Based on environmental monitoring data and meteorological observation data of the Chinese major energy province, Shanxi, from 2015 to 2020, using the satellite remote sensing data of Atmospheric Infrared Sounder Instrument (AIRS) and Ozone Monitoring Instrument (OMI) in 2017, we analyzed the characteristics of surface ozone (O₃) pollution and its correlation with meteorological factors, as well as the vertical distribution of O₃ in typical pollution cities in Shanxi Province. The results showed that surface O₃ became the primary pollutant in Shanxi. Surface O₃ has shown a zonal distribution with a high level in the south and a low level in the north region since 2017. Surface O₃ pollution was severe in 2019, and the maximum daily 8 h running average of O₃ (MDA8 O₃) decreased, but annual mean O₃ in northern and central regions showed a slow rising trend in 2020. Comprehensive analyses of the influence of meteorological factors on surface O₃ indicated that O₃ pollution in Linfen, Yuncheng and Taiyuan was mainly caused by local photochemical reactions, while that in Jincheng, Xinzhou, Lvliang and Yangquan resulted from regional transports. O₃ volume mixing ratios (VMR) in the middle and lower troposphere generally increased with altitude, peaking at 120 ppbv at approximately 400 hPa. The positive vertical gradient of O₃ in the boundary layer was obvious in Taiyuan in summer and significant in the surface layer in Taiyuan and Linfen during winter and spring, which was associated with greater atmospheric dynamic stability and suppressed vertical mixing. Due to the lack of direct detection of O₃ in the lower troposphere in this region, O₃ vertical distribution retrieved by satellite observation is critical for the study of vertical mixing and transport of local O₃, as well as its regional transport characteristics.

An appropriate representation of the NO${}_x/{\mathrm{NO}}_{\mathrm{2}}$ 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${}_x/{\mathrm{NO}}_{\mathrm{2}}$ 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${}_x/{\mathrm{NO}}_{\mathrm{2}}$ 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).

Coal-fired power generation dominates China’s electricity mix and accounts for over one third of fossil CO ₂ emissions, with substantial air pollutants. We estimate nitrogen oxides (NO _x ) and co-emitted CO ₂ emissions from more than 1000 coal-fired power plants in China during 2021–2024 using a lightweight top-down framework. The method combines TROPOMI NO ₂ observations, ERA5 wind fields, and variable NO _x /NO ₂ ratios from Goddard earth observing system composition forecast within an improved directional derivative approach to derive gridded anthropogenic NO _x flux. Plant-level NO _x emissions are obtained by integrating weighted contributions from multiple plants within a 15 km radius at monthly temporal resolution, facility-specific CO ₂ /NO _x emission ratios are applied to infer CO ₂ . Estimated annual NO _x emissions decrease from 5.49 to 4.78 Mt during 2021–2024, while CO ₂ emissions decrease from 4.61 to 4.10 Gt. Corresponding uncertainties are 30.9%–31.5% for NO _x and 31.3%–32.0% for CO ₂ . A confidence assessment based on internal statistical consistency and external physical plausibility constraints classifies 85.1% of plant-level estimates as high and medium confidence, showing good agreement between observational indicators and derived emissions and supporting the robustness of the framework. The approach performs less reliably for low-capacity facilities and for regions with sparse satellite coverage or relatively high wind speeds.

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).