| Popis: |
Nitrogen dioxide (NO2) is among the major air pollutants in Europe posing severe hazard to environmental and human health. The concentrations of surface NO2 are measured by ground monitoring stations which are fairly limited in representation and distribution. While NO2 estimates from chemical transport models are realistic, their complexity makes them computationally intensive. Satellite observations from instruments such as TROPOMI provide high spatiotemporal distribution of NO2. However, these instruments capture NO2 density only along the tropospheric column and not on the surface. Exploiting the availability of ground station measurements and spatially continuous information from TROPOMI, this study estimates surface NO2 concentrations over Europe at 1km spatial resolution for 2019-2021 using XGBoost machine learning model. While ground measurements are used as target reference features, satellite observations such as tropospheric column density of NO2 (from TROPOMI), night light radiance (from VIIRS), NDVI (from MODIS) and modelled meteorological parameters such as planetary boundary layer height, wind velocity, temperature are used as input features to the model. We find an overall mean absolute error of 7.87µg/m3, mean bias of -3.13µg/m3 and spearman correlation of 0.61 during model validation. We found that the performance of the model is influenced by NO2 concentration levels and is most reliable for predictions at concentration levels |
