Search Results (245)

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

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

Based on comprehensive experimental datasets—proximate/ultimate analyses, XPS, solid-state ¹³C NMR, and Raman spectroscopy—we constructed and optimized a compositionally faithful macromolecular model of SG coking coal. Using density-functional theory (DFT) calculations, we simulated electrostatic-potential (ESP) fields and frontier molecular orbitals (FMO) to probe elementary oxidation steps relevant to combustion, and focused on how heteroatom speciation and carbon ordering govern site-selective reactivity. Employing multi-peak deconvolution and parameter synthesis, we obtained an aromatic fraction f_a = 76.56%, a bridgehead-to-periphery ratio X_BP = 0.215, and Raman indices I_D1/I_G ≈ 1.45 (area) with FWHM(G) ≈ 86.7 cm⁻¹; the model composition C₁₉₀H₁₄₄N₂O₂₁S and its predicted ¹³C NMR envelope validated the structural assignment against experiment. ESP–FMO synergy revealed electron-rich hotspots at phenolic/ether/carboxyl and thiophenic domains and electron-poor belts at H-terminated edges/aliphatic bridges, rationalizing carbon-end oxidation of CO, weak electrostatic steering by O₂/CO₂, and a benzylic H-abstraction → edge addition → O-insertion/charge-transfer sequence toward CO₂/H₂O, with thiophenic sulfur comparatively robust. We quantified surface functionalities (C–O 65.46%, O–C=O 24.51%, C=O 10.03%; pyrrolic/pyridinic N dominant; thiophenic-S with minor oxidized S) and determined a naphthalene-dominant, stacked-polyaromatic architecture with sparse alkyl side chains after Materials Studio optimization. The findings are significant for mechanistic understanding and control of coking-coal oxidation, providing actionable hotspots and a reproducible workflow (multi-probe constraints → model building/optimization → DFT reactivity mapping → spectral back-validation) for blend design and targeted oxidation-inhibition strategies. 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

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) provides an advanced proxy for global vegetation productivity. Recently, new high-quality remote sensing SIF datasets and reanalysis products have significantly advanced the application of SIF. However, the lack of long-term, daily resolution datasets continues to limit the precise exploration of vegetation dynamics, primarily due to challenges in daily modeling accuracy, substantial data volume, and computational demands. In this study, supported by the Google Earth Engine (GEE) platform, we developed a data-driven approach based on the Moving Spatial–Temporal Window Sampling (MSTWS) strategy for reconstructing long-term daily SIF. By learning the relationship between high-spatial-resolution Orbiting Carbon Observatory (OCO)-3 SIF and MODIS surface reflectance, we established a spatially and temporally specific daily prediction model for each day of the year (DOY), reconstructing the long-term daily OCO-3 SIF (DOSIF) from 2001 to the present with a global contiguous distribution. The prediction framework demonstrated robust performance with an R² of 0.92 on the training set and 0.81 on the validation set, indicating strong predictive ability and resistance to overfitting. Systematic evaluation of the dataset showed that DOSIF accurately captures the expected spatiotemporal distribution patterns. Cross-sensor validation with independent airborne SIF measurements further enhanced the reliability of the DOSIF dataset. Full article

Accurate quantification of carbon dioxide (CO₂) sources and sinks is becoming a key aspect in recent carbon flux research; yet our understanding of satellite performance on regional scales remains insufficient. In this work, the column-averaged dry-air mole fraction of CO₂ retrieved from OCO-2 v11.1r and GOSAT v03.05 was evaluated against CarbonTracker (CT) using data from March 2022 to August 2023. Also, the satellite data were validated against those from the Total Carbon Column Observing Network (TCCON) for March 2022 to February 2024. Comparison with CT revealed that both satellites had a general negative bias over land and the best performance in spring. In Southern Hemisphere land regions, the satellites captured monthly variability reliably, with OCO-2 obtaining the most accurate monthly concentrations. In Northern Hemisphere land regions, CT demonstrated the best performance, although both satellites accurately quantified monthly variations in some regions. In tropical land regions, none of the satellites showed superior performance. OCO-2 data showed bias features in sub-regional areas such as East and South Asia. For ocean regions, the bias was the largest in spring. Phase offset, slight underestimation of concentrations, and seasonal biases were found over several ocean regions in OCO-2 time series, whereas GOSAT was unable to provide reasonable results. When comparing TCCON with OCO-2 and GOSAT data, we found systematic errors of −0.12 and −0.56 ppm and root mean square errors of 1.08 and 1.70 ppm, respectively, mainly contributed by topographic variation and aerosol load. The errors were the smallest in spring and larger in summer and winter. Both CT- and TCCON-based analyses indicated that current satellite products may have better performance in desert surfaces. Clouds, aerosols, and surface pressure still challenged OCO-2 retrieval, while the bias-correction process can be emphasized for GOSAT. 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

Solid oxide electrolysis cells (SOECs) have emerged as a promising technology for efficient energy storage and CO₂ utilization via H₂O–CO₂ co-electrolysis. While most previous studies focused on planar or tubular configurations, this work investigated a novel flat, tubular SOEC design using a comprehensive 3D multi-physics model developed in COMSOL Multiphysics 5.6. This model integrates charge transfer, gas flow, heat transfer, chemical/electrochemical reactions, and structural mechanics to analyze operational behavior and thermo-mechanical stress under different voltages and pressures. Simulation results indicate that increasing operating voltage leads to significant temperature and current density inhomogeneity. Furthermore, elevated pressure improves electrochemical performance, possibly due to increased reactant concentrations and reduced mass transfer limitations; however, it also increases temperature gradients and the maximum first principal stress. These findings underscore that the design and optimization of flat tubular SOECs in H₂O–CO₂ co-electrolysis should take the trade-off between performance and durability into consideration. Full article

Traditional calibration methods often assume constant parameters that remain unchanged across input conditions, which can limit predictive accuracy when parameters actually vary. To address this issue, we propose a novel calibration framework with time-varying parameters. Building on the idea of profile least squares, we first apply local linear smoothing to estimate the discrepancy function between the computer model and the true process, and then use local linear smoothing again to obtain pointwise estimates of the functional calibration parameter. Through rigorous theoretical analysis, we establish the consistency and asymptotic normality of the proposed estimator. Simulation studies and an application to NASA’s OCO-2 mission demonstrate that the proposed method effectively captures parameter variation and improves predictive performance. Full article

Current research on chili powder and oil has predominantly focused on cultivar selection and oil temperature, while the impact of thermal pretreatment methods on their quality and flavor profiles remains underexplored. In this study, the flavor profiles of raw untreated, stir-fried, oven-baked, and microwaved chili powders (RC, SC, OC, and MC) and their corresponding chili oils obtained through secondary flavor activation (RCO, SCO, OCO, and MCO) were analyzed using E-nose, GC-IMS, HS-SPME-GC-MS, LC-MS/MS, and sensory evaluation techniques. E-nose and GC-IMS 2D topographic plots revealed that thermal treatment increased the concentration of volatile flavor compounds. HS-SPME-GC-MS further detected 220 and 207 volatile compounds in chili powders and oils, respectively, with 74 and 35 identified as differential volatile compounds. Aldehydes ((E,E)-2,4-heptadienal, benzaldehyde), alcohols (1-nonanol, 2-furanmethanol), Maillard reaction products (ethyl pyrazine, 2,3-dimethylpyrazine, and 2-ethyl-6-methylpyrazine), and methyl acetate were significantly enhanced in SC, OC, and MC and their corresponding chili oils. Among them, OC and OCO showed the greatest increase in differential flavor substances. Additionally, all three treatments enhanced the release of taste-active substances and improved sensory overall acceptability. These findings provide new insights for the food industry in optimizing chili product processing. Full article

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

Transient increments of X-ray radiation and extreme ultraviolet (EUV) during solar flares are strong drivers of thermospheric dynamics on Mars, yet their class-dependent impacts remain poorly measured. This work provides the first direct, side-by-side study of Martian thermospheric reactions to flares X8.2 on 10 September 2017 and M6.0 on 17 September 2017. This study shows nonlinear, class-dependent effects, compositional changes, and recovery processes not recorded in previous investigations. Species-specific responses deviated significantly from irradiance proportionality, even though the soft X-ray flux in the X8.2 flare was 13 times greater. Argon (Ar) concentrations rose 3.28× (compared to 1.13× for M6.0), and radiative cooling led CO₂ heating to approach a halt at ΔT = +40 K (X8.2) against +19 K (M6.0) at exobase altitudes (196–259 km). N₂ showed the largest class difference, where temperatures rose by +126 K (X8.2) instead of +19 K (M6.0), therefore displaying flare-magnitude dependent thermal sensitivity. The 1.95× increase in O concentrations during X8.2 and the subsequent decrease following M6.0 (−39 K cooling) illustrate the contradiction between photochemical production and radiative loss. The O/CO₂ ratio at 225 km dropped 46% during X8.2, revealing compositional gradients boosted by flares. Recovery timeframes varied by class; CO₂ quickly re-equilibrated because of effective cooling, whereas inert species (Ar, N₂) stabilized within 1–2 orbits after M6.0 but needed >10 orbits of the MAVEN satellite after the X8.2 flare. The observations of the X8.2 flare came from the western limb of the Sun, but the M6.0 flare happened on the far side. The CME shock was the primary driver of Mars’ EUV reaction. These findings provide additional information on atmospheric loss and planetary habitability by indicating that Mars’ thermosphere has a saturation threshold where strong flares induce nonlinear energy partitioning that encourages the departure of lighter species. Full article

The introduction of the ”dual carbon” target has increased the need for products that can accurately measure carbon dioxide levels, reflecting the rising demand. Due to challenges in achieving the required spatiotemporal resolution, accuracy, and spatial continuity with current carbon dioxide concentration products, it is essential to explore methods for obtaining carbon dioxide concentration products with completeness in space and time. Based on the 2018 OCO-2 carbon dioxide products and environmental variables such as vegetation coverage (FVC, LAI), net primary productivity (NPP), relative humidity (RH), evapotranspiration (ET), temperature (T) and wind (U, V), this study constructed a multiple regression model to obtain the spatial continuous carbon dioxide concentration products in the provinces of Huai River Basin. Using indicators such as correlation coefficient, root mean square error (RMSE), local variance, and percentage of valid pixels, the performance of model was validated. The validation results are shown as follows: (1) Among the selected environmental variables, the primary factors affecting the spatiotemporal distribution of carbon dioxide concentration are ET, LAI, FVC, NPP, T, U, and RH. (2) Compared with the OCO-2 carbon dioxide products, the percentage of valid pixels of the reconstructed carbon dioxide concentration data increased from less than 1% to over 90%. (3) The local variance in reconstructed data was significantly larger than that of original OCO-2 ${\mathrm{C}\mathrm{O}}_2$ products. (4) The average monthly RMSE is 2.69. Therefore, according to the model developed in this study, we can obtain a carbon dioxide concentration dataset that is spatially complete, meets precision requirements, and is rich in local detail information, which can better reflect the spatial pattern of carbon dioxide concentration and can be used to examine the carbon cycle between the terrestrial environment, biosphere, and atmosphere. Full article

Fenton oxidation can contribute to meeting effluent standards for COD in actual wastewater treatment plant effluents. However, Fenton oxidation is prone to produce iron sludge waste. The application of heterogeneous Fenton-like systems based on Fenton iron mud carbon in wastewater treatment plants is essential for Fenton iron mud reduction and recycling. In this study, a Fenton iron mud carbon catalyst/Ferrate salts/H₂O₂ (FSC/Fe(VI)/H₂O₂) system was developed to remove chemical oxygen demand (COD) from secondary effluents at the pilot scale. The results showed that the FSC/Fe(VI)/H₂O₂ system exhibited excellent COD removal performance with a removal rate of 57% under slightly neutral conditions in laboratory experiments. In addition, the effluent COD was stabilized below 40 mg·L⁻¹ for 65 days at the pilot scale. Fe(IV) and ¹O₂ were confirmed to be the main active species in the degradation process through electron paramagnetic resonance (EPR) and quenching experiments. C=O, O-C=O, N sites and Fe⁰ were responsible for the generation of Fe(IV) and ¹O₂ in the FSC/Fe(VI)/H₂O₂ system. Furthermore, the cost per ton of water treated by the pilot-scale FSC/Fe(VI)/H₂O₂ system was calculated to be only 0.6209 USD/t, further confirming the application potential of the FSC/Fe(VI)/H₂O₂ system. This study promotes the engineering application of heterogeneous Fenton-like systems for water treatment. Full article