buildings

Review
Assessing Indoor Air Quality and Ventilation to Limit Aerosol
Dispersion—Literature Review
Nadine Hobeika * , Clara García-Sánchez and Philomena M. Bluyssen

Faculty of Architecture, Delft University of Technology, 2628BL Delft, The Netherlands

* Correspondence: n.hobeika@tudelft.nl

Abstract: The COVID-19 pandemic highlighted the importance of indoor air quality (IAQ) and
ventilation, which researchers have been warning about for years. During the pandemic, researchers
studied several indicators using different approaches to assess IAQ and diverse ventilation systems
in indoor spaces. To provide an overview of these indicators and approaches in the case of airborne
transmission through aerosols, we conducted a literature review, which covered studies both from
before and during the COVID-19 pandemic. We searched online databases for six concepts: aerosol
dispersion, ventilation, air quality, schools or offices, indicators, and assessment approaches. The
indicators found in the literature can be divided into three categories: dose-, building-, and occupant-
related indicators. These indicators can be measured in real physical spaces, in a controlled laboratory,
or modeled and analyzed using numerical approaches. Rather than organizing this paper according to
these approaches, the assessment methods used are grouped according to the following themes they
cover: aerosol dispersion, ventilation, infection risk, design parameters, and human behavior. The
first finding of the review is that dose-related indicators are the predominant indicators used in the
selected studies, whereas building- and occupant-related indicators are only used in specific studies.
Moreover, for a better understanding of airborne transmission, there is a need for a more holistic
definition of IAQ indicators. The second finding is that although different design assessment tools
and setups are presented in the literature, an optimization tool for a room’s design parameters seems
to be missing. Finally, to efficiently limit aerosol dispersion in indoor spaces, better coordination
Citation: Hobeika, N.;
between different fields is needed.
García-Sánchez, C.; Bluyssen, P.M.
Assessing Indoor Air Quality and
Keywords: indoor air quality; aerosol dispersion; ventilation; numerical modeling; computational
Ventilation to Limit Aerosol
fluid dynamics; experimental measurements
Dispersion—Literature Review.
Buildings 2023, 13, 742. https://
doi.org/10.3390/buildings13030742

Academic Editor: Ashok Kumar, 1. Introduction

Alejandro Moreno-Rangel, M. Amirul
Since 2020, a highly contagious virus (SARS-CoV-2) has ravaged the world, with
I. Khan and Michał Piasecki
six hundred and eighty million reported cases and six million eight hundred thousand
Received: 24 February 2023 deaths to date (7 March 2023) [1], leading to a crippling pandemic on both economic
Revised: 8 March 2023 and social levels. At the beginning of the pandemic, airborne transmission was ruled
Accepted: 9 March 2023 out because of the low reproduction number of the virus (R). Thus, it was assumed that
Published: 11 March 2023 the main transmission routes were either close contact or fomite transmission, resulting
in a list of measures that included keeping a 1.5 to 2 m distance and washing hands
regularly. However, this assumption was quickly contested by numerous organizations
Copyright: © 2023 by the authors.
and experts. On 28 April 2020 during the Build-Up webinar, the Federation of European
Licensee MDPI, Basel, Switzerland.
Heating, Ventilation, and Air-Conditioning Associations (REHVA) stated that “the safety
This article is an open access article distance of 2 m between people, regarding the risk of COVID-19 infection, is a myth” [2].
distributed under the terms and Later, on 6 July 2020, two hundred and thirty-nine scientists and researchers signed an
conditions of the Creative Commons open letter urging officials and the medical community to admit that airborne transmission
Attribution (CC BY) license (https:// of the virus was the dominant transmission route and that keeping a distance was not
creativecommons.org/licenses/by/ enough to stop the spread of the virus [3]. The letter was soon followed by a publication by
4.0/). thirty-six scientists [4] in which appropriate ventilation as an additional measure to limit

Buildings 2023, 13, 742. https://doi.org/10.3390/buildings13030742 https://www.mdpi.com/journal/buildings

Buildings 2023, 13, 742 2 of 27

the spread of the virus was advocated, highlighting current buildings’ poor indoor air
quality (IAQ).
However, this was not the first time that respiratory diseases indicated problems with
IAQ, particularly in relation to ventilation in indoor environments. In the last twenty
years, the Severe Acute Respiratory Syndrome (SARS-CoV-1) epidemic in 2003, H1N1
influenza epidemic in 2011, and Middle East Respiratory Syndrome (MERS) outbreak in
2012 were red flags that highlighted the problems with a lack of indoor ventilation [5].
Thus, several literature studies prior to the COVID-19 pandemic presented overviews of
existing research that assessed airborne transmission in relation to indoor ventilation. Some
studies reviewed the application of experimental measurement techniques to visualize
and analyze aerosol dispersion and airflow patterns [6–8], whereas other studies reviewed
the use of Computational Fluid Dynamics (CFD) and its limitations to assess airborne
transmission [9]. More recent reviews aimed to clarify terminology [10] or present a more
up-to-date overview of ventilation systems [11], CFD studies [12], and risk assessment
models [13].
Additionally, several researchers emphasized that current guidelines and regulations
for IAQ were insufficient to fulfill the needs of occupants [14,15]. These guidelines typ-
ically provide the maximum values of certain pollutants that should not be exceeded
indoors and/or the minimum required levels of ventilation. However, they do not present
specific ventilation standards or design recommendations to curb the spread of airborne
diseases [14]. The issue is not only limited to the lack of proper ventilation standards but
also extends to the indicators used to assess ventilation with regard to health and comfort
in indoor spaces. These indicators can be divided into three categories: dose-, occupant-,
and building-related indicators [16]. For airborne transmission studies, dose-related indi-
cators, such as CO2 concentrations, temperature, and relative humidity, are mainly used,
although their usability was questioned in the context of the SARS-CoV-2 virus.
Moreover, most studies that use these indicators focus on the general room level,
which makes an important assumption that pollutants dilute in a space. However, this
is not consistent with the physics underlying airborne transmission. When a virus-laden
particle is emitted, three propelling forces act upon it: gravity, inertia, and aerodynamic
force [17]. If gravity is dominant, meaning that the weight of the particle is greater than
all the other forces, the particle falls to the floor and is no longer inhaled. However, when
inertia and aerodynamic forces take over, the particle (in this case, an aerosol) is suspended
in the air and can travel further distances, potentially reaching other individuals in the
room. Studying the ventilation of the entire room might not be the most suitable solution
for limiting the dispersion of these aerosols in indoor spaces [14].
Another issue is that studies often focus on a single indicator, even though different
indicators may be correlated with each other [18,19]. Researchers were calling for a more
integrated analysis of indoor environments even before the outbreak of the coronavirus.
Thus, the aim of this literature review is to present the indicators and approaches used to
assess IAQ in the case of airborne transmission through pathogen-laden aerosols across
multiple disciplines. In addition, this study attempts to answer the following research
questions:
• How has IAQ been assessed so far to reduce the dispersion of aerosols?
• Which indicators and approaches have been used and for what purpose?

2. Materials and Methods

Scopus, Google Scholar, ScienceDirect, Wiley, SpringerLink, PubMed, and the TU
Delft library books were used to conduct research for this study. After an initial screening
of the literature relating to airborne transmission and IAQ, keywords from papers were
aggregated into six concepts. These concepts were then used as the search keywords:
airborne diseases, ventilation, air quality, schools or offices, indicators, and assessment
approaches. Table 1 shows the list of keywords used to search the databases. For the search
Buildings 2023, 13, 742 3 of 27

combinations, multiple keywords within a concept were connected using the word OR and
the concepts were connected using the word AND.

Table 1. Keywords for literature search.

Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept 6

airborne ventilation
air quality classrooms indicators assessment
diseases (system)
airborne ventilating indoor air
schools parameter measurements
transmission (system) quality
airborne
air diffuser offices criteria simulations
infection
aerosol
exhaust room models
dispersion
droplet
dynamics

The initial search covered articles published in the last ten years and studies relevant
to indoor air quality and airborne diseases were selected based on their titles. Papers that
mainly focused on energy consumption and ventilation were excluded. Moreover, the
search focused specifically on ventilation strategies that used airflow to extract pathogen-
laden aerosols from the room or proxies that studied those aerosols and did not include
other equally important strategies that purified the air from pathogens such as UVC and
ionizers. From the filtered sources, papers were chosen based on the content of their
abstracts. If a reference citation in a paper was found to be interesting, it was also included
in this review. This ultimately led to the inclusion of studies that went beyond the past
ten years and that covered a wider range of room settings and functions beyond educational
buildings or offices. Books available at the TU Delft library that were related to this topic
were also included in this review.
While sorting through the references, different indicators and assessment methods
were found. The indicators could be divided into dose-, occupant-, and building-related
indicators. The assessment methods could be divided into measurements and numerical
simulations. However, five main concepts could be identified relating to ventilation effi-
ciency with regard to airborne diseases: airborne transmission, ventilation, risk of infection,
design parameters, and human behavior. These concepts highlight the importance of
combining the dose-, occupant-, and building-related indicators. Therefore, the references
were first divided into these five concepts and then further divided into sub-concepts. Some
additional references were found to support, extend, or complement certain claims within
a reference found in the preliminary literature search. Figure 1 provides an overview of the
process used in this review.
Buildings 2023, 13, 742 4 of 27

Figure 1. Flow chart showing an overview of the process used in this review.
Buildings 2023, 13, 742 5 of 27

3. Results—Assessment of IAQ
3.1. General Overview of IAQ Assessment Methods
There are two main approaches used to assess IAQ and ventilation with regard to
airborne transmission: measurements and numerical modeling. Measurements can be
performed either in the field (real environment) or a laboratory (controlled environment).
Field measurements provide values for dose-related indicators and account for real-world
uncertainties resulting from human behavior and environmental conditions. Field mea-
surements rely on different measuring devices depending on the purpose of the field study.
In contrast, the advantage of laboratory measurements is that they allow the environmental
conditions to be controlled, resulting in a deeper understanding of certain parameters
without exposing the test subjects to any contaminants or pathogens [20].
With the increase in computational power, numerical modeling of aerosol dispersion
and ventilation strategies is more commonly used to study infection risk in indoor en-
vironments. Based on the findings of this review, two approaches are typically adopted
depending on the scale being studied: (1) zonal network methods are used for floor-scale
modeling, where a floor is a network of rooms and each room is a node, where equations
such as pressure and contaminant equations are solved; and (2) CFD methods allow for
more detailed modeling of the airflow in rooms with different ventilation systems or model-
ing of aerosol dispersion in a space from the source to the receptor [21–23]. In the literature
reviewed, different turbulence models were used for CFD simulations, including Large
Eddy Simulations (LES), Detached Eddy Simulations (DES) (more specifically the Spalart–
Allmaras model [24]), or Reynolds-averaged Navier–Stokes (RANS) models with either
a k-e [25] or a k-ω model [26]. The k-e model integrates two partial differential transport
equations, as well as the Navier–Stokes equations, for the description of turbulence, one for
kinetic energy (k) and the other for the rate of dissipation of turbulence (e) [27]. The k-ω
turbulence model adopts a modified turbulent kinetic energy equation from the standard
k-e model and introduces a new equation for ω. The k-ω model incorporates modifications
for low Reynolds number effects. It is applicable to flows limited by walls. The k-ω SST
model is used for the robustness of a k-ω model near the wall and the ability to transition
to a k-e model in the far field [28]. Table 2 provides more details about the CFD simulations
that were found in the literature.
In the literature selected in this review, these approaches are often combined to create
more holistic studies. More advanced approaches use Artificial Intelligence (AI) and com-
bine both field measurements and numerical approaches. AI methods rely on monitoring
and/or simulations, as well as the analysis of these data to predict aerosol dispersion, ven-
tilation performance, or human behavior. Therefore, this section is organized thematically
based on the topics discussed in the literature and how they were combined:
• Aerosol dispersion;
• Ventilation;
• Infection risk;
• Design parameters;
• Human behavior.
Each sub-section is organized according to the type of studies performed (field mea-
surements, laboratory experiments, or numerical simulations). Table 3 presents an overview
of the themes covered in each of the publications and the methods used (experiments, sim-
ulations, or both). It should be noted that if a study was only “numerical”, it does not
mean that it was not validated. It means that the study only addressed the simulation or
that it validated the simulation based on another study or simulation. Additionally, if a
study utilized both numerical and experimental approaches, it does not mean that the
results were validated. The researchers in the study may have used the experimental part
to determine the initial boundary conditions of the simulation.
Buildings 2023, 13, 742 6 of 27

Table 2. Summary of the CFD solvers and methods used in the selected references.

Paper Solver Type of Solver (un)Steady Model

Dai et al. [29] SIMPLEC Eulerian Steady state RNG k-e
Ovando-Chacon et al. [30] - Eulerian Steady state -
Faleiros et al. [31] DPM Eulerian–Lagrangian Unsteady state SST k-ω
Li et al. [32] SIMPLE Eulerian Steady state RNG k-e
De Simone et al. [33] buoyant-SimpleFoam Eulerian Steady state -
StarCCM + QDE
Foster and Kinzel [34] (Quanta Dispersion Eulerian–Lagrangian Unsteady state Spalart–Allmaras
Equation)
Muthusamy et al. [35] StarCCM Eulerian–Lagrangian Unsteady state (realizable) k-e
DPM (+ SIMPLE to
Wu and Weng [22] Eulerian–Lagrangian Steady state SST k-ω
decouple p and U)
Blocken et al. [36] SIMPLE Eulerian–Lagrangian Steady state realizable k-e
Dbouk and Drikakis
- Eulerian–Lagrangian Unsteady state SST k-ω
[37]
Dbouk and Drikakis
- Eulerian–Lagrangian Unsteady state -
[38]
Eulerian (in
Vuorinen et al. [10] buoyant-PimpleFoam Unsteady state implicit LES
OpenFOAM)
Xu et al. [39] DPM Eulerian–Lagrangian Unsteady state RNG k-e + SST k-ω
Jiao et al. [40] SIMPLE Eulerian Steady state realizable k-e
Murakami et al. [41] SIMPLE Eulerian Steady state standard k-e
Keshavarz et al. [42] DPM Eulerian–Lagrangian Unsteady state enhanced k-e
SST k-ω, standard k-ω,
Gilani et al. [43] SIMPLE Eulerian Steady state
3 k-e models
Villafruela et al. [23] PISO Eulerian Unsteady state RNG k-e
Romano et al. [44] DPM Eulerian–Lagrangian Steady state realizable k-e
van Hooff and Blocken
SIMPLEC Eulerian Unsteady state realizable k-e
[45]
Aliabadi et al. [46] - Eulerian–Lagrangian Unsteady state RNG k-e + DRW
Mao and Celik [47] - LS URANS + RFG -
Eulerian +
Lai and Chen [48] - Unsteady state RNG k-e
Eulerian–Lagrangian
Rohdin and Moshfegh standard k-e, realizable
SIMPLE Eulerian Steady state
[49] k-e, RNG k-e
Zhang et al. [50] SIMPLE Eulerian Steady state RNG k-e
Brohus et al. [51] - Eulerian Steady state standard k-e
Lee and Chen [52] SIMPLEC Eulerian Steady state RNG k-e
Buildings 2023, 13, 742 7 of 27

Table 3. Themes of the selected papers. E indicates experiments and N indicates numerical simula-
tions.

Ventilation

parameters
dispersion

Infection

behavior
Methods

Articles

Aerosol

Human
Design
risk
de Man et al. [53], Han et al. [54], Hebbink et al.
[55], Ho et al. [56], Li et al. [57], Ortiz et al. [58], Tan
E
et al. [59], Akhtar et al. [60], Kim et al. [61], Bourouiba
et al. [62], Hui et al. [63], Tang et al. [64]

Wu and Weng [22], Dbouk and Drikakis [37,38], Xu

et al. [39], Aliabadi et al. [46], Mao and Celik [47], Lai
N
and Chen [48], Lee and Chen [52], Chen et al.
[65], Wei and Li [66], Xie et al. [67]

E Fu et al. [6], Cao et al. [7], Sun and Zhang [8]

Gilani et al. [43], van Hooff and Blocken [45], Rohdin

both
and Moshfegh [49], Mumtaz et al. [68], Chen [69]
Lipinski et al. [11], Li et al. [32], Zhang et al.
N
[50], Zhou and Haghighat [70]

Miller et al. [71], Dai and Zhao [72], Sun and Zhai
N [73], Peng and Jimenez [74], Sze To and Chao
[75], Rudnick and Milton [76], Riley et al. [77]

Zhang and Bluyssen [78], Zhang et al. [79], Bluyssen

et al. [80], Bolashikov et al. [81], Nielsen et al.
E
[82], Nielsen [83], Nielsen et al. [84], Melikov et al.
[85]

both Villafruela et al. [23], Romano et al. [44]

Ovando-Chacon et al. [30], Muthusamy et al.
N
[35], Keshavarz et al. [42], Cao et al. [86]

E Hou et al. [87], Liu et al. [88], Wu et al. [89]

both Dai et al. [29], Shao et al. [90]

Xu et al. [17], Foster and Kinzel [34], Guo et al.

N
[91], Zivelonghi and Lai [92]

both Faleiros et al. [31]

E Bluyssen [15], Park et al. [93], Kembel et al. [94]

N Murakami et al. [41], Mohamed et al. [95]

N Kapoor et al. [96], Peng et al. [97]

Buildings 2023, 13, 742 8 of 27

Table 3. Cont.

Ventilation

parameters
dispersion

Infection

behavior
Methods

Articles

Aerosol

Human
Design
risk
both Ascione et al. [21]

N De Simone et al. [33], Jiao et al. [40], Afshari et al. [98]

E Wang et al. [99]

both Blocken et al. [36]

E Blocken et al. [100], Halvoňová and Melikov [101]

Vuorinen et al. [10], Brohus et al. [51], Duives et al.

N [102], Xiao et al. [103], Xu and Chraibi
[104], Mazumdar et al. [105]

3.2. Aerosol Dispersion

Aerosol dispersion is governed by aerodynamic forces due to the small size of aerosols.
This means that aerosol particles are transported by the airflow within a room and are
affected by the exhalation and movements of people. However, the aerodynamic physics
that govern aerosol dispersion vary depending on the distance from the source of the
aerosols being studied [46]. Aliabadi et al. [46] identified two types of aerosol dispersion:
• near-field dispersions, which impact a few meters from the source of the aerosol.
• far-field dispersions, which impact the entire ventilated space of a room.
A near-field aerosol dispersion depends on the type of aerosol-generating task such as
coughing, sneezing, speaking, or breathing. This section concerns the different approaches
that study near-field aerosol dispersion, whereas far-field aerosol dispersion is discussed in
a later section.
For measurement studies that aimed to understand aerosol dispersion, Particle-
Tracking Velocimetry (PTV) [56,61], Particle Image Velocimetry (PIV) [31,54,57], Schlieren
images [64,90], tracers, and air velocity sensors were commonly used. Sneezing and cough-
ing were extensively studied at the beginning of the COVID-19 pandemic. Asymptomatic
aerosol-generating tasks were also the focal point of several studies after the realization
of the important role played by asymptomatic and pre-symptomatic people in infecting
other people [59]. The focuses of these studies included comparing aerosol dispersion with
different types of masks and without masks [53,58,60,63,64]; assessing the difference in the
dispersion of different words, vowels, and consonants during speaking [59]; and studying
the flow physics underlying the dispersion of exhaled aerosols [55,62].
Numerical approaches used to study aerosol dispersion involved either CFD model-
ing or probabilistic models and machine learning. Since aerosol dispersion includes both
the air and the virus-laden droplets, it is a two-phase flow problem in CFD simulations.
There were two approaches used for CFD simulations of aerosol dispersion: Eulerian–
Eulerian (Eulerian approach where particles were modeled as a continuum in space) and
Eulerian–Lagrangian (Lagrangian approach where particles were modeled as discrete enti-
ties in space). The Eulerian approach provided the concentration of particles in a volume,
whereas the Lagrangian approach modeled or calculated the trajectory and collision of the
particles [48].
Some studies used CFD to model near-field aerosol behavior [37,52] to assess cross-
infection between a source (infected person) and a receptor (healthy person) [22] and model
Buildings 2023, 13, 742 9 of 27

the generation of aerosols from the alveoli into the air [39]. These studies typically used
k-ω SST as a turbulence model in their simulations because it accurately simulates the
transition from laminar to turbulent in the respiratory tract. CFD was also used to test
theories about the evaporation and behavior of aerosols in the air. Dbouk and Drikakis
[38] developed a new theory for the evaporation of the coronavirus particles that included
the transient Nusselt and Sherwood numbers in the Reynolds, Prandtl, and Schmidt
numbers. The results of their simulations were consistent with the study by Yang and
Marr [106], which hypothesized that the virus was not deactivated at high RH. Therefore,
temperature and relative humidity, as well as the various uncertainties that accompany
these two environmental conditions, should be included in the assessment of airborne
transmission.
Mathematical models were also used to model aerosol dispersion. To determine
how far droplets can travel in a space, Xie et al. [67] added more complexities to the
Wells evaporation–falling curve by accounting for RH, air speed, and respiratory jets.
Regarding the use of probabilistic models, different Lagrangian stochastic models were
used for particle tracking in turbulent flows: the Discontinuous Random Walk (DRW)
model, Continuous Random Walk (CRW) model, and Stochastic Differential Equation
(SDE) method. The DRW model was mostly used because it is the least computationally
expensive. Wei and Li [66] used the DRW model to model the cough jet and distance of the
dispersion of various sizes of particles in a space. In the study, the dispersion of aerosols
beyond the 2 m safe distance proved the long-range transmission of 10 µm particles in the
air. Other studies developed their own mathematical models based on existing equations
and models [47,65]. Chen et al. [65] combined the simple dynamics of expired jets with
available probabilistic models of inhalation and deposition to study cross-infection between
two people facing each other while having a conversation.

3.3. Ventilation
Studying ventilation involves either assessing the concentrations of specific pollutants
in a space or more generally assessing the ventilation rate, mean age of the air, and airflow
patterns and distributions in a space. These parameters mainly depend on the type of
ventilation (natural or mechanical) and the position and configuration of air inlets and
outlets [11]. Some researchers focused on the ventilation of the entire room, whereas others
focused on the ventilation near the source of infection. PIV was the most commonly used
measurement technique to assess the airflow in a room. Several researchers presented
reviews on how to use PIV to measure the airflow and air motion to experimentally assess
indoor spaces in laboratories and test chambers [6–8].
At the numerical level, CFD simulations were the most commonly used numerical ap-
proach to assess ventilation. For natural ventilation, running CFD is not so straightforward
because two levels of airflow are present in the computational domain, the urban wind flow
and the indoor airflow. Decoupled simulation strategies were typically adopted to study
indoor natural ventilation. This means that the urban wind flow was modeled first and
then the relevant values near the openings of the studied building were used to later model
the indoor ventilation [45]. However, due to the increase in computational power in the
past decade, several studies used coupled simulations, where outdoor and indoor air were
modeled together [41,95]. Using coupled simulations, van Hooff and Blocken [45] studied
the CO2 concentration decay over time inside a semi-enclosed stadium after a concert. For
mechanical ventilation, some studies used CFD to assess the airflow patterns in a space [43]
to test optimization strategies for the ventilation system using machine learning [70] and for
sensitivity analysis to either determine the most influential parameters of the mechanical
ventilation system or determine the most convenient locations for a limited number of
sensors for full-scale measurements [50].
CFD also allows for the testing of more advanced ventilation systems. Lipinski et al.
[11] presented an overview of the three main types of ventilation (recirculating ventilation,
mixing ventilation, and displacement ventilation). They ran CFD simulations of two types
Buildings 2023, 13, 742 10 of 27

of displacement ventilation: roof-mounted natural ventilation with an indoor diffuser and

façade-mounted buoyancy-driven intelligent ventilation. The results showed that the latter
decreased the CO2 concentration more efficiently than the former despite having a lower
air-change rate. CFD simulations were typically validated with full-scale measurements.
The performance of the turbulence models depended on the flow that was used, even
though several researchers showed that Re-Normalization Group (RNG) k-e was the most
stable turbulence model for indoor air simulations [49,69].
Several researchers used machine learning to predict and optimize ventilation per-
formance in indoor spaces. Mumtaz et al. [68] measured eight air pollutants, along with
the temperature and relative humidity, and applied a neural network model to predict
indoor pollutants inside a laboratory. The model achieved a prediction accuracy of 99.1%
compared to newly measured data, providing high confidence in the model’s performance.
As an alternative to physical measurements, Li et al. [32] used CFD simulations to train
a Back-Propagation Neural Network (BPNN), achieving a prediction accuracy of 98.5%
compared to newly run cases using CFD, and then a Particle Swarm Optimizer (PSO)
to optimize the IAQ. These machine learning algorithms showed high accuracy for new
models and can potentially help architects and engineers improve the performance of their
design solutions.

Aerosol Dispersion and Ventilation

Some studies also combined aerosol dispersion with ventilation to extract contami-
nants from a room. Zhang et al. [79] conducted on-site measurements to investigate the
numbers and locations of CO2 sensors needed to monitor a classroom. The researchers
showed that with natural ventilation, the wall opposite the windows and the wall near
the teacher had the highest CO2 levels and, therefore, recommended that at least these
two locations should be monitored. With mechanical ventilation and the resulting mixing
of CO2 , it was recommended that at least one location (the wall opposite the windows) be
measured, depending on the classroom’s layout. In their study, the CO2 levels near the
wall opposite the windows were the highest regardless of the ventilation system.
Laboratory measurements were also used to test aerosol dispersion and ventilation.
These measurements either produced qualitative results, such as a visualization of the
dispersion of aerosols, or quantitative results, such as the number of particles. Zhang and
Bluyssen [78] assessed the impact of ventilation on the concentration of particles and CO2 .
Bluyssen et al. [80] tested the efficiency of HEPA filters under different ventilation systems
by using soap bubbles and a camera to visualize the dispersion of bubbles (simulating
aerosols) in a Senselab [107]. Nielsen et al. [84] investigated the interaction between the
macro-environment and the micro-environment under different ventilation systems. They
concluded that a fully mixed macro-environment increased exposure for occupants within
the micro-environment compared to an unmixed macro-environment such as vertical
ventilation or displacement ventilation. Bolashikov et al. [81] tested wearable personal
exhaust ventilation in the case of an infected doctor using manikins and tracer gas, whereas
Melikov et al. [85] tested personalized ventilation installed on an office desk with a personal
computer. Nielsen [83] assessed personalized ventilation for individuals in a hospital ward,
whereas Cao et al. [86] used experiments to propose a protected zone form of ventilation
in offices that through a downward jet plane, creates individual sub-zones for occupants,
significantly reducing the risk of infection (40% theoretically).
Some CFD studies focused on particle concentration and ventilation [30,42], which is
particularly relevant to hospitals because contamination standards are already established,
unlike other types of buildings, where particle concentration standards at the breathing
level may not exist. Romano et al. [44] simulated an operation theater to test the impact of
differential airflow diffusion on particle concentration above the operating table in the case
of both external and internal contamination (opened/closed doors/slits). Other researchers
studied near- and far-field aerosol behavior under a specific ventilation system [35], cross-
infection depending on the breathing mode (mouth/nose), and the distance between
Buildings 2023, 13, 742 11 of 27

the source and the receptor [23,82]. In the paper by Villafruela et al. [23], the breathing
boundary conditions of the mouth and nostrils were sinusoidal equations that mimicked
the inhalation and exhalation patterns. In addition to the distance, Nielsen et al. [82] studied
the impact of the relative positions of two individuals on their exposure to particles. They
studied four positions: face to face, face to the side of the target individual, face to the
back of the target, and seated. Both papers showed that in the micro-environment of an
individual, the closer the target is to the source, the higher the exposure. Additionally, both
papers used the tracer gas N2O for the simulation of displacement ventilation.
Most of these studies drew similar conclusions regarding the importance of including
more design parameters, such as rooms with different sizes and shapes [80], or human
behavior, such as movable bodies [44], continuous breathing [35], or the level of activity [82],
to better understand and evaluate IAQ and the impact of ventilation in a given space.

3.4. Infection Risk

The assessment of the infection risk quantifies the infection probability of a certain
disease and is used to test the outcomes of preventive control strategies [17].

3.4.1. Infection Risk and Ventilation

Since ventilation is an important control strategy for airborne transmission, it cannot
be considered separately from the assessment of the infection risk. Two models are typi-
cally used to quantify and assess the infection risk of airborne pathogens under different
assumptions: the Wells–Riley model and the dose–response model [75].
The most widely used infection risk assessment model is the Wells–Riley model. The
model calculates the probability of infection based on the number of aerosols required for
infection, pulmonary ventilation rate, room ventilation rate, and exposure time [77]. This
model was extensively used at the beginning of the COVID-19 pandemic to demonstrate
the correlation between poor ventilation and COVID-19 infection [71–73]. Once CO2 levels
began to be considered a proxy for virus emission, some researchers adapted the Wells–
Riley equation to include CO2 measurements in the equation [74,76]. The Wells–Riley
equation was also adapted and extended by different studies to either include more control
strategies such as masks [92], simplify calculations [10], or predict the infection risk using
machine learning [87]. However, the Wells–Riley model suffers from the assumption that
the air in a confined space is in a steady state and is fully mixed, which is typically not the
case [91].
In contrast, other studies used the dose–response model because it can account for
transmission routes aside from airborne [75]. The dose–response approach provides de-
terministic (empirical) models, which follow the cumulative curve of the dose–response
relationship, and stochastic (semi-empirical) models, which take into account the random
distribution of pathogens in a space due to air turbulence and any exposure to the pathogen
can lead to infection regardless of the dose. Stochastic models are more biologically plausi-
ble than deterministic models. In general, the dose–response model produces more realistic
estimates than the Wells–Riley model; however, the equation requires biological data about
the virus that were not known at the beginning of the coronavirus pandemic [75].
The advantage of using infection risk models is that they can quickly quantify the
infection risk and provide a general idea about the transmission risk in a space. However,
these models oversimplify the problems using one equation for the entire 3D space, which
is not the case in reality.

3.4.2. Aerosol Dispersion, Ventilation, and Infection Risk

Wu et al. [89] conducted on-site measurements using tracer gas to test the zonal risk of
infection between different apartments. They used the Wells–Riley model to evaluate the
risk of infection in different apartments due to air filtration. A cross-apartment infection
route was verified and it was deduced that the horizontal transmission of the virus should
not be ignored even if the room of origin of the virus is naturally ventilated. Liu et al. [88]
Buildings 2023, 13, 742 12 of 27

tested a personalized ventilation system using breathing manikins and Collison nebulizers
to replicate aerosol generation. The intake fraction of the inhaling manikin was measured
with an aerodynamic particle sizer that measures the number and size of particles. The
objective of the study was to study the efficiency of a personalized ventilation (PV) system
when the occupants of a space change how they face each other. The side-by-side position
of individuals was determined to be the most critical position for the PV system.
Zivelonghi and Lai [92] studied the aerosol dispersion, ventilation, and infection
risk mathematically to provide recommendations to schools returning to physical edu-
cation classes for the 2021–2022 school year. They used mathematical equations for the
average Emission Rate (ER) to model aerosol emissions. For the infection risk, they used
the Gammaitoni–Nucci model, a Wells–Riley-based model that can account for the time
evolution of the viral charge using the thermal gradient airflow. The viral load equation
considered the exposure time, saturation, decay rates, and opening/closing of windows.
The study also tested different occupancy levels in classrooms and whether the teacher
or any of the students were infected. This study not only accounted for the number of
pathogens in the air but also added human behavior to the assessment of the infection
rate. In addition, they highlighted the importance of cumulative airborne risk and the
importance of time in understanding infection in indoor spaces. They also highlighted the
two critical factors that can affect the infection risk: the ventilation and volume of the room.
Foster and Kinzel [34] differentiated between mathematical models (risk infection
models) and numerical models (CFD simulations) to estimate the infection risk in a class-
room setting and compared the percentage of error between the two models in two different
ventilation settings: no ventilation and mechanical ventilation with heating. To enable a
meaningful comparison, they used the Gammaitoni and Nucci form of the Wells–Riley
model because it considers the Reynolds transport theorem and provides an infection risk
estimate closest to a CFD simulation. For the CFD simulation, the infection risk probability
was calculated based on the volume of the breathing zone of individuals in a room and the
concentration of particles in that breathing zone. With no ventilation, the Wells–Riley model
had a relative under-prediction error of 6% compared to the CFD simulations. However,
with ventilation, the under-prediction error increased to 29%, highlighting the added value
of modeling the entire space, including its turbulence complexities, to identify the areas
in a room with a higher risk of infection. Dai et al. [29] used CFD to model the impact of
natural ventilation on the dispersion of pollutants inside a building. They tested three wind
directions and used the Wells–Riley model to assess the infection risk originating from a
source room to the rest of the building. When the source room was facing the dominant
wind direction, the risk of infection in other rooms that faced the same direction was found
to be the highest. They also validated the results by conducting wind tunnel experiments
using a scaled model of the studied area.

3.5. Design Parameters of a Room

Studies that consider design parameters typically investigate the impact of the room
size and volume on the infection risk, as well as the size, position, and number of openings
such as doors, windows, and ventilation system’s inlets and outlets.

3.5.1. Ventilation and Design Parameters

Using field measurements, Park et al. [93] tested the ventilation rate of natural ven-
tilation in a classroom, considering different window-opening configurations and types
of ventilation (cross-ventilation or single-sided ventilation). Using CO2 as a tracer gas,
the ventilation rate was determined using the tracer gas decay method. Afterward, the
ventilation rate was used in the Wells–Riley equation to determine the infection risk proba-
bility of each window-opening configuration. It was concluded that cross-ventilation was
better at reducing the infection risk and that a window-opening ratio of 15% was enough
to maintain the infection risk lower than 1% for a stay of less than 3 h.
Buildings 2023, 13, 742 13 of 27

Regional climates were also used in several CFD studies to choose the best type of
ventilation system. Ascione et al. [21] studied the differences between air distribution
systems using diffusers and grilles in a school classroom in Campobasso in southern
Italy (cold climate). They computed the temperature, airflow velocity, and mean age of
the air. They determined the optimal air distribution system (linear slot diffusers) but
emphasized that it was not a universal solution and that it would only apply to similar
types of classrooms in similar climates. This means that optimized ventilation solutions
are case-dependent and architects and engineers should be cautious when deciding on a
particular ventilation system.
On a larger scale, Kembel et al. [94] addressed the biogeography of a set of rooms
and how they are connected by assessing their degree of centrality (the organization of the
space) through betweenness (the number of shortest paths that go through a space) and
connectance (number of intermediate spaces that connect two spaces). They determined
the impact of the organizational design of a space on the traffic of occupants through the
central node and, consequently, how this impacts the microbiology of a space. The greater
the number of people that share a space, the higher the risk of exposure to pathogens.
This emphasizes the importance of different design layouts in the formation of microbial
communities in a space. It also highlights the need to understand both microbiology and
indoor design to ensure better IAQ.

3.5.2. Infection Risk and Design Parameters

Peng et al. [97] developed an open-source tool/spreadsheet that implements an in-
fection box model using the Wells–Riley model, ventilation rate, and room volume. They
referred to the two latter indicators as building parameters. They also accounted for dif-
ferent uses of spaces (classroom, subway, supermarket, stadium). To account for these
different uses, they developed emission rate factors based on the relative quanta for the
different physical intensities and vocalization types (oral breathing, speaking, and loudly
speaking) of an activity. The volumetric breathing rates were chosen per age group and
activity intensity (from sleeping to high-intensity activity). In the end, they fitted the model
over data from real outbreaks documented in the literature in addition to data from the
coronavirus pandemic. Based on their findings, they concluded that the transmission rate
of SARS-CoV-2 was higher than tuberculosis but lower than measles.
Kapoor et al. [96] used an artificial neural network to predict the infection risk prob-
ability in an office space using the area of openings as one of the inputs. They measured
the temperature, relative humidity, and CO2 concentration in an office and used the data
to build their neural network. They used eleven input parameters: indoor temperature,
indoor relative humidity, area of the opening, number of occupants, area per person,
volume per person, CO2 concentration, air quality index, outdoor wind speed, outdoor
temperature, and outdoor relative humidity. The model achieved a prediction accuracy
of 99.92% compared to multiple other prediction methods, making it more efficient for
predicting infection risk in offices.

3.5.3. Aerosol Dispersion, Infection Risk, and Design Parameters

For assessing aerosol dispersion and the risk of infection in a given 3D space,
Faleiros et al. [31] developed an application based on unsteady CFD simulations and the
dose–response model. The computational domain was a confined room with ventilation,
which can only be accounted for in a dose–response model. The application required
several inputs:
1. The geometry of the space: the room dimensions, size, and position of the doors and
windows;
2. The room conditions: the temperature and relative humidity;
3. Occupant-related parameters: the number and location of the people and their orien-
tation, gender, height, weight, and clothing type;
4. The types of aerosol-generating tasks: breathing, speaking, coughing, sneezing;
Buildings 2023, 13, 742 14 of 27

5. Particle size;
6. Type of mask.
Based on the inputs, the geometry of the room, including the location of the occupants,
was created and used to run the simulation. The idea of the app was to provide an
accessible tool for non-CFD users to model the infection risk in a space based on certain
inputs. Including ventilation modeling in CFD, however, would provide more detailed
information about the infection risk in a space.

3.5.4. Aerosol Dispersion, Ventilation, and Design Parameters

Researchers also investigated the optimization of design layouts for increased venti-
lation efficiency rather than just assessing certain cases. Jiao et al. [40] investigated and
optimized seven different inlet–outlet layouts to control airborne toxic and explosive ma-
terial in confined industrial workshops using carbon monoxide as a contaminant. The
concentration of carbon monoxide was treated as a passive scalar in the simulation and was
used to identify the concentrations of pollutants in the air. De Simone et al. [33] used CFD to
study how people and furniture impact aerosol dispersion in a restaurant. They developed
a tool for architects to assess their designs while accounting for pathogen exposure. Aerosol
dispersion was modeled as a passive scalar. They demonstrated that their tool can be used
in the design phase to predict how changes in a space can affect the infection risk.
Afshari et al. [98] used a zonal network to assess cross-infection between rooms
according to the standard ventilation systems used in offices in Sweden, Denmark, and
Norway. They showed how a ventilation system can create a difference in pressure between
rooms and corridors, which can result in the spread of infection beyond the room where
the infected person is located. The typical Swedish ventilation system involves the transfer
of air from offices to corridors. This can lead to the spread of contamination to corridors
but not to other rooms. The typical Danish ventilation system is balanced and the same
amount of supplied air is exhausted from the entire building. However, this means that
at room level, the amount of supplied and exhausted air might not be equal, leading to
cross-contamination between rooms. The typical Norwegian ventilation system uses a
balanced-room ventilation approach, which means that the same amount of supplied air is
exhausted from each room. This type of ventilation system is the most effective in limiting
cross-infection between rooms and corridors. If ventilation systems are designed to prevent
room-to-room infection, it is easier to conduct studies at a room scale rather than at a larger
floor scale.
Based on the papers that studied airborne transmission with regard to design parame-
ters, a list of the input data needed to run a simulation, including the design parameters, is
provided in Table 4 and is illustrated in Figure 2.

Figure 2. Input for CFD simulations. Green indicates the technical parameters; purple indicates the
design parameters; blue indicates are stochastic input; black indicates additional information for
specific scenarios.
Buildings 2023, 13, 742 15 of 27

Table 4. Input parameters for CFD simulations.

Input Parameters
Initial boundary conditions for exhaled breath (velocity, pressure, relative humidity, temperature,
turbulence parameters)
Initial boundary conditions for ventilation system (velocity, pressure, turbulence parameters)
Turbulence model and solver
Mesh resolution
Ventilation type
Room volume (x, y, z)
“Human” parameters (dimension of mouth and nostrils, body surface temperature)
For mechanical ventilation, position, direction, and airflow of inlets and outlets and
ventilation rate
Ambient conditions (temperature, pressure, relative humidity)
For natural ventilation, number, size, and location of windows and doors, regional climate
Number of occupants
Number of infected people (usually one)
Position of occupants with respect to each other
Position of the source generating aerosols (i.e., occupant’s location)
Presence of personal ventilation
With or without masks
Particle injection (kinematic or particle cloud parameters) (in some cases, it was a tracer gas)

3.6. Human Behavior

Studies that cover human behavior include the movements of individuals, their inter-
actions in a space, and the impact that these have on aerosol dispersion and ventilation.

3.6.1. Aerosol Dispersion and Human Behavior

Laboratory experiments were used to study certain human behaviors and the aerosol
dispersion related to these behaviors. Wang et al. [99] used water tunnel experiments to
study particle dispersion and clustering while a manikin traveled up and down the stairs.
The goal was to study the effect of the unsteadiness of the flow caused by the movement on
the stairs. While moving up the stairs, aerosols traveled shorter distances, whereas while
moving down the stairs, aerosols traveled longer distances.
Numerical simulations were performed to study certain human behaviors with regard
to aerosol dispersion. Blocken et al. [36] studied the safe social distance for moving people,
considering that the 1.5m safe distance is assumed for people standing still. They accounted
for the aerodynamic effects of a fast-walking and a running person on a trailing person.
The conclusion was that the higher the speed of the movement, the larger the safe distance
needed in the slipstream. At a 4 km/h walk, the safe distance in the slipstream increased
to 5 m, and at a 14.4 km/h run, the safe distance in the slipstream increased to 10 m.
This means that walkers and runners should avoid the slipstream of the leading person.
However, because of some assumptions in the study, more research is needed to see whether
droplets can escape the slipstream of the leading person.

3.6.2. Aerosol Dispersion, Ventilation, and Human Behavior

Some studies assessed the impact of a moving person on the contaminant concen-
tration of a source. Halvoňová and Melikov [101] set up a laboratory experiment using
manikins to compare the influence of moving people on the quality of air inhaled with
displacement ventilation and ductless personalized ventilation. Mazumdar et al. [105] ran
Buildings 2023, 13, 742 16 of 27

CFD simulations to measure the air velocity, temperature, and contaminant concentration
under different scenarios such as a person walking, the changing of bed sheets, and the
swing of a door in an inpatient ward. Brohus et al. [51] also used CFD and contaminant
concentrations to demonstrate the impact of moving staff in an operation room on the
concentration of contaminants near the patient’s table. All studies showed that the closer
the moving person was to the source of the contaminant, the greater the disturbance.
Blocken et al. [100] used CFD simulations to study people emitting aerosols in gyms
with mixed mechanical ventilation and the effects of a high-quality air cleaner in a field
experiment. The experiment only involved stationary workouts, such as stationary bicycles,
treadmills, and weight-based equipment, to limit the re-suspension of particles. During
the experiment, they measured the aerosol particle concentration of particles smaller than
0.25 µm and up to 10 µm, temperature, and CO2 . One conclusion of this study was that
gyms should be considered complex spaces because of their large height (5.1 m, in this
case) since the source emitting the pollutant was far from the inlet and outlet of the mixed
ventilation system. Another conclusion of this study was that measurements of particles
should be conducted at higher levels than those already measured (at 1.367 m and 1.247 m)
to capture the vertical gradient change in the gym.
To include human behavior in the numerical simulations, some studies used prob-
abilistic models. Vuorinen et al. [10] used a Monte Carlo model to simulate the effect of
people entering and leaving a supermarket, with or without coughing, on the dispersion
and concentration of aerosols per cubic meter. They introduced “the domain of elevated
risk (DER)”, which is a volume of accumulated exhaled aerosols based on the breathing
rate and assumption of the critical exposure dose to virus-laden aerosols in the order of 100.

3.6.3. Infection Risk and Human Behavior

Several researchers used mathematical models to include pedestrian flow to make
the calculations more realistic. Xiao et al. [103] and Xu and Chraibi [104] used a hybrid
microscopic pedestrian model combined with viral transmission models to assess the
impact of some restrictions on the infection risk in a grocery store or supermarket such
as early closure, maximum entry limitation, maintaining a safe distance, and entering
with a shopping cart. These models only consider contact transmission; thus, they are a
simplification of reality and may over- or under-predict the infection risk [102]. In contrast,
Duives et al. [102] combined pedestrian models with a multi-route transmission model to
create a tool that can assess the infection risk in an indoor space based on the movement in
that space and the interactions between individuals.
All the studies in this literature review used indicators to assess IAQ and ventilation.
Table 5 groups these studies according to the indicators identified and summarizes the
indicators according to the three main categories: dose-, occupant-, and building-related in-
dicators.
Buildings 2023, 13, 742 17 of 27

Table 5. Studies grouped according to the three main categories of indicators: dose-, occupant-, and building-related indicators.

[6–8,11,53,54,

[32,35,37,39,

[29,34,73,76,
[43,49,50,70]

[10,36,86,88]
42,44,47,51,
52,57,59,60,

[45,46,100]
[48,55,56]

[17,83,90]

[41,69,95]

[103,104]
58,63,64]

81,84,99]

[66,106]

[82,101]

[87,92]
[22,30]

[38,68]

[23,85]

[75,77]

[72,74]
62,78–

91,97]

[102]

[105]
[71]

[31]

[40]

[21]

[33]
[61]

[67]

[65]

[89]

[96]

[98]

[93]

[94]
Articles’ References

Physics of particles x x x x x x x x x x x x x x x x x x x x x x x x x x
Dose-related

Airflow patterns x x x x x x x x x x x x x x x x x x x x x x x x x
indicators

Temperature x x x x x x x x x x x x x

Relative Humidity x x x x x x x x
Occupant-related

Infection (exposure) risk x x x x x x x x x x x x x x x x

indicators

Relative position x x x x x x

Duration of stay x x x x x x x
Building-related

Windows/doors x x x x x x x x
indicators

Inlet–outlet mechanical x x x x

Space layout/volume x x x x x x x x
Buildings 2023, 13, 742 18 of 27

4. Discussion
4.1. Indicators
Several IAQ indicators were used to assess aerosol dispersion in the papers selected
for this review. Dose-, occupant-, and building-related indicators were used to understand
and simulate the behavior of aerosol dispersion and ventilation in a given space. However,
dose-related indicators were predominantly used, as seen in Table 5. Only 4 out of the
95 reviewed papers did not use dose-related indicators and only 14 studies used all three
categories of indicators. This highlights the lack of a holistic approach to assessing IAQ and
ventilation regarding airborne transmission. Moreover, the use of some of these indicators
has been questioned during the coronavirus pandemic.
For instance, using CO2 levels as a proxy for virus-laden particles has been widely
criticized due to the disconnection between CO2 levels (used generally as occupancy indi-
cators) and infection risk [92,108,109]. In the study by SAGE-EMG [109], CO2 levels were
identified as good indicators of poor ventilation in high-occupancy spaces. Nevertheless,
this does not hold for low-occupancy or large-volume spaces where CO2 levels are diluted
but virus-laden aerosols still accumulate at breathing levels. Eykelbosh [108] also argued
that air filters can considerably reduce the risk of infection without affecting the CO2 levels
in a room, thus weakening the use of CO2 levels as the only indicator for infection risk.
Consequently, researchers have been working to develop new infection risk proxies by
combining CO2 levels and other chemical pollutants such as PMs or VOCs. For example,
Zhang and Bluyssen [78] explored the possibility of using CO2 as a proxy for exhaled
particles to predict the risk of indoor exposure to pathogens but could not find a significant
relationship between the number of indoor particles and the CO2 level.
Additionally, there was no overall consensus among researchers on the thresholds
of the environmental conditions. Studies showed contradictory results in terms of the
impact of temperature and relative humidity on the deactivation and transmission of
SARS-CoV-2 [110,111]. One reason could be the lack of a strong correlation between the
environmental conditions and the spread and viability of the virus [112]. Another reason
could be the use of experimental aerosols. More water added to the initial mixture can lead
to a delay in the evaporation process and not a higher relative humidity [106].
With the coronavirus, it has become necessary to consider these indicators using an
integrated approach, considering not only objective indicators but also the perception of
the occupants of a space. Similar to soundscape [113], which approaches sound in indoor
spaces in a more holistic way, the authors of this paper would like to introduce the term
airscape. Airscape covers the perception of the occupants in an indoor space such as
smell, draft, and thermal comfort. However, the term can be extended to combine all the
IAQ indicators, including but not limited to the indicators mentioned in this paper (air
pollutants, environmental conditions, ventilation, and effects on people) (see Figure 3).
Therefore, the airscape of a room is defined as the combination of all pollutants in the air
(environmental conditions, airflow and distribution patterns, ventilation rate, and mean
age of the air), the perception of the occupants (smell, draft, and thermal comfort), and the
health symptoms and health effects, which are expressed, for example, as an infection rate
or disease.

4.2. Numerical Simulations and Tools

Whether used to assess one case or build a tool for numerical simulations, several
decisions are made to set up and run a simulation. First, several input parameters need to
be identified, and, depending on the purpose of the study, these parameters are considered
either constants or variables. Table 4 shows a list of the input parameters found in the
literature for numerical simulations.
Buildings 2023, 13, 742 19 of 27

Figure 3. Airscape—IAQ indicators presented holistically.

The choice of some of these parameters is not always straightforward and depends on
the researcher and the purpose of the study. The most uncertain parameters are related to
the input of the CFD simulations. Table 2 shows that there is no consensus on technical CFD
setups. The physics involved in the modeling are chosen based on whether the simulation
is a single-phase flow simulation, i.e., air and particles are modeled as a continuous gas,
or a multi-phase flow, i.e., air is modeled as a continuous gas and particles as discrete
elements. After choosing the physics, the actual solver needs to be chosen. This depends on
the different assumptions that the simulation should take into account, such as the choice
of turbulence model and steadiness, which mainly depends on the overall scope of the
research and is typically limited by the computational and time costs of the CFD. This
makes the optimization of several ventilation systems a very challenging task that depends
on each different scenario.
Numerical simulations also include parameters that are affected by natural variations
between individuals such as their height, weight, age, and gender. These parameters are
estimated based on experimental measures and included in the final simulation. Nev-
ertheless, an adequate way to efficiently prevent and assess the risk of infection based
on aerosol dispersion in indoor environments is still needed. So far, researchers have
suggested different approaches to model aerosol dispersion; however, not all aerosols are
virus carriers. Therefore, more information is needed on the viral shedding of an aerosol.
In addition, inhaling the virus does not automatically lead to infection [10]; a certain viral
load needs to be reached to result in illness. The value of that viral load depends on the
virus and is an important factor in determining the infection risk in a space.
There appears to be a gap in the current literature in terms of an optimization tool that
utilizes the findings from previous assessment tools and proposes design solutions that are
tailored to specific built or newly conceived spaces. As can be seen in this review, many
fields have incorporated machine learning into their research to assess aerosol dispersion
and ventilation in a room based on relatively few validated training samples from existing
cases. A numerical tool can be built based on this data to propose design parameters that
best optimize the airscape in a room. This tool can inform architects and engineers about
the IAQ in their designs and the improvements needed either during the concept phase or
later stages of a building’s life cycle.

4.3. Need for a Holistic Approach

The studies reviewed in this paper are grouped according to the five main themes they
covered (see Figure 4).
Buildings 2023, 13, 742 20 of 27

Figure 4. A summary of the themes discussed in this review, the topics addressed, and the indica-
tors used.

Table 3 shows that the topics of aerosol dispersion, ventilation, and infection risk were
covered more extensively individually than in combination with one other or in comparison
with design parameters and human behavior. A possible reason is that introducing all
these factors would increase the complexity of the system to study and model. Another
reason could be that the papers did not necessarily represent the entire work of researchers
but rather defined certain milestones in their research. Moreover, Table 3 shows that there
was a lack of papers covering the topics of human behavior and design parameters in
assessing IAQ and airborne transmission. This is consistent with the increasing demand to
include these themes as parameters in studies addressing IAQ and airborne transmission
in indoor spaces. Consequently, there is a need for more interdisciplinary research between
experts from different fields. This means that more stakeholders need to be included in
studies such as building engineers, architects, epidemiologists, virologists, and behavioral
scientists [114–116]. Megahed and Ghoneim [115] proposed a “holistic IAQ management
plan” that includes all engineering fields in the architectural design process. Numerical
tools were proposed as the optimal way for the fields to collaborate to yield more holistic
performance-based design solutions. Figure 5 shows a more holistic approach to collabo-
ration among the various disciplines. In fact, aerosol dispersion and ventilation depend
on the design of a space and the intensity of human behavior in that space. Despite the
complexity of the system and its various biological and behavioral uncertainties, true
interdependence between these fields leads to a pathogen source-based design [117], where
the design of the indoor environment can be more resilient to airborne pathogens.
It should be noted that because of the use of “healthy” ventilation, energy consump-
tion is the sixth theme that was found in the literature but was not included in this review
because it is outside the scope of the research question. In fact, most references that con-
cluded that the improvement of ventilation systems could limit the dispersion of aerosols
highlighted that these improvements should not come at the expense of higher energy
consumption [11,118]. Therefore, another degree of complexity should also be added to the
system that includes energy efficiency. The challenge is to design smarter ventilation sys-
tems, including relying more on natural ventilation [11], installing personalized ventilation
to target the breathing area [88], installing sensors that indicate when ventilation is (not)
needed [119], or a combination of these strategies so that energy costs can be minimized.
Buildings 2023, 13, 742 21 of 27

Figure 5. The interdependence between the design and human behavior disciplines and the engi-
neering fields that deal with aerosol dispersion and ventilation. The term "Pathogen-source-based
Design" was found in Shen et al. [117].

5. Conclusions
The coronavirus pandemic has emphasized the underlying weaknesses in IAQ and
ventilation strategies in indoor spaces. It is clear that existing building regulations related
to IAQ need to be revised and better implemented given the understanding of airborne
transmission.
The aim of this review was to investigate the steps that have been taken so far to
reduce aerosol dispersion with regard to IAQ. This resulted in identifying the indicators
and assessment approaches used to assess IAQ and ventilation systems in the case of
aerosol dispersion. The studies reviewed in this paper were divided into five themes:
aerosol dispersion, ventilation, infection risk, design parameters, and human behavior.
The aerosol dispersion studies addressed the physics of aerosol-generating tasks and the
impact of environmental conditions on aerosolization. The ventilation studies covered the
various types of ventilation and ventilation rates to understand the airflow patterns in
spaces. The studies that covered both topics aimed to extract or dilute aerosols. Based on
aerosol dispersion and ventilation, the studies that included the infection risk assessed the
risk based on the concentration of aerosols. The design-related studies addressed a room’s
geometrical parameters and the human behavior studies tried to add more uncertainty to
the ventilation models.
An important conclusion of this review is the predominant use of dose-related indi-
cators compared to the use of occupant- or building-related indicators. This underlines
the need for a broader definition of the IAQ indicators to understand and mitigate the
dispersion of aerosols in indoor spaces. For this purpose, the authors introduced the term
airscape, which covers all IAQ indicators used in the reviewed papers with the addition of
the perception and impact of the occupants of a space.
Moreover, different design assessment tools were found in the literature but there was
no proper optimization tool identified. This was mainly attributed to the fact that different
scenarios may require different numerical setups, which makes it difficult to efficiently test
different room designs and determine an optimal solution.
Finally, the reviewed literature seems to focus on analyzing the topics of aerosol
dispersion, ventilation, and infection risk separately rather than together, mostly avoiding
the inclusion of the topics of design parameters and human behavior. However, separating
Buildings 2023, 13, 742 22 of 27

aerosol dispersion and ventilation from the parameters of the space where they occur does
not allow for a comprehensive understanding of the airflow at the breathing level and
the optimal solutions that are needed. An integrated approach to optimizing the space is
needed that is consistent with the growing demand to include design parameters when
assessing the IAQ of a room.
This requires interdisciplinary work among different stakeholders that approaches
the problem from different perspectives. We can no longer afford to allow our health to
depend on poor coordination among experts. Coupling existing IAQ knowledge, advanced
computational technologies, and architectural design is the future for healthy buildings
and, consequently, healthy societies.

Author Contributions: Conceptualization, N.H., C.G.S. and P.B.; methodology, N.H., C.G.S. and P.B.;
formal analysis, N.H.; investigation, N.H.; writing—original draft preparation, N.H., C.G.S. and P.B.;
writing—review and editing, N.H., C.G.S. and P.B.; supervision, C.G.S. and P.B.; All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations
The following abbreviations are used in this manuscript:

AI Artificial Intelligence
BPNN Back-Propagation Neural Network
CFD Computational Fluid Dynamics
CO2 Carbon dioxide
CRW Continuous Random Walk
DRW Discontinuous Random Walk
HEPA High-Efficiency Particulate Air
IAQ Indoor Air Quality
MERS Middle East Respiratory Syndrome
PIV Particle Image Velocimetry
PSO Particle Swarm Optimiser
PTV Particle-Tracking Velocimetry
REHVA Federation of European Heating, Ventilation, and Air-Conditioning Associations
RH Relative Humidity
RNG Re-Normalization Group
SARS-CoV-1 Severe Acute Respiratory Syndrome Coronavirus 1
SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2
SDE Stochastic Differential Equation

References
1. Worldometer. Coronavirus Cases. 2022. Available online: https://www.worldometers.info/coronavirus/ (accessed on 21
February 2022).
2. Gameiro da Silva, M. How to Operate and Use Building Services during the COVID-19 Crisis [Build Up Webinar]. 2020. Available
online: https://www.rehva.eu/fileadmin/user_upload/2020.04.28_COVID-19_BuildUp_webinar_by_REHVA.pdf (accessed on
21 February 2022).
3. Morawska, L.; Milton, D.K. It is time to address airborne transmission of coronavirus disease 2019 (COVID-19). Clin. Infect. Dis.
2020, 71, 2311–2313. [CrossRef] [PubMed]
4. Morawska, L.; Tang, J.W.; Bahnfleth, W.; Bluyssen, P.M.; Boerstra, A.; Buonanno, G.; Cao, J.; Dancer, S.; Floto, A.; Franchimon, F.;
et al. How can airborne transmission of COVID-19 indoors be minimised? Environ. Int. 2020, 142, 105832. [CrossRef]
Buildings 2023, 13, 742 23 of 27

5. Qian, H.; Zheng, X. Ventilation control for airborne transmission of human exhaled bio-aerosols in buildings. J. Thorac. Dis. 2018,
10, S2295. [CrossRef] [PubMed]
6. Fu, S.; Biwole, P.H.; Mathis, C. Particle tracking velocimetry for indoor airflow field: A review. Build. Environ. 2015, 87, 34–44.
[CrossRef]
7. Cao, X.; Liu, J.; Jiang, N.; Chen, Q. Particle image velocimetry measurement of indoor airflow field: A review of the technologies
and applications. Energy Build. 2014, 69, 367–380. [CrossRef]
8. Sun, Y.; Zhang, Y. An overview of room air motion measurement: Technology and application. HVAC&R Res. 2007, 13, 929–950.
[CrossRef]
9. Ai, Z.T.; Melikov, A.K. Airborne spread of expiratory droplet nuclei between the occupants of indoor environments: A review.
Indoor Air 2018, 28, 500–524. [CrossRef]
10. Vuorinen, V.; Aarnio, M.; Alava, M.; Alopaeus, V.; Atanasova, N.; Auvinen, M.; Balasubramanian, N.; Bordbar, H.; Erästö,
P.; Grande, R.; et al. Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2
transmission by inhalation indoors. Saf. Sci. 2020, 130, 104866. [CrossRef]
11. Lipinski, T.; Ahmad, D.; Serey, N.; Jouhara, H. Review of ventilation strategies to reduce the risk of disease transmission in high
occupancy buildings. Int. J. Thermofluids 2020, 7, 100045. [CrossRef]
12. Sheikhnejad, Y.; Aghamolaei, R.; Fallahpour, M.; Motamedi, H.; Moshfeghi, M.; Mirzaei, P.A.; Bordbar, H. Airborne and aerosol
pathogen transmission modeling of respiratory events in buildings: An overview of computational fluid dynamics. Sustain. Cities
Soc. 2022, 79, 103704. [CrossRef]
13. de Crane D’Heysselaer, S.; Parisi, G.; Lisson, M.; Bruyère, O.; Donneau, A.F.; Fontaine, S.; Gillet, L.; Bureau, F.; Darcis, G.; Thiry,
E.; et al. Systematic Review of the Key Factors Influencing the Indoor Airborne Spread of SARS-CoV-2. Pathogens 2023, 12, 382.
[CrossRef]
14. Morawska, L.; Allen, J.; Bahnfleth, W.; Bluyssen, P.M.; Boerstra, A.; Buonanno, G.; Cao, J.; Dancer, S.J.; Floto, A.; Franchimon, F.;
et al. A paradigm shift to combat indoor respiratory infection. Science 2021, 372, 689–691. [CrossRef]
15. Bluyssen, P.M. What do we need to be able to (re) design healthy and comfortable indoor environments? Intell. Build. Int. 2014,
6, 69–92. [CrossRef]
16. Bluyssen, P.M. Towards new methods and ways to create healthy and comfortable buildings. Build. Environ. 2010, 45, 808–818.
[CrossRef]
17. Xu, C.; Liu, W.; Luo, X.; Huang, X.; Nielsen, P. Prediction and control of aerosol transmission of SARS-CoV-2 in ventilated context:
From source to receptor. Sustain. Cities Soc. 2022, 76, 103416. [CrossRef] [PubMed]
18. Bluyssen, P.M. Towards an integrated analysis of the indoor environmental factors and its effects on occupants. Intell. Build. Int.
2020, 12, 199–207. [CrossRef]
19. Eijkelenboom, A.; Bluyssen, P.M. Comfort and health of patients and staff, related to the physical environment of different
departments in hospitals: A literature review. Intell. Build. Int. 2022, 14, 95–113. [CrossRef]
20. Bluyssen, P.M. The Healthy Indoor Environment: How to Assess Occupants’ Wellbeing in Buildings; Taylor & Francis: London, UK,
2014. [CrossRef]
21. Ascione, F.; De Masi, R.F.; Mastellone, M.; Vanoli, G.P. The design of safe classrooms of educational buildings for facing contagions
and transmission of diseases: A novel approach combining audits, calibrated energy models, building performance (BPS) and
computational fluid dynamic (CFD) simulations. Energy Build. 2021, 230, 110533. [CrossRef] [PubMed]
22. Wu, J.; Weng, W. COVID-19 virus released from larynx might cause a higher exposure dose in indoor environment. Environ. Res.
2021, 199, 111361. [CrossRef]
23. Villafruela, J.; Olmedo, I.; San José, J. Influence of human breathing modes on airborne cross infection risk. Build. Environ. 2016,
106, 340–351. [CrossRef]
24. Spalart, P.; Allmaras, S. A one-equation turbulence model for aerodynamic flows. In Proceedings of the 30th Aerospace Sciences
Meeting and Exhibit, Reno, NV, USA, 6–9 January 1992; p. 439. [CrossRef]
25. Launder, B.E.; Spalding, D.B. The numerical computation of turbulent flows. In Numerical Prediction of Flow, Heat Transfer,
Turbulence and Combustion; Elsevier: Amsterdam, The Netherlands, 1983; pp. 96–116. [CrossRef]
26. Wilcox, D.C. Reassessment of the scale-determining equation for advanced turbulence models. AIAA J. 1988, 26, 1299–1310.
[CrossRef]
27. Valen-Sendstad, K.; Mortensen, M.; Langtangen, H.P.; Reif, B.A.P.; Mardal, K.A. Implementing a k-e Turbulence Model in the
FEniCS Finite Element Programming Environment. In Proceedings of the Fifth National Conference on Computational Mechanics
(MekIT’09), Trondheim, Norway, 26–27 May 2013; p. 13.
28. Weaver, D.S.; Mišković, S. A Study of RANS Turbulence Models in Fully Turbulent Jets: A Perspective for CFD-DEM Simulations.
Fluids 2021, 6, 271. [CrossRef]
29. Dai, Y.; Xu, D.; Wang, H.; Zhang, F. CFD Simulations of Ventilation and Interunit Dispersion in Dormitory Complex: A Case
Study of Epidemic Outbreak in Shanghai. Int. J. Environ. Res. Public Health 2023, 20, 4603. [CrossRef] [PubMed]
30. Ovando-Chacon, G.E.; Ovando-Chacon, S.L.; Rodríguez-León, A.; Díaz-González, M. Numerical Study of Indoor Air Quality in a
University Professor’s Office. Sustainability 2023, 15, 4221. [CrossRef]
31. Faleiros, D.E.; van den Bos, W.; Botto, L.; Scarano, F. TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2
infection risk analysis. Sci. Total Environ. 2022, 826, 154143. [CrossRef]
Buildings 2023, 13, 742 24 of 27

32. Li, L.; Zhang, Y.; Fung, J.C.; Qu, H.; Lau, A.K. A coupled computational fluid dynamics and back-propagation neural network-
based particle swarm optimizer algorithm for predicting and optimizing indoor air quality. Build. Environ. 2022, 207, 108533.
[CrossRef]
33. De Simone, Z.; Kastner, P.; Dogan, T. Towards Safer Work Environments During the COVID-19 Crisis: A Study Of Different
Floor Plan Layouts and Ventilation Strategies Coupling Open FOAM and Airborne Pathogen Data for Actionable, Simulation-
based Feedback in Design. In Proceedings of the Building Simulation 2021: 17th Conference of IBPSA, Bruges, Belgium, 1–3
September 2021.
34. Foster, A.; Kinzel, M. Estimating COVID-19 exposure in a classroom setting: A comparison between mathematical and numerical
models. Phys. Fluids 2021, 33, 021904. [CrossRef] [PubMed]
35. Muthusamy, J.; Haq, S.; Akhtar, S.; Alzoubi, M.A.; Shamim, T.; Alvarado, J. Implication of coughing dynamics on safe social
distancing in an indoor environment—A numerical perspective. Build. Environ. 2021, 206, 108280. [CrossRef]
36. Blocken, B.; Malizia, F.; Van Druenen, T.; Marchal, T. Towards Aerodynamically Equivalent COVID-19 1.5 m Social Distancing for
Walking and Running. 2020, preprint. Available online: https://www.euroga.org/system/1/user_files/files/000/045/111/4511
1/150d3060c/original/Social_Distancing_v20_White_Paper.pdf (accessed on 20 June 2022).
37. Dbouk, T.; Drikakis, D. On coughing and airborne droplet transmission to humans. Phys. Fluids 2020, 32, 053310. [CrossRef]
38. Dbouk, T.; Drikakis, D. Weather impact on airborne coronavirus survival. Phys. Fluids 2020, 32, 093312. [CrossRef] [PubMed]
39. Xu, C.; Zheng, X.; Shen, S. A numerical study of the effect of breathing mode and exposure conditions on the particle inhalation
and deposition. Inhal. Toxicol. 2020, 32, 456–467. [CrossRef]
40. Jiao, Z.; Yuan, S.; Ji, C.; Mannan, M.S.; Wang, Q. Optimization of dilution ventilation layout design in confined environments
using Computational Fluid Dynamics (CFD). J. Loss Prev. Process Ind. 2019, 60, 195–202. [CrossRef]
41. Murakami, Y.; Ikegaya, N.; Hagishima, A.; Tanimoto, J. Coupled simulations of indoor-outdoor flow fields for cross-ventilation of
a building in a simplified urban array. Atmosphere 2018, 9, 217. [CrossRef]
42. Keshavarz, S.; Salmanzadeh, M.; Ahmadi, G. Computational modeling of time resolved exposure level analysis of a heated
breathing manikin with rotation in a room. J. Aerosol Sci. 2017, 103, 117–131. [CrossRef]
43. Gilani, S.; Montazeri, H.; Blocken, B. CFD simulation of stratified indoor environment in displacement ventilation: Validation
and sensitivity analysis. Build. Environ. 2016, 95, 299–313. [CrossRef]
44. Romano, F.; Marocco, L.; Gustén, J.; Joppolo, C.M. Numerical and experimental analysis of airborne particles control in an
operating theater. Build. Environ. 2015, 89, 369–379. [CrossRef]
45. van Hooff, T.; Blocken, B. CFD evaluation of natural ventilation of indoor environments by the concentration decay method: CO2
gas dispersion from a semi-enclosed stadium. Build. Environ. 2013, 61, 1–17. [CrossRef]
46. Aliabadi, A.A.; Rogak, S.N.; Green, S.I.; Bartlett, K.H. CFD simulation of human coughs and sneezes: A study in droplet
dispersion, heat, and mass transfer. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition,
Vancouver, BC, Canada, 12–18 November 2010; Volume 44441, pp. 1051–1060. [CrossRef]
47. Mao, S.; Celik, I.B. Modeling of indoor airflow and dispersion of aerosols using immersed boundary and random flow generation
methods. Comput. Fluids 2010, 39, 1275–1283. [CrossRef]
48. Lai, A.C.; Chen, F. Comparison of a new Eulerian model with a modified Lagrangian approach for particle distribution and
deposition indoors. Atmos. Environ. 2007, 41, 5249–5256. [CrossRef]
49. Rohdin, P.; Moshfegh, B. Numerical predictions of indoor climate in large industrial premises. A comparison between different
k–ε models supported by field measurements. Build. Environ. 2007, 42, 3872–3882. [CrossRef]
50. Zhang, T.; Chen, Q.Y.; Lin, C.H. Optimal sensor placement for airborne contaminant detection in an aircraft cabin. HVAC&R Res.
2007, 13, 683–696. [CrossRef]
51. Brohus, H.; Balling, K.; Jeppesen, D. Influence of movements on contaminant transport in an operating room. Indoor Air 2006,
16, 356–372. [CrossRef] [PubMed]
52. Lee, J.H.W.; Chen, G.Q. A numerical study of turbulent line puffs via the renormalization group (RNG) k–e model. Int. J. Numer.
Methods Fluids 1998, 26, 217–247. .:2%3C217::AID-FLD637%3E3.0.CO;2-G. [CrossRef]
53. de Man, P.; Ortiz, M.A.; Bluyssen, P.M.; de Man, S.J.; Rentmeester, M.J.; van der Vliet, M.; Wils, E.J.; Ong, D.S. Airborne
SARS-CoV-2 in home and hospital environments investigated with a high-powered air sampler. J. Hosp. Infect. 2022, 119, 126–131.
[CrossRef] [PubMed]
54. Han, M.; Ooka, R.; Kikumoto, H.; Oh, W.; Bu, Y.; Hu, S. Experimental measurements of airflow features and velocity distribution
exhaled from sneeze and speech using particle image velocimetry. Build. Environ. 2021, 205, 108293. [CrossRef]
55. Hebbink, R.H.J.; Elshof, J.; Wanrooij, S.; Lette, W.; Lokate, M.; Venner, C.H.; Duiverman, M.; Hagmeijer, R. Passive Tracer
Visualization to Simulate Aerodynamic Virus Transport in Noninvasive Respiratory Support Methods. Respiration 2021,
100, 1196–1207. . [CrossRef]
56. Ho, K.M.A.; Davies, H.; Epstein, R.; Bassett, P.; Hogan, Á.; Kabir, Y.; Rubin, J.; Shin, G.Y.; Reid, J.P.; Torii, R.; et al. Spatiotemporal
droplet dispersion measurements demonstrate face masks reduce risks from singing. Sci. Rep. 2021, 11, 24183. [CrossRef]
57. Li, X.; Mak, C.M.; Ma, K.W.; Wong, H.M. Evaluating flow-field and expelled droplets in the mockup dental clinic during the
COVID-19 pandemic. Phys. Fluids 2021, 33, 047111. [CrossRef]
58. Ortiz, M.A.; Ghasemieshkaftaki, M.; Bluyssen, P.M. Testing of outward leakage of different types of masks with a breathing
manikin head, ultraviolet light and coloured water mist. Intell. Build. Int. 2022, 14, 623–641. [CrossRef]
Buildings 2023, 13, 742 25 of 27

59. Tan, Z.P.; Silwal, L.; Bhatt, S.P.; Raghav, V. Experimental characterization of speech aerosol dispersion dynamics. Sci. Rep. 2021,
11, 3953. [CrossRef]
60. Akhtar, J.; Luna Garcia, A.; Saenz, L.; Kuravi, S.; Shu, F.; Kota, K. Can face masks offer protection from airborne sneeze and cough
droplets in close-up, face-to-face human interactions?—A quantitative study. Phys. Fluids 2020, 32, 127112. [CrossRef] [PubMed]
61. Kim, J.T.; Jeong, H.; Kang, Y.J.; Chamorro, L.P.; Rogers, J.A. Dynamics of droplets on COVID-19 transmission via a soft wireless
device and Particle Tracking Velocimetry. In Proceedings of the APS Division of Fluid Dynamics Meeting Abstracts, Virtual
Event, 20–24 November 2020; pp. Y01–002.
62. Bourouiba, L.; Dehandschoewercker, E.; Bush, J.W. Violent expiratory events: On coughing and sneezing. J. Fluid Mech. 2014,
745, 537–563. [CrossRef]
63. Hui, D.S.; Chow, B.K.; Chu, L.; Ng, S.S.; Lee, N.; Gin, T.; Chan, M.T. Exhaled air dispersion during coughing with and without
wearing a surgical or N95 mask. PLoS ONE 2012, 7, e50845. [CrossRef] [PubMed]
64. Tang, J.W.; Liebner, T.J.; Craven, B.A.; Settles, G.S. A schlieren optical study of the human cough with and without wearing masks
for aerosol infection control. J. R. Soc. Interface 2009, 6, S727–S736. [CrossRef]
65. Chen, W.; Zhang, N.; Wei, J.; Yen, H.L.; Li, Y. Short-range airborne route dominates exposure of respiratory infection during close
contact. Build. Environ. 2020, 176, 106859. [CrossRef]
66. Wei, J.; Li, Y. Enhanced spread of expiratory droplets by turbulence in a cough jet. Build. Environ. 2015, 93, 86–96. [CrossRef]
67. Xie, X.; Li, Y.; Chwang, A.; Ho, P.; Seto, W. How far droplets can move in indoor environments–revisiting the Wells evaporation-
falling curve. Indoor Air 2007, 17, 211–225. .%2D0668.2007.00469.x. [CrossRef]
68. Mumtaz, R.; Zaidi, S.M.H.; Shakir, M.Z.; Shafi, U.; Malik, M.M.; Haque, A.; Mumtaz, S.; Zaidi, S.A.R. Internet of Things (IoT)
based indoor air quality sensing and predictive analytic—A covid-19 perspective. Electronics 2021, 10, 184. [CrossRef]
69. Chen, Q. Ventilation performance prediction for buildings: A method overview and recent applications. Build. Environ. 2009,
44, 848–858. [CrossRef]
70. Zhou, L.; Haghighat, F. Optimization of ventilation system design and operation in office environment, Part I: Methodology.
Build. Environ. 2009, 44, 651–656. [CrossRef]
71. Miller, S.L.; Nazaroff, W.W.; Jimenez, J.L.; Boerstra, A.; Buonanno, G.; Dancer, S.J.; Kurnitski, J.; Marr, L.C.; Morawska, L.; Noakes,
C. Transmission of SARS-CoV-2 by inhalation of respiratory aerosol in the Skagit Valley Chorale superspreading event. Indoor
Air 2021, 31, 314–323. [CrossRef]
72. Dai, H.; Zhao, B. Association of the infection probability of COVID-19 with ventilation rates in confined spaces. In Proceedings of
the Building Simulation; Springer: Berlin/Heidelberg, Germany, 2020; Volume 13, pp. 1321–1327. [CrossRef]
73. Sun, C.; Zhai, Z. The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. Sustain. Cities
Soc. 2020, 62, 102390. [CrossRef] [PubMed]
74. Peng, Z.; Jimenez, J. Exhaled CO2 as COVID-19 infection risk proxy for different indoor environments and activities. Environ. Sci.
Technol. Lett. 2020[CrossRef]
75. Sze To, G.N.; Chao, C.Y.H. Review and comparison between the Wells–Riley and dose-response approaches to risk assessment of
infectious respiratory diseases. Indoor Air 2010, 20, 2–16. [CrossRef] [PubMed]
76. Rudnick, S.; Milton, D. Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor Air
2003, 13, 237–245. [CrossRef] [PubMed]
77. Riley, E.; Murphy, G.; Riley, R. Airborne spread of measles in a suburban elementary school. Am. J. Epidemiol. 1978, 107, 421–432.
[CrossRef]
78. Zhang, D.; Bluyssen, P.M. Exploring the possibility of using CO2 as a proxy for exhaled particles to predict the risk of indoor
exposure to pathogens. Indoor Built Environ. 2022. accepted[CrossRef]
79. Zhang, D.; Ding, E.; Bluyssen, P.M. Guidance to assess ventilation performance of a classroom based on CO2 monitoring. Indoor
Built Environ. 2022, 1420326X211058743. [CrossRef]
80. Bluyssen, P.M.; Ortiz, M.; Zhang, D. The effect of a mobile HEPA filter system on ‘infectious’ aerosols, sound and air velocity in
the SenseLab. Build. Environ. 2021, 188, 107475. [CrossRef]
81. Bolashikov, Z.D.; Barova, M.; Melikov, A.K. Wearable personal exhaust ventilation: Improved indoor air quality and reduced
exposure to air exhaled from a sick doctor. Sci. Technol. Built Environ. 2015, 21, 1117–1125. [CrossRef]
82. Nielsen, P.V.; Olmedo, I.; de Adana, M.R.; Grzelecki, P.; Jensen, R.L. Airborne cross-infection risk between two people standing in
surroundings with a vertical temperature gradient. HVAC&R Res. 2012, 18, 552–561. [CrossRef]
83. Nielsen, P.V. Control of airborne infectious diseases in ventilated spaces. J. R. Soc. Interface 2009, 6, S747–S755. [CrossRef]
[PubMed]
84. Nielsen, P.; Buus, M.; Winther, F.; Thilageswaran, M. Contaminant flow in the microenvironment between people under different
ventilation conditions. Ashrae Trans. 2008, 114, 632.
85. Melikov, A.K.; Cermak, R.; Majer, M. Personalized ventilation: Evaluation of different air terminal devices. Energy Build. 2002,
34, 829–836. [CrossRef]
86. Cao, G.; Nielsen, P.; Jensen, R.; Heiselberg, P.; Liu, L.; Heikkinen, J. Protected zone ventilation and reduced personal exposure to
airborne cross-infection. Indoor Air 2015, 25, 307–319. [CrossRef]
87. Hou, D.; Katal, A.; Wang, L.L. Bayesian calibration of using CO2 sensors to assess ventilation conditions and associated COVID-19
airborne aerosol transmission risk in schools. medRxiv 2021[CrossRef]
Buildings 2023, 13, 742 26 of 27

88. Liu, W.; Liu, L.; Xu, C.; Fu, L.; Wang, Y.; Nielsen, P.V.; Zhang, C. Exploring the potentials of personalized ventilation in mitigating
airborne infection risk for two closely ranged occupants with different risk assessment models. Energy Build. 2021, 253, 111531.
[CrossRef]
89. Wu, Y.; Tung, T.C.; Niu, J.l. On-site measurement of tracer gas transmission between horizontal adjacent flats in residential
building and cross-infection risk assessment. Build. Environ. 2016, 99, 13–21. [CrossRef] [PubMed]
90. Shao, S.; Zhou, D.; He, R.; Li, J.; Zou, S.; Mallery, K.; Kumar, S.; Yang, S.; Hong, J. Risk assessment of airborne transmission of
COVID-19 by asymptomatic individuals under different practical settings. J. Aerosol Sci. 2021, 151, 105661. [CrossRef]
91. Guo, Y.; Qian, H.; Sun, Z.; Cao, J.; Liu, F.; Luo, X.; Ling, R.; Weschler, L.B.; Mo, J.; Zhang, Y. Assessing and controlling infection
risk with Wells-Riley model and spatial flow impact factor (SFIF). Sustain. Cities Soc. 2021, 67, 102719. [CrossRef]
92. Zivelonghi, A.; Lai, M. Mitigating aerosol infection risk in school buildings: The role of natural ventilation, volume, occupancy
and CO2 monitoring. Build. Environ. 2021, 204, 108139. [CrossRef]
93. Park, S.; Choi, Y.; Song, D.; Kim, E.K. Natural ventilation strategy and related issues to prevent coronavirus disease 2019
(COVID-19) airborne transmission in a school building. Sci. Total Environ. 2021, 789, 147764. [CrossRef] [PubMed]
94. Kembel, S.W.; Meadow, J.F.; O’Connor, T.K.; Mhuireach, G.; Northcutt, D.; Kline, J.; Moriyama, M.; Brown, G.; Bohannan, B.J.;
Green, J.L. Architectural design drives the biogeography of indoor bacterial communities. PLoS ONE 2014, 9, e87093. [CrossRef]
[PubMed]
95. Mohamed, M.F.; King, S.; Behnia, M.; Prasad, D. Coupled outdoor and indoor airflow prediction for buildings using Computa-
tional Fluid Dynamics (CFD). Buildings 2013, 3, 399–421. [CrossRef]
96. Kapoor, N.R.; Kumar, A.; Kumar, A.; Kumar, A.; Kumar, K. Transmission Probability of SARS-CoV-2 in Office Environment Using
Artificial Neural Network. IEEE Access 2022, 10, 121204–121229. [CrossRef]
97. Peng, Z.; Rojas, A.P.; Kropff, E.; Bahnfleth, W.; Buonanno, G.; Dancer, S.; Kurnitski, J.; Li, Y.; Loomans, M.G.; Marr, L.C.; et al.
Practical indicators for risk of airborne transmission in shared indoor environments and their application to COVID-19 outbreaks.
Environ. Sci. Technol. 2022[CrossRef]
98. Afshari, A.; Hultmark, G.; Nielsen, P.V.; Maccarini, A. Ventilation system design and the coronavirus (COVID-19). Front. Built
Environ. Front. Built Environ. 2021, 7, 662489. [CrossRef]
99. Wang, H.; Li, Z.; Liu, Y.; Zhu, L.; Zhou, Z. Experimental study of the dispersion of cough-generated droplets from a person going
up or down stairs. AIP Adv. 2022, 12, 015002. [CrossRef]
100. Blocken, B.; van Druenen, T.; Ricci, A.; Kang, L.; van Hooff, T.; Qin, P.; Xia, L.; Ruiz, C.A.; Arts, J.; Diepens, J.; et al. Ventilation and
air cleaning to limit aerosol particle concentrations in a gym during the COVID-19 pandemic. Build. Environ. 2021, 193, 107659.
[CrossRef]
101. Halvoňová, B.; Melikov, A.K. Performance of “ductless” personalized ventilation in conjunction with displacement ventilation:
Impact of disturbances due to walking person(s). Build. Environ. 2010, 45, 427–436. [CrossRef]
102. Duives, D.; Chang, Y.; Sparnaaij, M.; Wouda, B.; Boschma, D.; Liu, Y.; Yuan, Y.; Daamen, W.; de Jong, M.; Teberg, C.; et al. The
multi-dimensional challenges of controlling SARS-CoV-2 transmission in indoor spaces: Insights from the linkage of a microscopic
pedestrian simulation and virus transmission models. medRxiv 2021[CrossRef]
103. Xiao, Y.; Yang, M.; Zhu, Z.; Yang, H.; Zhang, L.; Ghader, S. Modeling indoor-level non-pharmaceutical interventions during the
COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach. Transp. Policy 2021, 109, 12–23. [CrossRef]
[PubMed]
104. Xu, Q.; Chraibi, M. On the effectiveness of the measures in supermarkets for reducing contact among customers during COVID-19
period. Sustainability 2020, 12, 9385. [CrossRef]
105. Mazumdar, S.; Yin, Y.; Guity, A.; Marmion, P.; Gulick, B.; Chen, Q. Impact of moving objects oncontaminant concentration
distributions in an inpatient ward with displacement ventilation. Hvac&R Res. 2010, 16, 545–563. [CrossRef]
106. Yang, W.; Marr, L.C. Mechanisms by which ambient humidity may affect viruses in aerosols. Appl. Environ. Microbiol. 2012,
78, 6781–6788. [CrossRef] [PubMed]
107. Bluyssen, P.M.; van Zeist, F.; Kurvers, S.; Tenpierik, M.; Pont, S.; Wolters, B.; van Hulst, L.; Meertins, D. The creation of SenseLab:
A laboratory for testing and experiencing single and combinations of indoor environmental conditions. Intell. Build. Int. 2018,
10, 5–18. [CrossRef]
108. Eykelbosh, A. Indoor CO2 Sensors for COVID-19 Risk Mitigation: Current Guidance and Limitations; National Collaborating Centre
for Environmental Health: Vancouver, BC, Canada, 2021.
109. SAGE-EMG. Role of Ventilation in Controlling SARS-CoV-2 Transmission; Tech. Report; SAGE: London, UK, 2020.
110. Ijaz, M.; Brunner, A.; Sattar, S.; Nair, R.C.; Johnson-Lussenburg, C. Survival characteristics of airborne human coronavirus 229E. J.
Gen. Virol. 1985, 66, 2743–2748. [CrossRef] [PubMed]
111. Morris, D.H.; Yinda, K.C.; Gamble, A.; Rossine, F.W.; Huang, Q.; Bushmaker, T.; Fischer, R.J.; Matson, M.J.; Van Doremalen, N.;
Vikesland, P.J.; et al. Mechanistic theory predicts the effects of temperature and humidity on inactivation of SARS-CoV-2 and
other enveloped viruses. Elife 2021, 10, e65902. [CrossRef]
112. Yuan, S.; Jiang, S.C.; Li, Z.L. Do humidity and temperature impact the spread of the novel coronavirus? Front. Public Health 2020,
240. [CrossRef]
113. ISO/DIS 12913-1; Acoustics—Soundscape—Part 1: Definition and Conceptual Framework. ISO: Geneva, Switzerland, 2014.
Buildings 2023, 13, 742 27 of 27

114. Bluyssen, P. TU Delft—Ventilation against the Coronavirus—Philomena Bluyssen. 2022. Available online: https://www.youtube.
com/watch?v=bTiE6bF_PZo (accessed on 4 February 2022).
115. Megahed, N.A.; Ghoneim, E.M. Indoor Air Quality: Rethinking rules of building design strategies in post-pandemic architecture.
Environ. Res. 2021, 193, 110471. [CrossRef]
116. Mendell, M.J.; Fisk, W.J.; Kreiss, K.; Levin, H.; Alexander, D.; Cain, W.S.; Girman, J.R.; Hines, C.J.; Jensen, P.A.; Milton, D.K.; et al.
Improving the health of workers in indoor environments: Priority research needs for a national occupational research agenda.
Am. J. Public Health 2002, 92, 1430–1440. [CrossRef]
117. Shen, J.; Kong, M.; Dong, B.; Birnkrant, M.J.; Zhang, J. Airborne transmission of SARS-CoV-2 in indoor environments: A
comprehensive review. Sci. Technol. Built Environ. 2021, 27, 1331–1367. [CrossRef]
118. Sha, H.; Zhang, X.; Qi, D. Optimal control of high-rise building mechanical ventilation system for achieving low risk of COVID-19
transmission and ventilative cooling. Sustain. Cities Soc. 2021, 74, 103256. [CrossRef] [PubMed]
119. Heo, S.; Nam, K.; Loy-Benitez, J.; Li, Q.; Lee, S.; Yoo, C. A deep reinforcement learning-based autonomous ventilation control
system for smart indoor air quality management in a subway station. Energy Build. 2019, 202, 109440. [CrossRef]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.