Live Air Quality Dashboard

A real-time and historical air quality monitoring dashboard built with Python, Dash, Plotly, and DuckDB.

📌 Overview

This project is a live-updating dashboard that visualizes air quality data from OpenAQ (or other sources). It processes both real-time and historical air pollution data, stores it efficiently using DuckDB, and presents it in an interactive Dash (Plotly) dashboard.

Key features: ✅ Real-time data updates (pollutant levels, AQI, trends) ✅ Historical data analysis (time-series trends, comparisons) ✅ Scalable data pipeline (easily add new locations without downtime) ✅ Efficient storage with DuckDB (fast querying, low overhead) ✅ Interactive visualizations with Plotly & Dash

🛠️ Technologies Used

Category	Tools/Libraries
Backend	Python, DuckDB (for lightweight SQL-based storage)
Dashboard	Plotly, Dash (for interactive web visualizations)
Data Tools	Pandas (data processing), DBeaver (DB management)
Data Source	OpenAQ API (or other air quality APIs)

🚀 Installation & Setup

1. Clone the Repository

git clone https://github.com/YVandana/Live-Dashboard-for-Air-Quality-Data.git
cd Live-Dashboard-for-Air-Quality-Data

2. Set Up a Virtual Environment (Recommended)

python -m venv venv
source venv/bin/activate  # Linux/Mac
.\venv\Scripts\activate   # Windows

3. Install Dependencies

pip install -r requirements.txt

4. Configure Data Sources

• Rename config.example.py to config.py and add your OpenAQ API key (if required)
• Modify data_pipeline.py to adjust data fetching intervals or locations

5. Run the Dashboard

python app.py

Visit http://127.0.0.1:8050/ in your browser

🔧 How It Works

📂 Data Pipeline

1. Fetching Data

   • The app pulls real-time air quality data from OpenAQ (or another source)
   • Historical data is stored in DuckDB for fast querying

2. Processing & Storing

   • Data is cleaned and structured using Pandas
   • DuckDB provides lightweight, SQL-based storage (no need for a full DB server)

3. Dashboard Updates

   • The Dash app refreshes at a set interval (e.g., every 10 minutes)
   • Users can filter by:
     • Location (city, country)
     • Time range (last 24h, 7 days, custom range)
     • Pollutants (PM2.5, PM10, O₃, NO₂, etc.)

📊 Dashboard Features

✔ Live AQI (Air Quality Index) gauge ✔ Time-series trends for pollutants ✔ Geographical heatmap (if location data is available) ✔ Comparison between multiple stations ✔ Export data (CSV/JSON)

📈 Adding New Locations

The pipeline is designed to dynamically include new air quality stations without restarting.

1. Edit config.py to include new city/station IDs
2. The data fetcher will automatically incorporate them in the next update cycle
3. The dashboard will show them in the location dropdown

🤝 Contributing

Want to improve this project? Contributions are welcome!

1. Fork the repository
2. Create a new branch (git checkout -b feature/new-location-support)
3. Commit your changes (git commit -m "Add support for new data source")
4. Push to the branch (git push origin feature/new-location-support)
5. Open a Pull Request

📬 Contact

For questions or feedback: 📧 Email: [vandanayalla321@gmail.com]

🔗 Reference Tools

• OpenAQ – Open air quality data
• DuckDB – Lightweight analytical database
• Plotly Dash – Dashboard framework

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Enjoy monitoring air quality in real-time! 🌍💨