AirLens – Global Air Pollution Visualization

AirLens is a full-stack web application that visualizes global air pollution levels across four major gases — CO, NO₂, O₃, and SO₂ — using NASA GIOVANNI NetCDF datasets. It transforms complex atmospheric data into a simple, intuitive, and interactive 3D globe visualization.

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🚀 Overview

AirLens simplifies scientific pollution data into a Composite Pollution Value (0.0–1.0), computed using the average normalized concentrations of the four gases. This score reflects the region's overall cumulative pollution burden.

Each region on the 3D globe is color-coded based on its pollution level:

Color	Pollution Level	Meaning
🟢	0.0–0.25	Clean Air
🟡	0.26–0.50	Moderate Pollution
🔴	0.51–0.75	High Pollution
🟣	0.76–1.0	Hazardous Level

The goal is to make scientific air quality data accessible to everyone — from students to policymakers — by combining clarity, accuracy, and creativity.

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⚙️ Tech Stack

Backend:

• Python (Flask)
• Flask-CORS
• NumPy
• netCDF4
• Nominatim API (for geocoding)

Frontend:

• React
• react-globe.gl
• HTML5 / CSS3
• Axios

Data Source:

• NASA GIOVANNI NetCDF Archives

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🧠 How It Works

1. Fetch air pollution datasets (NetCDF format) from NASA GIOVANNI.
2. Extract gas concentration values using NumPy and netCDF4.
3. Compute a Composite Pollution Value (CPV) as the average of normalized gas concentrations.
4. Assign color codes (Green, Yellow, Red, Purple) based on the CPV.
5. Render the pollution visualization on a 3D interactive globe using React and react-globe.gl.
6. Provide search functionality (via Nominatim API) for city-level data breakdown.

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💻 How to Use

1️⃣ Clone the Repository

git clone https://github.com/your-username/AirLens.git
cd AirLens

2️⃣ Start the Backend (Flask)

cd backend
pip install flask flask-cors numpy netcdf4 geopy
python app.py

3️⃣ Start the Frontend (React)

cd frontend
npm install
npm start

Open the app in your browser at 👉 http://localhost:3000

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🎯 Purpose

AirLens aims to translate scientific satellite data into public awareness tools, bridging the gap between space technology and environmental insight.

🛰️ Data Source

All atmospheric datasets are sourced from NASA GIOVANNI.