Search Results (160)

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

The increase in the carbon dioxide (CO₂) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO₂ column concentration (XCO₂), but its global application is limited by the narrow observational swath. To address this, this study effectively integrates XCO₂ data retrieved from the GOSAT and OCO-2 satellites using atmospheric profile adjustment and spatial grid integration techniques. Based on this, a multi-machine learning ensemble algorithm (MLE) was developed, which successfully estimated the spatially continuous XCO₂ concentration in China from 2010 to 2022 (ChinaXCO₂-MLE). The results indicate that, compared to individual satellite observations, the integration of multi-source satellite XCO₂ data significantly improves the spatiotemporal coverage. The overall R² of the MLE model was 0.97, with an RMSE of 0.87 ppmv, outperforming single machine learning models. The ChinaXCO₂-MLE shows good consistency with the observational records from two background stations in China, with R² values of 0.93 and 0.78, and corresponding RMSEs of 1.00 ppmv and 1.32 ppmv. This study also reveals the seasonal and regional variations in China’s XCO₂ concentration: the highest concentration occurs in spring, the lowest concentration occurs in northern regions during summer, and the lowest concentration occurs in southern regions during autumn. From 2010 to 2022, the XCO₂ concentration continued to rise, but the growth rate has slowed due to the implementation of air pollution prevention and energy conservation policies. The spatially continuous XCO₂ data provide a more comprehensive understanding of carbon variation and offer a valuable reference for achieving China’s carbon neutrality goals. Full article

The Mongolian Plateau plays a crucial role in global carbon cycling, but the spatiotemporal characteristics of XCO₂ concentration and its driving mechanism remain insufficiently explored. To solve this scientific issue, the synergistic methodology of mathematical statistics—the Pearson correlation and random forest model—was established using the main source of Orbiting Carbon Observatory 2 (OCO-2) satellite data. Results indicate the following: (1) Average XCO₂ concentration of the Mongolian Plateau was 412 ppm, with an annual growth rate of 2.29 ppm/a from 2018 to 2022, along with higher values in the south and lower values in the north. The seasonal change displayed a clear temporal feature, in the order of spring (414.83 ppm) > winter (413.4 ppm) > autumn (411.3 ppm) > summer (409.12 ppm). The spatial distributions in spring, autumn, and winter were relatively consistent, all showing higher XCO₂ concentrations in the east and lower concentrations in the west, whereas summer exhibited the opposite pattern. (2) From the perspective of the natural environment, XCO₂ change was negatively correlated with the normalized difference vegetation index (NDVI), precipitation (PRE), and temperature (TEMP). Temporal analysis further revealed that this negative correlation was most pronounced in the eastern region, in which these three elements were all relatively high. (3) According to the random forest model, the influence of both single and interactive factors on the plateau’s XCO₂ varied significantly. A comparison of driving factors revealed that the NDVI had the highest contribution rate (0.35), followed by fossil fuel combustion emissions (ODIAC), wind direction (WD), and wind speed (WS). As for interaction effects, the combination of NDVI and ODIAC showed the highest contribution rate (over 0.25), indicating a strong joint influence on XCO₂. Other important interactions included WS and WD, ODIAC and WS, and NDVI and WS (all above 0.05). These findings provide valuable insights into the driving mechanisms of XCO₂ on the Mongolian Plateau, offering a reference for regional carbon emission reduction policies. Full article

Climate change poses a global threat, affecting both biodiversity and human populations. To implement efficient mitigating strategies, the consistency and accuracy of our monitoring of greenhouse gases at the local level must be improved. We can achieve this with more advanced monitoring instruments or an enhancement of our processing techniques, which will in turn improve data attributes such as spatial or temporal resolutions and accuracy. This paper presents a daily high spatial resolution XCO₂ dataset aiming to help monitor atmospheric CO₂ concentration on a global scale at a greater level of detail compared with existing datasets. Using a super resolution deep learning model, we increase the resolution of the OCO-2-derived dataset from 0.5° × 0.625° to 0.03° × 0.04° and show that our product maintains the quality of the original dataset while consistently improving the detail of the atmospheric pollution field. We conduct a benchmark that highlights how our dataset outperforms similar products and present a use case of CO₂ monitoring at the regional level. In conclusion, this work provides a complementary approach to the area of global continuous dataset reconstruction and focuses on the adjacent problem of improving specific features of existing datasets. Full article

Global warming caused by the increase in the atmospheric CO₂ content has become a focal environmental issue of common concern to the international community. As a key resource support for achieving the “dual carbon” goals in Western China, Sichuan Province requires a deep analysis of its carbon sources, carbon sinks, and its characteristics in terms of atmospheric environmental capacity, which is of great significance for formulating effective regional sustainable development strategies and responding to global climate change. In view of the unique geographical and climatic conditions in Sichuan Province and the current situation of a low and uneven distribution of atmospheric environmental capacity, this paper uses three forms of multi-source satellite data, OCO-2, OCO-3, and GOSAT, combined with other auxiliary data, to generate a daily XCO₂ concentration dataset with a spatial resolution of a 1km grid in Sichuan Province from 2015 to 2022. Based on the Optuna optimization method with 10-fold cross-validation, the optimal hyperparameter configuration of the four base learners of Stacking, random forest, gradient boosting decision tree, extreme gradient boosting, and the K nearest neighbor algorithm is searched for; finally, the logistic regression algorithm is used as the second-layer meta-learner to effectively improve the prediction accuracy and generalization ability of the Stacking ensemble learning model. According to the comparison of the performance of each model by cross-validation and TCCON site verification, the Stacking model significantly improved in accuracy, with an R², RMSE, and MAE of 0.983, 0.87 ppm and 0.19 ppm, respectively, which is better than those of traditional models such as RF, KNN, XGBoost, and GBRT. The accuracy verification of the atmospheric XCO₂ data estimated by the model based on the observation data of the two TCCON stations in Xianghe and Hefei showed that the correlation coefficients were 0.96 and 0.98, and the MAEs were 0.657 ppm and 0.639 ppm, respectively, further verifying the high accuracy and reliability of the model. At the same time, the fusion of multi-source satellite data significantly improved the spatial coverage of XCO₂ concentration data in Sichuan Province, effectively filling the gap in single satellite observation data. Based on the reconstructed XCO₂ dataset of Sichuan Province, the study revealed that there are significant regional and seasonal differences in the XCO₂ concentrations in the region, showing seasonal variation characteristics of being higher in spring and winter and lower in summer and autumn; in terms of the spatial distribution, the overall spatial distribution characteristics are high in the east and low in the west. This study helps to deepen our understanding of the carbon cycle and climate change, and can provide a scientific basis and risk assessment methods for policy formulation, effect evaluation, and international cooperation. Full article

The accurate quantification of anthropogenic carbon dioxide (CO₂) emissions in urban areas is hindered by high uncertainties in emission inventories. We assessed the spatial distributions of three anthropogenic CO₂ emission inventories in Shanghai, China—MEIC (0.25° × 0.25°), ODIAC (1 km × 1 km), and a local inventory (LOCAL) (4 km × 4 km)—and compared simulated CO₂ column concentrations (XCO₂) from WRF-CMAQ against OCO-3 satellite Snapshot Mode XCO₂ observations. Emissions differ by up to a factor of 2.6 among the inventories. ODIAC shows the highest emissions, particularly in densely populated areas, reaching 4.6 and 8.5 times for MEIC and LOCAL in the central area, respectively. Emission hotspots of ODIAC and MEIC are the city center, while those of LOCAL are point sources. Overall, by comparing the simulated XCO₂ values driven by three emission inventories and the WRF-CMAQ model with OCO-3 satellite XCO₂ observations, LOCAL demonstrates the highest accuracy with slight underestimation, whereas ODIAC overestimates the most. Regionally, ODIAC performs better in densely populated areas but overestimates by around 0.22 kt/d/km² in relatively sparsely populated districts. LOCAL underestimates by 0.39 kt/d/km² in the center area but is relatively accurate near point sources. Moreover, MEIC’s coarse resolution causes substantial regional errors. These findings provide critical insights into spatial variability and precision errors in emission inventories, which are essential for improving urban carbon inversion. Full article

Accurately assessing the impact of anthropogenic carbon dioxide (CO₂) emissions on CO₂ concentrations is essential for understanding regional climate change, particularly in high-emission countries like China. This study employed the GEOS-Chem chemical transport model to simulate and compare the spatiotemporal distributions of XCO₂ of three anthropogenic CO₂ emission inventories in mainland China for the 2018–2020 period and analyzed the effects of emission variations on atmospheric CO₂ concentrations. In eastern China, particularly in the Yangtze River Delta (YRD) and Beijing-Tianjin-Hebei (BTH) regions, column-averaged dry air mole fractions of CO₂ (XCO₂) can exceed 420 ppm during peak periods, with emissions from these areas contributing significantly to the national total. The simulation results were validated by comparing them with OCO-2 satellite observations and ground-based monitoring data, showing that more than 70% of the monitoring stations exhibited a correlation coefficient greater than 0.7 between simulated and observed data. The average bias relative to satellite observations was less than 1 ppm, with the Emissions Database for Global Atmospheric Research (EDGAR) showing the highest degree of agreement with both satellite and ground-based observations. During the study period, anthropogenic CO₂ emissions resulted in an increase in XCO₂ exceeding 10 ppm, particularly in the North China Plain and the YRD. In scenarios where emissions from either the BTH or YRD regions were reduced by 50%, a corresponding decrease of 1 ppm in XCO₂ was observed in the study area and its surrounding regions. These findings underscore the critical role of emission control policies in mitigating the rise in atmospheric CO₂ concentrations in densely populated and industrialized areas. This research elucidates the impacts of variations in anthropogenic emissions on the spatiotemporal distribution of atmospheric CO₂ and emphasizes the need for improved accuracy of CO₂ emission inventories. Full article

As carbon dioxide (CO₂) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO₂ retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO₂ retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO₂ column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method. Full article

The COllaborative Carbon Column Observing Network has become a reliable source of high-quality ground-based remote sensing network data that provide column-averaged dry-air mole fractions of carbon dioxide (XCO₂), methane (XCH₄), and carbon monoxide (XCO). The fiducial reference measurements of these gases from the COCCON complement the TCCON and NDACC-IRWG data. This study shows the application of COCCON data for the validation of existing greenhouse gas satellite products. This study includes the validation of XCH₄ and XCO products from the European Copernicus Sentinel-5 Precursor (S5P) mission, XCO₂ products from the American Orbiting Carbon Observatory-2 (OCO-2) mission, and XCO₂ and XCH₄ products from the Japanese Greenhouse gases Observing SATellite (GOSAT). A total of 27 datasets contributed to this study; some of these were collected in the framework of campaign activities and covered only a short time period. In addition, several permanent stations provided long-term observations. The random uncertainties in the validation results, specifically for S5P with a lot of coincidences pairs, are found to be similar to the comparison with the TCCON. The comparison results of OCO-2 land nadir and land glint observation modes to the COCCON on a global scale, despite limited coincidences, are very promising. The stations can, therefore, expand on the coverage of the already existing ground-based reference remote sensing sites from the TCCON and the NDACC network. The COCCON data can be used for future satellite and model validation studies and carbon cycle studies. Full article

Satellite XCO₂ retrievals have been widely used in estimating fossil fuel carbon (FFC) emissions at point and urban scales. However, at the regional scale, it remains a significant challenge. Furthermore, current global and regional atmospheric inversions often overlook the uncertainties associated with FFC emissions. To meet the needs of the global carbon stocktake, we developed an inversion method based on Bayesian statistical theory and OCO-2 satellite XCO₂ observations to optimize FFC emissions alongside terrestrial ecosystem carbon fluxes (NEE). The methodology’s core is to distinguish the contributions of NEE and FFC to the observed concentrations using their different spatial distributions. We designed an observing system simulation experiment to invert the 2016 FFC emissions. The results showed that posterior FFC emissions were significantly optimized during the non-growing seasons in the regions with high emissions, with the optimization effect diminishing as emissions shrank. Average FFC emissions uncertainty reductions are in the range of 13–82% in the non-growing season for the eight largest emitting regions globally. By assuming the same uncertainty reduction for FFC emissions in both the growing and non-growing seasons, we can optimize annual emissions for high-emission areas. We believe this study provides a new idea for the inversion of FFC emissions at the regional scale, which is important for achieving the goal of carbon neutrality. Full article

The investigation of the properties of Heusler compounds is an important task that will pave the way for new applications in various fields related to magnetics and thermoelectrics. This study examines the magnetic and thermoelectric properties of Fe₂CoGa Heusler compounds prepared by casting and subsequent annealing. The Fe₂CoGa Heusler compound was found to be ferromagnetic, with a large saturation magnetization of 110 emu/g and a high Curie temperature of 1011 K. The Fe₂CoGa Heusler compound was a good thermoelectric material, with a negative Seebeck coefficient of −44 μV/K, a low electrical resistivity of 0.60 μΩm, and a high-power factor of 3000 μW/mK² at room temperature. The maximum power factor of 3230 μW/mK² for the Fe₂CoGa Heusler compound was obtained at 400 K. In order to improve the magnetic and thermoelectric properties of the Fe₂CoGa Heusler compound, Fe_2-xCo_1+xGa (x = 0–1) Heusler compounds were also prepared by casting and subsequent annealing. In the Fe_2-xCo_1+xGa (x = 0–1) Heusler compounds, the saturation magnetization slightly decreased, but the Curie temperature increased with increasing Co content (x). As regards the thermoelectric properties, the electrical resistivity of the Fe_2-xCo_1+xGa (x = 0.25–1) Heusler compounds was smaller than that of the Fe₂CoGa Heusler compound. The Seebeck coefficient and power factor of the Fe_1.75Co_1.25Ga Heusler compound were more significant than those of the Fe₂CoGa Heusler compound. An increase in the Co content of the Fe₂CoGa Heusler compound did not improve the saturation magnetization but improved the Curie temperature and thermoelectric properties of the Fe₂CoGa Heusler compound. The Fe_1.75Co_1.25Ga Heusler compound exhibited a high-power factor value of over 4000 μW/mK², which was comparable to that of the Bi₂Te₃ compound. Full article

A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO₂) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO₂ fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO₂. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO₂ (XCO₂) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO₂ data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO₂ with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO₂ data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO₂ fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO₂ ecosystem fluxes from satellite XCO₂ observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO₂ fluxes are scarce. Full article