Air Aware

AirAware is an AI-powered air quality forecast app that explains why air quality changes and how it affects health, helping users make safer daily decisions beyond just AQI numbers.

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Inspiration

This project was inspired by the increasing amount of daily emissions released into our planet. As air pollution continues to rise, air quality is steadily declining, directly affecting the people who breathe this air every day—especially the elderly, children, pregnant women, and individuals with respiratory conditions like myself. Living with a history of respiratory issues made me realize how critical it is to not only know air quality levels, but also understand their real impact on health and daily life.

What it does

AirAware helps users understand the air quality in their current location and provides short-term AQI forecasts. More importantly, it offers personalized recommendations based on users’ habits and upcoming activities—such as whether outdoor exercise may pose health risks—so users can make safer, more informed decisions.

How we built it

I built AirAware using Cursor as my development environment, with FastAPI for the backend, Next.js for the frontend, and MongoDB to store user information. Docker was used to containerize the application, making it easier to reuse and deploy on Google Cloud Platform.

Challenges we ran into

Finding a reliable and free global AQI API was one of the biggest challenges. Many weather and air quality services charge high fees per request, and some available data sources lack accuracy. Given the limited development time, this significantly impacted progress. Fortunately, I was able to find a free API that provides accurate and suitable air quality data.

Accomplishments that we're proud of

This is my first complete project, and I’m proud of how much I’ve grown since I first started learning to program and how far I was able to take the idea.

What's next for AirAware

Next, I plan to add deeper user personalization by storing behavioral data and using AI to analyze user habits, allowing Gemini 3 API to generate more accurate and context-aware recommendations.

What we learned

Through this project, I learned how to design a full-stack application, integrate real-world data APIs, deploy containerized services to the cloud, and use AI to turn raw data into meaningful, user-focused insights.

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