In the quantification model of emission intensity of emission sources, the estimation of the background concentration of greenhouse gases near an emission source is an important problem. The traditional method of estimating the background concentration of greenhouse gases through statistical information often results in a certain deviation. In order to solve this problem, we propose an adaptive estimation method of CO₂ background concentrations near emission sources in this work, which takes full advantage of robust local regression and a Gaussian mixture model to achieve accurate estimations of greenhouse gas background concentrations. It is proved by experiments that when the measurement error is 0.2 ppm, the background concentration estimation error is only 0.08 mg/m³, and even when the measurement error is 1.2 ppm, the background concentration estimation error is less than 0.4 mg/m³. The CO₂ concentration measurement data all show a good background concentration assessment effect, and the accuracy of top-down carbon emission quantification based on actual measurements should be effectively improved in the future.

The traditional least squares method for the retrieval of CO${}_2$ emissions from CO${}_2$ emission sources is affected by the nonlinear characteristics of the Gaussian plume model, which leads to the optimal estimation of CO${}_2$ emissions easily falling into local minima. In this study, ACA–IPFM (ant colony algorithm and interior point penalty function) is proposed to remedy the shortcomings of the traditional least squares method, which makes full use of the global search property of the ant colony algorithm and the local exact search capability of the interior point penalty function to make the optimal estimation of CO${}_2$ emissions closer to the global optimum. We evaluate the errors of several parameters that are most likely to affect the accuracy of the CO${}_2$ emission retrieval and analyze these errors jointly. These parameters include wind speed measurement error, wind direction measurement error, CO${}_2$ concentration measurement error, and the number of CO${}_2$ concentration measurements. When the wind speed error is less than 20%, the inverse error of CO${}_2$ concentration emission is less than 1% and the uncertainty is less than 3%, when the wind direction error is less than 55 degrees, the inverse error is less than 1% and the uncertainty is less than 3%, when the CO${}_2$ concentration measurement error is less than 10%, the inverse error is less than 1% and the uncertainty is less than 3.3%, and when the measurement quantity is higher than 60, the inverse error is less than 1% and the uncertainty is less than 3%. In addition, we simulate the concentration observations on different paths under the same conditions, and invert the CO${}_2$ emissions based on these simulated values. Through the retrieval results, we evaluate the errors caused by different paths of measurements, and have demonstrated that different paths are affected by different emission sources to different degrees, resulting in different inversion accuracies for different paths under the same conditions in the end, which can provide some reference for the actual measurement route planning of the mobile system. Combined with the characteristics of the agility of the mobile system, ACA–IPFM can extend the monitoring of CO${}_2$ emissions to a wider area.

Methane emission reduction represents a critical pathway for near-term climate mitigation. Super-emitter control is widely recognized as the most cost-effective mitigation strategy; however, the prevalence of these sources varies significantly across countries and sectors, resulting in heterogeneity in abatement difficulty and policy priorities. In this study, we integrate recently emerging satellite-based point-source emission datasets to develop a cross-scale analytical framework that systematically characterizes methane emission rate distributions across countries and sectors. Analysis of the full Carbon Mapper dataset shows that sources exceeding 5000 kg h⁻¹ account for only 3.34% of total point sources, yet contribute more than 25.18% of total equivalent emissions. Gini coefficients range from 0.46 to 0.60 across countries, indicating pronounced inequality in emission distributions and mitigation costs. Integrating these distributional characteristics with economic capacity indicators further shows that countries with highly concentrated, high-intensity point sources—particularly oil- and gas-dominated nations such as Turkmenistan and Uzbekistan—offer the highest cost-effective mitigation potential and should be prioritized as global methane action breakthroughs. Among these, economically advanced countries are positioned to lead by demonstration, while nations with high mitigation potential but limited economic capacity represent optimal targets for international climate finance and technology transfer. These findings provide satellite-derived evidence to inform differentiated, country- and sector-specific mitigation pathways.

High-resolution imaging spectroscopy has enabled the detection and quantification of methane emissions from individual facilities, providing valuable information for identifying large emission sources and evaluating emission inventories. This review synthesizes research on broad-spectrum imaging spectrometers through an end-to-end framework linking spectral characterization, methane enhancement retrieval, plume segmentation and emission-rate estimation. We first review atmospheric radiative transfer and spectral characterization, followed by methane enhancement retrieval methods, including IMAP-DOAS, matched filters, and their variants. We then discuss the identification of methane plumes, with particular attention to false-positive suppression and recent developments in automated plume segmentation. Emission-rate estimation methods, including Gaussian plume inversion, cross-sectional flux, integrated mass enhancement, and machine learning approaches, are compared in terms of their underlying assumptions and sources of uncertainty. We also examine the probability of detection and emission persistence, which are essential for interpreting nondetections and estimating emissions from repeated observations. Applications across oil and gas infrastructure, coal mines, landfills, offshore facilities, and accidental releases demonstrate the capability of imaging spectroscopy to identify major emission sources and reveal their spatial and temporal variability. Continued advances in spectral calibration, methane enhancement retrieval, automated plume identification, and emission-rate estimation, together with coordinated multisensor observations, will further improve the accuracy, consistency, and temporal coverage of facility-scale methane monitoring.

This paper presents an algorithm for the retrieval of nitrous oxide profiles from the Atmospheric InfraRed Sounder (AIRS) on the Earth Observing System (EOS)/Aqua using a nonlinear optimal estimation method. First, an improved Optimal Sensitivity Profile (OSP) algorithm for channel selection is proposed based on the weighting functions and the transmissions of the target gas and interfering gases, with 13 channels selected for inversion in this algorithm. Next, the data of the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) aircraft and the Earth System Research Laboratory (ESRL) are used to verify the retrieval results, including the atmospheric nitrous oxide profile and the column concentration. The results show that using AIRS satellite data, the atmospheric nitrous oxide profile between 300–900 hPa can be well retrieved with an accuracy of ~0.1%, which agrees with the corresponding Jacobian peak interval of selected channels. Analysis of the AIRS retrievals demonstrates that the AIRS measurements provide useful information to capture the spatial and temporal variations in nitrous oxide between 300–900 hPa.