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║             DRONE-BASED AIR QUALITY MONITORING SYSTEM                   ║
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🚁 Real-time Environmental Monitoring from the Sky

Measure. Monitor. Protect. A cutting-edge air quality monitoring system designed for DJI Matrice drones, capable of measuring and logging key environmental parameters in real-time.

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🛠️ Tech Stack

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📊 System Overview

✨ Features

🌡️ Comprehensive Sensor Suite CO₂ (Carbon Dioxide) in ppm CO (Carbon Monoxide) in ppm CH₄ (Methane) in ppm NOₓ (Nitrogen Oxides) in ppm PM2.5 Particulate Matter in μg/m³ VOCs (Volatile Organic Compounds) Temperature & Humidity readings	🖥️ Modern Dashboard Real-time data visualization Live graphs and metrics CSV export for data analysis Built with Electron + TypeScript + Next.js Powered by Python backend Designed with v0.dev
🔌 Wireless Communication ESP-NOW protocol for long-range transmission Low latency data streaming Reliable connection between drone and ground station Real-time telemetry updates	🤖 ROS Integration ROS Noetic for robust communication UART connection between Raspberry Pi & ESP32 GPS data extraction from DJI drone Modular architecture for easy expansion
🛠️ Hardware Design Custom PCB (designed in-house) 3D-printed casing for DJI Matrice 210V2 Weather-resistant enclosure Matrice 350RTK support coming soon	⚡ Developer Experience PlatformIO for streamlined development Hot-reload dashboard during development Automated build scripts Comprehensive documentation

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🛠️ Getting Started (Software)

1. Prerequisites

• Familiarity with Git, terminal and basic UNIX commands
• Recommended: Linux host (Windows/Mac supported but requires extra configuration)
• Required Tools
  • C++ Compiler
    • Linux: sudo apt install build-essential
    • Mac: Xcode Command Line Tools (xcode-select --install)
    • Windows: Install MinGW or use WSL
  • VSCode (Or IDE of your choice)
  • Platform IO Extension for VSCode
  • ROS Noetic (This software was built in 2025 on Ubuntu 20.04)
  • [Optional but Recommended] PlatformIO Core CLI for terminal workflows

2. Clone the Repository on the Machine Used to Deploy to ESP32

cd ~
git clone https://github.com/MRU-Earth-and-Enviromental-Science/Low-Cost-Sensors.git
cd Low-Cost-Sensors
code . # Open in VSCode (or editor of your choice)

3. Upload Code to ESP32 on the Drone

• Connect your ESP32 dev board via USB.
• Open the Drone_System Directory on Visual Studio Code.

cd ~/Low-Cost-Sensors/drone_esp32

• Use PlatformIO to build and upload:
  • Click the right-facing arrow (➤) at the bottom of VSCode, or

# If using PIO command line
pio run --target upload

PlatformIO will automatically detect your environment and upload the firmware to the board.

4. Configure the ESP32 on Ground Station

cd ~/Low-Cost-Sensors/ground_station/ground_esp32

• Use PlatformIO to build and upload the code to the ESP32. (same as above)

5. Raspberry Pi Set-Up

• Running any Linux Distro between 16.04 and 20.04 (This was developed on Ubuntu 20.04 Server, Raspberry Pi 3)
• Requires ROS Noetic (Base) to be installed
• Requires a C++ compiler
• Requires CMake > 3.0
• Clone this repo on the Pi

cd ~
git clone https://github.com/MRU-Earth-and-Enviromental-Science/Low-Cost-Sensors.git

• Clone the DJI OSDK and DJI OSDK ROS Packages

cd ~
git clone https://github.com/dji-sdk/Onboard-SDK.git

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
catkin_init_workspace
git clone https://github.com/dji-sdk/Onboard-SDK-ROS/tree/master/src/dji_osdk_ros

Run the install script to setup the rest of the code

cd ~/Low-Cost-Sensors
chmod +x install_sdk.sh
./install_sdk.sh

6. Setting Up and Running the Dashboard

• Install the Dashboard from the 'Releases Section' of the GitHub repo linked below for your system.
  • For Windows: Download the .exe file.
  • For Mac: Download the .dmg file.
  • For Linux: Download the .AppImage file and make it executable:
• Alternatively, you can clone the sensor-dashboard submodule and build from source (must have npm installed):

# Clone the sensor-dashboard submodule
cd ~/Low-Cost-Sensors
git submodule update --init --recursive

cd ground_station/sensor-dashboard

npm install --legacy-peer-deps
npm run dev

# in a new terminal, run the following command to start the dashboard
npm run electron-dev

# to run the backend
python3 -m venv venv
source venv/bin/activate # This is a UNIX command for windows run: venv\Scripts\activate
pip3 install -r requirements.txt

python3 serial_backend.py # Run this in the venv

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🧰 Hardware Used

• ESP32 Dev Module (WROVER-E based)
• Raspberry Pi 3
• K30 CO₂ Sensor
• MQ Series Gas Sensors (e.g., MQ-7 for CO, MQ-135 for NOₓ, MQ-4 for CH₄)
• Plantower PMS7003 (PM2.5 Sensor)
• SGP30 (VOC Sensor)
• DHT11 (Temperature & Humidity)
• UART to USB Cable (Drone to Pi connection)
• Custom PCB Ordered from JLC PCB (Found in Repo)
• Bill of Materials Found in Repo

Casing

• STEP and STL Files available to mount to Matrice210

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📄 License

This project is licensed under the MIT License.

🛠️ Developed By

Mount Royal University, Faculty of Science and Technology, Department of Earth and Environmental Science

Gwen O'Sullivan — Vice Dean of Science and Technology @ MRU
Shivam Walia — Mechatronics Engineering @ UWaterloo

❓ Questions

shivamwalia2006 [at] gmail [dot] com
gosullivan [at] mtroyal [dot] ca