Quantifying CO<sub>2</sub> emissions of a city with the Copernicus Anthropogenic CO<sub>2</sub> Monitoring satellite mission

Abstract

Abstract. We investigate the potential of the Copernicus Anthropogenic Carbon Dioxide (CO2) Monitoring (CO2M) mission, a proposed constellation of CO2 imaging satellites, to estimate the CO2 emissions of a city on the example of Berlin, the capital of Germany. On average, Berlin emits about 20 Mt CO2 yr−1 during satellite overpass (11:30 local time). The study uses synthetic satellite observations of a constellation of up to six satellites generated from one year of high-resolution atmospheric transport simulations. The emissions were estimated by (1) an analytical atmospheric inversion applied to the plume of Berlin simulated by the same model that was used to generate the synthetic observations, and (2) a mass-balance approach that estimates the CO2 flux through multiple cross-sections of the city plume detected by a plume detection algorithm. The plume was either detected from CO2 observations alone or from additional nitrogen dioxide (NO2) observations on the same platform. The two approaches span the range between the optimistic assumption of a perfect transport model that provides an accurate prediction of plume location and CO2 background, and the pessimistic assumption that plume location and background can only be determined reliably from the satellite observations. Often unfavorable meteorological conditions allowed to successfully apply the analytical inversion to only 11 out of 61 overpasses per satellite per year on average. From a single overpass, the instantaneous emissions of Berlin could be estimated with an average precision of 3.0 to 4.2 Mt yr−1 (15–21 % of emissions during overpass) depending on the assumed instrument noise ranging from 0.5 to 1.0 ppm. Applying the mass balance approach required the detection of a sufficiently large plume, which on average was only possible on 3 overpasses per satellite per year when using CO2 observations for plume detection. This number doubled to 6 estimates when the plumes were detected from NO2 observations due to the better signal-to-noise ratio and lower sensitivity to clouds of the measurements. Compared to the analytical inversion, the mass balance approach had a lower precision ranging from 8.1 to 10.7 Mt yr−1 (40–53 %), because it is affected by additional uncertainties introduced by the estimation of the location of the plume, the CO2 background field, and the wind speed within the plume. These uncertainties also resulted in systematic biases, especially without the NO2 observations. An additional source of bias were non-separable fluxes from outside of Berlin. Annual emissions were estimated by fitting a low-order periodic spline to the individual estimates to account for the temporal variability of the emissions. The analytical inversion was able to estimate annual emissions with an accuracy of 40 %)) when using the CO2 observations alone. When using the NO2 observations to detect the plume, the accuracy could be greatly improved to 22 % and 13 % with two and three satellites, respectively. Using the complementary information provided by the CO2 and NO2 observations on the CO2M mission, it should be possible to quantify annual emissions of a city like Berlin with an accuracy of about 10 to 20 %, even in the pessimistic case that plume location and CO2 background have to be determined from the observations alone. This requires, however, that the temporal coverage of the constellation is sufficiently high to resolve the temporal variability of emissions.

Determination of the Emission Rates of CO<sub>2</sub> Point Sources with Airborne Lidar

“…To achieve this goal, satellite missions are indispensable. Satellite missions are expected to detect CO2 emissions from large power plants and cities, e.g., the future European Carbon Constellation CO2M (Kuhlmann et al, 2020;Broquet et al, 2018;Bézy et al, 2019), and other mission ideas still in the pre-development phase (Kiemle et al, 2017;Strandgren et al, 2020). Furthermore, CH4 emissions can also be detected, as is done by GHGSat-D for coal mine ventilation shafts (Varon et al, 2020), or the Sentinel-5 Precursor for the oil and natural gas producing sector (Zhang et al, 2020;Pandey et al, 2019).…”

Automated detection of atmospheric NO<sub>2</sub> plumes from satellite data: a tool to help infer anthropogenic combustion emissions

“…A growing body of work has been using satellite observations to study point sources of CO 2 (Bovensmann et al, 2010;Kort et al, 2012;Hakkarainen et al, 2016;Nassar et al, 2017;Broquet et al, 2018;Brunner et al, 2019;Kuhlmann et al, 2019;Zheng et al, 2019;Wang et al, 2019;Kuhlmann et al, 2020;Strandgren et al, 2020;Wang et al, 2020;Wu et al, 2020;Yang et al, 2020;Ye et al, 2020;Zheng et al, 2020) and methane (Varon et al, 2019;de Gouw et al, 2020;Varon et al, 2021), taking advantage of global measurement coverage, subject to clear skies. Even with the 0.3 % precision of CO 2 columns detected by the NASA Orbiting Carbon Observatory-2 instrument, dilution of point source emissions across a 3 km 2 grid box could potentially result in the directly overhead column being elevated but not elevate the measurements immediately downwind except under exceptional circumstances.…”

Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale

“…Previous analyses of the potential of high-resolution satellite imagery of XCO 2 (such as ESA, 2015; Wang et al, 2020;Santaren et al, 2021) have focused on its use as a standalone observation system and on the potential complementarity of images of co-emitted species co-registered with an instrument on board the same satellite or from another mission (Reuter et al, 2019;Kuhlmann et al, 2019Kuhlmann et al, , 2020. However, the distinction between FF and natural CO 2 signals and thus the separation between the FF and natural components in the flux estimates remain difficult, even when using highresolution images and satellite data on co-emitted species (Kuhlmann et al, 2020;Santaren et al, 2021;Sadiq et al, 2021). The separation between the emissions from biofuel (BF) and FF combustion is another challenge because BF emissions can be located in the same hotspots as FF ones (Ciais et al, 2020).…”