AeroSense AI

AI-powered air quality monitoring with prediction, insights, and smarter decision support.

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Inspiration

Air pollution is becoming a major challenge in modern cities, but most existing applications only display the Air Quality Index (AQI) without explaining the reasons behind poor air quality or suggesting practical solutions. We wanted to build an AI-powered platform that not only monitors air quality but also predicts risks, identifies pollution sources, and provides actionable recommendations for smarter environmental decision-making.

What it does

AeroSense AI is an AI-powered air quality monitoring and decision support platform.

It provides:

  • Real-time AQI monitoring
  • Interactive pollution maps
  • AQI hotspot detection
  • Root cause analysis
  • AI-powered recommendations
  • Risk prediction
  • Scenario simulation to compare pollution mitigation strategies

Our goal is to transform environmental data into meaningful insights for citizens, industries, and government agencies.

How we built it

We developed AeroSense AI as a hackathon prototype using AI-assisted development.

Technologies used:

  • TypeScript
  • Node.js
  • Express
  • OpenStreetMap
  • Mock environmental datasets
  • Google Gemini API

The project was developed with the assistance of AI tools including Replit AI, ChatGPT, and Google Gemini. Our team customized the generated code, improved the UI/UX, fixed errors, integrated the workflow, and refined the final prototype.

Challenges we ran into

  • Finding suitable environmental datasets
  • Designing an intuitive user interface
  • Connecting different modules into a smooth workflow
  • Working within limited hackathon time
  • Testing and refining AI-generated code to ensure a stable prototype

Accomplishments that we're proud of

  • Built a complete end-to-end prototype within the hackathon timeline.
  • Successfully integrated AI-powered analysis and recommendations.
  • Designed a clean, interactive, and user-friendly interface.
  • Created a solution that combines monitoring, prediction, analysis, and decision support in one platform.

What we learned

This project helped us learn:

  • AI-assisted software development
  • Environmental data visualization
  • UI/UX design for analytical dashboards
  • Team collaboration under time constraints
  • The importance of validating and improving AI-generated code instead of relying on it completely

What's next for AeroSense AI

Our current project is a functional demonstration prototype.

In the future, we plan to integrate:

  • Live government AQI APIs
  • Real satellite imagery
  • IoT sensor networks
  • Advanced machine learning models
  • Mobile application support
  • Smart City dashboards
  • Policy and decision-support tools for government agencies

Our vision is to evolve AeroSense AI into a production-ready platform that helps build cleaner, healthier, and smarter cities.

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