Column-averaged dry air mole fraction of atmospheric CO₂ (XCO₂), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO₂ concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO₂ from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO₂. The approach integrated XCO₂ from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO₂ (GM-XCO₂) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO₂ precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R² of 0.97 from cross-validation. GM-XCO₂ showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO₂ or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO₂ product may be also used in different carbon cycle research applications with different precision requirements.

A global mapping data of atmospheric carbon dioxide (CO₂) concentrations can help us to better understand the spatiotemporal variations of CO₂ and the driving factors of the variations to support the actions for emissions reduction and control. Greenhouse gases satellites that measure atmospheric CO₂, such as the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory (OCO-2), have been providing global observations of the column averaged dry-air mole fractions of CO₂ (XCO₂) since 2009. However, these XCO₂ retrievals are irregular in space and time with many gaps. In this paper, we mapped a global spatiotemporally continuous XCO₂ dataset (Mapping-XCO₂) using the XCO₂ retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps. The dataset covers a geographic range from 56° S to 65° N and 169° W to 180° E for a 1° grid interval in space and 3-day time interval. The uncertainties of the mapped XCO₂ values are generally less than 1.5 parts per million (ppm). The spatiotemporal characteristics of global XCO₂ that are revealed by the Mapping-XCO₂ are similar to the model data obtained from CarbonTracker. Compared to the ground observations, the overall standard bias is 1.13 ppm. The results indicate that this long-term Mapping-XCO₂ dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO₂ and can support studies related to the carbon cycle and anthropogenic CO₂ emissions. The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.

The continuing increase in atmospheric CO₂ concentration caused by anthropogenic CO₂ emissions significantly contributes to climate change driven by global warming. Satellite measurements of long-term CO₂ data with global coverage improve our understanding of global carbon cycles. However, the sensitivity of the space-borne measurements to anthropogenic emissions on a regional scale is less explored because of data sparsity in space and time caused by impacts from geophysical factors such as aerosols and clouds. Here, we used global land mapping column averaged dry-air mole fractions of CO₂ (XCO₂) data (Mapping-XCO₂), generated from a spatio-temporal geostatistical method using GOSAT and OCO-2 observations from April 2009 to December 2020, to investigate the responses of XCO₂ to anthropogenic emissions at both global and regional scales. Our results show that the long-term trend of global XCO₂ growth rate from Mapping-XCO₂, which is consistent with that from ground observations, shows interannual variations caused by the El Niño Southern Oscillation (ENSO). The spatial distributions of XCO₂ anomalies, derived from removing background from the Mapping-XCO₂ data, reveal XCO₂ enhancements of about 1.5–3.5 ppm due to anthropogenic emissions and seasonal biomass burning in the wintertime. Furthermore, a clustering analysis applied to seasonal XCO₂ clearly reveals the spatial patterns of atmospheric transport and terrestrial biosphere CO₂ fluxes, which help better understand and analyze regional XCO₂ changes that are associated with atmospheric transport. To quantify regional anomalies of CO₂ emissions, we selected three representative urban agglomerations as our study areas, including the Beijing-Tian-Hebei region (BTH), the Yangtze River Delta urban agglomerations (YRD), and the high-density urban areas in the eastern USA (EUSA). The results show that the XCO₂ anomalies in winter well capture the several-ppm enhancement due to anthropogenic CO₂ emissions. For BTH, YRD, and EUSA, regional positive anomalies of 2.47 ± 0.37 ppm, 2.20 ± 0.36 ppm, and 1.38 ± 0.33 ppm, respectively, can be detected during winter months from 2009 to 2020. These anomalies are slightly higher than model simulations from CarbonTracker-CO₂. In addition, we compared the variations in regional XCO₂ anomalies and NO₂ columns during the lockdown of the COVID-19 pandemic from January to March 2020. Interestingly, the results demonstrate that the variations of XCO₂ anomalies have a positive correlation with the decline of NO₂ columns during this period. These correlations, moreover, are associated with the features of emitting sources. These results suggest that we can use simultaneously observed NO₂, because of its high detectivity and co-emission with CO₂, to assist the analysis and verification of CO₂ emissions in future studies.

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.

The monitoring of anthropogenic CO₂ emissions, which increase the atmospheric CO₂ concentration, plays the most important role in the management of emission reduction and control. With the massive increase in satellite-based observation data related to carbon emissions, a data-driven machine learning method has great prospects for predicting anthropogenic CO₂ emissions. Training samples, which are used to model predictions of anthropogenic CO₂ emissions through machine learning algorithms, play a key role in obtaining accurate predictions for the spatial heterogeneity of anthropogenic CO₂ emissions. We propose an approach for predicting anthropogenic CO₂ emissions using the training datasets derived from the clustering of the atmospheric CO₂ concentration and the segmentation of emissions to resolve the issue of the spatial heterogeneity of anthropogenic CO₂ emissions in machine learning modeling. We assessed machine learning algorithms based on decision trees and gradient boosting (GBDT), including LightGBM, XGBoost, and CatBoost. We used multiple parameters related to anthropogenic CO₂-emitting activities as predictor variables and emission inventory data from 2019 to 2021, and we compared and verified the accuracy and effectiveness of different prediction models based on the different sampling methods of training datasets combined with machine learning algorithms. As a result, the anthropogenic CO₂ emissions predicted by CatBoost modeling from the training dataset derived from the clustering analysis and segmentation method demonstrated optimal prediction accuracy and performance for revealing anthropogenic CO₂ emissions. Based on a machine learning algorithm using observation data, this approach for predicting anthropogenic CO₂ emissions could help us quickly obtain up-to-date information on anthropogenic CO₂ emissions as one of the emission monitoring tools.

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.