ABOUT

Smart Vape Detection System

Introduction 

Indoor air quality has a direct impact on human health, comfort, and productivity. In recent  years, electronic cigarettes and vaping devices have introduced new forms of indoor air  pollution through aerosolized chemicals, nicotine, and solvents such as propylene glycol  and glycerin. Conventional smoke detectors are not designed to detect these aerosols  accurately, leading to undetected vaping in restricted areas. 

This project focuses on the development of a smart vape detection and air quality  monitoring system capable of identifying vaping events by detecting volatile organic  compounds (VOCs) and humidity spikes associated with vape emissions. By leveraging  low-cost sensors and a wireless-capable microcontroller, the system provides an effective  solution for monitoring and maintaining indoor air safety.

TEAM

OBJECTIVES

The main objectives of this project are: 

• To design a low-cost and reliable vape detection system. 

• To monitor indoor air quality parameters related to vaping activity. • To detect VOC concentration changes using a gas sensor. 

• To identify humidity variations caused by vape aerosols. 

• To process sensor data in real time using an ESP32 microcontroller. • To provide a scalable platform for alerts and future cloud integration.

Early prototype build of the Airsentinel Vape Detector

SYSTEM ANALYSIS PARAMETERS

A. Habitability Analysis 

The system evaluates indoor air safety by monitoring: 

• Sudden increases in VOC levels. 

• Abnormal humidity fluctuations. 

• Temperature conditions that may influence sensor readings.

B. Atmospheric Stability 

• Detection of rapid and short-duration air composition changes. • Identification of conditions that may irritate human respiratory systems. 

C. Danger Detection 

• Detection of airborne toxic compounds released during vaping. • Identification of pollutant spikes exceeding predefined thresholds.

PROPOSED METHODOLOGY

A. System Architecture 

The proposed system consists of: 

• An ESP32 microcontroller for data acquisition and processing. • An MQ-135 gas sensor to detect VOCs and gaseous pollutants. • A DHT11 sensor to measure temperature and humidity. • A ventilated enclosure to ensure proper airflow to sensors. 

- ESP32 microcontroller

- MQ135 sensor 

- DHT11 sensor 

B. Data Acquisition 

The MQ-135 provides an analog output proportional to VOC concentration, while the  DHT11 provides digital temperature and humidity readings. Sensor data is sampled at fixed  intervals and processed by the ESP32 to detect abnormal patterns indicative of vaping. 

C. Vape Detection Logic 

Vaping events are identified by combining: 

• Elevated MQ-135 sensor readings. 

• Sudden increases in humidity levels. 

Threshold-based logic is used to reduce false positives caused by perfumes or  cleaning agents. 

D. Air Purity Index 

A simple Air Purity Index (API) is computed using weighted sensor values, producing a  normalized score between 0 and 100, where lower values indicate degraded air quality.

HARDWARE & SOFTWARE IMPLEMENTATION

Hardware Implementation 

A. Microcontroller 

The ESP32 was selected due to its low power consumption, high processing capability, and  built-in Wi-Fi and Bluetooth support. 

B. Sensors 

• MQ-135: Detects VOCs, ammonia, alcohol vapors, and other gases commonly  found in vape aerosols. 

• DHT11: Measures temperature and humidity to identify aerosol-induced humidity  spikes. 

C. Power Supply 

The MQ-135 sensor operates at 5V, while the ESP32 and DHT11 operate at 3.3V. Proper  voltage regulation ensures safe and stable operation.

Software Implementation 

The system is programmed using the Arduino IDE. Sensor libraries are used for reliable  data acquisition, and serial monitoring is implemented for debugging and testing. The  software continuously reads sensor values, compares them against predefined  thresholds, and flags potential vape detection events.

TESTING AND RESULTS

A. Indoor Testing 

The system was tested in enclosed indoor environments such as rooms and corridors. 

B. Controlled Exposure Testing 

Incense sticks and sanitizer sprays were used to simulate vape aerosols. The MQ-135  sensor showed a significant increase in readings, while the DHT11 detected corresponding  humidity spikes. 

C. Results 

• Clean air MQ-135 readings ranged between 200–400. 

• Simulated vape exposure caused readings to exceed 700. 

• Humidity increased rapidly during exposure, confirming detection reliability.

APPLICATIONS LIMITATIONS AND FUTURE SCOPE

Applications 

• Schools and educational institutions. 

• Public restrooms and facilities. 

• Offices and indoor workplaces. 

• Hospitals and restricted indoor zones.

Limitations 

• MQ-135 requires warm-up time for stable readings. 

• Sensor calibration may vary with environmental conditions. 

• False positives may occur in highly polluted environments without additional  filtering. 

Future Scope 

• Integration with cloud platforms for remote monitoring. 

• Mobile application for alerts and analytics. 

• Machine learning–based classification for improved accuracy. 

• Miniaturization and mass deployment using custom PCBs.

CONCLUSION

This project demonstrates an effective and affordable approach to detecting vaping activity  through air quality monitoring. By combining VOC detection and humidity analysis, the  proposed system provides reliable vape detection in indoor environments. The modular  design and scalability make it suitable for real-world deployment and future  enhancements.

REFERENCES

1. World Health Organization, “Air Quality Guidelines,” WHO Press. 

2. Espressif Systems, “ESP32 Technical Reference Manual.” 

3. Adafruit Industries, “DHT11 Sensor Datasheet.” 

4. Hanwei Electronics, “MQ-135 Gas Sensor Datasheet.”