Publikasjonsdetaljer
Tidsskrift: Remote Sensing of Environment, vol. 312, 114321, 2024
Arkiv: hdl.handle.net/11250/3146278
Doi: doi.org/10.1016/j.rse.2024.114321
Sammendrag:
Satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) show significant potential for monitoring the spatiotemporal variability of NO2, however they typically provide vertically integrated measurements over the tropospheric column. In this study, we introduce a machine learning approach entitled ‘S-MESH’ (Satellite and ML-based Estimation of Surface air quality at High resolution) that allows for estimating daily surface NO2 concentrations over Europe at 1 km spatial resolution based on eXtreme gradient boost (XGBoost) model using primarily observation-based datasets over the period 2019–2021. Spatiotemporal datasets used by the model include TROPOMI NO2 tropospheric vertical column density, night light radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS), Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer (MODIS), observations of air quality monitoring stations from the European Environment Agency database and modeled meteorological parameters such as planetary boundary layer height, wind velocity, temperature. The overall model evaluation shows a mean absolute error of 7.77 μg/m3, a median bias of 0.6 μg/m3 and a Spearman rank correlation of 0.66. The model performance is found to be influenced by NO2 concentration levels, with the most reliable predictions at concentration levels of 10–40 μg/m3 with a bias of