# Experimental Device to Evaluate Aerosol Dispersion in Venues
## 4 Discussion  
Case studies worldwide indicate that airborne aerosols are a major transmission route for SARS-CoV-2 virus indoors [27, 28]. So far, most COVID-19 risk evaluations for indoor environments are based on calculations applying spreadsheet models including relevant environmental and physiological parameters [29, 30]. The typical settings to evaluate the infection risk represent classrooms or office spaces with space volumes below 400 m\({}^{3}\) and fewer than 50 persons present [31]. The DVV, with an ascending spectator area, represents good ventilation of the venue in all three scenarios, indicated by the low amount of absorbed aerosols. However, the observed aerosol plume was associated with highly exposed positions behind the aerosol generator, while the positions in front of and next to the aerosol generator remained almost unaffected, creating characteristic areas of low and high exposure. The position of the aerosol generator has a huge impact on the size of the aerosol plume and, therefore, on the aerosol exposure in the venue, as can be seen in Figure 3 between the rear position A and the front position C. The CFD data indicated that the further in front of the ascending spectator area that the aerosol generator is placed, the higher the aerosol load in the entire venue. This can be explained by the positioning of the aerosol generator in close proximity to the air vents (Figure 3 Case A), directly exhausting the emitted plume through the air  
Figure 8: Comparison of normalized data points from CFD simulation and experimental measurements. Each colour represents a different venue. The \(x\)-axis shows the total aerosol load from the experimental measurements, the \(y\)-axis the total load from the simulations. For better comparability, all values were normalized. The closer the values are to the black dashed line, the better the agreement between experiment and simulation. R-square values for the values compared to the perfect agreement (black dashed line): DVV R-square = 0.8633, MVV R-Square = \(-0.1845\) and NVV R-Square = 0.5155.  
vents. This observation is not supported by the experimental data, since in case C, a lower aerosol load is observed than in case B, despite a greater distance to the exhaust (Figure 6). However, this is possibly due to the unfortunate positioning of the absorbers. Only one absorber was positioned directly in the aerosol plume, so it is difficult to make a statement about the total aerosol load in the room. It seems that the room air flow of the DVV can be reproduced well with CFD simulations. Qualitatively, the distribution of aerosols in the simulation and the experiments match well. A closer look at the numbers shows that the high aerosol loads are overestimated by the simulation and the low ones are underestimated. However, numerical simulations seemed to adequately reproduce experimentally derived aerosol distribution patterns for DVV and are therefore a useful tool for aerosol distribution analysis when experimental measurements are not possible. For the MVV, the total amount of aerosol measured by the absorbers was 77%, higher than that in the DVV. In contrast to the DVV and NVV, it was observed that the amount of aerosol decreases with increasing distance from the emitter (Figure 7). This behaviour, which is shown in Figure 7, does not correspond to the common idea of ideal mixing ventilation, in which the air is uniformly mixed throughout the room. However, predicting highly exposed positions is more difficult because the air flow in the mixing ventilation venue is less directional and not dominated by strong thermal buoyancy. It is therefore less predictable and appears to react sensitively to marginal changes in boundary conditions. This might also be the reason for the poor match of the experimental and simulated data. It seems that the less-predictable air flow in the MVV cannot be reproduced by the CFD simulation to match the experimental results.