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This project explores how artificial intelligence (AI) can be used to make satellite observations of air pollution more accessible, interactive, and insightful. Students will design an AI agent capable of automatically retrieving and visualizing satellite-based observations of air quality, such as nitrogen dioxide (NO2), from geostationary and polar-orbiting sensors (e.g., NASA’s TEMPO and ESA’s TROPOMI missions). The AI agent will interpret natural-language commands from users (e.g., “Show today’s NO2 map over New Jersey” or “Display the weekly trend for New York”) and automatically produce corresponding spatial or temporal visualizations. This system will demonstrate how AI can simplify scientific data interaction, allowing non-specialists to explore real-world satellite products through conversational queries.
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