Global Pollen Data - Historical | Real-Time | Forecast | Climatology
Verified Data Provider|
timestamp
|
Risk.grass_pollen
|
Risk.tree_pollen
|
Risk.weed_pollen
|
Count.grass_pollen
|
Count.tree_pollen
|
Count.weed_pollen
|
Species.Grass.Grass / Poaceae
|
Species.Others
|
Species.Tree.Ash
|
Species.Tree.Birch
|
Species.Tree.Cypress / Juniper / Cedar
|
Species.Tree.Elm
|
Species.Tree.Maple
|
Species.Tree.Mulberry
|
Species.Tree.Oak
|
Species.Tree.Pine
|
Species.Tree.Poplar / Cottonwood
|
Species.Weed.Ragweed
|
SpeciesRisk.Ash
|
SpeciesRisk.Birch
|
SpeciesRisk.Cypress / Juniper / Cedar
|
SpeciesRisk.Elm
|
SpeciesRisk.Grass / Poaceae
|
SpeciesRisk.Maple
|
SpeciesRisk.Mulberry
|
SpeciesRisk.Oak
|
SpeciesRisk.Pine
|
SpeciesRisk.Poplar / Cottonwood
|
SpeciesRisk.Ragweed
|
lat
|
lng
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx |
| Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx |
| Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx |
| xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx |
| Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx |
| xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx |
| xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx |
| Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx |
| Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx |
| Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx |
Data Dictionary
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
timestamp
|
DateTime | 2025-07-04T06:00:00+00:00 | |
|
Risk.grass_pollen
|
String | Low | |
|
Risk.tree_pollen
|
String | Low | |
|
Risk.weed_pollen
|
String | Low | |
|
Count.grass_pollen
|
Integer | 8 | |
|
Count.tree_pollen
|
Integer | 2 | |
|
Count.weed_pollen
|
Integer | 8 | |
|
Species.Grass.Grass / Poaceae
|
Integer | 8 | |
|
Species.Others
|
Integer | 2 | |
|
Species.Tree.Ash
|
Integer | 0 | |
|
Species.Tree.Birch
|
Integer | 0 | |
|
Species.Tree.Cypress / Juniper / Cedar
|
Integer | 0 | |
|
Species.Tree.Elm
|
Integer | 0 | |
|
Species.Tree.Maple
|
Integer | 0 | |
|
Species.Tree.Mulberry
|
Integer | 0 | |
|
Species.Tree.Oak
|
Integer | 0 | |
|
Species.Tree.Pine
|
Integer | 0 | |
|
Species.Tree.Poplar / Cottonwood
|
Integer | 0 | |
|
Species.Weed.Ragweed
|
Integer | 8 | |
|
SpeciesRisk.Ash
|
String | Low | |
|
SpeciesRisk.Birch
|
String | Low | |
|
SpeciesRisk.Cypress / Juniper / Cedar
|
String | Low | |
|
SpeciesRisk.Elm
|
String | Low | |
|
SpeciesRisk.Grass / Poaceae
|
String | Low | |
|
SpeciesRisk.Maple
|
String | Low | |
|
SpeciesRisk.Mulberry
|
String | Low | |
|
SpeciesRisk.Oak
|
String | Low | |
|
SpeciesRisk.Pine
|
String | Low | |
|
SpeciesRisk.Poplar / Cottonwood
|
String | Low | |
|
SpeciesRisk.Ragweed
|
String | Low | |
|
lat
|
Float | 40.7128 | |
|
lng
|
Float | -74.006 |
Description
Country Coverage
History
Volume
| 30 | days of forecast |
Pricing
Suitable Company Sizes
Delivery
Use Cases
Categories
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Frequently asked questions
What is Global Pollen Data - Historical Real-Time Forecast Climatology?
High-resolution global pollen data built for precision health, allergy forecasting, and demand modeling. Hourly updates, species-level insights, and region-optimized models.
What is Global Pollen Data - Historical Real-Time Forecast Climatology used for?
This product has 5 key use cases. Ambee recommends using the data for Programmatic Advertising, Demand Forecasting, Retail Intelligence, Marketing Strategy, and Pharmaceutical Research. Global businesses and organizations buy Pollen Data from Ambee to fuel their analytics and enrichment.
Who can use Global Pollen Data - Historical Real-Time Forecast Climatology?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Pollen Data. Get in touch with Ambee to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Global Pollen Data - Historical Real-Time Forecast Climatology go?
This product has 9 years of historical coverage. It can be delivered on a hourly, daily, weekly, monthly, quarterly, yearly, real-time, and on-demand basis.
Which countries does Global Pollen Data - Historical Real-Time Forecast Climatology cover?
This product includes data covering 250 countries like USA, China, Japan, Germany, and India. Ambee is headquartered in India.
How much does Global Pollen Data - Historical Real-Time Forecast Climatology cost?
Pricing information for Global Pollen Data - Historical Real-Time Forecast Climatology is available by getting in contact with Ambee. Connect with Ambee to get a quote and arrange custom pricing models based on your data requirements.
How can I get Global Pollen Data - Historical Real-Time Forecast Climatology?
Businesses can buy Pollen Data from Ambee and get the data via S3 Bucket, SFTP, Email, UI Export, REST API, Compressed File, Snowflake Share, Google BigQuery, Google Cloud Storage, Azure Blob Storage, and Databricks Delta Share. Depending on your data requirements and subscription budget, Ambee can deliver this product in .csv, .geojson, .json, .parquet, .tiff, .txt, .xls, and .xml format.
What is the data quality of Global Pollen Data - Historical Real-Time Forecast Climatology?
You can compare and assess the data quality of Ambee using Datarade’s data marketplace.
