A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

Abstract. We present a novel method to infer CO 2 emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO 2 ) and demonstrate its utility on US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NO x ) emissions from isolated power plants using Ozone Monitoring Instrument (OMI) NO 2 tropospheric vertical column densities (VCDs) averaged over the ozone season (May–September) and a "top-down" approach that we previously developed. Second, we determine the relationship between NO x and CO 2 emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal type, boiler firing type, NO x emission control device type, and changes in operating conditions. Third, we estimate CO 2 emissions of the ozone season for a plant using the OMI-estimated NO x emissions and the CEMS NO x  / CO 2 emission ratio. We find that the CO 2 emissions estimated by our satellite-based method during 2005–2017 are in reasonable agreement with the CEMS measurements, with a relative difference of 8 % ± 41 % (mean ± standard deviation) for the selected US power plants in our analysis. Total uncertainty in the inferred CO 2 estimates is partly associated with the uncertainty associated with the OMI NO 2 VCD data, so we expect that it will decrease when our method is applied to OMI-like sensors with improved capabilities, such as TROPOspheric Monitoring Instrument (TROPOMI) and geostationary Tropospheric Emissions: Monitoring Pollution (TEMPO). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO 2 emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other estimates.