Air quality is strongly influenced by both local emissions and regional transport. Atmospheric chemical transport models can distinguish between emissions and regional transport sources in air pollutant concentrations. However, quantifying model inventories is challenging due to emission changes caused by the recent strict control measures taken by the Chinese government. In this study, we use NO₂ column observations from the Tropospheric Monitoring Instrument to retrieve top-down nitrogen oxide (NO_X) emissions and quantify the contributions of local emissions and regional transport to NO_x in Beijing (BJ), from 1 November 2018 to 28 February 2019 (W_2018) and 1 November 2019 to 29 February 2020 (W_2019). In W_2018 and W_2019, the BJ bottom-up NO_X emissions from the multi-resolution emission inventory for China in 2017 were overestimated by 11.8% and 40.5%, respectively, and the input of NO_X from other cities to BJ was overestimated by 10.9% and 51.6%, respectively. The simulation using our adjusted inventory exhibited a much higher spatial agreement (slope = 1.0, R² = 0.79) and reduced a mean relative error by 45% compared to those of bottom-up NO_X emissions. The top-down inventory indicated that (1) city boundary transport contributes approximately 40% of the NO_X concentration in BJ; (2) in W_2019, NO_X emissions and transport in BJ decreased by 20.4% and 17.2%, respectively, compared to those of W_2018; (3) in W_2019, NO_X influx substantially decreased (−699 g/s) in BJ compared to that of W_2018 despite negative meteorological conditions that should have increased NOx influx by +503 g/s. Overall, the contribution of intercity input to NOx in BJ has declined with decreasing emissions in the surrounding cities due to regional cooperative control measures, and the role of local emissions in BJ NOx levels was more prominent. Our findings indicate that local emissions may play vital roles in regional center city air quality.

Since its launch on 13 October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) mission has been measuring the solar radiation backscattered by Earth’s atmosphere and surface. In this study, we evaluate the TROPOMI operational methane (CH₄) and carbon monoxide (CO) products’ performance results covering about 3 years using the only two global Total Carbon Column Observing Network (TCCON) sites in China, i.e., the Hefei site and the Xianghe site. These two sites have recently joined the TCCON, and this study uses the both sites simultaneously to validate the TROPOMI products over China for the first time. We found that the systematic bias with rescaling between the TROPOMI CO products and the Hefei site is on average 1.78 ± 6.35 ppb or 1.18 ± 5.35%. The systematic bias with rescaling between the TROPOMI CO products and the Xianghe site is on average 5.33 ± 14.24 ppb or 3.85 ± 10.30%. Both the stations show a correlation above 0.9. The TROPOMI CO data are systematically higher than the two TCCON sites measurements in China. We found that the systematic bias with rescaling between the TROPOMI CH₄ products and the Hefei site is on average −4.13 ± 11.65 ppb or −0.22 ± 0.62%. The systematic bias between the TROPOMI CH₄ products and Xianghe site is on average −7.25 ± 10.72 ppb or −0.39 ± 0.57%. Both the stations show a correlation above 0.9. The TROPOMI CH₄ data are systematically lower than the two TCCON sites measurements in China. We found that the bias between the TROPOMI and the two sites’ data as a function of the coincident radius around the two sites is mostly affected by localized emissions for both CO and CH₄. We also observe a CO decreasing trend and a CH₄ increasing trend in the year-to-year relative changes from 2019 to 2021. Validating against reference from Hefei and Xianghe TCCON site demonstrates the high quality of TROPOMI CO and CH₄ data over China.

Long-term, high-resolution monitoring of carbon monoxide (CO) and methane (CH₄) is essential for understanding their spatiotemporal variability and guiding climate mitigation strategies. However, satellite observations like TROPOMI are often incomplete, and existing fusion methods have limitations in accuracy and continuity. This study proposes a signal-domain fusion approach combining 3D discrete cosine transform (DCT) and singular value decomposition (SVD) to integrate TROPOMI data with GEOS-Chem simulations. A lightweight residual U-Net is employed to refine the initial reconstruction by learning the residual field using meteorological drivers and model outputs, guided by a masked loss. The method produces global 0.25° and China-specific 0.05° daily gap-free XCO and XCH₄ datasets from 2019 to 2023. The fused results outperform GEOS-Chem and are comparable or superior to TROPOMI, with R² values of 0.92 for XCO and 0.85 for XCH₄. Trend analysis reveals regional patterns such as XCO increases in North America and declines in Eastern China, and widespread CH₄ growth. High-resolution data captures enhancements during the 2022 Chongqing wildfires, with average increases of 17.1 ppb in XCO and 24.5 ppb in XCH₄, and reveals lower XCH₄ increases over rice-growing areas compared to TROPOMI, with overestimation reduced by 17–26 %, and stronger XCO reductions, with satellite underestimations up to 38 %. These results highlight agricultural contributions and policy impacts. This approach effectively reconstructs missing observations and enhances the utility of satellite–model data for atmospheric research and emission assessments. The generated daily gap-free datasets are publicly available at https://doi.org/10.5281/zenodo.17936461.

Long-term, high-resolution monitoring of carbon monoxide (CO) and methane (CH ₄ ) is essential for understanding their spatiotemporal variability and supporting climate mitigation strategies. However, satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) are often spatially and temporally incomplete, while existing fusion methods still struggle to achieve both high accuracy and spatiotemporal continuity. Here, we propose a signal-domain fusion approach that combines three-dimensional discrete cosine transform (3D DCT) and singular value decomposition (SVD) to integrate TROPOMI retrievals with GEOS-Chem simulations. A lightweight residual U-Net is further employed to refine the initial reconstruction by learning residual fields from GEOS-Chem simulations and DCT/SVD reconstruction outputs, guided by a masked loss. The method generates global 0.25° and China-specific 0.05° daily gap-free XCO and XCH ₄ datasets from 2019 to 2023. In the time-series comparison analysis at representative sites, the fused datasets generally follow the temporal variations observed by the Total Carbon Column Observing Network (TCCON) and TROPOMI. Missing-rate-threshold experiments further show that the fused products perform comparably to original TROPOMI retrievals under low and moderate missing-rate conditions and show improved performance under sparse TROPOMI coverage (MR >0.5), with R ² values of 0.91 for XCO and 0.83 for XCH ₄ , along with reduced biases and standard deviations. The fused datasets also capture regional XCO increases in parts of North America, decreases over eastern China, and widespread XCH ₄ growth, wildfire-related enhancements in Chongqing in 2022, and short-term variations over rice-growing regions. These results indicate that the proposed framework can reconstruct missing satellite observations with improved continuity and provide useful fused datasets for studying regional variability, event-related enhancements, and atmospheric composition changes. The generated datasets are publicly available at https://doi.org/10.5281/zenodo.22010891 (An et al., 2026).

Long-term, high-resolution monitoring of carbon monoxide (CO) and methane (CH₄) is essential for understanding their spatiotemporal variability and guiding climate mitigation strategies. However, satellite observations like TROPOMI are often incomplete, and existing fusion methods have limitations in accuracy and continuity. This study proposes a signal-domain fusion approach combining 3D discrete cosine transform (DCT) and singular value decomposition (SVD) to integrate TROPOMI data with GEOS-Chem simulations. A lightweight residual U-Net is employed to refine the initial reconstruction by learning the residual field using meteorological drivers and model outputs, guided by a masked loss. The method produces global 0.25° and China-specific 0.05° daily gap-free XCO and XCH₄ datasets from 2019 to 2023. The fused results outperform GEOS-Chem and are comparable or superior to TROPOMI, with R² values of 0.92 for XCO and 0.85 for XCH₄. Trend analysis reveals regional patterns such as XCO increases in North America and declines in Eastern China, and widespread CH₄ growth. High-resolution data captures enhancements during the 2022 Chongqing wildfires, with average increases of 17.1 ppb in XCO and 24.5 ppb in XCH₄, and reveals lower XCH₄ increases over rice-growing areas compared to TROPOMI, with overestimation reduced by 17–26 %, and stronger XCO reductions, with satellite underestimations up to 38 %. These results highlight agricultural contributions and policy impacts. This approach effectively reconstructs missing observations and enhances the utility of satellite–model data for atmospheric research and emission assessments. The generated daily gap-free datasets are publicly available at https://doi.org/10.5281/zenodo.17936461.