Air pollution poses significant risks to respiratory health and ecosystem stability, yet current operational forecasting systems, such as those used by the Copernicus Atmospheric Monitoring Service (CAMS), are often costly to operate and struggle to capture local pollution patterns accurately. AQPlus4 set out to address this challenge by developing a forecasting system based on advanced artificial intelligence—specifically, Transformer models originally designed for language data. The goal was to create accurate, reliable, and low-cost forecasts that can be adapted globally.
The project focused on ground-level ozone (O₃) and leveraged over 30 years of observations from the Tropospheric Ozone Assessment Report (TOAR) Phase II database alongside ERA5 meteorological data from the European Centre for Medium Range Weather Forecasts (ECMWF). The prototype was built using a Temporal Fusion Transformer (TFT) architecture capable of learning long-range patterns and incorporating anthropogenic factors (such as land use and population density) that traditional models often overlook.
Building on the ERC Advanced Grant IntelliAQ, the project aimed to validate a scientifically grounded, operation-ready forecasting system capable of 4-day predictions. A key objective was to demonstrate the transferability of the model to new regions, specifically evaluating its performance in South Korea using minimal local data. Furthermore, the project developed the technical foundations for automated operational workflows, creating a blueprint for transitioning research-grade models into scalable, service-oriented applications which are affordable also for less affluent countries.
