The results of the numerical analysis are compared with experimental results for a mock-up cabin model, and a certain degree of validation of the prediction accuracy of numerical method is also provided. The infection risk model, although simple, is a model that has been applied widely, and a certain level of prediction accuracy is ensured.

The research theme and the numerical analysis method are not claimed to be novel, since they are based on previous studies, but they contain some novelty compared to previous studies, such as the use of a new index for evaluating exposure concentrations, PCR.

Overall, this manuscript is judged to have few shortcomings and to be well-considered/well-organized, and this reviewer would recommend this should be accepted.

Reviewer #2: The present study performs numerical simulations to analyse droplet dynamics in a Airbus A320 model, evaluating the effect of changing air supply rate on passengers’ exposure to droplets. The topic is interesting and worth of investigation; however, there are issues associated with the methodology and the CFD simulations, making the proposed results not reliable and the paper unsuitable for publication. Listed below are the main criticalities of the paper.

- The Authors present the analysis of the breathing zone as a strong point, but the dynamics of breathing and particle inhalation are completely neglected. These aspects considerably alters the distribution of particles in the breathing zone, making the results obtained unreliable.

- English language needs improvement. At some points, the sentence construction is convoluted; contracted forms are also present, which are not suitable for a scientific paper.

- The description of the simulated scenario is confusing. It is stated that 6 people have been simulated (line 152), but in Figure 1 more passengers are depicted. In addition, authors should demonstrate that the simulated domain (restricted to a section of the whole cabin) is representative of the problem under investigation.

- Authors should specify the software employed to carry out CFD simulations.

- It is not sufficient to state that “Building upon existing research findings, our simulations employed a mesh grid totaling 6.51 million polyhedral cells within the cross-section of the three-row cabin” (lines 152-154). Unless the authors have carried out previous studies on the same cabin model, a grid sensitivity analysis must be performed.

- A complete description of the boundary conditions should be provided. The periodic boundary condition set for the “periodic faces” needs to be discussed further.

- The governing equations presented in section 2.4 (Equation 1 and 2) are for a laminar, incompressible and unsteady flow. These are not the governing equations of the problem under investigation. The URANS equations must be provided.

In addition, the Boussinesq approximation seems to be used to model the effects of buoyancy (by considering the density constant in the transient and convective terms), but then in the gravitational term its dependence on temperature is not considered.

- The drag force (equation 3) should be evaluated as a function of the droplet Reynolds number.

- The scenario analyzed for particle emission is not realistic at all. It should also be described in more detail, not simply reporting the total number of particles emitted.

The diameters (1 and 5 µm) are not representative of a real scenario, nor are the velocity (fixed at 1 m/s) and the direction of release.

- How would the model proposed by the Authors improve the Wells-Riley model, by changing the perspective from global to local (section 2.5)?

In the Referee’s opinion, the proposed model is not reliable to provide quantitative information about the risk of infection; rather, it gives information about the relative weight between different zones. For this purpose, it would have been sufficient to show the concentration of particles in different zones.

- What is the error between PIV measurements and CFD results? The agreement seems to be very poor, especially for lines A and F (Figure 4). Such disagreement raises questions regarding the turbulence model, the boundary conditions and the grid sensitivity analysis.

In addition, the section of the experimental-numerical comparison should be highlighted in the computational domain and the scenario considered for the validation should be described.

- I find the representation in Figure 5 of little use, with the experimental vectors barely visible. The legend is also missing (as in the later images). It would be more useful to represent the entire measured velocity field.

In general, the representation with vectors is unreadable and does not allow to visualize the presence of recirculation zones; in this sense, streamlines would be more suitable.

- A picture depicting particle spatial distribution should be provided, commented with reference to the simulated velocity fields.

- A strange effect is present in the velocity fields of the simulated scenarios whereby the air jet is strongly drawn back to the wall, despite being released at a certain angle toward the inside of the cabin. This effect is not present in the validation scenario and should be explained by the authors.

- How is it possible for air to be completely carried behind (resulting in infection only for those passengers sitting behind the infected person)? The explanation provided by the authors (lines 379-383) is not convincing and the effect of the boundary conditions set at the "periodic faces" should be investigated.

- Representing the possibility of infection for the source (Figure 12) makes no sense. In addition, there is probably a typo in the legend (the highest probability is equal to 0.25%, which is very low).

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0309730.r002
Author response to Decision Letter 0
Submission Version1
13 May 2024

Response to Reviewer #1's Comments

We're truly grateful for the reviewer's supportive feedback, which means a lot to us.

Response to Reviewer #2's Comments

Comments:

1. The Authors present the analysis of the breathing zone as a strong point, but the dynamics of breathing and particle inhalation are completely neglected. These aspects considerably alters the distribution of particles in the breathing zone, making the results obtained unreliable.

We thank the reviewers for raising the insightful comments. The authors would like to clarify the major objective of this study is to propose a novel holistic infection risk assessment framework by incorporating the concept of the breathing zone and the Personal Contamination Ratio (PCR) into the original Wells-Riley model. Based on the detailed spatial-temporal information of the tracked droplets from the CFD simulations, it allowed us to obtain a more reasonable infection risk quantification considering the particle distribution.

We acknowledged the significance of considering dynamics related to breathing and particle inhalation, which was believed to enhance the overall robustness of the assessment. While those aspects were believed to be beneficial, including them into the simulation was expected to significantly improve the overall computational cost. Considering the complex transient nature of the breathing patterns (boundary condition variations), and multi-scale (from cabin environment, m3, to human nasal, mm3) and multi-coupling challenges, further including those setups within a densely-occupied cabin space was expected to exponentially increase the simulation difficulty and time. Moreover, many existing studies overlook the influence of the breathing pattern, as exemplified by the following instances:

[1] Kong B, Zou Y, Cheng M, Shi H, Jiang YJAS. Droplets transmission mechanism in a commercial wide-body aircraft cabin. 2022;12(10):4889.

[2] Wang F, Zhang TT, You R, Chen Q. Evaluation of infection probability of Covid-19 in different types of airliner cabins. Build Environ. 2023;234:110159.

[3] You R, Lin CH, Wei D, Chen QJIA. Evaluating the commercial airliner cabin environment with different air distribution systems. 2019;29(5):840-53.

[4] Choi E-S, Yook S-J, Kim M, Park DJT. Study on the Ventilation Method to Maintain the PM10 Concentration in a Subway Cabin below 35 μg/m3. 2022;10(10):560.

[5] Zhang M, Yu N, Zhang Y, Zhang X, Cui YJP. Numerical simulation of the novel coronavirus spread in commercial aircraft cabin. 2021;9(9):1601.

2. English language needs improvement. At some points, the sentence construction is convoluted; contracted forms are also present, which are not suitable for a scientific paper.

Thanks for raising this issue which we agree with. We checked the grammar and wording in the manuscript and revised them accordingly, as follows:

Page 2, lines 39-40. ‘Air cabins play an important role in spreading infectious diseases (8-11).’

Page 3, lines 63-64. ‘However, Yan, Li (18)'s investigations predominantly focused on droplets of a representative size, which may not be widely applicable to all scenarios.’

Page 10, lines 281-283. ‘Compared to previous methods used for predicting droplet spread, this study introduces a new exposure risk quantification method which named PCR (Personal Contamination Ratio).’

Page 12, lines 347-348. ‘Consequently, this research begins with visualizing cabin airflow characteristics to better understanding of the particle distribution.’

Page 13, lines 374-375. ‘The distribution of particle in the cabin is influenced not only by inlet air velocity but also by the direction of airflow.’

Page 17, lines 488-489. ‘Their trajectories are altered significantly in response to a substantial increase in inlet air velocity.’

3. The description of the simulated scenario is confusing. It is stated that 6 people have been simulated (line 152), but in Figure 1 more passengers are depicted. In addition, authors should demonstrate that the simulated domain (restricted to a section of the whole cabin) is representative of the problem under investigation.

Thanks for the reviewer’s comment. The authors would like to clarify that in this study, a total of 18 passengers were modelled within an aircraft cabin. 6 passengers were constructed in a single row with one aisle to mimic the real cabin layout. Based on the review’s comment, the authors found that the previous description of the model can be quite confusing for the readers and such description has now been revised based on the reviewer’s comment (please see page6, lines 154-155).

‘As shown in Figure 1, passengers and seats are arranged on two sides of the cabin. There are three rows, each accommodating six persons, making a total number of 18 passengers.’

As for the second concern, the authors acknowledged that investigating the in-cabin aerosol transmission scenario with a full-scale cabin model would be perfect as it provides a comprehensive picture of the particle transport and distribution characteristics. However, the authors would like to point out that restoring such phenomena can be really time-consuming (e.g. solving the large-scale, multi-phase simulation). In this study, the authors adopted the aforementioned layout as recent studies demonstrate that severe transmission generally occurs longitudinally, within the 3 rows [1]. Most existing studies are based on such outcomes to model the in-cabin transmission scenarios in recent years [2-5]. The authors acknowledge that the current cabin model would not be perfect, while based on the existing findings and mainstream studies, such a computational model was expected to be reasonable and representative. Additionally, we have added some text in page 5, lines 137-139. Thanks for the reviewer’s comment.

‘Building upon previous literature (18, 26, 27, 34, 35), to optimize computational resources, we have chosen to focus our analysis on a representative subset of the cabin section with three rows.’

[1] Silcott, David, et al. "TRANSCOM/AMC commercial aircraft cabin aerosol dispersion tests." (2020).

[2] Zhao, Yingjie, et al. "Numerical simulation study on air quality in aircraft cabins." Journal of Environmental Sciences 56 (2017): 52-61.

[3] Zee, Malia, et al. “Computational fluid dynamics modeling of cough transport in an aircraft cabin.” Scientific reports 11.1 (2021): 23329.

[4] Kong, Benben, et al. “Droplets transmission mechanism in a commercial wide-body aircraft cabin.” Applied Sciences 12.10 (2022): 4889.

[5] Yan, Yihuan, et al. "Evaluation of cough-jet effects on the transport characteristics of respiratory-induced contaminants in airline passengers’ local environments." Building and environment 183 (2020): 107206.

4. Authors should specify the software employed to carry out CFD simulations.

We appreciate the reviewer for reminding this issue. We have added specific details regarding the software used for the CFD simulations on page 8, lines 222-223.

‘This study utilized the commercial computational fluid dynamics (CFD) software ANSYS Fluent 2021 R1 for conducting all numerical calculations.’

5. It is not sufficient to state that “Building upon existing research findings, our simulations employed a mesh grid totaling 6.51 million polyhedral cells within the cross-section of the three-row cabin” (lines 152-154). Unless the authors have carried out previous studies on the same cabin model, a grid sensitivity analysis must be performed.

We appreciate the reviewer for bringing up this issue. We acknowledge the importance of conducting mesh sensitivity analysis for mesh generation, and we have provided the information about the mesh sensitivity analysis below. The horizontal axis of the table represents velocity, while the vertical axis represents the length of the validation line. Through the comparison from this picture, there is no considerable deviation in the velocity field noticed after increasing the number of mesh elements from 6.51 million to 7.84 million. Thus, we chose to use a mesh setup with 6.51 million elements for the simulations. Additionally, in the manuscript on page 6, lines 155-162 and 179-180, we have included a description of the mesh independence test as follow:

‘To achieve mesh independence, this study experimented with five sets of mesh configurations, utilizing total mesh elements of 2.60 million, 3.92 million, 5.20 million, 6.51 million, 7.84 million, respectively. Significant discrepancies in velocity were observed among the groups with grid numbers of 2.60, 3.92, and 5.20 million, while an increase in mesh elements from 6.51 million to 7.84 million revealed negligible deviation in the velocity field, as illustrated in Figure 3. This indicates that a mesh of 6.51 million elements is suitable for simulating airflow within the computer cabin. Therefore, in our simulations employed a mesh grid totaling 6.51 million polyhedral cells within the cross-section of the three-row cabin, achieving a maximum skewness of 0.81.’

Please see picture in the Response to Reviewers.docx

6. A complete description of the boundary conditions should be provided. The periodic boundary condition set for the “periodic faces” needs to be discussed further.

We agree with the constructive suggestion. The detailed description of the periodic boundary condition is added to page 7, line 200.

‘…we have set the floor, seats, walls, and ceiling as no-slip surfaces. Additionally, we employed translation as the periodic face type.’

7. The governing equations presented in section 2.4 (Equation 1 and 2) are for a laminar, incompressible and unsteady flow. These are not the governing equations of the problem under investigation. The URANS equations must be provided.

In addition, the Boussinesq approximation seems to be used to model the effects of buoyancy (by considering the density constant in the transient and convective terms), but then in the gravitational term its dependence on temperature is not considered.

Thanks for raising this issue. We have revised the formula, with specific modifications outlined on page 8, lines 226-233, as follows:

Please see detailes in the Response to Reviewers.docx

Additionally, we sincerely apologize for any confusion caused to readers due to our oversight. The misleading descriptions in the manuscript have now been corrected, as evidenced on page 8, lines 223-224 as follows:

‘The Navier-Stokes(N-S) equations with the Boussinesq approximation were used to simulate airflow field in Eulerian method’

8. The drag force (equation 3) should be evaluated as a function of the droplet Reynolds number.

Thanks for your reminder. The Reynolds number is already included in the formula. To enhance the clarity and conciseness of the formula expression, the first term on the right-hand side of the equation is separated and presented individually.

Please see equation in the Response to Reviewers.docx

9. The scenario analyzed for particle emission is not realistic at all. It should also be described in more detail, not simply reporting the total number of particles emitted.

The diameters (1 and 5 µm) are not representative of a real scenario, nor are the velocity (fixed at 1 m/s) and the direction of release.

Thank you for bringing up this question. We would like to clarify that the details of the particle information can be found in Figure 1, specifying both the location and angle of incidence. Additionally, we have provided further details regarding the particle emission location on pages 5, lines 146-151. The specifics are as follow.

In consideration of the velocity and direction of release, we carefully referred to peer-reviewed literature and set up the specifics based on a scenario involving a stationary seated individual in a steady state, as outlined in reference [3-4]. We believe this adequately explains the information regarding the particle inlet.

‘Following the recommendations of Kong, Zou (26), we have represented the CSP model's particle injection, located on the face, as a simplified circular inlet with a diameter of 1.24 cm. In accordance with findings from Haselton and Sperandio (36) and Kuga, Wargocki (28), the exhalation zone is identified to span from 27° and 33°. This study set a central value of 30° as the particle injection angle.’

As for the particle diameter, we have provided references as follow to support the significance of investigating diameters of 1 and 5 µm. In particular, the reference - [5] illustrates that the particle diameter of human respiratory particles is almost below 8 µm:

[1] Thomas, Richard James. "Particle size and pathogenicity in the respiratory tract." Virulence 4.8 (2013): 847-858.

[2] Shang, Y. D., K. Inthavong, and J. Y. Tu. "Detailed micro-particle deposition patterns in the human nasal cavity influenced by the breathing zone." Computers & Fluids 114 (2015): 141-150.

[3] Kuga K, Wargocki P, Ito KJIa. Breathing zone and exhaled air re‐inhalation rate under transient conditions assessed with a computer‐simulated person. 2022;32(2):e13003.

[4] Zhang M, Yu N, Zhang Y, Zhang X, Cui YJP. Numerical simulation of the novel coronavirus spread in commercial aircraft cabin. 2021;9(9):1601.

[5] Zhang, Hualing, et al. "Documentary research of human respiratory droplet characteristics." Procedia engineering 121 (2015): 1365-1374.

10. How would the model proposed by the Authors improve the Wells-Riley model, by changing the perspective from global to local (section 2.5)?

In the Referee’s opinion, the proposed model is not reliable to provide quantitative information about the risk of infection; rather, it gives information about the relative weight between different zones. For this purpose, it would have been sufficient to show the concentration of particles in different zones.

Thank you for your insightful question and feedback. The approach introduced in our study offers a significant departure from the global perspective typically employed in prior research. By modifying the methodology for calculating exposure risk (as highlight in the formula) and integrating the PCR factor into the infection risk calculation, we shift the focus from a global outlook to an individualized perspective. This shift enables us to accurately quantify the localised infection risk of each seated passenger rather than analysing the global infection risk, thereby enhancing the reliability of the quantitative information provided.

As for the second concern, we would like to clarify that the method employed in our study, the Eula-Laglang method, is specially tailored to analyze particle trajectory paths. Our primary objective is to comprehensively investigate particle trajectories from infection source to each individual's breathing zone. Within this framework, we have quantified particle numbers within each breathing zone to assess infection and exposure risks across different areas. Besides, we have revised the calculation formula for Possibility of Infection to ensure a more accurate characterization of each individual’s PI value.

The way to improve the Wells-Riley model in this study:

As for the improvement to the Wells-Riley model, we specifically adjust the calculation of the personal contamination ratio by shifting from considering the total particle amount in a zone to evaluating the particle concentration within each individual's breathing zone.

Please see equations in the Response to Reviewers.docx

Among them, N_vs (V) indicates the cumulative number of droplets in a single breathing zone, and N_vr (V) represents the cumulative number of droplets particles in all breathing zones. C represents the droplets concentration in the droplets discharged at the moment of discharge, and d_i is the original diameter of the i th expelled droplet.

Please see equations in the Response to Reviewers.docx

Among them, θ denotes the ratio coefficient of 〖HID〗_50 (median infective dose in humans) to 〖TCID〗_50, specifically we utilize the influenza data for estimation. N_particles represents the number of the particles, p signifies the pulmonary ventilation rate, and t represents the duration of the flight.

11. What is the error between PIV measurements and CFD results? The agreement seems to be very poor, especially for lines A and F (Figure 4)

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0309730.r003
Decision Letter 1
Pongpirul Krit Academic Editor
© 2024 Krit Pongpirul
2024
Krit Pongpirul
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
19 Jul 2024

PONE-D-24-00989R1Optimizing cabin air inlet velocities and personal risk assessment: introducing the Personal Contamination Ratio (PCR) method for enhanced aircraft cabin infection risk evaluationPLOS ONE

Dear Dr. Shang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 02 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Krit Pongpirul, MD, MPH, PhD.

Academic Editor

PLOS ONE

Additional Editor Comments:

Please carefully address the additional comments from the reviewers.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: The authors provide references to sources providing information about the conditions representing the tested case.

The authors provided drawings and data explaining the adopted methods and results to demonstrate the usefulness of the PCR method.

This study contributes important information on droplet transport dynamics and infection risk in aircraft cabins, highlighting the importance of optimizing air delivery rates. PCR can significantly contribute to better design of ventilation systems and public health strategies.

Reviewer #4: The manuscript entitled “Optimizing cabin air inlet velocities and personal risk assessment: introducing the Personal Contamination Ratio (PCR) method for enhanced aircraft cabin infection risk evaluation” is interesting. However, there are several major concerns that authors shall address.

1. Abstract: Measurable findings are required in the abstract session. Conclusion Is not clearly highlighted as in current form.

2. Introduction- suggest adding on other airborne infection statistics and its description, instead of COVID-19 only. These inclusions could show the importance of present study to be adopted in future.

3. Description of droplets shall be included, i.e., material, density, viscosity, other physical properties.

4. Is humidity being considered in this study? If no, please justify thoroughly. As far as reviewer concern, humidity could significantly affect the droplets transportation characteristics.

5. Authors shall justify why SIMPLE scheme for pressure velocity coupling and second order upwind scheme are chosen. Else, authors might need to find reference (similar study that investigate the effect of droplets dispersion in indoor) to support. Example: https://doi.org/10.1016/j.enbuild.2023.113439

6. Line 340- satisfactory agreement could be subjective. What is the relative error? This information is very crucial to support the reliability of result.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: Yes: Konrad Gumowski

Reviewer #4: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0309730.r004
Author response to Decision Letter 1
Submission Version2
2 Aug 2024

Ref: PONE-D-24-00989

Title: Optimizing cabin air inlet velocities and personal risk assessment: introducing the Personal Contamination Ratio (PCR) method for enhanced aircraft cabin infection risk evaluation

Dear Editor and Reviewers,

We greatly appreciate your valuable comments and suggestions which have helped enhance our manuscript. Based on your feedback, we have made improvements to our work. We have addressed each of your points in detail, with changes highlighted in red in the revised manuscript and responses marked in blue below.

Reviewer #3's Comments

The authors provide references to sources providing information about the conditions representing the tested case.

The authors provided drawings and data explaining the adopted methods and results to demonstrate the usefulness of the PCR method.

This study contributes important information on droplet transport dynamics and infection risk in aircraft cabins, highlighting the importance of optimizing air delivery rates. PCR can significantly contribute to better design of ventilation systems and public health strategies.

We are truly grateful for the reviewer's supportive feedback, which means a lot to us. We appreciate the time and effort invested in reviewing our manuscript, and we are pleased that the revisions have met with approval. Thank you for recognizing the value of our research and for your encouragement.

 

Reviewer #4's Comments

The authors would like to express their sincere gratitude to the reviewer for providing thoughtful and valuable feedback on this manuscript. The improvements made to the manuscript were greatly influenced by the reviewer's insightful comments.

The manuscript entitled “Optimizing cabin air inlet velocities and personal risk assessment: introducing the Personal Contamination Ratio (PCR) method for enhanced aircraft cabin infection risk evaluation” is interesting. However, there are several major concerns that authors shall address.

1.Abstract: Measurable findings are required in the abstract session. Conclusion Is not clearly highlighted as in current form.

Response: We appreciate the reviewer for reminding this issue. Based on the reviewer’s comment, we have revised the content of the abstract section as follows:

‘Recurrent epidemics of respiratory infections have drawn attention from the academic community and the general public in recent years. Aircraft plays a pivotal role in facilitating the cross-regional transmission of pathogens. In this study, we initially utilized an Airbus A320 model for computational fluid dynamics (CFD) simulations, subsequently validating the model's efficacy in characterizing cabin airflow patterns through comparison with empirical data. Building upon this validated framework, we investigate the transport dynamics of droplets of varying sizes under three air supply velocities. The Euler-Lagrangian method is employed to meticulously track key parameters associated with droplet transport. This study integrates acquired data into a novel PCR (Personal Contamination Rate) equation to assess individual contamination rates. Numerical simulations demonstrate that increasing air supply velocity leads to enhanced stability in the movement of larger particles compared to smaller ones. Results show that the number of potential infections in the cabin decreases by 51.8 % at the highest air supply velocity compared to the base air supply velocity, and the total exposure risk rate reduced by 26.4 %. Thus, optimizing air supply velocity within a specific range effectively reduces the potential infection area. In contrast to previous research, this study provides a more comprehensive analysis of droplet movement dynamics across various particle sizes. We introduce an improved method for calculating the breathing zone, thereby enhancing droplet counting accuracy. It is hoped that these findings can provide valuable insights for enhancing non-pharmacological public health interventions and improving cabin ventilation system design.’

Please see Page 1, lines 11-29 in the revised manuscript.

Additionally, we have also revised the content of the conclusion section as follows:

‘(1) The direction of airflow within the aircraft cabin is closely linked to the inlet velocity, a critical parameter in cabin ventilation systems. Our investigation reveals that air supplied through the ventilation systems converges near the lower section of the aisle before ascending, regardless of the inlet velocity. This airflow pattern is significantly influenced by thermal plumes generated by human occupants and their respiratory processes. Notably, as the inlet velocity increases, the rearward flow of air becomes more pronounced. These observations underscore the complex interplay between inlet velocity, thermal plumes, and the resultant airflow patterns within the cabin environment.

(2) The trajectories of small-sized droplets exhibit high sensitivity to variations in inlet air velocity. As the inlet air supply incrementally increases from its minimum threshold, these droplets demonstrate a tendency to converge, resulting in a relative reduction of potential infection areas. In contrast, larger droplets display a slower response to changes in inlet air velocity. Their trajectories undergo significant alterations only in response to substantial increases in inlet air velocity.

(3) Optimizing the inlet air velocity is crucial for effective infection control. Experimental results demonstrate that the minimum inlet velocity leads to the largest potential infection area. Conversely, maximum velocity reduces the potential infection area in the transverse direction but significantly increases it longitudinally. Notably, the number of potential infections within the cabin at the highest inlet air velocity decreases by 51.8 % compared to the basic inlet air velocity, and the total exposure risk rate is reduced by 26 %. Furthermore, the number of rows with infected passengers decreases to 4 rows under maximum inlet air velocity. The probability of infection for more than 50% of individuals is reduced by 65.2 % at the highest inlet air velocity and by 21.74 % at moderate inlet air velocity compared to the basic inlet air velocity. Additionally, the total number of individuals at risk of infection is nearly halved at the highest inlet air velocity. Considering passenger comfort alongside infection control, operating at a moderate velocity emerges as the optimal strategy. This balanced approach effectively mitigates infection risk while maintaining acceptable comfort levels for passengers. In this study, only one pathogen-releasing CSP, seated in position 5C, was considered.’

Please see Page 17, lines 488-502; Page 18, lines 503-515 in the revised manuscript.

2. Introduction- suggest adding on other airborne infection statistics and its description, instead of COVID-19 only. These inclusions could show the importance of present study to be adopted in future.

Response: Thanks for bring up this issue. We have searched the statistical data on various respiratory diseases and have provided a specific example for context. The details of the revision are as follows:

‘Airborne infectious diseases pose a significant threat to global public health, with the potential to spread rapidly and overwhelm healthcare systems during outbreaks. These respiratory-borne illnesses can lead to widespread infections, affecting millions of people worldwide each year. The transmission of such diseases has become a focal point of research, particularly in light of recent global health crises. Among the various airborne infectious diseases, some stand out due to their impact and prevalence. For instance, Respiratory Syncytial Virus (RSV) spreads extensively during certain periods each year, with a median duration of 4.6 months, affecting millions globally [1]. More recently, the COVID-19 pandemic has had an unprecedented global impact, resulting in 775.69 million confirmed cases and 6.95 million deaths. This pandemic has significantly raised awareness about the transmission of respiratory diseases and led to extensive research and vaccination campaigns. The emergence of influenza during the later stages of the COVID-19 pandemic further highlights the threat posed by respiratory diseases. These outbreaks have intensified the focus on transmission studies, revealing the potential for rapid spread and severe strain on healthcare resources. In the current globalization period, air travel in particular has become an essential way of linking people worldwide. In this mode of transportation, air cabin plays an important role in spreading infectious diseases.’

Please see Page 2, lines 32-47 in the revised manuscript.

Additionally, to support our findings, we have included relevant literature reference, which is listed below:

[1] Agca H, Akalin H, Saglik I, Hacimustafaoglu M, Celebi S, Ener B. Changing epidemiology of influenza and other respiratory viruses in the first year of COVID-19 pandemic. Journal of Infection and Public Health. 2021;14(9):1186-90.

3. Description of droplets shall be included, i.e., material, density, viscosity, other physical properties.

Response: Thank you for bringing this important issue to our attention. We agree that it is crucial for accurate simulation. Based on equivalent aerodynamic principles, this approach simplifies and predicts the motion, settling, and spreading behavior of real particles or droplets by comparing them to idealized spherical particles with the same aerodynamic properties. In our study, we defined droplets as inert particles and modeled them with a density of 1000 kg/m³ to reflect their water-liquid composition. Based on the reviewer’s comment, we have added a detailed description of the droplets as follows:

‘To simulate the droplets based on equivalent aerodynamic principles, we defined them as inert particles, allowing us to focus on their aerodynamic behavior and physical characteristics without involving complex chemical reactions or biodegradation processes. Based on previous research, the droplets are modeled using water-liquid, with a density of 1000 kg/m^3. This configuration ensures that we can accurately capture the droplets’ behavior in the airflow.’

Please see Page 8, lines 226-230 in the revised manuscript.

Furthermore, to support the configuration used in our study, we have provided relevant literature references as listed below. These references also set the droplets as inert particles, and the same physical properties as in our study. In addition, due to the low-humidity nature of the cabin environment during flight, our previous research demonstrated that droplets would rapidly evaporate into a residual [2-4], which is expected to minimize their effect on droplet transport characteristics. However, since this study modeled a large-scale, densely populated cabin environment and considering the high computational resources required, evaporation was temporarily not considered.

[2] Li P, Liu W, Zhang TT. CFD modeling of dynamic airflow and particle transmission in an aircraft lavatory. Building Simulation. 2023;16(8):1375-90.

[3] Ma B, Ruwet V, Corieri P, Theunissen R, Riethmuller M, Darquenne C. CFD simulation and experimental validation of fluid flow and particle transport in a model of alveolated airways. Journal of Aerosol Science. 2009;40(5):403-14.

[4] Park CIP, editor Simulating aerosol movement in experimental chambers using computational fluid dynamics. CSBE/SCGAB 2017 Annual Conference; 2017.

4. Is humidity being considered in this study? If no, please justify thoroughly. As far as reviewer concern, humidity could significantly affect the droplets transportation characteristics.

Response: Thank you for your insightful question regarding the consideration of humidity in our study. We appreciate your concern about the potential impact of humidity on droplet transport characteristics. In this study, we did not explicitly consider humidity as an important factor. Our decision was based on several considerations. Firstly, our previous research has shown that in the low humidity environment of an aircraft cabin, droplets tend to evaporate rapidly after emission. For 10 μm droplets, the evaporation process terminates within 1s. Notably, the smaller the droplet, the faster the evaporation process. This quick evaporation process minimizes the influence of environmental humidity on droplet transport [5-7].

Moreover, the primary focus of our research was to introduce the PCR (Personal Contamination Ratio) method. Given this emphasis, we chose to simplify certain environmental parameters in our simulation phase to highlight the efficacy of the PCR approach.

However, we acknowledge the importance of humidity in this filed study. Additionally, we included the following limitation section in the manuscript. Furthermore, considering that accounting for evaporation effects in the cabin environment would significantly increase computational cost, we have decided, based on our previous studies, to disregard the evaporation of droplets. Rather, we shall concentrate our research on developing the PCR method.

‘In this study, we did not explicitly account for the effects of humidity on droplet transport within the aircraft cabin environment. Our decision was informed by previous research indicating that droplets tend to evaporate rapidly in the extremely dry conditions of an aircraft cabin, thereby minimizing the impact of ambient humidity on droplet dynamics. This simplification was necessary to focus on the primary objective of our research: the development and validation of the Personal Contamination Ratio (PCR) method. While this approach allowed us to highlight the efficacy of the PCR method, we recognize that excluding humidity and evaporation effects may limit the generalizability of our findings to other environments. Future studies should consider more factors to provide a more comprehensive understanding of droplet behavior under varying humidity conditions.’

Please see Page 18, lines 520-529 in the revised manuscript.

[5] Shang Y, Dong J, Tian L, He F, Tu J. An improved numerical model for epidemic transmission and infection risks assessment in indoor environment. Journal of Aerosol Science. 2022 May 1;162:105943.

[6] Li X, Shang Y, Yan Y, Yang L, Tu J. Modelling of evaporation of cough droplets in inhomogeneous humidity fields using the multi-component Eulerian-Lagrangian approach. Building and Environment. 2018 Jan 15;128:68-76.

[7] Li X, Yan Y, Fang X, Tu J. Numerical studies of indoor particulate and gaseous micropollutant transport and its impact on human health in densely-occupied spaces. Environmental Pollution. 2024 Feb 1;342:123031.

5. Authors shall justify why SIMPLE scheme for pressure velocity coupling and second order upwind scheme are chosen. Else, authors might need to find reference (similar study that investigate the effect of droplets dispersion in indoor) to support. Example: https://doi.org/10.1016/j.enbuild.2023.113439

Response: We greatly appreciate your valuable question regarding our choice of the SIMPLE scheme for pressure-velocity coupling and the second-order upwind scheme. Furthermore, it has been demonstrated in earlier research that the second-order upwind scheme and the SIMPLE method for pressure-velocity coupling are especially well-suited for simulations of interior environments.

Following your suggestion, we have revised the description of the SIMPLE scheme as follows:

‘This study employs the SIMPLE scheme for pressure-velocity coupling and the second-order upwind scheme for momentum space discretization. This combination has been proven particularly suitable for indoor environment simulations, aligning well with the scope of our study [8,9].’

Please see Page 7, line 209 and Page 8, lines 210-211 in the revised manuscript.

The authors have carefully read the literature provided by the reviewers and it was found the literature provide valuable insight to our manuscript, and we have added the following references in the manuscript:

[8] Tan H, Othman MHD, Kek HY, Chong WT, Wong SL, Ern GKP, et al. Would sneezing increase the risk of passengers contracting airborne infection? A validated numerical assessment in a public elevator. Energy and Buildings. 2023;297:113439.

[9] Ho X, Ho

Attachment Submitted filename: Response to Reviewers_v6.docx

10.1371/journal.pone.0309730.r005
Decision Letter 2
Pongpirul Krit Academic Editor
© 2024 Krit Pongpirul
2024
Krit Pongpirul
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version2
19 Aug 2024

Optimizing cabin air inlet velocities and personal risk assessment: introducing the Personal Contamination Ratio (PCR) method for enhanced aircraft cabin infection risk evaluation

PONE-D-24-00989R2

Dear Dr. Shang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Krit Pongpirul, MD, MPH, PhD.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #4: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #4: All comments have been addressed well. Please send the manuscript for proofread before publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #4: No

**********

10.1371/journal.pone.0309730.r006
Acceptance letter
Pongpirul Krit Academic Editor
© 2024 Krit Pongpirul
2024
Krit Pongpirul
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
27 Aug 2024

PONE-D-24-00989R2

PLOS ONE

Dear Dr. Shang,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Assoc. Prof. Dr. Krit Pongpirul

Academic Editor

PLOS ONE
==== Refs
References

1 Jing H , Ge HW , Wang L , Choi S , Farnoud A , An ZN , et al . Investigating unsteady airflow characteristics in the human upper airway based on the clinical inspiration data. Physics of Fluids. 2023;35 (10 ). doi: 10.1063/5.0169597 WOS:001104461900004.
2 Ge H , Zhao P , Choi S , Deng T , Feng Y , Cui X . Effects of face shield on an emitter during a cough process: A large-eddy simulation study. Sci Total Environ. 2022;831 :154856. Epub 2022/04/01. doi: 10.1016/j.scitotenv.2022.154856 .35358516
3 Dong J , Sun Q , Shang Y , Zhang Y , Tian L , Tu J . Numerical comparison of inspiratory airflow patterns in human nasal cavities with distinct age differences. 2022;38 (3 ):e3565. doi: 10.1002/cnm.3565 34913265
4 Agca H , Akalin H , Saglik I , Hacimustafaoglu M , Celebi S , Ener B . Changing epidemiology of influenza and other respiratory viruses in the first year of COVID-19 pandemic. Journal of Infection and Public Health. 2021;14 (9 ):1186–90. doi: 10.1016/j.jiph.2021.08.004 34399190
5 WHO. WHO Coronavirus (COVID-19) Dashboard. 2024. 2024 .
6 Zhang SX , Wang Y , Rauch A , Wei FJPr . Unprecedented disruption of lives and work: Health, distress and life satisfaction of working adults in China one month into the COVID-19 outbreak. 2020;288 :112958.
7 Miao H , Huang K , Li Y , Li R , Zhou X , Shi J , et al . Optimization of formulation and atomization of lipid nanoparticles for the inhalation of mRNA. Int J Pharm. 2023;640 :123050. Epub 2023/05/19. doi: 10.1016/j.ijpharm.2023.123050 .37201764
8 Vaccinations Against COVID-19 May Have Averted Up To 140,000 Deaths In The United States. 2021;40 (9 ):1465–72. doi: 10.1377/hlthaff.2021.00619 .34406840
9 Takyi PO , Dramani JB , Akosah NK , Aawaar GJSA . Economic activities’ response to the COVID-19 pandemic in developing countries. 2023;20 :e01642.
10 Hâncean M-G , Slavinec M , Perc MJJoCN . The impact of human mobility networks on the global spread of COVID-19. 2020;8 (6 ):cnaa041.
11 Wang F , You R , Zhang T , Chen Q . Recent progress on studies of airborne infectious disease transmission, air quality, and thermal comfort in the airliner cabin air environment. 2022;32 (4 ):e13032. doi: 10.1111/ina.13032 35481932
12 Liu M , Liu J , Cao Q , Li X , Liu S , Ji S , et al . Evaluation of different air distribution systems in a commercial airliner cabin in terms of comfort and COVID-19 infection risk. Build Environ. 2022;208 :108590. Epub 2021/11/24. doi: 10.1016/j.buildenv.2021.108590 ; PubMed Central PMCID: PMC8599143.34812218
13 Tao Y , Yang W , Inthavong K , Tu J . Indoor particle inhalability of a stationary and moving manikin. Building and Environment. 2020;169 . doi: 10.1016/j.buildenv.2019.106545
14 You R , Lin CH , Wei D , Chen QJIA . Evaluating the commercial airliner cabin environment with different air distribution systems. 2019;29 (5 ):840–53. doi: 10.1111/ina.12578 31172603
15 Choi E-S , Yook S-J , Kim M , Park DJT . Study on the Ventilation Method to Maintain the PM10 Concentration in a Subway Cabin below 35 μg/m3. 2022;10 (10 ):560.
16 Cui F , Geng X , Zervaki O , Dionysiou DD , Katz J , Haig S-J , et al . Transport and Fate of Virus-Laden Particles in a Supermarket: Recommendations for Risk Reduction of COVID-19 Spreading. 2021;147 (4 ):04021007. doi: 10.1061/(ASCE)EE.1943-7870.0001870
17 Li J , Cao X , Liu J , Wang C , Zhang YJB , Environment. Global airflow field distribution in a cabin mock-up measured via large-scale 2D-PIV. 2015;93 :234–44.
18 Cao Q , Liu M , Li X , Lin C-H , Wei D , Ji S , et al . Influencing factors in the simulation of airflow and particle transportation in aircraft cabins by CFD. 2022;207 :108413. doi: 10.1016/j.buildenv.2021.108413 36568650
19 Yan Y , Li X , Shang Y , Tu JJB , environment. Evaluation of airborne disease infection risks in an airliner cabin using the Lagrangian-based Wells-Riley approach. 2017;121 :79–92.
20 Australia SW . Working with silica and silica containing products—Assessing the risk. 2019.
21 WHO. Hazard prevention and control in the work environment: Airborne dust. 2002.
22 Shang YD , Inthavong K , Tu JY . Detailed micro-particle deposition patterns in the human nasal cavity influenced by the breathing zone. Computers & Fluids. 2015;114 :141–50. doi: 10.1016/j.compfluid.2015.02.020
23 Bourouiba L. Turbulent Gas Clouds and Respiratory Pathogen Emissions: Potential Implications for Reducing Transmission of COVID-19. JAMA. 2020;323 (18 ):1837–8. doi: 10.1001/jama.2020.4756 32215590
24 Bourouiba L , Dehandschoewercker E , Bush JWJJoFM . Violent expiratory events: on coughing and sneezing. 2014;745 :537–63.
25 Chao CYH , Wan MP , Morawska L , Johnson GR , Ristovski Z , Hargreaves M , et al . Characterization of expiration air jets and droplet size distributions immediately at the mouth opening. 2009;40 (2 ):122–33. doi: 10.1016/j.jaerosci.2008.10.003 32287373
26 Zayas G , Chiang MC , Wong E , MacDonald F , Lange CF , Senthilselvan A , et al . Cough aerosol in healthy participants: fundamental knowledge to optimize droplet-spread infectious respiratory disease management. 2012;12 (1 ):1–12. doi: 10.1186/1471-2466-12-11 22436202
27 Kong B , Zou Y , Cheng M , Shi H , Jiang YJAS . Droplets transmission mechanism in a commercial wide-body aircraft cabin. 2022;12 (10 ):4889.
28 Zee M , Davis AC , Clark AD , Wu T , Jones SP , Waite LL , et al . Computational fluid dynamics modeling of cough transport in an aircraft cabin. 2021;11 (1 ):23329.
29 Kuga K , Wargocki P , Ito KJIa . Breathing zone and exhaled air re‐inhalation rate under transient conditions assessed with a computer‐simulated person. 2022;32 (2 ):e13003. doi: 10.1111/ina.13003 35225397
30 Abouelhamd I , Kuga K , Yoo S-J , Ito KJB , Environment. Identification of probabilistic size of breathing zone during single inhalation phase in semi-outdoor environmental scenarios. 2023;243 :110672.
31 Zhang M , Yu N , Zhang Y , Zhang X , Cui YJP . Numerical simulation of the novel coronavirus spread in commercial aircraft cabin. 2021;9 (9 ):1601.
32 Sun C , Zhai ZJSc , society. The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. 2020;62 :102390.
33 Riley E , Murphy G , Riley RJAjoe . Airborne spread of measles in a suburban elementary school. 1978;107 (5 ):421–32.
34 Shang Y , Dong J , Tian L , He F , Tu JJJoAS . An improved numerical model for epidemic transmission and infection risks assessment in indoor environment. 2022;162 :105943. doi: 10.1016/j.jaerosci.2021.105943 35034977
35 Silcott D , Kinahan S , Santarpia J , Silcott B , Silcott R , Silcott P , et al . TRANSCOM/AMC commercial aircraft cabin aerosol dispersion tests. 2020.
36 Zhao Y , Dai B , Yu Q , Si H , Yu GJJoES . Numerical simulation study on air quality in aircraft cabins. 2017;56 :52–61.
37 Haselton F , Sperandio PJJoAP . Convective exchange between the nose and the atmosphere. 1988;64 (6 ):2575–81.
38 Wang F , Zhang TT , You R , Chen Q . Evaluation of infection probability of Covid-19 in different types of airliner cabins. Build Environ. 2023;234 :110159. Epub 2023/03/11. doi: 10.1016/j.buildenv.2023.110159 ; PubMed Central PMCID: PMC9977471.36895516
39 You XY , Liu W , editors. Large eddy simulation of virus transport around buildings. 2nd International Conference on Biomedical Engineering and Informatics (BMEI); 2009 Oct 17–19; Tianjin Univ Technol, Tianjin, PEOPLES R CHINA2009.
40 ASHRAE I , Atlanta. American Society of Heating, Refrigerating Air-Conditioning Engineers, Standard 161–2007, air quality within commercial aircraft. 2007.
41 IATA. IATA Medical Manual.
42 Tan H , Othman MHD , Kek HY , Chong WT , Wong SL , Ern GKP , et al . Would sneezing increase the risk of passengers contracting airborne infection? A validated numerical assessment in a public elevator. Energy and Buildings. 2023;297 :113439. 10.1016/j.enbuild.2023.113439.
43 Ho X , Ho WS , Wong KY , Hassim MH , Hashim H , Ab Muis Z , et al . Study of fresh air supply vent on indoor airflow and energy consumption i n an enclosed space. 2021;83 .
44 Atkinson J. Natural ventilation for infection control in health-care settings. 2009.
45 Li P , Liu W , Zhang TT . CFD modeling of dynamic airflow and particle transmission in an aircraft lavatory. Building Simulation. 2023;16 (8 ):1375–90. doi: 10.1007/s12273-023-1031-3
46 Ma B , Ruwet V , Corieri P , Theunissen R , Riethmuller M , Darquenne C . CFD simulation and experimental validation of fluid flow and particle transport in a model of alveolated airways. Journal of Aerosol Science. 2009;40 (5 ):403–14. doi: 10.1016/j.jaerosci.2009.01.002 20161301
47 Park CIP , editor Simulating aerosol movement in experimental chambers using computational fluid dynamics. CSBE/SCGAB 2017 Annual Conference; 2017.
48 Shen JL , Kong M , Dong B , Birnkrant MJ , Zhang JS . Airborne transmission of SARS-CoV-2 in indoor environments: A comprehensive review. Science and Technology for the Built Environment. 2021;27 (10 ):1331–67. doi: 10.1080/23744731.2021.1977693 WOS:000698272300001.
49 Li X , Lester D , Rosengarten G , Aboltins C , Patel M , Cole I . A spatiotemporally resolved infection risk model for airborne transmission of COVID-19 variants in indoor spaces. Sci Total Environ. 2022;812 :152592. Epub 2021/12/27. doi: 10.1016/j.scitotenv.2021.152592 ; PubMed Central PMCID: PMC8695516.34954184
