About this eBook

Pollen tracking is very difficult, so it’s no wonder we frequently get asked how we provide such granular pollen monitoring with daily up-to-date and forecast information.

In this guide, we explain the science behind our pollen forecasting model and how we help millions around the world manage their exposure to pollen on a daily basis.

Certain wind-pollinating plants produce massive amounts of pollen as part of their reproductive process. These grains, which usually look like a fine, yellow powder, are usually 15-90 micrometers in size, but some can be as small as 2 micrometers.

This makes pollen small enough to enter human airways and cause allergic or even asthmatic reactions. For some, pollen grains can also irritate the skin or the eyes, causing rashes and itchiness. 

 

Pollen allergy symptoms include nose congestion, hives, watery eyes, breathing difficulties, fatigue, and headaches, as well as sneezing and sore throats.

Multiple studies suggest a strong link between human-induced climate change and worsening pollen seasons across the globe. This means individuals with strong sensitivities, and even those with only moderate reactions may begin experiencing more extreme symptoms.

 

a) Allergy Seasons are Getting Worse

 

Experts suggest annual pollen seasons are now producing more grains and impacting more people:

Pollen Allergy

b) Allergy Seasons are Harder to Predict

 

Four decades ago, North America’s main pollen allergy season used to start around mid-March, with some plants, like juniper, starting near December in southern US regions. Now, as NBC News reports, doctors find pollen-related allergy symptoms in patients are starting around a month earlier than in previous years. 

 

As our climate changes and environmental patterns become harder to predict, pollen seasons also become less regular and harder to predict - especially at the hyperlocal level.

 

c) No Two Pollen Allergy Sufferers are the Same

 

Even if we reliably estimate pollen counts in the air around us, predicting a person’s reaction is not so simple. 

 

Birch, for example, is the most prevalent tree pollen type in Northern and Central Europe, estimated to affect 16% of the population. However, birch pollen does not impact all allergy sufferers - some are more sensitive to ragweed or grass pollen and can exhibit stronger symptoms after exposure to those grains. 

 

Sufferers experience very different reactions depending on the pollen types and amount they are sensitive to:

  • Boys tend to be more sensitive to grass pollen than girls according to a 5-year Australian case study on children (ages 2-18). 

     

  • Pregnant women exposed to high pollen levels in the last 12 weeks of pregnancy can put their unborn baby at risk of early asthma-related hospitalization in the first year of life.

     

  • A study by UK medical school researchers suggests allergy  - including pollen allergy - has increased among middle-aged men in the last quarter of the 20th century. 

In order to provide personalized risk indicators to pollen-sensitive groups, it's essential for sufferers to be able to distinguish between grass, weed, and tree pollen, and different plant types.

The CDC estimates pollen allergy alone is responsible for over $3 billion in annual medical costs in the US each year, mostly due to prescription medicine. If pollen counts continue to increase with climate change as they are predicted to, the health and economic impacts are also likely to increase:

  • In Paris, researchers associated a seasonal increase of 17.6 grass (Poaceae) pollen grains per micrometer with a 54% increase in the likelihood of experiencing an asthma attack. 

     

  • In London, researchers found high pollen counts correlated with an increase of up to 46% in asthma-related hospitalizations roughly 3 days after exposure. 

Traditional approaches to pollen analysis involve rudimentary techniques that offer only limited reporting options. Many sources are typically unable to provide personalized, granular, and actionable information in a timely fashion, due to a number of limitations:

  • Manual Methods: Pollen counting stations/traps rely on manual analysis methods, making them slower to process, harder to validate, and more prone to error.

 

  • Geographically Limited: Due to being widely dispersed, the accuracy of pollen count stations is limited to their exact location. Cities rarely have more than a single pollen trap, if any. Therefore hyperlocal reporting with traditional methods is impossible. 

     

  • Delayed: Stations trap pollen during a full day/night cycle, using volumetric air pumps and sticky films. Lab analysis results are provided with a minimum 24-hours delay. In addition, holidays, illnesses, and other technician absences result in no data collection. Subsequently, delays can sometimes stretch to weeks.

     

  • Unregulated: There are very few pollen monitoring guidelines in most countries. Because pollen is a biological phenomenon, unlike man-made pollution, it isn’t (and can’t be) regulated - and therefore neither can pollen monitoring.

     

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  • No forecasting: Pollen trap stations can only provide past data, with no ability to predict tomorrow’s pollen counts or even report on threat levels of the present day.

     

  • Different pollen indexes: Pollen indexes vary between different countries and providers. They’re often averaged over a large region, based on pollen station measurements far away from most users. These indexes often offer no consideration for spatial variability, different pollen types, or health impact on asthma or seasonal allergy.

In order to deliver accurate and timely pollen forecasts, BreezoMeter moves beyond monitoring station information and regional models by leveraging sophisticated AI-driven data analysis and prediction methods. 

 

In addition to station information, we layer this information with data about pollen emission schedules, vegetation land covers, and climate and weather conditions - all of which serve to inform our pollen forecasting models. 

 

Using AI and machine learning algorithms, our model can forecast pollen dispersion and report hyperlocal threat levels:

 

How Does it Work?

 

  • Processing The Information

We combine government land cover databases and satellite imagery to calculate the percentage of pollen-producing plants at a 100-meter grid resolution. We then reference that data against local weather and climate to calculate pollen production schedules and total annual production for local plant types. 

  • Adding Pollen Emissions & Station Data

After establishing the beginning and end of pollen seasons and total annual productions, we forecast local pollen emissions for 3 consecutive days. We continue referencing dynamic weather changes, as well as data from pollen monitoring stations across the world to adjust daily results. 

  • Algorithms & Modeling 

The calculated emissions data is then fed into our AI-driven pollen dispersion model to estimate grains per cubic meter. These numbers are translated into BreezoMeter’s Pollen Index to provide a 5-level threat severity assessment based on scientific literature and continuous research.

 

BreezoMeter Pollen Forecasting

  • The Results

Hyperlocal and highly accurate 3-day pollen forecasts at a 1km resolution. Businesses can easily integrate data via our Pollen API to provide users with daily risk alerts, personalized actionable recommendations, and even color-coded pollen heatmaps visualizing location-based threat levels.

BreezoMeter’s Pollen Forecasts: What’s Included?

 

Breezometer’s sophisticated approach to pollen reporting enables businesses to engage users with location-based 3-day pollen threat forecasts personalized to individual sensitivities.

 

 

BreezoMeter Pollen App

This includes: 

  • 3-day pollen forecasts predicting individual exposure to assist in day-to-day allergy management. 

  • Accurate pollen data at a 1 km resolution, personalizing information based on local weather conditions and land cover.

  • Tree, weed, and grass pollen risk indication separation, making different allergy types easier to manage based on personal plant sensitivities.

  • Color-coded pollen heatmap accentuating threat levels from red to green based on BreezoMeter’s Pollen Index and local plant types.

  • 5-level pollen threat assessment based on daily data, with separate risk indications for 13 different types of tree, weed, and grass pollen.

Currently, BreezoMeter is the only provider of global pollen data capable of creating daily forecasts based on a health-focused pollen index, which categorizes risk levels by multiple grass, tree, and weed species.

Most common pollen indexes vary in how they calculate pollen concentrations. Most indexes only categorize general pollen threat levels based on pollen counts without indicating risk for specific plant sensitivities. 

 

Individuals often experience different allergic reactions to different pollen types, and lack of personalization creates some serious limitations:

  • Different ‘High’ and ‘Low’ Standards

A ‘low’ count in one pollen index can register as ‘medium’ or even ‘high’ in others, due to the use of different scales. Allergy sufferers that need to take medication in advance can become vulnerable to high pollen exposure considered ‘low’ by some indexes.

  • No Personalization

Many pollen indexes base their levels on average measurements in a wide geographical area, not daily variations based on granular location. In addition, they often don’t categorize measured pollen counts into plant species or types, which pose different risks based on individual sensitivities.

  • Not Health-based 

Common pollen indexes often don’t account for the impact on respiratory health, and reported levels don’t reflect the threat of causing or aggravating different allergy symptoms. Individuals with pollen sensitivities can’t refer to them to estimate personal risk. 

Understanding the BreezoMeter Pollen Index (BPI) 

 

Based on academic research analysis of over a dozen plant types, the BPI accurately represents the health impact of pollen on seasonal allergy sufferers. 

 

 

Ranging from 0-5, ‘none’ to ‘very high’, each threat level is based on specific pollen concentrations at a queried area and the potential impact on allergic symptoms based on the specific plant type. With a 1 km grid resolution, the BPI presents accurate daily pollen exposure risk based on the user’s location. 

 

How the BPI Enables Better Allergy Management:

  • 5 different health-focused pollen severity levels indicate reliable health risks for pollen allergy sufferers.

     

  • 13 plant-type threat-level breakdown, including grass, weed, and tree pollen separation, informs personal sensitivities for better protection.

     

  • Hyperlocal pollen measurements, accurate at a 1 km resolution, personalize exposure at the individual level.

To achieve a high level of accuracy, BreezoMeter adopts a multi-data layer approach and uses a geographical grid to model daily variations in pollen levels based on the user’s location. But what measures do we take to ensure the information we provide is reliable? 

  • Validating Collected Information

Utilizing multiple data sources enables BreezoMeter to maintain better information authentication. For example, we compare satellite imagery with land cover databases, thus enabling us to verify the relevant local flora population.

  • Comparison Studies with Pollen Monitoring Stations

In 2019, we compared BreezoMeter’s pollen forecast information to the Italian POLLnet monitoring stations and found our model agreed with the stations’ threat risk assessment over 95% of the time for a period of 6 months (April 1st, 2019 - Sept 29th, 2019). This demonstrates that BreezoMeter’s pollen model reported the correct pollen risk category (in terms of ‘Low’, ‘Medium’, ‘High’) almost 100% of the time.

  • Constant Assessment & Improvement

We constantly strive to improve our models by taking new research into account, by comparing our model results to measurement stations where available, and based on customer and user feedback.

Understanding our Approach to Accuracy

 

Although we consider the information presented by monitoring stations to be ‘the ground truth’ for their exact location, they do not provide predictive information - only retrospective and not for the area in between stations. 


By comparing our pollen forecasts with monitoring station data, we can be sure of a high confidence level when it comes to our pollen prediction model - which, unlike station data, does give individuals the power to plan ahead also at locations far away from pollen measurement stations.

Ready to Try Pollen Data?

Our health-focused daily pollen forecasts are already available to test via our live pollen map and air quality app. If you’re a business looking to try out the APIs for your own, you can request a free demo below: