NOVA: Air Quality Prediction & Monitoring

NOVA is a web application developed for the NASA Space Apps Challenge 2025, addressing the "From Earthdata to Action: Cloud Computing with Earth Observation Data for Predicting Cleaner, Safer Skies" challenge.

Project Overview

NOVA leverages NASA Earth observation data to predict and visualize air quality trends, helping users make informed decisions about outdoor activities and health precautions. The application combines satellite data with machine learning to provide accurate air quality forecasts and visualizations. The project is live here: https://nova-frontend-1086273935477.europe-west1.run.app/

Key Features

• Interactive Dashboard: Real-time air quality metrics and forecasts
• Geospatial Visualization: Map-based view of air quality across different regions
• Predictive Analytics: Machine learning models to forecast air quality trends
• Health Recommendations: Personalized advice based on air quality conditions

Tech Stack

Frontend

• React.js
• Chart.js for data visualization
• Leaflet for interactive maps
• Axios for API communication

Backend

• Flask (Python)
• Scikit-learn for machine learning models
• NumPy and Pandas for data processing

Getting Started

Prerequisites

• Node.js and npm
• Python 3.8+
• Git

Installation

1. Clone the repository:

   git clone https://github.com/shknth/NOVA.git
   cd NOVA
   

2. Set up the frontend:

   cd frontend
   npm install
   npm start
   

   The frontend will be available at http://localhost:3000

3. Set up the backend:

   cd ../backend
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   python app.py
   

   The backend API will be available at http://localhost:5000

Project Structure

NOVA/
├── frontend/           # React frontend application
│   ├── public/         # Static files
│   └── src/            # React components and logic
│       ├── components/ # UI components
│       └── ...
├── backend/            # Flask backend application
│   ├── app.py          # Main Flask application
│   ├── model.py        # ML model for air quality prediction
│   └── requirements.txt # Python dependencies
└── README.md           # Project documentation

Future Enhancements

• Integration with real-time NASA Earth observation data
• Advanced ML models for more accurate predictions
• User accounts for personalized monitoring
• Mobile application for on-the-go access
• Alerts and notifications for dangerous air quality conditions

Contributors

• Team CodeOrbiters - NASA Space Apps Challenge 2025

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• NASA for providing Earth observation data
• The Space Apps Challenge for the opportunity to contribute to cleaner, safer skies
• OpenAQ, MERRA for the open source dataset