Real-Time Air Pollution Monitor with AI chatbot

My project can view real-time air pollutant data, and with this data, users can communicate with an AI chatbot about environmental issues.

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Inspiration

  • I was inspired to create this program due to my passion for environmental sustainability and the urgent need to address air quality issues. As pollution levels rise, impacting both health and climate, I sought to empower individuals with real-time data. I believe accessible information fosters informed decision-making and drives community action. Incorporating an AI chatbot enables engaging conversations and personalized advice on environmental concerns. Ultimately, I aim to inspire positive change and increase awareness of the importance of clean air for all.

What it does

  • When a user searches for a Canadian city, the application retrieves real-time air pollutant data specific to that location. This data encompasses various pollutants including PM2.5, PM10, nitrogen dioxide, and ozone levels. The AI chatbot subsequently analyzes this information to provide relevant responses to the user's queries. Whether inquiring about current air quality or its health implications, the chatbot delivers tailored information. This seamless interaction empowers users to make informed decisions about their environment.

How I built it

  • I used the Spring Boot framework for the backend and the React framework for the frontend. On the frontend, the program collects the city name and environmental questions from the user. The backend application retrieves real-time data from the Google Air Quality API. Then, it shares the parameters with the OpenAI API, which I used to create an NLP chatbot.

Challenges I ran into

  • Designing the software architecture was a complex part of the project. The software design needed to meet the requirements of the user. One challenging decision was how to convert latitude and longitude into a city name. One option was to use the Google Map API to find the city. The other approach I took was to save the city's latitude and longitude in the frontend application. Luckily, it worked out.

Accomplishments that I am proud of

  • I’m really proud that I finished this project all by myself and on time. It was hard but also really satisfying. I put a lot of time and effort into making it work. I’ve done other projects before, but this was the first time using AI related API. Usually, full-stack application projects took me weeks to finish. But this time, I am happy that I finished the project of this scale within 2 days.

What I learned

  • I learned to use the Spring Boot framework and the AI framework. I also developed my time management skills and learned to work efficiently.

What's next for Real-Time Air Pollution Monitor with AI chatbot

  • I want to add complex data visualizations using D3.js on the frontend. Additionally, I aim to incorporate more features into the project, such as interacting with water pollution data and utilizing advanced machine learning models to derive new solutions.
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