
Air pollution is a health risk to millions of people in Europe. Heavier pollution occurs in densely populated or industrial areas where we can observe more combustion of fossil fuels. Relevant indicators for air quality are the concentrations of particles with sizes of ~10 µm (PM10) and ~2.5 µm (PM2.5), ozone (O3), and nitrogen-dioxide (NO2).
The EEA’s European Topic Center for Human Health and the Environment (ETC HE) has developed a complex approach for mapping these indicators, the Regression-Interpolation-Merging-Mapping (RIMM) method. It combines reliable in situ measurements collected by the EEA member states with atmospheric transport model outputs (ERA5, CAMS), satellite observations (Sentinel-5P Tropomi), and anthropogenic factors (land cover, population density, traffic) to produce Europe-wide annual maps at 1 km spatial resolution.
We implement this method as open source software and apply it to produce air quality maps for all four indicators at annual, monthly, and daily frequency. Minor activities explore possible improvements of this method with regard to prediction errors or computational cost.
What is the challenge?
Air quality is not a local problem! In this context the Copernicus Atmospheric Monitoring System (CAMS) provides daily forecasts of atmospheric concentrations for multiple pollutants on the continental scale. The European Environment Agency (EEA) provides the European Union and its member states with annual air quality maps based on in situ observations and various predictors (including CAMS), but only on an annual basis. The sweet spot lies in the middle: there is a need for maps that rely on accurate measurements, which are produced in a timely manner (e.g., near-real time).
Our solution
In this use case we aim to provide simple means for mapping air quality indicators using the best-available data: EEA in situ measurements, CAMS/ERA5 model outputs, and static anthropogenic factors (land cover, population density, traffic).

Who will benefit?
Health organizations and researchers will benefit by gaining reliable tools to apply a well-established method for air quality monitoring and reporting. Policymakers and the public benefit from the resulting air quality map, which can be explored interactively.
Scope
Target Partner Organizations
OEMC Leading Partner
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Type of output
Mapping air quality indicators using the best-available data: EEA in situ measurements, CAMS/ERA5 model outputs, and static anthropogenic factors (land cover, population density, traffic).
Technology readiness level
TRL5: Technology validated in relevant environment
Location
Europe