Oceans are widely regarded as major offsets for anthropogenic carbon emissions, leading to an evident lower measured atmospheric CO₂ concentration than expected. It is thus of great significance to develop effective means to monitor CO₂ fluxes over oceans globally. In this work, we utilized observations obtained by an airborne CO₂‐IPDA LIDAR to evaluate the potential of such means in estimating sea‐air CO₂ flux. During a flight experiment in 2019, we have estimated the CO₂ exchange rate, −1.5 ± 0.18 mmol/m²/h, between the Bohai Bay and the atmosphere using equilibrium atmospheric boundary layer theory. These findings indicated that the forthcoming space‐borne CO₂‐IPDA LIDAR is capable of identifying CO₂ uptakes over oceans qualitatively, which would be a novel means and a basis for monitoring CO₂ fluxes over global oceans.

China is likely the world's largest anthropogenic source of methane emissions, with coal mine methane (CMM) being the predominant contributor. Here, we deploy 2 years of satellite observations to survey facility‐level CMM emitters in Shanxi, the most prolific coal mining province in China. A total of 138 detected episodic events at 82 facilities are estimated to emit 1.20 (+0.24/−0.20, 95% CI) million tons of methane per year (Mt CH₄/yr) during 2021–2023, roughly equivalent to 4.2 times the integrated flux from the Permian plumes and four times of the integrated flux from the Four Corners plumes, two of the world's largest hotspots for oil and gas methane emissions. This work reveals the heavy‐tailed distribution characteristic of CMM emission sources for the first time, with 20% of emitters contributing approximately 50% of total emissions. Comparison with the Global Energy Monitor (GEM) inventory reveals that the GEM estimate is about 4.1 times our estimate.

With the rapid growth of greenhouse gas (GHG) monitoring satellites, more and more studies focused on the issue of inversion/optimization of carbon dioxide (CO ₂ ) fluxes using satellite-derived XCO ₂ observations in recent years. A common and critical challenge in this framework is the separation of background and anomalies from XCO ₂ observations, which directly affect the performance of the CO ₂ fluxes’ inversion. We proposed a novel method to accurately extract background XCO ₂ from satellite observations. A series of observing system simulation experiments (OSSEs) were performed to test the performance of the method. We found that the bias and uncertainty of the background concentration are below 0.01 and 0.05 ppm in the given cases, respectively. Based on this method, we selected five overpasses from 2014 to 2016 to demonstrate a regional-scale flux inversion near Riyadh. The comparison with the two previous methods shows that the posterior simulated XCO ₂ by the method proposed in this article can match better with the observed XCO ₂ from Orbiting Carbon Observatory-2 (OCO-2).

Methane is the second most important greenhouse gas after carbon dioxide. The intensity and distribution of methane source/sink in China are unknown. We collected the column-averaged dry air mixing ratio of CH₄ (abbreviated as XCH₄ hereafter) from TROPOMI for the period from 2018 to 2021, to study spatial distribution and temporal change of atmospheric CH₄ concentration, providing clues and foundations for understanding the source/sink in China. It was found that the distribution of XCH₄ is roughly high in the East, low in the West, high in the South and low in the North. Additionally, an evidently positive linear relationship between XCH₄ and population density was witnessed, suggesting anthropogenic emissions may account for a large portion of total methane emissions. XCH₄ exhibits evident seasonal characteristics, with the peak in summer and trough in winter, regardless of the different regions. Moreover, we used XCH₄ anomalies to identify the emission sources and found its great potential in the detection of methane emission from mining plants, landfill, rice fields and even geological fracture zones.

We propose a novel, to the best of our knowledge, active–passive collaborative retrieval method for measuring XCO₂ (APCRM-CO₂). This method simultaneously uses observations from spectrometers and integrated path differential absorption laser detection and ranging (IPDA LIDAR), thus making its products have both the coverage advantages of passive detection and the high information content of active detection. The results of simulation experiments show that APCRM-CO₂ can reduce the retrieval error by 15% to 70%. The minimum SNR for achieving 1 ppm is 24.7 dB. All these results are obtained without considering the LIDAR measured aerosol profile as input. Therefore, greater performance improvements on XCO₂ retrievals are expected in the future for real data processing. The first satellite mission, DQ-2, carrying both a spectrometer and an IPDA LIDAR is planned to be launched by 2025. With the help of APCRM-CO₂, its XCO₂ products would better assist us with understanding the carbon cycle.