Search Results (171)

This study developed a multi-source data assimilation system based on the WRF-Chem model integrated with 3DVAR and EAKF methods. By assimilating a multi-source satellite fused XCO₂ concentration dataset, the system achieved simultaneous optimization of CO₂ concentration fields and emission fluxes over China. During the December 2019 experiment, the system successfully reconstructed high-precision CO₂ concentration fields and dynamically corrected the MEIC inventory through emission error inversion derived from concentration differences before and after assimilation. Comparative analysis with the EDGAR inventory demonstrated the superior performance of the EAKF method, which reduced RMSE by 56% and increased the correlation coefficient to 0.360, while the 3DVAR method achieved a 9% RMSE reduction and improved the correlation coefficient to 0.294. In terms of total emissions, 3DVAR and EAKF increased national emissions by 13.6% and 5.1%, respectively, but reduced emissions in Xinjiang by 3.24 MT and 7.99 MT. A comparison of three simulation scenarios (prior emissions, 3DVAR-optimized, and EAKF-optimized) showed significant improvement over the EGG4 dataset, with systematic bias decreasing by approximately 75% and RMSE reduced by about 49%. The assimilation algorithm developed in this study provides a reliable methodological support for regional carbon monitoring and can be extended to multi-pollutant emissions and high-resolution satellite data integration. Full article

Atmospheric carbon dioxide in mole fraction (XCO₂) is one of the key parameters in estimating CO₂ fluxes at the air–sea interface. Satellite-derived column-averaged XCO₂ has been widely used in the estimates of air–sea CO₂ fluxes, yet the uncertainties induced by using column-averaged XCO₂ instead of atmospheric XCO₂ in the ocean boundary layer have been generally unknown. In this study, based on an extensive dataset of atmospheric XCO₂ measured in the ocean boundary layer from global ocean mooring arrays (N = 945,243) and historical cruises (N = 170,000) between 2002 and 2024, for the first time, we quantitatively evaluated the performance of four satellites, including the Greenhouse gases Observing SATellite (GOSAT and GOSAT-2), the Orbiting Carbon Observatory-2 (OCO-2), and the Atmospheric InfraRed Sounder (AIRS), in monitoring the atmospheric XCO₂ over oceanic regions. The atmospheric XCO₂ has been increasing from 375 ppm in 2002 to 417 ppm in 2024 based on the longest data record from AIRS. We found that the column-averaged atmospheric XCO₂ can serve as a good proxy for atmospheric XCO₂ in the ocean boundary layer, with associated uncertainties of 2.48 ppm (0.46%) for GOSAT, 1.01 ppm (0.24%) for GOSAT-2, 2.45 ppm (0.45%) for OCO-2, and 4.22 ppm (0.83%) for AIRS. We also investigated the consistency of these satellites in monitoring the growth rates of atmospheric XCO₂ in the global ocean basins. Based on the longest data record from AIRS, the atmospheric XCO₂ has been increasing at a rate of 1.87–1.97 ppm year⁻¹ over oceanic regions in the past two decades. These findings contribute to improving the reliability of satellite-derived column-averaged XCO₂ observations in the estimates of air–sea CO₂ fluxes and support future efforts in monitoring ocean carbon dynamics through satellite remote sensing. Full article

Accurate simulation and assimilation of CO₂ concentrations are of great significance for global carbon cycle research, carbon emission monitoring, and climate policy formulation. In this study, we conducted simulation and assimilation of CO₂ concentrations over central, eastern, and southern China from March to August 2020 using the WRF-Chem model (Weather Research and Forecasting model coupled with Chemistry) coupled with the Ensemble Adjustment Kalman Filter (EAKF) assimilation method. We designed four progressive experiments (CTRL, MET_DA, CO₂_DA, and FULL_DA) to evaluate the impacts of assimilating meteorological observations and multi-satellite fused XCO₂ concentrations on CO₂ simulation performance. Compared to the CTRL simulation, the MET_DA experiment shows that the correlation coefficients (R) for meteorological elements, including wind speed, temperature, and relative humidity, improved by approximately 9.68%, 2.03%, and 16.05%, respectively. The CO₂_DA experiment showed improved accuracy in CO₂ concentration simulation. The validation against WDCGG (World Data Centre for Greenhouse Gases) and TCCON (Total Carbon Column Observing Network) observations demonstrated that R increased to 0.970 and 0.830, respectively, with corresponding RMSEs reduced to 2.598 ppm and 2.042 ppm. Building upon the improvements of CO₂_DA, the FULL_DA experiment achieved greater accuracy, with R reaching 0.972 and 0.875, and RMSE reduced to 2.309 ppm and 1.693 ppm, respectively. In addition, the bias was lowered by 46.74% and 77.58%. The results show that assimilation of both meteorological and multi-source fused XCO₂ datasets achieves optimal performance in enhancing the accuracy of CO₂ concentration simulations. This study employs an hourly, multi-source fused CO₂ dataset that features an increased number of observations and greater spatial coverage. By assimilating this dataset, we achieve more accurate simulations of CO₂ concentrations, thereby providing reliable support for carbon monitoring and emission estimation. Full article

Cities are major sources of anthropogenic carbon dioxide (CO₂) emissions, making the study of intra-urban CO₂ concentration patterns an emerging research priority. However, limited data availability and the complexity of urban environments have impeded detailed spatiotemporal analyses at the city scale. To address these challenges, an analysis supported by multi-source data and GeoAI methods is carried out to examine the spatial distribution, vertical variation, temporal dynamics, and driving factors of CO₂ concentrations in urban areas. We combined OCO-2 satellite-derived XCO₂ data (2014–2024) with ground-based measurements from the Shanghai Tower (August 2024 to March 2025), alongside meteorological and socioeconomic variables. The analysis employed spatial interpolation (inverse distance weighting), nonparametric testing (Mann–Whitney U test), time series decomposition, ordinary least squares (OLS) regression, and machine learning techniques including random forest and SHAP (SHapley Additive exPlanations) analysis. Results reveal that CO₂ concentrations are significantly higher in central urban districts compared to suburban areas, with notable spatial heterogeneity. Elevated levels were detected near ports and ferry routes, with airports and industrial emissions identified as principal contributors. Vertically, CO₂ concentrations decline with increasing altitude but exhibit a peak at mid-level heights. Temporally, a pronounced seasonal pattern was observed, characterized by higher concentrations in winter and lower levels in summer. Both OLS regression and machine learning models highlight proximity to emission sources, wind speed, and temperature as key determinants of spatial CO₂ variability, with these factors collectively explaining 67% of the variance in OLS models. This study demonstrates how multi-source data and advanced methods can capture the spatial, vertical, and seasonal dynamics and driving factors of urban CO₂ concentrations, offering insights for policy, planning, and mitigation. Full article

As one of the most important greenhouse gases, carbon dioxide (CO₂) exhibits spatiotemporal variations that directly affect the accuracy of global carbon inventories. In recent years, multiple satellites have successively been deployed for observing the column-averaged CO₂ dry-air mole fraction (XCO₂). However, these satellites perform quite differently, so it is crucial to evaluate their XCO₂ products systematically for both scientific and practical reasons. Most existing studies rely on ground-based observations or the CarbonTracker (CT) model data as reference benchmarks. Nevertheless, because ground-based stations are sparsely distributed and model data are subject to prior errors, biases may be introduced into the evaluation results. In contrast, the Three-Cornered Hat (TCH) method can estimate the relative errors of multi-source data without true values. Based on this, the current study systematically evaluates the XCO₂ products of the four following satellites—Greenhouse Gases Observing Satellite (GOSAT), GOSAT-2, Orbiting Carbon Observatory 2 (OCO-2), and OCO-3—over China by integrating the TCH method, ground-based observations and CarbonTracker model data. The results show that the monthly coverage of the four satellite XCO₂ products in China is limited. In terms of overall performance, the OCO-series outperforms the GOSAT-series, with OCO-3 showing the relatively best performance. Additionally, the TCH method proves to be applicable and reliable for uncertainty analysis of XCO₂ data. This study provides a new perspective for the quality grading and fusion application of multi-source satellite XCO₂ data, and is of great significance for carbon assimilation models. Full article

Understanding the dynamics of terrestrial carbon sources and sinks is crucial for addressing climate change, yet significant uncertainties remain at regional scales. We developed the Monitoring and Evaluation of Greenhouse gAs Flux (MEGA) inversion system with satellite data assimilation and applied it to China using OCO-2 V11.1r XCO₂ retrievals. Our results show that China’s terrestrial ecosystems acted as a carbon sink of 0.28 ± 0.15 PgC yr⁻¹ during 2018–2023, consistent with other inversion estimates. Validation against surface CO₂ flask measurements demonstrated significant improvement, with RMSE and MAE reduced by 30%–46% and 24–44%, respectively. Six sets of prior sensitivity experiments conclusively demonstrated the robustness of MEGA. In addition, this study is the first to systematically compare model-derived and observation-based background fields in satellite data assimilation. Ten sets of background sensitivity experiments revealed that model-based background fields exhibit superior capability in resolving seasonal flux dynamics, though their performance remains contingent on three key factors: (1) initial fields, (2) flux fields, and (3) flux masks (used to control regional flux switches). These findings highlight the potential for further refinement of the atmospheric inversion system. Full article

One of the leading causes of climate change and global warming is the rise in carbon dioxide (${\mathrm{CO}}_2$) levels. For a precise assessment of ${\mathrm{CO}}_2$’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing ${\mathrm{CO}}_2$ sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) ${\mathrm{XCO}}_2$ retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled ${\mathrm{CO}}_2$ concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 ${\mathrm{km}}^2$) ${\mathrm{XCO}}_2$. When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and ${\mathrm{R}}^2$ of 0.90. Full article

Carbon satellites, as the most direct means of observing carbon dioxide globally, offer credible and scientifically robust methods for estimating carbon emissions. To enhance the accuracy and timeliness of urban-scale carbon emission estimates, this study proposes an innovative model that integrates top-down carbon satellite data with high-resolution spatial proxies, including points of interest, road networks, and population distribution. The K-means clustering method was employed to study the relationship between carbon emissions and XCO₂ anomalies. Based on this, the local adaptive carbon emission estimation model was constructed. Further, by integrating the spatial distribution and weights of proxy data, carbon emissions were reallocated to generate a high-resolution urban carbon emission map at a 1 km × 1 km resolution. Taking Urumqi, the XCO₂ background concentration ranged from approximately 408 ppm to 415 ppm in 2020, and the corresponding ${∆\mathrm{XCO}}_2$ ranged from −1.58 ppm to 1.13 ppm. The total carbon emission estimated by the local adaptive model amounted to approximately 58.26718 million tons in 2020, close to the EDGAR dataset, with most monthly relative error within ±10%. The Pearson correlation coefficient between the ODIAC dataset and spatially redistributed carbon emission was 0.192, and their comparison showed that high carbon emission areas in the spatially redistributed carbon emission aligned closely with urban industrial parks and commercial centers, offering a more detailed representation of urban carbon emission spatial characteristics. This method contributed to exploring the potential of carbon satellites for quantitatively measuring anthropogenic emissions and offers improved insights into monitoring urban-scale carbon dioxide emissions. Full article

This study provides the first comprehensive characterization of fluid components in cordierites from both moderate- to high-pressure granulite facies of the Aldan Shield (Sutam and Nimnyr blocks), and granulite–amphibolite facies of the Yenisei Ridge (Kan and Yenisei series of the Angara–Kan complex), Russia, using integrated infrared and Raman spectroscopy coupled with pyrolysis-free gas chromatography–mass spectrometry (GC-MS). Granulite-facies cordierites record CO₂-dominated fluids (X_CO2 = CO₂/(H₂O + CO₂) = 0.74–0.99) with elevated values (X_CO2 = 0.89–0.99) in high-pressure, high-temperature (high-P-T) samples from the Sutam block and Kan series compared to moderate-P-T samples from the Nimnyr block (X_CO2 = 0.74–0.84). Amphibolite-facies cordierites (Yenisei series) show significantly lower CO₂ contents (X_CO2 = 0.51–0.57) and higher H₂O concentrations relative to high-pressure granulites. Critically, we report the first identification in cordierites of at least 12 homologous series of organic compounds and nitrogenated, sulfonated, and halogenated compounds. These results provide new constraints on fluid behavior across metamorphic facies transitions. Full article

Beyond its renowned gemological value, diamond serves as a vital economic mineral and a unique messenger from Earth’s deep interior, preserving invaluable geological information. Since the Mengyin region is the source of China’s greatest diamond deposits, research on the diamonds there not only adds to our understanding of their origins but also offers an essential glimpse into the development of the North China Craton’s mantle lithosphere. In this article, 50 diamond samples from Mengyin were investigated using gemological microscopy, Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, DiamondView™, and X-ray micro-computed tomography (CT) scanning technologies. The types of Mengyin diamonds are mainly Type IaAB, Type IaB, and Type IIa, and the impurity elements are N and H. Inclusions in diamonds serve as direct indicators of mantle-derived components, providing crucial constraints on the pressure–temperature (P–T) conditions during their crystallization. Mengyin diamonds have both eclogite-type and peridotite-type inclusions. It formed at depths ranging from 147 to 176 km, which corresponds to source pressures of approximately 4.45–5.35 GPa, as determined by the Raman shifts of olivine inclusions. The discovery of coesite provides key mineralogical evidence for subduction of an ancient oceanic plate in the source region. The surface morphology of diamonds varies when they are reabsorbed by melts from the mantle, reflecting distinctive features that record subsequent geological events. Distinctive surface features observed on Mengyin diamonds include fusion pits, tile-like etch patterns, and growth steps. Specifically, regular flat-bottomed negative trigons are mainly formed during diamond resorption in kimberlite melts with a low CO₂ (X_CO2 < ~0.5) and high H₂O content. The samples exhibit varying fluorescence under DiamondView™, displaying blue, green, and a combination of blue and green colors. This diversity indicates that the diamonds have undergone a complex process of non-uniform growth. The nitrogen content of the melt composition also varies significantly throughout the different growth stages. The N3 center is responsible for the blue fluorescence, suggesting that it originated in a long-term, hot, high-nitrogen craton, and the varied ring band structure reveals localized, episodic environmental variations. Radiation and medium-temperature annealing produce H3 centers, which depict stagnation throughout the ascent of kimberlite magma and are responsible for the green fluorescence. Full article

Greenhouse gas monitoring satellites provide extensive observational data for the global remote sensing of atmospheric carbon dioxide (CO₂), yet a critical limitation in utilizing these data is the dependence of the full physics retrieval accuracy on a priori CO₂ profiles. This challenge is pronounced due to the significant time delay inherent in data assimilation products of high quality, whose latency prevents their use for retrieval in real time. The resulting temporal mismatch between the a priori constraint and the actual atmospheric state is a primary source of systematic bias in the retrieved CO₂. To address this issue, this paper develops a random forest-based CO₂ profile emulator (RF-CPE) with the core novelty of emulating the high-quality Carbon Tracker CO₂ profiles in real time. By learning the complex relationships between multisource features and the corresponding Carbon Tracker profiles, the emulator generates a dynamic profile specific to each observation. The application of this emulator-based approach to TanSat observations from 2017 to 2018 demonstrates significant performance gains, reducing the mean retrieval bias by 44.11% (from 2.63 ppm to 1.47 ppm) compared to using a static prior. The emulator itself exhibits high performance, with an R² of 0.71 and an RMSE of 2.13 ppm, in agreement with the Carbon Tracker data. Ultimately, this work presents a robust and computationally efficient solution that resolves the conflict between the accuracy and timeliness of a priori constraints, effectively translating the performance of a delayed assimilation system into a real-time retrieval framework to significantly enhance the reliability of satellite CO₂ monitoring. Full article

Cities house more than half of the world’s population and are responsible for more than 70% of the world anthropogenic CO₂ emissions. Therefore, quantifications of emissions from major cities, which are only less than a hundred intense emitting spots across the globe, should allow us to monitor changes in global fossil fuel CO₂ emissions in an independent, objective way. The study adopted a high-spatiotemporal-resolution regional assimilation method using satellite observation data and atmospheric transport model WRF-Chem/DART to assimilate CO₂ concentration and fluxes in Lisbon, a major city in Portugal. It is based on Zhang’s assimilation method, combined OCO-2 XCO₂ retrieval data, ODIAC 1 km anthropogenic CO₂ emissions and Ensemble Adjustment Kalman Filter Assimilation. By employing three two-way nested domains in WRF-Chem, we refined the spatial resolution of the CO₂ concentrations and fluxes over Lisbon to 3 km. The spatiotemporal distribution characteristics and main driving factors of CO₂ concentrations and fluxes in Lisbon and its surrounding cities and countries were analyzed in March 2020, during the period affected by COVID-19 pandemic. The results showed that the monthly average CO₂ and XCO₂ concentrations in Lisbon were 420.66 ppm and 413.88 ppm, respectively, and the total flux was 0.50 Tg CO₂. From a wider perspective, the findings provide a scientific foundation for urban carbon emission management and policy-making. Full article

Balance control in pirouettes has previously been characterized by constraint of the topple angle. However, there is a paucity of research using the margin of stability (MoS) as a dynamic measure of balance related to pirouettes. Therefore, this study aimed primarily to examine the MoS as a metric of balance during a single-turn en dehors pirouette in healthy female amateur ballet dancers. Four participants performed pirouettes until five successful pirouettes were achieved without hopping or loss of balance. Three-dimensional motion capture was used to record the motion trajectories of anatomical markers based on the Plug-in-Gait and Oxford Foot models. Motion synchronized with ground reaction forces was used to calculate the center of pressure (CoP), base of support (BoS), center of the pivot foot, center of mass (CoM), and extrapolated center of mass (XCoM) throughout the turn phase, using laboratory (LCS) and virtual left foot (LFT) coordinate systems. In the LCS and LFT coordinate system, the excursions and patterns of motion of both the CoM and XCoM relative to the CoP were similar, suggesting a neurological relationship. Two different measures of the margin of stability (MoS) in the LFT coordinate system were tabulated: the distance between the (1) XCoM and CoP and (2) XCoM and BoS center. The magnitude of both versions of the MoS was greatest at turn initiation and toe-touch, which was associated with two foot contacts. The MoS values were at a minimum approximately 50% of the stance during the turn phase: close to zero along the anteroposterior (A/P) axis and approximately 50 mm along the mediolateral (M/L) axis. On average, MoS magnitudes were reduced (mean across participants: approximately 20 mm) along the A/P axis, and larger MoS magnitudes (mean across participants: approximately 50 mm) along the M/L axis throughout the turn phase. Although all turns analyzed were completed successfully, the larger MoS values along the M/L axis suggest a fall potential. The variability between trials within a dancer and across participants and trials was documented and showed moderate inter-trial (16% to 51%) and across-participant CV% (range: 10% to 28%), with generally larger variations along the A/P axis. Although our results are preliminary, they suggest that the MoS may be useful for detecting faults in the control of dynamic balance in dehors pirouette performance, as a part of training and rehabilitation following injury. Full article

Understanding the trends and drivers of greenhouse gases (GHGs) is vital to making effective climate mitigation strategies and benefiting human health. In this study, we investigate carbon dioxide (CO₂) trends in the top three emitting states in the U.S. (i.e., Texas, California, and Florida) using column-averaged CO₂ concentrations (XCO₂) from the Greenhouse Gases Observing Satellite (GOSAT) from 2010 to 2022. Annual XCO₂ enhancements are derived by removing regional background values (XCO_{2, enhancement}), and their interannual changes (ΔXCO_{2, enhancement}) are analyzed against key influencing factors, including population, gross domestic product (GDP), nonrenewable and renewable energy consumption, and normalized vegetation difference index (NDVI). Overall, interannual changes in socioeconomic factors, particularly GDP and energy consumption, are more strongly correlated with ΔXCO_{2, enhancement} in Florida. In contrast, NDVI and state-specific environmental policies appear to play a more influential role in shaping XCO₂ trends in California and Texas. These differences underscore the importance of regionally tailored approaches to emissions monitoring and mitigation. Although renewable energy use is increasing, CO₂ trends remain primarily influenced by nonrenewable sources, limiting progress toward atmospheric CO₂ reduction. Full article

Accurate estimation of anthropogenic CO₂ emissions is crucial for effective climate change mitigation policies. This study aims to improve CO₂ emission estimates in China using remote sensing measurements of column-averaged dry air mole fractions of CO₂ (XCO₂) and a neural network approach. We evaluated XCO₂ anomalies derived from three background XCO₂ concentration approaches: CHN (national median), LAT (10-degree latitudinal median), and NE (N-nearest non-emission grids average). We then applied the Generalized Regression Neural Network model, combined with a partition modeling strategy using the K-means clustering algorithm, to estimate CO₂ emissions based on XCO₂ anomalies, net primary productivity, and population data. The results indicate that the NE method either outperformed or was at least comparable to the LAT method, while the CHN method performed the worst. The partition modeling strategy and inclusion of population data effectively improved CO₂ emission estimates. Specifically, increasing the number of partitions from 1 to 30 using the NE method resulted in mean absolute error (MAE) values decreasing from 0.254 to 0.122 gC/m²/day, while incorporating population data led to a decrease in MAE values between 0.036 and 0.269 gC/m²/day for different partitions. The present methods and findings offer critical insights for supporting government policy-making and target-setting. Full article