From leadership research to district action: Smart Vape Detection in Schools for Mitigating Student E-Cigarette Use

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract Highlights Public health relevance—How does this work relate to a public health issue? Youth vaping has emerged as a significant public health concern, particularly in school environments where exposure to e-cigarette aerosols can occur in confined indoor spaces. Monitoring indoor air quality in high-risk school locations, such as restrooms, may provide new approaches for identifying and responding to vaping behaviour. Public health significance—Why is this work of significance to public health? This study presents a real-world deployment of environmental sensors across multiple school restrooms to detect aerosol signatures associated with vaping events. The results demonstrate that e-cigarette use generates rapid spikes in particulate matter concentrations that can be detected using low-cost indoor air quality monitoring systems. Public health implications—What are the key implications or messages for practitioners, policy makers and/or researchers in public health? Environmental monitoring systems may assist schools in identifying vaping activity and implementing targeted prevention or response strategies. Effective deployment requires integration of sensor systems with clear governance, response protocols, and stakeholder engagement to ensure that monitoring data leads to meaningful action. Abstract Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM2.5 and PM10) data at one-minute intervals over an 18-month period, where PM2.5 and PM10 refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤10 µm, respectively, reported in micrograms per cubic metre (µg/m3. Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM2.5 and PM10 excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Keywords: indoor air quality (IAQ), adolescent vaping, vape detection, particulate matter (PM2.5, PM10), school environments, Internet of Things (IoT), real-time aerosol monitoring

Study methods

A longitudinal embedded case study design was adopted to evaluate the performance of a low-cost, IoT-enabled Long Range, low-bit-rate wireless protocol for IoT (LoRaWAN) vape detection system and to examine patterns of institutional engagement over time. The study was conducted at a large independent secondary school in Australia, occupying approximately 20 hectares and comprising more than 40 buildings, including academic blocks, sports facilities, and shared community spaces. While the Australian academic year aligns with the calendar year, the school concluded its academic term in early December and resumed in the fourth week of January 2024. During holiday periods, the campus remained partially active, with ongoing use for extracurricular sporting, community, and facility-related activities, meaning monitored environments were not entirely unoccupied. The site was selected due to its demonstrated commitment to collaborative, applied research focused on IAQ and student wellbeing. A de-identified deployment map and location index for all monitored spaces are provided in Appendix E.

Limitations

The vape detection system deployed in this study represents an integrated end-to-end IoT architecture, purpose-built for indoor aerosol monitoring in school environments. It leverages a LoRaWAN-based wireless sensor network, a secured cloud computing infrastructure, and real-time alerting logic to support both proactive monitoring and post-incident response.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Choose one school improvement question with teachers. Protect a short shared planning cycle, agree an instructional change and use a common rubric for examining student work. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Review implementation fidelity, teacher time and independently scored student work against a recorded baseline. Examine access differences and competing explanations before attributing changes to leadership. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Robert Sharon; Lidia Morawska; Lindy Osborne Burton. Smart Vape Detection in Schools for Mitigating Student E-Cigarette Use. International Journal of Environmental Research and Public Health (2026-04-14). https://doi.org/10.3390/ijerph23040501

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.