Impacts of Spatial Resolution and XCO2 Precision on Satellite Capability for CO2 Plumes Detection.

Greenhouse gas satellites can provide consistently global CO2 data which are important inputs for the top-down inverse estimation of CO2 emissions and their dynamic changes. By tracking greenhouse gas emissions, policymakers and businesses can identify areas where reductions are needed most and implement effective strategies to reduce their impact on the environment. Monitoring greenhouse gases provides valuable data for scientists studying climate change. The requirements for CO2 emissions monitoring and verification support capacity drive the payload design of future CO2 satellites. In this study, we quantitatively evaluate the performance of satellite in detecting CO2 plumes from power plants based on an improved Gaussian plume model, with focus on impacts of the satellite spatial resolution and the satellite-derived XCO2 precision under different meteorological conditions. The simulations of CO2 plumes indicate that the enhanced spatial resolution and XCO2 precision can significantly improve the detection capability of satellite, especially for small-sized power plants with emissions below 6 Mt CO2/yr. The satellite-detected maximum of XCO2 enhancement strongly varies with the wind condition. For a satellite with a XCO2 precision of 0.7 ppm and a spatial resolution of 2 km, it can recognize a power plant with emissions of 2.69 Mt CO2/yr at a wind speed of 2 m/s, while its emission needs be larger than 5.1 Mt CO2/yr if the power plant is expected to be detected at a wind speed of 4 m/s. Considering the uncertainties in the simulated wind field, the satellite-derived XCO2 measurements and the hypothesized CO2 emissions, their cumulative contribution to the overall accuracy of the satellite's ability to identify realistic enhancement in XCO2 are investigated in the future. The uncertainties of ΔXCO2 caused by the uncertainty in wind speed is more significant than those introduced from the uncertainty in wind direction. In the case of a power plant emitting 5.1 Mt CO2/yr, with the wind speed increasing from 0.5 m/s to 4 m/s, the simulated ΔXCO2 uncertainty associated with the wind field ranges from 3.75 ± 2.01 ppm to 0.46 ± 0.24 ppm and from 1.82 ± 0.95 ppm to 0.22 ± 0.11 ppm for 1 × 1 km2 and 2 × 2 km2 pixel size, respectively. Generally, even for a wind direction with a higher overall uncertainty, satellite still has a more effective capability for detecting CO2 emission on this wind direction, because there is more rapid growth for simulated maximal XCO2 enhancements than that for overall uncertainties. A designed spatial resolution of satellite better than 1 km and a XCO2 precision higher than 0.7 ppm are suggested, because the CO2 emission from small-sized power plants is much more likely be detected when the wind speed is below 3 m/s. Although spatial resolution and observed precision parameters are not sufficient to support the full design of future CO2 satellites, this study still can provide valuable insights for enhancing satellite monitoring of anthropogenic CO2 emissions.

Reconstructing annual XCO2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method.

Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO2 datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO2 data (GLM-XCO2). The 1-km-spatial-resolution dataset containing XCO2 values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R2 = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO2 showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO2 concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO2 over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO2 in the scale of nation and city agglomeration. These long-term and high resolution XCO2 data help understand the spatiotemporal variations in XCO2, thereby improving policy decisions and planning about carbon reduction.

The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data.

The spatiotemporal evolution patterns of carbon emissions and their influence mechanisms are important topics for regional climate change monitoring and research on sustainable development goals. At present, due to the limitation of statistical data collection scale, it is difficult to analyze the spatiotemporal variation of carbon emission and its influence mechanism at a finer scale in China. With the development of new remote sensing platforms and technologies, multisource remote sensing data such as nighttime light remote sensing data and XCO2 concentration data have become important information resources for carbon emission monitoring. Therefore, this study monitors the spatiotemporal evolution of carbon emissions in China based on multisource remote sensing data and conducts impact mechanism research. The main conclusions of this study include: (1) The partial least squares carbon emission estimation model and the downscaled inversion model estimate carbon emissions with high accuracy. The estimated carbon emissions of both have high correlation with statistical carbon emissions, with R2 of 0.86 and 0.87, respectively, and no significant overestimation or underestimation. (2) The overall spatial pattern of energy consumption carbon emissions in China from 2010 to 2018 is high in the east and low in the west and high in the north and low in the south, but this spatial distribution pattern is gradually weakening. China's energy consumption carbon emissions varied considerably from 2010 to 2018, with an overall slow positive growth trend. (3) The mechanisms of population growth, economic development, urbanization and industrialization on carbon emissions are more complex, and most of their influencing factors promote carbon emission generation, while carbon emission impacts have spatial spillover. This study designs and studies a regional energy consumption carbon emission estimation model in China based on multisource remote sensing data, and explores the characteristics of regional multiscale carbon emission spatiotemporal variation and its influence mechanism, so as to provide scientific references for China's carbon emission reduction targets.

Long-Term Highly Stable High-Voltage LiCoO2 Synthesized via a Solid Sulfur-Assisted One-Pot Approach.

Commercialized lithium cobalt oxide (LCO) only shows a relatively low capacity of ≈175 mAh g-1 despite a high theoretical capacity of ≈274 mAh g-1 . As an effective and direct strategy, increasing its charge cutoff voltage can, in principle, escalate the capacity, which is however precluded by the irreversible phase transition, oxygen loss, and severe side reactions with electrolytes normally. Herein, an in situ sulfur-assisted solid-state approach is proposed for one-pot synthesis of long-term highly stable high-voltage LCO with a novel compound structure. The coating of coherent spinel Lix Co2 O4 shells on and the gradient doping of SO4 2- polyanions into LCO are in situ realized simultaneously in terms of gas-solid interface reactions between metal oxides and generated SO2 gas from sulfur during synthesis. At 4.6 V, this LCO shows the discharge capacities of 232.4 mAh g-1 at 0.1 C (1 C = 280 mA g-1 ), 215 mAh g-1 at 1 C and 139 mAh g-1 even at 20 C and the capacity retentions of 97.4% (89.7%) after 100 (300) cycles at 1 C. This approach is facile, low-cost and up-scalable and may provide a route to improve the performance of LCO and other electrode materials greatly.

Constraining China's land carbon sink from emerging satellite CO2 observations: Progress and challenges.

Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space-based CO2 measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO2 , and the recent progresses on the inversion of China's land carbon sink using satellite CO2 measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite-based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space-based CO2 measurements.

Characterization of Regional Combustion Efficiency using ΔXCO: ΔXCO2 Observed by a Portable Fourier-Transform Spectrometer at an Urban Site in Beijing.

Measurements of column-averaged dry-air mole fractions of carbon dioxide and carbon monoxide, CO2 (XCO2) and CO (XCO), were performed throughout 2019 at an urban site in Beijing using a compact Fourier Transform Spectrometer (FTS) EM27/SUN. This data set is used to assess the characteristics of combustion-related CO2 emissions of urban Beijing by analyzing the correlated daily anomalies of XCO and XCO2 (e.g., ΔXCO and ΔXCO2). The EM27/SUN measurements were calibrated to a 125HR-FTS at the Xianghe station by an extra EM27/SUN instrument transferred between two sites. The ratio of ΔXCO over ΔXCO2 (ΔXCO:ΔXCO2) is used to estimate the combustion efficiency in the Beijing region. A high correlation coefficient (0.86) between ΔXCO and ΔXCO2 is observed. The CO:CO2 emission ratio estimated from inventories is higher than the observed ΔXCO:ΔXCO2 (10.46 ± 0.11 ppb ppm-1) by 42.54%-101.15%, indicating an underestimation in combustion efficiency in the inventories. Daily ΔXCO:ΔXCO2 are influenced by transportation governed by weather conditions, except for days in summer when the correlation is low due to the terrestrial biotic activity. By convolving the column footprint [ppm (µmol m-2 s-1)-1] generated by the Weather Research and Forecasting-X-Stochastic Time-Inverted Lagrangian Transport models (WRF-X-STILT) with two fossil-fuel emission inventories (the Multi-resolution Emission Inventory for China (MEIC) and the Peking University (PKU) inventory), the observed enhancements of CO2 and CO were used to evaluate the regional emissions. The CO2 emissions appear to be underestimated by 11% and 49% for the MEIC and PKU inventories, respectively, while CO emissions were overestimated by MEIC (30%) and PKU (35%) in the Beijing area.

Spatial Distribution of Multi-Fractal Scaling Behaviours of Atmospheric XCO2 Concentration Time Series during 2010-2018 over China.

Exploring the spatial distribution of the multi-fractal scaling behaviours in atmospheric CO2 concentration time series is useful for understanding the dynamic mechanisms of carbon emission and absorption. In this work, we utilise a well-established multi-fractal detrended fluctuation analysis to examine the multi-fractal scaling behaviour of a column-averaged dry-air mole fraction of carbon dioxide (XCO2) concentration time series over China, and portray the spatial distribution of the multi-fractal scaling behaviour. As XCO2 data values from the Greenhouse Gases Observing Satellite (GOSAT) are insufficient, a spatio-temporal thin plate spline interpolation method is applied. The results show that XCO2 concentration records over almost all of China exhibit a multi-fractal nature. Two types of multi-fractal sources are detected. One is long-range correlations, and the other is both long-range correlations and a broad probability density function; these are mainly distributed in southern and northern China, respectively. The atmospheric temperature and carbon emission/absorption are two possible external factors influencing the multi-fractality of the atmospheric XCO2 concentration. Highlight: (1) An XCO2 concentration interpolation is conducted using a spatio-temporal thin plate spline method. (2) The spatial distribution of the multi-fractality of XCO2 concentration over China is shown. (3) Multi-fractal sources and two external factors affecting multi-fractality are analysed.

Application of Quality by Design to the robust preparation of a liposomal GLA formulation by DELOS-susp method.

Fabry disease is a lysosomal storage disease arising from a deficiency of the enzyme α-galactosidase A (GLA). The enzyme deficiency results in an accumulation of glycolipids, which over time, leads to cardiovascular, cerebrovascular, and renal disease, ultimately leading to death in the fourth or fifth decade of life. Currently, lysosomal storage disorders are treated by enzyme replacement therapy (ERT) through the direct administration of the missing enzyme to the patients. In view of their advantages as drug delivery systems, liposomes are increasingly being researched and utilized in the pharmaceutical, food and cosmetic industries, but one of the main barriers to market is their scalability. Depressurization of an Expanded Liquid Organic Solution into aqueous solution (DELOS-susp) is a compressed fluid-based method that allows the reproducible and scalable production of nanovesicular systems with remarkable physicochemical characteristics, in terms of homogeneity, morphology, and particle size. The objective of this work was to optimize and reach a suitable formulation for in vivo preclinical studies by implementing a Quality by Design (QbD) approach, a methodology recommended by the FDA and the EMA to develop robust drug manufacturing and control methods, to the preparation of α-galactosidase-loaded nanoliposomes (nanoGLA) for the treatment of Fabry disease. Through a risk analysis and a Design of Experiments (DoE), we obtained the Design Space in which GLA concentration and lipid concentration were found as critical parameters for achieving a stable nanoformulation. This Design Space allowed the optimization of the process to produce a nanoformulation suitable for in vivo preclinical testing.

Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments.

The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km² and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO2 in the flight area using the limited flight test data and the approximate surface of XCO2, which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO2 in the flight test area, which takes the approximate surface of XCO2 as its driving field and the XCO2 observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO2 were constructed with HASM based on the flight's observations. The results showed that the mean XCO2 in the flight test area is about 400 ppm and that XCO2 over urban areas is much higher than in other places. Compared with OCO-2's XCO2, the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO2 surface based on the flight test of the TanSat instruments fell within an expected and acceptable range.

Improved estimation of CO2 emissions from thermal power plants based on OCO-2 XCO2 retrieval using inline plume simulation.

CO2 emissions from power plants are the dominant source of global CO2 emissions, thus in the context of global warming, accurate estimation of CO2 emissions from power plants is essential for the effective control of carbon emissions. Based on the XCO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) and the Gaussian Plume Model (GPM), a series of studies have been carried out to estimate CO2 emission from power plants. However, the GPM is an ideal model, and there are a number of assumptions that need to be made when using this model, resulting in large uncertainties in the inverted emissions. Here, based on 6 cases of power plant plumes observed by the OCO-2 satellite over the Yangtze River Delta, China, we use an inline plume rise module coupled in the Community Multi-scale Air Quality model (CMAQ) to simulate the plumes and invert the emissions, and compare the simulated plumes and inverted emissions using the GPM model. We found that CO2 emissions can be significantly overestimated or underestimated based on the GPM simulations, and that the CMAQ inline plume simulation could significantly improve the estimates. However, the simulation bias in wind speed can significantly affect the inversion results. These results indicate that accurate meteorological field and plume simulations are critical for future inversion of point source emissions.

Generating daily high-resolution and full-coverage XCO2 across China from 2015 to 2020 based on OCO-2 and CAMS data.

China has set a goal to achieve carbon neutrality by 2060, and satellite remote sensing allows for acquiring large-range and high-resolution carbon dioxide (CO2) data, which can aid in achieving this goal. However, satellite-derived column-averaged dry-air mole fraction of CO2 (XCO2) products often suffer from substantial spatial gaps due to the impacts of narrow swath and clouds. Here, this paper generates daily full-coverage XCO2 data at a high spatial resolution of 0.1° over China during 2015-2020, by fusing satellite observations and reanalysis data in a deep neural network (DNN) framework. Specifically, DNN constructs the relationships between Orbiting Carbon Observatory-2 satellite XCO2 retrievals, Copernicus Atmosphere Monitoring Service (CAMS) XCO2 reanalysis data, and environmental factors. Then, daily full-coverage XCO2 data can be generated based on CAMS XCO2 and environmental factors. Results show that a satisfactory performance is reported in multiform validations, with RMSE and R2 of 0.99 ppm and 0.963 in terms of the sample-based cross-validation, respectively. The independent in-situ validation also indicates high consistency (R2 = 0.866 and RMSE = 1.71 ppm) between XCO2 estimates and ground measurements. Based on the generated dataset, spatial and seasonal distributions of XCO2 across China are investigated, and a growth rate of 2.71 ppm/yr is found from 2015 to 2020. This paper generates long time series of full-coverage XCO2 data, which helps promote our understanding of carbon cycling. The dataset is available from https://doi.org/10.5281/zenodo.7793917.

Spatiotemporal variation analysis of global XCO2 concentration during 2010-2020 based on DINEOF-BME framework and wavelet function.

Combining with Carbon dioxide column concentration (XCO2) remote sensing data, it is of great scientific significance to obtain XCO2 long time series data with high precision and high spatio-temporal coverage. In this study, the combination framework of DINEOF and BME were employed to integrate the XCO2 data of GOSAT, OCO-2 and OCO-3 satellites for generating global XCO2 data from January 2010 to December 2020, with the average monthly space coverage rate of more than 96 %. Through cross-validation and comparison of The Total Carbon Column Observing Network (TCCON) XCO2 data and DINEOF-BME interpolation XCO2 products, it is verified that DINEOF-BME method has better interpolation accuracy, and the coefficient of determination of interpolated XCO2 products and TCCON data is 0.920. The long time series of global XCO2 products showed a wave rising trend, with a total increase of ~23 ppm; obviously seasonal characteristics were also detected with the highest XCO2 value in spring and the lowest in autumn. According to the zonal integration analysis, the values of XCO2 in the northern hemisphere is higher than the southern hemisphere during January-May and October-December, while the values of XCO2 in the southern hemisphere is higher than the northern hemisphere during June-September, which accords with the seasonal law. Through EOF mapping, the first mode accounted for 88.93 % of the total variability, and its variation trend is consistent with that of XCO2 concentration, which verifies the variation rule of XCO2 from the time and space pattern. Through wavelet analysis, the time scale corresponding to the first main cycle of XCO2 change is 59-month, which has obvious regularity on the time scale. DINEOF-BME technology framework has good generality, while XCO2 long time series data products and the spatio-temporal variation of XCO2 revealed by the research provide a solid theoretical basis and data support for related research.

Characterizing Average Seasonal, Synoptic, and Finer Variability in Orbiting Carbon Observatory-2 XCO2 Across North America and Adjacent Ocean Basins.

Variations in atmosphere total column-mean CO2 (XCO2) collected by the National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 satellite can be used to constrain surface carbon fluxes if the influence of atmospheric transport and observation errors on the data is known and accounted for. Due to sparse validation data, the portions of fine-scale variability in XCO2 driven by fluxes, transport, or retrieval errors remain uncertain, particularly over the ocean. To better understand these drivers, we characterize variability in OCO-2 Level 2 version 10 XCO2 from the seasonal scale, synoptic-scale (order of days, thousands of kilometers), and mesoscale (within-day, hundreds of kilometers) for 10 biomes over North America and adjacent ocean basins. Seasonal and synoptic variations in XCO2 reflect real geophysical drivers (transport and fluxes), following large-scale atmospheric circulation and the north-south distribution of biosphere carbon uptake. In contrast, geostatistical analysis of mesoscale and finer variability shows that real signals are obscured by systematic biases across the domain. Spatial correlations in along-track XCO2 are much shorter and spatially coherent variability is much larger in magnitude than can be attributed to fluxes or transport. We characterize random and coherent along-track XCO2 variability in addition to quantifying uncertainty in XCO2 aggregates across typical lengths used in inverse modeling. Even over the ocean, correlated errors decrease the independence and increase uncertainty in XCO2. We discuss the utility of computing geostatistical parameters and demonstrate their importance for XCO2 science applications spanning from data reprocessing and algorithm development to error estimation and carbon flux inference.

Study of mid-latitude retrieval XCO2 greenhouse gas: Validation of satellite-based shortwave infrared spectroscopy with ground-based TCCON observations.

Carbon dioxide (CO2) is a major greenhouse gas. This study investigated the performance of three common algorithms, namely NIES, ACOS, and Remo Tec (SRFP). These algorithms were compared using GOSAT observation satellite data with reference data obtained from TCCON during the period 2009-2021. According to statistical evaluation, the SRFP and NIES algorithms achieved the lowest and highest correlation values of the 13 year (2009_2021) average of all sites, respectively. The average bias error values of NIES and ACOS was estimated to be less than that of SRFP approximately 0.5 ppm, while the bias within SRFP was of about 2 ppm. Comparing the RMSE and CRMS error values showed that the highest and lowest error values were related to the SRFP and NIES algorithms respectively, which were 0.37-1.67 and ppm 1.46-7.9. The researchers also compared them with monthly time changes based on ground measurements, and observed a time series of CO2 concentration changes that well matched the trend of gas concentration values at ground stations obtained by NIES algorithm. The results showed that in most cases NIES was an effective algorithm to retrieve carbon dioxide gas concentrations, allowing the researchers to identify the main sources of greenhouse gas emissions in different areas. The clustering result in the study area showed that the continental CO2 columnar concentration has a specific seasonal cycle, with the maximum and minimum values appearing in winter-early spring and spring-late summer, respectively. In conclusion, cluster analysis can classify the surface CO2 column concentration values and determine the spatial distribution pattern of CO2.

The status of carbon neutrality of the world's top 5 CO2 emitters as seen by carbon satellites.

China, the Unite States (US), the European Union (EU), India, and Russia are the world's top 5 fossil fuel and cement CO2 (FFC) emitting countries or regions (CRs). It is very important to understand their status of carbon neutrality, and to monitor their future changes of net carbon fluxes (NCFs). In this study, we implemented a well-established global carbon assimilation system (GCAS, Version 2) to infer global surface carbon fluxes from May 2009 to December 2019 using both GOSAT and OCO-2 XCO2 retrievals. The reductions of flux uncertainty and XCO2 bias, and the evaluation of posterior flux show that GCAS has comparable and good performance in the 5 CRs. The results suggest that Russia has achieved carbon neutrality, but the other 4 are still far from being carbon neutral, especially China. The mean annual NCFs in China, the US, the EU, India, and Russia are 2.33 ± 0.29, 0.82 ± 0.20, 0.42 ± 0.16, 0.50 ± 0.12, and -0.33 ± 0.23 PgC yr-1, respectively. From 2010 to 2019, the NCFs showed an increasing trend in the US and India, a slight downward trend after 2013 in China, and were stable in the EU. The changes of land sinks in China and the US might be the main reason for their trends. India's trend was mainly due to the increase of FFC emission. The relative contributions of NCFs to the global land net carbon emission of China and the EU have decreased, while those of the US and India have increased, implying the US and India must take more active measures to control carbon emissions or increase their sinks. This study indicates that satellite XCO2 could be successfully used to monitor the changes of regional NCFs, which is of great significance for major countries to achieve greenhouse gas control goals.

Simulation and analysis of XCO2 in North China based on high accuracy surface modeling.

As an important cause of global warming, CO2 concentrations and their changes have aroused worldwide concern. Establishing explicit understanding of the spatial and temporal distributions of CO2 concentrations at regional scale is a crucial technical problem for climate change research. High accuracy surface modeling (HASM) is employed in this paper using the output of the CO2 concentrations from weather research and forecasting-chemistry (WRF-CHEM) as the driving fields, and the greenhouse gases observing satellite (GOSAT) retrieval XCO2 data as the accuracy control conditions to obtain high accuracy XCO2 fields. WRF-CHEM is an atmospheric chemical transport model designed for regional studies of CO2 concentrations. Verified by ground- and space-based observations, WRF-CHEM has a limited ability to simulate the conditions of CO2 concentrations. After conducting HASM, we obtain a higher accuracy distribution of the CO2 in North China than those calculated using the classical Kriging and inverse distance weighted (IDW) interpolation methods, which were often used in past studies. The cross-validation also shows that the averaging mean absolute error (MAE) of the results from HASM is 1.12 ppmv, and the averaging root mean square error (RMSE) is 1.41 ppmv, both of which are lower than those of the Kriging and IDW methods. This study also analyses the space-time distributions and variations of the XCO2 from the HASM results. This analysis shows that in February and March, there was the high value zone in the southern region of study area relating to heating in the winter and the dense population. The XCO2 concentration decreased by the end of the heating period and during the growing period of April and May, and only some relatively high value zones continued to exist.

A comparison of satellite observations with the XCO2 surface obtained by fusing TCCON measurements and GEOS-Chem model outputs.

Ground observations can capture CO2 concentrations accurately but the number of available TCCON (Total Carbon Column Observing Network) sites is too small to support a comprehensive analysis (i.e. validation) of satellite observations. Atmospheric transport models can provide continuous atmospheric CO2 concentrations in space and time, but some information is difficult to generate with model simulations. The HASM platform can model continuous column-averaged CO2 dry air mole fraction (XCO2) surface taking TCCON observations as its optimum control constraints and an atmospheric transport model as its driving field. This article presents a comparison of the satellite observations with a HASM XCO2 surface obtained by fusing TCCON measurements with GEOS-Chem model results. We first verified the accuracy of the HASM XCO2 surface using six years (2010-2015) of TCCON observations and the GEOS-Chem model XCO2 results. The validation results show that the largest MAE of bias between the HASM results and observations was 0.85ppm and the smallest MAE was only 0.39ppm. Next, we modeled the HASM XCO2 surface by fusing the TCCON measurements and GEOS-Chem XCO2 model results for the period 9/1/14 to 8/31/15. Finally, we compared the GOSAT and OCO-2 observations with the HASM XCO2 surface and found that the global OCO-2 XCO2 estimates more closely resembled the HASM XCO2 surface than the GOSAT XCO2 estimates.