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Observations of atmospheric carbon dioxide (CO) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO can be analyzed and modeled by geostatistical methods, and CO values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO, and does not consider temporal variation in the satellite-observed CO data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO data sets in China. Prediction of monthly CO using the spatiotemporal variogram model and space-time kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient ( r), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO than those from the spatial-only approach. [ABSTRACT FROM AUTHOR]

The emergence of lithium-ion solid electrolytes greatly increases the safety risk of liquid electrolytes. Among the many solid electrolytes, aluminum-doped Li 1.3 Al 0.3 Ti 1.7 (PO 4) 3 (LATP) has become a research hotspot due to its high ionic conductivity and good air stability. However, the traditional high-temperature sintering process has the problems of lithium volatilization and secondary phase formation. Therefore, it is very difficult to prepare high-quality LATP solid electrolytes. The X-ray diffraction pattern and analysis showed that the LATP samples prepared in this paper showed rhombohedral structure and could be indexed by the R 3 c space group, and a new method of excess lithium compensation sintering was proposed, in which lithium compensation (LiNO 3) could well reduce the impact of lithium volatilization. It can be used as a sintering additive to promote the sintering densification of LATP. In this paper, the optimal sample for sintering at 900°C was determined by measuring the microstructure and electrochemical properties of the electrolyte, and lithium doping was studied. The LATP solid electrolyte with 10 wt% lithium doped has a conductivity of 7.2× 10−4 S/cm and a low activation energy (0.278 eV). Its mechanical properties (elastic modulus and hardness) are the best, which are 103.024 GPa and 8.053 GPa, respectively, and the value of its elastic modulus is much greater than that of lithium metal shear modulus of 4.25 GPa, which can effectively inhibit the growth of lithium dendrites. And the magnitude of the grain boundary conductivity and particle size of the lithium-doped sample correspond to the dielectric constant results. • The appropriate sintering temperature is a key factor in obtaining high-performance LATP solid electrolytes. • The ionic conductivity of the prepared LATP solid electrolyte is as high as 7.2×10−4S/cm. • The elastic modulus and hardness of LATP reach the maximum of 103.024 GPa and 8.053Gpa. [ABSTRACT FROM AUTHOR]

As a greenhouse gas with strong global warming potential, atmospheric methane (CH4) emissions have attracted a great deal of attention. Remote sensing measurements can provide information about CH4 sources and emissions. However, accurate assessment of CH4 emissions is challenging due to the influence of aerosol scattering in the atmosphere. In this study, imaging spectroscopic measurements from the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) in the short-wave infrared are used to analyze the impact of aerosol scattering on CH4 retrievals. Using a numerically efficient two-stream-exact-single-scattering radiative transfer model, we also simulate AVIRIS-NG measurements for different scenarios and quantify the impact of aerosol scattering using two retrieval techniques - the traditional Matched Filter (MF) method and the Optimal Estimation (OE) method, which is a popular approach for trace gas retrievals. The results show that the MF method exhibits up to 50% lower fractional retrieval bias compared to the OE method at high CH4 concentrations (>100% enhancement over typical background values) and is suitable for detecting strong CH4 emissions, while the OE method is an optimal technique for diffuse sources (<50% enhancement), showing up to five times smaller fractional retrieval bias than the MF method. In addition, the impacts of aerosol scattering as a function of different parameters, such as surface albedo, CH4 concentration, aerosol optical depth, single scattering albedo and asymmetry parameter, are also discussed. [ABSTRACT FROM AUTHOR]

Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with "top-down" satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing "bottom-up" biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies. [ABSTRACT FROM AUTHOR]

The regional uncertainty of the column-averaged dry air mole fraction of CO2 (XCO2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO2 within a latitude band of 37-42° N segmented into 8 cells in a grid of 5° from west to east (80-120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and builtup areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO2 retrievals by quantifying and attributing the consistency of XCO2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7-1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0-1.6 ppm) with a highbrightness surface from the pairwise comparison results of XCO2 retrievals. (2) Compared with XCO2 simulated by GEOS-Chem (GEOS-XCO2), the XCO2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO2. (3) Viewing attributions of XCO2 in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO2 emissions, which implies that XCO2 from satellite observations could be reliably applied in the assessment of atmospheric CO2 enhancements induced by anthropogenic CO2 emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo. [ABSTRACT FROM AUTHOR]

Anthropogenic carbon dioxide (CO2) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO2 with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO2 emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO2 (XCO2). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO2 anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO2. The model is trained using long-term datasets of XCO2 and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO2 values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R2 is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R2 is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories. [ABSTRACT FROM AUTHOR]

Satellite observations of atmospheric CO are able to truly capture the variation of global and regional CO concentration. The model simulations based on atmospheric transport models can also assess variations of atmospheric CO concentrations in a continuous space and time, which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO in a global scale. Satellite observations and model simulations of CO offer us two different approaches to understand the atmospheric CO. However, the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO. In this study, we compared and assessed the spatio-temporal variation of atmospheric CO using two datasets of the column-averaged dry air mole fractions of atmospheric CO (XCO) in a year from June 2009 to May 2010, respectively from GOSAT retrievals (V02.xx) and from Goddard Earth Observing System-Chemistry (GEOS-Chem), which is a global 3-D chemistry transport model. In addition to the global comparison, we further compared and analyzed the difference of CO between the China land region and the United States (US) land region from two datasets, and demonstrated the reasonability and uncertainty of satellite observations and model simulations. The results show that the XCO retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average, which is close to the validation conclusion for GOSAT by ground measures. This difference of XCO between the two datasets, however, changes with the different regions. In China land region, the difference is large, from 0.6 to 5.6 ppm, whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region. The goodness of fit test between the two datasets is 0.81 in the US land region, which is higher than that in the global land region (0.67) and China land region (0.68). The analysis results further indicate that the inconsistency of CO concentration between satellite observations and model simulations in China is larger than that in the US and the globe. This inconsistency is related to the GOSAT retrieval error of CO caused by the interference among input parameters of satellite retrieval algorithm, and the uncertainty of driving parameters in GEOS-Chem model. [ABSTRACT FROM AUTHOR]

Spatiotemporal patterns of column-averaged dry air mole fraction of CO2 (XCO2) have not been well characterized on a regional scale due to limitations in data availability and precision. This paper addresses these issues by examining such patterns in China using the long-term mapping XCO2 dataset (2009–2016) derived from the Greenhouse gases Observing SATellite (GOSAT). XCO2 simulations are also constructed using the high-resolution nested-grid GEOS-Chem model. The following results are found: Firstly, the correlation coefficient between the anthropogenic emissions and XCO2 spatial distribution is nearly zero in summer but up to 0.32 in autumn. Secondly, on average, XCO2 increases by 2.08 ppm every year from 2010 to 2015, with a sharp increase of 2.6 ppm in 2013. Lastly, in the analysis of three typical regions, the GOSAT XCO2 time series is in better agreement with the GEOS-Chem simulation of XCO2 in the Taklimakan Desert region (the least difference with bias 0.65±0.78 ppm), compared with the northern urban agglomeration region (−1.3±1.2 ppm) and the northeastern forest region (−1.4±1.4 ppm). The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations. [ABSTRACT FROM AUTHOR]

Methane (CH4) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH4 concentrations in space and time can help us better to understand the characteristics and driving factors of CH4 variation as to support the actions of CH4 emission reduction for preventing the continuous increase of atmospheric CH4 concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH4 dataset (Mapping-XCH4) in 1° grid and three days globally using column averaged dry air mole fraction of CH4 (XCH4) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH4 data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH4 dataset by comparing with the XCH4 simulations from the CarbonTracker model and primarily investigating the variations of XCH4 from April 2009 to April 2020. The results showed that the mean annual increase in XCH4 was 7.5 ppb/yr derived from Mapping-XCH4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH4 is larger in South Asia and eastern China than in the other regions, which agrees with the XCH4 simulations. The Mapping-XCH4 shows a significant linear relationship and a correlation coefficient of determination (R2) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH4 could detect the reduction of XCH4 in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH4 dataset has great potential for understanding the spatiotemporal variations of CH4 concentrations induced by natural processes and anthropogenic emissions at a global and regional scale. [ABSTRACT FROM AUTHOR]