Deriving regional and point source nitrogen oxides emissions in China from TROPOMI using the directional derivative approach with nonlinear chemical lifetime fitting

An appropriate representation of the NO_x/NO₂ ratio and NO_x lifetime is essential for estimating NO_x emissions from satellite NO₂ observations. We introduce a satellite-based, data-driven approach that applies variable NO_x/NO₂ ratio and derives the nonlinear chemical lifetime using a piecewise fitting method based on the directional derivative approach (DDA). This method enables the estimation of both regional and point-source NO_x emissions across China, representing the first application of a lightweight, satellite-driven method to directly capture nonlinear NO_x lifetime for emission estimation over large, topographically complex region. The incorporation of a variable NO_x/NO₂ ratio enhances the accuracy of source divergence and emission estimates and the improved fitting scheme captures the nonlinear behavior of NO_x chemistry. Anthropogenic contributions are isolated by subtracting natural sources from satellite-derived total emissions, with natural NO_x identified using a seasonal criterion and further constrained by Nighttime Light (NTL) data. Estimated anthropogenic NO_x emissions in China from 2019 to 2024 are 20.2, 18.5, 19.4, 18.9, 20.7 and 18.8 Tg, respectively, with annual uncertainties of 27 %–30 %. These values show good agreement with both bottom-up inventories and top-down inversions, with national scale discrepancies ranging from -11.8 % to 0.8 %. The DDA captures key spatial and temporal emission patterns, including consistent decline in NO_x emissions in megacities and provincial disparities linked to urbanization and economic development. The DDA estimates are consistent with previous studies on power plant emissions, and emissions from 124 plants vary between 0.02–2.13 kg s⁻¹ for 2019–2024, with uncertainties spanning 4 %–78 %, averaging 16 %. This satellite-based, lightweight method enables low-latency, long-term monitoring of NO_x emissions and offers a promising alternative to bottom-up inventories and resource-intensive top-down models. The data are publicly available at https://doi.org/10.5281/zenodo.18923337 (Chen et al., 2026).