Anthropogenic carbon dioxide (CO₂) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO₂ with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO₂ emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO₂ (XCO₂). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO₂ anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO₂. The model is trained using long-term datasets of XCO₂ and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO₂ values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R² is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R² is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories.

Methane (CH₄) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH₄ concentrations in space and time can help us better to understand the characteristics and driving factors of CH₄ variation as to support the actions of CH₄ emission reduction for preventing the continuous increase of atmospheric CH₄ concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH₄ dataset (Mapping-XCH₄) in 1° grid and three days globally using column averaged dry air mole fraction of CH₄ (XCH₄) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH₄ data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH₄ dataset by comparing with the XCH₄ simulations from the CarbonTracker model and primarily investigating the variations of XCH₄ from April 2009 to April 2020. The results showed that the mean annual increase in XCH₄ was 7.5 ppb/yr derived from Mapping-XCH_4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH₄ is larger in South Asia and eastern China than in the other regions, which agrees with the XCH₄ simulations. The Mapping-XCH₄ shows a significant linear relationship and a correlation coefficient of determination (R²) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH₄ could detect the reduction of XCH₄ in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH₄ dataset has great potential for understanding the spatiotemporal variations of CH₄ concentrations induced by natural processes and anthropogenic emissions at a global and regional scale.

Space-based measurements, such as the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite, provide global observations of the column-averaged CH₄ concentration (XCH₄). Due to the irregular observations and data gaps in the retrievals, studies on the spatial and temporal variations of regional atmospheric CH₄ concentrations are limited. In this paper, we mapped XCH₄ data over monsoon Asia using GOSAT and TROPOMI observations from April 2009 to December 2021 and analyzed the spatial and temporal pattern of atmospheric CH₄ variations and emissions. The results show that atmospheric CH₄ concentrations over monsoon Asia have long-term increases with an annual growth rate of roughly 8.4 ppb. The spatial and temporal trends of XCH₄ data are significantly correlated with anthropogenic CH₄ emissions from the bottom-up emission inventory of EDGAR. The spatial pattern of gridded XCH₄ temporal variations in China presents a basically consistent distribution with the Heihe–Tengchong Line, which is mainly related to the difference in anthropogenic emissions in the eastern and western areas. Using the mapping of XCH₄ data from 2019 to 2021, this study further revealed the response of atmospheric CH₄ concentrations to anthropogenic emissions in different urban agglomerations. For the urban agglomerations, the triangle of Central China (TCC), the Chengdu–Chongqing City Group (CCG), and the Yangtze River Delta (YRD) show higher CH₄ concentrations and emissions than the Beijing–Tianjin–Hebei region and nearby areas (BTH). The results reveal the spatial and temporal distribution of CH4 concentrations and quantify the differences between urban agglomerations, which will support further studies on the drivers of methane emissions.

Anthropogenic CO₂ emissions are one of the primary drivers of the increase in atmospheric CO₂ concentrations. It has been indicated that reducing emitted pollution gases can simultaneously bring out anthropogenic CO₂ reduction, known as the synergistic effects of pollution and carbon reduction for controlling increases in CO₂ and pollution gas concentrations. This study aims to assess these synergistic effects, which are still not clearly understood, by analyzing the mechanisms of atmospheric CO₂ and NO₂ concentration variability in response to human emission reduction activities. We utilize satellite-observed NO₂, which is a short-lived anthropogenic pollution gas with the same emission sources as CO₂, along with CO₂ concentration data to detect their simultaneous response to anthropogenic CO₂ emissions, thereby assessing and comparing the synergistic effects of pollution and carbon reduction in the two study areas of China and the United States, as well as in a special scenario of abrupt reductions in anthropogenic CO₂ emissions. The results show that the synergistic effects of pollution and carbon reduction in the United States are likely better than those in China, as the United States demonstrates a stronger response (R² = 0.53) between atmospheric NO₂ and anthropogenic CO₂ emission compared with China (R² = 0.36). This difference is attributable to the CO₂ emissions from coal-fired power generation in China are much more than those in the United States, where oil and natural gas dominate. Furthermore, the analysis of special scenarios during the COVID-19 pandemic (2020–2022) in China demonstrates that the types of anthropogenic emission sources are the main factors influencing the synergistic effects of pollution and carbon reduction. Specifically, the megacity regions, where fossil fuel power plants and transportation are the main emission sources, presented stronger synergistic effects of pollution and carbon reduction than those regions dominated by coal-based metallurgical and chemical plants.

The spatial and temporal variations in the atmospheric CO₂ concentrations evidently respond to anthropogenic CO₂ emission activities. NO₂, a pollutant gas emitted from fossil fuel combustion, comes from the same emission sources as CO₂. Exploiting the simultaneous emissions characteristics of NO₂ and CO₂, we proposed an XCO₂ prediction approach to reconstruct XCO₂ data based on the data-driven machine learning algorithm using multiple predictors, including satellite observation of atmospheric NO₂, to resolve the issue of data gaps in satellite observation of XCO₂. The prediction model showed good predictive performance in revealing CO₂ concentrations in space and time, with a total deviation of 0.17 ± 1.17 ppm in the cross-validation and 1.03 ± 1.15 ppm compared to ground-based XCO₂ measurements. As a result, the introduction of NO₂ obtained better improvements in the CO₂ concentration responding to the anthropogenic emissions in space. The reconstructed XCO₂ data not only filled the gaps but also enhanced the signals of anthropogenic CO₂ emissions by using NO₂ data, as NO₂ strongly responds to anthropogenic CO₂ emissions (R² = 0.92). Moreover, the predicted XCO₂ data preferred to correct the abnormally low XCO₂ retrievals at satellite observing footprints, where the XCO₂_uncertainity field in the OCO-2 and OCO-3 products indicated a larger uncertainty in the inversion algorithm.