Air purifier location and position are key in reducing transmission of coronavirus aerosol
Air purifier location and position are key in reducing transmission of coronavirus aerosol lead image
Aerosol particles are one of the main routes of transmission of the COVID-19 illness, as small particles may remain suspended in the air for hours.
Personal protective measures, such as masks, will never be able to remove all virus-laden particles in the air. Therefore, ventilation is of utmost importance as an additional measure.
Approaches such as air purifiers that generate a defined volumetric air flow through an efficient filter material represent promise in removing infectious indoor aerosol particles.
Tobisch et al. examined the effect of mobile air purifiers on reducing the spread of the coronavirus, using an exemplary lecture hall and thermal dummies. They found position and orientation are key in reducing particle deposition.
For each trial, researchers set up a purifier in a different corner of the room. Positioning the purifier at the back of the classroom at a right angle to the presenter at the front and facing backwards achieves the best results. But the same position facing into the room is the least desirable.
“The experimental setup using particle sensory networks may be used to study aerosol transport in many different situations, like cars, trains or buses,” said author Jennifer Niessner. “These networks may eventually be used to not only measure but control air purifiers for reducing infection risk and ensuring an energetically efficient operation.”
The researchers demonstrated favorable configuration of purifiers can reduce particle concentrations by 86%, with purifiers in all cases reducing particle load by 61%.
The researchers further suggest key points to address when operating an air purifier in an indoor environment, such as ensuring the flow covers the whole room by obstructing the outlet stream of the purifier.
Source: “Reducing indoor particle exposure using mobile air purifiers - experimental and numerical analysis,” by Adrian Tobisch, Lukas Springsklee, Lisa-Fraziska Schäfer, Nico Sussmann, Martin J. Lehmann, Frederik Weis, Raoul Zöllner, and Jennifer Niessner. AIP Advances (2021). The article can be accessed at https://doi.org/10.1063/5.0064805 .
