Powered by satellite observations, weather forecasts, and machine learning, our proprietary model estimates pollen risk at local and regional scales.
Simply put, we combine data from multiple sources, process it in real time, and turn it into actionable pollen forecasts that help people plan ahead and better manage their allergies.
Our forecasts start with detailed vegetation maps. Using satellite imagery, environmental data, and machine learning, we identify where different pollen-producing plants and trees are located.
We then estimate the start and progression of the pollen season for each species based on historical observations, weather forecasts, and environmental conditions such as temperature, precipitation, and soil moisture.
Airmine provides pollen risk alerts, helping you understand where and when pollen exposure is likely to occur. Similar to a weather warning, a high pollen risk indicates conditions that may lead to elevated pollen levels, helping you plan ahead and be prepared.
We continuously test and refine our models using historical data, measurements, and aggregated symptom reports from our users. As new and relevant data sources become available, we incorporate them to improve forecast performance.
Pollen forecasts are predictions of future conditions. While no forecast can be perfect, we work every day to make ours more accurate, reliable, and actionable.
From pollen maps and local forecasts to APIs and integrations, Airmine makes pollen information available where it matters most.
Our goal is simple: To be the trusted source to help people better understand pollen risk, plan ahead, and enjoy outdoor life with fewer allergy symptoms.