Monitoring ground level nitrogen dioxide concentration in complex terrain areas using satellite Sentinel 5P total column observations

Air pollution is currently a major environmental issue to human health and natural ecosystems so improving air quality monitoring techniques, traditionally based on ground-based observation networks, is essential. Satellite remote sensing of air pollutants has made significant strides in recent years and now serves as a complementary data source alongside ground sensors. For example, different studies have explored the relationship between satellite-derived NO₂ total column data and ground-level concentration but none of them focused on complex terrain areas. The aim of this work is to evaluate the feasibility of using NO₂ column data from the Sentinel 5P satellite over complex terrain such as the Pyrenees Mountain area covering France, Spain and Andorra to estimate ground level values. For this purpose, a number of models considering the separation of temporal average and fluctuations are considered for both satellite and ground sensor data. The primary objective of these models is to enhance the signal-to-noise ratio. Initially, the periodicities are identified and subtracted from the original data, resulting in a residual series. These residual series are then filtered to eliminate noise while retaining the significant events. Finally, these new series are combined with the previously identified periodicity.  

Preliminary results over Andorra show that our models can enhance Pearson's correlation between the temporal series of the satellite and ground sensor, improving it from 0.415 to 0.650. In addition, it has been found that the NO₂ annual cycle in Andorra can be detected with a correlation of 0.950 between the model and the ground sensor NO₂ series. Furthermore, a weekly cycle during winter has been detected in the Sentinel NO₂ series too. These findings suggest that satellite estimates can identify days with high risk of exceeding NO₂ ground level thresholds, enabling the creation of risk maps for areas lacking ground sensors. Such results could profoundly impact air quality monitoring in major towns located in valleys of mountain areas. Peak concentrations that deviate from average cycles have also been quantified. These deviations will be compared with other locations characterized by simpler topography to gain a deeper understanding of the limitations of satellite estimates. Subsequently, the next phase involves integrating these models into Machine Learning Algorithms to expand the application of Sentinel 5P data to complex terrain areas. This study is supported by the project “Towards a climate resilient cross-border mountain community in the Pyrenees (LIFE-SIP PYRENEES4CLIMA)”.