Temporal variability of NOx emissions from power plants: a comparison of satellite- and inventory-based estimates

Abstract

Satellite observations of nitrogen dioxide (NO2) are a valuable tool for estimating nitrogen oxides (NOx) emissions from point sources and can support carbon dioxide (CO2) monitoring through emission ratios. We assess the capability of TROPOMI NO2 measurements to quantify the seasonal and day-to-day temporal variability of NOx emissions from eighteen power plants in Europe and the United States. Using the cross-sectional flux (CSF) method implemented in the ddeq Python library (version 1.1), we derive top-down emissions and compare two NOx chemistry corrections approaches: a “local” method based on plume-resolving MicroHH simulations and a “global” method based on GEOS-Chem simulations. Annual top-down estimates using the local approach agree well with bottom-up estimates from the CORSO point source database, with a mean bias of 9 ± 20 % when aggregating point sources within 30 km. A regression analysis yields a slope of 1.05 ± 0.17 and a coefficient of determination of 0.68. The local correction yields emissions that are 58 ± 8 % higher than the global approach. Satellite-based estimates successfully captured seasonal and short-term variability in bottom-up emissions estimated from electricity generation in Europe and continuous emissions monitoring systems (CEMS) in the USA. However, limitations remain due to reduced winter coverage, emissions below the detection limit, overlapping plumes, and uncertainties in NOx chemistry corrections especially for non-isolated facilities. Overall, our findings demonstrate that satellite NO2 observations can effectively monitor the seasonality of NOx emissions from power plants. Addressing remaining uncertainties will be essential for future emission monitoring systems and upcoming satellite missions targeting both NO2 and CO2.