We estimated CO₂ emissions from the Beijing region of China using differential ground-based observations of the column-averaged dry air mole fractions of CO₂ ( Δ X C O 2 , obs ; urban minus upwind background column observations) in winter (1 November 2020–1 March 2021). Beijing is one of the most world’s populated cities and the CO₂ emissions from this region contain large uncertainties in different bottom-up anthropogenic inventories. Differential measurements are potentially able to capture urban signals over Beijing (3.46 ± 2.35 ppm). The simulated XCO₂ enhancements were calculated ( Δ X C O 2 , sim ) based on three emission inventories (the Open-source Data Inventory for Anthropogenic CO₂ (ODIAC), Multiresolution Emission Inventory for China (MEIC) and Emissions Database for Global Atmospheric Research (EDGAR) datasets) for Beijing. The Δ X C O 2 , sim values based on the ODIAC dataset were much higher than the observations, whereas the values from the EDGAR dataset were much lower and the MEIC dataset was more consistent. We performed a Lagrangian inversion framework based on Bayesian theory. The average and uncertainty of a priori estimates (12.18 ± 8.0, 7.09 ± 7.5 and 3.53 ± 11.4 μmol (m² s)⁻¹) were optimized to the posterior emissions (9.44 ± 5.7, 7.13 ± 4.9 and 7.15 ± 5.7 μmol (m² s)⁻¹), suggesting that the three posterior estimates tended to converge to become more consistent, transport errors (especially the horizontal transport errors) and the spatially uneven corrections in Beijing were the main reason for the differences between the posterior estimates. Sensitivity tests suggested that the prescribed spatial and temporal structures affected up to about 12.9%, 4.9% and 20.8%, respectively, of the three inventories.

The increase in atmospheric greenhouse gas concentrations of CO₂ and CH₄, due to human activities, is the main driver of the observed increase in surface temperature by more than 1 °C since the pre-industrial era. At the 2015 United Nations Climate Change Conference held in Paris, most nations agreed to reduce greenhouse gas emissions to limit the increase in global surface temperature to 1.5 °C. Satellite remote sensing of CO₂ and CH₄ is now well established thanks to missions such as NASA’s OCO-2 and the Japanese GOSAT missions, which have allowed us to build a long-term record of atmospheric GHG concentrations from space. They also give us a first glimpse into CO₂ and CH₄ enhancements related to anthropogenic emission, which helps to pave the way towards the future missions aimed at a Monitoring & Verification Support (MVS) capacity for the global stock take of the Paris agreement. China plays an important role for the global carbon budget as the largest source of anthropogenic carbon emissions but also as a region of increased carbon sequestration as a result of several reforestation projects. Over the last 10 years, a series of projects on mitigation of carbon emission has been started in China, including the development of the first Chinese greenhouse gas monitoring satellite mission, TanSat, which was successfully launched on 22 December 2016. Here, we summarise the results of a collaborative project between European and Chinese teams under the framework of the Dragon-4 programme of ESA and the Ministry of Science and Technology (MOST) to characterize and evaluate the datasets from the TanSat mission by retrieval intercomparisons and ground-based validation and to apply model comparisons and surface flux inversion methods to TanSat and other CO₂ missions, with a focus on China.

Monitoring the atmospheric CO${}_2$ columns inside and around a city is of great importance to understand the temporal–spatial variation of XCO${}_2$ near strong anthropogenic emissions. In this study, we use two FTIR CO${}_2$ column measurements in Beijing (Bruker EM27/SUN) and Xianghe (Bruker IFS 125HR) between 2019 and 2021 to investigate the differences of XCO${}_2$ between Beijing (urban) and Xianghe (suburb) in North China and to validate the OCO-2 and OCO-3 satellite XCO${}_2$ retrievals. The mean and standard deviation (std) of the $\Delta$XCO${}_2$ between Beijing and Xianghe (Beijing–Xianghe) observed by two FTIR instruments are 0.206 ± 1.736 ppm, which has a seasonal variation and varies with meteorological conditions (wind speed and wind direction). The mean and std of the XCO${}_2$ differences between co-located satellite and FTIR measurements are −0.216 ± 1.578 ppm in Beijing and −0.343 ± 1.438 ppm in Xianghe for OCO-2 and 0.637 ± 1.594 ppm in Beijing and 1.206 ± 1.420 ppm in Xianghe for OCO-3. It is found that the OCO-3 snapshot area mode (SAM) measurements can capture the spatial gradient of XCO${}_2$ between urban and suburbs well. However, the FTIR measurements indicate that the OCO-3 SAM measurements are about 0.9–1.4 ppm overestimated in Beijing and Xianghe.

The COVID-19 pandemic has led to ongoing reductions in economic activity and anthropogenic emissions. Beijing was particular badly affected by lockdown measures during the early months of the COVID-19 pandemic. It has significantly reduced the CO₂ emission and toxic air pollution (CO and NO₂). We use column-averaged dry-air mole fractions of CO₂ and CO (XCO₂ and XCO) observed by a ground-based EM27/SUN Fourier transform spectrometer (FTS), the tropospheric NO₂ column observed by MAX-DOAS and satellite remote sensing data (GOSAT and TROPOMI) to investigate the variations in anthropogenic CO₂ emission related to COVID-19 lockdown in Beijing. The anomalies describe the spatio-temporal enhancement of gas concentration, which relates to the emission. Anomalies in XCO₂ and XCO, and XNO₂ (ΔXCO₂, ΔXCO, and ΔXNO₂) for ground-based measurements were calculated from the diurnal variability. Highly correlated daily XCO and XCO₂ anomalies derived from FTS time series data provide the ΔXCO to ΔXCO₂ ratio (the correlation slope). The ΔXCO to ΔXCO₂ ratio in Beijing was lower in 2020 (8.2 ppb/ppm) than in 2019 (9.6 ppb/ppm). The ΔXCO to ΔXCO₂ ratio originating from a polluted area was significantly lower in 2020. The reduction in anthropogenic CO₂ emission was estimated to be 14.2% using FTS data. A comparable value reflecting the slowdown in growth of atmospheric CO₂ over the same time period was estimated to be 15% in Beijing from the XCO₂ anomaly from GOSAT, which was derived from the difference between the target area and the background area. The XCO anomaly from TROPOMI is reduced by 8.7% in 2020 compared with 2019, which is much smaller than the reduction in surface air pollution data (17%). Ground-based NO₂ observation provides a 21.6% decline in NO₂. The NO₂ to CO₂ correlation indicates a 38.2% decline in the CO₂ traffic emission sector. Overall, the reduction in anthropogenic CO₂ emission relating to COVID-19 lockdown in Beijing can be detected by the Bruker EM27/SUN Fourier transform spectrometer (FTS) and MAX-DOAS in urban Beijing.

Satellite measurements of urban CO₂ plumes offer a global approach to track CO₂ emissions for large cities. To examine and to quantify the feasibility of space‐based monitoring, an intensive measurement campaign (MERCI‐CO₂) using seven solar‐tracking Fourier transform infrared (FTIR) spectrometers has been conducted over the Mexico City Metropolitan Area (MCMA) to monitor urban emissions and to evaluate Snapshot Area Map (SAM) observations from the NASA's Orbiting Carbon Observatory‐3 (OCO‐3) mission. Once adjusted for their respective averaging kernels, we diagnosed a positive difference between OCO‐3 and FTIR column measurements (1.06 ppm). Thanks to this unprecedented amount of column observations over a large city, we demonstrate that XCO₂ gradients within OCO‐3 SAMs align with the inter‐calibrated FTIR measurements (mean bias of 0.3 ppm), confirming the potential to track CO₂ emissions from space over large metropolitan areas. XCO₂ urban‐rural differences across the FTIR network, show a strong correlation with observed gradients, with Pearson's correlation coefficients (R) around 0.92. The correlation is significantly lower when considering intra‐urban gradients, where R drop to around 0.24. Simulated XCO₂ enhancements (ΔXCO₂) based on X‐STILT for both FTIR and OCO‐3 show relatively high correlations (R is around 0.6) using high‐resolution footprints and two gridded inventories. Spatial correlations with OCO‐3 improve when aggregating satellite retrievals at coarser resolutions (10 km). Our study demonstrates the capabilities of detecting urban gradients by FTIR network and OCO‐3 SAM observations over MCMA, a promising result to evaluate the evolution of MCMA's emissions over the coming decade.

The Mexico City Metropolitan Area (MCMA) stands as one of the most densely populated urban regions globally. To quantify the urban emissions in the MCMA, we independently assimilated observations from a dense column‐integrated Fourier transform infrared (FTIR) network and OCO‐3 Snapshot Area Map observations between October 2020 and May 2021. Applying a computationally efficient analytical Bayesian inversion technique, we inverted for surface fluxes at high spatio‐temporal resolutions (1‐km and 1‐hr). The fossil fuel (FF) emission estimates of 5.08 and 6.77 Gg/hr reported by the global and local emission inventories were optimized to 4.85 and 5.51 Gg/hr based on FTIR observations over this 7 month period, highlighting a convergence of posterior estimates. The modeled biogenic flux estimate of −0.14 Gg/hr was improved to −0.33 to −0.27 Gg/hr, respectively. It is worth noting that utilizing observations from three primary sites significantly enhanced the accuracy of estimates (13.6 29.2%) around the other four. Using FTIR posterior estimates can improve simulation with the OCO‐3 data set. OCO‐3 shows a similar decreasing trend in FF emissions (from 6.37 Gg/hr to 6.36 and 5.04 Gg/hr) as FTIR, but its correction trends for biogenic sources differ, changing from 0.37 to 0.48 Gg/hr. The primary reason is OCO‐3's lower temporal sampling density. Aligning the FTIR inversion timing with that of OCO‐3 yielded comparable corrections for FF emissions, yet discrepancies in biogenic emissions persisted, which can be attributed to their different sampling locations in the rural region and discrepancy in X observations. Our findings mark a significant step toward validating OCO‐3 and FTIR inversion results in metropolitan region.