Air pollution is invisible. Understanding it at the level needed to protect public health requires something far more sophisticated than a network of measuring stations, it requires data, models, ethics, and policy working together. That is exactly what ClimAIr is building.
ClimAIr investigates the complex relationships between climate change, air pollution, and non-communicable respiratory diseases using advanced AI tools. By integrating interdisciplinary research and innovative methodologies, the project seeks to provide scientific evidence to inform policies that mitigate pollution-related health risks. Advanced modelling techniques, such as the System for Integrated modelling of Atmospheric composition (SILAM), are used to provide high-resolution environmental data and predict future pollution levels. The project operates across nine target cities in Europe.
You cannot measure everywhere
The starting point for any serious air quality research is a simple but uncomfortable truth: we will never have enough sensors. “We can’t measure air quality everywhere,” says Ari Karppinen, research manager at the Finnish Meteorological Institute. “So the models are needed to fill these gaps, showing conditions where no measurement data exists.” Models are also the only tools that allow us to understand and predict air quality and climate impacts far ahead into the future.
This is where remote sensing comes in. Werner Wiedemann, from Remote Sensing Solutions in Munich, explains that his team collects multiple layers of data, satellite imagery, ground-based measurements, traffic density, vegetation, and building heights, and harmonizes them into a single dataset shared across all project partners. But building that dataset is far from simple. Each of the nine target cities comes with its own data infrastructure, standards, and gaps. Some cities have very high-resolution datasets that are publicly available; for others, the resolution is poor and gathering the data is complex and time-intensive.
From data to models: understanding how pollution moves
Once data is collected and harmonized, meteorological modelling allows researchers to understand how pollutants behave once released into the atmosphere. Without it, “we can only estimate the emissions, but we don’t know what happens after the emission,” says Karppinen. ClimAIr focuses particularly on urban environments, where accurately modelling meteorological conditions is especially challenging.
The models draw on a wide range of sources: not only measuring stations in the city, but also surrounding agricultural fields, different tree types, and lidar data on vegetation density. This level of detail matters directly for the project’s focus on allergic respiratory diseases, for example, combining vegetation density with wind speed data makes it possible to model how pollen spreads across a city.
Models for the future, not just the present
Perhaps the most consequential aspect of ClimAIr’s modelling work is its long-term dimension. These tools are not designed to describe air quality today alone, they project what will happen in ten, twenty, or fifty years. “Before making political decisions about emissions, the models will help us understand what those decisions will practically mean twenty or fifty years ahead,” explains Karppinen. “Here the models can really make the difference.”
When science meets policy: the readiness gap
Generating accurate models is one thing. Ensuring that institutions are equipped to act on them is another challenge entirely. George Manea, founder and director of the Euro-Atlantic Diplomacy Society, identifies three distinct readiness layers that must be addressed: the capacity of institutions to absorb and govern the data ClimAIr produces; the training of the people inside those institutions to integrate, manage, and interpret what the project delivers; and, perhaps most underestimated, resistance to change. When all three layers are addressed, the result is an institutional ecosystem capable of working both vertically, from local to national to EU level, and horizontally across health, environment, and urban planning.
Building public trust across cultures
Even the most robust policy framework will fail if the public does not trust the proposed solutions. For a project operating across nine European countries with different languages, cultures, and political contexts, this is a genuine challenge. Key ingredients include transparent communication about risks and benefits, early stakeholder engagement, and participatory co-design with communities. Crucially, linking air quality data with health impacts in ways people can relate to on a daily basis also helps build trust and relevance. And when it comes to communication, one size will not fit all, messages must be adapted to local context in order to achieve social legitimacy and support long-term adaptation.
A shared infrastructure for a shared challenge
The health stakes are clear: air pollution causes an estimated 7 million deaths every year, mainly from non-communicable diseases such as chronic respiratory diseases, cardiovascular diseases, and cancer. ClimAIr takes that urgency and builds the full chain, the models that make sense of incomplete data, the institutions that need to be ready to act, and the communities that need to trust what is being proposed. By the end of the project, the goal is to launch the ClimAIr tool: a web app designed to help doctors, urban planners, policymakers, and citizen scientists with reliable information on disease prediction, training on pollution and health impacts, and guidelines for decision-makers.
Video: Modelling air pollution: from data collection to climate projections