Our objectives
Apply modern artificial intelligence to obtain reliable assessments of the global state of air pollution and its changes over time
Build a comprehensive and consistent collection of global surface air quality observations
Improve air quality forecasts at the local scale with help of novel data combinations and deep learning
Explore the use of machine learning for quality assurance (QA) of air pollutant concentration measurements
Novel applications of state-of-the-art artificial intelligence techniques to analyse large and heterogeneous datasets related to air pollution
Stringent validation methods for deep learning applications in the context of air quality and weather research
Large-scale and scalable data management procedures with web service end points to allow participation of researchers from around the world
We develop
We integrate
Long-term air quality monitoring data from thousands of stations worldwide
High-resolution geospatial datasets to characterize measurement locations in a globally consistent manner
Multi-year data from numerical weather prediction models
The composition of the air we breathe is relevant for human health, agriculture, natural ecosystems, and the climate system. Poor air quality ranks among the top causes of premature deaths according to the World Health Organization (WHO). It causes substantial economic losses due to reduced work capacity, crop losses, and damages to buildings and infrastructure. The design of effective control strategies for air pollution hinges on the availability of adequate information on pollutant concentrations and their variations across space and time. Presently, this information is insufficient due to gaps in measurement networks and insufficient statistical analysis techniques. With funding from the European Research Council and in collaboration with the global scientific community, a team of researchers at Forschungszentrum Jülich has begun to change this.