Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

An Overview of Experiments and Numerical Simulations on Airflow and Aerosols Deposition in Human Airways and the Role of Bioaerosol Motion in COVID-19 Transmission

An Overview of Experiments and Numerical Simulations on Airflow and Aerosols Deposition in Human... Special Issue on COVID-19 Aerosol Drivers, Impacts and Mitigation (II) Aerosol and Air Quality Research, 20: 1172–1196, 2020 Publisher: Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online https://doi.org/10.4209/aaqr.2020.04.0185 An Overview of Experiments and Numerical Simulations on Airflow and Aerosols Deposition in Human Airways and the Role of Bioaerosol Motion in COVID-19 Transmission 1 1* 2,3,4* Justus Kavita Mutuku , Wen-Che Hou , Wei-Hsin Chen Department of Environmental Engineering, National Cheng Kung University, Tainan 70101, Taiwan Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan Department of Chemical and Materials Engineering, College of Engineering, Tunghai University, Taichung 407302, Taiwan Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan ABSTRACT Determining the hotspots and deposition efficiencies (DEs) for aerosols in human airways is important for both research and medical purposes. The complexity of the human airways and the breathing process limit the application of in vitro measurements to only two consecutive branches of the human airway. Herein, in-depth information on in vitro experiments and state-of-the-art review on various computational fluid dynamics (CFD) applications and finite element methods on airflow and aerosol motion in both healthy and obstructed human airways are provided. A brief introduction of the application of one-dimensional and two-dimensional mathematical models to investigate airflow and particle motion in the lungs are further discussed. As evident in this review, aerosol deposition in the upper and central human airway regions has been extensively studied under different inhalation statuses and conditions such as humidity as well as different aerosol sizes, shapes, and properties. However, there is little literature on the lower sections of the human airways. Herein, a detailed review of the fundamentals for both in vitro experiments and numerical simulation at different sections of human airways is done. Exceptional features and essential developments in numerical methods for aerosol motion in healthy and diseased human airways are also discussed. Challenges and limitations associated with the applications of in vitro experiments and CFD methods on both human-specific and idealized models are highlighted. The possibility of airborne transmission pathways for COVID-19 has been discussed. Overall, this review provides the most useful approach for carrying out two- phase flow investigations at different sections of the human lungs and under different inhalation statuses. Additionally, new research gaps that have developed recently on the role of bioaerosols motion in COVID-19 transmission, as well as the deposition of aerosols in impaired human airways due to coronavirus (COVID-19) are underlined. Keywords: Aerosol physics; Asthma and COPD; in vitro experiment; Numerical methods; Two-phase flow; Deposition efficiencies (DEs); Coronavirus (COVID-19). NOMENCLATURE D Diameter (mm) d Particle diameter (µ m) A Amplitude of the sinusoidal curve (Reynolds number) F Force (N) A Amplitude of the folds (cm) G Generation of Weibel’s airway fold A Cross-sectional area of the lumen (cm ) G Turbulent kinetic energy 0 k –3 C Concentration (cm ) G Specific dissipation rate C Drag coefficient L Length of the real airway (m) D A DF Deposition fraction L Length of the geometric model (m) m Mass of a single particle (µ g) n Number of folds P Pressure (Pa) * –1 Corresponding author. Q Flow rate (L s ) E-mail address: whou@mail.ncku.edu.tw (W.C. Hou); R Radius (mm) weihsinchen@gmail.com; chenwh@mail.ncku.edu.tw r Radial coordinate (W.H. Chen) Re Reynolds number Copyright: The Author(s) 2020. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1173 t Time (s) 2010; Hwang and Park, 2019). Despite PM affecting the 2.5 –1 u Particle velocity (m s ) entire population, the adverse impacts are worse for special –1 U Velocity at the real airway (m s ) groups such as infants, the elderly, and patients with obstructive –1 U Average velocity at the airway’s entrance (m s ) conditions as they experience the highest deposition mean –1 U Max velocity at the airway’s entrance (m s ) efficiencies (Longest et al., 2006; Chen et al., 2012; Adeloye max –1 U Velocity at the geometric model (m s ) et al., 2015). Common obstructive pulmonary diseases include V Control volume chronic obstructive pulmonary disease (COPD), asthma, cystic fibrosis, and acute respiratory distress syndrome. Epidemiological reports show that the prevalence of COPD Greek Letters –3 ρ Density (kg m ) and asthma are the highest among the obstructive diseases –2 σ Stress (N m ) (Mathers and Loncar, 2006; Mannino and Buist, 2007; µ Viscosity Coefficient Adeloye et al., 2015; Soriano et al., 2017). They are both –1 υ Averaged inlet velocity of parent branch (m s ) characterized by persistent and limited airflow inside the α Fluid volume fraction lungs and are usually exacerbated by inhalation of toxic θ Angular coordinate (°) gasses and PM (Viegas et al., 1996). Therefore, past studies have mostly focused on airflow, aerosols transportation, transformation, and deposition inside healthy and obstructed Subscripts p particle human lungs during breathing (McCreanor et al., 2007; H hydraulic Zhang and Papadakis, 2010; Chen et al., 2012). mean average value Meanwhile, coronavirus disease 2019 (COVID-19), which peak peak value broke out in December 2019, has been shown to cause deadly cases of pneumonia and is, therefore, receiving a great deal of attention lately. This disease has been shown to INTRODUCTION cause a 3.4% mortality rate globally according to the estimate In vitro experiments and computational fluid dynamics from WHO as of March 2020. Analysis of radiographic and (CFD) coupled with finite element method (FEM) have been computed tomography (CT) findings of COVID-19 patients used as tools for investigating airflow and aerosol motions showed the presence of patchy, confluent, or nodular shaped in human airways for a few years. They are used to lesions and pulmonary opacities concentrated mostly in the investigate the differences in deposition efficiencies (DEs) peripheral lungs (Yoon et al., 2020). A statistical analysis of and deposition patterns of toxic and pharmaceutical aerosols the dominant shapes of lesions showed that patchy to for both healthy and obstructed human airways. Those confluent lesions were more dominant as compared to the approaches are useful in the assessment of the performance nodular ones. Further, CT imaging studies have shown that of existing drug-aerosol delivery technologies to the human the lesions are more concentrated on the lower lobes as well lungs (Asgharian et al., 2001; Darquenne, 2012). Findings as the dorsal part of the lungs (Çinkooğlu et al., 2020). from these studies can be applied in the accurate estimation According to the radiologic evidence presented in a study by of risk levels caused by toxic aerosols or in design Li et al. (2020a), the presence of edema and acute lung modifications to overcome the limitations of inhalers used injury are common in critical stages of patients with severe by patients with respiratory diseases (Kolanjiyil and COVID-19. Acute lung inflammation and long-term damage Kleinstreuer, 2017; Chen et al., 2018a). to the alveolar walls are among the main adverse effects Investigations in the current and past centuries have suffered by COVID-19 survivors (Hosseiny et al., 2020; Xu linked air pollution to a wide range of acute and chronic et al., 2020b). Permanent lung damage associated with the health defects (Brook et al., 2010; Chowdhury et al., 2019). disease presents a new challenge of understanding deposition A case in point is the statistically significant association patterns and efficiencies for toxic, pharmaceutical, or between long-term exposure to fine particulate matter (PM) biological aerosols in patients after recovery. and reduced life expectancy (Asadi et al., 2020). Although To study the motion of aerosols such as PM and bioaerosols this information is only available for developed and middle- such as bacteria and viruses, their size distributions are income countries in the world countries, model estimations important. Coronaviruses such as COVID-19, which is the depict a worse situation in developing countries whose true cause of the latest pandemic of respiratory tract infections, situation remains conclusively unknown (Cohen et al., have an average size of between 65 and 125 nm, with an 2005; Soriano et al., 2017). Reports on the relationship of envelope diameter and spikes measuring about 80 nm and some aspects of PM, for instance, chemical composition, 20 nm, respectively (Velavan and Meyer, 2020). The size of toxicity, and particle size show an inverse relationship aerosols released during coughing and sneezing are very between toxicity and PM’s size (Chen et al., 2019; Li et al., important in studying airborne infectious disease transmission. 2020b). Consequently, fine (PM ) and ultra-fine PMs tend Their sizes have been shown to vary in healthy and diseased 2.5 to be the most toxic among the total suspended solids individuals. For healthy individuals, droplets were found to (Valavanidis et al., 2008; Zhang et al., 2018). range between 341.5–398.1 µ m for unimodal distribution There is a fascinating but not very conclusive understanding (Han et al., 2013). On the other hand, individuals affected of the possible pathways that associate exposure to PM by influenza were found to sneeze droplets whose size ranged 2.5 and mortality due to cardiovascular diseases (Brook et al., between 0.35 to 10 µ m (Lindsley et al., 2012). 1174 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 The geometry of the human lung is very complex. to the airflow rate due to amplified secondary flow. Therefore, to study the motions of aerosols inside the lungs, Since the development of CFD, different types of fluid flow in vitro experiments and CFD analysis usually employ have been thoroughly described, including continuous flow simplified geometries (Lennon et al., 1998; Delvadia et al., systems in the industrial, environmental, and physiological 2012). The main simplified models include (1) Weibel’s applications (Chen, 2001a; Longest et al., 2006; Chen et al., geometry which is asymmetrical and with 23 generations 2011; Zhang et al., 2019). The physiological processes include and (2) Horsfield’s asymmetrical geometry (Weibel, 1963b; airflow in the airways and blood flow inside the human body Horsfield and Cumming, 1967). Other geometries developed (Soni and Aliabadi, 2013; Chen et al., 2018c). CFD has later, for instance, by Hammersley and Olson (1992), are matured into a proper tool whose application in describing more suitable for investigations in the sixth to twelfth airflow in the human airways has almost replaced the traditional generations (G6–G12). in vitro experiments. This increased adoption of CFD methods Traditionally, investigations about the deposition efficiencies for airflow analysis has been aggravated by the need to produce for aerosols inside the human lungs were conducted using detailed results on airflow as well as the high costs and huge experiments (Chang and El Masry, 1982; Lennon et al., time consumed during in vitro experiments (Solchenbach and 1998). It is 5 decades since the first in vitro measurements Trottenberg, 1988). Additionally, CFD blends visualization for airflow and particle motion inside the lungs were done. techniques as well as mathematical physics and methods for Currently, this approach presents a great challenge due to the the production of optimized results (Chen, 2001b). limited ability to accurately assess instantaneous airflow Previously reviews have been conducted on the applications velocity and pressure as well as the DEs (Lambert et al., of CFD to study the deposition of aerosol medicine in a 2011). The development and advancement of computing whole lung airway model by Longest et al. (2019). Another capability in the world have enhanced the application of review by Islam et al. (2020) covered the recent developments FEM and CFD models to carry out investigations on airflow on airflow analysis and particle deposition in both upper and and particle deposition in human airways (Chen et al., 2012; lower regions of human airways. Furthermore, particle Rahimi-Gorji et al., 2015). clearance mechanisms in the lungs have also reviewed Important items before the implementation of a CFD study (Hussain et al., 2011). The simulations of particle formation include the generation of geometry, solving the governing and localized deposition in human airways using 1-D, 2-D, equations along with appropriate boundary conditions, and and 3-D models were reviewed by Guzman (2020). Despite sometimes incorporating user-defined functions (UDFs). other authors writing reviews on aerosol deposition and The shape and size of geometry are not only dependent on particle clearance in human lungs, none has combined particle the generation of the human airways under investigation but deposition and lung clearance mechanism. This review fills also on whether it is healthy or affected by a disease (Sul et the gap by reviewing the application of experiments and al., 2014; Chen et al., 2018a). COPD has been represented CFD with FEM in the assessment of airflow as well as by an axisymmetric constriction at the center of a bifurcation, aerosol motion and deposition in healthy and deformed while asthma has been represented by sinusoidal folds at the human airways. Fundamentals for in vitro experiments circumference of the affected bifurcation (Yang et al., 2006; involving airflow and particle motion inside the human lungs Zhang and Papadakis, 2010). Typical boundary conditions are discussed. Additionally, those for numerical simulation, imposed for airflow include velocity distribution at the inlet, for instance, simplified geometries of the human lungs, CFD no-slip boundary condition along the walls, and pressure at models for different sections of the human airway are covered. the outlets (Zhang and Kleinstreuer, 2002). Dominant lung clearance mechanisms at different sections In COPD cases, investigations on airflow have attributed of the human airways are briefly discussed. Exceptional the shortness of breath to the presence of stagnation and features and essential aspects of results from both experiments recirculation zones at the proximity of the obstructed are CFD analyses are also discussed. bifurcation (Luo et al., 2007; Chen et al., 2012; Mutuku and Chen, 2018). Pressure distributions in COPD showed that jet HUMAN AIRWAY STRUCTURE flow phenomena at the cross-section affected by the obstruction resulted in low pressures which were inadequate Gaseous exchange between the atmosphere and the human to drive the required airflow mass to the later sections of the blood is accomplished through the human respiratory system. human airway (Yang et al., 2006). Skewed airflow velocities Upon inhalation, air travels through the nose, pharynx, larynx, influenced the mass flow ratios at bifurcations in the and into the trachea, after which it goes into one of the two generations affected by the obstruction and subsequent ones bronchi that lead the way into the left and right lungs. The (Mutuku and Chen, 2018). Sudden reduction in the effective two bronchi divide into smaller and smaller bronchioles cross-sectional area for asthma cases caused increased airflow until they reach the alveoli. The alveoli, which are sac-like velocities and high complex secondary flows, which in turn structures, mark the end of the lungs and the region responsible led to higher deposition fractions for PM as compared to for gaseous exchange between the inhaled air and the blood 2.5 healthy human airways (Chen et al., 2018a). Airflow resistance, in the circulatory system (Horsfield and Cumming, 1968). which is normally represented by the ratio of total pressure drop, has been applied to characterize and evaluate obstructions Geometric Models for Human Airways in the human lungs (Sul et al., 2014). Previous research has In vivo measurements on the deposition efficiencies and shown that the pressure drop is usually directly proportional airflow dynamics inside the lungs are impossible due to the Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1175 delicate and complex nature of the human airways. Therefore, where D is the hydraulic diameter for the idealized model, in vitro measurements and numerical simulations have been while the area and perimeter are measured from the real applied in the investigations for airflow as well as particle airway geometry. motion and deposition. Due to the complex nature of the Historically, whole lung-semi-empirical models were human lungs, in vitro experiments on the human lungs require applied to establish the deposition patterns and airflow simplification of the lungs’ geometry. Several researchers phenomenon in the human airways. However, a more feasible have developed simplified lung geometries (Weibel, 1963b; approach has been developed recently and it involves the Horsfield et al., 1971; Yeates and Aspin, 1978; Hammersley application of multistage modeling with the development of and Olson, 1992; Van Ertbruggen et al., 2005; Lindsley et large scale models representing the central airways and al., 2012; Chen et al., 2017; Zhao et al., 2020). However, small scale models to represent peripheral airways (Kolanjiyil only two of these are commonly used for in vitro experiments and Kleinstreuer, 2017). The approach is aimed at obtaining and numerical analysis; they are Horsefield’s model (Horsfield a whole lung airway model which is physiologically and Cumming, 1968) and Weibel’s model (Weibel, 1963a). accurate. Investigations on airflow and particle motion using These geometric models are applied for in vitro experiments the resultant geometric representations can be used to build or numerical simulations to investigate airflow or particle an understanding of the whole human lung. motion and deposition inside the human airways. In some According to a study by Park and Wexler (2007), there is a studies, CT scans and magnetic resonance imaging (MRI) significant degree of mixing, especially in Weibel’s bifurcation. measurements have been applied to develop more realistic As air advances to the terminal airways, the aggregate cross- geometric models. However, airflow dynamics, aerosol motion, sectional area of the human airways increases, and this and deposition in the human airways face tremendous inter- amplifies recirculation phenomena and hence increases the subjective inconsistencies and therefore the results from mixing, especially with the pulsating airflow. It is important such human-specific studies cannot be projected to the entire to understand the fluid mechanics for airflow inside the human population. Furthermore, curvatures and anatomy of the real airways, especially for area expansion, curvatures, secondary human airways are more complicated compared to the flow phenomena, flow re-organization, and stagnation and simplified models and as such, their airflow patterns are recirculation zones in each bifurcation (Hammersley and equally complicated (Hwang and Park, 2019). Despite the Olson, 1992; Lambert et al., 2011). use of CT scans and MRI measurements providing the most realistic geometries, they cannot provide the dimensions of Weibel Geometric Model th th the bifurcation beyond the 7 or 9 generation due to According to the study of Weibel (1963b), the inadequate clarity of the scanned images (Walters and Luke, tracheobronchial system can be simplified using a dichotomous 2010). This makes simplified models more attractive as branching network of pipes and a total of 23 levels of ducts compared to real ones obtained from CT scans for application (bifurcations). The bifurcations are numbered depending on in studies involving the human airways, especially for how far downstream they are from the trachea. Therefore, central and lower sections of the lung. the trachea is G0, the left and right bronchi which are both In the study of airflow dynamics and particulate phase G1, the four branches after the bronchi G1 are G2, and so motion in the trachea-bronchial bifurcations, a huge task lies forth until the last section of the alveoli (G20–G23). A in striking a balance between ease of measurement and simplified representation of Weibel’s model is presented in physiologically realistic airway geometry. The Weibel’s Fig. 1(a). It is widely accepted that the application of a geometry assumes a symmetrical geometry, while the reduced number of branches with suitable boundary conditions Horsfield's model assumes an asymmetrical structure (Weibel, can give results with an acceptable degree of accuracy if the 1963b; Horsfield et al., 1971). Scientific evidence suggests region of interest is not at the outlet branches (Sul et al., that the human lung is asymmetrical and the number of 2014). A 4 generations bifurcation for a healthy human generations in a particular pathway is variable (Horsfield airway is shown in Fig. 2(a). In this model, the human lung and Cumming, 1968; Chen et al., 2020). However, Weibel’s geometry can be split into three main regions; G0–16 is the geometric model which is idealized, regular, and symmetrical conductive zone, G17–19 is respiratory bronchioles, and remains the most popular due to its ability to provide G20–23 is composed of alveoli ducts and alveoli. The sufficient information on gas and particulate flow within a bifurcations have a constant length to diameter ratio (L/D) short computational time. which is approximately 3. The ratio of the parent bifurcation The walls in airways are commonly assumed to be rigid and diameter to that of the child (D /D ) is also constant in this n n+1 smooth with a circular cross-section (Chang and El Masry, model and ranges from 1.17 to 1.5 (Weibel, 1963a). 1982; Chen et al., 2018a). The concept of hydraulic diameter is important as it equates the circular cross-sections in idealized Horsfield GEOMETRIC MODEl models to the bronchial surface over which airflow experiences This model was aimed at capturing the effects of a shear force in the real human airway (Hammersley and asymmetry on airflow dynamics and particle deposition Olson, 1992). The hydraulic diameter is expressed as (Horsfield and Cumming, 1967). The effects of asymmetry on airflow and particle motion increases as air advances from the oro-nasal cavity towards the alveoli region. The 4 Area D  (1) approach by Horsfield and Cumming (1967) applied formulas perimeter by which the asymmetry of the dichotomous branching 1176 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 could be represented. Details provided in the model include Other Models the angle of the branches, the radius of the curvature, and the A few other irregular dichotomy models have been proposed cross-sectional shape. The numbering of the bifurcations based on morphometric data from Horsfield’s model (Yeates also differs from the one proposed by Weibel (1963b), in the and Aspin, 1978; Van Ertbruggen et al., 2005). Yeates and sense that bifurcations are numbered starting from the alveoli Aspin, (1978) carried out a study on the physiological region. (Horsfield and Cumming, 1967). A typical solid implications of the morphological structure proposed by geometry and mesh for a model adapted from Horsfield’s Horsfield, whereby mathematical expressions for the model are presented in Fig. 1(b). bifurcation system of the intralobular bronchi from th e (a) (b) Fig. 1. (a) A representation of Weibel’s symmetrical models of generations 0–23 and (b) A representation of the G0–G3 of the Horsfield’s asymmetrical model. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1177 trachea to the alveoli were described. Additional anatomical models have also been established using mathematical algorithms and imaging techniques (Tgavalekos et al., 2007; Velavan and Meyer, 2020; Xu et al., 2020b). The latest airway geometry model was developed by Lindsley et al. (2012) and it described the first 17 generations of an asymmetrical human airway geometry. A more practical cadaver model was developed to handle smaller airways for bifurcations between G6 and G12 (Hammersley and Olson, 1992). A mathematical method of obtaining a physiologically accurate geometry for the first five generations of the human airway was developed by Zhao et al. (2020). Semi-automatic approaches have been developed to help in the development of the lung’s morphological structure (Sauret et al., 1999). In a study by Lindsley et al. (2012), high-resolution tomography coupled with image processing algorithms were applied to th develop precise models of the human airways up to the 17 generation of the Horsefield’s geometry. Another hybrid model using more than two techniques to characterize the geometry was by Van Ertbruggen et al. (2005), which used the work by Horsfield and Cumming (1967) for characteristics of individual generation coupled with imaging techniques to obtain local branch orientations. Anatomical Features of Diseased Human Lungs Since asthma and COPD are the most prevalent obstructive pulmonary diseases, there is a relatively higher volume of literature covering aerosol motion and deposition in human airways affected by these conditions (Yang et al., 2006; Luo et al., 2007; Zhang and Papadakis, 2010; Chen et al., 2018a). Geometric characteristics of the obstructed human airways such as for asthma and COPD can be represented by slightly altered geometries from the regular idealized ones. Asthmatic human airways are usually represented by uniformly distributed folds along the circumference of the affected generation (Zhang and Papadakis, 2010; Chen et al., 2018a). According to the study of Zhang and Papadakis (2010), a human airway affected by asthma can be represented as a circular cross-section surrounded by several sinusoidal folds which are distributed along the circumference. The equation can be expressed in the polar coordinate system as: r(θ) = R + A cos(nθ) (2) fold where r, θ and R are the radial coordinate, angular coordinate, and effective radius of the affected cross-section. Additionally, A and n stand for the amplitude of the fold in centimeters fold and the number of folds in the affected cross-section, respectively. A typical configuration of the asthmatic airway discretized with 40% of normal lumen area and 10 folds is Fig. 2. Typical geometries for (a) healthy human airway, (b) shown in Fig. 2(b). human airway affected by asthma, and (c) human airway On the other hand, airways affected by COPD are affected by COPD. represented by axisymmetric constriction in one or more of the bifurcations (Yang et al., 2006; Chen et al., 2012). A typical figure of a human airway affected by COPD as human-specific airway geometries, CT scans have provided shown in Fig. 2(c). images that are clear enough for the reproduction of 3D The lung damages caused by COVID- 19 will affect geometries up to the G9 of the human airways. The lesions airflow as well as aerosol motion and deposition inside the associated with COVID -19 are mostly concentrated in the human airways of survivors. In previous studies, based on alveolar region, and therefore, it is difficult if not impossible 1178 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 to obtain CT images at a clarity level which would be conditions or stochastically coupled boundary conditions. A adequate for the production of 3D geometries of the diseased truncated model was used by Tena et al. (2015) whereby the regions. Therefore, new approaches are needed to carry out same velocity vector fields were imposed on the truncated investigations on airflow and particle motion in the lungs of sections as the corresponding sections of the developed COVID-19 survivors. branches of the airway. In another study by Walters and Luke (2010), where 50% of the airway paths were truncated, static pressure values at corresponding sections of the Whole Lung Airway Model vs. Localized Simulations. There have been few attempts to simulate the resistance remaining bifurcations were used as the boundary conditions at of flow, and deposition efficiency in the entire human lung. the truncated sites. In a study by Gemci et al. (2008) a 17- A summary of the studies has been presented in Fig. 3. In a generation model was partially solved using 1,453 bronchi study by Chen et al. (2017), the resistance of flow in the rather than 131,072. This was achieved through truncation upper airway was found to contribute 45–81% of the total and duplication of the boundary conditions to cater for the resistance of the entire human lung depending on the truncated sections. In a study by Walters and Luke (2010), a frequency of ventilation. During the study, hybrid 3D bifurcation angle of 70° was applied for a geometry geometries for the upper, central, small airways, and alveoli consisting of G4–G12, the plane for each bifurcation was were used. Some of the generations between G4 and G6 selected randomly for angles between 0° and 180°. In this were truncated. In a study on the deposition of 1–30 µ m study, branches were truncated past G6 such that only one particles in the tracheobronchial generations between the branch followed all through to G12. Stochastically coupled mouth to G10, deposition mostly happened in the large- and boundary conditions were imposed on the truncated medium-sized generations (Ma and Lutchen, 2009). sections. The latest state-of-the-art approach in CFD involves the On the other hand, studies on localized deposition of application of partially resolved models of a truncated airway particles include the work of Nowak et al. (2003) where a tree. After truncation, appropriate boundary conditions are numerical analysis for airflow and 10 µ m particle motion imposed on the truncated sections by applying prior pressure was carried out based on a four-generation Weibel’s model Rahimi- G1~G17 Gorgi et al., Ma and Mouth~G10 Lutchen, 2009 Without truncation Soni et al., Mouth – G10 Simulations for WLAM Ma and G0~G23 With Lutchen, truncation Walters and G1~G17 Luke, 2010 Kolanjiyil, and Mouth~G23 Kleinstreuer, Fig. 3. Attempts towards carrying out aerosol motion investigations in a whole lung airway model. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1179 (G0–G3) and a cadaver lung cast. Results from the deposition U L U L M M A A  (3) efficiencies done for both inhalation and exhalation flow vv MA conditions showed a maximum deposition of 12% in the carina region that succeeds generation 2 for unsteady where U and L represent linear velocity and dimension, inhalation using Weibel's geometry. The maximum DE of respectively, while v designates the kinematic viscosity of 6.5% for the geometry from the CT scan was highest at G0 the gas phase. The subscripts M and A denote the geometric for both steady and unsteady inhalation conditions (Hwang model and the real airway, respectively. and Park, 2019). In an in vitro experiment, by Lennon et al. To conduct experiments on obstructed airways in the (1998) whereby deposition efficiencies were compared for lungs, copper tubes, which are packed with steel capillary nasal and oral inhalation, the maximum deposition efficiency tubes, are applied as obstructions inside the tubular model. of 43.6% occurred during nasal inhalation at the nasal region The longer the resistor, the more severe the obstruction as compared to 5.4% at the oral cavity during oral inhalation. (Chang and El Masry, 1982). Plastic conic resistors provide The effect of cartilage on airflow in the trachea and the main the transition from the airway terminals to resistors. A bronchi were investigated using CFX and Fluent solvers precision metering valve is applied to control the airflow rate (Russo et al., 2008). Results showed that for a laminar flow during both inhalation and exhalation. –1 characterized by 15 L min at the inlet DEs using Fluent exceeded those obtained using CFX by an average of 2.4%. Methods of Measuring Flow Velocity and Pressure –1 However, for a turbulent flow of 60 L min , the DEs of Distribution Fluent fell behind those of CFX by about 4.2% for a smooth Airflow velocity is usually constant for experiments. As airway channel, and they were equal for ringed trachea and can be seen on the VOSviewer map in Fig. 5 airflow is bronchi. Further details on localised deposition are tabulated among the most important parameters in aerosol motion in Table 1. inside the human airways. Axial flow velocities are usually The whole lung airway model can provide information measured using hot-wire anemometer probes. The working about the airflow dynamics, particle motion, and deposition mechanisms of the wire probes involve the application of the of particles inside the human airways. However, the degree cooling effect of the wire due to airflow to estimate the of details required to achieve an improved understanding of velocity of air at the cross-section fitted with the anemometer regional deposition is not practical while using a whole lung wire (Chang and El Masry, 1982; Isabey and Chang, 1982; airway model. This reason makes localized particle deposition Cheng et al., 1999). Secondary flow velocities are captured models more popular. Additionally, accurate prediction of using still photographs by continuous frontal illumination DEs in hotspots and other vulnerable sites can provide more with an angle of 45° as the incidence angle (Schroter and relevant methods for the assessment parts of the respiratory Sudlow, 1969). Pressure drop over a given lateral length is system which are more prone to injury or diseases due to usually measured using a sensitive differential pressure higher deposition efficiencies of aerosols (Hofmann et al., transducer (Chang and El Masry, 1982; Yanai et al., 1992). 1995; Nazridoust and Asgharian, 2008). In a study by Stapleton et al. (2000), overall pressure drop during a turbulent flow in a mouth-throat geometry was FUNDAMENTALS OF IN VITRO EXPERIMENTS measured by attaching a pressure transducer to both the inlet and exit using pressure traps. For a long time, experiments have been performed to understand airflow resistance in the human lungs, mixing of Estimating Deposition Patterns and Respective the intrapulmonary gases and deposition of particles from Deposition Efficiencies ambient air (Yanai et al., 1992; Cheng et al., 1999). The The aerosol deposition is among the most important aspects main goal for these experiments is usually to shed some light of studies on aerosol motion in the human airways as can be on the airflow dynamics inside the human lungs and also the seen in Fig. 5. The application of chemicals with unique factors influencing particle deposition inside them. The properties under illumination is helpful in the estimation of setup for an in vitro analysis is shown in Fig. 4. deposition efficiencies. In an experimental study by Lennon et al. (1998), regional deposition efficiencies for fluorescent Generating Casts particles with diameters of 0.3 µ m and 0.7 µ m were estimated Solid casts to represent the human airways are applied in by measuring the fluorescent intensity using a fluorescence experiments. Their dimensions are usually human-specific spectrophotometer. Overall deposition efficiency was or following popular simplified human airway geometries. estimated by comparing the number of particles injected to Solid tubular models are usually constructed to represent the the ones which exited the cast at the outlet. Aerosol deposition airway bronchus from materials such as acrylic plastic in a study by Cheng et al. (1999) was investigated using (Perspex), silicone rubber, milled steel blocks, premade Y- different sizes of polystyrene latex fluorescence particles. connectors, bored aluminum, or plumbing fixtures (Chang Fluorescent content and consequently the deposition fractions and El Masry, 1982; Lennon et al., 1998). Even though the were estimated using a fluorescence spectrometer. An physical geometry is usually at a larger scale to ensure ease aerodynamic particle sizer which gives the particle size and of data collection, the Reynolds number should be kept the number concentration can be used to measure the total constant to ensure dynamic similarity, as expressed in the deposition (Häußermann et al., 2002). following equation (Chang and El Masry, 1982). 1180 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 Table 1. Literature review of past studies. Algorithm Maximum Experiment Reynolds Particle Particle Type Generations Healthy/ Inhalation/ Mesh (type of CFD (pressure- deposition or Status number density diameter Reference of flow covered Unhealthy Exhalation elements) code velocity fraction –3 simulation at the inlet (kg m ) (µM) coupling) (%) Transient G3–G6 Healthy Simulation Inhalation Rest 388 CFX4.3 SIMPLEC 500–5000 3–7 Zhang and – – Light activity 805 Kleinstreuer Moderate 1586 (2002) exercise Exhalation Rest 340 Light activity 696 Moderate 1418 exercise Transient G5–G8 COPD Simulation Inhalation Light activity 362.24 PISO 19 Chen et al. Steady 362.24 FLUENT 1000–2650 5 17 (2012) Transient G4–G13 Healthy Simulation Inhalation Light activity 319 Fully CAMEL Fourth order 998.2 10 20 Soni and Steady G4–G13 Healthy Simulation Inhalation Light activity 319 unstructured runge-kutta 15 Aliabadi (2013) Steady G3–G5 Healthy Simulation Inhalation N/A 500–2000 Unstructured CFX 4.2 500–2000 3, 5, 7 Comer et – – solver al. (2000) Steady G4–G15 Healthy Simulation Inhalation Rest 519 Hexahedral FLUENT SIMPLE 1000 1 0.6 Piglione et and 6.3 2 1.43 al. (2012) experiment 5 14 10 71 20 100 Moderate 1038 1 1.33 activity 2 1.83 5 17 10 83 20 100 Steady G0–G3 Healthy Simulation Inhalation N/A 400–1200 Unstructured FLUENT SIMPLE N/A N/A N/A Guha et al. G0–G4 triangular and (2016) G0–G5 tetrahedral elements Steady G0–G5 Healthy Simulation Inhalation N/A 400, 100 unstructured FLUENT SIMPLE N/A N/A N/A Guha and and 1600 tetrahedral Pradhan elements and (2017) 0-grids for the boundary layer G8–G14 Healthy Simulation Inhalation Light activity 169.4 Unstructured FLUENT SIMPLE N/A N/A N/A Sul et al. Symmetric and Moderate 296.2 meshes 14 (2014) (COPD) exhalation exercise Asymmetric Light activity 169.4 (COPD) Random Moderate 296.2 (COPD) exercise Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1181 Fig. 4. Schematics of a typical arrangement for an in vitro experiment set-up on airflow dynamics and particle motion inside the human airways. Fig. 5. VOSviewer map of the current state of research on aerosol motion and deposition in human airways. 1182 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 airways. Governing equations are also applied at the important FUNDAMENTALS OF NUMERICAL ANALYSIS sections of the control volume to represent the boundary Algebraic deposition models came before CFD methods, conditions in different sections (Schreck and Mockros, 1970; but their tendency to overestimate the effects of impaction on Chen, 2002). Some of the governing equations for fluid particle deposition led to the development and more extensive flow, particle motion, drag coefficient, the velocity at the inlet, adoption of the CFD method which has higher accuracies. Reynolds number, and deposition efficiencies are summarized Mathematical dispersion models of bolus dispersion have in Table 2. Fundamental steps for a CFD analysis are shown also been applied in the prediction of particle motion and in Fig. 6. deposition in the human airways. The mathematical models have a drawback in the sense that they cannot account for Mesh and Boundary Conditions information on particle trajectories and hence no available Mesh generation is the process of subdividing a control information on hotspots (Lambert et al., 2011). Only CFD volume into discrete geometric and topological grids. methods will be covered under numerical analysis due to its Meshes for the control volumes are usually generated using extensive adoption as compared to other numerical methods Gambit, ANSYS, or other mesh generating applications for airflow simulation. (Inthavong et al., 2010; Chen et al., 2011; Deng et al., 2018). The main advantage of numerical analysis of airflow and The shape of the elements is important and it can be particle motion inside the human airways is that it gives unstructured, hexahedral, tetrahedral, non-orthogonal blocks, researchers the ability to study airflow phenomenon and and triangular prism (Rahimi-Gorji et al., 2015; Chen et al., particle motion in the respiratory system in ways that are 2018a). In a study of particle deposition inside a real human experimentally impractical or incalculable. Additionally, it airway (G0–G2) by Rahimi-Gorji et al. (2015), the elements is an affordable and non-invasive method of gaining useful had unstructured tri/tetrahedral hybrid. In a study by Tena et information on flow inside the human airways for medical al. (2015), tetrahedral meshes were used for the lung model or research purposes (Walters and Luke, 2010). With CFD due to their flexibility and ability to cope with complex solid tools, it is possible to carry out investigations using healthy geometries. and diseased human lung geometries on the resistance of In an investigation comparing the effects of different airflow, distribution of mass flow rates, shear stress on the boundary conditions, Nazridoust and Asgharian (2008) found walls, complex secondary flow phenomena, and deposition out that the highest particle deposition was during unsteady patterns as well as efficiencies (Walters and Luke, 2010; flow boundary conditions imposed at the inlet of the control Huang and Zhang, 2011; Tian et al., 2017). volume. Initial boundary conditions for the inlet and outlet are usually specified and can be based on either pressure or velocity. The velocity distribution at the inlet of any section Geometrical Structures for the Human Airways Flow inside a confining geometry is strongly influenced of the human lungs can be uniform, symmetric parabolic, or by the geometric shape and as such creation of the human skewed parabolic, depending on the position of the control airway geometries is usually a vital step towards running a volume in the lungs (Yang et al., 2006). For simulations successful CFD simulation. A few software applications have concerning the upper section of the lungs, for instance, the been used before in the creation of human airway geometry, oral cavity or nasal air passages, a uniform velocity distribution for instance, solid works and AutoCAD (Sul et al., 2014; is sufficient (Moskal and Gradoń, 2002). In a study by Gemci –1 Chen et al., 2018a). Important parameters in the creation of a et al. (2008) a uniform velocity distribution of 2.896 m s physiologically accurate lung model include diameter, length, was imposed at the inlet. In a study of secondary flow branching angle, and radius of the curvature. Carina regions phenomena, a non-uniform velocity distribution was imposed are usually smoothened after lofting mother and daughter at the inlet of the control volume (Guha and Pradhan, 2017). branches using the fillet options available in Computer-aided A no-slip boundary condition is usually imposed on the design applications. Information on the generations which walls for airflow (Chen et al., 2012; Rahimi-Gorji et al., are most likely to be affected by obstructions is obtained 2015), while a “trap upon impact” boundary condition is from bronchoscopy studies which are useful in choosing the imposed for the aerosols due to the presence of mucus on the obstructed generations in the creation of the solid geometries airway walls (Ma and Lutchen, 2009; Chen et al., 2018a). It (Yanai et al., 1992). The extend of obstruction is chosen in is important to define a gauge static pressure at the outlet of a way that the volume reduction and airway surface reduction the control volume and it is 1 atm for some studies (Ma and are consistent with histological studies conducted on Lutchen, 2009; Guha and Pradhan, 2017; Chen et al., 2018a). obstructed airways for instance asthma and COPD (Zhang and Papadakis, 2010; Chen et al., 2012). Tools for 2D and Inhalation Curves 3D geometrical development are applied in the development Traditionally, the constant velocity at the inlets was of 2 dimensional and solid geometries which can either be assumed more so for in vitro experimental studies on airflow human-specific or from the generalized human lung airway in the human lungs (Yeh and Schum, 1980; Chang and El models discussed earlier. Masry, 1982). Later, real inhalation curves at the inlets were imposed in carrying out CFD analysis (Zhang et al., 2002; Chen et al., 2012; Mutuku and Chen, 2018). Due to the Governing Equations Several governing equations are applied for the numerical similarity between real inhalation curves and sinewave analysis of fluid flow and particle motion inside the human curves, some studies have used velocity distribution at the Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1183 Table 2. A summary of governing equations, boundary conditions, and dimensionless numbers Governing equations Airflow Particles’ motion Continuity equation Particles’ trajectories du Ui 0, 1, 2, 3   m   C u u u u  F fi   p p D p p fp x dt 8 Momentum equation Momentum equation   fP     fP    U   U U      U   U U      F             f i f j i f ij f i f j i f ij pf t x  x x t x  x x j i j j i j i = 1, 2, 3 and j = 1, 2, 3 i = 1, 2, 3 and j = 1, 2, 3 Particles’ drag coefficient  C    d 1 Re Re N 2 Reynolds number Reynolds number UD  v v D pp mean Re  Re  d N   Boundary conditions Velocity distribution at the inlet Deterministic-parabolic distribution of particles at the inlet G 2 Q 2  Cr in   r  V  G  21 2    D /4 G CR    Velocity at the walls   rr  22 ba V = 0 (No slip) n  Int 2C r  r   p  b a 2R    Dimensionless numbers Reynolds number UD mean Re  Deposition fraction The number of particles deposited on a section of the walls DF (%)100 The total number of particles entering that section of the wall 2. Numerical 4. Visualization analysis • Iterative methods • Continuity equation • CFD Solvers e.g., • Contour • Discretization • Momentum equation SIMPLE, SIMPLEC, • Vectors of the • Energy equations PISO geometry – • Line plots • Newton's second law of • DPM - dispersed finite element • Animation motion phase method (F EM) • Deposition 1. Setting up of • Boundary patterns governing conditions 3. Solutions to the air • Hostspots equations flow and aerosol motion problems Fig. 6. Fundamental steps for developing a CFD and DPM solution for airflow and aerosol deposition in healthy and obstructed human airways. 1184 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 inlet in the form of a sinewave (Chen et al., 2018a). Typical work by Tian et al. (2017), the importance of particle size real inhalation curves for rest, light activity, and moderate distribution in estimating the dosimetry was established. exercise, and their equivalent sinewave curves are shown in Findings showed that deposition equations and particle size Fig. 7. Usually, a spirometry test is used to acquire the distribution were vital in the estimation of dosimetry for velocity variation at the inlet with time as in the case of a exposure risk assessment caused by nanoparticles. study by Tena et al. (2015). Many studies tend to differentiate In the CFD simulation for the deposition of PM in 2.5 the breathing statuses based on the level of the human healthy and asthmatic human airways, the typical particle activity and consequently the air mass flow rates during size distribution for PM in Taiwan was used (Chen et al., 2.5 inhalation. Common breathing statuses include rest condition 2018a; Mutuku et al., 2020). Ideally, the distribution of (sedentary), light activity, and moderate exercise. During particles at the inlet should be random, hence several studies normal breathing, the length and diameter variations in the have applied a random distribution of aerosols at the inlet large airways of the lungs are moderate and the rates of (Zhang and Kleinstreuer, 2001; Russo et al., 2008; Chen et variation are insignificant compared to the axial airflow al., 2012; Chen et al., 2018b). A uniform particle distribution velocity, therefore the effect of wall movement is assumed at the inlet has also been applied for investigations on to be very minimal (Hughes et al., 1972). Sometimes, volume aerosol deposition (Russo et al., 2008; Zierenberg et al., flow rates are defined for localized investigations on the 2013; Kolanjiyil and Kleinstreuer, 2017). In a numerical human airways, for instance, the case of Gemci et al. (2008) analysis by Zhang and Kleinstreuer (2001), the effects of –3 where 28.3 L min was applied. random-parabolic, random uniform, random-random, and deterministic-parabolic particle distributions at the inlet on Particle Size Distribution for Aerosols the deposition patterns in a triple bifurcation were investigated. It is not enough to estimate particle dosage using particle The findings proved that a satisfactory representation of the number, mass, and surface area available for deposition deposition patterns was achievable for both aerosol medicine only. In a combined experimental and numerical analysis and particulate matters if a parabolic-deterministic particle Fig. 7. Typical real inhalation curves and corresponding sinewaves for (a) rest, (b) light activity, and (c) moderate exercise. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1185 distribution was used at the inlet instead of a realistic random shear stress, and stagnation and recirculation zones, on the distribution. Consequently, some studies have used parabolic- walls (Zhang et al., 2002; Kleinstreuer and Zhang, 2010; deterministic particle distribution at the inlet to analyze the Chen et al., 2012). deposition efficiencies in a human lung bifurcation (Comer et al., 2000; Chen et al., 2018a). Airflow Velocity Distribution and Pressure Drop Airflow fields are important in determining the deposition patterns of particulate matter in the human airways. Airflow Solvers Numerical calculations for the Navier-stokes equations velocity distribution also affects pressure distribution since governing laminar airflow and newton’s second law of the two are closely linked (Luo et al., 2007; Kang et al., motion governing particle motion in the lungs can be solved 2011). The airflow distribution is an important indicator of using CFD solvers and discrete phase models provided by sections of the lungs suffering from inadequate ventilation applications such as ANSYS-fluent among others (Kelecy, as a result of lung obstructive diseases. The velocity 2008). Simulation of two-phase flow in the human airways distribution inside the control volume is responsible for the usually applies one-way coupling, whereby the gas phase formation of stagnation and recirculation zones as well as jet affects the solid phase only but there is no feedback from the flow phenomena in constrictions (Chen et al., 2012; Chen et dispersed phase (Comer et al., 2000; Luo et al., 2007; al., 2018a). Flow phenomena resulting from velocity Rahimi-Gorji et al., 2015). The discrete phase model of CFD distributions greatly affect particle deposition patterns in is activated by defining several parameters of the dispersed both health and deformed human airways. phase such as position in the control volume, velocity, In a study by Gemci et al. (2008), a static pressure drop diameter, temperature, mass flow, and time of injection of 50 Pa was associated with a volumetric flow rate of –3 (Rahimi-Gorji et al., 2015). The trajectory calculations use 28.3 L min between G0 and G17. The further pressure the initial location and parameters for the calculations. drop across the entire Weibel’s geometry was determined to Several CFD codes have been used in the simulation of be 60 Pa (Pedley, 1977). This had been previously investigated fluid flow including CFX, Camel, FLUENT, and TASC flow through an experiment by Hyatt and Wilcon (1963), whereby 3D. CFX, which is a finite volume code and user-enhanced the pressure drop in the entire human lung was found to be FORTRAN programs were applied at the beginning of this 75 Pa. In a study by Qi et al. (2014), patients suffering from millennium to investigate airflow and aerosol motion in left pulmonary artery sling (LPAS) were found to have a idealized lung models (Zhang and Kleinstreuer, 2002; pressure drop in the two bronchi ranging between 78.9– Zhang et al., 2002). TASCflow, which is also a commercial 914.5 Pa, as compared to a usual pressure drop of 0.7 Pa in CFD code with a standard k-ε turbulence model was applied a healthy individual. in a study by Stapleton et al. (2000) to compare the results of numerical analysis to that of in vivo experiment on aerosol Jets, Recirculation Zones and Secondary Flow Phenomena deposition in the mouth and throat. Fluent CFD solver has Jets tend to form in constriction according to the results two basic solver algorithms: density-based coupled solver from most CFD simulations and comparisons of healthy and (DBCS) and pressure-based coupled solver (PBCS). The obstructed airways. Boundary layer separation in the regions former solves the equations of conservation of continuity, near the constrictions led to the recirculating phenomenon momentum, and energy in a coupled manner, while the latter (Sul et al., 2014). Vorticity in obstructed airways can be solves the same equations in an uncoupled manner. Even obtained by the curl of the velocity field. In obstructed though pressure-based algorithms are vigorous and resourceful, airways, the vorticity is usually on the ranges of 100-fold their applications in complex geometries are not applicable higher as compared to a normal airway. since their convergence rates are not satisfactory. The efficiency of the DBCS comes at a cost as it requires double Shear Stresses the memory per element compared to PBCS since memory Amplified wall shear stresses in human airways are is needed for the coupled matrix equations (Kelecy, 2008). responsible for inducing the disease defense mechanism by Other important parameters in the selection of a solver activating the release of Adenosine triphosphate (ATP) as besides the memory requirements include time per iteration, well as the release of intracellular calcium (Fisher et al., iterations to converge, and time to convergence. DBCS is 2001; Garcia et al., 2006; Sidhaye et al., 2008). It has also associated with a high computational cost. Consequently, been previously proven that low-level shear stress bears an PBCS is more popular for investigations of fluid flow inside inverse relationship with the permeability of epithelial cells the human airways (Kelecy, 2008). Some of the most common (Sidhaye et al., 2008), while excessive wall shear stress can algorithms for airflow investigations in the human lungs are cause epithelial damage, especially in the re-opening a shown in Fig. 8(a), including the semi-implicit method for previously collapsed airway (Bilek et al., 2003). Therefore, pressure-linked equations (SIMPLE), the semi-implicit method understanding shear stress distribution can explain the for pressure-linked equations-consistent (SIMPLEC), pressure- healing process of wounds in the epithelial lining of the implicit with splitting of operators (PISO), and coupled. lungs (Suh and Park, 2018). Models for Turbulent Flows Airflow Dynamics in Human Airways Many parameters can be used to explain fluid flow in the Flow in the human lungs is largely laminar and as such human airways for instance; velocity distribution, jet flows, most of the CFD investigations in which are based on central 1186 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 (a) (b) Fig. 8. (a) Common algorithms applied in CFD models to evaluate particle motion and airflow dynamics (b) Models for turbulent flows in the human airways. and lower regions do not require turbulent flow models LES is better at simulating turbulent flow as compared to a (Chalupa et al., 2004; Zhang and Papadakis, 2010; Chen et combination of RANS and EIMs. This is because RANS al., 2012). However, different models (turbulent) are applied turbulence models tend to average out the turbulent effects in investigations of airflow in the upper region and part of and hence may not capture fully the resultants effects of the central region of the human airway (Inthavong et al., recirculation on the particle motion (Lambert et al., 2011). 2010; Zhang and Kleinstreuer, 2011; Rahimi-Gorji et al., Fluent applies a finite volume approach in obtaining the 2015). The upper section of the human lung experiences numerical solutions to Navier-Stokes and continuity equations laminar-turbulent transitional regions as air progresses from in a control volume with the appropriate geometry and the oral cavity and larynx to the trachea and bronchioles boundary conditions. For the upper section of the respiratory (Kolanjiyil and Kleinstreuer, 2017). As such modeling airflow system that is the oro-nasal cavity, trachea, and bronchus, through these sections of the respiratory system requires airflow is usually turbulent. Therefore, turbulence models models that can handle the turbulent flow. Usually, Reynolds and LES are applied (Gemci et al., 2008). LES models can averaged Navier Stokes (RANS) are combined with Random- provide instantaneous velocity fluctuations and vortex walk eddy interaction models (EIMs) to effectively cover structures, whereas RANs cannot. However, LES models are the effect of turbulent flow on particle motion in the upper 100 times more costly in terms of computational time when sections of the human airways. Large-eddy simulation (LES) compared to RANS models. Direct numerical simulation can also effectively account for the effect of turbulent flow (DNS) is another alternative approach but it is limited to on the particle’s motion. Recent developments show that certain types of control volumes. It also requires excessively Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1187 huge computational resources (Zhang and Kleinstreuer, mean velocity gradients and the generation of ω respectively. 2011). Turbulent models are useful for modeling airflow in Γ and Γ are the effective diffusivities of k and ω, k ω the upper sections of the respiratory system. This is because, respectively while Y and Y are the dissipation of k and ω, k ω in these regions, for instance, the throat, airflow experiences respectively, due to turbulence. Lastly, D is the cross-diffusion local area reductions, which increases the Reynolds number term, and S and S are user-defined source terms. A summary k ω and causes turbulent flow. Thereafter, due to the subsequent of the available turbulent models is shown in Fig. 8(b). increase in effective cross-sectional area, the Reynolds number decreases and the flow gets re-laminarized. Due to MECHANISMS FOR AEROSOL DEPOSITION AND the above-mentioned limitations of both LES and DNS, CLEARANCE RANS finds preference amongst researchers in predicting airflow behavior. The human airways are usually lined with mucus, which In an investigation by Zhang and Kleinstreuer (2011), helps to trap suspended particulate matters in the inhaled air insignificant differences of less than 0.5% in DEs were mass. It thus follows that a trap upon impact is usually the found in the performance of LES, LRN k-ω and SST boundary condition imposed on the walls of the airways transition. This finding was made while doing a numerical during the numerical analysis of two-phase flow inside the analysis of the deposition efficiencies for nanoparticles human airways. From the analysis of particle deposition, (1 nm–50 nm) during the transition to turbulent flow in the important information includes deposition patterns, hot oral airways model. The SST transition model proved better spots, and deposition efficiencies. results in the prediction of kinetic energy profiles while the LES model could provide information on instantaneous Mechanisms of Deposition velocity fluctuations. There are several mechanisms through which inhaled The standard k-ω turbulence model is more effective for aerosols deposit on the airways’ walls. The dominance of predictions near the wall region as compared to the k-ω, but these mechanisms of deposition varies as the particles advance it performs poorly in the far-field. On the other hand, the from the oral cavity, through the upper, central, lower section SST k-ω combines a bit of both and is, therefore, more of the human lungs, and later into the alveolar region. There favorable for turbulent and transitional flows (Tena et al., are 5 main deposition mechanisms, including turbulent mixing, 2015). The SST k-ω equations are expressed as inertial impaction, gravitational sedimentation, Brownian motion, and electrostatic precipitation (Finlay and Martin, 2008; Darquenne, 2012). A summary of the mechanisms of     k  k  ku  Γ  G  Y  S (4)  aerosol deposition and their respective regions of dominance i k k k k  t x x x i i j  is shown in Table 3. Turbulent Mixing        u  Γ  G  Y  D  S      Unlike the central and lower sections of the human airways i       t x x x i j j  where the flow is usually laminar, the upper section of the (5) lungs is associated with turbulent airflow. Rapid changes in both the magnitude and direction of flow by the air-aerosol where ρ is the air density, k represents the turbulence kinetic mixture lead to an eventual impaction on the airway walls (Darquenne, 2012). Furthermore, branch curvature as air energy, ω represents the specific dissipation rate. G and G k ω refer to the generation of turbulence kinetic energy because of progresses from the parent bifurcation to the child branch Table 3. Dominant aerosol deposition mechanisms and lung clearance mechanisms at different sections of the human airway. Dominant aerosol deposition Region Dominant aerosol clearance mechanism mechanism Extra-thoracic region Deposition by Mechanical clearance (sneezing, coughing or (Oro-nasal passages) 1. Turbulent flow swallowing of the inhaled aerosols) 2. Inertial impaction Tracheal-bronchial region Deposition by Mucociliary clearance (for insoluble particles in the TB (TB) 1. Turbulent mixing region. (24 hours) – operates like an escalator driving 2. Inertial impaction aerosol loaded mucus from the TB region to the larynx where mechanical clearance mechanism takes over. Bronchiolar region Deposition by Translocation 1. Inertial impaction 2. Diffusion Acinar region (G16-23) Deposition by Translocation 1. Diffusion Microphage arbitrated clearance where white blood cells 2.Gravitational sedimentation engulf and relocate the particles towards the bronchiolar 3.Electrostatic deposition region, circulatory system, or the lymphatic region. 1188 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 induces lateral convective motions (Hammersley and Olson, particles as a result of collisions with the gas molecules. It 1992). This convective motion initially purposed to ensure happens where the velocity of airflow is low, especially in the mixing of the inhaled gases also causes particle the alveoli region (Darquenne, 2012). Deposition by this deposition on the airway walls. Turbulent mixing has been mechanism occurs predominantly for particles with a diameter proven through experiments to affect the local deposition in of smaller than 0.5 µ m, and is usually proportional to the G3–G5 by influencing the initial velocity and particle Brownian diffusion coefficient which can be expressed as; motion (Longest and Vinchurkar, 2007). ckT D  (9) Inertial Impaction 3 d This mechanism greatly affects particles whose diameter exceeds 5 µm. This is because heavier particles are incapable where D , c, k, and T represent the deposition by Brownian of changing the direction of motion with a sudden change in motion, Cunningham’s correction factor, Boltzmann’s the direction of fluid flow. This results in a deviation from constant, and the absolute temperature, respectively. The the streamlines of flow and eventual impaction on the Cunningham’s correction factor accounts for the reduced air airways’ walls (Darquenne, 2012). The property of deviation resistance as a result of slippage when the particles’ diameters from the streamlines of flow for two-phase flow is best approach the mean free paths of the gas molecules. defined using the stokes number which is expressed by; After experimentation, Yeh and Schum (1980) reported that deposition by diffusion was different for both laminar  du pp flow and turbulent flow. For laminar flow, the probability of St  (6) 18 d deposition by diffusion can be expressed as;  7.315 x 44.63 x 114 x where St is the Stokes number, d and ρ are the diameter and p p P  1 0.819e  0.0976e  0.0325e density of the suspended particles, respectively, u and µ are the 79.31x 0.0509e (10) average velocity and dynamic viscosity of the gas phase, respectively, and d is the diameter of the airway. Overall, LD x deposition by inertial impaction increases with an increase 2 2R  in the Stokes number. In an experimental study by Yeh and Schum (1980), the formula for impaction deposition where P is the probability of deposition by diffusion, D is probability was summarized as follows; the diffusion coefficient of the particles, R is the radius of the airway’s bifurcation,  is the mean flow velocity, and  d pp  11 L is the length of the bifurcation. For turbulent flow, it can P  1 cos   St sin 2 cos   St V  g   Is   18 be expressed as; for θ × St < 1  P = 1 for θ × St > 1 (7) 22 Dt Dt 1 2 1 2 P  1  ...  2.828x 1 0.314x  ...     RR 9  where P is the impaction deposition probability and θ is the (11) angle of the bend in radians. where t represents the time needed for the flow to cover the Gravitational Sedimentation bifurcation’s length, i.e., L / This mechanism of deposition is absent in upper airways For a pause, but strongly present in central and lower airways. This is because a shorter distance exists between the particles and  the airway walls for central and lower airways (Darquenne, 5.784KTC p t P  1 exp  (12)  2012). This property is best represented by the terminal 2  6rR  settling velocity of the particles, which can be expressed as; where t is the length of time in the pause, K is the Boltzman  d pp constant, T is the temperature in K, C is the Cunningham slip Vg  (8) 18 correction factor, r is the particle’s radius, µ is the fluid’s viscosity, and the superscript p represents pause (Yeh and where g is the acceleration due to gravitational force. The Schum, 1980). most important factors during deposition by sedimentation are particle size and particle residence time in the airways Electrostatic Precipitation and alveoli. This mechanism of deposition mostly affects This process is only effective for particles in possession particles whose diameter ranges between 1 and 8 µ m. of charges. Charges are induced at the walls of the airways by charged particles in close proximity. As a result, the walls Brownian Diffusion attract electrically charged particles leading to deposition This mechanism results from the random motion of the (Darquenne, 2012). Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1189 Interception to increase by ×10 to ×100 (Longest et al., 2006). In a study The efficiency of this mechanism of deposition is dependent on the effect of COPD in human airways, Chen et al. (2012) on the shape and hygroscopicity of the particles, whereby found a hotspot for the deposition of 6 µ m particles at the elongated and hygroscopic particles deposit more easily. constriction (G6) in a geometry consisting of G5–G8. This mechanism involves a particle coming into contact with Furthermore, the deposition efficiencies of 0.075, 0.15, 0.3, the airway walls while still following a streamline of flow and 0.6 µ m particulate matters in an asthmatic human airway (Darquenne, 2012). Deposition by interception decreases were found to exceed that in a healthy human airway by significantly for particles with a spherical shape. 1.19%, 2.5%, 14.3%, and 25%, respectively, during a moderate exercise (Chen et al., 2018a). These elevated deposition Factors Influencing Particle Deposition efficiencies lead to aggravated risks due to inhalation of Particle deposition efficiencies and patterns are dependent toxic aerosols. on several factors including, the geometry of the Enhanced condensational growth (ECG) is a recently tracheobronchial model, inhalation status, and chemical and developed method of pharmaceutical aerosol delivery, physical properties of the particles. aimed at increasing their deposition efficiencies. Usually in Due to the bifurcating nature of the human airways, the this approach, a stream of medicinal nanoparticles is injected inhalation segment of the breathing cycle provides a larger at the mouth region followed by an air stream that is surface area for particle deposition through impaction as supersaturated (Kulmala et al., 2004; Phalen et al., 2010; compared to the exhalation phase. Consequently, the deposition Tian et al., 2011). This is done to minimize depositional losses efficiencies during the inhalation phase tend to exceed those at the extrathoracic region and encourage deeper penetration of the exhalation phase for the same local stokes number of the aerosols into the central and lower section of the (Zhang et al., 2002). Additionally, the curved walls of the human airways. It is believed that aerosols with a diameter bifurcating geometries provide surface areas for particle of 2–4 µ m have almost perfect retention inside the lungs. interception as the particles are driven onto the walls by the induced dean vortices (Chen et al., 2018a; Mutuku and Chen, Methods of Quantifying Deposition 2018). In the investigation of Guha and Pradhan (2017), new There are two ways of quantifying deposition. The use of secondary flow structures including dean vortices and anti- DEs is the most commonly used method of quantifying the dean vortices were found to develop in the curved human deposition of aerosols in the human airways either through airway generations. in vitro experiments or numerical analysis. Deposition The physical parameters of the particles also have an enhancement factors (DEFs) are used to quantify the impact on the deposition efficiencies. Specifically, the diameter deposition in a certain zone as compared to deposition in an of inhaled aerosols is directly linked to the Stokes number entire region of consideration. DEFs for microns exceed of the particles for a constant density. Previous research has those of nanoparticles. Specifically, in a study by Guzman shown that deposition efficiencies for aerosols increase with (2020), the DEFs for 40 µ m particles were found to exceed an increase in their Stokes numbers since impaction is the those of 40 nm particles. main deposition mechanism (Cheng et al., 1999; Zhang et Establishing the position of hotspots is an important al., 2002). Usually, in performing numerical analysis, the aspect of studies on particle deposition. Hotspots are localized particles are assumed to have a spherical shape and hence regions of high deposition. This is important because it makes their Stokes number tends to increase with an increase in their it possible to quantify particle dissolution, particle clearance, diameters. In a study by Cheng et al. (1999), the deposition and the uptake of the dissolved chemical compositions into efficiency was found to correlate to the stokes number of the the epithelial layer. Hotspots have been associated with lung particles by the following equation. cancer and tumors. A 30% contraction in the upper tracheobronchial airways due to asthma could increase the ɳ = 1 – exp(–αSt) (13) DEs by 10–100 times. Disproportionate amounts of aerosols are known to enter the left bronchi, despite a higher mass flow where ɳ is the deposition efficiency, St is the Stokes number, of air going into the right bronchi (Lambert et al., 2011). and α is the best fit parameter whereby it was 6.66 ± 0.418 (SEM) and r = 0.976 (Cheng et al., 1999). Lung Clearance Mechanisms The deposition of particles on the airway walls is greatly The lung structure is designed to allow the mixing of air influenced by their solubility in water. The particles which as it flows towards the alveolar region. However, this leads are soluble in water tend to deposit more easily. This is to the impaction of the suspended particles on the walls as because the moisture content inside the human lungs air progresses into the acinar region (Tsuda et al., 2008). The increases their weight and hence their chances of depositing lung has several clearance mechanisms that help to rid the through inertial impaction (Ferron et al., 1989). The shapes airway walls of deposited particles. The first clearance of the aerosols have been shown to affect their deposition mechanism is mechanical clearance and it takes the form of efficiencies in previous researches. For instance, elongated coughing, sneezing, or swallowing. This mechanism is aerosols can easily deposit through interception. dominant in the upper section of the airways, specifically the The presence of deformities in the lung’s geometry, for oro-nasal region (Hussain et al., 2011). Mucociliary clearance instance, the presence of tumors, asthma, and COPD, which is the second mechanism and involves the propulsion of usually result in obstructions, leads to local deposition rated aerosol filled mucus from the middle section of the human 1190 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 lungs towards the oro-nasal region so that they can be coughing can range between 0.6–125 µ m (Guzman, 2020). ejected out of the respiratory system through the mechanical In the investigation by Asadi et al. (2020), particles with a clearance. The third clearance mechanism involves the diameter of about 1 µ m were found to be released during macrophages which engulf the aerosols and get into the normal breathing and speech. This size was adequate to circulatory system or the lymphatic system. This clearance carry SARS-CoV-2 viruses which have a diameter of about mechanism is dominant in the alveolar region of the human 65–125 nm. The bioaerosols with a diameter of 1–5 µ m are airways (Hussain et al., 2011). A summary of the lung’s the most potent in terms of airborne transmission of diseases clearance mechanism and their respective region of (Wang and Du, 2020). In a study by Xu et al. (2020a), dominance is shown in Table 3. aerosols generated from infectious feces are also thought to drive the spreading of the virus. Altogether, the load pathogenic bioaerosol in the ambient air has been shown to COVID-19 AND THE RESPIRATORY SYSTEM retain its viability for up to 3 hours. This is suspected to play a major role in the transmission of this deadly virus. Effects of COVID-19 to Human Lungs Lungs are very delicate and, consequently, get easily Available knowledge and epidemiological studies indicate damaged. The latest global pandemic of COVID-19 is a that the recommended distance of 2 m to prevent the spread classic case of diseases which is easily transmitted through of COVID-19 might be inadequate or effective only if respiratory droplets and can be fatal or lead to permanent everyone wears a facemask (Setti et al., 2020). In the studies lung damage. In a recent study of Olds and Kabbani (2020), carried out in Wuhan and Nebraska university hospital, exposure to nicotine through smoking was shown to propel SARS-CoV-2 RNA was found in ambient air samples, individuals to higher risk from COVID-19 due to the impact proving that the virus stayed viable inside aerosol droplets. on the putative receptor for the virus (ACE2). Older people In a study by van Doremalen et al. (2020), the half-life of and those with weakened immunity systems were more SARS-CoV-2 RNA suspended in the ambient air was found to susceptible to the adverse effects caused by COVID-19. be 1 hour. According to literature, the aerosol size distribution COVID-19 patients suffered from a serious inflammation of and prevailing wind conditions can support the transportation the lungs after which the alveoli were filled with water, pus, of contaminated aerosols for up to 10 m. Contaminated and debris from epithelial cells destroyed by the immune aerosols merge with PM at high concentrations and stable 2.5 system in the process of fighting the infection (Yoon et al., atmospheric conditions further aiding in the transportation 2020). and deposition of the viruses in the deeper regions of the Results from CT scans showed that the lungs of patients human airways (Chen et al., 2017). Indoor environments had lesions whose density could be hardly depicted using with low temperature and low relative humidity can lead to conventional radiography. Consequently, a routine exercise rapid evaporation rates at the surface of aerosol droplets of obtaining high-resolution chest CT examination was seen forming smaller droplets that can stay airborne for longer. to be key in the diagnosis of the disease (Agostini et al., Bioaerosol motion in the transmission of the disease has 2020; Zhao et al., 2020). The abnormalities associated with been an important object in recent investigations. Therefore, COVID-19 according to a study by Yoon et al. (2020). In a future studies might apply 3D human airways models from study by Li et al. (2020a), pathological alterations caused by the COVID-19 survivors to determine the motion and DEs of disease include lung edema and acute lung injury (ALI) which aerosols such as suspended toxic particulates (PM and PM ) 10 2.5 eventually caused acute respiratory distress syndrome (ARDS). and pharmaceutical aerosols in the deformed airways. ALI came as a result of the activation of the epithelial and In the study of disease exposure through inhalation of endothelial cells and the consequent overproduction of pro- bioaerosols, it is important to establish the relationship inflammatory cytokines. As the severity of COVID-19 disease between viability if the virus, diameter of the bioaerosols, increases, patients have been shown to suffer multiple organ and distance travelled by the bioaerosol in the ambient air. failure and eventual death. For COVID-19, that is still in debate. But earlier studies have shown that it may vary depending on prevailing wind speed and presence of obstructions. Role of Bioaerosols in COVID-19 Transmission, Possible Control Strategies and Future Challenges Even though the novel SARS-CoV-2 virus has affected The most familiar mode of transmission of SARS-CoV-2 the world for more than 5 months now, several unknowns have virus involves a healthy individual coming into contact with inhibited the full assessment of the situation in the world surfaces that have been contaminated by infected aerosols. with regards to the spread of the virus. Amongst them is the As such, a 2 m distance has been recommended among minimum viral load required to cause an infection. There is persons as a form of social distancing to curb the spread of also a need to carry out investigations on the transport analysis the disease. This social distancing is aimed at preventing to verify if the spread of the virus is airborne. Demystifying bioaerosols with SARS-CoV-2 from reaching the respiratory the unknowns is vital for proper epidemic control strategies. system of healthy individuals (Guzman, 2020). Even though earlier investigations ruled out the airborne OUTLOOK transmission in the role of the spread of SARS-CoV-2, recent investigations showed that this might not be the case. Recent Advancements in the Field The diameter of bioaerosols released from individuals infected Although many numerical modeling methods have been with COVID-19 during breathing, talking, sneezing, and developed in the past, CFD has now become a categorically Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1191 influential and universal tool for many applications in the CONCLUSIONS st 21 century. This is mostly due to its ability to provide solutions representing a rich blend of numerical methods, In vitro experiments for estimating deposition of particles user interfaces, mathematical physics, and state-of-the-art in the human airway geometries uses constant velocity at the visualization systems (Xia and Sun, 2002). The recent adoption inlet of the geometry. Even though experiments have helped of CFD for applications in particulate matter and airflow in scientists develop empirical methods for evaluations of the airways has been driven by the high costs associated with respiratory system, it is the subsequent development of CFD experimentation and analytical modeling methods for solving that has proved to be an important tool for the evaluation of fluid flow and two-phase flow problems. The trend has been airflow and particulate matter deposition in the human fueled further by the recent development of numerical solutions airways when applied for localized deposition. Algebraic for Navier-stokes equations and advancement in computing deposition models came before CFD methods, but their technology, making it a viable option for application in tendency to overestimate the effects of impaction on particle industry and science (Norton and Sun, 2006). deposition led to the development and more extensive Understanding airflow and particle deposition in obstructed adoption of the CFD method which has higher accuracies. and healthy airways are complex processes. Developing Complex physical phenomena can be broken down in CFD relatively simple equations for predicting the two phenomena and derived from otherwise inaccessible regions for one would help in building up an improved understanding of the applying experiments. The two most popular human airway correlation between deposition in the respiratory tract and a models are Weibel and Horsfield. The former finds more wide range of health outcomes. applications due to its simplicity, accuracy as well as saving on A possible potential application of the findings from CFD computational costs. For diseased human airway geometries, simulations could be in the development of diagnostic asthmatic human airways are usually represented by techniques based on the images of airflow patterns in the uniformly distributed folds along the circumference of the lungs. Breakthroughs in the field of CFD involving human affected generation, while COPD geometries consist of an airway geometries could be applied to design more efficient axisymmetric constriction in one or more of the bifurcations delivery methods for inhaled pharmaceuticals and to also affected by the obstruction. In the discretization of the understand the adverse health effects induced by toxic air human airway geometry for numerical investigations, the pollutants for instance use of enhanced condensational following mesh types can apply, unstructured, hexahedral, growth and eventual deposition. tetrahedral, non-orthogonal blocks, and triangular prism. The tremendous improvements in CFD methods have placed Challenges them on the verge of fully replacing experimental studies. Although histological measurements are usually included Important factors in carrying out a numerical analysis for in the generation of geometries to represent obstructed instance; computational cost and time are mentioned as airways, accurate representations of the exact architectures some of the most important factors to consider in a associated with such obstructions are not easily achievable. numerical simulation. Unlike the central and lower sections This is because smooth surfaces are usually applied to connect of the human airways where the flow is usually laminar, the healthy portions of the airway to the ones affected by upper section of the lungs is associated with turbulent obstructions and hence affecting the validity of the results. airflow. Density-based coupled solvers (DBCS) have high During the simulations, a 100% trapping efficiency is computational costs. Consequently, pressure-based coupled assumed, but this might not be the case in real life. The real solvers (PBCS) are more popular for investigations of fluid value would be established best using empirical approaches. flow inside the human airways. Due to the similarity CT scanned images of the human lung are clear enough for between real inhalation curves and sinewave curves, some application in 3D modeling only up to G7, this leaves the rest studies have used velocity distribution at the inlet in the form of the generation depending on idealized human geometries for of a sinewave. A uniform velocity distribution is usually studies. Despite some previous attempts towards studying imposed at the inlet for simulations in the upper section of the the entire human lung, so far, none of the studies has been human airways. However, for the central and lower branches, a successful. The complexity of human airways and breathing parabolic velocity distribution is more suitable. The dominance processes limit the application of in vitro measurements to of the 5 main deposition mechanisms, including turbulent only two consecutive branches of the human airway. In most mixing, inertial impaction, gravitational sedimentation, numerical simulations, the walls of the human airway are Brownian motion, and electrostatic precipitation, vary as the assumed to be stationary. However, this is not the case in particles advance from the oral cavity, through the upper, reality as the airway walls move in and out during inhalation central, and lower sections of the human lungs, and later into and exhalation. So far, numerical analysis of two-phase flow the alveolar region. Due to the bifurcating nature of human inside the human airways uses one-way coupling whereby airways, the inhalation segment of the breathing cycle the gas phase affects the solid or liquid phase but there is no provides a larger surface area for particle deposition through feedback. However, in reality, there should be a reaction impaction as compared to the exhalation phase. the diameter force for every action force and this would impact the of inhaled aerosols is directly linked to the Stokes number aerosols’ trajectories and eventually change the deposition of the particles for a constant density. The recent development fractions of particles under investigation. of pneumonia caused by SARS-CoV-2 virus affects patients whereby, they suffer from a serious inflammation of the 1192 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 lungs after which the alveoli are filled with water, pus, and velocity profiles. Resp. Physiol. 49: 75–95. https://doi.org/ debris from epithelial cells destroyed by the immune system 10.1007/s11547-020-01179-x in the process of fighting the infection. Therefore, future studies Chen, G., Zhang, W., Li, S., Williams, G., Liu, C., Morgan, might apply 3D human airways models from COVID-19 G.G., Jaakkola, J.J. and Guo, Y. (2017). Is short-term survivors to determine the motion and DEs of aerosols such exposure to ambient fine particles associated with measles as suspended toxic particulates (PM and PM ) and incidence in China? A multi-city study. Environ. Res. 156: 10 2.5 pharmaceutical aerosols in the deformed airways. 306–311. https://doi.org/10.1016/j.envres.2017.03.046 Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y. and Wei, Y. (2020). ACKNOWLEDGMENTS Epidemiological and clinical characteristics of 99 cases of The authors acknowledge financial support from the 2019 novel coronavirus pneumonia in Wuhan, China: A Ministry of Science and Technology Taiwan, ROC, under descriptive study. Lancet 395: 507–513. https://doi.org/1 the grant numbers MOST 108-2221-E-006-127-MY3, 108- 0.1016/S0140-6736(20)30211-7 2622-E-006-017-CC1 and 109-3116-F-006-016-CC1 for Chen, S., Cui, K., Yu, T.Y., Chao, H.R., Hsu, Y.C., Lu, I.C., this research. Arcega, R.D., Tsai, M.H., Lin, S.L. and Chao, W.C. (2019). A big data analysis of PM and PM from low 2.5 10 cost air quality sensors near traffic areas. Aerosol Air REFERENCES Qual. Res. 19: 1721–1733. https://doi.org/10.4209/aaqr.2 Adeloye, D., Chua, S., Lee, C., Basquill, C., Papana, A., 019.06.0328 Theodoratou, E., Nair, H., Gasevic, D., Sridhar, D. and Chen, W.H. (2001a). Dynamics of sulfur dioxide absorption Campbell, H. (2015). Global and regional estimates of in a raindrop falling at terminal velocity. Atmos. Environ. COPD prevalence: Systematic review and meta–analysis. 35: 4777–4790. https://doi.org/10.1016/S1352-2310(01) J. Glob. Health5: 020415. https://doi.org/10.7189/jogh.0 00274-6 5.020415 Chen, W.H. (2001b). Unsteady absorption of sulfur dioxide Agostini, A., Floridi, C., Borgheresi, A., Badaloni, M., by an atmospheric water droplet with internal circulation. Pirani, P.E., Terilli, F., Ottaviani, L. and Giovagnoni, A. Atmos. Environ. 35: 2375–2393. https://doi.org/10.1016/ (2020). Proposal of a low-dose, long-pitch, dual-source S1352-2310(00)00536-7 chest CT protocol on third-generation dual-source CT Chen, W.H. (2002). An analysis of gas absorption by a using a tin filter for spectral shaping at 100 kVp for liquid aerosol in a stationary environment. Atmos. CoronaVirus Disease 2019 (COVID-19) patients: A Environ. 36: 3671–3683. https://doi.org/10.1016/S1352- feasibility study. Radiol. Med. 125: 365–373. 2310(02)00244-3 https://doi.org/10.1007/s11547-020-01179-x Chen, W.H., Chen, Y.Y. and Hung, CI. (2011). A Simplified Asadi, S., Bouvier, N., Wexler, A.S. and Ristenpart, W.D. model of predicting SO absorption by single atmospheric (2020). The coronavirus pandemic and aerosols: Does raindrops with chemical dissociation and internal circulation. COVID-19 transmit via expiratory particles? Aerosol Sci. Aerosol Air Qual. Res. 11: 860–872. https://doi.org/10.42 Technol. 54: 635-638. https://doi.org/10.1080/02786826. 09/aaqr.2011.08.0130 2020.1749229 Chen, W.H., Lee, K.H., Mutuku, J.K. and Hwang, C.J. Asgharian, B., Hofmann, W. and Bergmann, R. (2001). (2018a). Flow dynamics and PM deposition in healthy 2.5 Particle deposition in a multiple-path model of the human and asthmatic airways at different inhalation statuses. lung. Aerosol Sci. Technol. 34: 332–339. https://doi.org/ Aerosol Air Qual. Res. 18: 866–883. https://doi.org/10.4 10.1080/02786820119122 209/aaqr.2018.02.0058 Bilek, A.M., Dee, K.C. and Gaver III, D.P. (2003). Chen, X., Zhong, W., Sun, B., Jin, B. and Zhou, X. (2012). Mechanisms of surface-tension-induced epithelial cell Study on gas/solid flow in an obstructed pulmonary damage in a model of pulmonary airway reopening. J. airway with transient flow based on CFD–DPM approach. Appl. Physiol. 94: 770–783. https://doi.org/10.1152/jappl Powder Technol. 217: 252–260. https://doi.org/10.1016/ physiol.00764.2002 j.powtec.2011.10.034 Brook, R.D., Rajagopalan, S., Pope, C.A., Brook, J.R., Chen, X., Feng, Y., Zhong, W., Sun, B. and Tao, F. (2018b). Bhatnagar, A., Diez-Roux, A.V., Holguin, F., Hong, Y., Numerical investigation of particle deposition in a triple Luepker, R.V. and Mittleman, M.A. (2010). Particulate bifurcation airway due to gravitational sedimentation and matter air pollution and cardiovascular disease: An update inertial impaction. Powder Technol. 323: 284–293. to the scientific statement from the American Heart https://doi.org/10.1016/j.powtec.2017.09.050 Association. Circulation 121: 2331–2378. https://doi.org/ Chen, Z., Jena, S.K., Giridharan, G.A., Koenig, S.C., 10.1161/cir.0b013e3181dbece1 Slaughter, M.S., Griffith, B.P. and Wu, Z.J. (2018c). Flow Chalupa, D.C., Morrow, P.E., Oberdörster, G., Utell, M.J. features and device‐induced blood trauma in CF-VADs and Frampton, M.W. (2004). Ultrafine particle deposition under a pulsatile blood flow condition: A CFD in subjects with asthma. Environ. Health Perspect. 112: comparative study. Int. J. Numer. Methods Biomed. Eng. 879. https://doi.org/10.1289/ehp.6851 34: e2924. https://dx.doi.org/10.1002%2Fcnm.2924 Chang, H. and El Masry, O.A. (1982). A model study of Cheng, Y.S., Zhou, Y. and Chen, B.T. (1999). Particle flow dynamics in human central airways. Part I: Axial deposition in a cast of human oral airways. Aerosol Sci. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1193 Technol. 31: 286–300. https://doi.org/10.1080/02786829 123602. https://doi.org/10.1063/1.4971315 9304165 Guha, A. and Pradhan, K. (2017). Secondary motion in Chowdhury, P.H., Honda, A., Ito, S., Okano, H., Onishi, T., three-dimensional branching networks. Phys. Fluids 29: Higashihara, M., Okuda, T., Tanaka, T., Hirai, S. and 063602. https://doi.org/10.1063/1.4984919 Takano, H. (2019). Effects of ambient PM collected Guzman, M. (2020). Bioaerosol size effect in COVID-19 2.5 using cyclonic separator from Asian cities on human transmission. Preprints 2020: 2020040093. https://doi.org/ airway epithelial cells. Aerosol Air Qual. Res. 19: 1808– 10.20944/preprints202004.0093.v1 1819. https://doi.org/10.4209/aaqr.2019.01.0016 Hammersley, J.R. and Olson, D. (1992). Physical models of Çinkooğlu, A., Bayraktaroğlu, S. and Savaş, R. (2020). the smaller pulmonary airways. J. App. Physiol. 72: Lung changes on chest CT during 2019 novel coronavirus 2402–2414. https://doi.org/10.1152/jappl.1992.72.6.2402 (COVID-19) Pneumonia. Eur. J. Breast Health 16: 89. Han, Z., Weng, W. and Huang, Q. (2013). Characterizations https://dx.doi.org/10.5152%2Fejbh.2020.010420 of particle size distribution of the droplets exhaled by Cohen, A.J., Ross Anderson, H., Ostro, B., Pandey, K.D., sneeze. J. R. Soc. Interface 10: 20130560. https://doi.org/ Krzyzanowski, M., Künzli, N., Gutschmidt, K., Pope, A., 10.1098/rsif.2013.0560 Romieu, I. and Samet, J.M. (2005). The global burden of Häußermann, S., Bailey, A., Bailey, M., Etherington, G. and disease due to outdoor air pollution. J. Toxicol. Environ. Youngman, M. (2002). The influence of breathing Health Part A 68: 1301–1307. https://doi.org/10.1080/15 patterns on particle deposition in a nasal replicate cast. J. 287390590936166 Aerosol Sci. 33: 923–933. https://doi.org/10.1016/S0021- Comer, J., Kleinstreuer, C., Hyun, S. and Kim, C. (2000). 8502(02)00044-7 Aerosol transport and deposition in sequentially bifurcating Hofmann, W., Balásházy, I. and Koblinger, L. (1995). The airways. J. Biomech. Eng. 122: 152–158. https://doi.org/ effect of gravity on particle deposition patterns in 10.1115/1.429636 bronchial airway bifurcations. J. Aerosol Sci. 26: 1161– Darquenne, C. (2012). Aerosol deposition in health and 1168. https://doi.org/10.1016/0021-8502(95)00044-D disease. J. Aerosol Med. Pulm. Drug Del. 25: 140–147. Horsfield, K. and Cumming, G. (1967). Angles of branching https://dx.doi.org/10.1089%2Fjamp.2011.0916 and diameters of branches in the human bronchial tree. Delvadia, R.R., Longest, P.W. and Byron, P.R. (2012). In Bull. Math. Biol. 29: 245–259. https://doi.org/10.1007/BF vitro tests for aerosol deposition. I: Scaling a physical 02476898 model of the upper airways to predict drug deposition Horsfield, K. and Cumming, G. (1968). Morphology of the variation in normal humans. J. Aerosol Med. Pulm. Drug bronchial tree in Man. J. Appl. Physiol. 24: 373–383. Del. 25: 32–40. https://doi.org/10.1089/jamp.2011.0905 https://doi.org/10.1152/jappl.1968.24.3.373 Deng, Q., Ou, C., Chen, J. and Xiang, Y. (2018). Particle Horsfield, K., Dart, G., Olson, D.E., Filley, G.F. and deposition in tracheobronchial airways of an infant, child Cumming, G. (1971). Models of the human bronchial and adult. Sci. Total Environ. 612: 339–346. tree. J. Appl. Physiol. 31: 207–217. https://doi.org/10.115 https://doi.org/10.1016/j.scitotenv.2017.08.240 2/jappl.1971.31.2.207 Ferron, G., Oberdörster, G. and Henneberg, R. (1989). Hosseiny, M., Kooraki, S., Gholamrezanezhad, A., Reddy, Estimation of the deposition of aerosolized drugs in the S. and Myers, L. (2020). Radiology perspective of human respiratory tract due to hygroscopic growth. J. coronavirus disease 2019 (COVID-19): Lessons from Aerosol Med. 2: 271–284. https://doi.org/10.1089/jam.19 severe acute respiratory syndrome and Middle East 89.2.271 respiratory syndrome. Am. J. Roentgenol. 214: 1078– Finlay, W.H. and Martin, A.R. (2008). Recent advances in 1082. https://doi.org/10.2214/AJR.20.22969 predictive understanding of respiratory tract deposition. J. Huang, J. and Zhang, L. (2011). Numerical simulation of Aerosol Med. Pulm. Drug Del. 21: 189–206. micro-particle deposition in a realistic human upper https://doi.org/10.1089/jamp.2007.0645 respiratory tract model during transient breathing cycle. Fisher, A.B., Chien, S., Barakat, A.I. and Nerem, R.M. Particuology 9: 424–431. https://doi.org/10.1016/j.partic. (2001). Endothelial cellular response to altered shear 2011.02.004 stress. Am. J. Physiol. Lung Cell. Mol. Physiol. 281: L529– Hughes, J., Hoppin Jr, F. and Mead, J. (1972). Effect of lung L533. https://doi.org/10.1152/ajplung.2001.281.3.l529 inflation on bronchial length and diameter in excised Garcia, C., Prota, L., Morales, M., Romero, P., Zin, W. and lungs. J. Appl. Physiol. 32: 25–35. https://doi.org/10.1152 Rocco, P. (2006). Understanding the mechanisms of lung /jappl.1972.32.1.25 mechanical stress. Braz. J. Med. Biol. Res. 39: 697–706. Hussain, M., Madl, P. and Khan, A. (2011). Lung deposition https://doi.org/10.1590/S0100-879X2006000600001 predictions of airborne particles and the emergence of Gemci, T., Ponyavin, V., Chen, Y., Chen, H. and Collins, R. contemporary diseases. Part-I. theHealth 2: 51–59. (2008). Computational model of airflow in upper 17 Hwang, S.H. and Park, D.U. (2019). Ambient endotoxin and generations of human respiratory tract. J. Biomech. 41: chemical pollutant (PM , PM , and O ) levels in south 10 2.5 3 2047–2054. https://doi.org/10.1016/j.jbiomech.2007.12. Korea. Aerosol Air Qual. Res. 19: 786–793. 019 https://doi.org/10.4209/aaqr.2018.06.0235 Guha, A., Pradhan, K. and Halder, P.K. (2016). Finding Hyatt, R.E. and Wilcon, R.E. (1963). The pressure-flow order in complexity: A study of the fluid dynamics in a relationships of the intrathoracic airway in man. J. Clin. three-dimensional branching network. Phys. Fluids 28: Invest. 42: 29–39. https://doi.org/10.1172/jci104693 1194 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 Inthavong, K., Choi, L.T., Tu, J., Ding, S. and Thien, F. Longest, P.W., Vinchurkar, S. and Martonen, T. (2006). (2010). Micron particle deposition in a tracheobronchial Transport and deposition of respiratory aerosols in airway model under different breathing conditions. Med. models of childhood asthma. J. Aerosol Sci. 37: 1234– Eng. Phys. 32: 1198–1212. https://doi.org/10.1016/j.med 1257. https://doi.org/10.1016/j.jaerosci.2006.01.011 engphy.2010.08.012 Longest, P.W. and Vinchurkar, S. (2007). Validating CFD Isabey, D. and Chang, H. (1982). A model study of flow predictions of respiratory aerosol deposition: Effects of dynamics in human central airways. Part II: Secondary upstream transition and turbulence. J. Biomech. 40: 305– flow velocities. Resp. Physiol. 49: 97–113. https://doi.org/ 316. https://doi.org/10.1016/j.jbiomech.2006.01.006 10.1016/0034-5687(82)90105-0 Longest, P.W., Bass, K., Dutta, R., Rani, V., Thomas, M.L., Islam, M.S., Paul, G., Ong, H.X., Young, P.M., Gu, Y. and El-Achwah, A. and Hindle, M. (2019). Use of Saha, S.C. (2020). A review of respiratory anatomical computational fluid dynamics deposition modeling in development, air flow characterization and particle respiratory drug delivery. Expert Opin. Drug Del. 16: 7–26. deposition. Int Environ Res. Public Health 17: 380. https://dx.doi.org/10.1080%2F17425247.2019.1551875 https://doi.org/10.3390/ijerph17020380 Luo, H., Liu, Y. and Yang, X. (2007). Particle Deposition in Kang, M.Y., Hwang, J. and Lee, J.W. (2011). Effect of Obstructed Airways. J. Biomech. 40: 3096–3104. geometric variations on pressure loss for a model bifurcation https://doi.org/10.1016/j.jbiomech.2007.03.027 of the human lung airway. J. Biomech. 44: 1196–1199. Ma, B. and Lutchen, K.R. (2009). CFD simulation of aerosol https://doi.org/10.1016/j.jbiomech.2011.02.011 deposition in an anatomically based human large-medium Kelecy, F.J. (2008). Coupling momentum and continuity airway model. Ann. Biomed. Eng. 37: 271. https://doi.org/ increases CFD robustness. Ansys Advantage 2: 49–51. 10.1007/s10439-008-9620-y Kleinstreuer, C. and Zhang, Z. (2010). Airflow and particle Mannino, D.M. and Buist, A.S. (2007). Global burden of transport in the human respiratory system. Annu. Rev. COPD: Risk factors, prevalence, and future trends. Lancet Fluid Mech. 42: 301–334. https://doi.org/10.1146/annurev- 370: 765–773. https://doi.org/10.1016/S0140-6736(07)61 fluid-121108-145453 380-4 Kolanjiyil, A.V. and Kleinstreuer, C. (2017). Computational Mathers, C.D. and Loncar, D. (2006). Projections of global analysis of aerosol-dynamics in a human whole-lung mortality and burden of disease from 2002 to 2030. PLoS airway model. J. Aerosol Sci. 114: 301–316. https://doi.org/ Med. 3: e442. https://doi.org/10.1371/journal.pmed.0030 10.1016/j.jaerosci.2017.10.001 442 Kulmala, M., Laakso, L., Lehtinen, K.E.J., Riipinen, I., Dal McCreanor, J., Cullinan, P., Nieuwenhuijsen, M.J., Stewart- Maso, M., Anttila, T., Kerminen, V.M., Hõrrak, U., Vana, Evans, J., Malliarou, E., Jarup, L., Harrington, R., M. and Tammet, H. (2004). Initial steps of aerosol growth. Svartengren, M., Han, I.K. and Ohman-Strickland, P. Atmos. Chem. Phys. 4: 2553–2560. https://doi.org/10.519 (2007). Respiratory Effects of Exposure to Diesel Traffic 4/acp-4-2553-2004 in Persons with Asthma. N. Engl. J. Med. 357: 2348– Lambert, A.R., O'shaughnessy, P.T., Tawhai, M.H., 2358. https://doi.org/10.1056/NEJMoa071535 Hoffman, E.A. and Lin, C.L. (2011). Regional deposition Moskal, A. and Gradoń, L. (2002). Temporary and spatial of particles in an image-based airway model: large-eddy deposition of aerosol particles in the upper human airways simulation and left-right lung ventilation asymmetry. during breathing cycle. J. Aerosol Sci. 33: 1525–1539. Aerosol Sci. Technol. 45: 11–25. https://doi.org/10.1080/ https://doi.org/10.1016/S0021-8502(02)00108-8 02786826.2010.517578 Mutuku, J.K. and Chen, W.H. (2018). Flow characterization Lennon, S., Zhang, Z., Lessmann, R. and Webster, S. in healthy airways and airways with chronic obstructive (1998). Experiments on particle deposition in the human pulmonary disease (COPD) during different inhalation upper respiratory system. Aerosol Sci. Technol. 28: 464– conditions. Aerosol Air Qual. Res. 18: 2680–2694. 474. https://doi.org/10.1080/02786829808965538 https://doi.org/10.4209/aaqr.2018.06.0232 Li, L., Huang, Q., Wang, D.C., Ingbar, D.H. and Wang, X. Mutuku, J.K., Hou, W.C. and Chen, W.H. (2020). Two- (2020a). Acute lung injury in patients with COVID-19 phase flow dynamics and PM deposition in healthy and 2.5 infection. Clin. Transl. Med. 10: 20–27. https://doi.org/10. obstructed human airways during inhalation. Aerosol Air 1002/ctm2.16 Qual. Res. 20: 1091–1110. https://doi.org/10.4209/aaqr.2 Li, Z., Guo, S., Li, Z., Wang, Y., Hu, Y., Xing, Y., Liu, G., 020.03.0107 Fang, R. and Zhu, H. (2020b). PM associated phenols, Nazridoust, K. and Asgharian, B. (2008). Unsteady-state 2.5 phthalates, and water soluble ions from five stationary airflow and particle deposition in a three-generation combustion sources. Aerosol Air Qual. Res. 20: 61–71. human lung geometry. Inhalation Toxicol. 20: 595–610. https://doi.org/10.4209/aaqr.2019.11.0602 https://doi.org/10.1080/08958370801939374 Lindsley, W.G., Pearce, T.A., Hudnall, J.B., Davis, K.A., Norton, T. and Sun, D.W. (2006). Computational fluid Davis, S.M., Fisher, M.A., Khakoo, R., Palmer, J.E., dynamics (CFD) – an effective and efficient design and Clark, K.E. and Celik, I. (2012). Quantity and size analysis tool for the food industry: A review. Trends Food distribution of cough-generated aerosol particles produced Sci. Technol. 17: 600–620. https://doi.org/10.1016/j.tifs.2 by influenza patients during and after illness. J. Occup. 006.05.004 Environ. Hyg. 9: 443–449. https://doi.org/10.1080/15459 Nowak, N., Kakade, P.P. and Annapragada, A.V. (2003). 624.2012.684582 Computational fluid dynamics simulation of airflow and Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1195 aerosol deposition in human lungs. Ann. Biomed. Eng. 31: 265–281. https://doi.org/10.1016/0167-8191(88)90047-6 374–390. https://doi.org/10.1114/1.1560632 Soni, B. and Aliabadi, S. (2013). Large-scale CFD simulations Olds, J.L. and Kabbani, N. (2020). Is nicotine exposure of airflow and particle deposition in lung airway. Comput. linked to cardiopulmonary vulnerability to COVID-19 in Fluids 88: 804–812. https://doi.org/10.1016/j.compfluid. the general population? FEBS J. https://doi.org/10.1111/ 2013.06.015 febs.15303 Soriano, J.B., Abajobir, A.A., Abate, K.H., Abera, S.F., Park, S. and Wexler, A. (2007). Particle deposition in the Agrawal, A., Ahmed, M.B., Aichour, A.N., Aichour, I., pulmonary region of the human lung: A semi-empirical Aichour, M.T.E., Alam, K., Alam, N., Alkaabi, J.M., Al- model of single breath transport and deposition. J. Maskari, F., Alvis-Guzman, N., Amberbir, A., Amoako, Aerosol Sci. 38: 228–245. https://doi.org/10.1016/j.jaeros Y.A., Ansha, M.G., Antó, J.M., Asayesh, H., … Vos, T. ci.2006.11.009 (2017). Global, regional, and national deaths, prevalence, Pedley, T. (1977). Pulmonary Fluid Dynamics. Annu. Rev. disability-adjusted life years, and years lived with Fluid Mech. 9: 229–274. https://doi.org/10.1063/1.3517737 disability for chronic obstructive pulmonary disease and Phalen, R.F., Mendez, L.B. and Oldham, M.J. (2010). New asthma, 1990–2015: A systematic analysis for the Global Developments in Aerosol Dosimetry. Inhalation Toxicol. Burden of Disease Study 2015. Lancet Respir. Med. 5: 22: 6–14. https://doi.org/10.3109/08958378.2010.516031 691–706. https://doi.org/10.1016/S2213-2600(17)30293-X Piglione, M.C., Fontana, D. and Vanni, M. (2012). Simulation Stapleton, K.W., Guentsch, E., Hoskinson, M. and Finlay, of particle deposition in human central airways. Eur. J. W. (2000). On the suitability of k–ε turbulence modeling Mech. B. Fluids 31: 91–101. https://doi.org/10.1016/j.eur for aerosol deposition in the mouth and throat: A omechflu.2011.08.003 comparison with experiment. J. Aerosol Sci. 31: 739–749. Qi, S., Li, Z., Yue, Y., van Triest, H.J. and Kang, Y. (2014). https://doi.org/10.1016/S0021-8502(99)00547-9 Computational Fluid Dynamics Simulation of Airflow in Suh, Y. and Park, J.Y. (2018). Effect of off-plane bifurcation the Trachea and Main Bronchi for the Subjects with Left angles of primary bronchi on expiratory flows in the human Pulmonary Artery Sling. BioMed Eng. OnLine 13: 85. trachea. Comput. Biol. Med. 95: 63–74. https://doi.org/10. https://doi.org/10.1186/1475-925X-13-85 1016/j.compbiomed.2018.01.014 Rahimi-Gorji, M., Pourmehran, O., Gorji-Bandpy, M. and Sul, B., Wallqvist, A., Morris, M.J., Reifman, J. and Rakesh, Gorji, T. (2015). CFD simulation of airflow behavior and V. (2014). A computational study of the respiratory particle transport and deposition in different breathing airflow Characteristics in normal and obstructed Human conditions through the realistic model of human airways. airways. Comput. Biol. Med. 52: 130–143. https://doi.org/ J. Mol. Liq. 209: 121–133. https://doi.org/10.1016/j.moll 10.1016/j.compbiomed.2014.06.008 iq.2015.05.031 Tena, A., Francos, J., Alvarez, E. and Casan, P. (2015). A Russo, J., Robinson, R. and Oldham, M.J. (2008). Effects of three dimensional in silico model for the simulation of cartilage rings on airflow and particle deposition in the inspiratory and expiratory airflow in humans. Eng. Appl. trachea and main bronchi. Med. Eng. Phys. 30: 581–589. Comput. Fluid Mech. 9: 187–198. https://doi.org/10.1080 https://doi.org/10.1016/j.medengphy.2007.06.010 /19942060.2015.1004819 Sauret, V., Goatman, K., Fleming, J. and Bailey, A. (1999). Tgavalekos, N.T., Musch, G., Harris, R., Melo, M.V., Semi-automated tabulation of the 3D topology and Winkler, T., Schroeder, T., Callahan, R., Lutchen, K. and morphology of branching networks using CT: Application Venegas, J. (2007). Relationship between airway narrowing, to the airway tree. Phys. Med. Biol. 44: 1625. patchy ventilation and lung mechanics in asthmatics. Eur. https://doi.org/10.1088/0031-9155/44/7/304 Respir. J. 29: 1174–1181. https://doi.org/10.1183/09031 Schroter, R. and Sudlow, M. (1969). Flow patterns in 936.00113606 models of the human bronchial airways. Respiration Tian, G., Longest, P.W., Su, G. and Hindle, M. (2011). Physiol. 7: 341–355. https://doi.org/10.1016/0034-5687( Characterization of respiratory drug delivery with enhanced 69)90018-8 condensational growth using an individual path model of Schreck, R. and Mockros, L. (1970). Fluid dynamics in the the entire tracheobronchial airways. Ann. Biomed. Eng. 39: rd upper pulmonary airways. AIAA 3 Fluid and Plasma 1136–1153. https://doi.org/10.1007/s10439-010-0223-z Dynamics Conference, Los Angeles, California. Tian, L., Shang, Y., Chen, R., Bai, R., Chen, C., Inthavong, Setti, L., Passarini, F., Gennaro, G.D., Barbieri, P., Perrone, K. and Tu, J. (2017). A combined experimental and M.G., Borelli, M., Palmisani, J., Gilio, A.D., Piscitelli, P. numerical study on upper airway dosimetry of inhaled and Miani, A. (2020). Airborne transmission route of nanoparticles from an electrical discharge machine shop. COVID-19: Why 2 meters/6 feet of inter-personal distance Part. Fibre Toxicol. 14: 24. https://doi.org/10.1186/s12989- could not be enough. Int. J. Environ. Res. Public Health 017-0203-7 17: 2932. https://doi.org/10.3390/ijerph17082932 Tsuda, A., Henry, F.S. and Butler, J.P. (2008). Gas and Sidhaye, V.K., Schweitzer, K.S., Caterina, M.J., Shimoda, L. aerosol mixing in the acinus. Respir. Physiol. Neurobiol. and King, L.S. (2008). Shear stress regulates aquaporin-5 163: 139–149. https://doi.org/10.1016/j.resp.2008.02.010 and airway epithelial barrier function. PNAS 105: 3345– Valavanidis, A., Fiotakis, K. and Vlachogianni, T. (2008). 3350. https://doi.org/10.1073/pnas.0712287105 Airborne particulate matter and human health: Toxicological Solchenbach, K. and Trottenberg, U. (1988). SUPRENUM: assessment and importance of size and composition of System essentials and grid applications. Parallel Comput. 7: particles for oxidative damage and carcinogenic 1196 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 mechanisms. J. Environ. Sci. Health., Part C 26: 339–362. https://doi.org/10.1016/j.jbiomech.2005.10.009 https://doi.org/10.1080/10590500802494538 Yeates, D.B. and Aspin, N. (1978). A mathematical van Doremalen, N., Bushmaker, T., Morris, D.H., Holbrook, description of the airways of the human lungs. Respir. M.G., Gamble, A., Williamson, B.N., Tamin, A., Harcourt, Physiol. 32: 91–104. https://doi.org/10.1016/0034-5687(7 J.L., Thornburg, N.J. and Gerber, S.I. (2020). Aerosol and 8)90102-0 surface stability of SARS-CoV-2 as compared with Yeh, H.C. and Schum, G. (1980). Models of human lung SARS-CoV-1. N. Engl. J. Med. 382: 1564–1567. airways and their application to inhaled particle deposition. https://doi.org/10.1056/NEJMc2004973 Bull. Math. Biol. 42: 461–480. https://doi.org/10.1016/S0 Van Ertbruggen, C., Hirsch, C. and Paiva, M. (2005). 092-8240(80)80060-7 Anatomically based three-dimensional model of airways Yoon, S.H., Lee, K.H., Kim, J.Y., Lee, Y.K., Ko, H., Kim, to simulate flow and particle transport using computational K.H., Park, C.M. and Kim, Y.H. (2020). Chest fluid dynamics. J. Appl. Physiol. 98: 970–980. radiographic and CT findings of the 2019 novel https://doi.org/10.1152/japplphysiol.00795.2004 coronavirus disease (COVID-19): Analysis of nine Velavan, T.P. and Meyer, C.G. (2020). The COVID-19 patients treated in Korea. Korean J. Radiol. 21: 494–500. Epidemic. Trop. Med. Int. Health 25: 278–280. https://doi.org/10.3348/kjr.2020.0132 https://doi.org/10.1111/tmi.13383 Zhang, H. and Papadakis, G. (2010). Computational analysis Viegas, C.A., Ferrer, A., Montserrat, J.M., Barbera, J.A., of flow structure and particle deposition in a single Roca, J. and Rodriguez-Roisin, R. (1996). Ventilation- asthmatic human airway bifurcation. J. Biomech. 43: 2453– perfusion response after fenoterol in hypoxemic patients 2459. https://doi.org/10.1016/j.jbiomech.2010.05.031 with stable COPD. Chest 110: 71–77. https://doi.org/10.1 Zhang, P., Duan, J., Chen, G. and Wang, W. (2019). 378/chest.110.1.71 Numerical investigation on gas-solid flow in a circumfluent Walters, D.K. and Luke, W.H. (2010). A method for three- cyclone separator. Aerosol Air Qual. Res. 19: 971–980. dimensional navier-stokes simulations of large-scale https://doi.org/10.4209/aaqr.2018.05.0197 regions of the human lung airway. J. Fluids Eng. 132: Zhang, X., Kang, J., Chen, H., Yao, M. and Wang, J. (2018). 051101. https://doi.org/10.1115/1.4001448 PM meets blood: In vivo damages and immune defense. 2.5 Wang, J. and Du, G. (2020). COVID-19 may transmit Aerosol Air Qual. Res. 18: 456–470. https://doi.org/10.42 through aerosol. Ir. J. Med. Sci. https://doi.org/10.1007/s1 09/aaqr.2017.05.0167 1845-020-02218-2 Zhang, Z. and Kleinstreuer, C. (2001). Effect of particle inlet Weibel, E.R. (1963a). Geometric and dimensional airway distributions on deposition in a triple bifurcation lung models of conductive, transitory and respiratory zones of airway model. J. Aerosol Med. 14: 13–29. https://doi.org/ the human lung. In Morphometry of the human lung, 10.1089/08942680152007864 Weibel, E.R. (Ed.), Springer, pp. 136–142. Zhang, Z. and Kleinstreuer, C. (2002). Transient airflow Weibel, E.R. (1963b). Geometry and dimensions of airways structures and particle transport in a sequentially branching of conductive and transitory zones. In Morphometry of the lung airway model. Phys. Fluids 14: 862–880. human lung, Weibel, E.R. (Ed.), Springer, pp. 110–135. https://doi.org/10.1063/1.1433495 Xia, B. and Sun, D.W. (2002). Applications of Zhang, Z., Kleinstreuer, C. and Kim, C. (2002). Gas–solid computational fluid dynamics (CFD) in the food industry: two-phase flow in a triple bifurcation lung airway model. A review. Comput. Electron. Agric. 34: 5–24. Int. J. Multiphase Flow 28: 1021–1046. https://doi.org/10. https://doi.org/10.1016/S0168-1699(01)00177-6 1016/S0301-9322(02)00011-3 Xu, C., Luo, X., Yu, C., and Cao, S.J. (2020). The 2019- Zhang, Z. and Kleinstreuer, C. (2011). Laminar-to-turbulent nCoV epidemic control strategies and future challenges of fluid-nanoparticle dynamics simulations: Model building healthy smart cities. Indoor Built Environ. comparisons and nanoparticle-deposition applications. 1420326X20910408. https://doi.org/10.1177%2F142032 Int. J. Numer. Methods Biomed. Eng. 27: 1930–1950. 6X20910408 https://doi.org/10.1002/cnm.1447 Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Zhao, W., Zhong, Z., Xie, X., Yu, Q. and Liu, J. (2020). Liu, S., Zhao, P., Liu, H., Zhu, L., Tai, Y., Bai, C., Gao, Relation between chest CT findings and clinical conditions T., Song, J., Xia, P., Dong, J., Zhao, J., and Wang, F.S. of coronavirus disease (COVID-19) pneumonia: A (2020). Pathological findings of COVID-19 associated multicenter study. Am. J. Roentgenol. 214: 1072–1077. with acute respiratory distress syndrome. Lancet Respir. https://doi.org/10.2214/AJR.20.22976 Med. 8: 420–422. https://doi.org/10.1016/S2213-2600(20) Zierenberg, J.R., Halpern, D., Filoche, M., Sapoval, B. and 30076-X Grotberg, J.B. (2013). An asymptotic model of particle Yanai, M., Sekizawa, K., Ohrui, T., Sasaki, H. and deposition at an airway bifurcation. Math. Med. Biol. 30: Takishima, T. (1992). Site of airway obstruction in 131–156. https://doi.org/10.1093/imammb/dqs002 pulmonary disease: Direct measurement of intrabronchial pressure. J. Appl. Physiol. 72: 1016–1023. https://doi.org/ 10.1152/jappl.1992.72.3.1016 Received for review, April 30, 2020 Yang, X., Liu, Y. and Luo, H. (2006). Respiratory flow in Revised, May 22, 2020 obstructed airways. J. Biomech. 39: 2743–2751. Accepted, May 24, 2020 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aerosol and Air Quality Research Unpaywall

An Overview of Experiments and Numerical Simulations on Airflow and Aerosols Deposition in Human Airways and the Role of Bioaerosol Motion in COVID-19 Transmission

Aerosol and Air Quality ResearchJan 1, 2020

Loading next page...
 
/lp/unpaywall/an-overview-of-experiments-and-numerical-simulations-on-airflow-and-yl9PNa1zFF

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Unpaywall
ISSN
1680-8584
DOI
10.4209/aaqr.2020.04.0185
Publisher site
See Article on Publisher Site

Abstract

Special Issue on COVID-19 Aerosol Drivers, Impacts and Mitigation (II) Aerosol and Air Quality Research, 20: 1172–1196, 2020 Publisher: Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online https://doi.org/10.4209/aaqr.2020.04.0185 An Overview of Experiments and Numerical Simulations on Airflow and Aerosols Deposition in Human Airways and the Role of Bioaerosol Motion in COVID-19 Transmission 1 1* 2,3,4* Justus Kavita Mutuku , Wen-Che Hou , Wei-Hsin Chen Department of Environmental Engineering, National Cheng Kung University, Tainan 70101, Taiwan Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan Department of Chemical and Materials Engineering, College of Engineering, Tunghai University, Taichung 407302, Taiwan Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan ABSTRACT Determining the hotspots and deposition efficiencies (DEs) for aerosols in human airways is important for both research and medical purposes. The complexity of the human airways and the breathing process limit the application of in vitro measurements to only two consecutive branches of the human airway. Herein, in-depth information on in vitro experiments and state-of-the-art review on various computational fluid dynamics (CFD) applications and finite element methods on airflow and aerosol motion in both healthy and obstructed human airways are provided. A brief introduction of the application of one-dimensional and two-dimensional mathematical models to investigate airflow and particle motion in the lungs are further discussed. As evident in this review, aerosol deposition in the upper and central human airway regions has been extensively studied under different inhalation statuses and conditions such as humidity as well as different aerosol sizes, shapes, and properties. However, there is little literature on the lower sections of the human airways. Herein, a detailed review of the fundamentals for both in vitro experiments and numerical simulation at different sections of human airways is done. Exceptional features and essential developments in numerical methods for aerosol motion in healthy and diseased human airways are also discussed. Challenges and limitations associated with the applications of in vitro experiments and CFD methods on both human-specific and idealized models are highlighted. The possibility of airborne transmission pathways for COVID-19 has been discussed. Overall, this review provides the most useful approach for carrying out two- phase flow investigations at different sections of the human lungs and under different inhalation statuses. Additionally, new research gaps that have developed recently on the role of bioaerosols motion in COVID-19 transmission, as well as the deposition of aerosols in impaired human airways due to coronavirus (COVID-19) are underlined. Keywords: Aerosol physics; Asthma and COPD; in vitro experiment; Numerical methods; Two-phase flow; Deposition efficiencies (DEs); Coronavirus (COVID-19). NOMENCLATURE D Diameter (mm) d Particle diameter (µ m) A Amplitude of the sinusoidal curve (Reynolds number) F Force (N) A Amplitude of the folds (cm) G Generation of Weibel’s airway fold A Cross-sectional area of the lumen (cm ) G Turbulent kinetic energy 0 k –3 C Concentration (cm ) G Specific dissipation rate C Drag coefficient L Length of the real airway (m) D A DF Deposition fraction L Length of the geometric model (m) m Mass of a single particle (µ g) n Number of folds P Pressure (Pa) * –1 Corresponding author. Q Flow rate (L s ) E-mail address: whou@mail.ncku.edu.tw (W.C. Hou); R Radius (mm) weihsinchen@gmail.com; chenwh@mail.ncku.edu.tw r Radial coordinate (W.H. Chen) Re Reynolds number Copyright: The Author(s) 2020. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1173 t Time (s) 2010; Hwang and Park, 2019). Despite PM affecting the 2.5 –1 u Particle velocity (m s ) entire population, the adverse impacts are worse for special –1 U Velocity at the real airway (m s ) groups such as infants, the elderly, and patients with obstructive –1 U Average velocity at the airway’s entrance (m s ) conditions as they experience the highest deposition mean –1 U Max velocity at the airway’s entrance (m s ) efficiencies (Longest et al., 2006; Chen et al., 2012; Adeloye max –1 U Velocity at the geometric model (m s ) et al., 2015). Common obstructive pulmonary diseases include V Control volume chronic obstructive pulmonary disease (COPD), asthma, cystic fibrosis, and acute respiratory distress syndrome. Epidemiological reports show that the prevalence of COPD Greek Letters –3 ρ Density (kg m ) and asthma are the highest among the obstructive diseases –2 σ Stress (N m ) (Mathers and Loncar, 2006; Mannino and Buist, 2007; µ Viscosity Coefficient Adeloye et al., 2015; Soriano et al., 2017). They are both –1 υ Averaged inlet velocity of parent branch (m s ) characterized by persistent and limited airflow inside the α Fluid volume fraction lungs and are usually exacerbated by inhalation of toxic θ Angular coordinate (°) gasses and PM (Viegas et al., 1996). Therefore, past studies have mostly focused on airflow, aerosols transportation, transformation, and deposition inside healthy and obstructed Subscripts p particle human lungs during breathing (McCreanor et al., 2007; H hydraulic Zhang and Papadakis, 2010; Chen et al., 2012). mean average value Meanwhile, coronavirus disease 2019 (COVID-19), which peak peak value broke out in December 2019, has been shown to cause deadly cases of pneumonia and is, therefore, receiving a great deal of attention lately. This disease has been shown to INTRODUCTION cause a 3.4% mortality rate globally according to the estimate In vitro experiments and computational fluid dynamics from WHO as of March 2020. Analysis of radiographic and (CFD) coupled with finite element method (FEM) have been computed tomography (CT) findings of COVID-19 patients used as tools for investigating airflow and aerosol motions showed the presence of patchy, confluent, or nodular shaped in human airways for a few years. They are used to lesions and pulmonary opacities concentrated mostly in the investigate the differences in deposition efficiencies (DEs) peripheral lungs (Yoon et al., 2020). A statistical analysis of and deposition patterns of toxic and pharmaceutical aerosols the dominant shapes of lesions showed that patchy to for both healthy and obstructed human airways. Those confluent lesions were more dominant as compared to the approaches are useful in the assessment of the performance nodular ones. Further, CT imaging studies have shown that of existing drug-aerosol delivery technologies to the human the lesions are more concentrated on the lower lobes as well lungs (Asgharian et al., 2001; Darquenne, 2012). Findings as the dorsal part of the lungs (Çinkooğlu et al., 2020). from these studies can be applied in the accurate estimation According to the radiologic evidence presented in a study by of risk levels caused by toxic aerosols or in design Li et al. (2020a), the presence of edema and acute lung modifications to overcome the limitations of inhalers used injury are common in critical stages of patients with severe by patients with respiratory diseases (Kolanjiyil and COVID-19. Acute lung inflammation and long-term damage Kleinstreuer, 2017; Chen et al., 2018a). to the alveolar walls are among the main adverse effects Investigations in the current and past centuries have suffered by COVID-19 survivors (Hosseiny et al., 2020; Xu linked air pollution to a wide range of acute and chronic et al., 2020b). Permanent lung damage associated with the health defects (Brook et al., 2010; Chowdhury et al., 2019). disease presents a new challenge of understanding deposition A case in point is the statistically significant association patterns and efficiencies for toxic, pharmaceutical, or between long-term exposure to fine particulate matter (PM) biological aerosols in patients after recovery. and reduced life expectancy (Asadi et al., 2020). Although To study the motion of aerosols such as PM and bioaerosols this information is only available for developed and middle- such as bacteria and viruses, their size distributions are income countries in the world countries, model estimations important. Coronaviruses such as COVID-19, which is the depict a worse situation in developing countries whose true cause of the latest pandemic of respiratory tract infections, situation remains conclusively unknown (Cohen et al., have an average size of between 65 and 125 nm, with an 2005; Soriano et al., 2017). Reports on the relationship of envelope diameter and spikes measuring about 80 nm and some aspects of PM, for instance, chemical composition, 20 nm, respectively (Velavan and Meyer, 2020). The size of toxicity, and particle size show an inverse relationship aerosols released during coughing and sneezing are very between toxicity and PM’s size (Chen et al., 2019; Li et al., important in studying airborne infectious disease transmission. 2020b). Consequently, fine (PM ) and ultra-fine PMs tend Their sizes have been shown to vary in healthy and diseased 2.5 to be the most toxic among the total suspended solids individuals. For healthy individuals, droplets were found to (Valavanidis et al., 2008; Zhang et al., 2018). range between 341.5–398.1 µ m for unimodal distribution There is a fascinating but not very conclusive understanding (Han et al., 2013). On the other hand, individuals affected of the possible pathways that associate exposure to PM by influenza were found to sneeze droplets whose size ranged 2.5 and mortality due to cardiovascular diseases (Brook et al., between 0.35 to 10 µ m (Lindsley et al., 2012). 1174 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 The geometry of the human lung is very complex. to the airflow rate due to amplified secondary flow. Therefore, to study the motions of aerosols inside the lungs, Since the development of CFD, different types of fluid flow in vitro experiments and CFD analysis usually employ have been thoroughly described, including continuous flow simplified geometries (Lennon et al., 1998; Delvadia et al., systems in the industrial, environmental, and physiological 2012). The main simplified models include (1) Weibel’s applications (Chen, 2001a; Longest et al., 2006; Chen et al., geometry which is asymmetrical and with 23 generations 2011; Zhang et al., 2019). The physiological processes include and (2) Horsfield’s asymmetrical geometry (Weibel, 1963b; airflow in the airways and blood flow inside the human body Horsfield and Cumming, 1967). Other geometries developed (Soni and Aliabadi, 2013; Chen et al., 2018c). CFD has later, for instance, by Hammersley and Olson (1992), are matured into a proper tool whose application in describing more suitable for investigations in the sixth to twelfth airflow in the human airways has almost replaced the traditional generations (G6–G12). in vitro experiments. This increased adoption of CFD methods Traditionally, investigations about the deposition efficiencies for airflow analysis has been aggravated by the need to produce for aerosols inside the human lungs were conducted using detailed results on airflow as well as the high costs and huge experiments (Chang and El Masry, 1982; Lennon et al., time consumed during in vitro experiments (Solchenbach and 1998). It is 5 decades since the first in vitro measurements Trottenberg, 1988). Additionally, CFD blends visualization for airflow and particle motion inside the lungs were done. techniques as well as mathematical physics and methods for Currently, this approach presents a great challenge due to the the production of optimized results (Chen, 2001b). limited ability to accurately assess instantaneous airflow Previously reviews have been conducted on the applications velocity and pressure as well as the DEs (Lambert et al., of CFD to study the deposition of aerosol medicine in a 2011). The development and advancement of computing whole lung airway model by Longest et al. (2019). Another capability in the world have enhanced the application of review by Islam et al. (2020) covered the recent developments FEM and CFD models to carry out investigations on airflow on airflow analysis and particle deposition in both upper and and particle deposition in human airways (Chen et al., 2012; lower regions of human airways. Furthermore, particle Rahimi-Gorji et al., 2015). clearance mechanisms in the lungs have also reviewed Important items before the implementation of a CFD study (Hussain et al., 2011). The simulations of particle formation include the generation of geometry, solving the governing and localized deposition in human airways using 1-D, 2-D, equations along with appropriate boundary conditions, and and 3-D models were reviewed by Guzman (2020). Despite sometimes incorporating user-defined functions (UDFs). other authors writing reviews on aerosol deposition and The shape and size of geometry are not only dependent on particle clearance in human lungs, none has combined particle the generation of the human airways under investigation but deposition and lung clearance mechanism. This review fills also on whether it is healthy or affected by a disease (Sul et the gap by reviewing the application of experiments and al., 2014; Chen et al., 2018a). COPD has been represented CFD with FEM in the assessment of airflow as well as by an axisymmetric constriction at the center of a bifurcation, aerosol motion and deposition in healthy and deformed while asthma has been represented by sinusoidal folds at the human airways. Fundamentals for in vitro experiments circumference of the affected bifurcation (Yang et al., 2006; involving airflow and particle motion inside the human lungs Zhang and Papadakis, 2010). Typical boundary conditions are discussed. Additionally, those for numerical simulation, imposed for airflow include velocity distribution at the inlet, for instance, simplified geometries of the human lungs, CFD no-slip boundary condition along the walls, and pressure at models for different sections of the human airway are covered. the outlets (Zhang and Kleinstreuer, 2002). Dominant lung clearance mechanisms at different sections In COPD cases, investigations on airflow have attributed of the human airways are briefly discussed. Exceptional the shortness of breath to the presence of stagnation and features and essential aspects of results from both experiments recirculation zones at the proximity of the obstructed are CFD analyses are also discussed. bifurcation (Luo et al., 2007; Chen et al., 2012; Mutuku and Chen, 2018). Pressure distributions in COPD showed that jet HUMAN AIRWAY STRUCTURE flow phenomena at the cross-section affected by the obstruction resulted in low pressures which were inadequate Gaseous exchange between the atmosphere and the human to drive the required airflow mass to the later sections of the blood is accomplished through the human respiratory system. human airway (Yang et al., 2006). Skewed airflow velocities Upon inhalation, air travels through the nose, pharynx, larynx, influenced the mass flow ratios at bifurcations in the and into the trachea, after which it goes into one of the two generations affected by the obstruction and subsequent ones bronchi that lead the way into the left and right lungs. The (Mutuku and Chen, 2018). Sudden reduction in the effective two bronchi divide into smaller and smaller bronchioles cross-sectional area for asthma cases caused increased airflow until they reach the alveoli. The alveoli, which are sac-like velocities and high complex secondary flows, which in turn structures, mark the end of the lungs and the region responsible led to higher deposition fractions for PM as compared to for gaseous exchange between the inhaled air and the blood 2.5 healthy human airways (Chen et al., 2018a). Airflow resistance, in the circulatory system (Horsfield and Cumming, 1968). which is normally represented by the ratio of total pressure drop, has been applied to characterize and evaluate obstructions Geometric Models for Human Airways in the human lungs (Sul et al., 2014). Previous research has In vivo measurements on the deposition efficiencies and shown that the pressure drop is usually directly proportional airflow dynamics inside the lungs are impossible due to the Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1175 delicate and complex nature of the human airways. Therefore, where D is the hydraulic diameter for the idealized model, in vitro measurements and numerical simulations have been while the area and perimeter are measured from the real applied in the investigations for airflow as well as particle airway geometry. motion and deposition. Due to the complex nature of the Historically, whole lung-semi-empirical models were human lungs, in vitro experiments on the human lungs require applied to establish the deposition patterns and airflow simplification of the lungs’ geometry. Several researchers phenomenon in the human airways. However, a more feasible have developed simplified lung geometries (Weibel, 1963b; approach has been developed recently and it involves the Horsfield et al., 1971; Yeates and Aspin, 1978; Hammersley application of multistage modeling with the development of and Olson, 1992; Van Ertbruggen et al., 2005; Lindsley et large scale models representing the central airways and al., 2012; Chen et al., 2017; Zhao et al., 2020). However, small scale models to represent peripheral airways (Kolanjiyil only two of these are commonly used for in vitro experiments and Kleinstreuer, 2017). The approach is aimed at obtaining and numerical analysis; they are Horsefield’s model (Horsfield a whole lung airway model which is physiologically and Cumming, 1968) and Weibel’s model (Weibel, 1963a). accurate. Investigations on airflow and particle motion using These geometric models are applied for in vitro experiments the resultant geometric representations can be used to build or numerical simulations to investigate airflow or particle an understanding of the whole human lung. motion and deposition inside the human airways. In some According to a study by Park and Wexler (2007), there is a studies, CT scans and magnetic resonance imaging (MRI) significant degree of mixing, especially in Weibel’s bifurcation. measurements have been applied to develop more realistic As air advances to the terminal airways, the aggregate cross- geometric models. However, airflow dynamics, aerosol motion, sectional area of the human airways increases, and this and deposition in the human airways face tremendous inter- amplifies recirculation phenomena and hence increases the subjective inconsistencies and therefore the results from mixing, especially with the pulsating airflow. It is important such human-specific studies cannot be projected to the entire to understand the fluid mechanics for airflow inside the human population. Furthermore, curvatures and anatomy of the real airways, especially for area expansion, curvatures, secondary human airways are more complicated compared to the flow phenomena, flow re-organization, and stagnation and simplified models and as such, their airflow patterns are recirculation zones in each bifurcation (Hammersley and equally complicated (Hwang and Park, 2019). Despite the Olson, 1992; Lambert et al., 2011). use of CT scans and MRI measurements providing the most realistic geometries, they cannot provide the dimensions of Weibel Geometric Model th th the bifurcation beyond the 7 or 9 generation due to According to the study of Weibel (1963b), the inadequate clarity of the scanned images (Walters and Luke, tracheobronchial system can be simplified using a dichotomous 2010). This makes simplified models more attractive as branching network of pipes and a total of 23 levels of ducts compared to real ones obtained from CT scans for application (bifurcations). The bifurcations are numbered depending on in studies involving the human airways, especially for how far downstream they are from the trachea. Therefore, central and lower sections of the lung. the trachea is G0, the left and right bronchi which are both In the study of airflow dynamics and particulate phase G1, the four branches after the bronchi G1 are G2, and so motion in the trachea-bronchial bifurcations, a huge task lies forth until the last section of the alveoli (G20–G23). A in striking a balance between ease of measurement and simplified representation of Weibel’s model is presented in physiologically realistic airway geometry. The Weibel’s Fig. 1(a). It is widely accepted that the application of a geometry assumes a symmetrical geometry, while the reduced number of branches with suitable boundary conditions Horsfield's model assumes an asymmetrical structure (Weibel, can give results with an acceptable degree of accuracy if the 1963b; Horsfield et al., 1971). Scientific evidence suggests region of interest is not at the outlet branches (Sul et al., that the human lung is asymmetrical and the number of 2014). A 4 generations bifurcation for a healthy human generations in a particular pathway is variable (Horsfield airway is shown in Fig. 2(a). In this model, the human lung and Cumming, 1968; Chen et al., 2020). However, Weibel’s geometry can be split into three main regions; G0–16 is the geometric model which is idealized, regular, and symmetrical conductive zone, G17–19 is respiratory bronchioles, and remains the most popular due to its ability to provide G20–23 is composed of alveoli ducts and alveoli. The sufficient information on gas and particulate flow within a bifurcations have a constant length to diameter ratio (L/D) short computational time. which is approximately 3. The ratio of the parent bifurcation The walls in airways are commonly assumed to be rigid and diameter to that of the child (D /D ) is also constant in this n n+1 smooth with a circular cross-section (Chang and El Masry, model and ranges from 1.17 to 1.5 (Weibel, 1963a). 1982; Chen et al., 2018a). The concept of hydraulic diameter is important as it equates the circular cross-sections in idealized Horsfield GEOMETRIC MODEl models to the bronchial surface over which airflow experiences This model was aimed at capturing the effects of a shear force in the real human airway (Hammersley and asymmetry on airflow dynamics and particle deposition Olson, 1992). The hydraulic diameter is expressed as (Horsfield and Cumming, 1967). The effects of asymmetry on airflow and particle motion increases as air advances from the oro-nasal cavity towards the alveoli region. The 4 Area D  (1) approach by Horsfield and Cumming (1967) applied formulas perimeter by which the asymmetry of the dichotomous branching 1176 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 could be represented. Details provided in the model include Other Models the angle of the branches, the radius of the curvature, and the A few other irregular dichotomy models have been proposed cross-sectional shape. The numbering of the bifurcations based on morphometric data from Horsfield’s model (Yeates also differs from the one proposed by Weibel (1963b), in the and Aspin, 1978; Van Ertbruggen et al., 2005). Yeates and sense that bifurcations are numbered starting from the alveoli Aspin, (1978) carried out a study on the physiological region. (Horsfield and Cumming, 1967). A typical solid implications of the morphological structure proposed by geometry and mesh for a model adapted from Horsfield’s Horsfield, whereby mathematical expressions for the model are presented in Fig. 1(b). bifurcation system of the intralobular bronchi from th e (a) (b) Fig. 1. (a) A representation of Weibel’s symmetrical models of generations 0–23 and (b) A representation of the G0–G3 of the Horsfield’s asymmetrical model. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1177 trachea to the alveoli were described. Additional anatomical models have also been established using mathematical algorithms and imaging techniques (Tgavalekos et al., 2007; Velavan and Meyer, 2020; Xu et al., 2020b). The latest airway geometry model was developed by Lindsley et al. (2012) and it described the first 17 generations of an asymmetrical human airway geometry. A more practical cadaver model was developed to handle smaller airways for bifurcations between G6 and G12 (Hammersley and Olson, 1992). A mathematical method of obtaining a physiologically accurate geometry for the first five generations of the human airway was developed by Zhao et al. (2020). Semi-automatic approaches have been developed to help in the development of the lung’s morphological structure (Sauret et al., 1999). In a study by Lindsley et al. (2012), high-resolution tomography coupled with image processing algorithms were applied to th develop precise models of the human airways up to the 17 generation of the Horsefield’s geometry. Another hybrid model using more than two techniques to characterize the geometry was by Van Ertbruggen et al. (2005), which used the work by Horsfield and Cumming (1967) for characteristics of individual generation coupled with imaging techniques to obtain local branch orientations. Anatomical Features of Diseased Human Lungs Since asthma and COPD are the most prevalent obstructive pulmonary diseases, there is a relatively higher volume of literature covering aerosol motion and deposition in human airways affected by these conditions (Yang et al., 2006; Luo et al., 2007; Zhang and Papadakis, 2010; Chen et al., 2018a). Geometric characteristics of the obstructed human airways such as for asthma and COPD can be represented by slightly altered geometries from the regular idealized ones. Asthmatic human airways are usually represented by uniformly distributed folds along the circumference of the affected generation (Zhang and Papadakis, 2010; Chen et al., 2018a). According to the study of Zhang and Papadakis (2010), a human airway affected by asthma can be represented as a circular cross-section surrounded by several sinusoidal folds which are distributed along the circumference. The equation can be expressed in the polar coordinate system as: r(θ) = R + A cos(nθ) (2) fold where r, θ and R are the radial coordinate, angular coordinate, and effective radius of the affected cross-section. Additionally, A and n stand for the amplitude of the fold in centimeters fold and the number of folds in the affected cross-section, respectively. A typical configuration of the asthmatic airway discretized with 40% of normal lumen area and 10 folds is Fig. 2. Typical geometries for (a) healthy human airway, (b) shown in Fig. 2(b). human airway affected by asthma, and (c) human airway On the other hand, airways affected by COPD are affected by COPD. represented by axisymmetric constriction in one or more of the bifurcations (Yang et al., 2006; Chen et al., 2012). A typical figure of a human airway affected by COPD as human-specific airway geometries, CT scans have provided shown in Fig. 2(c). images that are clear enough for the reproduction of 3D The lung damages caused by COVID- 19 will affect geometries up to the G9 of the human airways. The lesions airflow as well as aerosol motion and deposition inside the associated with COVID -19 are mostly concentrated in the human airways of survivors. In previous studies, based on alveolar region, and therefore, it is difficult if not impossible 1178 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 to obtain CT images at a clarity level which would be conditions or stochastically coupled boundary conditions. A adequate for the production of 3D geometries of the diseased truncated model was used by Tena et al. (2015) whereby the regions. Therefore, new approaches are needed to carry out same velocity vector fields were imposed on the truncated investigations on airflow and particle motion in the lungs of sections as the corresponding sections of the developed COVID-19 survivors. branches of the airway. In another study by Walters and Luke (2010), where 50% of the airway paths were truncated, static pressure values at corresponding sections of the Whole Lung Airway Model vs. Localized Simulations. There have been few attempts to simulate the resistance remaining bifurcations were used as the boundary conditions at of flow, and deposition efficiency in the entire human lung. the truncated sites. In a study by Gemci et al. (2008) a 17- A summary of the studies has been presented in Fig. 3. In a generation model was partially solved using 1,453 bronchi study by Chen et al. (2017), the resistance of flow in the rather than 131,072. This was achieved through truncation upper airway was found to contribute 45–81% of the total and duplication of the boundary conditions to cater for the resistance of the entire human lung depending on the truncated sections. In a study by Walters and Luke (2010), a frequency of ventilation. During the study, hybrid 3D bifurcation angle of 70° was applied for a geometry geometries for the upper, central, small airways, and alveoli consisting of G4–G12, the plane for each bifurcation was were used. Some of the generations between G4 and G6 selected randomly for angles between 0° and 180°. In this were truncated. In a study on the deposition of 1–30 µ m study, branches were truncated past G6 such that only one particles in the tracheobronchial generations between the branch followed all through to G12. Stochastically coupled mouth to G10, deposition mostly happened in the large- and boundary conditions were imposed on the truncated medium-sized generations (Ma and Lutchen, 2009). sections. The latest state-of-the-art approach in CFD involves the On the other hand, studies on localized deposition of application of partially resolved models of a truncated airway particles include the work of Nowak et al. (2003) where a tree. After truncation, appropriate boundary conditions are numerical analysis for airflow and 10 µ m particle motion imposed on the truncated sections by applying prior pressure was carried out based on a four-generation Weibel’s model Rahimi- G1~G17 Gorgi et al., Ma and Mouth~G10 Lutchen, 2009 Without truncation Soni et al., Mouth – G10 Simulations for WLAM Ma and G0~G23 With Lutchen, truncation Walters and G1~G17 Luke, 2010 Kolanjiyil, and Mouth~G23 Kleinstreuer, Fig. 3. Attempts towards carrying out aerosol motion investigations in a whole lung airway model. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1179 (G0–G3) and a cadaver lung cast. Results from the deposition U L U L M M A A  (3) efficiencies done for both inhalation and exhalation flow vv MA conditions showed a maximum deposition of 12% in the carina region that succeeds generation 2 for unsteady where U and L represent linear velocity and dimension, inhalation using Weibel's geometry. The maximum DE of respectively, while v designates the kinematic viscosity of 6.5% for the geometry from the CT scan was highest at G0 the gas phase. The subscripts M and A denote the geometric for both steady and unsteady inhalation conditions (Hwang model and the real airway, respectively. and Park, 2019). In an in vitro experiment, by Lennon et al. To conduct experiments on obstructed airways in the (1998) whereby deposition efficiencies were compared for lungs, copper tubes, which are packed with steel capillary nasal and oral inhalation, the maximum deposition efficiency tubes, are applied as obstructions inside the tubular model. of 43.6% occurred during nasal inhalation at the nasal region The longer the resistor, the more severe the obstruction as compared to 5.4% at the oral cavity during oral inhalation. (Chang and El Masry, 1982). Plastic conic resistors provide The effect of cartilage on airflow in the trachea and the main the transition from the airway terminals to resistors. A bronchi were investigated using CFX and Fluent solvers precision metering valve is applied to control the airflow rate (Russo et al., 2008). Results showed that for a laminar flow during both inhalation and exhalation. –1 characterized by 15 L min at the inlet DEs using Fluent exceeded those obtained using CFX by an average of 2.4%. Methods of Measuring Flow Velocity and Pressure –1 However, for a turbulent flow of 60 L min , the DEs of Distribution Fluent fell behind those of CFX by about 4.2% for a smooth Airflow velocity is usually constant for experiments. As airway channel, and they were equal for ringed trachea and can be seen on the VOSviewer map in Fig. 5 airflow is bronchi. Further details on localised deposition are tabulated among the most important parameters in aerosol motion in Table 1. inside the human airways. Axial flow velocities are usually The whole lung airway model can provide information measured using hot-wire anemometer probes. The working about the airflow dynamics, particle motion, and deposition mechanisms of the wire probes involve the application of the of particles inside the human airways. However, the degree cooling effect of the wire due to airflow to estimate the of details required to achieve an improved understanding of velocity of air at the cross-section fitted with the anemometer regional deposition is not practical while using a whole lung wire (Chang and El Masry, 1982; Isabey and Chang, 1982; airway model. This reason makes localized particle deposition Cheng et al., 1999). Secondary flow velocities are captured models more popular. Additionally, accurate prediction of using still photographs by continuous frontal illumination DEs in hotspots and other vulnerable sites can provide more with an angle of 45° as the incidence angle (Schroter and relevant methods for the assessment parts of the respiratory Sudlow, 1969). Pressure drop over a given lateral length is system which are more prone to injury or diseases due to usually measured using a sensitive differential pressure higher deposition efficiencies of aerosols (Hofmann et al., transducer (Chang and El Masry, 1982; Yanai et al., 1992). 1995; Nazridoust and Asgharian, 2008). In a study by Stapleton et al. (2000), overall pressure drop during a turbulent flow in a mouth-throat geometry was FUNDAMENTALS OF IN VITRO EXPERIMENTS measured by attaching a pressure transducer to both the inlet and exit using pressure traps. For a long time, experiments have been performed to understand airflow resistance in the human lungs, mixing of Estimating Deposition Patterns and Respective the intrapulmonary gases and deposition of particles from Deposition Efficiencies ambient air (Yanai et al., 1992; Cheng et al., 1999). The The aerosol deposition is among the most important aspects main goal for these experiments is usually to shed some light of studies on aerosol motion in the human airways as can be on the airflow dynamics inside the human lungs and also the seen in Fig. 5. The application of chemicals with unique factors influencing particle deposition inside them. The properties under illumination is helpful in the estimation of setup for an in vitro analysis is shown in Fig. 4. deposition efficiencies. In an experimental study by Lennon et al. (1998), regional deposition efficiencies for fluorescent Generating Casts particles with diameters of 0.3 µ m and 0.7 µ m were estimated Solid casts to represent the human airways are applied in by measuring the fluorescent intensity using a fluorescence experiments. Their dimensions are usually human-specific spectrophotometer. Overall deposition efficiency was or following popular simplified human airway geometries. estimated by comparing the number of particles injected to Solid tubular models are usually constructed to represent the the ones which exited the cast at the outlet. Aerosol deposition airway bronchus from materials such as acrylic plastic in a study by Cheng et al. (1999) was investigated using (Perspex), silicone rubber, milled steel blocks, premade Y- different sizes of polystyrene latex fluorescence particles. connectors, bored aluminum, or plumbing fixtures (Chang Fluorescent content and consequently the deposition fractions and El Masry, 1982; Lennon et al., 1998). Even though the were estimated using a fluorescence spectrometer. An physical geometry is usually at a larger scale to ensure ease aerodynamic particle sizer which gives the particle size and of data collection, the Reynolds number should be kept the number concentration can be used to measure the total constant to ensure dynamic similarity, as expressed in the deposition (Häußermann et al., 2002). following equation (Chang and El Masry, 1982). 1180 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 Table 1. Literature review of past studies. Algorithm Maximum Experiment Reynolds Particle Particle Type Generations Healthy/ Inhalation/ Mesh (type of CFD (pressure- deposition or Status number density diameter Reference of flow covered Unhealthy Exhalation elements) code velocity fraction –3 simulation at the inlet (kg m ) (µM) coupling) (%) Transient G3–G6 Healthy Simulation Inhalation Rest 388 CFX4.3 SIMPLEC 500–5000 3–7 Zhang and – – Light activity 805 Kleinstreuer Moderate 1586 (2002) exercise Exhalation Rest 340 Light activity 696 Moderate 1418 exercise Transient G5–G8 COPD Simulation Inhalation Light activity 362.24 PISO 19 Chen et al. Steady 362.24 FLUENT 1000–2650 5 17 (2012) Transient G4–G13 Healthy Simulation Inhalation Light activity 319 Fully CAMEL Fourth order 998.2 10 20 Soni and Steady G4–G13 Healthy Simulation Inhalation Light activity 319 unstructured runge-kutta 15 Aliabadi (2013) Steady G3–G5 Healthy Simulation Inhalation N/A 500–2000 Unstructured CFX 4.2 500–2000 3, 5, 7 Comer et – – solver al. (2000) Steady G4–G15 Healthy Simulation Inhalation Rest 519 Hexahedral FLUENT SIMPLE 1000 1 0.6 Piglione et and 6.3 2 1.43 al. (2012) experiment 5 14 10 71 20 100 Moderate 1038 1 1.33 activity 2 1.83 5 17 10 83 20 100 Steady G0–G3 Healthy Simulation Inhalation N/A 400–1200 Unstructured FLUENT SIMPLE N/A N/A N/A Guha et al. G0–G4 triangular and (2016) G0–G5 tetrahedral elements Steady G0–G5 Healthy Simulation Inhalation N/A 400, 100 unstructured FLUENT SIMPLE N/A N/A N/A Guha and and 1600 tetrahedral Pradhan elements and (2017) 0-grids for the boundary layer G8–G14 Healthy Simulation Inhalation Light activity 169.4 Unstructured FLUENT SIMPLE N/A N/A N/A Sul et al. Symmetric and Moderate 296.2 meshes 14 (2014) (COPD) exhalation exercise Asymmetric Light activity 169.4 (COPD) Random Moderate 296.2 (COPD) exercise Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1181 Fig. 4. Schematics of a typical arrangement for an in vitro experiment set-up on airflow dynamics and particle motion inside the human airways. Fig. 5. VOSviewer map of the current state of research on aerosol motion and deposition in human airways. 1182 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 airways. Governing equations are also applied at the important FUNDAMENTALS OF NUMERICAL ANALYSIS sections of the control volume to represent the boundary Algebraic deposition models came before CFD methods, conditions in different sections (Schreck and Mockros, 1970; but their tendency to overestimate the effects of impaction on Chen, 2002). Some of the governing equations for fluid particle deposition led to the development and more extensive flow, particle motion, drag coefficient, the velocity at the inlet, adoption of the CFD method which has higher accuracies. Reynolds number, and deposition efficiencies are summarized Mathematical dispersion models of bolus dispersion have in Table 2. Fundamental steps for a CFD analysis are shown also been applied in the prediction of particle motion and in Fig. 6. deposition in the human airways. The mathematical models have a drawback in the sense that they cannot account for Mesh and Boundary Conditions information on particle trajectories and hence no available Mesh generation is the process of subdividing a control information on hotspots (Lambert et al., 2011). Only CFD volume into discrete geometric and topological grids. methods will be covered under numerical analysis due to its Meshes for the control volumes are usually generated using extensive adoption as compared to other numerical methods Gambit, ANSYS, or other mesh generating applications for airflow simulation. (Inthavong et al., 2010; Chen et al., 2011; Deng et al., 2018). The main advantage of numerical analysis of airflow and The shape of the elements is important and it can be particle motion inside the human airways is that it gives unstructured, hexahedral, tetrahedral, non-orthogonal blocks, researchers the ability to study airflow phenomenon and and triangular prism (Rahimi-Gorji et al., 2015; Chen et al., particle motion in the respiratory system in ways that are 2018a). In a study of particle deposition inside a real human experimentally impractical or incalculable. Additionally, it airway (G0–G2) by Rahimi-Gorji et al. (2015), the elements is an affordable and non-invasive method of gaining useful had unstructured tri/tetrahedral hybrid. In a study by Tena et information on flow inside the human airways for medical al. (2015), tetrahedral meshes were used for the lung model or research purposes (Walters and Luke, 2010). With CFD due to their flexibility and ability to cope with complex solid tools, it is possible to carry out investigations using healthy geometries. and diseased human lung geometries on the resistance of In an investigation comparing the effects of different airflow, distribution of mass flow rates, shear stress on the boundary conditions, Nazridoust and Asgharian (2008) found walls, complex secondary flow phenomena, and deposition out that the highest particle deposition was during unsteady patterns as well as efficiencies (Walters and Luke, 2010; flow boundary conditions imposed at the inlet of the control Huang and Zhang, 2011; Tian et al., 2017). volume. Initial boundary conditions for the inlet and outlet are usually specified and can be based on either pressure or velocity. The velocity distribution at the inlet of any section Geometrical Structures for the Human Airways Flow inside a confining geometry is strongly influenced of the human lungs can be uniform, symmetric parabolic, or by the geometric shape and as such creation of the human skewed parabolic, depending on the position of the control airway geometries is usually a vital step towards running a volume in the lungs (Yang et al., 2006). For simulations successful CFD simulation. A few software applications have concerning the upper section of the lungs, for instance, the been used before in the creation of human airway geometry, oral cavity or nasal air passages, a uniform velocity distribution for instance, solid works and AutoCAD (Sul et al., 2014; is sufficient (Moskal and Gradoń, 2002). In a study by Gemci –1 Chen et al., 2018a). Important parameters in the creation of a et al. (2008) a uniform velocity distribution of 2.896 m s physiologically accurate lung model include diameter, length, was imposed at the inlet. In a study of secondary flow branching angle, and radius of the curvature. Carina regions phenomena, a non-uniform velocity distribution was imposed are usually smoothened after lofting mother and daughter at the inlet of the control volume (Guha and Pradhan, 2017). branches using the fillet options available in Computer-aided A no-slip boundary condition is usually imposed on the design applications. Information on the generations which walls for airflow (Chen et al., 2012; Rahimi-Gorji et al., are most likely to be affected by obstructions is obtained 2015), while a “trap upon impact” boundary condition is from bronchoscopy studies which are useful in choosing the imposed for the aerosols due to the presence of mucus on the obstructed generations in the creation of the solid geometries airway walls (Ma and Lutchen, 2009; Chen et al., 2018a). It (Yanai et al., 1992). The extend of obstruction is chosen in is important to define a gauge static pressure at the outlet of a way that the volume reduction and airway surface reduction the control volume and it is 1 atm for some studies (Ma and are consistent with histological studies conducted on Lutchen, 2009; Guha and Pradhan, 2017; Chen et al., 2018a). obstructed airways for instance asthma and COPD (Zhang and Papadakis, 2010; Chen et al., 2012). Tools for 2D and Inhalation Curves 3D geometrical development are applied in the development Traditionally, the constant velocity at the inlets was of 2 dimensional and solid geometries which can either be assumed more so for in vitro experimental studies on airflow human-specific or from the generalized human lung airway in the human lungs (Yeh and Schum, 1980; Chang and El models discussed earlier. Masry, 1982). Later, real inhalation curves at the inlets were imposed in carrying out CFD analysis (Zhang et al., 2002; Chen et al., 2012; Mutuku and Chen, 2018). Due to the Governing Equations Several governing equations are applied for the numerical similarity between real inhalation curves and sinewave analysis of fluid flow and particle motion inside the human curves, some studies have used velocity distribution at the Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1183 Table 2. A summary of governing equations, boundary conditions, and dimensionless numbers Governing equations Airflow Particles’ motion Continuity equation Particles’ trajectories du Ui 0, 1, 2, 3   m   C u u u u  F fi   p p D p p fp x dt 8 Momentum equation Momentum equation   fP     fP    U   U U      U   U U      F             f i f j i f ij f i f j i f ij pf t x  x x t x  x x j i j j i j i = 1, 2, 3 and j = 1, 2, 3 i = 1, 2, 3 and j = 1, 2, 3 Particles’ drag coefficient  C    d 1 Re Re N 2 Reynolds number Reynolds number UD  v v D pp mean Re  Re  d N   Boundary conditions Velocity distribution at the inlet Deterministic-parabolic distribution of particles at the inlet G 2 Q 2  Cr in   r  V  G  21 2    D /4 G CR    Velocity at the walls   rr  22 ba V = 0 (No slip) n  Int 2C r  r   p  b a 2R    Dimensionless numbers Reynolds number UD mean Re  Deposition fraction The number of particles deposited on a section of the walls DF (%)100 The total number of particles entering that section of the wall 2. Numerical 4. Visualization analysis • Iterative methods • Continuity equation • CFD Solvers e.g., • Contour • Discretization • Momentum equation SIMPLE, SIMPLEC, • Vectors of the • Energy equations PISO geometry – • Line plots • Newton's second law of • DPM - dispersed finite element • Animation motion phase method (F EM) • Deposition 1. Setting up of • Boundary patterns governing conditions 3. Solutions to the air • Hostspots equations flow and aerosol motion problems Fig. 6. Fundamental steps for developing a CFD and DPM solution for airflow and aerosol deposition in healthy and obstructed human airways. 1184 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 inlet in the form of a sinewave (Chen et al., 2018a). Typical work by Tian et al. (2017), the importance of particle size real inhalation curves for rest, light activity, and moderate distribution in estimating the dosimetry was established. exercise, and their equivalent sinewave curves are shown in Findings showed that deposition equations and particle size Fig. 7. Usually, a spirometry test is used to acquire the distribution were vital in the estimation of dosimetry for velocity variation at the inlet with time as in the case of a exposure risk assessment caused by nanoparticles. study by Tena et al. (2015). Many studies tend to differentiate In the CFD simulation for the deposition of PM in 2.5 the breathing statuses based on the level of the human healthy and asthmatic human airways, the typical particle activity and consequently the air mass flow rates during size distribution for PM in Taiwan was used (Chen et al., 2.5 inhalation. Common breathing statuses include rest condition 2018a; Mutuku et al., 2020). Ideally, the distribution of (sedentary), light activity, and moderate exercise. During particles at the inlet should be random, hence several studies normal breathing, the length and diameter variations in the have applied a random distribution of aerosols at the inlet large airways of the lungs are moderate and the rates of (Zhang and Kleinstreuer, 2001; Russo et al., 2008; Chen et variation are insignificant compared to the axial airflow al., 2012; Chen et al., 2018b). A uniform particle distribution velocity, therefore the effect of wall movement is assumed at the inlet has also been applied for investigations on to be very minimal (Hughes et al., 1972). Sometimes, volume aerosol deposition (Russo et al., 2008; Zierenberg et al., flow rates are defined for localized investigations on the 2013; Kolanjiyil and Kleinstreuer, 2017). In a numerical human airways, for instance, the case of Gemci et al. (2008) analysis by Zhang and Kleinstreuer (2001), the effects of –3 where 28.3 L min was applied. random-parabolic, random uniform, random-random, and deterministic-parabolic particle distributions at the inlet on Particle Size Distribution for Aerosols the deposition patterns in a triple bifurcation were investigated. It is not enough to estimate particle dosage using particle The findings proved that a satisfactory representation of the number, mass, and surface area available for deposition deposition patterns was achievable for both aerosol medicine only. In a combined experimental and numerical analysis and particulate matters if a parabolic-deterministic particle Fig. 7. Typical real inhalation curves and corresponding sinewaves for (a) rest, (b) light activity, and (c) moderate exercise. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1185 distribution was used at the inlet instead of a realistic random shear stress, and stagnation and recirculation zones, on the distribution. Consequently, some studies have used parabolic- walls (Zhang et al., 2002; Kleinstreuer and Zhang, 2010; deterministic particle distribution at the inlet to analyze the Chen et al., 2012). deposition efficiencies in a human lung bifurcation (Comer et al., 2000; Chen et al., 2018a). Airflow Velocity Distribution and Pressure Drop Airflow fields are important in determining the deposition patterns of particulate matter in the human airways. Airflow Solvers Numerical calculations for the Navier-stokes equations velocity distribution also affects pressure distribution since governing laminar airflow and newton’s second law of the two are closely linked (Luo et al., 2007; Kang et al., motion governing particle motion in the lungs can be solved 2011). The airflow distribution is an important indicator of using CFD solvers and discrete phase models provided by sections of the lungs suffering from inadequate ventilation applications such as ANSYS-fluent among others (Kelecy, as a result of lung obstructive diseases. The velocity 2008). Simulation of two-phase flow in the human airways distribution inside the control volume is responsible for the usually applies one-way coupling, whereby the gas phase formation of stagnation and recirculation zones as well as jet affects the solid phase only but there is no feedback from the flow phenomena in constrictions (Chen et al., 2012; Chen et dispersed phase (Comer et al., 2000; Luo et al., 2007; al., 2018a). Flow phenomena resulting from velocity Rahimi-Gorji et al., 2015). The discrete phase model of CFD distributions greatly affect particle deposition patterns in is activated by defining several parameters of the dispersed both health and deformed human airways. phase such as position in the control volume, velocity, In a study by Gemci et al. (2008), a static pressure drop diameter, temperature, mass flow, and time of injection of 50 Pa was associated with a volumetric flow rate of –3 (Rahimi-Gorji et al., 2015). The trajectory calculations use 28.3 L min between G0 and G17. The further pressure the initial location and parameters for the calculations. drop across the entire Weibel’s geometry was determined to Several CFD codes have been used in the simulation of be 60 Pa (Pedley, 1977). This had been previously investigated fluid flow including CFX, Camel, FLUENT, and TASC flow through an experiment by Hyatt and Wilcon (1963), whereby 3D. CFX, which is a finite volume code and user-enhanced the pressure drop in the entire human lung was found to be FORTRAN programs were applied at the beginning of this 75 Pa. In a study by Qi et al. (2014), patients suffering from millennium to investigate airflow and aerosol motion in left pulmonary artery sling (LPAS) were found to have a idealized lung models (Zhang and Kleinstreuer, 2002; pressure drop in the two bronchi ranging between 78.9– Zhang et al., 2002). TASCflow, which is also a commercial 914.5 Pa, as compared to a usual pressure drop of 0.7 Pa in CFD code with a standard k-ε turbulence model was applied a healthy individual. in a study by Stapleton et al. (2000) to compare the results of numerical analysis to that of in vivo experiment on aerosol Jets, Recirculation Zones and Secondary Flow Phenomena deposition in the mouth and throat. Fluent CFD solver has Jets tend to form in constriction according to the results two basic solver algorithms: density-based coupled solver from most CFD simulations and comparisons of healthy and (DBCS) and pressure-based coupled solver (PBCS). The obstructed airways. Boundary layer separation in the regions former solves the equations of conservation of continuity, near the constrictions led to the recirculating phenomenon momentum, and energy in a coupled manner, while the latter (Sul et al., 2014). Vorticity in obstructed airways can be solves the same equations in an uncoupled manner. Even obtained by the curl of the velocity field. In obstructed though pressure-based algorithms are vigorous and resourceful, airways, the vorticity is usually on the ranges of 100-fold their applications in complex geometries are not applicable higher as compared to a normal airway. since their convergence rates are not satisfactory. The efficiency of the DBCS comes at a cost as it requires double Shear Stresses the memory per element compared to PBCS since memory Amplified wall shear stresses in human airways are is needed for the coupled matrix equations (Kelecy, 2008). responsible for inducing the disease defense mechanism by Other important parameters in the selection of a solver activating the release of Adenosine triphosphate (ATP) as besides the memory requirements include time per iteration, well as the release of intracellular calcium (Fisher et al., iterations to converge, and time to convergence. DBCS is 2001; Garcia et al., 2006; Sidhaye et al., 2008). It has also associated with a high computational cost. Consequently, been previously proven that low-level shear stress bears an PBCS is more popular for investigations of fluid flow inside inverse relationship with the permeability of epithelial cells the human airways (Kelecy, 2008). Some of the most common (Sidhaye et al., 2008), while excessive wall shear stress can algorithms for airflow investigations in the human lungs are cause epithelial damage, especially in the re-opening a shown in Fig. 8(a), including the semi-implicit method for previously collapsed airway (Bilek et al., 2003). Therefore, pressure-linked equations (SIMPLE), the semi-implicit method understanding shear stress distribution can explain the for pressure-linked equations-consistent (SIMPLEC), pressure- healing process of wounds in the epithelial lining of the implicit with splitting of operators (PISO), and coupled. lungs (Suh and Park, 2018). Models for Turbulent Flows Airflow Dynamics in Human Airways Many parameters can be used to explain fluid flow in the Flow in the human lungs is largely laminar and as such human airways for instance; velocity distribution, jet flows, most of the CFD investigations in which are based on central 1186 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 (a) (b) Fig. 8. (a) Common algorithms applied in CFD models to evaluate particle motion and airflow dynamics (b) Models for turbulent flows in the human airways. and lower regions do not require turbulent flow models LES is better at simulating turbulent flow as compared to a (Chalupa et al., 2004; Zhang and Papadakis, 2010; Chen et combination of RANS and EIMs. This is because RANS al., 2012). However, different models (turbulent) are applied turbulence models tend to average out the turbulent effects in investigations of airflow in the upper region and part of and hence may not capture fully the resultants effects of the central region of the human airway (Inthavong et al., recirculation on the particle motion (Lambert et al., 2011). 2010; Zhang and Kleinstreuer, 2011; Rahimi-Gorji et al., Fluent applies a finite volume approach in obtaining the 2015). The upper section of the human lung experiences numerical solutions to Navier-Stokes and continuity equations laminar-turbulent transitional regions as air progresses from in a control volume with the appropriate geometry and the oral cavity and larynx to the trachea and bronchioles boundary conditions. For the upper section of the respiratory (Kolanjiyil and Kleinstreuer, 2017). As such modeling airflow system that is the oro-nasal cavity, trachea, and bronchus, through these sections of the respiratory system requires airflow is usually turbulent. Therefore, turbulence models models that can handle the turbulent flow. Usually, Reynolds and LES are applied (Gemci et al., 2008). LES models can averaged Navier Stokes (RANS) are combined with Random- provide instantaneous velocity fluctuations and vortex walk eddy interaction models (EIMs) to effectively cover structures, whereas RANs cannot. However, LES models are the effect of turbulent flow on particle motion in the upper 100 times more costly in terms of computational time when sections of the human airways. Large-eddy simulation (LES) compared to RANS models. Direct numerical simulation can also effectively account for the effect of turbulent flow (DNS) is another alternative approach but it is limited to on the particle’s motion. Recent developments show that certain types of control volumes. It also requires excessively Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1187 huge computational resources (Zhang and Kleinstreuer, mean velocity gradients and the generation of ω respectively. 2011). Turbulent models are useful for modeling airflow in Γ and Γ are the effective diffusivities of k and ω, k ω the upper sections of the respiratory system. This is because, respectively while Y and Y are the dissipation of k and ω, k ω in these regions, for instance, the throat, airflow experiences respectively, due to turbulence. Lastly, D is the cross-diffusion local area reductions, which increases the Reynolds number term, and S and S are user-defined source terms. A summary k ω and causes turbulent flow. Thereafter, due to the subsequent of the available turbulent models is shown in Fig. 8(b). increase in effective cross-sectional area, the Reynolds number decreases and the flow gets re-laminarized. Due to MECHANISMS FOR AEROSOL DEPOSITION AND the above-mentioned limitations of both LES and DNS, CLEARANCE RANS finds preference amongst researchers in predicting airflow behavior. The human airways are usually lined with mucus, which In an investigation by Zhang and Kleinstreuer (2011), helps to trap suspended particulate matters in the inhaled air insignificant differences of less than 0.5% in DEs were mass. It thus follows that a trap upon impact is usually the found in the performance of LES, LRN k-ω and SST boundary condition imposed on the walls of the airways transition. This finding was made while doing a numerical during the numerical analysis of two-phase flow inside the analysis of the deposition efficiencies for nanoparticles human airways. From the analysis of particle deposition, (1 nm–50 nm) during the transition to turbulent flow in the important information includes deposition patterns, hot oral airways model. The SST transition model proved better spots, and deposition efficiencies. results in the prediction of kinetic energy profiles while the LES model could provide information on instantaneous Mechanisms of Deposition velocity fluctuations. There are several mechanisms through which inhaled The standard k-ω turbulence model is more effective for aerosols deposit on the airways’ walls. The dominance of predictions near the wall region as compared to the k-ω, but these mechanisms of deposition varies as the particles advance it performs poorly in the far-field. On the other hand, the from the oral cavity, through the upper, central, lower section SST k-ω combines a bit of both and is, therefore, more of the human lungs, and later into the alveolar region. There favorable for turbulent and transitional flows (Tena et al., are 5 main deposition mechanisms, including turbulent mixing, 2015). The SST k-ω equations are expressed as inertial impaction, gravitational sedimentation, Brownian motion, and electrostatic precipitation (Finlay and Martin, 2008; Darquenne, 2012). A summary of the mechanisms of     k  k  ku  Γ  G  Y  S (4)  aerosol deposition and their respective regions of dominance i k k k k  t x x x i i j  is shown in Table 3. Turbulent Mixing        u  Γ  G  Y  D  S      Unlike the central and lower sections of the human airways i       t x x x i j j  where the flow is usually laminar, the upper section of the (5) lungs is associated with turbulent airflow. Rapid changes in both the magnitude and direction of flow by the air-aerosol where ρ is the air density, k represents the turbulence kinetic mixture lead to an eventual impaction on the airway walls (Darquenne, 2012). Furthermore, branch curvature as air energy, ω represents the specific dissipation rate. G and G k ω refer to the generation of turbulence kinetic energy because of progresses from the parent bifurcation to the child branch Table 3. Dominant aerosol deposition mechanisms and lung clearance mechanisms at different sections of the human airway. Dominant aerosol deposition Region Dominant aerosol clearance mechanism mechanism Extra-thoracic region Deposition by Mechanical clearance (sneezing, coughing or (Oro-nasal passages) 1. Turbulent flow swallowing of the inhaled aerosols) 2. Inertial impaction Tracheal-bronchial region Deposition by Mucociliary clearance (for insoluble particles in the TB (TB) 1. Turbulent mixing region. (24 hours) – operates like an escalator driving 2. Inertial impaction aerosol loaded mucus from the TB region to the larynx where mechanical clearance mechanism takes over. Bronchiolar region Deposition by Translocation 1. Inertial impaction 2. Diffusion Acinar region (G16-23) Deposition by Translocation 1. Diffusion Microphage arbitrated clearance where white blood cells 2.Gravitational sedimentation engulf and relocate the particles towards the bronchiolar 3.Electrostatic deposition region, circulatory system, or the lymphatic region. 1188 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 induces lateral convective motions (Hammersley and Olson, particles as a result of collisions with the gas molecules. It 1992). This convective motion initially purposed to ensure happens where the velocity of airflow is low, especially in the mixing of the inhaled gases also causes particle the alveoli region (Darquenne, 2012). Deposition by this deposition on the airway walls. Turbulent mixing has been mechanism occurs predominantly for particles with a diameter proven through experiments to affect the local deposition in of smaller than 0.5 µ m, and is usually proportional to the G3–G5 by influencing the initial velocity and particle Brownian diffusion coefficient which can be expressed as; motion (Longest and Vinchurkar, 2007). ckT D  (9) Inertial Impaction 3 d This mechanism greatly affects particles whose diameter exceeds 5 µm. This is because heavier particles are incapable where D , c, k, and T represent the deposition by Brownian of changing the direction of motion with a sudden change in motion, Cunningham’s correction factor, Boltzmann’s the direction of fluid flow. This results in a deviation from constant, and the absolute temperature, respectively. The the streamlines of flow and eventual impaction on the Cunningham’s correction factor accounts for the reduced air airways’ walls (Darquenne, 2012). The property of deviation resistance as a result of slippage when the particles’ diameters from the streamlines of flow for two-phase flow is best approach the mean free paths of the gas molecules. defined using the stokes number which is expressed by; After experimentation, Yeh and Schum (1980) reported that deposition by diffusion was different for both laminar  du pp flow and turbulent flow. For laminar flow, the probability of St  (6) 18 d deposition by diffusion can be expressed as;  7.315 x 44.63 x 114 x where St is the Stokes number, d and ρ are the diameter and p p P  1 0.819e  0.0976e  0.0325e density of the suspended particles, respectively, u and µ are the 79.31x 0.0509e (10) average velocity and dynamic viscosity of the gas phase, respectively, and d is the diameter of the airway. Overall, LD x deposition by inertial impaction increases with an increase 2 2R  in the Stokes number. In an experimental study by Yeh and Schum (1980), the formula for impaction deposition where P is the probability of deposition by diffusion, D is probability was summarized as follows; the diffusion coefficient of the particles, R is the radius of the airway’s bifurcation,  is the mean flow velocity, and  d pp  11 L is the length of the bifurcation. For turbulent flow, it can P  1 cos   St sin 2 cos   St V  g   Is   18 be expressed as; for θ × St < 1  P = 1 for θ × St > 1 (7) 22 Dt Dt 1 2 1 2 P  1  ...  2.828x 1 0.314x  ...     RR 9  where P is the impaction deposition probability and θ is the (11) angle of the bend in radians. where t represents the time needed for the flow to cover the Gravitational Sedimentation bifurcation’s length, i.e., L / This mechanism of deposition is absent in upper airways For a pause, but strongly present in central and lower airways. This is because a shorter distance exists between the particles and  the airway walls for central and lower airways (Darquenne, 5.784KTC p t P  1 exp  (12)  2012). This property is best represented by the terminal 2  6rR  settling velocity of the particles, which can be expressed as; where t is the length of time in the pause, K is the Boltzman  d pp constant, T is the temperature in K, C is the Cunningham slip Vg  (8) 18 correction factor, r is the particle’s radius, µ is the fluid’s viscosity, and the superscript p represents pause (Yeh and where g is the acceleration due to gravitational force. The Schum, 1980). most important factors during deposition by sedimentation are particle size and particle residence time in the airways Electrostatic Precipitation and alveoli. This mechanism of deposition mostly affects This process is only effective for particles in possession particles whose diameter ranges between 1 and 8 µ m. of charges. Charges are induced at the walls of the airways by charged particles in close proximity. As a result, the walls Brownian Diffusion attract electrically charged particles leading to deposition This mechanism results from the random motion of the (Darquenne, 2012). Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1189 Interception to increase by ×10 to ×100 (Longest et al., 2006). In a study The efficiency of this mechanism of deposition is dependent on the effect of COPD in human airways, Chen et al. (2012) on the shape and hygroscopicity of the particles, whereby found a hotspot for the deposition of 6 µ m particles at the elongated and hygroscopic particles deposit more easily. constriction (G6) in a geometry consisting of G5–G8. This mechanism involves a particle coming into contact with Furthermore, the deposition efficiencies of 0.075, 0.15, 0.3, the airway walls while still following a streamline of flow and 0.6 µ m particulate matters in an asthmatic human airway (Darquenne, 2012). Deposition by interception decreases were found to exceed that in a healthy human airway by significantly for particles with a spherical shape. 1.19%, 2.5%, 14.3%, and 25%, respectively, during a moderate exercise (Chen et al., 2018a). These elevated deposition Factors Influencing Particle Deposition efficiencies lead to aggravated risks due to inhalation of Particle deposition efficiencies and patterns are dependent toxic aerosols. on several factors including, the geometry of the Enhanced condensational growth (ECG) is a recently tracheobronchial model, inhalation status, and chemical and developed method of pharmaceutical aerosol delivery, physical properties of the particles. aimed at increasing their deposition efficiencies. Usually in Due to the bifurcating nature of the human airways, the this approach, a stream of medicinal nanoparticles is injected inhalation segment of the breathing cycle provides a larger at the mouth region followed by an air stream that is surface area for particle deposition through impaction as supersaturated (Kulmala et al., 2004; Phalen et al., 2010; compared to the exhalation phase. Consequently, the deposition Tian et al., 2011). This is done to minimize depositional losses efficiencies during the inhalation phase tend to exceed those at the extrathoracic region and encourage deeper penetration of the exhalation phase for the same local stokes number of the aerosols into the central and lower section of the (Zhang et al., 2002). Additionally, the curved walls of the human airways. It is believed that aerosols with a diameter bifurcating geometries provide surface areas for particle of 2–4 µ m have almost perfect retention inside the lungs. interception as the particles are driven onto the walls by the induced dean vortices (Chen et al., 2018a; Mutuku and Chen, Methods of Quantifying Deposition 2018). In the investigation of Guha and Pradhan (2017), new There are two ways of quantifying deposition. The use of secondary flow structures including dean vortices and anti- DEs is the most commonly used method of quantifying the dean vortices were found to develop in the curved human deposition of aerosols in the human airways either through airway generations. in vitro experiments or numerical analysis. Deposition The physical parameters of the particles also have an enhancement factors (DEFs) are used to quantify the impact on the deposition efficiencies. Specifically, the diameter deposition in a certain zone as compared to deposition in an of inhaled aerosols is directly linked to the Stokes number entire region of consideration. DEFs for microns exceed of the particles for a constant density. Previous research has those of nanoparticles. Specifically, in a study by Guzman shown that deposition efficiencies for aerosols increase with (2020), the DEFs for 40 µ m particles were found to exceed an increase in their Stokes numbers since impaction is the those of 40 nm particles. main deposition mechanism (Cheng et al., 1999; Zhang et Establishing the position of hotspots is an important al., 2002). Usually, in performing numerical analysis, the aspect of studies on particle deposition. Hotspots are localized particles are assumed to have a spherical shape and hence regions of high deposition. This is important because it makes their Stokes number tends to increase with an increase in their it possible to quantify particle dissolution, particle clearance, diameters. In a study by Cheng et al. (1999), the deposition and the uptake of the dissolved chemical compositions into efficiency was found to correlate to the stokes number of the the epithelial layer. Hotspots have been associated with lung particles by the following equation. cancer and tumors. A 30% contraction in the upper tracheobronchial airways due to asthma could increase the ɳ = 1 – exp(–αSt) (13) DEs by 10–100 times. Disproportionate amounts of aerosols are known to enter the left bronchi, despite a higher mass flow where ɳ is the deposition efficiency, St is the Stokes number, of air going into the right bronchi (Lambert et al., 2011). and α is the best fit parameter whereby it was 6.66 ± 0.418 (SEM) and r = 0.976 (Cheng et al., 1999). Lung Clearance Mechanisms The deposition of particles on the airway walls is greatly The lung structure is designed to allow the mixing of air influenced by their solubility in water. The particles which as it flows towards the alveolar region. However, this leads are soluble in water tend to deposit more easily. This is to the impaction of the suspended particles on the walls as because the moisture content inside the human lungs air progresses into the acinar region (Tsuda et al., 2008). The increases their weight and hence their chances of depositing lung has several clearance mechanisms that help to rid the through inertial impaction (Ferron et al., 1989). The shapes airway walls of deposited particles. The first clearance of the aerosols have been shown to affect their deposition mechanism is mechanical clearance and it takes the form of efficiencies in previous researches. For instance, elongated coughing, sneezing, or swallowing. This mechanism is aerosols can easily deposit through interception. dominant in the upper section of the airways, specifically the The presence of deformities in the lung’s geometry, for oro-nasal region (Hussain et al., 2011). Mucociliary clearance instance, the presence of tumors, asthma, and COPD, which is the second mechanism and involves the propulsion of usually result in obstructions, leads to local deposition rated aerosol filled mucus from the middle section of the human 1190 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 lungs towards the oro-nasal region so that they can be coughing can range between 0.6–125 µ m (Guzman, 2020). ejected out of the respiratory system through the mechanical In the investigation by Asadi et al. (2020), particles with a clearance. The third clearance mechanism involves the diameter of about 1 µ m were found to be released during macrophages which engulf the aerosols and get into the normal breathing and speech. This size was adequate to circulatory system or the lymphatic system. This clearance carry SARS-CoV-2 viruses which have a diameter of about mechanism is dominant in the alveolar region of the human 65–125 nm. The bioaerosols with a diameter of 1–5 µ m are airways (Hussain et al., 2011). A summary of the lung’s the most potent in terms of airborne transmission of diseases clearance mechanism and their respective region of (Wang and Du, 2020). In a study by Xu et al. (2020a), dominance is shown in Table 3. aerosols generated from infectious feces are also thought to drive the spreading of the virus. Altogether, the load pathogenic bioaerosol in the ambient air has been shown to COVID-19 AND THE RESPIRATORY SYSTEM retain its viability for up to 3 hours. This is suspected to play a major role in the transmission of this deadly virus. Effects of COVID-19 to Human Lungs Lungs are very delicate and, consequently, get easily Available knowledge and epidemiological studies indicate damaged. The latest global pandemic of COVID-19 is a that the recommended distance of 2 m to prevent the spread classic case of diseases which is easily transmitted through of COVID-19 might be inadequate or effective only if respiratory droplets and can be fatal or lead to permanent everyone wears a facemask (Setti et al., 2020). In the studies lung damage. In a recent study of Olds and Kabbani (2020), carried out in Wuhan and Nebraska university hospital, exposure to nicotine through smoking was shown to propel SARS-CoV-2 RNA was found in ambient air samples, individuals to higher risk from COVID-19 due to the impact proving that the virus stayed viable inside aerosol droplets. on the putative receptor for the virus (ACE2). Older people In a study by van Doremalen et al. (2020), the half-life of and those with weakened immunity systems were more SARS-CoV-2 RNA suspended in the ambient air was found to susceptible to the adverse effects caused by COVID-19. be 1 hour. According to literature, the aerosol size distribution COVID-19 patients suffered from a serious inflammation of and prevailing wind conditions can support the transportation the lungs after which the alveoli were filled with water, pus, of contaminated aerosols for up to 10 m. Contaminated and debris from epithelial cells destroyed by the immune aerosols merge with PM at high concentrations and stable 2.5 system in the process of fighting the infection (Yoon et al., atmospheric conditions further aiding in the transportation 2020). and deposition of the viruses in the deeper regions of the Results from CT scans showed that the lungs of patients human airways (Chen et al., 2017). Indoor environments had lesions whose density could be hardly depicted using with low temperature and low relative humidity can lead to conventional radiography. Consequently, a routine exercise rapid evaporation rates at the surface of aerosol droplets of obtaining high-resolution chest CT examination was seen forming smaller droplets that can stay airborne for longer. to be key in the diagnosis of the disease (Agostini et al., Bioaerosol motion in the transmission of the disease has 2020; Zhao et al., 2020). The abnormalities associated with been an important object in recent investigations. Therefore, COVID-19 according to a study by Yoon et al. (2020). In a future studies might apply 3D human airways models from study by Li et al. (2020a), pathological alterations caused by the COVID-19 survivors to determine the motion and DEs of disease include lung edema and acute lung injury (ALI) which aerosols such as suspended toxic particulates (PM and PM ) 10 2.5 eventually caused acute respiratory distress syndrome (ARDS). and pharmaceutical aerosols in the deformed airways. ALI came as a result of the activation of the epithelial and In the study of disease exposure through inhalation of endothelial cells and the consequent overproduction of pro- bioaerosols, it is important to establish the relationship inflammatory cytokines. As the severity of COVID-19 disease between viability if the virus, diameter of the bioaerosols, increases, patients have been shown to suffer multiple organ and distance travelled by the bioaerosol in the ambient air. failure and eventual death. For COVID-19, that is still in debate. But earlier studies have shown that it may vary depending on prevailing wind speed and presence of obstructions. Role of Bioaerosols in COVID-19 Transmission, Possible Control Strategies and Future Challenges Even though the novel SARS-CoV-2 virus has affected The most familiar mode of transmission of SARS-CoV-2 the world for more than 5 months now, several unknowns have virus involves a healthy individual coming into contact with inhibited the full assessment of the situation in the world surfaces that have been contaminated by infected aerosols. with regards to the spread of the virus. Amongst them is the As such, a 2 m distance has been recommended among minimum viral load required to cause an infection. There is persons as a form of social distancing to curb the spread of also a need to carry out investigations on the transport analysis the disease. This social distancing is aimed at preventing to verify if the spread of the virus is airborne. Demystifying bioaerosols with SARS-CoV-2 from reaching the respiratory the unknowns is vital for proper epidemic control strategies. system of healthy individuals (Guzman, 2020). Even though earlier investigations ruled out the airborne OUTLOOK transmission in the role of the spread of SARS-CoV-2, recent investigations showed that this might not be the case. Recent Advancements in the Field The diameter of bioaerosols released from individuals infected Although many numerical modeling methods have been with COVID-19 during breathing, talking, sneezing, and developed in the past, CFD has now become a categorically Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1191 influential and universal tool for many applications in the CONCLUSIONS st 21 century. This is mostly due to its ability to provide solutions representing a rich blend of numerical methods, In vitro experiments for estimating deposition of particles user interfaces, mathematical physics, and state-of-the-art in the human airway geometries uses constant velocity at the visualization systems (Xia and Sun, 2002). The recent adoption inlet of the geometry. Even though experiments have helped of CFD for applications in particulate matter and airflow in scientists develop empirical methods for evaluations of the airways has been driven by the high costs associated with respiratory system, it is the subsequent development of CFD experimentation and analytical modeling methods for solving that has proved to be an important tool for the evaluation of fluid flow and two-phase flow problems. The trend has been airflow and particulate matter deposition in the human fueled further by the recent development of numerical solutions airways when applied for localized deposition. Algebraic for Navier-stokes equations and advancement in computing deposition models came before CFD methods, but their technology, making it a viable option for application in tendency to overestimate the effects of impaction on particle industry and science (Norton and Sun, 2006). deposition led to the development and more extensive Understanding airflow and particle deposition in obstructed adoption of the CFD method which has higher accuracies. and healthy airways are complex processes. Developing Complex physical phenomena can be broken down in CFD relatively simple equations for predicting the two phenomena and derived from otherwise inaccessible regions for one would help in building up an improved understanding of the applying experiments. The two most popular human airway correlation between deposition in the respiratory tract and a models are Weibel and Horsfield. The former finds more wide range of health outcomes. applications due to its simplicity, accuracy as well as saving on A possible potential application of the findings from CFD computational costs. For diseased human airway geometries, simulations could be in the development of diagnostic asthmatic human airways are usually represented by techniques based on the images of airflow patterns in the uniformly distributed folds along the circumference of the lungs. Breakthroughs in the field of CFD involving human affected generation, while COPD geometries consist of an airway geometries could be applied to design more efficient axisymmetric constriction in one or more of the bifurcations delivery methods for inhaled pharmaceuticals and to also affected by the obstruction. In the discretization of the understand the adverse health effects induced by toxic air human airway geometry for numerical investigations, the pollutants for instance use of enhanced condensational following mesh types can apply, unstructured, hexahedral, growth and eventual deposition. tetrahedral, non-orthogonal blocks, and triangular prism. The tremendous improvements in CFD methods have placed Challenges them on the verge of fully replacing experimental studies. Although histological measurements are usually included Important factors in carrying out a numerical analysis for in the generation of geometries to represent obstructed instance; computational cost and time are mentioned as airways, accurate representations of the exact architectures some of the most important factors to consider in a associated with such obstructions are not easily achievable. numerical simulation. Unlike the central and lower sections This is because smooth surfaces are usually applied to connect of the human airways where the flow is usually laminar, the healthy portions of the airway to the ones affected by upper section of the lungs is associated with turbulent obstructions and hence affecting the validity of the results. airflow. Density-based coupled solvers (DBCS) have high During the simulations, a 100% trapping efficiency is computational costs. Consequently, pressure-based coupled assumed, but this might not be the case in real life. The real solvers (PBCS) are more popular for investigations of fluid value would be established best using empirical approaches. flow inside the human airways. Due to the similarity CT scanned images of the human lung are clear enough for between real inhalation curves and sinewave curves, some application in 3D modeling only up to G7, this leaves the rest studies have used velocity distribution at the inlet in the form of the generation depending on idealized human geometries for of a sinewave. A uniform velocity distribution is usually studies. Despite some previous attempts towards studying imposed at the inlet for simulations in the upper section of the the entire human lung, so far, none of the studies has been human airways. However, for the central and lower branches, a successful. The complexity of human airways and breathing parabolic velocity distribution is more suitable. The dominance processes limit the application of in vitro measurements to of the 5 main deposition mechanisms, including turbulent only two consecutive branches of the human airway. In most mixing, inertial impaction, gravitational sedimentation, numerical simulations, the walls of the human airway are Brownian motion, and electrostatic precipitation, vary as the assumed to be stationary. However, this is not the case in particles advance from the oral cavity, through the upper, reality as the airway walls move in and out during inhalation central, and lower sections of the human lungs, and later into and exhalation. So far, numerical analysis of two-phase flow the alveolar region. Due to the bifurcating nature of human inside the human airways uses one-way coupling whereby airways, the inhalation segment of the breathing cycle the gas phase affects the solid or liquid phase but there is no provides a larger surface area for particle deposition through feedback. However, in reality, there should be a reaction impaction as compared to the exhalation phase. the diameter force for every action force and this would impact the of inhaled aerosols is directly linked to the Stokes number aerosols’ trajectories and eventually change the deposition of the particles for a constant density. The recent development fractions of particles under investigation. of pneumonia caused by SARS-CoV-2 virus affects patients whereby, they suffer from a serious inflammation of the 1192 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 lungs after which the alveoli are filled with water, pus, and velocity profiles. Resp. Physiol. 49: 75–95. https://doi.org/ debris from epithelial cells destroyed by the immune system 10.1007/s11547-020-01179-x in the process of fighting the infection. Therefore, future studies Chen, G., Zhang, W., Li, S., Williams, G., Liu, C., Morgan, might apply 3D human airways models from COVID-19 G.G., Jaakkola, J.J. and Guo, Y. (2017). Is short-term survivors to determine the motion and DEs of aerosols such exposure to ambient fine particles associated with measles as suspended toxic particulates (PM and PM ) and incidence in China? A multi-city study. Environ. Res. 156: 10 2.5 pharmaceutical aerosols in the deformed airways. 306–311. https://doi.org/10.1016/j.envres.2017.03.046 Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y. and Wei, Y. (2020). ACKNOWLEDGMENTS Epidemiological and clinical characteristics of 99 cases of The authors acknowledge financial support from the 2019 novel coronavirus pneumonia in Wuhan, China: A Ministry of Science and Technology Taiwan, ROC, under descriptive study. Lancet 395: 507–513. https://doi.org/1 the grant numbers MOST 108-2221-E-006-127-MY3, 108- 0.1016/S0140-6736(20)30211-7 2622-E-006-017-CC1 and 109-3116-F-006-016-CC1 for Chen, S., Cui, K., Yu, T.Y., Chao, H.R., Hsu, Y.C., Lu, I.C., this research. Arcega, R.D., Tsai, M.H., Lin, S.L. and Chao, W.C. (2019). A big data analysis of PM and PM from low 2.5 10 cost air quality sensors near traffic areas. Aerosol Air REFERENCES Qual. Res. 19: 1721–1733. https://doi.org/10.4209/aaqr.2 Adeloye, D., Chua, S., Lee, C., Basquill, C., Papana, A., 019.06.0328 Theodoratou, E., Nair, H., Gasevic, D., Sridhar, D. and Chen, W.H. (2001a). Dynamics of sulfur dioxide absorption Campbell, H. (2015). Global and regional estimates of in a raindrop falling at terminal velocity. Atmos. Environ. COPD prevalence: Systematic review and meta–analysis. 35: 4777–4790. https://doi.org/10.1016/S1352-2310(01) J. Glob. Health5: 020415. https://doi.org/10.7189/jogh.0 00274-6 5.020415 Chen, W.H. (2001b). Unsteady absorption of sulfur dioxide Agostini, A., Floridi, C., Borgheresi, A., Badaloni, M., by an atmospheric water droplet with internal circulation. Pirani, P.E., Terilli, F., Ottaviani, L. and Giovagnoni, A. Atmos. Environ. 35: 2375–2393. https://doi.org/10.1016/ (2020). Proposal of a low-dose, long-pitch, dual-source S1352-2310(00)00536-7 chest CT protocol on third-generation dual-source CT Chen, W.H. (2002). An analysis of gas absorption by a using a tin filter for spectral shaping at 100 kVp for liquid aerosol in a stationary environment. Atmos. CoronaVirus Disease 2019 (COVID-19) patients: A Environ. 36: 3671–3683. https://doi.org/10.1016/S1352- feasibility study. Radiol. Med. 125: 365–373. 2310(02)00244-3 https://doi.org/10.1007/s11547-020-01179-x Chen, W.H., Chen, Y.Y. and Hung, CI. (2011). A Simplified Asadi, S., Bouvier, N., Wexler, A.S. and Ristenpart, W.D. model of predicting SO absorption by single atmospheric (2020). The coronavirus pandemic and aerosols: Does raindrops with chemical dissociation and internal circulation. COVID-19 transmit via expiratory particles? Aerosol Sci. Aerosol Air Qual. Res. 11: 860–872. https://doi.org/10.42 Technol. 54: 635-638. https://doi.org/10.1080/02786826. 09/aaqr.2011.08.0130 2020.1749229 Chen, W.H., Lee, K.H., Mutuku, J.K. and Hwang, C.J. Asgharian, B., Hofmann, W. and Bergmann, R. (2001). (2018a). Flow dynamics and PM deposition in healthy 2.5 Particle deposition in a multiple-path model of the human and asthmatic airways at different inhalation statuses. lung. Aerosol Sci. Technol. 34: 332–339. https://doi.org/ Aerosol Air Qual. Res. 18: 866–883. https://doi.org/10.4 10.1080/02786820119122 209/aaqr.2018.02.0058 Bilek, A.M., Dee, K.C. and Gaver III, D.P. (2003). Chen, X., Zhong, W., Sun, B., Jin, B. and Zhou, X. (2012). Mechanisms of surface-tension-induced epithelial cell Study on gas/solid flow in an obstructed pulmonary damage in a model of pulmonary airway reopening. J. airway with transient flow based on CFD–DPM approach. Appl. Physiol. 94: 770–783. https://doi.org/10.1152/jappl Powder Technol. 217: 252–260. https://doi.org/10.1016/ physiol.00764.2002 j.powtec.2011.10.034 Brook, R.D., Rajagopalan, S., Pope, C.A., Brook, J.R., Chen, X., Feng, Y., Zhong, W., Sun, B. and Tao, F. (2018b). Bhatnagar, A., Diez-Roux, A.V., Holguin, F., Hong, Y., Numerical investigation of particle deposition in a triple Luepker, R.V. and Mittleman, M.A. (2010). Particulate bifurcation airway due to gravitational sedimentation and matter air pollution and cardiovascular disease: An update inertial impaction. Powder Technol. 323: 284–293. to the scientific statement from the American Heart https://doi.org/10.1016/j.powtec.2017.09.050 Association. Circulation 121: 2331–2378. https://doi.org/ Chen, Z., Jena, S.K., Giridharan, G.A., Koenig, S.C., 10.1161/cir.0b013e3181dbece1 Slaughter, M.S., Griffith, B.P. and Wu, Z.J. (2018c). Flow Chalupa, D.C., Morrow, P.E., Oberdörster, G., Utell, M.J. features and device‐induced blood trauma in CF-VADs and Frampton, M.W. (2004). Ultrafine particle deposition under a pulsatile blood flow condition: A CFD in subjects with asthma. Environ. Health Perspect. 112: comparative study. Int. J. Numer. Methods Biomed. Eng. 879. https://doi.org/10.1289/ehp.6851 34: e2924. https://dx.doi.org/10.1002%2Fcnm.2924 Chang, H. and El Masry, O.A. (1982). A model study of Cheng, Y.S., Zhou, Y. and Chen, B.T. (1999). Particle flow dynamics in human central airways. Part I: Axial deposition in a cast of human oral airways. Aerosol Sci. Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1193 Technol. 31: 286–300. https://doi.org/10.1080/02786829 123602. https://doi.org/10.1063/1.4971315 9304165 Guha, A. and Pradhan, K. (2017). Secondary motion in Chowdhury, P.H., Honda, A., Ito, S., Okano, H., Onishi, T., three-dimensional branching networks. Phys. Fluids 29: Higashihara, M., Okuda, T., Tanaka, T., Hirai, S. and 063602. https://doi.org/10.1063/1.4984919 Takano, H. (2019). Effects of ambient PM collected Guzman, M. (2020). Bioaerosol size effect in COVID-19 2.5 using cyclonic separator from Asian cities on human transmission. Preprints 2020: 2020040093. https://doi.org/ airway epithelial cells. Aerosol Air Qual. Res. 19: 1808– 10.20944/preprints202004.0093.v1 1819. https://doi.org/10.4209/aaqr.2019.01.0016 Hammersley, J.R. and Olson, D. (1992). Physical models of Çinkooğlu, A., Bayraktaroğlu, S. and Savaş, R. (2020). the smaller pulmonary airways. J. App. Physiol. 72: Lung changes on chest CT during 2019 novel coronavirus 2402–2414. https://doi.org/10.1152/jappl.1992.72.6.2402 (COVID-19) Pneumonia. Eur. J. Breast Health 16: 89. Han, Z., Weng, W. and Huang, Q. (2013). Characterizations https://dx.doi.org/10.5152%2Fejbh.2020.010420 of particle size distribution of the droplets exhaled by Cohen, A.J., Ross Anderson, H., Ostro, B., Pandey, K.D., sneeze. J. R. Soc. Interface 10: 20130560. https://doi.org/ Krzyzanowski, M., Künzli, N., Gutschmidt, K., Pope, A., 10.1098/rsif.2013.0560 Romieu, I. and Samet, J.M. (2005). The global burden of Häußermann, S., Bailey, A., Bailey, M., Etherington, G. and disease due to outdoor air pollution. J. Toxicol. Environ. Youngman, M. (2002). The influence of breathing Health Part A 68: 1301–1307. https://doi.org/10.1080/15 patterns on particle deposition in a nasal replicate cast. J. 287390590936166 Aerosol Sci. 33: 923–933. https://doi.org/10.1016/S0021- Comer, J., Kleinstreuer, C., Hyun, S. and Kim, C. (2000). 8502(02)00044-7 Aerosol transport and deposition in sequentially bifurcating Hofmann, W., Balásházy, I. and Koblinger, L. (1995). The airways. J. Biomech. Eng. 122: 152–158. https://doi.org/ effect of gravity on particle deposition patterns in 10.1115/1.429636 bronchial airway bifurcations. J. Aerosol Sci. 26: 1161– Darquenne, C. (2012). Aerosol deposition in health and 1168. https://doi.org/10.1016/0021-8502(95)00044-D disease. J. Aerosol Med. Pulm. Drug Del. 25: 140–147. Horsfield, K. and Cumming, G. (1967). Angles of branching https://dx.doi.org/10.1089%2Fjamp.2011.0916 and diameters of branches in the human bronchial tree. Delvadia, R.R., Longest, P.W. and Byron, P.R. (2012). In Bull. Math. Biol. 29: 245–259. https://doi.org/10.1007/BF vitro tests for aerosol deposition. I: Scaling a physical 02476898 model of the upper airways to predict drug deposition Horsfield, K. and Cumming, G. (1968). Morphology of the variation in normal humans. J. Aerosol Med. Pulm. Drug bronchial tree in Man. J. Appl. Physiol. 24: 373–383. Del. 25: 32–40. https://doi.org/10.1089/jamp.2011.0905 https://doi.org/10.1152/jappl.1968.24.3.373 Deng, Q., Ou, C., Chen, J. and Xiang, Y. (2018). Particle Horsfield, K., Dart, G., Olson, D.E., Filley, G.F. and deposition in tracheobronchial airways of an infant, child Cumming, G. (1971). Models of the human bronchial and adult. Sci. Total Environ. 612: 339–346. tree. J. Appl. Physiol. 31: 207–217. https://doi.org/10.115 https://doi.org/10.1016/j.scitotenv.2017.08.240 2/jappl.1971.31.2.207 Ferron, G., Oberdörster, G. and Henneberg, R. (1989). Hosseiny, M., Kooraki, S., Gholamrezanezhad, A., Reddy, Estimation of the deposition of aerosolized drugs in the S. and Myers, L. (2020). Radiology perspective of human respiratory tract due to hygroscopic growth. J. coronavirus disease 2019 (COVID-19): Lessons from Aerosol Med. 2: 271–284. https://doi.org/10.1089/jam.19 severe acute respiratory syndrome and Middle East 89.2.271 respiratory syndrome. Am. J. Roentgenol. 214: 1078– Finlay, W.H. and Martin, A.R. (2008). Recent advances in 1082. https://doi.org/10.2214/AJR.20.22969 predictive understanding of respiratory tract deposition. J. Huang, J. and Zhang, L. (2011). Numerical simulation of Aerosol Med. Pulm. Drug Del. 21: 189–206. micro-particle deposition in a realistic human upper https://doi.org/10.1089/jamp.2007.0645 respiratory tract model during transient breathing cycle. Fisher, A.B., Chien, S., Barakat, A.I. and Nerem, R.M. Particuology 9: 424–431. https://doi.org/10.1016/j.partic. (2001). Endothelial cellular response to altered shear 2011.02.004 stress. Am. J. Physiol. Lung Cell. Mol. Physiol. 281: L529– Hughes, J., Hoppin Jr, F. and Mead, J. (1972). Effect of lung L533. https://doi.org/10.1152/ajplung.2001.281.3.l529 inflation on bronchial length and diameter in excised Garcia, C., Prota, L., Morales, M., Romero, P., Zin, W. and lungs. J. Appl. Physiol. 32: 25–35. https://doi.org/10.1152 Rocco, P. (2006). Understanding the mechanisms of lung /jappl.1972.32.1.25 mechanical stress. Braz. J. Med. Biol. Res. 39: 697–706. Hussain, M., Madl, P. and Khan, A. (2011). Lung deposition https://doi.org/10.1590/S0100-879X2006000600001 predictions of airborne particles and the emergence of Gemci, T., Ponyavin, V., Chen, Y., Chen, H. and Collins, R. contemporary diseases. Part-I. theHealth 2: 51–59. (2008). Computational model of airflow in upper 17 Hwang, S.H. and Park, D.U. (2019). Ambient endotoxin and generations of human respiratory tract. J. Biomech. 41: chemical pollutant (PM , PM , and O ) levels in south 10 2.5 3 2047–2054. https://doi.org/10.1016/j.jbiomech.2007.12. Korea. Aerosol Air Qual. Res. 19: 786–793. 019 https://doi.org/10.4209/aaqr.2018.06.0235 Guha, A., Pradhan, K. and Halder, P.K. (2016). Finding Hyatt, R.E. and Wilcon, R.E. (1963). The pressure-flow order in complexity: A study of the fluid dynamics in a relationships of the intrathoracic airway in man. J. Clin. three-dimensional branching network. Phys. Fluids 28: Invest. 42: 29–39. https://doi.org/10.1172/jci104693 1194 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 Inthavong, K., Choi, L.T., Tu, J., Ding, S. and Thien, F. Longest, P.W., Vinchurkar, S. and Martonen, T. (2006). (2010). Micron particle deposition in a tracheobronchial Transport and deposition of respiratory aerosols in airway model under different breathing conditions. Med. models of childhood asthma. J. Aerosol Sci. 37: 1234– Eng. Phys. 32: 1198–1212. https://doi.org/10.1016/j.med 1257. https://doi.org/10.1016/j.jaerosci.2006.01.011 engphy.2010.08.012 Longest, P.W. and Vinchurkar, S. (2007). Validating CFD Isabey, D. and Chang, H. (1982). A model study of flow predictions of respiratory aerosol deposition: Effects of dynamics in human central airways. Part II: Secondary upstream transition and turbulence. J. Biomech. 40: 305– flow velocities. Resp. Physiol. 49: 97–113. https://doi.org/ 316. https://doi.org/10.1016/j.jbiomech.2006.01.006 10.1016/0034-5687(82)90105-0 Longest, P.W., Bass, K., Dutta, R., Rani, V., Thomas, M.L., Islam, M.S., Paul, G., Ong, H.X., Young, P.M., Gu, Y. and El-Achwah, A. and Hindle, M. (2019). Use of Saha, S.C. (2020). A review of respiratory anatomical computational fluid dynamics deposition modeling in development, air flow characterization and particle respiratory drug delivery. Expert Opin. Drug Del. 16: 7–26. deposition. Int Environ Res. Public Health 17: 380. https://dx.doi.org/10.1080%2F17425247.2019.1551875 https://doi.org/10.3390/ijerph17020380 Luo, H., Liu, Y. and Yang, X. (2007). Particle Deposition in Kang, M.Y., Hwang, J. and Lee, J.W. (2011). Effect of Obstructed Airways. J. Biomech. 40: 3096–3104. geometric variations on pressure loss for a model bifurcation https://doi.org/10.1016/j.jbiomech.2007.03.027 of the human lung airway. J. Biomech. 44: 1196–1199. Ma, B. and Lutchen, K.R. (2009). CFD simulation of aerosol https://doi.org/10.1016/j.jbiomech.2011.02.011 deposition in an anatomically based human large-medium Kelecy, F.J. (2008). Coupling momentum and continuity airway model. Ann. Biomed. Eng. 37: 271. https://doi.org/ increases CFD robustness. Ansys Advantage 2: 49–51. 10.1007/s10439-008-9620-y Kleinstreuer, C. and Zhang, Z. (2010). Airflow and particle Mannino, D.M. and Buist, A.S. (2007). Global burden of transport in the human respiratory system. Annu. Rev. COPD: Risk factors, prevalence, and future trends. Lancet Fluid Mech. 42: 301–334. https://doi.org/10.1146/annurev- 370: 765–773. https://doi.org/10.1016/S0140-6736(07)61 fluid-121108-145453 380-4 Kolanjiyil, A.V. and Kleinstreuer, C. (2017). Computational Mathers, C.D. and Loncar, D. (2006). Projections of global analysis of aerosol-dynamics in a human whole-lung mortality and burden of disease from 2002 to 2030. PLoS airway model. J. Aerosol Sci. 114: 301–316. https://doi.org/ Med. 3: e442. https://doi.org/10.1371/journal.pmed.0030 10.1016/j.jaerosci.2017.10.001 442 Kulmala, M., Laakso, L., Lehtinen, K.E.J., Riipinen, I., Dal McCreanor, J., Cullinan, P., Nieuwenhuijsen, M.J., Stewart- Maso, M., Anttila, T., Kerminen, V.M., Hõrrak, U., Vana, Evans, J., Malliarou, E., Jarup, L., Harrington, R., M. and Tammet, H. (2004). Initial steps of aerosol growth. Svartengren, M., Han, I.K. and Ohman-Strickland, P. Atmos. Chem. Phys. 4: 2553–2560. https://doi.org/10.519 (2007). Respiratory Effects of Exposure to Diesel Traffic 4/acp-4-2553-2004 in Persons with Asthma. N. Engl. J. Med. 357: 2348– Lambert, A.R., O'shaughnessy, P.T., Tawhai, M.H., 2358. https://doi.org/10.1056/NEJMoa071535 Hoffman, E.A. and Lin, C.L. (2011). Regional deposition Moskal, A. and Gradoń, L. (2002). Temporary and spatial of particles in an image-based airway model: large-eddy deposition of aerosol particles in the upper human airways simulation and left-right lung ventilation asymmetry. during breathing cycle. J. Aerosol Sci. 33: 1525–1539. Aerosol Sci. Technol. 45: 11–25. https://doi.org/10.1080/ https://doi.org/10.1016/S0021-8502(02)00108-8 02786826.2010.517578 Mutuku, J.K. and Chen, W.H. (2018). Flow characterization Lennon, S., Zhang, Z., Lessmann, R. and Webster, S. in healthy airways and airways with chronic obstructive (1998). Experiments on particle deposition in the human pulmonary disease (COPD) during different inhalation upper respiratory system. Aerosol Sci. Technol. 28: 464– conditions. Aerosol Air Qual. Res. 18: 2680–2694. 474. https://doi.org/10.1080/02786829808965538 https://doi.org/10.4209/aaqr.2018.06.0232 Li, L., Huang, Q., Wang, D.C., Ingbar, D.H. and Wang, X. Mutuku, J.K., Hou, W.C. and Chen, W.H. (2020). Two- (2020a). Acute lung injury in patients with COVID-19 phase flow dynamics and PM deposition in healthy and 2.5 infection. Clin. Transl. Med. 10: 20–27. https://doi.org/10. obstructed human airways during inhalation. Aerosol Air 1002/ctm2.16 Qual. Res. 20: 1091–1110. https://doi.org/10.4209/aaqr.2 Li, Z., Guo, S., Li, Z., Wang, Y., Hu, Y., Xing, Y., Liu, G., 020.03.0107 Fang, R. and Zhu, H. (2020b). PM associated phenols, Nazridoust, K. and Asgharian, B. (2008). Unsteady-state 2.5 phthalates, and water soluble ions from five stationary airflow and particle deposition in a three-generation combustion sources. Aerosol Air Qual. Res. 20: 61–71. human lung geometry. Inhalation Toxicol. 20: 595–610. https://doi.org/10.4209/aaqr.2019.11.0602 https://doi.org/10.1080/08958370801939374 Lindsley, W.G., Pearce, T.A., Hudnall, J.B., Davis, K.A., Norton, T. and Sun, D.W. (2006). Computational fluid Davis, S.M., Fisher, M.A., Khakoo, R., Palmer, J.E., dynamics (CFD) – an effective and efficient design and Clark, K.E. and Celik, I. (2012). Quantity and size analysis tool for the food industry: A review. Trends Food distribution of cough-generated aerosol particles produced Sci. Technol. 17: 600–620. https://doi.org/10.1016/j.tifs.2 by influenza patients during and after illness. J. Occup. 006.05.004 Environ. Hyg. 9: 443–449. https://doi.org/10.1080/15459 Nowak, N., Kakade, P.P. and Annapragada, A.V. (2003). 624.2012.684582 Computational fluid dynamics simulation of airflow and Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 1195 aerosol deposition in human lungs. Ann. Biomed. Eng. 31: 265–281. https://doi.org/10.1016/0167-8191(88)90047-6 374–390. https://doi.org/10.1114/1.1560632 Soni, B. and Aliabadi, S. (2013). Large-scale CFD simulations Olds, J.L. and Kabbani, N. (2020). Is nicotine exposure of airflow and particle deposition in lung airway. Comput. linked to cardiopulmonary vulnerability to COVID-19 in Fluids 88: 804–812. https://doi.org/10.1016/j.compfluid. the general population? FEBS J. https://doi.org/10.1111/ 2013.06.015 febs.15303 Soriano, J.B., Abajobir, A.A., Abate, K.H., Abera, S.F., Park, S. and Wexler, A. (2007). Particle deposition in the Agrawal, A., Ahmed, M.B., Aichour, A.N., Aichour, I., pulmonary region of the human lung: A semi-empirical Aichour, M.T.E., Alam, K., Alam, N., Alkaabi, J.M., Al- model of single breath transport and deposition. J. Maskari, F., Alvis-Guzman, N., Amberbir, A., Amoako, Aerosol Sci. 38: 228–245. https://doi.org/10.1016/j.jaeros Y.A., Ansha, M.G., Antó, J.M., Asayesh, H., … Vos, T. ci.2006.11.009 (2017). Global, regional, and national deaths, prevalence, Pedley, T. (1977). Pulmonary Fluid Dynamics. Annu. Rev. disability-adjusted life years, and years lived with Fluid Mech. 9: 229–274. https://doi.org/10.1063/1.3517737 disability for chronic obstructive pulmonary disease and Phalen, R.F., Mendez, L.B. and Oldham, M.J. (2010). New asthma, 1990–2015: A systematic analysis for the Global Developments in Aerosol Dosimetry. Inhalation Toxicol. Burden of Disease Study 2015. Lancet Respir. Med. 5: 22: 6–14. https://doi.org/10.3109/08958378.2010.516031 691–706. https://doi.org/10.1016/S2213-2600(17)30293-X Piglione, M.C., Fontana, D. and Vanni, M. (2012). Simulation Stapleton, K.W., Guentsch, E., Hoskinson, M. and Finlay, of particle deposition in human central airways. Eur. J. W. (2000). On the suitability of k–ε turbulence modeling Mech. B. Fluids 31: 91–101. https://doi.org/10.1016/j.eur for aerosol deposition in the mouth and throat: A omechflu.2011.08.003 comparison with experiment. J. Aerosol Sci. 31: 739–749. Qi, S., Li, Z., Yue, Y., van Triest, H.J. and Kang, Y. (2014). https://doi.org/10.1016/S0021-8502(99)00547-9 Computational Fluid Dynamics Simulation of Airflow in Suh, Y. and Park, J.Y. (2018). Effect of off-plane bifurcation the Trachea and Main Bronchi for the Subjects with Left angles of primary bronchi on expiratory flows in the human Pulmonary Artery Sling. BioMed Eng. OnLine 13: 85. trachea. Comput. Biol. Med. 95: 63–74. https://doi.org/10. https://doi.org/10.1186/1475-925X-13-85 1016/j.compbiomed.2018.01.014 Rahimi-Gorji, M., Pourmehran, O., Gorji-Bandpy, M. and Sul, B., Wallqvist, A., Morris, M.J., Reifman, J. and Rakesh, Gorji, T. (2015). CFD simulation of airflow behavior and V. (2014). A computational study of the respiratory particle transport and deposition in different breathing airflow Characteristics in normal and obstructed Human conditions through the realistic model of human airways. airways. Comput. Biol. Med. 52: 130–143. https://doi.org/ J. Mol. Liq. 209: 121–133. https://doi.org/10.1016/j.moll 10.1016/j.compbiomed.2014.06.008 iq.2015.05.031 Tena, A., Francos, J., Alvarez, E. and Casan, P. (2015). A Russo, J., Robinson, R. and Oldham, M.J. (2008). Effects of three dimensional in silico model for the simulation of cartilage rings on airflow and particle deposition in the inspiratory and expiratory airflow in humans. Eng. Appl. trachea and main bronchi. Med. Eng. Phys. 30: 581–589. Comput. Fluid Mech. 9: 187–198. https://doi.org/10.1080 https://doi.org/10.1016/j.medengphy.2007.06.010 /19942060.2015.1004819 Sauret, V., Goatman, K., Fleming, J. and Bailey, A. (1999). Tgavalekos, N.T., Musch, G., Harris, R., Melo, M.V., Semi-automated tabulation of the 3D topology and Winkler, T., Schroeder, T., Callahan, R., Lutchen, K. and morphology of branching networks using CT: Application Venegas, J. (2007). Relationship between airway narrowing, to the airway tree. Phys. Med. Biol. 44: 1625. patchy ventilation and lung mechanics in asthmatics. Eur. https://doi.org/10.1088/0031-9155/44/7/304 Respir. J. 29: 1174–1181. https://doi.org/10.1183/09031 Schroter, R. and Sudlow, M. (1969). Flow patterns in 936.00113606 models of the human bronchial airways. Respiration Tian, G., Longest, P.W., Su, G. and Hindle, M. (2011). Physiol. 7: 341–355. https://doi.org/10.1016/0034-5687( Characterization of respiratory drug delivery with enhanced 69)90018-8 condensational growth using an individual path model of Schreck, R. and Mockros, L. (1970). Fluid dynamics in the the entire tracheobronchial airways. Ann. Biomed. Eng. 39: rd upper pulmonary airways. AIAA 3 Fluid and Plasma 1136–1153. https://doi.org/10.1007/s10439-010-0223-z Dynamics Conference, Los Angeles, California. Tian, L., Shang, Y., Chen, R., Bai, R., Chen, C., Inthavong, Setti, L., Passarini, F., Gennaro, G.D., Barbieri, P., Perrone, K. and Tu, J. (2017). A combined experimental and M.G., Borelli, M., Palmisani, J., Gilio, A.D., Piscitelli, P. numerical study on upper airway dosimetry of inhaled and Miani, A. (2020). Airborne transmission route of nanoparticles from an electrical discharge machine shop. COVID-19: Why 2 meters/6 feet of inter-personal distance Part. Fibre Toxicol. 14: 24. https://doi.org/10.1186/s12989- could not be enough. Int. J. Environ. Res. Public Health 017-0203-7 17: 2932. https://doi.org/10.3390/ijerph17082932 Tsuda, A., Henry, F.S. and Butler, J.P. (2008). Gas and Sidhaye, V.K., Schweitzer, K.S., Caterina, M.J., Shimoda, L. aerosol mixing in the acinus. Respir. Physiol. Neurobiol. and King, L.S. (2008). Shear stress regulates aquaporin-5 163: 139–149. https://doi.org/10.1016/j.resp.2008.02.010 and airway epithelial barrier function. PNAS 105: 3345– Valavanidis, A., Fiotakis, K. and Vlachogianni, T. (2008). 3350. https://doi.org/10.1073/pnas.0712287105 Airborne particulate matter and human health: Toxicological Solchenbach, K. and Trottenberg, U. (1988). SUPRENUM: assessment and importance of size and composition of System essentials and grid applications. Parallel Comput. 7: particles for oxidative damage and carcinogenic 1196 Mutuku et al., Aerosol and Air Quality Research, 20: 1172–1196, 2020 mechanisms. J. Environ. Sci. Health., Part C 26: 339–362. https://doi.org/10.1016/j.jbiomech.2005.10.009 https://doi.org/10.1080/10590500802494538 Yeates, D.B. and Aspin, N. (1978). A mathematical van Doremalen, N., Bushmaker, T., Morris, D.H., Holbrook, description of the airways of the human lungs. Respir. M.G., Gamble, A., Williamson, B.N., Tamin, A., Harcourt, Physiol. 32: 91–104. https://doi.org/10.1016/0034-5687(7 J.L., Thornburg, N.J. and Gerber, S.I. (2020). Aerosol and 8)90102-0 surface stability of SARS-CoV-2 as compared with Yeh, H.C. and Schum, G. (1980). Models of human lung SARS-CoV-1. N. Engl. J. Med. 382: 1564–1567. airways and their application to inhaled particle deposition. https://doi.org/10.1056/NEJMc2004973 Bull. Math. Biol. 42: 461–480. https://doi.org/10.1016/S0 Van Ertbruggen, C., Hirsch, C. and Paiva, M. (2005). 092-8240(80)80060-7 Anatomically based three-dimensional model of airways Yoon, S.H., Lee, K.H., Kim, J.Y., Lee, Y.K., Ko, H., Kim, to simulate flow and particle transport using computational K.H., Park, C.M. and Kim, Y.H. (2020). Chest fluid dynamics. J. Appl. Physiol. 98: 970–980. radiographic and CT findings of the 2019 novel https://doi.org/10.1152/japplphysiol.00795.2004 coronavirus disease (COVID-19): Analysis of nine Velavan, T.P. and Meyer, C.G. (2020). The COVID-19 patients treated in Korea. Korean J. Radiol. 21: 494–500. Epidemic. Trop. Med. Int. Health 25: 278–280. https://doi.org/10.3348/kjr.2020.0132 https://doi.org/10.1111/tmi.13383 Zhang, H. and Papadakis, G. (2010). Computational analysis Viegas, C.A., Ferrer, A., Montserrat, J.M., Barbera, J.A., of flow structure and particle deposition in a single Roca, J. and Rodriguez-Roisin, R. (1996). Ventilation- asthmatic human airway bifurcation. J. Biomech. 43: 2453– perfusion response after fenoterol in hypoxemic patients 2459. https://doi.org/10.1016/j.jbiomech.2010.05.031 with stable COPD. Chest 110: 71–77. https://doi.org/10.1 Zhang, P., Duan, J., Chen, G. and Wang, W. (2019). 378/chest.110.1.71 Numerical investigation on gas-solid flow in a circumfluent Walters, D.K. and Luke, W.H. (2010). A method for three- cyclone separator. Aerosol Air Qual. Res. 19: 971–980. dimensional navier-stokes simulations of large-scale https://doi.org/10.4209/aaqr.2018.05.0197 regions of the human lung airway. J. Fluids Eng. 132: Zhang, X., Kang, J., Chen, H., Yao, M. and Wang, J. (2018). 051101. https://doi.org/10.1115/1.4001448 PM meets blood: In vivo damages and immune defense. 2.5 Wang, J. and Du, G. (2020). COVID-19 may transmit Aerosol Air Qual. Res. 18: 456–470. https://doi.org/10.42 through aerosol. Ir. J. Med. Sci. https://doi.org/10.1007/s1 09/aaqr.2017.05.0167 1845-020-02218-2 Zhang, Z. and Kleinstreuer, C. (2001). Effect of particle inlet Weibel, E.R. (1963a). Geometric and dimensional airway distributions on deposition in a triple bifurcation lung models of conductive, transitory and respiratory zones of airway model. J. Aerosol Med. 14: 13–29. https://doi.org/ the human lung. In Morphometry of the human lung, 10.1089/08942680152007864 Weibel, E.R. (Ed.), Springer, pp. 136–142. Zhang, Z. and Kleinstreuer, C. (2002). Transient airflow Weibel, E.R. (1963b). Geometry and dimensions of airways structures and particle transport in a sequentially branching of conductive and transitory zones. In Morphometry of the lung airway model. Phys. Fluids 14: 862–880. human lung, Weibel, E.R. (Ed.), Springer, pp. 110–135. https://doi.org/10.1063/1.1433495 Xia, B. and Sun, D.W. (2002). Applications of Zhang, Z., Kleinstreuer, C. and Kim, C. (2002). Gas–solid computational fluid dynamics (CFD) in the food industry: two-phase flow in a triple bifurcation lung airway model. A review. Comput. Electron. Agric. 34: 5–24. Int. J. Multiphase Flow 28: 1021–1046. https://doi.org/10. https://doi.org/10.1016/S0168-1699(01)00177-6 1016/S0301-9322(02)00011-3 Xu, C., Luo, X., Yu, C., and Cao, S.J. (2020). The 2019- Zhang, Z. and Kleinstreuer, C. (2011). Laminar-to-turbulent nCoV epidemic control strategies and future challenges of fluid-nanoparticle dynamics simulations: Model building healthy smart cities. Indoor Built Environ. comparisons and nanoparticle-deposition applications. 1420326X20910408. https://doi.org/10.1177%2F142032 Int. J. Numer. Methods Biomed. Eng. 27: 1930–1950. 6X20910408 https://doi.org/10.1002/cnm.1447 Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Zhao, W., Zhong, Z., Xie, X., Yu, Q. and Liu, J. (2020). Liu, S., Zhao, P., Liu, H., Zhu, L., Tai, Y., Bai, C., Gao, Relation between chest CT findings and clinical conditions T., Song, J., Xia, P., Dong, J., Zhao, J., and Wang, F.S. of coronavirus disease (COVID-19) pneumonia: A (2020). Pathological findings of COVID-19 associated multicenter study. Am. J. Roentgenol. 214: 1072–1077. with acute respiratory distress syndrome. Lancet Respir. https://doi.org/10.2214/AJR.20.22976 Med. 8: 420–422. https://doi.org/10.1016/S2213-2600(20) Zierenberg, J.R., Halpern, D., Filoche, M., Sapoval, B. and 30076-X Grotberg, J.B. (2013). An asymptotic model of particle Yanai, M., Sekizawa, K., Ohrui, T., Sasaki, H. and deposition at an airway bifurcation. Math. Med. Biol. 30: Takishima, T. (1992). Site of airway obstruction in 131–156. https://doi.org/10.1093/imammb/dqs002 pulmonary disease: Direct measurement of intrabronchial pressure. J. Appl. Physiol. 72: 1016–1023. https://doi.org/ 10.1152/jappl.1992.72.3.1016 Received for review, April 30, 2020 Yang, X., Liu, Y. and Luo, H. (2006). Respiratory flow in Revised, May 22, 2020 obstructed airways. J. Biomech. 39: 2743–2751. Accepted, May 24, 2020

Journal

Aerosol and Air Quality ResearchUnpaywall

Published: Jan 1, 2020

There are no references for this article.