Tracking Urban Atmospheric Pollutants using Sentinel-5P Satellite Data
For urban air-quality assessment in data-scarce regions, this work provides an interpretable and scalable tool using satellite observations alone.
This study presents a satellite-based framework using Sentinel-5P/TROPOMI data to track urban NO2 pollution in Guayas Province, Ecuador, employing distributional metrics and K-means clustering. Results show that highly urbanized cantons have elevated extreme NO2 values and greater variability compared to less urbanized areas.
Urban nitrogen dioxide ($NO_2$) is a key indicator of combustion-related air pollution and exhibits strong spatial and temporal variability in cities. This study presents a satellite-based framework for tracking urban $NO_2$ pollution using tropospheric column observations from Sentinel-5P/TROPOMI over Guayas Province, Ecuador. Rather than estimating surface concentrations, the methodology emphasizes robust distributional metrics, including the median and upper-tail percentiles ($P_{90}$, $P_{95}$, and $P_{99}$), to characterize background conditions and localized pollution extremes at the canton scale. Multi-year satellite observations are aggregated annually and analyzed using unsupervised K-means clustering to identify characteristic pollution regimes without predefined thresholds. Results show that highly urbanized cantons consistently exhibit elevated extreme $NO_2$ values and greater variability, while less urbanized areas display lower and more homogeneous patterns. The proposed approach provides an interpretable and scalable tool for urban air-quality assessment in data-scarce regions using satellite observations alone. The implementation is publicly available on GitHub https://hvelesaca.github.io/sentinel-5P-clustering/.