Predicting Fine-Scale Daily NO₂ for 2005–2016 Incorporating OMI Satellite Data Across Switzerland

NO₂ remains an important traffic-related pollutant associated with both short- and long-term health effects. de Hoogh et al. conducted one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m²) and temporal (daily) variation of NO₂ across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO₂. The predicted NO₂ concentrations will be made available to facilitate health research in Switzerland.

de Hoogh et al., Environmental Science & Technology 2019 53 (17), 10279-10287, DOI: 10.1021/acs.est.9b03107

Detection of Strong NO_x Emissions from Fine-scale Reconstruction of the OMI Tropospheric NO₂ Product

Satellite NO₂ columns have been widely used in assessing bottom-up NO_x emissions from large cities, industrial facilities, and power plants. However, the satellite data fail to identify strong NO_x emissions from sources less than the satellite’s pixel size, while significantly underestimating their emission intensities (i.e., smoothing effect). Lee et al. reconstruct the OMI tropospheric NO₂ vertical column density (VCD) over South Korea to a fine-scale product (3 × 3 km²) using a conservative spatial downscaling method. Their findings highlight a potential capability of the fine-scale reconstructed OMI NO₂ product in detecting directly strong NO_x emissions, and emphasize the inherent methodological uncertainty in interpreting the reconstructed satellite product at a high-resolution grid scale.

The spatial distributions of the fine-scale reconstructed OMI NO₂ VCDs over South Korean obtained by applying the spatial-weight kernels from two AQ models: (a) WRF-Chem and (b) WRF/CMAQ.

Lee et al., Remote Sensing, 2019 11(16), 1861, https://doi.org/10.3390/rs11161861.