To cope with global climate change and monitor global CO ₂ concentration distribution, the first Chinese carbon dioxide satellite (TanSat) has been successfully launched in December 2016. In this study, we implemented a CO ₂ retrieval scheme by calibrating the TanSat sun-glint (GL) mode spectra and adapting the Iterative Maximum $A$ Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm for CO ₂ spectral retrieval. The global terrestrial CO ₂ total vertical column density (VCD) and column-averaged dry-air mole fractions of CO ₂ ( $\text{X}_{\text {CO2}}$ ) were simultaneously retrieved from TanSat GL spectral observations. Then, a comprehensive verification was performed between TanSat CO ₂ retrieval and other measurements including Total Carbon Column Observing Network (TCCON), the Japanese Greenhouse gases Observing SATellite (GOSAT), and the US Orbiting Carbon Observatory-2 (OCO-2). Further comparisons between our TanSat CO ₂ retrieval and ground-based FTIR measurements from TCCON indicated a good correlation with the mean bias of −0.78 ppm, the standard deviation at 1.75 ppm, and the Pearson correlation coefficient of 0.81. In addition, cross-satellite CO ₂ validations of TanSat with GOSAT and OCO-2 showed consistently spatiotemporal trends for both CO ₂ VCD and $\text{X}_{\text {CO2}}$ . In summary, we can conclude that the presented CO ₂ retrieval scheme has achieved global CO ₂ retrieval from TanSat GL mode spectra with high precision and accuracy, as suggested by the results of independent ground-based and satellite validations.

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.