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.

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.

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.