Inspiration
Air pollution is one of India's biggest environmental and public health challenges. Millions of people are exposed to poor air quality every day, but pollution data is often difficult to access or understand. We wanted to create a simple, visually engaging platform that makes real-time air quality information accessible to everyone, encouraging healthier and more informed decisions.
What it does
AirSense AI is a web platform that displays real-time air quality data for major Indian cities using trusted government data. It provides pollutant levels including PM2.5, PM10, SO₂, and NO₂, classifies air quality into easy-to-understand categories, and presents the information through an intuitive dashboard for quick monitoring.
How we built it Developed the frontend using HTML, CSS, and JavaScript. Designed a responsive and modern user interface. Integrated the data.gov.in Air Quality API to retrieve live pollution data. Processed pollutant values dynamically using JavaScript. Displayed live air quality cards for major Indian cities with pollution status indicators. Planned a secure Flask backend architecture for future API protection and AI integration. Challenges we ran into Finding a reliable source of real-time air quality data. Understanding the structure of the government API. Handling different pollutant records for each city. Designing a responsive dashboard that works across devices. Converting raw environmental data into simple, user-friendly visualizations. Accomplishments that we're proud of Successfully integrated live government air quality data. Built a clean and responsive dashboard from scratch. Created an intuitive interface that makes pollution data easy to understand. Developed a scalable architecture that can support future AI-powered features. Demonstrated how environmental data can be presented in a meaningful and accessible way. What we learned
Through this project, we learned about API integration, asynchronous JavaScript, frontend design, responsive web development, environmental data visualization, and best practices for handling API keys securely. We also gained a deeper understanding of India's air quality monitoring system and the importance of presenting data in a way that users can quickly interpret.
What's next for AirSense AI
Our future roadmap includes:
AI-based AQI forecasting using machine learning. Personalized health recommendations based on pollution levels. Interactive pollution trend graphs and historical analysis. Location-based air quality monitoring using GPS. Push notifications for hazardous pollution levels. Weather integration to better understand pollution patterns. Mobile application support. Multilingual support for wider accessibility across India.
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