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The Selective Detection of Individual Respiratory Droplets in Air
Matjaž Malok1,2, Darko Kavšek1, Maja Remškar1,3
1Jozef Stefan Institute, 1000 Ljubljana, Slovenia.
Abstract:
Preventing the spread of airborne diseases in crowded indoor spaces is a global challenge. Infected individuals release virus-laden respiratory droplets (RDs) that can remain suspended in air and infectious for hours. Current monitoring methods cannot distinguish these droplets from airborne particulate matter (PM) in a real time. Here, we present a capacitive sensor that selectively detects and counts the individual droplets in indoor spaces, regardless the presence of PM. The device exploits the dielectric constant (ε) of water (78.2) to differentiate the droplets from solid PM particles (ε < 15). In a nonventilated conference-room study, RDs concentrations (40-330 RDs/L) were found to be correlated with human occupancy, but not with PM2.5 levels. The developed technology enables a real-time monitoring of number concentration of RDs, which represent a potential health risk when they carry viral or bacterial infections. The detected increase in RD concentration can serve as a trigger for data-driven ventilation and infection-prevention measures, providing an effective tool for mitigating the spread of respiratory diseases in hospitals, schools and other public spaces.
Insights
A new sensor detects airborne virus-laden respiratory droplets (RDs) in real-time, distinguishing them from particulate matter (PM). This technology aids in controlling disease spread in indoor public spaces.
Area of Science:
- Environmental Health Engineering
- Sensor Technology
- Infectious Disease Control
Background:
- Airborne diseases spread easily in crowded indoor environments via respiratory droplets (RDs).
- Existing monitoring systems cannot differentiate RDs from particulate matter (PM) in real-time.
- Accurate detection of RDs is crucial for implementing timely infection-prevention measures.
Purpose of the Study:
- To develop a novel sensor for selective, real-time detection and counting of individual RDs.
- To differentiate RDs from airborne PM based on their dielectric properties.
- To assess the correlation between RD concentration, human occupancy, and PM levels in indoor spaces.
Main Methods:
- A capacitive sensor was designed utilizing the difference in dielectric constants between water (RDs) and solid PM.
- The sensor was tested in a non-ventilated conference room to measure RD concentrations.
- RD counts were compared with human occupancy and PM2.5 levels.
Main Results:
- The sensor successfully detected and counted individual RDs in real-time, independent of PM presence.
- RD concentrations (40-330 RDs/L) correlated with human occupancy.
- No correlation was found between RD concentrations and PM2.5 levels.
Conclusions:
- The developed sensor provides a real-time monitoring tool for airborne RDs, a significant health risk.
- Increased RD detection can trigger data-driven ventilation and infection-control strategies.
- This technology can enhance respiratory disease mitigation in public spaces like hospitals and schools.
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