🌍 Air Quality Monitoring System

🚀 A real-time IoT-based Air Quality Monitoring System powered by ESP32 + Gas & Dust Sensors.
The system streams live environmental data to ThingSpeak, processes it with a Flask backend, and visualizes it on a modern, animated dashboard.

It also integrates Machine Learning models for:

• 📈 Predicting air quality trends
• ⚠️ Detecting anomalies in sensor readings
• 🧠 Providing intelligent insights & alerts

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🔴 Live Air Quality Status

🌫️ Dust (PM2.5):

🔥 CO (Carbon Monoxide - MQ7):

💨 CO₂ / Air Quality (MQ135):

🔥 LPG/Smoke (MQ2):

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✨ Features

✅ Real-time air quality monitoring with ESP32
✅ ThingSpeak integration for IoT data storage & retrieval
✅ Flask-based dashboard with 3 tabs:

• Live Data
• Past Analysis
• About Project
  ✅ Machine Learning models for prediction & anomaly detection
  ✅ Interactive charts & visualizations (Charts.js, Matplotlib)
  ✅ Modern UI/UX with animated backgrounds 🌈

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🛠️ Tech Stack

• Hardware: ESP32 + MQ-2, MQ-7, MQ-135, Dust Sensor
• Firmware: Arduino IDE (C++ for ESP32)
• Backend: Python (Flask)
• Frontend: HTML + CSS + Bootstrap (with animated background 🎨)
• IoT Cloud: ThingSpeak
• Visualization: Charts.js / Matplotlib
• Machine Learning:
  • Regression models for AQI prediction
  • Classification models for hazard detection
  • Time-series forecasting for trend analysis

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⚙️ Installation & Setup

1️⃣ ESP32 Firmware

Upload the ESP32 code using Arduino IDE.
Make sure to set your Wi-Fi SSID, Password, and ThingSpeak API key in the code.

2️⃣ Flask Server

# Clone repo
git clone https://github.com/<your-username>/<repo-name>.git
cd <repo-name>

# Create & activate virtual environment
python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

# Install dependencies
pip install -r requirements.txt

# Run server
python app.py```

📊 Dashboard Tabs

1️⃣ Current Values → live ESP32 readings (fetched via ThingSpeak API)
2️⃣ Past Analysis → historical charts + ML insights
3️⃣ About → project details & scope

🚀 Future Enhancements

🌐 MQTT support for faster real-time updates
📲 Mobile app integration (Flutter/React Native)
🤖 Advanced ML for AQI forecasting
☁️ Cloud deployment on AWS/GCP/Azure

📌 Project Status
🟢 Active Development – ML model improvements coming soon!

📬 Contact
👩‍💻 Developer: Vijaya Lakshmi M, Chindhana K, Harshini Ganga T S