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Spatial Downscaling of Nitrogen Dioxide Data

Though the native resolution of the European Space Agency’s TROPOspheric Monitoring Instrument (TROPOMI) is finer than Aura's Ozone Monitoring Instrument (OMI), it is still not fine enough for many research applications. In addition, the TROPOMI data record is only 2 years long as compared to OMI’s 15 years. Over the last few years, a number of researchers have applied various techniques (e.g., land-use regression models) to create finer-scale Aura OMI NO2 data for use in research. Two recent studies are highlighted here.

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.

Earth Observation Modeling and Modelled Air Pollution Surfaces

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

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

Satellite NO2 columns have been widely used in assessing bottom-up NOx emissions from large cities, industrial facilities, and power plants. However, the satellite data fail to identify strong NOx 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 NOx 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.

Earth Observation Modeling and Modelled Air Pollution Surfaces

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


Scientific significance, societal relevance, and relationships to future missions:

de Hoogh et al.: NO2 remains an important traffic-related pollutant associated with both short- and long-term health effects. de Hoogh et al. aim to model daily average [NO2] in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from OMI, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. They derived robust models explaining ∼58% (R2 range, 0.56–0.64) of the variation in measured [NO2] using mixed-effect models at a 1 × 1 km2 resolution. The random forest models explained ∼73% (R2 range, 0.70–0.75) of the overall variation in the residuals at a 100 × 100 m2 resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) 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] will be made available to facilitate health research in Switzerland.

Lee et al.: Satellite NO2 columns have been widely used in assessing bottom-up NOx emissions from large cities, industrial facilities, and power plants. However, the satellite data fail to quantify strong NOx emissions from sources less than the satellite’s pixel size, while significantly underestimating their emission intensities (smoothing effect). The poor monitoring of the emissions makes it difficult to enforce pollution restriction regulations. 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, and investigate the methodological fidelity in quantifying the major Korean area and point sources that are smaller than the satellite’s pixel size. Multiple high-fidelity air quality models of the Weather Research and Forecast-Chemistry (WRF-Chem) and the Weather Research and Forecast/Community Multiscale Air Quality modeling system (WRF/CMAQ) were used to investigate the downscaling uncertainty in a spatial-weight kernel estimate. The analysis results showed that the fine-scale reconstructed OMI NO2 VCD revealed the strong NOx emission sources with increasing atmospheric NO2 column concentration and enhanced their spatial concentration gradients near the sources, which was accomplished by applying high-resolution modeled spatial-weight kernels to the original OMI NO2 product. The downscaling uncertainty of the reconstructed OMI NO2 product was inherent and estimated at 11.1% ± 10.6% over South Korea. The smoothing effect of the original OMI NO2 product was estimated at 31.7% ± 13.1% for the 6 urbanized area sources and 32.2% ± 17.1% for the 13 isolated point sources on an effective spatial resolution that is defined to reduce the downscaling uncertainty. Finally, it was found that the new reconstructed OMI NO2 product had a potential capability in quantifying NOx emission intensities of the isolated strong point sources with a good correlation of R = 0.87, whereas the original OMI NO2 product failed not only to identify the point sources, but also to quantify their emission intensities (R = 0.30). Their findings highlight a potential capability of the fine-scale reconstructed OMI NO2 product in detecting directly strong NOx emissions, and emphasize the inherent methodological uncertainty in interpreting the reconstructed satellite product at a high-resolution grid scale


References:

Kees de Hoogh, Apolline Saucy, Alexandra Shtein, Joel Schwartz, Erin A. West, Alexandra Strassmann, Milo Puhan, Martin Röösli, Massimo Stafoggia, and Itai Kloog, Predicting Fine-Scale Daily NO2 for 2005–2016 Incorporating OMI Satellite Data Across Switzerland, Environmental Science & Technology, 2019 53 (17), 10279-10287, DOI: 10.1021/acs.est.9b03107

Jae-Hyeong Lee, Sang-Hyun Less, and Hyun Cheol Kim, Detection of Strong NOx Emissions from Fine-scale Reconstruction of the OMI Tropospheric NO2 Product, Remote Sensing, 2019 11(16), 1861, https://doi.org/10.3390/rs11161861.




11.2019


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