Data for modeling nitrogen dioxide concentration levels across Germany.

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Geographic Information Systems (GIS)

Background:

• Nitrogen dioxide (NO2) is a key air pollutant with significant health implications.
• Accurate spatial modeling of NO2 concentrations is crucial for environmental monitoring and policy-making.
• Existing datasets may lack the granularity or specific features required for detailed land use regression models.

Purpose of the Study:

• To present a comprehensive secondary dataset for modeling conditional mean NO2 concentration levels in Germany.
• To provide detailed metadata including monitoring site information, geocoordinates, altitudes, land use, and road network data.
• To facilitate reproducible research by providing open-access raw data and processing code.

Main Methods:

• Data compilation from various open-access sources.
• Data pre-processing and analysis using the statistical software R.
• Gridding of data to a 1x1 km resolution for spatial modeling.

Main Results:

• A comprehensive dataset for modeling NO2 concentrations across Germany is established.
• The dataset integrates air quality monitoring data with detailed geospatial and land use information.
• All data processing scripts are available in an online repository, ensuring transparency and reproducibility.

Conclusions:

• The presented secondary data offer a robust foundation for developing and validating land use regression models for NO2.
• This dataset supports research into factors influencing air pollution, such as urban agglomeration and infrastructure.
• The open-access nature of the data and code promotes further research and collaboration in air quality assessment.