While fertilizer is great for growing our food, some of it escapes from farms into the air as an invisible gas called ammonia. This hidden pollution can harm air quality and damage natural ecosystems. The biggest challenge is that we don't have a good way to track how much ammonia is being released or where it's coming from.

To solve this, we propose a new framework to measure this pollution. Our approach combines three strategies working together. First, we will place a few powerful, high-end sensors in key locations to get extremely accurate data. Then, to see the bigger picture, we will deploy a wide network of simpler, low-cost sensors to cover much more ground. Finally, we will use existing data from weather and air quality stations to fill in the rest of the map.

The innovative part is deciding where to put these sensors. We use machine learning to analyze environmental data like soil, weather, and plant life across the country. Machine learning identifies zones that behave similarly when it comes to ammonia fluxes. This allows us to strategically place our instruments to build the comprehensive and accurate picture, helping us protect our environment while supporting our farms.