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IntelliAQ Workflow

The IntelliAQ project develops novel approaches for the analysis and synthesis of global air quality data based on deep neural networks. The foundation of this project is one of the world’s largest collection of surface air quality measurements, which was recently assembled at the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich and plays a pivotal role in the ongoing first comprehensive Tropospheric Ozone Assessment Report (TOAR).

This database is complemented with data from OpenAQ, the world’s leading effort to collect global air pollutant measurements in near realtime. Through combination of this unprecedented treasure of global air quality data with high-resolution geodata, weather model output, and satellite retrievals of atmospheric composition, huge training data sets for deep learning will be constructed, which provide a globally consistent characterization of individual measurement locations and regional air pollution patterns.

State-of-the-art deep learning methods are applied to this unprecedented dataset in order to

  1. fill observation gaps in space and time,
  2. provide short-term forecasts of air quality, and
  3. assess the quality of air pollutant information from diverse measurements.

The combination of diverse data sources is unique, and the project is the first to apply the full potential of deep neural networks on global air quality data. The achievement of the three IntelliAQ objectives will shift the analysis of global air pollutant observations to a new level and provide a basis for the future development of innovative air quality services with robust scientific underpinning.