Refining Spatial and Temporal XCO2 Characteristics Observed by Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3 Using Sentinel-5P Tropospheric Monitoring Instrument NO2 Observations in China

Abstract

The spatial and temporal variations in the atmospheric CO2 concentrations evidently respond to anthropogenic CO2 emission activities. NO2, a pollutant gas emitted from fossil fuel combustion, comes from the same emission sources as CO2. Exploiting the simultaneous emissions characteristics of NO2 and CO2, we proposed an XCO2 prediction approach to reconstruct XCO2 data based on the data-driven machine learning algorithm using multiple predictors, including satellite observation of atmospheric NO2, to resolve the issue of data gaps in satellite observation of XCO2. The prediction model showed good predictive performance in revealing CO2 concentrations in space and time, with a total deviation of 0.17 ± 1.17 ppm in the cross-validation and 1.03 ± 1.15 ppm compared to ground-based XCO2 measurements. As a result, the introduction of NO2 obtained better improvements in the CO2 concentration responding to the anthropogenic emissions in space. The reconstructed XCO2 data not only filled the gaps but also enhanced the signals of anthropogenic CO2 emissions by using NO2 data, as NO2 strongly responds to anthropogenic CO2 emissions (R2 = 0.92). Moreover, the predicted XCO2 data preferred to correct the abnormally low XCO2 retrievals at satellite observing footprints, where the XCO2_uncertainity field in the OCO-2 and OCO-3 products indicated a larger uncertainty in the inversion algorithm.