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Coal-fired power plants are a major carbon source in China. In order to assess the evaluation of China’s carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon flux intensity from coal-fired power plants. Previous studies have calculated CO2emissions from point sources based on Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3) satellite measurements, but the factors affecting CO2flux estimations are uncertain. In this study, we employ a Gaussian Plume Model to estimate CO2emissions from three power plants in China based on OCO-3 XCO2measurements. Moreover, flux uncertainties resulting from wind information, background values, satellite CO2measurements, and atmospheric stability are discussed. This study highlights the CO2flux uncertainty derived from the satellite measurements. Finally, satellite-based CO2emission estimates are compared to bottom-up inventories. The satellite-based CO2emission estimates at the Tuoketuo and Nongliushi power plants are ∼30 and ∼10 kt d−1smaller than the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) respectively, but ∼10 kt d−1larger than the ODIAC at Baotou.

Atmospheric ammonia (NH3) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH3concentration based on the absorption lines of NH3in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH3column from the Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Chinese FengYun (FY)-3D satellite and present the first atmospheric NH3column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH3hotspots around the world, e.g., India, West Africa, and East China, where large NH3emissions exist. The HIRAS NH3columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer (IASI) measurements, and we find that the two instruments observe a consistent NH3global distribution, with correlation coefficient (R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH3retrieval are discussed.

Atmospheric carbon dioxide (CO2) is the primary anthropogenic driver of climate change, accounting for more than half of the total effective radiative forcing (ERF). Active remote-sensing technique using differential absorption light detection and ranging (LiDAR) (DIAL) is recognized as the most promising remote sensing means for atmospheric CO2 measurements. The aerosol and carbon detection LiDAR (ACDL) instrument onboard the DaQi-1 (DQ-1) is dedicated to quantifying the global spatial distribution of atmospheric CO2. To meet the requirement of accuracy and precision, a reasonable averaging scheme for ACDL measurements is needed to minimize the effect of random noise of observations on CO2 retrievals. In this study, three averaging schemes were conducted in the retrieval process: averaging of CO2 columns (AVX), averaging of differential absorption optical depth (AVD), and averaging of signals (AVS). The performances were compared at three representative sites. The experiments were first carried out on simulations. The results show that the optimal size of the averaging window is 50 km, corresponding to an averaging of measurements over 150 pulse pairs. In addition, the AVX and AVD schemes are less affected by altitude variations and can be applied to surfaces with moderate and severe topographic variation, such as hills and mountains, whereas the AVS method is more suitable for surfaces with slight topographic variation, such as oceans, plains, and terraces. Furthermore, the ACDL observations were also retrieved by applying three averaging schemes and validated against ground-based Total Carbon Column Observing Network (TCCON) measurements at the Xianghe station. The AVS scheme exhibits better performance than the AVX and AVD methods with the lowest biases of less than 0.5 ppm, which is consistent with the simulation results.

Measurements of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) are of great importance in the Qinghai-Tibetan region, as it is the highest and largest plateau in the world affecting global weather and climate systems. In this study, for the first time, we present CO2, CH4, and CO column measurements carried out by a Bruker EM27/SUN Fourier-transform infrared spectrometer (FTIR) at Golmud (36.42°E, 94.91°N, 2808 m) in August 2021. The mean and standard deviation of the column-average dry-air mixing ratio of CO2, CH4, and CO (XCO2, XCH4, and XCO) are 409.3 ± 0.4 ppm, 1905.5 ± 19.4 ppb, and 103.1 ± 7.7 ppb, respectively. The differences between the FTIR co-located TROPOMI/S5P satellite measurements at Golmud are 0.68 ± 0.64% (13.1 ± 12.2 ppb) for XCH4and 9.81 ± 3.48% (−10.7 ± 3.8 ppb) for XCO, which are within their retrieval uncertainties. High correlations for both XCH4and XCO are observed between the FTIR and S5P satellite measurements. Using the FLEXPART model and satellite measurements, we find that enhanced CH4and CO columns in Golmud are affected by anthropogenic emissions transported from North India. This study provides an insight into the variations of the CO2, CH4, and CO columns in the Qinghai-Tibetan Plateau.

Despite the importance of understanding the urban emission characteristics of greenhouse gases (GHGs) and air pollutants, few studies have conducted integrated assessments across diverse urban environments. Herein, we conducted a comprehensive evaluation of the emission characteristics of GHGs and air pollutants in seven cities in the Northern Hemisphere using ground‐based Fourier transform spectrometers. Our analysis primarily focused on emission ratios of excess column‐averaged dry‐air mole fractions of carbon monoxide (CO) to carbon dioxide (CO2) (∆XCO:∆XCO2) and those of methane (CH4) to CO2(∆XCH4:∆XCO2). We found that the emission ratios varied significantly across cities. Xianghe (China) and Pasadena (USA), known for severe air pollution, showed the highest emission ratios. Notably, Seoul (South Korea) showed lower ∆XCO:∆XCO2(3.32 ± 0.10 ppb/ppm) but relatively higher ∆XCH4:∆XCO2(4.85 ± 0.04 ppb/ppm), which was comparable to the ∆XCH4:∆XCO2value of Xianghe (5.15 ± 0.10 ppb/ppm), suggesting that targeted CH4reduction strategies may be required for climate change mitigation in Seoul. Many cities experience significant greenhouse gas and air pollutant emissions. We comprehensively analyzed the characteristics of these emissions using ground‐based solar‐viewing Fourier transform spectrometers (FTSs) in Northern Hemisphere cities including Seoul (South Korea), Thessaloniki (Greece), Tsukuba (Japan), Karlsruhe (Germany), Xianghe (China), Pasadena (USA), and Paris (France). First, we focused on variations in carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO). Based on these variations, their emission ratios were estimated in terms of excess column‐averaged dry‐air mole fractions of CO to CO2(∆XCO:∆XCO2) and CH4to CO2(∆XCH4:∆XCO2) in urban sites. We found that the emission ratios differed across cities. For example, Xianghe and Pasadena, which generally exhibit high levels of air pollution, had the highest emission ratios. However, Seoul had lower emission ratios of ∆XCO:∆XCO2, but higher levels of ∆XCH4:∆XCO2. This study suggests that ground‐based Fourier transform spectrometer measurements can be used as a tool for comprehensively evaluating the emission characteristics of greenhouse gases and air pollutants in urban environments. Furthermore, extending our study to longer periods and more sites could further aid in formulating effective mitigation strategies for climate change. We comprehensively assessed the emission characteristics of greenhouse gases and air pollutants in seven cities in the Northern HemisphereGround‐based Fourier transform spectrometers offers a comprehensive analysis of the urban emission characteristics of greenhouse gases and air pollutantsOur results can further support effective strategies for mitigating climate change We comprehensively assessed the emission characteristics of greenhouse gases and air pollutants in seven cities in the Northern Hemisphere Ground‐based Fourier transform spectrometers offers a comprehensive analysis of the urban emission characteristics of greenhouse gases and air pollutants Our results can further support effective strategies for mitigating climate change

Quantifying atmospheric CO2over long periods from space is crucial in understanding the carbon cycle's response to climate change. However, a single satellite offers limited spatiotemporal coverage, making comprehensive monitoring challenging. Moreover, biases among various satellite retrievals hinder their direct integration. This study proposed a machine learning framework for fusing the column‐averaged dry‐air mole fraction of CO2(XCO2) retrievals from Greenhouse Gases Observing Satellite (GOSAT) and OCO‐2 satellites. The best model (R2= 0.85) presented improved consistency of GOSAT retrievals by reducing 71.5% of the average monthly bias while using OCO‐2 retrievals as a benchmark, indicating the fusion data set's potential to enhance observation coverage. Incorporating the adjusted GOSAT XCO2retrievals into the OCO‐2 data set added an average of 84.7 thousand observations annually, enhancing the yearly temporal coverage by 53.6% (from 14 to 21.5 days per grid). This method can be adapted to other satellites, maximizing satellite resources for a more robust carbon flux inversion. CO2sources and sinks are primarily regulated by anthropogenic emissions, photosynthesis and respiration on land and in the ocean, as well as by physical dissolution and carbonate chemistry with ocean circulation. The consistent long‐term quantification of atmospheric CO2concentrations using satellite observations plays a pivotal role in understanding the response of global and regional carbon cycles to climate change. However, satellites have a revisit period, and factors like cloud and aerosol scattering impact the quality and quantity of their observations. A single satellite currently falls short of the demand to monitor global carbon sources and sinks, necessitating the integration of observations from various satellites to conduct carbon flux inversions. Different satellites come with distinct sampling patterns, instrument parameters, and retrieval algorithms, which leads to biases in their retrieval products. Our study, focusing on OCO‐2 and GOSAT, employs machine learning models to improve consistency between retrievals derived from these two satellites, thus generating a harmonized data set. The bias‐corrected GOSAT XCO2retrievals exhibit high spatiotemporal consistency with OCO‐2 XCO2retrievals, immensely enhancing the observational constraints for carbon flux inversions. This method holds promise for application to recently launched and future satellites, aiming to offer carbon flux inversions with amplified spatiotemporal observational constraints. This study employs machine learning (ML) models to enhance XCO2consistency between OCO‐2 and Greenhouse Gases Observing Satellite (GOSAT), reducing monthly inconsistency by 71.5%Integrating the OCO‐2 data set with GOSAT retrievals increased yearly observations by 56.2%Fusing satellite data through ML models can pave the way for improved carbon flux inversions in the future This study employs machine learning (ML) models to enhance XCO2consistency between OCO‐2 and Greenhouse Gases Observing Satellite (GOSAT), reducing monthly inconsistency by 71.5% Integrating the OCO‐2 data set with GOSAT retrievals increased yearly observations by 56.2% Fusing satellite data through ML models can pave the way for improved carbon flux inversions in the future

Methane (CH4) is one of the most important greenhouse gases in the atmosphere, making it worthwhile to study its temporal and vertical distributions in source areas, e.g., North China. For this purpose, a ground-based high-resolution Fourier transform infrared spectrometer (FTIR), the Bruker IFS 125HR, along with an in-situ instrument, the Picarro G2301, were deployed in Xianghe County (39.8°N,117.0°E), Hebei Province, China. Data have been recorded since June 2018. For the FTIR measurements, we used two observation modes to retrieve the mole fraction of CH4: the Total Carbon Column Observing Network (TCCON) method (retrieval algorithm: GGG2014), and the Network for the Detection of Atmospheric Composition Change (NDACC) method (retrieval algorithm: SFIT4). Combining FTIR with in-situ measurements, we found the temporal and vertical distributions of atmospheric CH4within three vertical layers (near the ground, in the troposphere, and in the stratosphere), and throughout the whole atmosphere. Regarding the diurnal variation of CH4near the ground, the concentration at night was higher than during the daytime. Regarding the seasonal variation, CH4was low in spring and high in summer, for all three vertical layers. In addition, there was a peak of CH4in winter near the ground, both in the troposphere and the whole atmosphere. We found that variation of CH4in the tropospheric column was close to that of the in-situ measurements near the ground. Furthermore, the variations of CH4in the stratospheric column could be influenced by vertical motions, since it was higher in summer and lower in winter.

ABSTRACTHigh-accuracy continuous measurements of atmospheric concentrations of CO2were made from May 2016 to December 2017 using the Picarro G2301 analyzer at Xinglong station (40°24′N, 117°30′E, 940 MSL), 150 km northeast of Beijing. The near-ground CO2measurements were calibrated by standards based on WMO procedures. The regional background measurements were filtered by the robust extraction of baseline signal method to study the seasonal and diurnal cycles. The regional background CO2concentrations were low in summer. The maximum difference between the local sources and regional background CO2concentrations occurred in summer and autumn, indicating a strong influence from local sources. Cluster analysis and potential source contribution function analysis showed that the long-distance transport of anthropogenic emissions in the Beijing–Tianjin–Hebei metropolitan area influenced the CO2concentrations in Xinglong, especially in summer. The diurnal variation of CO2was mainly influenced by the various vertical transport conditions of the tropospheric atmosphere in a day.

Based on the optimal estimation method, a satellite XCO2retrieval algorithm was constructed by combining LBLRTM with VLIDORT. One-year GOSAT/TANSO observations over four TCCON stations were tested by our algorithm, and retrieval results were compared with GOSAT L2B products and ground-based FTS measurements. Meanwhile, the influence of CO2line mixing effect on retrieval was estimated, and the research showed that neglecting CO2line mixing effect could result in approximately 0.25% XCO2underestimation. The accuracy of XCO2retrievals was similar to GOSAT L2B products at cloud-free footprints with aerosol optical depth less than 0.3, and 1% accuracy of XCO2retrievals can be reached based on the validation result with TCCON measurements.