SMART-RADON: A Self-Monitoring Adaptive Real Time System for Indoor Radon Reduction.

In this video, I describe my project, SMART-RADON: A Self-Monitoring Adaptive Real-Time System for Indoor Radon Reduction. This unique 3-in-1 integrated system improves indoor air safety by automatically responding to changes in radon levels.

Radon exposure is an often overlooked but serious health risk in Canada. I first heard of this issue through a radio podcast, which told the story of a non-smoking Albertan who developed lung cancer caused by radon. This highlighted how dangerous radon can be, even for individuals without traditional risk factors for lung cancer, such as smoking, and motivated me to further research radon.

Radon is a naturally occurring, colourless, odourless, and tasteless gas produced by the breakdown of uranium and thorium in soil. While it is less harmful in outdoor environments due to dilution. It can accumulate indoors, particularly in basements, entering through cracks in foundations. Prolonged exposure to elevated radon levels significantly increases the risk of lung cancer. According to Health Canada, radon is the leading cause of lung cancer among non-smokers and causes over 3,000 deaths annually in Canada.

I realized that there is no product currently available that combines radon detection, automated reduction, and real-time remote monitoring into one integrated system.

To address this gap, I developed SMART-RADON, a 3-in-1 system to monitor radon levels and automatically activate ventilation when levels become elevated. Through the app I developed, users can check radon levels remotely in real time, making the system more practical for everyday home use.

This project benefits any household exposed to radon, people, as well as pets living in areas with higher radon risk, by helping them monitor and reduce indoor radon levels and lower the risk of radon-related lung cancer.

For the completion of this project, a multi-step methodology was used.

1. ESP32 Setup and Sensor Integration

Code was tested and uploaded to the ESP32 using the Arduino IDE software to receive signals from the RD200M pulsed ion chamber. All components were initially connected using a breadboard.

2. Printed Circuit Board(PCB) Design and Manufacturing

PCBs were designed using EasyEDA software. Two identical PCBs were soldered for the radon sensor modules, and one PCB for the ventilator control module.

3. Mobile App Development

A mobile app, including a simulation, was developed using MIT App Inventor to monitor/control the ventilation system remotely.

4. System Testing and Troubleshooting

After assembly, the full system was connected to the home Wi-Fi and tested for

functionality. Proper data transmission from the sensors, stable operation of

the ESP32, and correct operation of the ventilator control were checked.

5. Sensor and Ventilator Housing Design

Custom enclosures were designed using Fusion 360 software and 3D printed to fit all

components.

6. Calibration

The radon monitoring sensor units were calibrated at the Radiation Safety Institute

of Canada, Saskatoon. Serial numbers were assigned for sensor 1 (SN: 202601-12)

and sensor 2 (SN: 202601-10)

7. Installation

One radon monitoring unit was placed in the basement, and the other was placed on

an upper floor of the home to collect comparison data. The ventilator

controller was connected to the home's central ventilation switch to activate

if the radon level exceeded the safe threshold.

8. Data Collection and Analysis

Over 3,500 data points were collected in my home over a month. Sensors display the average radon value every 10 minutes. In addition to my home, several other homes across Alberta were tested. The data was exported and analyzed using Microsoft Excel to create graphs to verify the functionality of the device.

RD200M Pulsed Ion Chamber

A pulsed ion chamber detects radon by using an electric field in an air-filled chamber. When radon decays, it emits alpha particles that ionize the air, creating electrical pulses. The pulse rate indicates the radon concentration in the air. Every 10 minutes, the data is averaged and sent to the ESP32 microcontroller for processing and communication.

ESP32 Microcontroller inside the sensor unit

The ESP32 microcontroller processes data from the pulsed ion chamber into radon values displayed on a Liquid Crystal Display (LCD). It creates a Comma-Separated Values (CSV) file for data analysis and uses Wi-Fi and Message Queuing Telemetry Transport (MQTT) to send readings to a mobile app and communicate with the ventilation controller. This enables the SMART-RADON prototype to monitor radon levels, store data, and control ventilation automatically.

ESP32 Microcontroller inside the ventilation controller

The ESP32 microcontroller interacts with sensors and compares the radon values to the set point. If the radon level is higher than the set point, the ESP32 sends a signal to the relay switch to turn on the home ventilation system. This ESP32 also communicates with the mobile app to display the ventilation system status.

LM2596 step-down voltage regulator

The RD200M pulsed ion chamber operates at 12V, while the ESP32 microcontroller requires 5V. Therefore, an LM2596 buck switching regulator is used to convert 12V to 5V for the ESP32.

Massachusetts Institute of Technology (MIT) App Inventor App

The app, built with MIT App Inventor, runs on a Samsung Galaxy tablet and lets users remotely monitor radon values and switch to a simulation mode for testing.

Ventilation Controller

This device automatically controls the home’s Heating, Ventilation, and Air Conditioning (HVAC) system to keep radon levels below a safe threshold.

Results

Graph 1 (calibration graph): shows the reference radon value using green dots.

Sensor 1 has a -5.79% relative error, and Sensor 2 has a 7.89% relative error.

Graph 2: The average basement radon level in our home is 39.64 Bq/m³, while the upper floor averages 26.48 Bq/m³.

Graph 3: Home 1 records an average radon level of 168.15 Bq/m³, Home 2 averages 46.44 Bq/m³ and Home 3 shows an average of 43.56 Bq/m³.

Graph 4: Five Calgary homes were tested: The results show the averages for home 1 at 218.43 Bq/m³, home 2 at 33.13 Bq/m³, home 3 at 310.25 Bq/m³, and home 4 at 230.75 Bq/m³.

Graph 5: In home 5, I connected my ventilation controller to the existing two-stage HVAC system. The initial radon level is around 440 Bq/m³. During the ventilation system activation, the radon level drops to an average of 108.25 Bq/m³. After the system turns off, the radon level rises to around 310.25 Bq/m³.

The main objective of this project was to prove that it is possible to create a functional, affordable, automatic, and efficient radon monitoring and reduction system for household use.

The home comparison graph (graph 2) shows that radon levels are usually higher in basements than on upper floors, which supports the idea that radon enters mainly from the ground and collects in lower areas of a house.

According to the Health Canada guideline, the safe threshold for indoor radon is up to 200 Bq/m³. For my home (graph 2), the indoor radon level fluctuated around 40 Bq/m3, which was selected as the threshold for this project to activate the HVAC system. It is also evident that radon levels can vary from house to house. Some Calgary homes measure above the Canadian threshold.

Graph 5 shows that when the SMART-RADON system turns on the HVAC, letting fresh air in, to drop radon levels from high to much lower levels. This automatic response is important because it eliminates the need for constant human monitoring and allows the system to respond quickly when radon levels rise.

However, there are limitations with very high radon levels (e.g.,1000Bq/m3), as this procedure requires extremely high ventilation power with higher blower fan speed, which is not practically achievable (graph 6) using a home HVAC system.

Overall, the graphs support that the SMART-RADON prototype can successfully detect rising radon levels, activate ventilation, and automatically reduce indoor radon to a safer level within limitations.

In the future, I plan to add temperature, air pressure, and humidity sensors to study how these factors affect the indoor radon levels. I will also incorporate a rechargeable battery to ensure uninterrupted data collection during relocation of the sensors and power outages.

Additionally, I intend to design and build my own pulsed ion chamber.

As there have been requests from residents, I will volunteer to measure radon levels in homes within my community. Further, will collect data during different seasons over three months.

I would use the gathered data to contribute to updating the Canadian Radon Map.

I would like to thank Mr. Nisal Dayan for mentoring me and guiding my coding skills since Grade 2.

I also thank Mr. Brian Bjorndal, Director at the Radiation Safety Institute of Canada, for calibrating my sensors.

I am grateful to Professor Aaron Goodarzi at the University of Calgary for his valuable feedback to improve my device.

I also thank Ms. Pam Warkentin, Executive Director of CARST, for guiding me on including my data in the Canadian radon map.

I further express my gratitude towards Mr. Miguel Borges, who helped me with editing my project video.

Thank you to my science teacher, Ms. Wilson, the Wood Buffalo Regional Science Fair committee, my parents, and my loving little brother for their continuous support throughout this journey.

This would not have been possible without you all.

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