Street- and census-level NO₂ data for Barcelona with uncertainty and exceedance probability mapping

Area of Science:

• Environmental Science
• Public Health
• Data Science

Background:

• Air pollution, particularly nitrogen dioxide (NO2), poses significant public health risks.
• Street-level air quality data platforms are scarce, and uncertainty in estimates is often not reported.
• High-resolution air quality data is vital for public awareness, health research, and urban planning.

Purpose of the Study:

• To present a comprehensive six-year (2019-2024) dataset of nitrogen dioxide (NO2) concentrations at street-level resolution for Barcelona.
• To incorporate and map the uncertainty associated with these high-resolution air quality estimates.
• To provide exceedance probability maps aligned with European Air Quality Directives and WHO guidelines.

Main Methods:

• Developed high-resolution (25m x 25m) annual NO2 concentration estimates.
• Generated daily NO2 concentrations at the census-tract level for citywide temporal assessments.
• Integrated uncertainty quantification into the air quality data and visualized it spatially.
• Created exceedance probability maps based on regulatory thresholds.

Main Results:

• A six-year database of high-resolution NO2 concentrations and associated uncertainty for Barcelona.
• The first citywide daily NO2 assessment with full spatial coverage.
• Spatial maps illustrating NO2 concentrations, uncertainty, and exceedance probabilities.
• Data accessible via the uncertAIR platform for diverse stakeholders.

Conclusions:

• The study delivers a unique, high-resolution, and uncertainty-aware NO2 dataset for Barcelona.
• This open data platform advances street-scale air quality monitoring and reporting globally.
• The findings support evidence-based decision-making in public health, urban planning, and environmental policy.