Intelligent forecasts for health, media & industry

Pollen and air quality data for EU countries

We provide reliable and predictive environmental data for data-driven companies. Our focus is on robust pollen information, enhanced by additional Health and AirQuality indicators such as AQI, UV radiation, particulate matter (PMx) and sulphur dioxide (SOx).

With our unique recipe of measured pollen and weather data, combined with state-of-the-art machine learning and artificial intelligence algorithms, we deliver highly accurate forecasts for the coming hours and days.

Integrate our forecast data seamlessly into your website, data science projects or other applications. This allows you to stay one step ahead of the pollen and offer your customers an even more reliable service.

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Which pollen types do we cover?

Our data includes > 35 available pollen types based on pollen measurement stations, including…

… Fir, maple, horse chestnut, alder, ragweed, mugwort, composite, birch, hornbeam, chestnut, goosefoot, hazel, crucifer, sour grass, heather, beech, ash, mold, rennet, Hops, touch-me-not, walnut, larch, spruce, pine, plantain, sycamore, grasses, poplar, oak, holm oak, dock, willow, rye, yew, lime, elm, nettle.

In addition to the most relevant allergenic pollen types, we offer numerous other Health & AirQuality indicators, such as AQI, UV radiation, particulate matter (PMx) and sulphur dioxide (SOx).

The classification is based on current scientific standards and is updated regularly.

High quality pollen forecast

The Pollen Loading Index parameter in our API summarizes the daily exposure to different types of pollen in one easy-to-use value – ideal for integration into health services, weather portals or pharmaceutical applications. This parameter is calculated by combining different factors to provide accurate and reliable pollen count index values.

7-day forecast at zip code level
Forecasts updated hourly
Regional availability throughout Europe
High spatial resolution
Easy API connection

Our forecasts are based on data from automatic pollen traps of the Electronic Pollen Information Network in Europe (EAN) as well as machine learning algorithms and specially developed AI models that take historical data, weather developments and regional differences into account.

This is how our state-of-the-art pollen count forecast for the coming days is created.