Development of a Smart Portable Air Quality Monitoring Device for Detecting E-Cigarette and Traditional Smoking Exposure
Ashleen Jisoo Lee
Korea International School, Pangyo, South Korea
Publication date: July 10, 2026
Korea International School, Pangyo, South Korea
Publication date: July 10, 2026
DOI: http://doi.org/10.34614/JIYRC2026I31
ABSTRACT
This study aims to develop a portable device and web-integrated detection system capable of identifying both e-cigarette aerosol and traditional cigarette smoke within school environments. The system incorporates multiple sensors—PM1.0, PM2.5, PM10, VOC, and MQ9—to capture real-time particulate and gas-phase signatures associated with different smoking behaviors. Experimental results demonstrated that the PM1.0 sensor exhibited the highest sensitivity to both e-cigarette aerosol and conventional cigarette smoke, while the VOC and MQ9 sensors responded selectively to combustion-related emissions, enabling differentiation between the two smoke types. The device uses Wi-Fi and Bluetooth to transmit time-stamped sensor data to a cloud-based dashboard and an Android app for real-time visualization and record management. By integrating multi-sensor detection, automated logging, and a user-linked monitoring interface, this system addresses the limitations of traditional stationary detectors. The findings highlight the potential application of this technology in schools, healthcare settings, and other sensitive environments to reduce exposure to secondhand smoke and improve public health monitoring capabilities.
This study aims to develop a portable device and web-integrated detection system capable of identifying both e-cigarette aerosol and traditional cigarette smoke within school environments. The system incorporates multiple sensors—PM1.0, PM2.5, PM10, VOC, and MQ9—to capture real-time particulate and gas-phase signatures associated with different smoking behaviors. Experimental results demonstrated that the PM1.0 sensor exhibited the highest sensitivity to both e-cigarette aerosol and conventional cigarette smoke, while the VOC and MQ9 sensors responded selectively to combustion-related emissions, enabling differentiation between the two smoke types. The device uses Wi-Fi and Bluetooth to transmit time-stamped sensor data to a cloud-based dashboard and an Android app for real-time visualization and record management. By integrating multi-sensor detection, automated logging, and a user-linked monitoring interface, this system addresses the limitations of traditional stationary detectors. The findings highlight the potential application of this technology in schools, healthcare settings, and other sensitive environments to reduce exposure to secondhand smoke and improve public health monitoring capabilities.
