# Measurement and Simulation of Biocontamination in an Enclosed Habitat

Anniina Salmela, Ilpo Kulmala, Aku Karvinen, Virginie Taillebot, Peter

Weiss, Thibaud Gobert, Audrey Berthier, Vincenzo Guarnieri, Stephanie

Raffestin, Pertti Pasanen

► To cite this version:

Anniina Salmela, Ilpo Kulmala, Aku Karvinen, Virginie Taillebot, Peter Weiss, et al. Measurement and Simulation of Biocontamination in an Enclosed Habitat. Aerosol Science and Engineering, 2020, 4, pp.101 - 110. 10.1007/s41810-020-00057-3. hal-04517566

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# Measurement and Simulation of Biocontamination in an Enclosed Habitat

Anniina Salmela<fnref n="1" /> · Ilpo Kulmala<fnref n="2" /> · Aku Karvinen<fnref n="2" /> · Virginie Taillebot<fnref n="3" /> · Peter Weiss<fnref n="3" /> · Thibaud Gobert<fnref n="3" /> · Audrey Berthier<fnref n="4" /> · Vincenzo Guarnieri<fnref n="5" /> · Stephanie Raffestin<fnref n="6" /> · Pertti Pasanen<fnref n="1" />

Received: 14 August 2019 / Revised: 5 March 2020 / Accepted: 7 March 2020 / Published online: 20 March 2020
© The Author(s) 2020

## Abstract

Microbial aerosols can be used as model particles for examining the dispersion and deposition of particles as well as assessing the reliability of the simulation methods. For example, the computational fluid dynamics model (CFD) can be used in the evaluation of indoor microbial contamination and the possible spread of harmful microbes in spaces with high densities of people or in special hermetic environments. The aim of this study was to compare the results of the CFD simulation, which predicts the deposition of biological particles on the surfaces of a spacecraft, and real particle deposition, using *Bacillus licheniformis/aerius* bacterium particles as the model organism. The results showed that the particles were mainly deposited on floor surfaces, but also onto the supply air diffusers, where bacterial concentrations were higher than on the wall and ceiling surfaces. The CFD simulation showed similar trends with actual particle dispersal, conducted in this experiment with *Bacillus* particles.

**Keywords** Bioaerosol · Modelling · Spacecraft · Wet generation · *Bacillus*

## 1 Introduction

Bioaerosols such as bacteria, fungi, viruses and all other particles that contain living organisms or which can be released from living organisms are recognized as important negative contributors to the indoor air quality (Reponen et al. 2011). Indoors, bioaerosols can originate from humans, pets, plants or they can enter from the outdoor air. Humans are major

sources of bacteria and viruses indoors, while moulds mostly originate from human activities such as handling of root vegetable and firewood (Reponen et al. 1992; Nazaroff 2004; National Research Council 2005). In addition, contaminated clothes can act as a source of fungal spores.

Crew members are a major source of microorganisms on spacecraft and station and, although most of the microbes released are generally harmless, there may also be some opportunistic pathogens (Pierson 2001; Checinska et al. 2015; Ichijo et al. 2016). The bioaerosols emitted by the crew members originate mainly from the respiratory tract and skin, although emissions from the gastrointestinal tract in some off-nominal events are also possible (National Research Council 2005). Microorganisms are ubiquitous ecological partners of humans, materials and devices also during manned space flight and in other hermetically sealed environments. Microbes can be transmitted and enter into the human body naturally by inhalation, by direct contact between persons or through indirect contact, e.g. with environmental surfaces or by ingestion through contaminated food and water (Siegel et al. 2007). Furthermore, airborne biocontamination is considered especially important under spaceflight conditions, because microgravity means that the bioaerosol dispersion will be very different from that on

<figure><img src="image_2.png" /></figure>

<footnote>Anniina Salmela
salmela.annii@gmail.com</footnote>

<footnote n="1">¹ Department of Environmental and Biological Science,
University of Eastern Finland, P.O. Box 1627,
70211 Kuopio, Finland</footnote>

<footnote n="2">² VTT Technical Research Centre of Finland, P.O. Box 1000,
02044 Espoo, Finland</footnote>

<footnote n="3">³ Compagnie Maritime D'Expertises, 36 Boulevard de
l'Océan, 13009 Marseille, France</footnote>

<footnote n="4">⁴ Institute for Space Medicine and Physiology, Cedex 4,
P.O. Box 74404, 31405 Toulouse, France</footnote>

<footnote n="5">⁵ Thales Alenia Space Italia SpA, Str. Antica Di Collegno,
253, 10146 Turin, Italy</footnote>

<footnote n="6">⁶ European Space Agency, Keplerlaan 1, 2201 AZ Noordwijk,
the Netherlands</footnote>
Earth. It is also known that an enclosed environment (such as in ISS space station) can produce resistant microbial and more virulent strains (Wilson et al. 2007, 2008; Parra et al. 2008; Zea et al. 2017). During the envisioned future long-duration flights, it will be mandatory to prevent and control bioaerosol contamination, and this will demand a better understanding about the contamination mechanisms ranging from potential sources to actual deposition of the aerosolized particle.

In a space station, most of the bioaerosols are exhausted and subsequently removed by the high efficiency particulate air (HEPA) filters installed in the recirculation air (Van Houdt et al. 2012; Checinska et al. 2015). The HEPA filters used in a space station retain > 99.97% particles of size 0.3 µm and larger. However, a small fraction of the airborne microbes will still become deposited on surfaces. If one considers microbial contamination deposition under normal gravity, then large particles carrying bacteria settle rapidly and are thus removed from the air onto solid surfaces. Under microgravity conditions, the settling of larger particles is less significant and other removal mechanisms become more dominant. According to Van Houdt et al. (2018), continuous ventilation and efficient particle filtration will remove the majority of these airborne bioaerosols, but imperfect mixing of air may result in areas where there is a risk of unwanted deposition onto surfaces.

Particle deposition on surfaces can be studied using generated bacterial and fungal aerosols. These bacterial and fungal aerosols can be exploited also as model particles when examining the dispersion and deposition of particles as well as when assessing the reliability of the models. The models could be applied to evaluate microbial contamination and the possible spread of harmful microbes in crowded areas, such as transport hubs. Another example would be to determine the spread of pathogens in airports or in special hermetic environments such as submarines or a space station.

CFD models have been widely used to study indoor air quality, thermal comfort, fire safety, particle concentration levels and HVAC system performance (Chen 2009). In addition to particulate matter, CFD has also been used to monitor indoor air quality which has been deteriorated by gaseous contaminants (Chen 2009). Typical environments for these studies have included various buildings (commercial buildings, residential buildings, schools, health-care facilities, institutional buildings, and industrial buildings), underground facilities, public transportation vehicles, greenhouses, animal facilities, etc. Particle deposition has been modelled by many researchers. The deposition of particles on indoor smooth surfaces has been investigated as a function of particle size and density (Lai and Nazaroff 2000; Lai and Chen 2006; Zhao et al. 2008; Zhang and Chen 2009). Also, the behaviour of bioaerosols can be simulated with computational fluid dynamics (CFD) which offers

detailed predictions about airflows and particle transport in mechanically ventilated enclosed spaces (Zhao et al. 2004). Advances in computational fluid dynamics (CFD) as well as the underlying algorithms and the developments in computational power have made it possible to better resolve indoor air movements and transport of contaminants. The simulations presented in this article are expected to be performed in computational cluster environments, mainly currently existing in research institutions. The simulations have focused on studying airflow patterns and contaminant concentration dispersion in the air. In addition, by modifying the gravity force in the CFD simulation, the method can be exploited also to predict the deposition of bioaerosols on surfaces in a microgravity environment. However, a suitable Earth analogue of the space ecosystem is not available and the comparison of the simulation results with the experimental results is currently feasible only under normal gravity conditions.

The objective of this study was to develop and compare a model to predict microbial contamination in an enclosed environment using *Bacillus aerius*/licheniformis bacteria isolated from the ISS: the transportation of bioaerosols and the concurrent spread of biocontamination. A more elaborate aim was to produce a robust modelling tool for predicting airborne microbial contamination transport in manned spacecraft and vehicles, since this can help to develop adequate control programmes and countermeasures.

## 2 Materials and Methods

Experiments were conducted in an environmentally controlled and a hermetically sealed 26.4 m³ chamber (Hydro-sphere Habitat (HH) of COMEX, FR), which was divided into three distinct compartments: D = displacement ventilation zone, M = mixing ventilation zone and E = exhaust ventilation zone (Fig. 1).

In zone D, a low-velocity air diffuser was installed to make it possible to study a displacement ventilation configuration. Zone M hosted four separate air diffusers to simulate a mixing ventilation configuration similar to the current one onboard the Columbus module in the International Space Station (ISS). Columbus is a research laboratory module; it is a part of the International Space Station and is the largest single contribution made by the European Space Agency (ESA) to the ISS. Zone E did not contain an air supply system, but instead there was a ventilation system exhausting the air supplied by the displacement and mixing ventilation as transfer air.

The ventilation system of chamber was an HEPA-powered (high efficiency particulate air filter) filtration unit (Oy Lifa Air Ltd, FIN) that produces a sufficient HEPA-filtered airflow. The ventilation was arranged with the closed circulation principle in which the exhaust air was led to a filtration

<figure><img src="image_3.png" /></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 1** Test volume CAD view describing the three air distribution zones and dimensions in the research chamber hydro-sphere habitat</figcaption></figure>

unit and supplied back to the air distribution system. The instrumentation of the airflow and microbial measurements systems has also been incorporated into the chamber and coupled with environmental measurement devices. Airflow rate and air velocities in the empty chamber were measured using a hot wire comfort anemometer, i.e. the omnidirectional thermoanemometer (TSI model 8475-075-1). Temperature and relative humidity were measured (HM70, Vaisala, FIN) also before and after each test period. Inlet air temperature and relative humidity in the chamber varied during the different test days from 20 ± 4 °C and from 70 ± 10%, respectively. The corresponding values for outdoor air were 17.5 ± 2.5 °C and 65 ± 25%, respectively.

Before the microbial experimental campaign, pre-tests with non-biological diethylhexyl sebacate (DESH) particles and airflow measurements were performed to adjust the air distribution system as well as allowing calibration of the airflow pattern in the model. The concentration of the used DESH particles were much smaller than bioaerosol concentration in experiments, but because particle-particle interactions were not taken into account, the relative results are comparable. A measurement ring and an adjustment

damper were installed upstream of each diffuser to measure and adjust the airflow rate. The flow rates were balanced so that the total supply flow rate of the displacement ventilation was 150 m³/h. The supply flow rate of the mixing ventilation chamber was 160 m³/h, divided equally with the four supply devices, giving 40 m³/h for each. The total airflow rate was thus 310 m³/h. Figure 2 presents the mean velocities in selected planes of interest. Some unsteady non-convergent structures can be seen, but they are mainly due to logarithmic scale used in the figure; deviations from the smooth flow field are insignificant.

Computational fluid dynamics is determined by solving of the governing equations of the fluid flow; in the case of the incompressible flow, these are the continuity equation and the momentum equation, i.e. Navier–Stokes equations. Equations are presented, e.g. in Ferziger et al. (2020). Together with the boundary conditions and material properties, these equations are all that are needed to obtain time-dependent fluid flow field in every spatial location of the domain. The method based on solving these equations without any simplifications is called a direct numerical simulation (DNS). The commonly used CFD methods are based

<figure><img src="image_5.png" /><figcaption>**Fig. 2** Mean velocity field in selected planes. Note: logarithmic</figcaption></figure>
on the domain and equation discretization and the solving of the equations by marching in a time stepwise manner. Apart from rare case of laminar flow, DNS requires a very dense computational grid and small time steps and is, therefore, computationally too expensive to be used as a part of a routine simulation process. The traditional procedure applied to overcome this problem is to conduct time averaging of the governing equations yielding the Reynolds averaged Navier–Stokes (RANS) equations and to assume that there is a time-independent steady-state solution. The nonlinear nature of the momentum equation generates an additional term to the time-averaged equation, which needs to be modelled using some turbulence closure, for example, the well-known $k-\epsilon$ model (Launder & Spalding 1974). Turbulence models are always empirical models and require fine-tuning of the parameters in the models. This means that the models usually give good results only for the cases in which they are meant to be used and therefore there is no universal model that works well in every case. The method applied in this study can be considered to lie between the DNS and RANS, both with respect to computational demands and accuracy. The method is called a large eddy simulation (LES). In LES, the case is simulated as an unsteady case, but in contrast to DNS, all the turbulence scales are not simulated but the smallest one is modelled using a sub-grid scale model. This substantially reduces the needed computational power as compared to DNS. In this study, a WALE (wall-adapting local eddy viscosity) model was used for the sub-grid model (Nicoud and Ducros 1999).

The size and quality of the computational grid affect the results as well as influencing the convergence of the simulations. In general, a higher density of grid points is needed where gradients of the simulated variables are large. Special caution is needed when modelling near surfaces. The geometry of the simulated domain should naturally be as close as possible to the real one. The calculation domain was divided into discrete cells so that the grid density was higher in locations where high mean velocity gradients were expected such as in the vicinity of surfaces and supply outlets. The computational grid had about 81 million cells. The typical size of the grid cell in the core region of the domain was 2 cm. We applied a threefold refinement near the wall grids, yielding a grid size of 2.5 mm. Subsequently, the nearest wall cell was divided into three cells using a thickness ratio of 2. The typical distance from the centre point of the wall adjacent cell to the wall of different surfaces of the domain was about 0.35 mm. The computational geometry was devised using open source software, Salome-platform (www.salome-platform.org). A computational grid was produced using utility of the open source CFD package OpenFOAM (www.openfoam.org).

The actual simulation was performed using an LES WALE turbulence model implemented in the open source

software OpenFOAM. The case was initially simulated for 300 s to depict the situation where the statistics of the flow field are no longer varying. After that, the flow field was simulated for 180 s and time average field over that period was used in the particle simulation stage. A simulation time using 400 CPU cores of modern Linux cluster was about 3 months in the air velocity flow field simulation phase, whereas the particle simulation required only days using one CPU core.

Particle simulation was performed using time-averaged gas velocity field and the so-called Lagrangian method in which trajectories of the particles are calculated taking into account the forces influencing the particles. Since the influence of turbulence is lost in the time-averaging process, it should be included by incorporating turbulence quantities into the particle tracking process. Traditionally, this is done by modifying the trajectory of the particle using turbulence quantities obtained directly from the turbulence model being used. In a large eddy simulation, however, only part of the turbulence is modelled using a turbulence model and the rest of the turbulence is simulated as a part of the unsteady simulation procedure. In this work, this simulated part of the turbulence is taken into account by calculating the averages of the fluctuating velocity components in the unsteady simulation stage. Hence, both modelled and simulated turbulence of the gas flow field are used in the particle trajectory modification procedure, in which the fluctuation of the particle trajectories is much more vigorous than in the case in which only modelled turbulence is considered.

The microbial experimental campaign was carried out with a wet generated *Bacillus* bacteria bioaerosol. Briefly according Salmela et al. (2017), *Bacillus* suspension was prepared as follows: *B. aerius/licheniformis* was inoculated with loop from stock culture on trypticase soy agar (TSA, Sigma-Aldrich Co., St. Luis, MO, US) and grown for 24 ± 2 h at 37 °C. After incubation, bacterial colonies were harvested with an inoculation loop, inoculated in potato agar (PA, Thermo Fisher Scientific–Oxoid Limited, UK) and incubated for 48 ± 2 h at 37 °C. After the 48 ± 2 h incubation of the PA plates, the incubation was continued at room temperature (21 ± 1 °C) under natural light for 5 days until the amount of spores was > 80%. The number of spores was pre-checked with Schaeffer–Fulton staining. After incubation, the *B. aerius/licheniformis* suspension was prepared by applying 5 ml of sterile deionized water onto the sporulating bacterial culture growing in PA plates. Bacterial spores were suspended into water by swapping the culture with a sterile inoculation loop and decanting the suspension into a test tube (V= 50 mL) with the suspension being mixed well. The relative amount of spores in suspension was determined with Schaeffer–Fulton staining. A total of 400 spores and vegetative cells were counted under oil immersion in a light microscope at 1000× magnification and the relative amount

<figure><img src="image_6.png" /></figure>
of spores (> 80% of all) was calculated. The concentration of suspension was adjusted to $10^8$ microbes/mL by using a hemacytometer (e.g. Fuchs–Rosenthal: Hirschmann EM Techcolor) (Salmela et al. 2017). A diluted *Bacillus suspension* was generated by using 6-jet Collison Nebulizer (BGI, MA, US) at airflow 6 L/min. Compressed oil and particle-free air were used to generate the aerosol from the bacterial suspension solution. The suspension load was 25 mL and it produced stable aerosols for 2 h. The tests were conducted in triplicate.

From the air of the chamber, both the stability of bioaerosol generation and the total particle concentration were measured with an optical particle counter (OPS 3330, TSI, MN, US). Particle detection is based on light scattering; when a particle passes through a beam of light, some of the light is scattered and thus the number of particles is determined by counting the pulses of scattered light reaching the detector (Burkart et al. 2010, Salmela 2018). OPC counts all the particles in air in a predetermined size range (e.g. 0.3–10 µm). The deposition of bacteria cells and spores was studied at 34 different sampling points (Fig. 3) with contact agar (sampling area = 20 cm²) (Petrifilm AC, 3 M, US) and spread plate (sampling area = 25 cm²) (Quantiswab, Copan, IT) methods on the surface of the sampling sheets (glass material). In the spread plate method, dilution series were done up to $10^{-1}$ and two dilutions of each sample were inoculated onto TSA plates with 0.1 ml per dilution each conducted in duplicate. The detection limit was 0.2 cfu/cm² and 1 cfu/20 cm² for the spread plate method and the contact agar method, respectively. TSA plates and contact agars were incubated at 37 °C for 3 days. The agglomeration stage and the possible presence of other particles in the generated bioaerosol were analysed by using filter sampling. The generated bioaerosols were collected from the air into polycarbonate filters (MB PCB ø25 mm, 0.2 µm, Merck KGaA, DE)

with airflow of $1.8 \pm 0.2$ L/min. The SEM slides were prepared by coating the filters with gold and then visualized in a high-vacuum scanning electron microscope (SEM) (Menzah-Attipoe et al. 2016). From SEM images, the agglomerate stage was evaluated by counting either 40 fields or 400 endospores and cells, whichever came first (Paerl 1978).

Decontamination of surfaces and air was undertaken before the first microbe generation and after every bioaerosol generation. Decontamination was achieved by wiping the glass sampling sheets with 6% hydrogen peroxide solution, followed by hydrogen vapour treatment (generated from 35% aqueous $H_2O_2$ solution). Control samples were collected onto the surfaces after each decontamination processes with contact agars. In addition, air samples using Andersen impactors (sampling time 10 min, 28.3 L/min, LOD 3.5 cfu/m³) (Andersen 1958) were taken after the final decontamination. The decontamination results of contact agars were less than 0.5 cfu/cm², which was below the set trigger value for contamination, i.e. 50 cfu/100 cm²: the criteria are between grades B and C in the EU Guidelines to Good Manufacturing Practice of Sterile Medicinal Products (GMP) (EudraLex 2018). The microbial air concentration after the final decontamination as measured by Andersen impactors was <LOD in compartments D, M and E.

## 3 Results and Discussion

Optical particle counter (OPC) measurements revealed that the production of the bioaerosol was stable for 2 h, which was a sufficient time to allow us to conduct deposition measurements (Fig. 4). The particles were mostly in the size range of 0.5–3 µm, which is similar to the size of *Bacillus licheniformis/aerius* spores 1.2 µm length and 0.8 µm breadth, respectively (Carrera et al. 2007; Rey et al.

<figure><img src="image_7.png" /><figcaption>Fig. 3 Surface sampling points; floors, ceilings, walls and supply air diffusers, in COMEX' Test chamber</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 4 Particle concentration during 2 h bioaerosol generation with Collision Nebulizer starting at time point 20 min</figcaption></figure>

2004; Chada et al. 2003). The total particle concentration was on average 63,000 particle/m³ and 9000 particle/m³ in size class 0.5–1 µm and 1–3 µm during 2 h wet generation from $10^8$ cfu/ml suspension with a Collision Nebulizer, respectively. Changes in the stability were followed in all compartments with OPC by successive samples from D, M and E zones. However, changes between different compartments were rather small.

Figure 5 shows the averages of culturable concentration of deposited particles on the sampling points of the three repetitions measured with the spread plate method and with contact agar. Culturable surface concentrations analysed by the spread plate method varied from <LOD to $110 \text{ cfu/cm}^2$ and in contact agar samples from <LOD to $44 \text{ cfu/cm}^2$. The results showed that particles became deposited mostly on the

floors, at sampling points DF1–4, MF1–4 and EF1–4, where deposition concentrations ranged from 25 to $110 \text{ cfu/cm}^2$ and from 20 to $44 \text{ cfu/cm}^2$ in TSA plate samples and in contact agar, respectively. More than 90% of viable particles were deposited onto floor surfaces. In addition, sampling points on the wall, points DV1–4, were also emphasized. The deposition concentrations on the walls varied from <LOD to $6 \text{ cfu/cm}^2$ and <LOD–9 $\text{cfu/cm}^2$ by the spread plate and contact agar methods, respectively. There was only minor deposition on ceiling surfaces (<LOD to $2 \text{ cfu/cm}^2$). In contrast, there were also high deposition concentrations detected on the supply air diffusers (SL1–2, SR1–2) (4–18 $\text{cfu/cm}^2$). Due to their small surface area, only swab sampling with the spread plate method was done in the supply air diffusers. The highest culturable concentrations were observed in zone D.

<figure><img src="image_9.png" /><figcaption>Fig. 5 Averages and ranges of culturable concentrations of surface densities of cultivation *Bacillus* aerosols (n=3) with swab and contact agar (n=3) samples from all sampling points</figcaption></figure>

<figure><img src="image_10.png" /></figure>
However, no significant differences were detected between
the compartments.

In general, the deposition of biological particles was
highest on upward-facing surfaces. Gravitational settling is
likely to have primarily resulted on deposition onto floors,
whereas turbulent and Brownian diffusions were dominant
processes when particles were deposited on walls and ceil-
ings (Lai 2002; Kohli and Mittal 2009). Other external fac-
tors which can affect particle deposition were not studied. It
is known that in addition to particle size, the deposition of
bioaerosols onto surfaces depends also on the characteristics
of the surface such as moisture, hydrophilic interactions,
electrostatic forces and physical configuration, i.e. smooth,
rough, porous or irregular surface (Tamburini et al. 2015).
Also temperature may have an effect (thermophoretic force)
(Han et al. 2011). Moreover, air turbulences and physical
disturbances play a role in whether a particle will adhere
or be re-aerosolized. However, the size of a bioaerosol is
the most important parameter that determines its deposition
properties. Bioaerosols with a size less than 5.0 µm gener-
ally remain in the air, and thus they will follow the air flow
that carries them, whereas larger particles (> 10 µm) are
soon deposited on surfaces (Mohr 2001; Ghosh et al. 2015).

The bioaerosols selected in this study, *Bacillus licheniformis* cells and endospores, were of a size less than 2 µm and their movements were influenced by the airflow pattern (Lu et al 1996; Carrera et al. 2007; Hurst et al. 2007; Wong et al. 2010) and that is the reason why more particles were in air than deposited on surfaces.

The comparison between the two different surface sam-
pling methods revealed that one obtains similar results with
the cultivation method and Petrifilm contact agar methods.
The manufacturer of Quantiswabs (Biomerieux S.A.) has
claimed that their nylon flocked swab used in this study
should be able to recover 60% of microorganisms from a
surface, whereas traditional fibre wound swabs achieve only

around 20% recovery (Dalmaso et al. 2007). The Petrifilm
manufacturer has not provided comparable information
about sampling efficiency, but several studies (e.g. Salo et al.
2000, Nelson et al. 2013, Warren et al. 2015) have com-
pared the Petrifilm contact agar method to the conventional
spread plate method and the data have indicated that the
Petrifilm method is as sensitive as the conventional spread
plate method. In addition, the Petrifilm AC method is sim-
pler and less time-consuming than the conventional spread
plate method as noted also by Kudaka et al. (2010).

According to use of the cultivation method for determi-
nation of deposited particles, a well-known limitation of
cultivation is that it detects only a small portion (< 1–10%)
of the microorganisms present in different environmental
samples and it has a short sampling period (Amann et al.
1995; Nazaroff 2004). Heidelberg et al. (1997) and Torsvik
et al. (1994) have found that only a very small proportion,
i.e. 10%, of the microorganisms present in the environment
could be cultured and identified and less than 1% of total
microbial species in any environmental sample could be ana-
lysed by cultivation (Hugenholtz 2002). According to Oliver
(2005), the other microorganisms were naturally viable but
were non-culturable in laboratory conditions remaining in
the “viable but non-culturable” stage.

The filter samples collected from the air of the chamber
showed that the *B. aerius/licheniformis* spores were released
mostly as single spores, which were sized < 2 µm, during
the wet generation (Fig. 6). The image analysis detected
also some agglomerates, but these made up less than 20%
of the total counts and there were also particles of smaller
sizes, < 0.5 µm. The number of particles under 0.5 µm was
approximately ten times more than particles of spore size.
These particles may be tissue fragments such as cell walls
or cytoplasmic material (Terzieva et al. 1996; Reponen et al.
1997; Després et al. 2012). An explanatory factor may be
an aerosolization stress. The aerosolization stress evoked

<figure><img src="image_11.png" /><figcaption>Fig. 6 Spores and nanoparticles (a) and agglomerate (b) (the size of the agglomerate 1.2×1.8 mm), on the filter</figcaption></figure>
by the Collison Nebulizer may induce structural changes in cells as well as cumulative metabolic injuries in the bacteria and thus increase the fragment concentration emitted from the Collison Nebulizer (Mainelis et al. 2005; Ibrahim et al. 2015). Furthermore, this process may affect the survivability of microorganisms, which is shown to depend on growth conditions prior to aerosolization, environmental conditions during aerosolization, methods of aerosolization and methods of collection and enumeration (Marthi et al. 1990). Nevertheless, survivability can also depend on the species of microorganism being aerosolized. However, spore forming *Bacillus* should be stress-resistant bacteria (Marthi et al. 1990, Griffits et al. 1996, Reponen et al. 1997, Thorne et al. 1992, Johansson et al. 2011, Ibrahim et al. 2015).

<figure><img src="image_12.png" /><figcaption>**Fig. 7** Predicted particle deposition (black points) after 30 min simulation onto indoor surfaces in the habitat</figcaption></figure>

In general, the calculated deposition of particles was highest on upward-facing surfaces (floor), which is similar to the measurements (Figs. 5, 7 and 8). The highest deposition rates were predicted to be in zone M, whereas the experimental study resulted in more equal deposition rates to the floor of all compartments. The calculated deposition on vertical surfaces was in general clearly lower than that on upward-facing surfaces, which was also confirmed by measurements. However, in contrast to the experiments, there were quite high-predicted deposition rates on the ceiling in the mixing ventilation and exhaust zones. Overall, the experimental results were mostly less than ten times higher than predicted deposition. However, there was also up to 200 times higher experimental deposition than calculated results (mixing ventilation zone). Although the deposition rates were not predicted exactly, the calculated trends follow clearly the measured ones. There are also uncertainties in the measured values because of the indoor air microbe concentration as a result of adjusted *Bacillus* suspension, and also because varying surface sampling efficiency.

## 4 Conclusions

The development of a reliable model to predict contaminant dispersion and deposition in an enclosed space requires considerable effort. It requires the development of the model itself and then high-quality experimentation to validate the results. The experimentation had several phases which had to

<figure><img src="image_13.png" /><figcaption>**Fig. 8** Predicted particle deposition after 30 min simulation onto indoor surfaces in the habitat as compared to experimental bioaerosol deposition (spread plate method) (particle/cm²)</figcaption></figure>
be properly performed from the bioaerosol generation, sam-
pling to handling, cultivation and counting of the samples.
Careful actions and efficient decontamination of the meas-
urement environment between experiments were needed to
avoid contamination which could ruin the experiments.

In conclusion, the results from this study proved that a *Bacillus* aerosol is a useful material when one wishes to assess bioaerosol deposition; the method produced results that were comparable with those obtained by mathematical modelling. However, the dispersion of generated particles was not very well predicted, perhaps because the aerosol generator was placed in the region where the flow field was not properly predicted. However, the predicted deposition rates showed similar trends with experimentation.

In spite of these results, the CFD model can be still used
to predict the aerosol particle dispersion and deposition in
ventilated enclosed multi-zone spaces. Computational fluid
dynamics model can provide detailed information about the
airflow and particle movements and deposition in micro-
gravity spacecraft environment, information that would not
otherwise be possible to collect under Earth conditions.

This work increases our knowledge about indoor bioaero-
sol transport and deposition; this will be useful in design,
construction, operation and maintenance operations. In addi-
tion to spaceflight applications, it may be possible to exploit
these results in situations where the spread of airborne
pathogens is of interest, e.g. hospitals, operating theatres,
airborne infection isolation rooms, pharmaceutical industry,
clean rooms, airplanes and other mass transport vehicles.

**Acknowledgements** Open access funding provided by University of Eastern Finland (UEF) including Kuopio University Hospital. This study was supported by the European Space Agency, ESA Basic Technology Research Programme ESA ESTEC AO/1-7603/13/NL/RA within the BIOMODEXO project.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

References

Amann RI, Ludwig W, Schleifer KH (1995) Phylogenetic identification and in situ detection of individual microbial cells without cultivation. *Microbiol Rev* 59(1):143–169

Andersen AA (1958) New sampler for the collection, sizing, and enu-
eration of viable airborne particles. J Bacteriol 76:471-484

Carrera M, Zandomeni R, Fitzgibbon J, Sagripanti J (2007) Difference between the spore sizes of *Bacillus anthracis* and other Bacillus species. J Appl Microbiol 102:303–312

Chada VG, Sanstad EA, Wang R, Driks A (2003) Morphogenesis of bacillus spore surfaces. J Bacteriol 185:6255–6261

Checinska A, Probst AJ, Vaishampayan P, White JR, Kumar D, Stepanov VG et al (2015) Microbiomes of the dust particles collected from the International Space Station and Spacecraft Assembly Facilities. Microbiome 3(1):50

Dalmaso G, Ferrari M, Paris A, (2007). Validation of the new Irradi-
ated Nylon™ flocked

Després V, Huffman JA, Burrows S, Hoose C, Safatov A, Buryak G, Fröhlich-Nowoisky J, Elbert W, Andreae M, Pöschl U (2012) Primary biological aerosol particles in the atmosphere: a review. Tellus B Chem Phys Meteorol 64:15598

EudraLex (2018) Good manufacturing practice (GMP) Guidelines Volume 4, European Union

Ferziger JH, Peric M, Street RL (2020) Computational methods for
fluid dynamics. Vol. 4. Springer International Publishing, Ber-
lin. Doi: 10.1007/978-3-319-99693-6.

Ghosh B, Lal H, Srivastava A (2015) Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms. Environ Int 85:254–272

Griffiths WD, Stewart IW, Reading AR, Futter SJ (1996) Effect of aerosolization, growth phase and residence time in spray and collection fluids on the culturability of cells and spores. J Aero-sol Sci 27(5):803–820

Han Y, Hu Y, Qian F (2011) Effects of air temperature and humidity on particle deposition. Chem Eng Res Des 89(10):2063-2069

Heidelberg JF, Shahamat M, Levin M, Rahman I, Stelma G, Grim C, Colwell RR (1997) Effect of aerosolization on culturability and viability of gram-negative bacteria. Appl Environ Microbiol 63(9):3585–3588

Hugenholtz P. (2002). Exploring prokaryotic diversity in the genomic era. Genome biology 3(2): reviews00003. 1.

Hurst CJ, Crawford RL, Garland JL, Lipson DA, (2007). Manual of environmental microbiology. American Society for Microbiology Press.

Ibrahim E, Harnish D, Kinney K, Heimbuch B, Wander J (2015) An experimental investigation of the performance of a Colli-son nebulizer generating H1N1 influenza aerosols. Biotechnol Biotechnol Equip 29:1142–1148

Ichijo T, Yamaguchi N, Tanigaki F, Shirakawa M, Nasu M, (2016) Four-year bacterial monitoring in the International Space Station—Japanese Experiment Module” Kibo” with culture-independent approach. npj Microgravity, 2:16007.

Johansson E, Adhikari A, Reponen T, Yermakov M, Grinshpun SA (2011) Association between increased DNA mutational frequency and thermal inactivation of aerosolized Bacillus spores exposed to dry heat. Aerosol Sci Technol 45(3):376–381

Kohli R, Mittal KL, (2009) Developments in surface contamination and cleaning-Vol 2: Particle deposition, control and removal, William Andrew.

Kudaka J, Horii T, Tamanaha K, Itokazu K, Nakamura M, Taira K, Nidaira M, Okano S, Kitahara A (2010) Evaluation of the petrifilm aerobic count plate for enumeration of aerobic marine bacteria from seawater and *Caulerpa lentillifera*. J Food Prot 73:1529–1532

Lai AC, Nazaroff WW (2000) Modeling indoor particle deposi-
tion from turbulent flow onto smooth surfaces. J Aerosol Sci
3(4):463–476

Oliver JD (2005) The viable but nonculturable state in bacteria. J Microbiol 43(1):93–100
Lai AC (2002) Particle deposition indoors: a review. Indoor Air 12:211–214

Lai AC, Chen F (2006) Modeling particle deposition and distribution in a chamber with a two-equation Reynolds-averaged Navier-Stokes model. J Aerosol Sci 37(12):1770–1780

Launder BE, Spalding DB (1974) The numerical computation of turbulent flows. Comput Methods Appl Mech Eng 3:269–289

Lu W, Howarth AT, Adam N, Riffat SB (1996) Modelling and measurement of airflow and aerosol particle distribution in a ventilated two-zone chamber. Build Environ 31:417–423

Mainelis G, Berry D, An HR, Yao M, DeVoe K, Fennell DE, Jaeger R (2005) Design and performance of a single-pass bubbling bioaerosol generator. Atmos Environ 39:3521–3533

Marthi B, Fieland VP, Walter M, Seidler RJ (1990) Survival of bacteria during aerosolization. Appl Environ Microbiol 56(11):3463–3467

Mensah-Attipoe J, Saari S, Veijalainen A, Pasanen P, Keskinen J, Leskinen JT, Reponen T (2016) Release and characteristics of fungal fragments in various conditions. Sci Total Environ 547:234–243

Mohr A (2001) Fate and transport of mechanism in air. Manual of Environmental Microbiology, Third Edition, American Society of Microbiology, pp. 961–971.

National Research Council (2005) Sensor systems for biological agent attacks: protecting buildings and military bases. National Academies Press, Washington

Nazaroff WW (2004) Indoor bioaerosol dynamics. Indoor Air 14:175–183

Nelson MT, LaBudde RA, Tomasino SF, Pines RM (2013) Comparison of 3M™ Petrifilm™ Aerobic count plates to standard plating methodology for use with AOAC antimicrobial efficacy methods 955.14, 955.15, 964.02, and 966.04 as an alternative enumeration procedure: collaborative study. J AOAC Int 96:717–722

Nicoud F, Ducros F (1999) Subgrid-scale stress modelling based on the square of the velocity gradient tensor. Flow, Turbul Combust 62:183–200

Paerl HW (1978) Effectiveness of various counting methods in detecting viable phytoplankton. N Z J Mar Freshwat Res 12:67–72

Parra M, Ricco AJ, Yost B, McGinnis MR, Hines JW (2008) Studying space effects on microorganisms autonomously: GeneSat, PharmaSat and the Future of Bionanosatellites. Gravit Space Biol 21:9–17

Pierson DL (2001) Microbial contamination of spacecraft. Gravit Space Biol Bull 14(2):1–6

QUANTISWAB™ for the quantitative recovery of micro-organisms in critical clean room environments. https://www.biomerieux-usa.com/sites/subsidiary_us/files/quantiswab-microorganism-recovery-1.pdf.

Reponen T, Lehtonen M, Raunemaa T, Nevalainen A (1992) Effect of indoor sources on fungal spore concentrations and size distributions. J Aerosol Sci 23:663–666

Reponen T, Willeke K, Ulevicius V, Grinshpun SA, Donnelly J (1997) Techniques for dispersion of microorganisms into air. Aerosol Sci Technol 27:405–421

Reponen T, Willeke K, Grinshpun S, Nevalainen A (2011) Aerosol measurement: principles, techniques, and applications. Biological particle sampling. Third edition, pp 549–570.

Rey MW, Ramaiya P, Nelson BA, Brody-Karpin SD, Zaretsky EJ, Tang M, de Leon AL, Xiang H, Gusti V, Clausen IG (2004) Complete genome sequence of the industrial bacterium *Bacillus licheniformis* and comparisons with closely related *Bacillus* species. Genome Biol 5:r77

Salmela A, Kokkonen E, Kulmala I, Veijalainen A, van Houdt R, Leys N, Berthier A, Viacheslav I, Kharin S, Morozova J, Pasanen P

(2017) Production and characterization of bioaerosols for model validation in spacecraft environment. J Environ Sci 69:227–238

Salo S, Laine A, Alanko T, Sjöberg A, Writanen G (2000) Validation of the microbiological methods Hygicult dipslide, contact plate, and swabbing in surface hygiene control: a Nordic collaborative study. J AOAC Int 83:1357–1365

Siegel JD, Rhinehart E, Jackson M, Chiarello L, Committee HCICPA (2007) Guideline for isolation precautions: preventing transmission of infectious agents in health care settings. Am J Infect Control 35:S65–S164

Tamburini E, Donegà V, Marchetti MG, Pedrini P, Monticelli C, Balbo A (2015) Study on microbial deposition and contamination onto six surfaces commonly used in chemical and microbiological laboratories. Int J Environ Res Public Health 12:8295–8311

Terzieva S, Donnelly J, Ulevicius V, Grinshpun SA, Willeke K, Stelma GN, Brenner KP (1996) Comparison of methods for detection and enumeration of airborne microorganisms collected by liquid impingement. Appl Environ Microbiol 62:2264–2272

Thorne PS, Kiekhaefer MS, Whitten P, Donham KJ (1992) Comparison of bioaerosol sampling methods in barns housing swine. Appl Environ Microbiol 58(8):2543–2551

Torsvik V, Goksoyr J, Daae F, Sorheim R, Michalsen J, Salte K (1994) Use of DNA analysis to determine the diversity of microbial communities. In: Ritz K, Dighton J, Giller KE (eds) Beyond the biomass: compositional and functional analysis of soil microbial communities. Wiley, Chichester, pp 39–48

Van Houdt R, Mijnendonckx K, Leys N (2012) Microbial contamination monitoring and control during human space missions. Planet Space Sci 60:115–120

Van Houdt R, Kokkonen E, Lehtimäki M, Pasanen P, Leys N, Kulmala I (2018) Requirements for modeling airborne microbial contamination in space stations. Acta Astronaut 144:380–387 (Epub 2018 Jan 4)

Warren CA, Weber SK, Crespo R (2015) Comparison of conventional plating methods and Petrifilm™TM for the recovery of aerobic bacteria and mold from hatchery fluff samples. J Appl Poultry Res 25:48–53

Wilson JW, Ott CM, Bentrup KH, Ramamurthy R, Quick L, Porwollik S et al (2007) Space flight alters bacterial gene expression and virulence and reveals a role for global regulator. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.0707155104

Wilson JW, Ott CM, Quick L, Davis R, Bentrup KH, Crabbé A et al (2008) Media in composition controls regulatory and virulence response of Salmonella in spaceflight. PLoS ONE 3:e3923. https://doi.org/10.1371/journal.pone.0003923

Wong L, Chan W, Mui K, Lai A (2010) An experimental and numerical study on deposition of bioaerosols in a scaled chamber. Aerosol Sci Technol 44:117–128

Zea L, Larsen M, Estante F, Qvortrup K, Moeller R, Dias de Oliveira S, Stodieck L, Klaus D (2017) Phenotypic changes exhibited by *E. coli* cultured in space. Front Microbiol. https://doi.org/10.3389/fmicb.2017.01598

Zhao B, Zhang Y, Li X, Yang X, Huang D (2004) Comparison of indoor aerosol particle concentration and deposition in different ventilated rooms by numerical method. Build Environ 39:1–8

Zhao B, Yang C, Yang X, Liu S (2008) Particle dispersion and deposition in ventilated rooms: testing and evaluation of different Eulerian and Lagrangian models. Build Environ 43(4):388–397

Zhang Z, Chen Q (2009) Prediction of particle deposition onto indoor surfaces by CFD with a modified Lagrangian method. Atmos Environ 43(2):319–328

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