# Experimental measurement of respiratory particles dispersed by wind
instruments and analysis of the associated risk of infection transmission

Oliver Schlenczek  Birte Thiede  Laura Turco  Katja Stieger  Jana M. Kosub
Rudolf Müller  Simone Scheithauer  Eberhard Bodenschatz
<EMAIL_ADDRESS>Gholamhossein Bagheri
<EMAIL_ADDRESS>Max Planck Institute for Dynamics and Self-
Organization (MPIDS), Göttingen 37077 , Germany  Institute for Dynamics of
Complex Systems, University of Göttingen, Göttingen 37077 , Germany  Institute
of Infection Control and Infectious Diseases, University Medical Center,
Göttingen 37075, Germany  Institute for Music and Aerosols, Bad Wünnenberg
33181, Germany  Laboratory of Atomic and Solid State Physics and Sibley School
of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853,
USA

###### Abstract

Activities such as singing or playing a wind instrument release respiratory
particles into the air that may contain pathogens and thus pose a risk for
infection transmission. Here we report measurements of the size distribution,
number, and volume concentration of exhaled particles from 31 healthy
musicians playing 20 types of wind instruments using aerosol spectrometry and
in-line holography in a strictly controlled cleanroom environment. We find
that playing wind instruments carries a lower risk of airborne disease
transmission than speaking or singing. We attribute this to the fact that the
resonators of wind instruments act as filters for particles
$>$$10\text{\,}\mathrm{\SIUnitSymbolMicro m}$ in diameter. We have also
measured the size-dependent filtering properties of different types of filters
that can be used as instrument masks. Based on these measurements, we
calculated the risk of airborne transmission of SARS-CoV-2 in different near-
and far-field scenarios with and without masking and/or distancing. We
conclude that in all cases where there is a possibility that the musician is
infectious, the only safe measure to prevent airborne transmission of the
disease is the use of well-fitting and well-filtering masks for the instrument
and the susceptible person.

###### keywords:

Aerosol, wind instrument, airborne disease, face mask, filtration,
transmission risk

††journal: arXiv

##  1 Introduction

Numerous contagious diseases are known to spread via exhaled infectious
particles that are inhaled by a susceptible, e.g. influenza, tuberculosis,
Severe Acute Respiratory Syndrome (SARS), and most recently the Coronavirus
Disease 2019 (COVID-19), which is caused by the Severe Acute Respiratory
Syndrome CoronaVirus 2 (SARS-CoV-2) Leung [2021], Morawska and Cao [2020],
Nannu Shankar et al. [2022]. Currently, the ongoing COVID-19 pandemic is still
considered a public health emergency of international concern WHO Team [2022].
About 8% of the persons tested positive for SARS-CoV-2 with no or very mild
symptoms of COVID-19 (Alpha strain of SARS-CoV-2) had a very high viral load
(more than $10^{9}$ genome copies of the SARS-CoV-2 virus per swab), which
promotes disease transmission by highly-infectious asymptomatic individuals if
high viral load is associated with high infectivity Jones et al. [2021]. It is
well-known that various respiratory maneuvers, like speaking and singing
produce particles [see e.g. Morawska et al., 2009, Pöhlker et al., 2021,
Bagheri et al., 2021a, and references therein]. However, the fact that wind
instruments also release respiratory particles [see e.g. Lai et al., 2011, He
et al., 2021, Kähler and Hain, 2020, Parker and Crookston, 2020, Spahn et al.,
2021, Stockman et al., 2021, Abraham et al., 2021, McCarthy et al., 2021,
Firle et al., 2021, Gantner et al., 2022] indicates that band or orchestra
rehearsals or concerts are also associated with an enhanced risk of airborne
infection transmission (hereafter called “infection risk”). Because of this
potential risk, most forms of live music events were forbidden in most of 2020
in Germany and also other countries. Governmental rules forced opera houses
and theatres to close, and instrument education / singing lessons could not
take place as usual. This unfortunate situation is mainly due to the fact that
much is unknown about the emitted amount of respiratory particles and
effective measures to mitigate the risk of infection during live performances
of singers and wind instrument players.

Among the particle emission from different respiratory activities, not much
attention was given to wind instrument players prior to the COVID-19 pandemic.
It was found that playing a very simple brass instrument such as a vuvuzela
releases much more particles than a person who is shouting Lai et al. [2011].
A study by He et al. [2021] has shown that many wind instruments release more
particles during the play in comparison to normal breathing or speaking.
Instruments in the low register (e.g. tuba, bassoon) typically had less
particle emission compared to instruments in the high register (e.g. clarinet,
trumpet). The selection of instruments examined by He et al. [2021] covers
approximately two orders of magnitude in particle emission from the lowest
(tuba) to the highest emitter (trumpet). For wind instruments with a simple
geometry, increasing loudness was associated with increasing particle
emission, but the net effect of loudness on the emitted particle concentration
was low He et al. [2021]. Abraham et al. [2021] also examined the pitch
dependence and found negative correlation between pitch and particle emission
for instruments in the low register (in particular the bass trombone and the
bassoon) while a positive correlation was found between pitch and particle
emission for instruments in the high register (clarinet, oboe, trumpet). Short
notes played on the trumpet led to more particles compared to long notes of
the same pitch. However, it is still unknown which notes or playing styles on
which instrument lead to the highest emission of particles. A study by
McCarthy et al. [2021] came to the conclusion that the particle size
distributions from breathing and from playing a wind instruments are similar,
and that the mean particle emission is also comparable. Stockman et al. [2021]
however measured higher particle number concentration compared to the data
shown in He et al. [2021] for clarinets (2 times more), bassoon (4 times
more), oboe (4 times more) and French horn (3 times more). The emitted
particle number concentration from the trumpet measured by Stockman et al.
[2021] was 3 times lower compared to He et al. [2021]. Emitted particle number
concentration shown in Firle et al. [2021] is also higher for playing flute or
oboe compared to speaking. In all of the studies mentioned above, and also in
Brosseau et al. [2022], the conclusion was drawn that the highest number
density of emitted particles from wind instruments is
<$1\text{\,}\mathrm{\SIUnitSymbolMicro m}$.

Furthermore, these studies [except for Firle et al., 2021] used an Aerodynamic
Particle Sizer (APS) from TSI Inc. as the primary aerosol size spectrometer,
which nominally measures the aerodynamic diameter of particles in a size range
from 0.5 to $20\text{\,}\mathrm{\SIUnitSymbolMicro m}$. However, it cannot
detect smaller than $0.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ that may
include a significant number of particles containing
$\sim$$0.1\text{\,}\mathrm{\SIUnitSymbolMicro m}$ SARS-CoV-2 virions and, as
pointed out in Bagheri et al. [2021a] and other studies [e.g. see Pöhlker et
al., 2021, and references therein], APS suffers from low detection efficiency
for larger $4\text{\,}\mathrm{\SIUnitSymbolMicro m}$ particles. Furthermore,
it is extremely important to perform measurements in a certified cleanroom
with controlled relative humidity and temperature. For a quantitative
assessment of the infection risk, either the size distribution of the dry
residues or the size distribution of the particles right after emission needs
to be known as described in Nordsiek et al. [2021] since particles in any
state in between exhalation and fully dried would introduce huge uncertainties
in the infection risk calculation. The importance of controlled measurements
becomes clear when one considers that the particle volume decreases by a
factor of about 90 (almost two orders of magnitude) from exhalation to fully
dry Bagheri et al. [2021a].

In terms of infection risk, the most frequently discussed scenario is a well-
mixed room with accumulation of infectious particles. The well-mixed room is
recently used by several investigations [e.g. Buonanno et al., 2020, Nordsiek
et al., 2021, Miller et al., 2020, Jimenez, 2020, Bagheri et al., 2020, Firle
et al., 2021, Kriegel et al., 2022]. Miller et al. [2020] as well as Kriegel
et al. [2022] determined the emission rate of pathogen copies from
retrospective analyses of superspreading events. This is an insightful
simplification of the dose-response model, but it remains unclear which size
fraction of emitted particles drives the release of pathogen copies into the
room. To give a reasonable estimation on the efficacy of infection mitigation
measures, the relationship between particle size and number concentration of
pathogen copies needs to be known. The risk calculation done by Firle et al.
[2021] was based on a constant conversion factor from pathogen copies to
particle number concentration within a specified diameter range. Numerical
simulations carried out by Stockman et al. [2021] have shown that the well-
mixed room assumption is reasonable for indoor spaces of
$18\text{\,}{\mathrm{m}}^{2}$ area and $63\text{\,}{\mathrm{m}}^{3}$ volume if
the susceptible is not standing downwind of the infectious (i.e. the donor of
pathogens) at close distance.

Apart from that, the near-field is of particular relevance in larger indoor
environments [see Hejazi et al., 2022, Bagheri et al., 2021b, and references
therein], for which the the horizontal and vertical spread and dilution of
particles released from the bell end of wind instruments becomes important.
Spahn et al. [2021] investigated the airflow from wind instruments
(contrabassoon, bassoon, bass clarinet, English horn, tenor saxophone,
clarinet, alto flute, oboe, recorder, piccolo, tuba, French horn, trombone and
trumpet) played by professional musicians and found that for all instruments
measured the flow had relaxed to that of the room at a radius of more than
$1.5\text{\,}\mathrm{m}$ from the bell end. This question was also
investigated by Abraham et al. [2021], who found that the so-called aerosol
influence zone (i.e. the distance up to which the released particles were
detectable above the background in the Minnesota orchestra hall) was less than
$0.3\text{\,}\mathrm{m}$ for all instruments examined. These findings suggest
that the source is highly localized before the emission plume gets mixed with
the ambient air, mainly due to the wide opening of the instrument bells and
the resulting low air speed.

As shown in the diagrams of measured wind speed and particle concentration in
Abraham et al. [2021] and in the schlieren photos of Becher et al. [2021], the
approximation of the particle emission plume as a jet emanating from the bell
end of a wind instrument seems reasonable. This is supported by Brosseau et
al. [2022] who with Particle Image Velocimetry (PIV) found a jet flow
geometry. At a distance between 2 to $6\text{\,}\mathrm{m}$, the center
velocity of the air jet from the wind instruments reached room air draft level
very much in agreement with the results by Spahn et al. [2021]. Particle
dispersion to more than a meter away from the wind instrument was also
reported by Gantner et al. [2022]. With this, the far-field is more reasonable
for larger distances and well mixed room models otherwise turbulent dispersion
and transports need to be considered Hejazi et al. [2022].

Due to low flow velocity and the tendency of aerosol-laden air from the wind
instrument to move vertically upwards, the usage of air purifiers above the
instruments seems to be very effective to mitigate accumulation of potentially
infectious aerosol Abraham et al. [2021]. In their computational fluid
dynamics (CFD) simulations Abraham et al. [2021] treated each particle
released from a wind instrument as infectious. The reduction of infectious
particles by the usage of mobile air purifiers was also examined by Narayanan
and Yang [2021] in a music classroom. Air purifiers have a higher cleaning
efficacy at high concentration of aerosols, i.e. close to the source of the
infectious particles Narayanan and Yang [2021]. However, their disadvantage is
emission of sound that may disturb the music played.

The use of a filter mask at the bell end of wind instruments was shown as a
possible mitigation measure by Parker and Crookston [2020] and also examined
and discussed by Abraham et al. [2021], Stockman et al. [2021], and Firle et
al. [2021]. Parker and Crookston [2020], Abraham et al. [2021] and Stockman et
al. [2021] examined particle number concentration with and without a mask and
report penetration for total number concentration. Neither work provided
information on size-specific penetration, so it is not possible to estimate
the risk of infection Nordsiek et al. [2021], Bagheri et al. [2021b]. Firle et
al. [2021] found, in contrast to the results in Abraham et al. [2021] and
Stockman et al. [2021], that surgical masks on the instrument bell do not
reduce the particle concentration emitted from flute, oboe, clarinet or
trumpet. Again, no details on mask penetration versus particle size were given
and mask fit to the instrument bell was not discussed.

Our literature review shows that it is fair to say existing data are
inconsistent when it comes to the concentration of particles produced by a
given musical instrument in different studies, the importance of near-field
versus far-field infection risk, and the influence of instrument masking on
infection risk. In addition, it is critical to consider particle shrinkage in
ambient air and its impact on risk of airborne disease transmission, as well
as size-dependent filtration, which have not yet been considered. The
importance of particle shrinkage becomes clear when one considers that
particle volume can decrease by a factor of 90 in a fraction of a second,
e.g., from exhalation to measurement Bagheri et al. [2021a]. The impact of
size-dependent filtration on infection risk has also been shown to be
extremely important Bagheri et al. [2021b]. These problems can be attributed
in part to due to the large variability in measurement methods, the different
levels of environmental control, limitations of measurement instruments,
uncontrolled sampling, and/or lack of representative statistics.

Our primary goal in this study is to address these issues all at once. We have
studied different types of wind instruments in a strictly controlled cleanroom
and under controlled sampling conditions using aerosol spectrometry and in-
line holography. We have studied the size and number concentration of
particles produced in the air exhaled by 31 musicians playing a total of 20
wind instrument types. In total 300 minutes of spectrometry data and 24000
holograms to present the field-standardized, normalized number-size
distribution in the size range of $1.5\text{\,}$ to
$40\text{\,}\mathrm{\SIUnitSymbolMicro m}$ diameter is collected. The particle
shrinkage is taken into account by controlling the relative humidity of the
sampling tubes and recalculating the wet diameter of the particles at the time
of exhalation. We characterized 12 types of filter pieces used as instrument
masks and generated size-dependent filtration curves. Using these data, we
performed a through analysis of the risk of transmission in the near field
according to the upper bound concept presented in Bagheri et al. [2021b] and
also in the far field in a room for different masking and social distancing
scenarios.

In the following, we first describe the particle measurement setup, the
experimental procedure, data processing, information about the musicians, and
the infection risk model. This is then followed by the Results and Discussion
section, in which the particle size distributions measured from different wind
instruments are presented in detail and compared with particle emission during
normal breathing, speaking, and singing. Finally, the upper bound of infection
risk is presented in different exposure scenarios with and without social
distancing and/or instrument/susceptible masking in the near and far field. In
contrast to previous studies, we also estimate the the risk of infection for
musicians playing the wind-instruments, as they are typically unable to
protect themselves with face masks. The manuscripts ends with a summary of the
main findings and conclusions.

##  2 Materials and Methods

###  2.1 Experimental setup and measurement procedure

For measurement of the particulate matter emitted by playing wind-instruments,
we use a similar setup as described in Bagheri et al. [2021a]. In contrast to
previous studies [e.g. He et al., 2021, McCarthy et al., 2021], we measured
the size distribution of the particle residues (dry at RH $<30\%$) and
calculate the full wet size distribution at exit from the instrument from the
diameter shrinkage factor derived in Bagheri et al. [2021a], i.e. we multiply
the dried diameter by a factor of 4.5 to obtain the diameter at the time of
production (hereafter referred to as the exhaled diameter). All measurements
were performed in a nominal ISO Class 6 cleanroom (i.e. less than 1 million
particles per $\text{\,}{\mathrm{m}}^{3}$). However, our measurements have
shown that the cleanroom met at least ISO Class 4 criteria, i.e. less than
1000 >$0.3\text{\,}\mathrm{\SIUnitSymbolMicro m}$ particles per
$\text{\,}{\mathrm{m}}^{3}$.

Setup

The main setup consisted of two aerosol size spectrometers, which were an
Optical Particle Sizer Type 3330 (OPS) and a Nanoscale Scanning Mobility
Particle Sizer Type 3910 (SMPS), both from TSI Inc. In the very beginning of
the study, a third spectrometer, the Aerodynamic Particle Sizer Model 3321
(APS) from TSI Inc., was also connected to the setup. The SMPS uses a flow
rate of $0.75\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ and the OPS uses
$1\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$. If the APS was connected
to the setup, it added $5\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ to
the total flow rate ($1\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ sample
flow plus $4\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ sheath flow).
Both OPS and SMPS had sampling times of one minute per sample. While all
experiments had valid OPS data, valid SMPS and APS data are only available for
some of the measurements (see Table 1 in Supplementary Information (SI)). In
the 16 logarithmically equidistant channels of the OPS, we obtain less
resolution, but better counting efficiency (less losses) for particles larger
than $4\text{\,}\mathrm{\SIUnitSymbolMicro m}$ compared to an APS [see Pöhlker
et al., 2021, for more details], and we had two channels measuring particles
smaller than $0.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$. There is a
reasonable agreement between OPS and APS up to
$4\text{\,}\mathrm{\SIUnitSymbolMicro m}$ particle diameter (see Fig. 2 of
SI). According to the SMPS user manual, each of the 13 SMPS channels is
scanned for approximately $4.6\text{\,}\mathrm{s}$ per sample. As the sample
flow rate is $0.75\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$, the sample
volume for each channel in each one-minute sample is
$58\text{\,}{\mathrm{cm}}^{3}$, which yields a physical detection limit of
$0.017\text{\,}{\mathrm{cm}}^{-3}$ for the SMPS to measure one count. The
spectrometers are connected via electrically conductive polytetrafluorethylene
(PTFE) tubing with $6\text{\,}\mathrm{mm}$ inner diameter to a flow splitter,
which is connected via the same kind of tubing to two serial diffusion dryers
(Grimm Model 7813), filled with silica gel. The outermost diffusion dryer is
connected via the same kind of tubing to a sampling funnel made of plastic.
Via intercomparison between the particle size distributions measured with the
setup described in Fig. 1 A and playing the instrument directly into the inlet
nozzle of the OPS, we could verify that the material of the funnel (non-
conductive plastic) did not introduce notable electrostatic losses (see Fig. 3
and 4 of SI). Particle losses due to the diffusion dryers are approximately
25% and independent of the particle size (see Bagheri et al. [2021a] for more
details). The total tube length was $4.2\text{\,}\mathrm{m}$ from the funnel
to each aerosol size spectrometer. For measuring the particle size
distribution from wind instruments, we used two collection funnels of
different size (a smaller one with $100\text{\,}\mathrm{mm}$ diameter, and a
larger one with $150\text{\,}\mathrm{mm}$), depending on the bell diameter of
the instrument. We expect acceptable inlet efficiency around 50 - 80% for the
setup with the collection funnel for particles
<$3.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ (more details in Subsection 1.2.
and Fig. 1 in SI). The standard setup for measuring the particle emission at
the end piece or bell end of a wind instrument is shown in Fig. 1A. During
those measurements where particle leakage from the nose or embouchure (i.e.
next to the mouth) was measured in parallel with emission at bell end, a
second funnel was placed in front of mouth and nose, connected to another OPS
(see Fig. 1B). For instruments with tone holes, we did additional measurements
as described in Fig. 1C to determine the particle leakage from the tone holes
in comparison to the particles emitted from the bell end. The role of leakage
from mouth and nose, and the leakage from the tone holes is discussed in
detail in Subsection 2.5. of the SI. Between the funnel and the bell end,
there was a narrow gap for air to escape in order to operate the aerosol size
spectrometers at ambient air pressure. As already studied by Bouhuys [1964],
the peak exhale volume of wind instrument players is much larger than the
sampling flow rate used in our setup (up to
$100\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ for a tubist compared to
a maximum sample flow rate of
$6.75\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$), so dilution due to the
small gap at the side can be assumed to be negligible. In addition to the
measurements of the particle emission at the open bell end, we examined the
penetration of masks made of different materials similar to setup A in Fig. 1.
Mask penetration was also measured with dolomite test dust as described in
Bagheri et al. [2021b] to obtain better counting statistics in particular for
large particles. More details about the mask materials and their penetration
can be found in Subsections 2.13. to 2.16. of the SI. In the following we call
these filter pieces “instrument-masks”.

Figure 1: Schematics of measurement setups used inside the cleanroom for
characterizing exhale particles produced by playing wind instruments. The
standard configuration is setup A, where a funnel is placed at the bell end of
the instrument and the particle-laden air passes two diffusion dryers (orange
boxes) before reaching the aerosol size spectrometers. Setup B is used to
measure particle emission from mouth and nose during the play of the wind
instrument, here no dryer is used. Setup C is used for some woodwind
instruments to measure leakage from the tone holes without a funnel connected
to the tube end. Both setups B and C were optional while setup A was used for
all instrument / subject combinations. If an instrument-mask was used, the
funnel in setup A was placed onto the fabric (which covers the bell end) to
avoid cleanroom air bypassing the funnel edge.

In addition to the dry particle measurements, the exhaled diameter of droplets
within the first ten centimeters from the source was measured with the same
holographic setup as described in Bagheri et al. [2021a]. The subject
positions the bell end of the wind instrument or the tube end of a brass
mouthpiece at about $2\text{\,}$ to $3\text{\,}\mathrm{cm}$ upstream of the
holographic sample volume and at the same horizontal level as the laser beam.
The air with the particles crosses the sample volume of the holography unit
and then approaches a collection funnel with an inner diameter of
$150\text{\,}\mathrm{mm}$, which acts as aerosol sampling device and guides
the particles that are small enough to follow the flow via the two dryers to
the aerosol size spectrometers. Due to the larger distance between aerosol
source and collection funnel, dilution will occur and these data are used only
for qualitative comparison. Results of the holographic measurements can be
found in Subsection 2.12. of the SI and in Subsection 3.3. The view volume of
the holographic setup is about $37\text{\,}{\mathrm{cm}}^{3}$ and its volume
sampling rate is about $13.6\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$.
The holograms contain the diffraction pattern from each object in the sample
volume, and via numerical reconstruction on a high performance computation
(HPC) cluster, the size, location and shape of each particle from about
$6\text{\,}\mathrm{\SIUnitSymbolMicro m}$ to some $\text{\,}\mathrm{mm}$ of
particle diameter can be obtained [details in Fugal et al., 2009, Schlenczek,
2018]. With this setup, we measured the particle size distribution and the
local number density of particles $>$$6\text{\,}\mathrm{\SIUnitSymbolMicro
m}$. Details about the size calibration of the holographic setup can be found
in the Supplementary Information of Bagheri et al. [2021a].

Measurement procedure

For each experimental run the subject(s) as well as the examiner(s) were
required to wear ISO 4/5 clean-room overalls, hoods, and overboots. If
possible they also wore clean-room gloves and face masks. All papers used were
clean-room certified [details in Bagheri et al., 2021a]. Instruments.
computers, and wind instruments were wiped with clean-room cloth to minimize
contamination of the results by dust. Before the measurements in order to
avoid artifacts from dust from inside the wind-instrument, they were either
flushed with particle-free air, or were played for at least two minutes before
the actual experiment started. In contrast to measuring particle emission from
mouth and nose, for wind-instruments we need to account for the time of
particle transport from the mouthpiece through the wind instrument to the
particle spectrometers. Thus, the activities started at least
$30\text{\,}\mathrm{s}$ before sampling was started. This time for particle
transport was increased to $90\text{\,}\mathrm{s}$ for wind-instruments with
large volume (e.g. B-flat tuba, see Fig. 6 in SI). Each measurement sample was
$60\text{\,}\mathrm{s}$ long, and each experiment consisted of at least one
sample. The subject played at least one piece of music selected by themselves,
some also played single long notes at different pitch, or multiple pieces of
music.

###  2.2 Data processing

For the OPS data, a detectability correction was applied. Basically, the
sensitivity for $0.3\text{\,}\mathrm{\SIUnitSymbolMicro m}$ and
$0.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ given in the calibration sheet was
interpolated linearly to match the center diameter of the first two OPS
channels. For $0.3\text{\,}\mathrm{\SIUnitSymbolMicro m}$ particle diameter,
the sensitivity was typically around 0.5, and for
$0.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ particle diameter, the sensitivity
was close to 1. The sensitivity correction factor is the inverse of the
sensitivity interpolated for the channel mid-points. With this sensitivity
correction, a good agreement was found between optical diameter of the OPS and
electrical mobility diameter of the SMPS (an example is shown in Subsection
3.3).

Due to the fact that the particle emission is concentrated in the center
region of the holographic sample volume, the sample volume used for the data
analysis has been restricted to $\pm$ $3\text{\,}\mathrm{cm}$ in crosswise
direction from the center of the emission plume (i.e. where most particles
were found), and the outer $2.5\text{\,}\mathrm{mm}$ on both sides in
streamwise and vertical direction have been cut off. The remaining sample
volume is
$$1.44\text{\,}\mathrm{cm}$\times$6\text{\,}\mathrm{cm}$\times$0.8\text{\,}\mathrm{cm}$=$
$6.9\text{\,}{\mathrm{cm}}^{3}$. We also found that the sizes below
$8\text{\,}\mathrm{\SIUnitSymbolMicro m}$ have reduced detectability, so the
concentrations below $8\text{\,}\mathrm{\SIUnitSymbolMicro m}$ particle
diameter in the holography data need to be interpreted with care. From
$8\text{\,}\mathrm{\SIUnitSymbolMicro m}$ to larger sizes, we did not see a
notable reduction in detectability. In the size range above
$50\text{\,}\mathrm{\SIUnitSymbolMicro m}$, we are typically limited by
counting statistics when examining human exhale particles.

###  2.3 Subjects, instruments and music

A call for participation was published via various communication channels.
Flyers were displayed at the University Medical Center Göttingen (UMG), an
announcement was posted on the UMG Facebook page, music students at the
Hanover University of Music, Drama and Media (Hochschule für Musik, Theater
und Medien Hannover HMTMH) were informed by their professors, and all staff
involved in the study asked their friends to participate if they played a
wind-instrument. An overview of the subjects, the instruments played and the
number of experiments can be found in Table S1 of the SI. A total of thirty-
one musicians, ranging in age from 14 to 80 years, with an average age of 35
years, participated in the study. Five participants were from HMTMH, 8 played
in a traditional German brass ensemble (”Blasorchester”), four were from a
traditional German wind ensemble, four participants played in a trombone choir
(”Posaunenchor”), 2 played in jazz bands and one played in a marching band
(”Spielmannszug”). Due to fundamental differences in instrumentation and style
between international wind ensembles, we decided to use the German term below
in addition to the English. At the time the study was conducted, neither
point-of-care tests nor vaccinations against SARS-CoV-2 were available. For
all participants, an anamnesis was performed for possible risk situations due
to close contact with persons infected with SARS-CoV-2 or travel to risk areas
or virus variant areas as defined by the German Robert Koch Institute within
the last 14 days, and they were asked about their health status. During all
phases of the study, the study team and the participants strictly adhered to
the official regulations and the Institute’s hygiene plan. With the exception
of the musician whose particle emission was measured, all participants wore
FFP2 masks throughout. None of the subjects felt ill on the day of the
measurements and no COVID-19 infections were traceable to the study.
Participants with symptoms or people closely exposed to someone tested
positive for SARS-CoV-2 were excluded from the study. The study was ethically
approved by the Max Planck Society. All subjects participated voluntarily,
were informed in advance about the conduct of the experiment and subsequently
consented to their participation. The subjects had to be legally competent and
not impaired to carry out activities in this study. For participants below the
age of 18 years, both the participants themselves and their legal guardians
gave consent. Participation could be revoked at any time during the study.

Instruments

For the purpose of this study we use the following categories for wind-
instruments: Brass instruments are wind-instruments where the tone is
generated by fast opening and closing of the player’s lips within the
mouthpiece. Although the instruments are typically made of brass this also
includes other materials like metals, plastics, or wood. Woodwind instruments
we put into three categories: edge-blown , i.e., piccolo, flute, fife, and
recorder ; single-reed, i.e., clarinet and saxophone; and double-reed, i.e.,
oboe, bassoon and chanter of bagpipes. The sound from edge-blown woodwind
instruments is created by blowing a fast air stream onto a sharp edge. In a
single-reed instrument, the sound is created by the fast opening and closing
of the air channel between the reed and the actual mouthpiece. Double-reed
instruments use the same principle but the air channel is formed between both
reeds. All clarinets in the study were clarinets in B-flat, all trumpets were
trumpets in B-flat (with piston or rotary valves), and all trombones were
tenor slide trombones in B-flat. The double horn in our study was played in
B-flat only. The single-reed instruments in the study are all exposed single-
reed instruments (the reed is actually in the player’s mouth) while all
double-reed instruments examined in this work are double-reeds in a windcap.
An illustration of the wind instruments examined in our study is shown in Fig.
2.

Figure 2: Illustration of the wind instrument types examined in this study
grouped in different categories, namely brass instruments (top row: piccolo
trumpet, trumpet, Kuhlo horn, middle row: hunting horn, tenor trombone,
tenorhorn, bottom row: double horn, F tuba, Bb tuba), edge-blown woodwind
instruments (top row: fife, alto recorder, bottom row: piccolo, flute),
single-reed woodwind instruments (left: clarinet, soprano sax, middle: alto
sax, right: tenor sax), and double-reed woodwind instruments (from left to
right: practice chanter, Great Highland Bagpipes, Scottish Smallpipes).

Music

The music pieces were chosen by the volunteers, each of whom chose something
comparable to what they often play and have experience with. Some played
pieces for symphony orchestras or small ensembles, others played pieces from
traditional music, film music, jazz and military music. This differs from He
et al. [2021], where a standardised pattern was played. In this study, we
focused on music that is typically played over a long period of time,
comparable to a rehearsal or concert situation for the volunteers. We chose
this approach to better understand real-life music events. This allowed us to
identify scenarios with lower or higher emissions. Some subjects performed a
series of experiments where they played long tones at different pitches to
investigate the relationship between aerosol emission and the tone played (see
Table S1 in the SI for more details).

###  2.4 Exposure Scenarios

Though there are four possible combinations of susceptible and infectious in a
person-to-person exposure scenario, here we will mainly focus on an infectious
musician (with “musician” we mean a wind instrument player) and a susceptible
other person. For the different exposure scenarios, we need to differentiate
between near-field and far-field exposure. The following subsections present
in detail the methods and assumptions used to calculate the risk of infection
from near- and far-field exposure in our study, but first we explain the
general assumptions that apply to both exposure scenarios.

For the susceptible wearing a fitted FFP2 mask, we assume effective
penetration as in Bagheri et al. [2021b] for the “adjusted FFP2 mask”, which
is fitted to the face without fit-test. From the different materials tested
for filtering outflow of music instruments (see Subsection 2.13 with Figures
S11 and S12 of SI), we found “filter05” to have the lowest penetration and
decided to use this material for the instrument-mask. The effect of using
other mask materials will be also discussed in the Results section. This
material also had acceptable acoustic impedance according to the musicians who
used it during the measurements. Size-specific penetration is taken into
account by using the parameters obtained by the power-law fit for penetration
of dry dolomite dust in Subsection 2.13. of SI. We assume that the infectious
particles are already dry when they reach the mask material resulting in a
lower filtration efficiency compared to the assumed diameter of the exhaled
particles and thus a higher risk of infection. For all scenarios we assume
that the wind instrument player plays the instrument without any interruption
(i.e. no pause), which precludes a breakdown of the air jet emanating from the
instrument bell and makes distancing less effective. Effects from e.g.
different breathing rates will be discussed in the respective Results
subsection.

####  2.4.1 Exposure Scenarios: near-field

In all near-field scenarios, we assume that there is a distance of $r_{1}=$
$0.5\text{\,}\mathrm{m}$ between the face of the infectious musician and the
bell end of the wind instrument. Also, we assume that there is an air-jet
emanating from the nose during breathing, and from the bell end of the wind
instrument during playing. It is assumed the musician is not speaking or
singing. To give an upper bound on exposure, we assume that the air-jet from
the nose goes horizontally towards the susceptible. In scenarios with
distance, the distance between susceptible and instrument bell end will be
$r_{2}=$ $1.5\text{\,}\mathrm{m}$. We will consider three near-field exposure
scenarios, which are illustrated in Fig. 3.

  * 1.

Distancing: Instrument bell end of infectious and face of susceptible are
separated by a distance $r_{2}=$ $1.5\text{\,}\mathrm{m}$ and oriented face-
to-face. None of the two uses a mask.

  * 2.

Mixed: Instrument bell end of infectious and face of susceptible are separated
by a distance $r_{2}=$ $1.5\text{\,}\mathrm{m}$ and oriented face-to-face.
Susceptible is wearing an adjusted FFP2 mask.

  * 3.

Masked: Instrument bell end of infectious and face of susceptible are
separated by a distance $r_{2}=$ $0\text{\,}\mathrm{m}$ and oriented face-to-
face. The instrument bell is covered with an instrument-mask and the
susceptible is wearing an adjusted FFP2 mask. The air inhaled by the
susceptible is primarily the undiluted air that was exhaled by the infectious
through the instrument and filtered by the instrument-mask.

For each scenario, we will show two extremes, which are a wind instrument with
a high particle-volume-concentration emission and an instrument with a low
particle-volume-concentration emission. They will be highlighted by “Max” and
“Min”. Particle sizes from $1.5\text{\,}$ to
$45\text{\,}\mathrm{\SIUnitSymbolMicro m}$ exhaled diameter
($0.3\text{\,}\mathrm{\SIUnitSymbolMicro m}$ to
$10\text{\,}\mathrm{\SIUnitSymbolMicro m}$ measured dry diameter ) are taken
into account.

Figure 3: Exposure scenarios in the near field with one infectious musician
(left) and one susceptible (right). Between the nose of the infectious and the
instrument bell, a distance of $r_{1}=$$0.5\text{\,}\mathrm{m}$ (in blue) is
assumed. In the distancing and mixed scenario, the susceptible keeps a
distance of $r_{2}=$$1.5\text{\,}\mathrm{m}$ (in red) to the instrument bell.
In the masked scenario, the wind instrument is covered with an instrument-
mask, and the susceptible stands directly in front of the (covered) instrument
bell, i.e. $r_{1}=$$0.5\text{\,}\mathrm{m}$, $r_{2}=$$0\text{\,}\mathrm{m}$.
In the mixed and masked scenario, the susceptible wears an FFP2 mask fitted
manually to the face without fit test as detailed in Bagheri et al. [2021b].

####  2.4.2 Exposure Scenarios: far-field

The far-field scenarios are not as complicated as the near-field scenarios
because the distance between the infectious musician and the susceptible is by
definition large enough to have no near-field exposure. This allows the use of
well-mixed model, where it is assumed that all particles exhaled by the
infectious musicians are immediately mixed throughout the whole volume of the
room as long as the room is not too large, e.g., no larger than a classroom or
small office. Here, we can examine all possible mask combinations as also
shown in Fig. 4:

  * 1.

Room-Unmasked: Neither infectious musician nor susceptible use or wear a
instrument/face mask.

  * 2.

Room-Instrument-mask: Infectious musician uses an instrument-mask on the wind
instrument, susceptible does not wear a mask.

  * 3.

Room-Mixed: Infectious musician does not use an instrument-mask, susceptible
wears an adjusted FFP2 mask.

  * 4.

Room-Masked: Infectious musician uses an instrument-mask and susceptible wears
an adjusted FFP2 mask.

Figure 4: Illustration of the exposure scenarios in the far-field. Distances
are assumed to be large enough such that no exposure to the near-field occurs.
The scenarios are “Room-Unmasked” where neither infectious nor susceptible
uses a mask, “Room-Mixed” where the susceptible wears a fitted FFP2 mask,
“Room-Instrument-mask” where the infectious uses a mask for the instrument,
and “Room-Masked” where both use masks. The room has a specific volume and
also a specific ventilation rate.

###  2.5 Calculation of the risk of infection transmission

By measuring the emitted particle size distribution from different musicians
playing different wind instruments, we obtain the basis for calculating the
risk of infection transmission (hereafter “infection risk”) of a susceptible
person (i.e. a person who is exposed to a potential pathogen and can become
infected) for SARS-CoV-2 in a typical live music environment if the musician
is considered to be the infectious, i.e. the donor of pathogen copies. We
assume that we have one susceptible sitting in the audience, and one
infectious musician playing a wind instrument. It is assumed that the
susceptible has not absorbed any SARS-CoV-2 copies prior to exposure. In this
paper, we assume a dose-response relationship such that the absorption of a
given pathogen dose by the susceptible will lead to some probability of
infection. Even though individuals with some degree of immunity (e.g.
vaccinated or recovered individuals) might not get infected at all after being
exposed to a high number of pathogen copies, we assume that our susceptible
individual does not have any immunity against the respective pathogen. This
assumption is part of the upper bound concept as described in Bagheri et al.
[2021b]. As mentioned above, we consider both near- and far-field infection
risk in a number of scenarios. As the risk of infection is the result of a
stochastic process, adding more infectious or susceptible persons to the
scenario increases complexity but does not significantly improve insight.
Similar to the scenarios in Bagheri et al. [2021b], we examine the upper bound
of infection risk for near-field exposure, but also for the far-field
scenario. For the calculation of the risk of infection, the exponential model
as described in Nordsiek et al. [2021] is used in a simplified way, i.e. the
classical mono-pathogen model, as for SARS-CoV-2 the differences to consider
the poly-pathogen are negligible. The mean infection risk $R_{I}$ is given by
Eq. 1 where $\mu$ is the absorbed dose (which is time-dependent), and
$\textnormal{ID}_{63.21}$ is the infectious dose required for 63.21% chance of
infection.

$R_{I}=1-\exp(-\mu/\textnormal{ID}_{63.21})$ (1)

Here we assume $\textnormal{ID}_{63.21}=200$ as in Bagheri et al. [2021b],
which is a good approximation of the infectious dose for the B.1.617.2 (Delta)
strain of SARS-CoV-2. A discussion about $\textnormal{ID}_{63.21}$ and
infection risk for different strains of SARS-CoV-2, e.g. wild type, Delta and
Omicron variants, is given in Subsection 2.17 of SI. The absorbed dose $\mu$
depends on exposure duration $t$, inhaled volumetric flow rate of the
susceptible $Q_{S}$ (which is the same as the exhaled volumetric flow rate),
the concentration of pathogen copies in the inhaled air per particle diameter
$n_{P,k}$, the dilution factor $f_{d}$ for mixing of the exhaled air with the
ambient air before inhalation and the effective penetration $P_{I,k}$ of the
mask the infectious is wearing, and the effective penetration $P_{S,k}$ of the
mask the susceptible is wearing. Effective penetration means that total inward
leakage is taken into account. Unmasked susceptible means $P_{S,k}=1\forall k$
and unmasked infectious means $P_{I,k}=1\forall k$. Also, the deposition
within the respiratory tract $D_{rt,k}$ as a function of particle size needs
to be known. Finally, $\mu$ can be expressed by Eq. 2 for $k=1,2,...,K$
particle size channels considered. We assume conservation of mass in a sense
that exhalation and inhalation flow rates are equal.

$\mu=\underbrace{t}_{\text{exp. time}}\cdot\underbrace{Q_{S}}_{\text{inhal.
susc.}}\cdot\underbrace{f_{d}}_{\text{dilution}}\cdot\sum_{k=1}^{K}{\underbrace{n_{P,k}}_{\text{path.
conc.}}\cdot\underbrace{P_{I,k}}_{\text{mask
inf.}}\cdot\underbrace{P_{S,k}}_{\text{mask
susc.}}\cdot\underbrace{D_{rt,k}}_{\text{dep. r.t.}}}$ (2)

Values for $D_{rt,k}$ and $Q_{S}$ are taken from International Commission on
Radiological Protection [1994] while hygroscopic particle growth in the
susceptible airways is also taken into account as explained in Bagheri et al.
[2021b] (to be discussed later).

To calculate $n_{P,k}$ for each size channel $k$, we need $N_{k}$ and also
need to take into account that the particle diameter at exhale $D_{0}$ is by a
factor of 4.5 larger than the particle diameter $D_{dry}$ measured after the
diffusion dryers as described in Bagheri et al. [2021a]. We also need to know
the concentration of pathogens $\rho_{p}$ in the airway surface liquid (ASL).
For $D_{dry}$ in $\text{\,}\mathrm{\SIUnitSymbolMicro m}$, $\rho_{p}$ in
pathogen copies $\text{\,}{\mathrm{cm}}^{-3}$ and $N_{k}$ in particles
$\text{\,}{\mathrm{cm}}^{-3}$, we get Eq. 3 with $n_{P,k}$ in the unit of
pathogen copies per liter of exhaled air.

$n_{P,k}=10^{-9}\pi\rho_{p}N_{k}(4.5D_{dry,k})^{3}/6$ (3)

Here, $N_{k}$ for the respective activity needs to be used (e.g. mean or
maximum particle emission for playing a specific wind instrument). For
$\rho_{p}$, we assume a value of $10^{8.5}$ $\text{\,}{\mathrm{cm}}^{-3}$.
There is some evidence that the viral load is not the same within the entire
respiratory tract Coleman et al. [2021]. Recent knowledge points at particles
<$5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ dry diameter (up to
$22\text{\,}\mathrm{\SIUnitSymbolMicro m}$ diameter at exhalation) carrying
the majority of virions exhaled for the Delta variant but particles
originating from the lung are expected to have lower viral load for the
Omicron variant [see e.g. Chen et al., 2021, Chan et al., 2021, Halfmann et
al., 2022]. Coleman et al. [2021] found a high concentration of virions in the
fine particle fraction for phonetic activities, but it remains unclear where
the main source of infectious particles is located. It could for example be
saliva, or the epithelial lining fluid (ELF) in the deep airways, or the mucus
covering the vocal folds and the throat, or nasal discharge running from the
nose into the throat.

####  2.5.1 Calculation of the infection risk in the near-field

For a near-field exposure scenario, the dilution factor $f_{d}$ depends on the
distance between infectious and susceptible. For a distancing scenario,
$f_{d}$ can be calculated via Eq. 4 where $a$ is the aperture radius of the
source (e.g. mouth radius or radius of bell end of a wind instrument), $r$ is
the distance between source and susceptible, and $\alpha$ is the cone half-
angle of the exhaled air jet as described in Yang et al. [2020].

$f_{d}=a/(r\tan(\alpha))$ (4)

Here we chose $a=$$1.8\text{\,}\mathrm{cm}$ and $\alpha=$
$10\text{\,}\mathrm{\SIUnitSymbolDegree}$ for the emission from mouth or nose
of the infectious [same as in Bagheri et al., 2021b], and
$a=$$3\text{\,}\mathrm{cm}$ and $\alpha=$
$10\text{\,}\mathrm{\SIUnitSymbolDegree}$ for the emission from the wind
instrument. This yields $f_{d}=0.11$ for the emission from the wind instrument
at $r=$ $1.5\text{\,}\mathrm{m}$, $f_{d}=0.05$ for the emission from mouth and
nose at $r=$ $2\text{\,}\mathrm{m}$, and $f_{d}=0.2$ for the emission from
mouth and nose at $r=$ $0.5\text{\,}\mathrm{m}$. If the infectious is using a
mask, we assume undiluted exhaled air ($f_{d}=1$) inhaled by the susceptible.
a jet flow from the instrument bell and from mouth or nose of the infectious
means implicitly that particle transport by convection is neglected. The role
of convection is discussed for example in Hedworth et al. [2021] Also, the
possibility of the susceptible being lucky as a non-zero background flow
carries away the infectious particles is not considered here.

In terms of emission sources, we need to take into account that an infectious
musician is not only playing the wind instrument (with infectious particles
released mostly from the bell end) but also breathing, with infectious
particles released from mouth and nose. By taking this into account, Eq. 2
needs to be modified. As the volumetric flow rate for exhalation through the
wind instrument is typically different from the volumetric flow rate for
normal breathing, we decided to take the volume-weighted average of emitted
pathogen concentration at the location of the susceptible. The additional
terms are the exhalation flow rate $Q_{w}$ of the infectious through the wind
instrument, the exhalation flow rate $Q_{b}$ of the infectious breathing, the
dilution factor $f_{d,w}$ for the exhale from the wind instrument, and
$f_{d,b}$ for the exhale from breathing. Note that the specific emitted
concentration of pathogen copies is $n_{P,k,w}$ for the wind instrument, and
$n_{P,k,b}$ for breathing. Here, an instrument-mask is considered in the term
$P_{I,k,w}$ and we assume that mouth and nose of the infectious are unmasked.
Finally, $\mu$ for the near-field exposure can be calculated via Eq. 5.

$\mu={t}{Q_{S}}\sum_{k=1}^{K}(Q_{w}f_{d,w}{n_{P,k,w}}{P_{I,k,w}}+Q_{b}f_{d,b}n_{P,k,b}){P_{S,k}}{D_{rt,k}}/(Q_{w}+Q_{b})$
(5)

####  2.5.2 Calculation of the infection risk in the far-field

A real-world exposure scenario is expected to be somewhere in between a near-
field exposure and a far-field exposure scenario. We examine the infection
risk in the far-field by assuming a small, well-mixed room. The room is
assumed to have an area $A=$ $12\text{\,}{\mathrm{m}}^{2}$ and a ceiling
height of $h=$ $2.5\text{\,}\mathrm{m}$, which yields a volume $V=$
$30\text{\,}{\mathrm{m}}^{3}$. This room is assumed continuously cleaned from
particles by an air purifier with an equivalent clean air delivery rate (CADR)
of 4 air-changes-per-hour (ACH). Only the infectious and the susceptible are
in this room. The initial concentration of pathogen copies in the room air is
zero. In a well-mixed room of volume $V$, we use the pathogen concentration
$n_{P,k}$ derived from the particle concentration $N_{k}$ and size to
calculate the infection risk as explained in detail in Nordsiek et al. [2021].
The rate of change in $n_{P,k}$ as expressed in Eq. 6 is given by the source
term (which is the emission of pathogen copies into the room by the
infectious) and four sink term: (i) the removal of pathogens via inhalation by
the infectious, (ii) the removal of pathogens via inhalation by the
susceptible (this yields the absorbed dose per time for the susceptible, which
determines the infection risk), (iii) the removal of pathogens due to
ventilation of the room or usage of an air purifier with $\beta_{v}$ in the
unit of air changes per second, and (iv) the pathogen inactivation $\gamma$,
which is approximately $0.64\text{\,}{\mathrm{h}}^{-1}$ as described in van
Doremalen et al. [2020]. A recent study by Oswin et al. [2022] shows that
pathogen inactivation might depend on relative humidity and ambient CO2
concentration and could actually be faster compared to the data in van
Doremalen et al. [2020]. As we are interested in the upper bound on exposure
to infectious particles, we use the more conservative inactivation rate by van
Doremalen et al. [2020].

$dn_{P,k}/dt=\underbrace{S_{k}}_{\text{emission}}\underbrace{-Q_{I}D_{rt,k}n_{P,k}/V}_{\text{inhal.
inf.}}\underbrace{-Q_{S}D_{rt,k}n_{P,k}/V}_{\text{inhal.
sus.}}\underbrace{-\beta_{v}n_{P,k}}_{\text{ventilation}}\underbrace{-\gamma
n_{P,k}}_{\text{inactiv.}}$ (6)

The reduction in pathogen concentration due to inhalation by the susceptible
changes if the susceptible is wearing a mask. With $P_{S,k}$ being the
effective mask penetration (including leakage), we get
$-Q_{S}(1-P_{S,k}^{2}(1-D_{rt,k}))n_{P,k}/V$ as the third term in Eq. 6.

For removal of infectious particles by an air purifier with a non-ideal
filter, $\beta_{v}=Q_{v}(1-P_{f,k})/V$ with the clean air delivery rate
$Q_{v}$, room volume $V$ and size-dependent filter penetration $P_{f,k}$. The
source term $S_{k}$ may consist of multiple emission terms if there are
combined activities, e.g. breathing and also playing a wind instrument. For
both breathing and playing a wind instrument, we can write the source term as
in Eq. 7

$S_{k}=\underbrace{Q_{I,w}n_{P,k,w}}_{\text{path. from
instrument}}\underbrace{P_{I,k}}_{\text{mask
penetr.}}+\underbrace{Q_{I,b}n_{P,k,b}}_{\text{path. from breathing}}$ (7)

where $Q_{I}$ is the exhale volumetric flow rate with $w$ for playing wind
instrument and $b$ for breathing, and the size-dependent emitted concentration
of pathogen copies $n_{P,k,w}$ and $n_{P,k,b}$ for each activity. $P_{I,k}$ is
the penetration of an instrument-mask which covers the instrument but not
mouth and nose of the infectious musician. For an upper bound on exposure, we
use $Q_{I,w}$ from Bouhuys [1964], and $Q_{I,b}$ from International Commission
on Radiological Protection [1994] where we assume relaxed breathing while
seated ($Q_{I,b}=$ $9\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$). This
is the same volumetric flow rate as for the susceptible ($Q_{S}$) who breathes
while seated.

The effect of ventilation or particle removal by an air purifier in this model
is limited to the removal after the exhale has been mixed with the air of the
entire room such that there is no concentration gradient anymore. In typical
settings, however, the air cleaner or exhaust is located close to the source.
If this is the case, the source term needs to be modified to allow direct
capture of infectious particles, as shown in Eq. 8.

$S_{k}=(\underbrace{Q_{I,w}n_{P,k,w}P_{I,k}}_{instrument\;w/\;mask}+\underbrace{Q_{I,b}n_{P,k,b}}_{breathing})(1-\underbrace{c}_{direct\;capture}\underbrace{(1-P_{f,k})}_{air\;cleaner\;eff.})$
(8)

Here, $0\leq c\leq 1$ is the fraction of particles captured directly, and
$P_{f,k}$ is the size-dependent penetration of the filter which is used in the
air cleaner. If a good filter is used ($P_{f,k}$ is small for all particle
sizes), the effect of direct capture is effectively a linear reduction of the
source strength, so the air cleaner would act like a mask. However, the flow
pattern in the vicinity of an air cleaner is very complicated and it requires
computational fluid dynamics (CFD) models or experiments under very controlled
conditions to assess the capture efficiency $c$ as a function of distance
between source and air cleaner, volumetric flow rate, and inlet and outlet
geometry of the air cleaner. For a real exposure scenario, this would then
still be limited to situations where few people are in the room and the
airflow from the air cleaner is not altered by persons, furniture, air inlets
or outlets, and other air cleaners.

##  3 Results and discussion

###  3.1 Experimentally-measured size distribution of respiratory particles
exhaled through wind instruments

Normalized number concentration of exhaled particles produced during playing
wind instruments measured by the OPS and SMPS as a function of exhale diameter
$D_{0}$ are shown in Fig. 5. Particles between 0.1 and
$0.3\text{\,}\mathrm{\SIUnitSymbolMicro m}$ represent between 1% and 20% of
the total volume concentration, so the particle size distribution measured by
the OPS, for which more data is available, covers 80 to 99% of the emitted
volume concentration. Apart from the recorder and the flute (second row of
Fig. 5), the emitted concentration of particles smaller than
$1\text{\,}\mathrm{\SIUnitSymbolMicro m}$ while playing a wind instrument is
about an order of magnitude higher compared to normal breathing found by
Bagheri et al. [2021a]. In addition, the fitted particle emission for a person
speaking is shown in Fig. 5 as dashed blue lines.

While the particle concentration tends to increase after
$10\text{\,}\mathrm{\SIUnitSymbolMicro m}$ towards larger sizes for speaking,
there is a rapid decrease of particle concentration in this size range
observed in the wind instrument data. As already explained in Materials and
Methods, Subsection 2.1, we can trust the measured particle concentrations to
about $16\text{\,}\mathrm{\SIUnitSymbolMicro m}$ diameter at exhalation, which
is $3.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ dry diameter. As the observable
decrease in particle concentration released by wind instruments starts at
smaller sizes, we think the decrease is real and caused by the wind
instruments themselves acting as a filter. We know from the results discussed
in Bagheri et al. [2021a] that a substantial amount of large
($D_{0}$>$10\text{\,}\mathrm{\SIUnitSymbolMicro m}$) particles is released
during phonetic activities. It is only the high sampling and detection
efficiency of the holographic setup that enables reliable characterization of
such large particles. We have also conducted experiments with several wind
instrument being played right into the sample volume of the holographic setup.
These experiments were done with four trumpets, one tenor trombone, two
clarinets, two transverse flutes, one alto recorder, one soprano sax, one alto
sax and two tenor saxophones. In none of the experiments, we were able to
detect particles >$6\text{\,}\mathrm{\SIUnitSymbolMicro m}$ at the bell end of
any wind instrument with the holographic setup. These results give us
confidence that we are not missing an important mode of the size distribution
at larger sizes. In general, we found that wind instrument players release
higher concentration of particles up to $10\text{\,}\mathrm{\SIUnitSymbolMicro
m}$ exhaled diameter when playing their instrument compared to breathing and
speaking.

Figure 5: Normalized emitted particle concentration (dN/dLogD0) from SMPS and
OPS obtained from two brass (orange), two edge-blown woodwind (blue) and four
single-reed woodwind instruments (red) as a function of exhaled diameter
$D_{0}$. Shown are arithmetic mean and standard error (black symbols and
errorbars), median (solid lines), and range from 25th percentile to maximum
(shaded area). Dashed blue (Fit breathing) and red lines (Fit speaking)
represent multi-modal log-normal fits for breathing and speaking as published
in Bagheri et al. [2021a]. For each wind instrument type, the base or
reference note is given in parenthesis.

In addition to the combined SMPS and OPS data presented in Fig. 5, we examined
the entire set of OPS data and performed multi-modal lognormal fits to the
mean size distributions per instrument group similar to the procedure
described in Bagheri et al. [2021a]. The size distributions were fitted with
the function described in Eq. 9 where $D_{i}$ is the $i$-th mode diameter at
exhalation, $\sigma_{i}$ is the geometric standard deviation of the $i$-th
mode, and $A_{i}$ is the mode concentration.

$dN/dLogD_{0}=\sum_{i=1}^{n}A_{i}\exp(-(\ln(D_{0}/D_{i})/\sigma_{i})^{2})$ (9)

To all measured size distributions, we could fit a mode at
$D_{1}=$$0.56\text{\,}\mathrm{\SIUnitSymbolMicro m}$ as in Bagheri et al.
[2021a]. However, the geometric standard deviation $\sigma_{1}$ needed to be
varied for the different wind instrument types to achieve a good coefficient
of determination ($r^{2}$). To avoid overfitting, the first step was to fit
the first mode to the data with fixed $D_{1}$ to determine $A_{1}$ and
$\sigma_{1}$. Then, the fitted first mode was subtracted from the data and
another mode was fitted to the remaining data. For most instrument types, two
modes could explain the measured data very well. For some instruments
(practice chanter, Great Highland Bagpipes, Scottish Smallpipes, F tuba), a
third mode was visible and fitted to the data remaining after mode 1 and 2
were subtracted. In the final step, all $D_{i}$ and $\sigma_{i}$ were fixed,
based on the results of the previous fits, and the coefficients $A_{i}$ were
used as free parameters for the fit to the original data. All fit parameters
and coefficient of determination are shown in Table 1. A weighting function
was used for the final fit, which is defined as $1/\sigma(dNdLogD_{0})$ where
$\sigma(dNdLogD_{0})$ is the standard error of the data. This choice was taken
to yield better agreement between the fit and the measured concentration of
particles >$10\text{\,}\mathrm{\SIUnitSymbolMicro m}$.

Table 1: Parameters of the multi-modal lognormal fits to the data for each instrument type. The mode diameter $D_{1}$ was fixed at $0.56\text{\,}\mathrm{\SIUnitSymbolMicro m}$. If a third mode was fitted as well, the parameters of the third mode are given in the columns of $A_{2}$, $D_{2}$ and $\sigma_{2}$ in parenthesis. Instrument |  $A_{1}$ [$\text{\,}{\mathrm{cm}}^{-3}$] | $\sigma_{1}$ |  $A_{2}$ [$\text{\,}{\mathrm{cm}}^{-3}$] |  $D_{2}$ [$\text{\,}\mathrm{\SIUnitSymbolMicro m}$] | $\sigma_{2}$ | $r^{2}$
---|---|---|---|---|---|---
Piccolo | 1.42 | 1.03 | 0.04 | 2.6 | 0.66 | 0.96
Fife | 1.92 | 1.15 | 0.05 | 3.8 | 0.55 | 0.96
Flute | 8.6 | 0.96 | 0.58 | 2.7 | 0.75 | 0.97
Recorder | 20.1 | 0.89 | 0.94 | 2.7 | 0.74 | 0.98
Clarinet | 44.1 | 1.05 | 1.42 | 5.9 | 0.58 | 0.99
Sop. sax | 14.5 | 1.03 | 0.47 | 5.1 | 0.71 | 0.99
Alt. sax | 12.3 | 0.93 | 0.07 | 2.8 | 1.27 | 0.97
Ten. sax | 13.3 | 0.90 | 0.43 | 3.3 | 0.91 | 0.99
Pr. chanter | 484 | 0.82 | 2.31 (0.004) | 2.9 (20.1) | 0.54 (0.41) | 0.98
Bagpipes | 24.5 | 1.11 | 0.05 (0.006) | 3.0 (27.0) | 0.59 (0.40) | 0.99
Smallpipes | 1.86 | 1.10 | 0.08 (0.009) | 3.5 (19.5) | 0.81 (0.48) | 0.98
P. trumpet | 30.0 | 0.97 | 0.27 | 3.5 | 1.16 | 0.98
Trumpet | 75.5 | 0.84 | 0.31 | 3.9 | 1.01 | 0.99
Kuhlo horn | 27.0 | 1.02 | 0.78 | 5.9 | 0.66 | 0.99
Hunting horn | 22.2 | 0.89 | 0.02 | 6.4 | 1.18 | 0.94
Trombone | 143 | 0.95 | 1.34 | 5.0 | 0.63 | 0.99
Tenorhorn | 83.4 | 0.80 | 0.62 | 1.8 | 0.91 | 0.99
Double horn | 10.4 | 1.01 | 0.29 | 6.8 | 0.74 | 0.99
F tuba | 49.5 | 0.90 | 1.96 (0.004) | 2.8 (30.8) | 0.75 (0.30) | 0.99
Bb tuba | 5.5 | 1.06 | 0.06 | 3.8 | 0.67 | 0.99

It needs to be emphasized that the data presented in Fig. 5 are those data
where the SMPS provided valid size distributions. Size distributions measured
by the OPS, including subjects and instruments without valid SMPS data, are
shown in Fig. 6, 7 and 8. The general trend of most wind instrument players
emitting much higher particle concentration when playing the instrument
compared to normal breathing is also visible in Fig. 6 \- 8.

We found that playing edge-blown woodwind instruments tends to produce less
particle number-concentration compared to playing brass instruments or reed
instruments (see Fig. 7). Both piccolo and fife had emitted size distributions
very similar to the size distribution emitted during normal breathing in terms
of shape. In direct comparison between wind instruments in Fig. 6 \- 8 and
phonetic activities in Fig. 8, most brass and reed instruments emit a higher
number of particles per volume in the size range from $1.5\text{\,}$ to
$10\text{\,}\mathrm{\SIUnitSymbolMicro m}$ than a person speaking or singing
(the total volume emitted, which is most important for the risk of infection,
is discussed later). There does not appear to be a direct correlation between
the instrument-tube length and the concentration of emitted particles.

From Fig. 6 it can be seen that the highest number-concentration is for the
tenor trombone, which has about double the tube length of a trumpet or Kuhlo
horn, and four times the tube length of a piccolo trumpet. The emitted
particle concentration from a F-tuba is not so much different from the
particle emission by a Kuhlo horn or a trumpet despite the tube of the tuba
being three times as long. In the group of the reed instruments in Fig. 7
(clarinet and saxophones) we find the highest variability in particle emission
in the clarinet data. The mean emitted particle concentration from the soprano
sax is somewhat lower, and the alto sax and tenor sax emit less particles than
the soprano sax. For the emission by the clarinet, we found a very strong
pitch dependence with lowest emission in the chalumeau register and
substantially higher emission in the clarion and altissimo register (see
Subsection 2.11. of SI). The highest average emission was found for the tenor
trombone and the clarinet. However, we found that a large amount of particles
is produced primarily by the vibrating lips when playing a brass mouthpiece.
The effect of the instrument resonator on the emitted particle size
distribution is discussed in Subsection 3.3. The fact that we could not detect
particles with the holographic setup emitted by wind instruments is in
accordance with the results by McCarthy et al. [2021] who used water-sensitive
cards and also could not detect particles
>$20\text{\,}\mathrm{\SIUnitSymbolMicro m}$ containing water at the bell end
of any wind instrument.

Figure 6: As in Fig. 5 for eight brass instruments in the measurement range of
the OPS. Mean values and standard error are shown by the black symbols with
errorbars, the black solid line represents a multi-modal lognormal fit to the
data.
Figure 7: As in Fig. 6 for eight woodwind instruments (edge-blown woodwinds in
blue, single-reed woodwinds in red).
Figure 8: As in Fig. 6 for four other instruments including hunting horn
(brass, in orange), practice chanter (directly mouth-blown double-reed, in
green) and two bagpipes (indirectly mouth-blown double-reed, in green).

###  3.2 Parameters influencing particle emission and significance of leakage

Similar to the particle emission from breathing and phonetic activities as
shown in Bagheri et al. [2021a], we found that the emission measured from a
given wind instrument also depends on the player. If a person has for example
an emitted particle concentration for normal breathing which is higher than
the geometric mean plus the geometric standard deviation of emitted particle
concentration in the examined cohort for the same activity, this person is
called a super-emitter [see Asadi et al., 2019, Bagheri et al., 2021a]. Our
data indicate that this is also the case here, i.e. some musicians produce
more respiratory particles with the same instrument and playing technique as
others, just because of different physiological properties (see Subsection
2.9. of SI for more detailed discussion). In addition to physiological reasons
for higher or lower emission, the music that was played does also have an
influence on the particle emission. We typically found higher particle
emission in the high register for brass players as well as for clarinet
players (see Fig. 9 of SI). For other wind instruments, the results were
inconclusive (details in Subsection 2.11. of SI).

To show the combined variability for most of the instruments, we present the
total emitted volume concentration of particles
$1.3\text{\,}\mathrm{\SIUnitSymbolMicro
m}$$<D_{0}<$$45\text{\,}\mathrm{\SIUnitSymbolMicro m}$ for all subject-
instrument-music combinations of playing the wind instrument normalized to
that of breathing (the mean of more than 130 subjects aged between 5 and 80
years) in Fig. 9. Compared with the dashed black line, which marks a relative
emitted volume concentration of 1 indicating the same volume concentration to
that of breathing, we see that most wind instruments typically are above.
However, the mean volume concentration produced by wind instruments is well
below that of the speaking or singing (if we consider only particles with
$D_{0}<$$45\text{\,}\mathrm{\SIUnitSymbolMicro m}$). Only the piccolo and the
fife have an emitted volume concentration that is mostly below the emitted
volume concentration from normal breathing. In case of the flute, the data
appear to be clustered with some data points at rather low emitted volume
concentration and some at high emitted volume concentration. Here we need to
mention that two of the flutists were super-emitters in normal breathing but
only one of them had a comparably high emission when playing the flute. The
between-subject variability of emitted volume concentration in the cohort of
the flutists for playing the same note on their flutes was as high as a factor
of 20 between the lowest and the highest emitter. On the other hand, we saw
that two super-emitters in normal breathing in the cohort of trumpet players
were not the subjects with the highest emission when playing the instrument.
This was also the case for the clarinet, where the one super-emitter in normal
breathing had much lower emission when playing the clarinet compared to the
subject who had the highest emission recorded during playing clarinet.

Between the different classes of wind instruments, we saw some trends. For
example, the emission measured from edge-blown woodwind instruments (piccolo,
fife, flute, recorder) was on average lower compared to most brass instruments
(except for the B-flat tuba) and single-reed woodwind instruments. A possible
explanation is the generation of the sound for these different instrument
types. Edge-blown woodwind instruments need a fast, narrow airstream with
virtually no resistance to make a good sound whereas the lips of a brass
player or the mouthpiece of a single-reed woodwind instrument have a notable
resistance to the exhaled air. The pressure within the respiratory tract needs
to be higher for playing a brass or a single-reed woodwind instrument compared
to playing an edge-blown woodwind instrument and we already found an increase
in emitted particle concentration for exhalation at higher pressure (see
Subsection 2.7 of SI).

Figure 9: Relative emitted particle volume concentration from directly mouth-
blown wind instruments (blue circles, brass in top panel, woodwind in bottom
panel) compared to normal breathing (dashed black lines), speaking (dashed
blue lines) and singing (dashed magenta lines). Each blue circle represents
one experiment (which is one subject-instrument-music combination at some
time). Red squares denote the arithmetic mean for the respective wind
instrument. ”Pr. chanter” is the abbreviation for ”practice chanter”. Relative
volume concentrations for singing and speaking values are from Bagheri et al.
[2021a] for particles with $1.3\text{\,}\mathrm{\SIUnitSymbolMicro
m}$$<D_{0}<$$45\text{\,}\mathrm{\SIUnitSymbolMicro m}$, which are almost
indistinguishable from each other in this plot.

###  3.3 Particle origin for different classes of wind instruments

The fact that edge-blown instruments typically release less particles compared
to reed instruments and brass instruments when being played can be explained
by the air pressure that is needed to generate the sound. On the other hand,
we know from Bagheri et al. [2021a] that the oral cavity and the lips are very
important sources of particles released by a person speaking or singing. When
playing a brass instrument, the sound is generated by the vibrating lips. If
they are wetted before they are opened and closed, they can produce a large
amount of particles >$30\text{\,}\mathrm{\SIUnitSymbolMicro m}$. We
investigated the production of particles by a brass player with the
holographic setup and OPS-based measurements of particles that passed the
mouthpiece of a trombone. To study the role of the resonator (the trombone),
we also measured the particle size distribution at the trombone bell end and
recalculated the particle size at exhalation from the measured dry diameter as
shown in Fig. 10. The size distributions are based on three subsequent
experiments executed by the same subject playing the same piece of music for
the same duration at same dynamics, which were (i)“Mouthpiece @ Holo”: playing
the trombone mouthpiece into the holographic sample volume (no correction
needed due to negligible drying), (ii)“Mouthpiece @ OPS”: playing the trombone
mouthpiece into the OPS inlet (cannot be corrected due to partial drying), and
(iii)“Trombone @ Setup A”: playing the trombone into the standard setup
(corrected assuming full drying to obtain $D_{0}$). As expected from the size
distributions in Fig. 5, the particle concentration emitted by the trombone
player through the instrument is about two orders of magnitude higher than the
typical emission from breathing. Around $6\text{\,}\mathrm{\SIUnitSymbolMicro
m}$ exhaled diameter, the fitted data from the trombone mouthpiece measured by
the holographic setup intersect with the corrected (measured dry) data from
the trombone bell end measured via Setup A. Due to partial drying, it was not
possible to correct the data measured by the OPS directly from the trombone
mouthpiece. What we can derive from the data shown in Fig. 10 is that the
trombone acts as a filter with a transmission $T$ of $\sim 1$% for $D_{0}$
>$20\text{\,}\mathrm{\SIUnitSymbolMicro m}$ and $\sim 0.1$% for $D_{0}$
>$30\text{\,}\mathrm{\SIUnitSymbolMicro m}$. It should be emphasized that up
to $20\text{\,}\mathrm{\SIUnitSymbolMicro m}$ exhaled diameter, the data
obtained via Setup A are trustworthy. Moreover, it is actually possible to
approximate the transmission of the trombone resonator by
$T\sim\exp(-0.3(D_{0}-D_{1}))$ with $D_{1}=$
$3.8\text{\,}\mathrm{\SIUnitSymbolMicro m}$. This is the same functional
relationship as found for penetration of non-charged melt-blown filter
fabrics, the only difference is that $D_{1}=0$ for the filter fabrics (see
Subsection 3.4 and Subsection 2.13 of SI for more details).

Figure 10: Particle size distributions measured from a trombone mouthpiece
directly with the holography setup (red, uncorrected due to no drying). For
comparison, the exhaled particle size distribution measured at the trombone
bell via the standard setup (green, fully dry and corrected) is shown. The two
first unfilled data points of the holography setup indicate the size range
with poor detectability. Errorbars show the variability from counting
statistics. Multi-modal log-normal fits to the bell end data (solid black
line) and mouthpiece data (dashed black line) are shown in comparison with
fitted size distribution for breathing (blue) in Bagheri et al. [2021a].

However, if these particles are captured somewhere in the resonator, they
might eventually become resuspended. Also, the removal of large particles by
the resonator (most likely via wet deposition) can explain the minimal
difference in mean emitted particle volume concentration between reed and
brass instruments. We think that the particle production mechanism for
>$5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ exhaled diameter is the same in
both brass and reed instruments. While the lips, which are usually covered
with some saliva, open and close up to several hundred times per second when
playing a brass instrument, the same happens with the mouthpiece of a single-
reed instrument, or with a double-reed if the reed is covered with some
saliva.

####  3.3.1 Leakage

We found that concentration of particles leaking from the tone holes of
woodwind instruments is in some cases about as much as the that of the bell
end, and they have a very much comparable particle size distribution (see Fig.
8 and Subsection 2.5. of SI). Brass instruments do not have leakage at the
valves, but another source of infectious particles is particles released by
normal breathing, either through the nose or the mouth. The particle leakage
from tone holes is taken into account in our infection risk calculations by
assuming the upper bound of exhalation volume flow rate for professional wind
instrument players given by Bouhuys [1964] for different instrument types. For
some of the brass and woodwind instrument players, we examined the size
distribution of particles from mouth or nose during the play with an
additional measurement setup and found that the particle size distributions
were very close to the particle size distributions from normal breathing (see
Fig. 7 and Subsection 2.5. of SI). From these findings, two consequences
arise: Firstly, woodwind instruments need to be covered entirely with an
instrument-mask as leakage from the tone holes is an issue. Secondly, particle
emission from normal breathing while playing the instrument needs to be taken
into account for the calculation of infection risk.

###  3.4 Masks for wind instruments

We have also investigated the possibility of using instrument-masks to reduce
dispersion of potentially infectious particles. Different melt-blown fabrics
were tested on various wind instruments and their size-dependent penetration
was measured with dry dolomite dust. In Fig. 11 we show four examples of the
tested fabrics. As visible in the dashed versus solid lines, charged melt-
blown and uncharged melt-blown fabrics have a different functional
relationship between penetration and particle size. Penetration $P$ as a
function of particle diameter $D$ follows either a power law as expressed by
Eq. 10 if the fabric is charged, or, in case of an uncharged fabric, an
exponential decay as expressed in Eq. 11. $P_{0}$, $P_{1}$ and $\beta$ are the
fabric-specific parameters. More details can be found in Subsection 2.13 of
SI.

$\displaystyle P(D)=$ $\displaystyle
P_{1}\cdot(D/$1\text{\,}\mathrm{\SIUnitSymbolMicro m}$)^{-\beta}$ (10)
$\displaystyle P(D)=$ $\displaystyle
P_{0}\cdot\exp\left({-\beta}\cdot(D/$1\text{\,}\mathrm{\SIUnitSymbolMicro
m}$)\right)$ (11)

Due to wind instruments emitting the largest fraction of volume concentration
in a size range $D_{0}$<$10\text{\,}\mathrm{\SIUnitSymbolMicro m}$ (i.e.
<$2.2\text{\,}\mathrm{\SIUnitSymbolMicro m}$ fully dried diameter), it is
utmost important to use charged melt-blown fabric for instrument-masks. The
acoustic properties as described by the musicians can be summarized in fabrics
with higher weight per area yielding higher playing resistance and stronger
acoustic attenuation. The mask materials had a typical pressure drop between 7
and $45\text{\,}\mathrm{Pa}$ for a mean air velocity of
$7.3\text{\,}\mathrm{cm}\text{\,}{\mathrm{s}}^{-1}$ through the mask. This
velocity corresponds to about
$100\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ of breathing through a
typical FFP2 mask. However, the material with lowest measured penetration was
quite thick (about $120\text{\,}\mathrm{g}\text{\,}{\mathrm{m}}^{-2}$) but the
sound quality was acceptable for low brass instruments (trombones, tenorhorns,
tubas). High brass instruments (in particular trumpets) had a notable loss of
brightness in the sound due to attenuation of the high harmonics. For them, a
thinner material with $55\text{\,}\mathrm{g}\text{\,}{\mathrm{m}}^{-2}$
(filter03) was better suited.

Figure 11: Measured penetration for two uncharged fabrics (filter06,
filter10), and two charged fabrics (filter03 and filter05). Shown are mean
penetration (squares), standard deviation (errorbars) and fitted penetration
with parameters given in the lower left corner (solid lines).

###  3.5 Risk of airborne disease transmission

With the measured emitted particle size distribution and concentration for
different wind instruments and the models introduced in Materials and Methods,
Subsection 2.5, we now calculate the risk of infection transmission for
different exposure scenarios. As we find the range of emitted volume
concentration for some of the instruments to be almost a factor 1000 (e.g.
clarinet) in Fig. 9, we need to use a conservative estimation of the emitted
volume concentration when calculating the infection risk. The arithmetic mean
values of emitted volume concentration from the trombone and the clarinet are
almost equal (see the red squares in Fig. 9). These numbers will be used to
represent a high emitter. As low emitter, we chose the piccolo. With these
choices the typical emission from any wind instrument is within these two
extremes. In scenarios with instrument-masks, we chose the “filter05” material
as mask material. We assume that all inhalation rates are the same as the
corresponding exhalation rates. Breathing rates are assumed to be $Q_{I,w}=$
$47\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ for the infectious playing
the trombone (Max), $Q_{I,w}=$
$44\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ for the infectious playing
the piccolo (Min), and additional $Q_{I,b}=$
$9\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$ for breathing. The values
for the trombone and piccolo are taken from Bouhuys [1964] and the value for
normal breathing is taken from International Commission on Radiological
Protection [1994]. For the near-field exposure, we use the volume averaged
concentration of pathogen copies from both sources (instrument and nose of
infectious). The susceptible breathes with a constant volumetric flow rate
$Q_{S,b}=$ $9\text{\,}\mathrm{L}\text{\,}{\mathrm{min}}^{-1}$. For the
interpretation of the infection risk, it needs to be mentioned that we show
the upper bound of infection risk, and that the infection risk is cumulative.
An instrument lesson, band rehearsal or concert might not be the only exposure
scenario during the day. So it makes sense to draw the line of an acceptable
infection risk somewhere on the low end.

####  3.5.1 Near-field exposure

Figure 12 shows clearly that there is already an upper bound of 50% risk of
infection transmission in the “Distancing” scenario after just 4 minutes of
exposure if we consider a wind instrument with high particle emission (solid
blue line in Fig. 12). As expected, the infection risk for the same time is
lower if a low emission instrument is played (dashed blue line in Fig. 12).
Though the infection risk is lower, the time to reach 50% risk of infection is
still only about 2.5 hours.

In the “Mixed” scenario, the infection risk is largely reduced in comparison
to the distancing scenario, but for a high emission instrument (solid red line
in Fig. 12), the level of protection for the susceptible is still insufficient
for relatively long exposures as the infection risk is still about 50% after
1.5 hours.

In the masked scenario, we find no difference between minimum and maximum
(solid and dashed black line in Fig. 12 are on top of each other), which means
that the particles emitted from mouth or nose of the infectious during
breathing dominate the infection risk (see Fig. 12). Even after an exposure
time of 12 hours, the infection risk is still <5%. This is because the
instrument-mask is able to reduce the particle emission from the instrument to
about 1% of that produced by breathing.

The “leakage” from breathing could actually be reduced by the infectious
musician wearing an adjusted FFP2 mask with a feedthrough for the mouthpiece,
but this additional protection proved to be impractical for most settings with
live music (e.g. music education, rehearsals, concerts). Nonetheless, given
that we have calculated a very conservative upper bound, the Masked scenario
can be assumed safe for up to several hours when the near-field transmission
of pathogens is considered only.

Figure 12: Infection risk for near-field exposure as a function of time. Shown
are the exposure scenarios as defined in Fig. 3 with the “Distancing” scenario
(blue lines), the “Mixed” scenario (red lines) and the “Masked” scenario
(black lines). Infection risk is shown for maximum (solid lines) and minimum
(dashed lines) mean particle emission by the examined wind instruments; the
instruments are explained in the text.

The selection of the mask material has an influence on the risk of infection.
If the thinnest of our examined materials without charge, which is “filter10”,
is used for the instrument-mask, the “Mixed” scenario leads to about the same
risk as the “Masked” scenario as the reduced distance increases the risk of
infection by about the same amount as the mask reduces it (see Subsection 2.18
and Fig. 17 of SI). This intercomparison shows what happens if low-efficacy
masks are used and no distance is kept between infectious and susceptible.
Thus, it is utmost important to use charged melt-blown materials for
instrument-masks. Even with a thin charged melt-blown material such as
“filter03”, the “Masked” scenario is the safest of all near-field exposure
scenarios.

####  3.5.2 Far-field exposure

The infection risk for the far-field exposure is shown in Fig. 13. While it
took only $4\text{\,}\mathrm{min}$ to reach 50% infection risk in the
“Distancing” scenario with the high emission instrument (see Fig. 12), the
exposure time for 50% infection risk in the “Room-Unmasked” scenario in the
far-field is about $30\text{\,}\mathrm{min}$ (see Fig. 13). Avoiding exposure
to the near-field of an infectious increases the exposure time for the same
risk by almost a factor ten, however, eliminating the risk of infection
transmission associated with near-field exposure in real world conditions is
not trivial to achieve. Nonetheless, the “Room-Unmasked” scenario is the one
with the highest risk of infection transmission and the “Room-Masked” scenario
is the safest. The “Room-Instrument-mask” scenario yields a lower infection
risk after 12 hours compared to the “Room-Mixed” scenario for the high-
emission wind instrument. From Fig. 13, it can be concluded that the “Room-
Masked” scenario is associated with a low risk of infection transmission
($<1\%$) even after 12 hours.

Figure 13: As in Fig. 12 for far-field exposure. Shown are “Room-Unmasked”,
“Room-Mixed”, “Room-Instrument-mask” and “Room-Masked” scenario for maximum
and minimum of the mean particle emission among the examined wind instruments.

###  3.6 The HEADS web-app: investigating other far-field scenarios

For the far-field exposure scenario, we have shown a typical example
representative of some music activities in the previous section. Other
scenarios, where multiple infectious and/or multiple susceptible individuals
are in rooms with different ventilation characteristics, can be studied with
the HEADS (Human Emission of Aerosol and Droplet Statistics) web app, which
has a user-friendly interface Bagheri et al. [2020]. In HEADS, the effect of
room size, ventilation rate, number of susceptible and infectious individuals,
activities, pathogen concentration, infectious dose, pathogen deactivation
rate, particle deposition, intake efficiency of the susceptible, particle
drying and their re-hydration of particles in susceptible airways, and poly-
pathogen aerosols on the risk of infection transmission for any airborne
disease can be studied. The HEADS “Advanced View” allows also mixtures of
different activities (for example, 80% breathing, 10% speaking, 10% singing).
Another powerful tool in HEADS is the analysis of subsequent exposure
scenarios. If the relevant parameters are known, it is possible to add for
example the use of public transportation to/from the concert. The HEADS web
app is available in English, German and some other languages.

###  3.7 Other considerations for the infection risk assessments

The use of conservative estimates allowed us to calculate a situation-
independent upper bound of infection risk for typical SARS-CoV-2 input
parameters that is reliable when the results are interpreted with statistical
reasoning. However, there are fundamental limitations and uncertainties that
need to be discussed. For example, in rare situations atypical values for
SARS-CoV-2 can be expected to occur, e.g. $\rho_{p}$ for an individual with
SARS-CoV-2 and considered infectious ranges from about $10^{6}$ to $10^{11}$
$\text{\,}{\mathrm{cm}}^{-3}$ instead of the $10^{8.5}$
$\text{\,}{\mathrm{cm}}^{-3}$ considered here. This span by itself introduces
an uncertainty of $\pm 2.5$ orders of magnitude (factor 300) for the time to
exceed a given threshold of the infection risk. In other words, a given time
to exceed a threshold of the risk of infection transmission of 5 minutes could
actually vary between 1 second for an infectious with extremely high virus
concentration, and 1 day for an infectious with low virus concentration. Also,
the value of $\textnormal{ID}_{63.21}$ is not exactly known and could vary
between 100 and 1000 virions for the wild type of SARS-CoV-2 [see e.g.
Nordsiek et al., 2021]. However, it is unlikely that the uncertainty caused by
$\textnormal{ID}_{63.21}$ will affect the real-world application of the
results presented, at least for the near-field scenarios, since we have
already used extremely conservative estimates for other parameters. Another
aspect that introduces uncertainty to the model is that the dependence of
$\rho_{p}$ on particle origin site and time during the course of the disease
is not known. There is also a recent discussion about the inactivation rate
for particles containing SARS-CoV-2, which could depend on several parameters
of the room air and could be higher Oswin et al. [2022]. On the other hand,
simpler models like the ones used in Miller et al. [2020], Stockman et al.
[2021] and Firle et al. [2021] have the same issues while they also do not
take the size to volume ratio of the particles into account. In this regard,
the upper bound we have presented for the near-field calculations should
remain valid as we have not considered the deactivation term.

In addition, the volumetric flow rates for exhalation are based on studies
with a small number of subjects (e.g. the paper by Bouhuys [1964]), however,
we have used conservative estimates. Another possible limitation is the
aggregation of wind instruments of the same type, e.g. the ”trumpet” class
consists of several different trumpet models in B-flat with both rotary and
piston valves and there is evidence that the instrument geometry and/or
mouthpiece geometry can have an effect on emitted particle concentration and
size distribution. One trumpet player played exactly the same pieces of music
on two different trumpet models at same dynamics and tempo with the result
that the mean emitted particle number concentrations differed systematically
by a factor 2 between the two trumpet models. The assumption of a diverging
jet flow is rather crude but useful to discuss the effect of dilution as a
function of distance between source and receiver. Finally, the limitations
discussed need to be taken into account when interpreting the results of the
infection risk calculation. Nevertheless, the infection risk calculations
presented can provide very useful guidance for planning rehearsals,
instrumental lessons, and live music events.

##  4 Conclusion

We have shown that playing wind instruments releases particles in a volume
concentration that is typically 0.5 to 25 times higher than the particle
release from normal breathing, which is, on average, still lower than the
particle release from speaking or singing. 18 out of 20 examined wind
instrument types had a higher mean emitted particle volume concentration
compared to breathing. Edge-blown woodwind instruments had on average lower
emitted particle volume concentration compared to single-reed or double-reed
woodwind instruments and brass instruments. We did not observe a systematic
trend in a way that for example brass instruments with a shorter tube emitted
more particles than brass instruments with a longer tube. For some instruments
(most brass and also clarinets), higher emission was found for higher notes
played. Reed and brass instruments also had similar mean emitted volume
concentration, though the playing technique is much different.

We have also found that playing wind instruments can produce very low
concentration of $D_{0}>$$20\text{\,}\mathrm{\SIUnitSymbolMicro m}$, which is
in stark contrast to phonetic activities like speaking or singing. More than
80% of the particle volume emitted from wind instruments lies within a
particle diameter range from $0.3\text{\,}$ to
$4.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ after fully dried, i.e.
$D_{0}\sim$1.5 to $20\text{\,}\mathrm{\SIUnitSymbolMicro m}$.

Different melt-blown fabrics have been examined as candidates for instrument-
masks. We found that only charged melt-blown fabrics can substantially reduce
emission of infectious particles. The material penetration as a function of
particle size follows a power law for charged materials, and an exponential
decay for uncharged materials. This explains why uncharged mask materials have
such a low capture efficacy for particles
<$1\text{\,}\mathrm{\SIUnitSymbolMicro m}$. We tested a mask material with
penetration <$10^{-4}$ for $1\text{\,}\mathrm{\SIUnitSymbolMicro m}$ particles
on several wind instruments and found minor influence of the instrument-mask
on the sound quality of brass instruments playing in the same tonal range as a
tenor trombone or lower. A practical solution for reed instruments still needs
to be found as leakage from tone holes had similar particle concentration and
size distribution as emission measured at the instrument bell end.

The emitted particles may pose a substantial risk of infection transmission in
a near-field exposure as well as in the well-mixed room considered here if the
musician is infectious. Without masks, the risk of infection transmission
exceeds 50 % after 4 minutes for a high-emission instrument. Using an FFP2
mask for a susceptible at $1.5\text{\,}\mathrm{m}$ distance to the bell end of
the instrument results in 50% risk of infection transmission after 1 hour.
When susceptible wears an FFP2 mask and instrument-mask is used, transmission
risk in near-field is only 5% even after 12 hours of continuous exposure. In a
well-mixed room situation (i.e. far-field scenarios) like a band rehearsal
(with one wind instrument) or private instrument lessons, the risk of
infection transmission is >50% after 20 minutes but stays below 0.5% even
after 12 hours with a moderate air exchange rate (4 ACH), instrument-mask and
FFP2 masks for the attendees.

With the web-app HEADS [Bagheri et al., 2020], it is possible to examine
different exposure scenarios with multiple infectious sources and susceptible
individuals at once, and investigate how changes in distance and masks as well
as other effects, such as point-of-care testing, ventilation and particle
deposition, could influence the infection risk. In this paper, we limited
ourselves to a few exposure scenarios representative of instrument education
or band / ensemble rehearsal to characterize the risk of infection
transmission in one of these events in general and evaluate possible
mitigation measures. By transferring the data published in this paper also to
HEADS, we enable the reader to investigate different exposure scenarios by
themselves through the HEADS easy-to-use interface.

In this work we could quantify the upper bound of infection risk and showed
that universal usage of well-fitting and well-filtering instrument and
individual masks can reduce the risk of infection transmission significantly
while distancing alone or non-universal masking are found to be high risk
measures.

## Author contributions

OS, GB and BT wrote the draft of the main paper and the Supplementary
Information. OS, GB, EB, RM, SS and LT designed the experimental procedures
for the subject measurements. SS advised all medical aspects of the
investigation. EB wrote the ethics application. OS, LT, KS, BT, JK, EB and GB
conducted the experiments on human subjects. OS, KS, BT and EB did the filter
penetration measurements. RM advised all musical aspects of the investigation
and assisted in some of the experiments on human subjects. OS, BT and GB with
the help of EB analyzed and interpreted the data. OS prepared the holography
data. All authors wrote the final text.

## Conflict of Interest

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.

## Ethics statement

The non-invasive exhaled aerosol sampling study which led to this paper was
approved by the Ethics Commission of the Max Planck Society (Submission
2020_23). The participants provided their written informed consent to
participate in this study.

## Data availability

A part of the datasets generated and analyzed for this study can be found in
the HEADS web app [https://aerosol.ds.mpg.de]. Data not published within HEADS
are available from the corresponding authors on reasonable request.

## Acknowledgments

Without the great support of our volunteers, this study would not have been
possible. We are grateful for all suggestions we received by some of the
volunteers, which finally enabled us to discover some of the ultra-high
emission scenarios we might have never seen otherwise. We like to thank the
Posaunenwerk der Landeskirche Hannovers, the Hochschule für Musik, Theater und
Medien Hannover, the Blasmusikverband Nordrhein-Westfalen e.V., and the
Jacobi-Kantorei Göttingen for sharing the call for volunteers and encouraging
their members to participate in our study. Special thanks go to the machine
shop of the Max Planck Institute for Dynamic and Self-Organization, and Udo
Schminke (head of construction) for making customized tools and adaptors
needed for the aerosol measurements. We gratefully acknowledge the support by
our High Performance Computation (HPC) work group to provide resources for
analysis of the holographic data and keep the HPC cluster in a working state.
We would also like to thank the companies producing melt-blown filter fabrics
(namely Sandler AG, Filter GmbH Preussel Langenfeld, and Brigitte Hoffmann
Filterprodukte KG) for providing test samples of different filter materials.

## Funding

We thank the Bundesministerium für Bildung und Forschung (BMBF) for funding
within the project B-FAST (Bundesweites Netzwerk Angewandte Surveillance und
Teststrategie) (01KX2021) within the NUM (Netzwerk Universitätsmedizin),
Deutsche Forschungsgemeinschaft (project number: 469107130) and the Max Planck
Society. The sponsors had no influence on the study design, data collection
and analysis, decision to publish, or preparation of the manuscript.

## References

  * Abraham et al. [2021] Abraham, A., He, R., Shao, S., Kumar, S.S., Wang, C., Guo, B., Trifonov, M., Placucci, R.G., Willis, M., Hong, J., 2021.  Risk assessment and mitigation of airborne disease transmission in orchestral wind instrument performance.  Journal of Aerosol Science 157, 105797\.  URL: https://www.sciencedirect.com/science/article/pii/S0021850221005292, doi:10.1016/j.jaerosci.2021.105797.
  * Asadi et al. [2019] Asadi, S., Wexler, A.S., Cappa, C.D., Barreda, S., Bouvier, N.M., Ristenpart, W.D., 2019\.  Aerosol emission and superemission during human speech increase with voice loudness.  Scientific Reports 9.  doi:10.1038/s41598-019-38808-z.
  * Bagheri et al. [2020] Bagheri, G., Schlenczek, O., Turco, L., Nordsiek, F., Clauberg, S., Stieger, K., Kosub, J.M., Scheithauer, S., Kaba, H., Bodenschatz, E., 2020\.  HEADS — Human Emission of Aerosol and Droplet Statistics.  https://aerosol.ds.mpg.de.  Accessed: 2021-12-08.
  * Bagheri et al. [2021a] Bagheri, G., Schlenczek, O., Turco, L., Thiede, B., Stieger, K., Kosub, J.M., Pöhlker, M., Pöhlker, C., Molacek, J., Scheithauer, S., Bodenschatz, E., 2021a.  Exhaled particles from nanometre to millimetre and their origin in the human respiratory tract.  medRxiv preprint URL: https://doi.org/10.1101/2021.10.01.21264333, doi:10.1101/2021.10.01.21264333. version 1, 03 Oct 2021\.
  * Bagheri et al. [2021b] Bagheri, G., Thiede, B., Hejazi, B., Schlenczek, O., Bodenschatz, E., 2021b.  An upper bound on one-to-one exposure to infectious human respiratory particles.  Proceedings of the National Academy of Sciences 118\.  URL: https://www.pnas.org/content/118/49/e2110117118, doi:10.1073/pnas.2110117118.
  * Becher et al. [2021] Becher, L., Gena, A.W., Alsaad, H., Richter, B., Spahn, C., Voelker, C., 2021\.  The spread of breathing air from wind instruments and singers using schlieren techniques.  Indoor Air 31, 1798–1814.  URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/ina.12869, doi:10.1111/ina.12869.
  * Bouhuys [1964] Bouhuys, A., 1964.  Lung volumes and breathing patterns in wind-instrument players.  Journal of Applied Physiology 19, 967–975.  URL: https://doi.org/10.1152/jappl.1964.19.5.967, doi:10.1152/jappl.1964.19.5.967. PMID: 14207753\.
  * Brosseau et al. [2022] Brosseau, Q., Ran, R., Graham, I., Jerolmack, D.J., Arratia, P.E., 2022.  Flow and aerosol dispersion from wind musical instruments.  arXiv preprint doi:10.48550/arXiv.2201.10386, arXiv:2201.10386. version 1, 25 Jan 2022.
  * Buonanno et al. [2020] Buonanno, G., Stabile, L., Morawska, L., 2020.  Estimation of airborne viral emission: Quanta emission rate of sars-cov-2 for infection risk assessment.  Environment International 141, 105794\.  URL: https://www.sciencedirect.com/science/article/pii/S0160412020312800, doi:10.1016/j.envint.2020.105794.
  * Chan et al. [2021] Chan, M., Hui, K., Ho, J., Cheung, M., Ng, K., Ching, R., Lai, K., Kam, T., Gu, H., Sit, K., Hsin, M., Au, W., Poon, L., Peiris, M., Nicholls, J., 2021\.  SARS-CoV-2 Omicron variant replication in human respiratory tract ex vivo.  Research Square preprint URL: https://doi.org/10.21203/rs.3.rs-1189219/v1, doi:10.21203/rs.3.rs-1189219/v1. version 1, 22 Dec 2021\.
  * Chen et al. [2021] Chen, J., Wang, R., Gilby, N.B., Wei, G.W., 2021.  Omicron (B.1.1.529): Infectivity, vaccine breakthrough, and antibody resistance.  arXiv preprint doi:10.48550/arXiv.2112.01318, arXiv:2112.01318. version 1, 01 Dec 2021.
  * Coleman et al. [2021] Coleman, K.K., Tay, D.J.W., Tan, K.S., Ong, S.W.X., Than, T.S., Koh, M.H., Chin, Y.Q., Nasir, H., Mak, T.M., Chu, J.J.H., Milton, D.K., Chow, V.T.K., Tambyah, P.A., Chen, M., Tham, K.W., 2021.  Viral Load of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Respiratory Aerosols Emitted by Patients With Coronavirus Disease 2019 (COVID-19) While Breathing, Talking, and Singing.  Clinical Infectious Diseases URL: https://doi.org/10.1093/cid/ciab691, doi:10.1093/cid/ciab691. ciab691.
  * van Doremalen et al. [2020] van Doremalen, N., Bushmaker, T., Morris, D.H., Holbrook, M.G., Gamble, A., Williamson, B.N., Tamin, A., Harcourt, J.L., Thornburg, N.J., Gerber, S.I., Lloyd-Smith, J.O., de Wit, E., Munster, V.J., 2020.  Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1.  New England Journal of Medicine 382, 1564--1567.  URL: https://doi.org/10.1056/NEJMc2004973, doi:10.1056/NEJMc2004973.
  * Firle et al. [2021] Firle, C., Steinmetz, A., Stier, O., Stengel, D., Ekkernkamp, A., 2021.  Aerosol emission rates from playing wind instruments \- implications for covid-19 transmission during music performance.  medRxiv preprint URL: https://www.medrxiv.org/content/early/2021/12/11/2021.12.08.21267466, doi:10.1101/2021.12.08.21267466. version 1, 11 Dec 2021\.
  * Fugal et al. [2009] Fugal, J.P., Schulz, T.J., Shaw, R.A., 2009.  Practical methods for automated reconstruction and characterization of particles in digital in-line holograms.  Measurement Science and Technology 20, 075501.  URL: https://doi.org/10.1088/0957-0233/20/7/075501, doi:10.1088/0957-0233/20/7/075501.
  * Gantner et al. [2022] Gantner, S., Echternach, M., Veltrup, R., Westphalen, C., Köberlein, M., Kuranova, L., Peters, G., Jakubaß, B., Benthaus, T., Döllinger, M., Kniesburges, S., 2022.  Impulse dispersion of aerosols during playing wind instruments.  PLoS ONE 17, e0262994.  doi:10.1371/journal.pone.0262994.
  * Halfmann et al. [2022] Halfmann, P.J., Iida, S., Iwatsuki-Horimoto, K., Maemura, T., Kiso, M., Scheaffer, S.M., Darling, T.L., Joshi, A., Loeber, S., Singh, G., Foster, S.L., Ying, B., Case, J.B., Chong, Z., Whitener, B., Moliva, J., Floyd, K., Ujie, M., Nakajima, N., Ito, M., Wright, R., Uraki, R., Warang, P., Gagne, M., Li, R., Sakai-Tagawa, Y., Liu, Y., Larson, D., Osorio, J.E., Hernandez-Ortiz, J.P., Henry, A.R., Ciouderis, K., Florek, K.R., Patel, M., Odle, A., Wong, L.Y.R., Bateman, A.C., Wang, Z., Edara, V.V., Chong, Z., Franks, J., Jeevan, T., Fabrizio, T., DeBeauchamp, J., Kercher, L., Seiler, P., Gonzalez-Reiche, A.S., Sordillo, E.M., Chang, L.A., van Bakel, H., Simon, V., Alburquerque, B., Alshammary, H., Amoako, A.A., Aslam, S., Banu, R., Cognigni, C., Espinoza-Moraga, M., Farrugia, K., van de Guchte, A., Khalil, Z., Laporte, M., Mena, I., Paniz-Mondolfi, A.E., Polanco, J., Rooker, A., Sominsky, L.A., Douek, D.C., Sullivan, N.J., Thackray, L.B., Ueki, H., Yamayoshi, S., Imai, M., Perlman, S., Webby, R.J., Seder, R.A., Suthar, M.S., García-Sastre, A., Schotsaert, M., Suzuki, T., Boon, A.C.M., Diamond, M.S., Kawaoka, Y., Consortium Mount Sinai Pathogen Surveillance (PSP) study group, 2022.  SARS-CoV-2 Omicron virus causes attenuated disease in mice and hamsters.  Nature 603, 687--692.  URL: https://doi.org/10.1038/s41586-022-04441-6, doi:10.1038/s41586-022-04441-6.
  * He et al. [2021] He, R., Gao, L., Trifonov, M., Hong, J., 2021.  Aerosol generation from different wind instruments.  Journal of Aerosol Science 151, 105669.  URL: https://www.sciencedirect.com/science/article/pii/S0021850220301555, doi:10.1016/j.jaerosci.2020.105669.
  * Hedworth et al. [2021] Hedworth, H.A., Karam, M., McConnell, J., Sutherland, J.C., Saad, T., 2021.  Mitigation strategies for airborne disease transmission in orchestras using computational fluid dynamics.  Science Advances 7, eabg4511.  URL: https://www.science.org/doi/abs/10.1126/sciadv.abg4511, doi:10.1126/sciadv.abg4511.
  * Hejazi et al. [2022] Hejazi, B., Schlenczek, O., Thiede, B., Bagheri, G., Bodenschatz, E., 2022.  On the risk of infection by infectious aerosols in large indoor spaces.  arXiv preprint doi:10.48550/arXiv.2105.10357, arXiv:2105.10357. version 3, 24 Jan 2022.
  * International Commission on Radiological Protection [1994] International Commission on Radiological Protection (Ed.), 1994\.  Human respiratory tract model for radiology protection: a report of a Task Group of the International Commission on Radiological Protection.  Number 66 in ICRP publication. 1\. ed ed., Pergamon, Oxford.  OCLC: 832441728.
  * Jimenez [2020] Jimenez, J.L., 2020.  COVID-19 Aerosol Transmission Estimator.  Online, Version 3.4.19 released 2020 Oct. 27 [Cited 2020 Nov. 26].  URL: https://tinyurl.com/covid-estimator.
  * Jones et al. [2021] Jones, T.C., Biele, G., Mühlemann, B., Veith, T., Schneider, J., Beheim-Schwarzbach, J., Bleicker, T., Tesch, J., Schmidt, M.L., Sander, L.E., Kurth, F., Menzel, P., Schwarzer, R., Zuchowski, M., Hofmann, J., Krumbholz, A., Stein, A., Edelmann, A., Corman, V.M., Drosten, C., 2021\.  Estimating infectiousness throughout SARS-CoV-2 infection course.  Science 373.  URL: https://science.sciencemag.org/content/373/6551/eabi5273, doi:10.1126/science.abi5273.
  * Kähler and Hain [2020] Kähler, C.J., Hain, R., 2020\.  Singing in choirs and making music with wind instruments ‒ Is that safe during the SARS-CoV-2 pandemic?  Preprint doi:10.13140/RG.2.2.36405.29926. retrieved 26 Apr 2022\.
  * Kriegel et al. [2022] Kriegel, M., Hartmann, A., Buchholz, U., Seifried, J., Baumgarte, S., Gastmeier, P., 2022\.  SARS-CoV-2 Aerosol Transmission Indoors: A Closer Look at Viral Load, Infectivity, the Effectiveness of Preventive Measures and a Simple Approach for Practical Recommendations.  International Journal of Environmental Research and Public Health 19.  URL: https://www.mdpi.com/1660-4601/19/1/220, doi:10.3390/ijerph19010220.
  * Lai et al. [2011] Lai, K.M., Bottomley, C., McNerney, R., 2011.  Propagation of Respiratory Aerosols by the Vuvuzela.  PLoS ONE 6, e20086.  doi:10.1371/journal.pone.0020086.
  * Leung [2021] Leung, N., 2021.  Transmissibility and transmission of respiratory viruses.  Nature Reviews Microbiology 19, 528--545.  doi:10.1038/s41579-021-00535-6.
  * McCarthy et al. [2021] McCarthy, L.P., Orton, C.M., Watson, N.A., Gregson, F.K.A., Haddrell, A.E., Browne, W.J., Calder, J.D., Costello, D., Reid, J.P., Shah, P.L., Bzdek, B.R., 2021.  Aerosol and Droplet Generation from Performing with Woodwind and Brass Instruments.  Aerosol Science and Technology 55, 1277--1287.  URL: https://doi.org/10.1080/02786826.2021.1947470, doi:10.1080/02786826.2021.1947470.
  * Miller et al. [2020] 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., 2020\.  Transmission of SARS-CoV-2 by inhalation of respiratory aerosol in the Skagit Valley Chorale superspreading event.  Indoor Air 00, 1--10.  doi:10.1111/ina.12751.
  * Morawska and Cao [2020] Morawska, L., Cao, J., 2020\.  Airborne transmission of SARS-CoV-2: The world should face the reality.  Environment International 139, 105730\.  doi:10.1016/j.envint.2020.105730.
  * Morawska et al. [2009] Morawska, L., Johnson, G., Ristovski, Z., Hargreaves, M., Mengersen, K., Corbett, S., Chao, C., Li, Y., Katoshevski, D., 2009.  Size distribution and sites of origin of droplets expelled from the human respiratory tract during expiratory activities.  Journal of Aerosol Science 40, 256--269.  doi:10.1016/j.jaerosci.2008.11.002.
  * Nannu Shankar et al. [2022] Nannu Shankar, S., Witanachchi, C.T., Morea, A.F., Lednicky, J.A., Loeb, J.C., Alam, M.M., Fan, Z.H., Eiguren-Fernandez, A., Wu, C.Y., 2022.  SARS-CoV-2 in residential rooms of two self-isolating persons with COVID-19.  Journal of Aerosol Science 159, 105870\.  URL: https://www.sciencedirect.com/science/article/pii/S0021850221006005, doi:10.1016/j.jaerosci.2021.105870.
  * Narayanan and Yang [2021] Narayanan, S.R., Yang, S., 2021\.  Airborne transmission of virus-laden aerosols inside a music classroom: Effects of portable purifiers and aerosol injection rates.  Physics of Fluids 33, 033307\.  URL: https://doi.org/10.1063/5.0042474, doi:10.1063/5.0042474.
  * Nordsiek et al. [2021] Nordsiek, F., Bodenschatz, E., Bagheri, G., 2021.  Risk assessment for airborne disease transmission by poly-pathogen aerosols.  PLoS ONE 16, 1--41.  doi:10.1371/journal.pone.0248004.
  * Oswin et al. [2022] Oswin, H.P., Haddrell, A.E., Otero-Fernandez, M., Mann, J.F., Cogan, T.A., Hilditch, T., Tian, J., Hardy, D., Hill, D.J., Finn, A., Davidson, A.D., Reid, J.P., 2022\.  The Dynamics of SARS-CoV-2 Infectivity with Changes in Aerosol Microenvironment.  medRxiv preprint URL: https://www.medrxiv.org/content/early/2022/01/10/2022.01.08.22268944, doi:10.1101/2022.01.08.22268944. version 1, 10 Jan 2022\.
  * Parker and Crookston [2020] Parker, A.S., Crookston, K., 2020\.  Investigation into the Release of Respiratory Aerosols by Brass Instruments and Mitigation Measures with Respect to Covid-19.  medRxiv preprint URL: https://www.medrxiv.org/content/early/2020/08/04/2020.07.31.20165837, doi:10.1101/2020.07.31.20165837. version 1, 04 Aug 2020\.
  * Pöhlker et al. [2021] Pöhlker, M.L., Krüger, O.O., Förster, J.D., Berkemeier, T., Elbert, W., Fröhlich-Nowoisky, J., Pöschl, U., Pöhlker, C., Bagheri, G., Bodenschatz, E., Huffman, J.A., Scheithauer, S., Mikhailov, E., 2021.  Respiratory aerosols and droplets in the transmission of infectious diseases.  arXiv preprint doi:10.48550/arXiv.2103.01188, arXiv:2103.01188. version 4, 04 Aug 2021.
  * Schlenczek [2018] Schlenczek, O., 2018.  Airborne and ground-based holographic measurement of hydrometeors in liquid-phase, mixed phase and ice clouds.  University of Mainz, PhD dissertation.
  * Spahn et al. [2021] Spahn, C., Hipp, A.M., Schubert, B., Axt, M.R., Stratmann, M., Schmölder, C., Richter, B., 2021.  Airflow and Air Velocity Measurements While Playing Wind Instruments, with Respect to Risk Assessment of a SARS-CoV-2 Infection.  International Journal of Environmental Research and Public Health 18, 5413.  URL: https://doi.org/10.3390/ijerph18105413, doi:10.3390/ijerph18105413.
  * Stockman et al. [2021] Stockman, T., Zhu, S., Kumar, A., Wang, L., Patel, S., Weaver, J., Spede, M., Milton, D.K., Hertzberg, J., Toohey, D., Vance, M., Srebric, J., Miller, S.L., 2021.  Measurements and Simulations of Aerosol Released while Singing and Playing Wind Instruments.  ACS Environmental Au 1, 71--84.  URL: https://doi.org/10.1021/acsenvironau.1c00007, doi:10.1021/acsenvironau.1c00007.
  * WHO Team [2022] WHO Team, 2022.  Statement on the tenth meeting of the International Health Regulations (2005) Emergency Committee regarding the coronavirus disease (COVID-19) pandemic.  URL: https://www.who.int/news/item/19-01-2022-statement-on-the-tenth-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-coronavirus-disease-(covid-19)-pandemic. Retrieved 20 Jan 2022.
  * Yang et al. [2020] Yang, F., Pahlavan, A.A., Mendez, S., Abkarian, M., Stone, H.A., 2020.  Towards improved social distancing guidelines: Space and time dependence of virus transmission from speech-driven aerosol transport between two individuals.  Physical Review Fluids 5, 122501\.  URL: https://link.aps.org/doi/10.1103/PhysRevFluids.5.122501, doi:10.1103/PhysRevFluids.5.122501.