AetherScan: trying to make sense of air quality data

I worked on AetherScan to answer a simple problem: air-quality data exists everywhere, but it’s scattered, inconsistent, and hard to read in one place. This project pulls data from multiple sources, puts it on a map, and lets you explore it as layers instead of raw numbers. You can switch between pollution views, satellite data, fire activity, infrastructure, and more, and actually see how things relate instead of guessing. The backend is built around separate flows for AQI calculation, layer generation, and tile rendering, so each part stays predictable and doesn’t interfere with the others. That structure helped keep the system stable even when combining different data sources. One part I spent time on was a hybrid approach to data processing: taking ideas from quantum algorithms (like superposition-style aggregation and search patterns) and implementing them in a classical way that actually runs reliably in this setup. It’s not about calling it “quantum”, it’s about using the ideas