Carbon dioxide (CO2) emissions from strong point sources account for a significant proportion of the global greenhouse gas emissions, and their associated uncertainties in bottom-up estimates remain substantial. Imaging spectrometers provide a capability to monitor large point source CO2 emissions and help reduce the uncertainties. In this study, we assess the capability of an airborne monitoring system with temporally sparse observations to constrain annual emissions at both facility and regional scales. We use observations of power plant emissions from 2022 and 2023 and compare the derived emission rates at facility scale to in-stack emission observations across the United States. We show that CO2 concentration enhancements retrieved using a log-normal matched filter are suitable for CO2 quantification, achieving low bias and uncertainty in estimated emission rates. We find that annual emissions at the regional scale can be effectively constrained by offsetting errors identified at the facility scale, with a 30% uncertainty.

The rapid growth of liquefied natural gas (LNG) exports underscores the importance of CO2 monitoring for LNG export terminals. This study presents a method for measuring CO2 emissions using remote sensing imaging spectroscopy applied to LNG terminals. The method is first validated using 47 power plant emission events with in situ measured data, then applied to 22 emission events in Sabine Pass and Cameron LNG terminals. The power plant dataset shows a robust correlation between our emission rate estimates and in situ data, with R2 0.9146 and the average error −2%. At Sabine Pass, 8 point sources are identified with emission rates ranging from 219.69 ± 54.95 to 1083.22 ± 308.06 t/hr. At Cameron, 3 point sources are identified with emission rates ranging from 91.64 ± 25.81 to 265.61 ± 67.80 t/hr. This study illustrates the potential of remote sensing to validate environmental reporting and CO2 inventories for industrial facilities.

Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Here, we estimate CO2 emission rates from a small number of collocated observations from the Orbiting Carbon Observatory-3 (OCO-3) and the Earth Surface Mineral Dust Source Investigation (EMIT), both onboard the International Space Station (ISS). These near-simultaneous measurements allow for an unprecedented comparison of two unique space-based CO2 sensors over both isolated coal-fired power plants and multi-source scenes in China. We estimate CO2 emission rates using integrated mass enhancement and a Gaussian plume model. Where validation data is available, 15 of the 19 estimated emission rates have errors less than 37%. For the multi-source scenes, EMIT can estimate emissions from individual facilities but its aggregate emissions are 33% lower than OCO-3, likely because it cannot detect small sources or diffuse emissions. OCO-3, with its excellent precision, may better constrain CO2 emissions over the entire scene.