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Updated: May 5, 2026

A Microcontroller Operated Device for the Generation of Liquid Extracts from Conventional Cigarette Smoke and Electronic Cigarette Aerosol
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Smart Vape Detection in Schools for Mitigating Student E-Cigarette Use.

Robert Sharon1,2, Lidia Morawska2,3,4, Lindy Osborne Burton2,5

  • 1Blue IoT Pty Ltd., Mulgrave, VIC 3170, Australia.

International Journal of Environmental Research and Public Health
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

A new Internet of Things (IoT) vape detection system accurately identified over 300 adolescent vaping incidents in schools. While technically reliable, sustained effectiveness requires strong organizational commitment and clear response protocols to combat alert fatigue.

Keywords:
Internet of Things (IoT)PM10)adolescent vapingindoor air quality (IAQ)particulate matter (PM2.5real-time aerosol monitoringschool environmentsvape detection

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Area of Science:

  • Environmental Health
  • Behavioral Science
  • Technology in Education

Background:

  • Adolescent vaping presents a significant health and behavioral challenge in educational settings.
  • Many schools lack effective tools for detecting and responding to concealed e-cigarette use.
  • Existing detection methods may not adequately address the nuances of vaping behavior in schools.

Purpose of the Study:

  • To evaluate the real-world performance of a low-cost Internet of Things (IoT) vape detection system in Australian schools.
  • To determine the system's accuracy in identifying vaping events and providing insights into student behavior.
  • To assess the system's potential to support timely staff intervention and inform school health governance.

Main Methods:

  • A longitudinal case study involving an IoT vape detection system deployed across 37 high-risk locations in an Australian school.
  • Continuous monitoring of particulate matter (PM2.5 and PM10) data at one-minute intervals over 18 months.
  • Utilized threshold-based alerting, cloud processing, and CCTV verification to assess detection accuracy and operational responses.

Main Results:

  • The system detected over 300 vaping incidents, with temporal patterns aligning with school schedules and higher prevalence among senior students.
  • High operational detection performance was observed, characterized by rapid PM2.5 and PM10 increases consistent with vaping aerosols.
  • Staff responsiveness decreased over time due to alert fatigue, and a major smoke event highlighted the need for context-aware logic to minimize false positives.

Conclusions:

  • Real-time aerosol monitoring via IoT systems is technically reliable and effective for detecting school-based vaping.
  • System effectiveness is contingent on user engagement, managing alert fatigue, and robust institutional follow-through.
  • Successful vape detection integration requires organizational commitment, defined response workflows, and alignment with broader school wellbeing and policy strategies.