Quantifying CO2 Emissions of Power Plants With CO2 and NO2 Imaging Satellites

One important goal of the Copernicus CO2 monitoring (CO2M) mission is to quantify CO2 emissions of large point sources. We analysed the feasibility of such quantifications using synthetic CO2 and NO2 observations for a constellation of CO2M satellites. Observations were generated from kilometer-scale COSMO-GHG simulations over parts of the Czech Republic, Germany and Poland. CO2 and NOx emissions of the 15 largest power plants (3.7-40.3 Mt CO2 / yr) were quantified using a data-driven method that combines a plume detection algorithm with a mass-balance approach. CO2 and NOx emissions could be estimated from single overpasses with 39-150% and 33-116% uncertainty (10-90th percentile), respectively. NO2 observations were essential for estimating CO2 emissions as they helped detecting and constraining the shape of the plumes. The uncertainties are dominated by uncertainties in the CO2M observations (2-72%) and limitations of the mass-balance approach to quantify emissions of complex plumes (25-95%). Annual CO2 emissions could be estimated with 23-119% and 18-65% uncertainties with two and three satellites, respectively. The uncertainty in the temporal variability of emissions contributes about half to the total uncertainty. The estimated uncertainty was extrapolated to determine uncertainties for point sources globally, suggesting that two satellites would be able to quantify the emissions of up to 300 point sources with <30% uncertainty, while adding a third satellite would double the number to about 600 point sources. Annual NOx emissions can be determined with better accuracy of 16-73% and 13-52% with two and three satellites, respectively. Estimating CO2 emissions from NOx emissions using a CO2:NOx emission ratio may thus seem appealing, but this approach is significantly limited by the high uncertainty in the emission ratios as determined from the same CO2M observations. The mass-balance approach studied here will be particularly useful for estimating emissions in countries where power plant emissions are not routinely monitored and reported. Further reducing the uncertainties will require the development of advanced atmospheric inversion systems for emission plumes and an improved constraint on the temporal variability of emissions using additional sources of information such as other satellite observations or energy demand statistics.