Improving NO₂ Episode Detection with TROPOMI: A Decomposition Approach Across Diverse Orography

Air pollution remains a critical environmental challenge for human health and ecosystems, requiring improved monitoring beyond traditional ground-based networks. The EU Directive 2024/2881 enforces stricter NO₂ limits by 2030, making advanced modeling essential, particularly in sensor-scarce regions. Satellite remote sensing has emerged as a key complement, with recent studies [1–4]  examining links between satellite-derived NO₂ columns and surface concentrations. However, these studies rely on simple temporal averages, removing short-term structures relevant for identifying pollution episodes and do not address the possible influence of orography.

This study introduces a fluctuation-aware decomposition framework to enhance NO₂ pollution episode detection using the Tropospheric Monitoring Instrument (TROPOMI) aboard Sentinel 5 Precursor (Sentinel-5P) satellite. The method isolates trend, seasonal, and fluctuation components. Explicitly, fluctuations are modeled to retain short-term variability associated with NO₂ events, enhancing the signal-to-noise ratio. This approach was applied to TROPOMI NO₂ vertical tropospheric column density (TrC-NO2) data and surface-level NO₂ concentration measurements (OBS-NO2) from 150 stations across northeastern Spain, Andorra, and southern France, including the Pyrenees, spanning May 2018 to December 2023, and accounting for varying terrain complexity and different NO₂ dynamics.

Performance was assessed via Pearson correlation and alarm rates (True Positive Rate, TPR; False Alarm Rate, FAR) across event intensities. Results show that our models outperform raw data for episodes lasting 3 days, reducing error and improving correlation in ≥98% of stations, regardless of terrain complexity. To our knowledge, this is the first study to assess terrain effects on TROPOMI NO₂ retrievals and to demonstrate their reliability in mountainous regions. These findings provide a robust framework for integrating satellite data into air quality monitoring and compliance strategies under the EU Directive, especially where ground networks are sparse.

References

1. Cersosimo A, Serio C, Masiello G. TROPOMI NO2 Tropospheric Column Data: Regridding to 1 km Grid-Resolution and Assessment of their Consistency with In Situ Surface Observations. Remote Sens. 2020 Jan;12(14):2212. 

2. Jeong U, Hong H. Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea. Remote Sens. 2021 Jan;13(10):1877. 

3. Petetin H, Guevara M, Compernolle S, Bowdalo D, Bretonnière PA, Enciso S, et al. Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula. Atmospheric Chem Phys. 2023 Apr 3;23(7):3905–35. 

4. Morillas C, Alvarez S, Serio C, Masiello G, Martinez S. TROPOMI NO2 Sentinel-5P data in the Community of Madrid: A detailed consistency analysis with in situ surface observations. Remote Sens Appl Soc Environ. 2024 Jan 1;33:101083.