Abstract The Orbiting Carbon Observatory‐3 (OCO‐3) provides precise observations of the column‐averaged dry air mole fraction of carbon dioxide () with the ability to target specific areas using the Snapshot Area Maps (SAMs). This study utilizes OCO‐3 observations in a Bayesian inversion framework, together with the column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model driven by the Weather Research and Forecasting (WRF) model to estimate emissions from the Bełchatów power plant in Poland. Many previous studies quantifying power plant emissions have relied on Gaussian plume modeling (GPM), cross‐sectional flux (CSF), and Integrated Mass Enhancement (IME) approaches. These methods have been effective for estimating point source emissions, but they do not explicitly simulate the emission plume. We used the WRF model at a 1 km resolution to simulate the emission plume from the Bełchatów power plant. We used nine SAMs over Bełchatów between April 2020 and October 2022, together with nitrogen dioxide () observations from the TROPOspheric Monitoring Instrument (TROPOMI) to isolate the plume in the OCO‐3 data. We found that the satellite‐derived emissions are in good agreement with independent emission estimates derived from hourly reported power generation data as well as estimates from previous studies. Our results demonstrate the potential of Bayesian inverse modeling with WRF‐X‐STILT for quantifying emissions from power plants.
Xiao et al. (Wed,) studied this question.