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

Abstract. 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 a 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 Tg, 18.5 Tg, 19.4 Tg, 18.9 Tg, 20.7 Tg 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 coal-fired 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, timely 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://zenodo.org/records/16787342 (Chen et al., 2025).