Get precise pollen count data with Ambee's pollen API

Pollen count shows the number of pollen grains that are present in the air for a standard volume.

The types of pollen allergies are classified by severity and the specific pollen type. According to allergy data, the three primary types are tree, grass, and weed allergies.

Pollen count levels range from 0 to 50,000 grains depending on the type of pollen. Safest pollen counts are when the pollen index is low. The higher the pollen index, more is the number of pollen count level.

API key can be copied and inserted in the tab on the Ambee dashboard to make pollen API calls. Ambee's pollen app give you real-time pollen count without an API key.

The pollen index is a measure of the amount of pollen in the air. It indicates the concentration of pollen in a cubic meter of air. A low pollen index means low pollen levels in the air, while a high pollen index signifies high pollen levels. High pollen levels can lead to increased rates of allergic reactions for individuals with pollen allergies.

Pollen forecasts are verified through several methods. One way is to compare the forecast to actual pollen counts collected from pollen traps or air samplers. Another approach involves comparing the forecast to other sources of information about pollen levels, such as reports from other pollen monitoring stations in the same region. While these methods provide valuable insights, the most accurate verification often involves a combination of both. At Ambee, we further enhance pollen forecast accuracy by utilizing proprietary AI and ML models, ensuring maximum precision in our predictions.

You can get historical pollen count data up to 48 hours on our APIs and more than 10+ years on demand by contacting our customer service. Our historical pollen data is accurate with street level granularity. Get highly insightful data on historical pollen counts by just heading over to our API dashboard.

Allergy forecasts can be quite accurate, but their reliability can be influenced by factors like weather patterns and local conditions. At Ambee, we enhance forecast accuracy by analyzing historical pollen data from multiple stations, considering phenological factors and environmental behaviors, and leveraging proprietary AI and ML models. This approach enables our virtual pollen monitoring stations to provide reliable and accurate pollen counts by zip code, empowering you to better manage your allergies.

Ambee's pollen data achieves up to 93% correlation with ground-truth measurements through our proprietary AI models that combine satellite imagery, ground sensors, and meteorological data. Learn more about our accuracy.

Unlike traditional pollen traps that sample limited areas and require manual counting, Ambee uses machine learning algorithms to integrate multiple data sources, providing real-time, global coverage with significantly higher spatial and temporal resolution.

We continuously validate our forecasts against a network of 1,200+ reference stations worldwide, using robust statistical methods including correlation analysis, mean absolute error, and confidence intervals. View our verification methodology.