Search Results (51)

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

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

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

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

This study presents the first column-averaged dry-air mole fractions of carbon dioxide (XCO₂), methane (XCH₄) and carbon monoxide (XCO) in the coal mine aggregation area in Shanxi, China, using two portable Fourier transform infrared spectrometers (EM27/SUNs), in the framework of the Collaborative Carbon Column Observing Network (COCCON). The measurements, collected over two months, were analyzed. Significant daily variations were observed, particularly in XCH₄, which highlight the impact of coal mining emissions as a major CH₄ source in the region. This study also compares COCCON XCO with measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite, revealing good agreement, with a mean bias of 7.15 ± 9.49 ppb. Additionally, comparisons were made between COCCON XCO₂ and XCH₄ data and analytical data from the Copernicus Atmosphere Monitoring Service (CAMS). The mean biases between COCCON and CAMS were −6.43 ± 1.75 ppm for XCO₂ and 15.40 ± 31.60 ppb for XCH₄. The findings affirm the stability and accuracy of the COCCON instruments for validating satellite observations and detecting local greenhouse gas sources. Operating COCCON spectrometers in coal mining areas offers valuable insights into emissions from these high-impact sources. Full article

The tracking of carbon and aerosol co-emissions is essential for environmental management. Satellite-based atmospheric synoptic observation networks provide large-scale and multifaceted data to help resolve emission behaviors. This study employs a comprehensive analysis of atmospheric dynamics, combustion byproducts, and aerosol characteristics around power plants. Strong correlations between Aerosol Optical Depth (AOD) at 500 nm and the column-averaged dry-air mole fraction of carbon dioxide (XCO₂) were observed, revealing synchronous peaks in their emission patterns. The investigation into combustion completeness utilized metrics such as the ratio of carbon monoxide (CO)/XCO₂ and Black Carbon Extinction (BCEXT)/Total Aerosol Extinction (TOTEXT). Discrepancies in these ratios across cases suggest variations in combustion efficiency and aerosol characteristics. Nitrogen dioxide (NO₂) distributions closely mirrored XCO₂, indicating consistent emission patterns, while variations in sulfur dioxide (SO₂) distributions implied differences in sulfide content in the coal used. The influence of coal composition on AOD/XCO₂ ratios was evident, with sulfide content contributing to variations besides combustion efficiency. This multifactorial analysis underscores the complex interplay of combustion completeness, aerosol composition, and coal components in shaping the air quality around power stations. The findings highlight the need for a nuanced understanding of these factors for effective air quality management. Full article

Carbon dioxide (CO₂) is the most important long-lived greenhouse gas and can be retrieved using solar absorption spectra recorded by a ground-based Fourier-transform infrared spectrometer (FTIR). In this study, we investigate the CO₂ retrieval strategy using the Network for the Detection of Atmospheric Composition Change–Infrared Working Group (NDACC–IRWG) type spectra between August 2018 and April 2022 (~4 years) at Xianghe, China, aiming to find the optimal observed spectra, retrieval window, and spectroscopy. Two spectral regions, near 2600 and 4800 cm⁻¹, are analyzed. The differences in column-averaged dry-air mole fraction of CO₂ (XCO₂) derived from spectroscopies (ATM18, ATM20, HITRAN2016, and HITRAN2020) can be up to 1.65 ± 0.95 ppm and 7.96 ± 2.02 ppm for NDACC-type 2600 cm⁻¹ and 4800 cm⁻¹ retrievals, respectively, which is mainly due to the CO₂ differences in air-broadened Lorentzian HWHM coefficient (γ_air) and line intensity (S). HITRAN2020 provides the best fitting, and the retrieved CO₂ columns and profiles from both 2600 and 4800 cm⁻¹ are compared to the co-located Total Column Carbon Observing Network (TCCON) measurements and the greenhouse gas reanalysis dataset from the Copernicus Atmosphere Monitoring Service (CAMS). The amplitude of XCO₂ seasonal variation derived from the NDACC-type (4800 cm⁻¹) is closer to the TCCON measurements than that from the NDACC-type (2600 cm⁻¹). Moreover, the NDACC-type (2600 cm⁻¹) retrievals are strongly affected by the a priori profile. For tropospheric XCO₂, the correlation coefficient between NDACC-type (4800 cm⁻¹) and CAMS model is 0.73, which is higher than that between NDACC-type (2600 cm⁻¹) and CAMS model (R = 0.56). Full article

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO₂) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO₂ from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO₂, CO, and NO₂ across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO₂ and CO₂ emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO₂ and CO emissions and a weaker correlation of 0.37 between CO and NO₂ emissions. Notably, we identify the NO₂ concentration as a reliable indicator of CO₂ emission levels, in which a 1% increase in NO₂ concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO₂ emissions. Enhancement ratios among NO₂, CO, and XCO₂ are also calculated, uncovering that high ΔNO₂: ΔXCO₂ ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO₂ ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making. Full article

Carbon dioxide (CO₂) is one of the most significant greenhouse gases, and its concentration and distribution in the atmosphere have always been a research hotspot. To study the temporal and spatial characteristics of atmospheric CO₂ globally, it is crucial to evaluate the consistency of observation data from different carbon observation satellites. This study utilizes data from the Total Carbon Column Observing Network (TCCON) to verify the column-averaged dry air mole fractions of atmospheric CO₂ (XCO₂) retrieved by satellites from October 2014 to May 2016, specifically comparing the XCO₂ distributions from the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory 2 (OCO-2). Our analysis indicates a strong correlation between the TCCON and both the GOSAT (correlation coefficient of 0.85) and OCO-2 (correlation coefficient of 0.91). Cross-validation further reveals that the measurements of the GOSAT and OCO-2 are highly consistent, with an average deviation and standard deviation of 0.92 ± 1.16 ppm and a correlation coefficient of 0.92. These differences remain stable over time, indicating that the calibration in the data set is reliable. Moreover, monthly averaged time-series and seasonal climatology comparisons were also performed separately over the six continents, i.e., Asia, North America, Europe, Africa, South America, and Oceania. The investigation of monthly XCO₂ values across continents highlights greater consistency in Asia, North America, and Oceania (standard deviation from 0.15 to 0.27 ppm) as compared to Europe, South America, and Africa (standard deviation from 0.45 to 0.84 ppm). A seasonal analysis exhibited a high level of consistency in spring (correlation coefficient of 0.97), but lower agreement in summer (correlation coefficient of 0.78), potentially due to cloud cover and aerosol interference. Although some differences exist among the datasets, the overall findings demonstrate a strong correlation between the satellite measurements of XCO₂. These results emphasize the importance of continued monitoring and calibration efforts to ensure the accurate assessment and understanding of atmospheric CO₂ levels. Full article

Accurate global monitoring of carbon dioxide (CO₂) is essential for understanding climate change and informing policy decisions. This study compares column-averaged dry-air mole fractions of CO₂ (XCO₂) between ACOS_L2_Lite_FP V9r for Japan’s Greenhouse Gases Observing Satellite (GOSAT), OCO-2_L2_Lite_FP V10r for the USA’s Orbiting Carbon Observatory-2 (OCO-2), and IAPCAS V2.0 for China’s Carbon Dioxide Observation Satellite (TANSAT) collectively referred to as GOT, with data from the Total Carbon Column Observing Network (TCCON). Our findings are as follows: (1) Significant data quantity differences exist between OCO-2 and the other satellites, with OCO-2 boasting a data volume 100 times greater. GOT shows the highest data volume between 30–45°N and 20–30°S, but data availability is notably lower near the equator. (2) XCO₂ from GOT exhibits similar seasonal variations, with lower concentrations during June, July, and August (JJA) (402.72–403.74 ppm) and higher concentrations during December, January, and February (DJF) (405.74–407.14 ppm). XCO₂ levels are higher in the Northern Hemisphere during March, April, and May (MAM) and DJF, while slightly lower during JJA and September, October, and November (SON). (3) The differences in XCO₂ (ΔXCO₂) reveal that ΔXCO₂ between OCO-2 and TANSAT are minor (−0.47 ± 0.28 ppm), whereas the most significant difference is observed between GOSAT and TANSAT (−1.13 ± 0.15 ppm). Minimal differences are seen in SON (with the biggest difference between GOSAT and TANSAT: −0.84 ± 0.12 ppm), while notable differences occur in DJF (with the biggest difference between GOSAT and TANSAT: −1.43 ± 0.17 ppm). Regarding latitudinal variations, distinctions between OCO-2 and TANSAT are most pronounced in JJA and SON. (4) Compared to TCCON, XCO₂ from GOT exhibits relatively high determination coefficients (R² > 0.8), with GOSAT having the highest root mean square error (RMSE = 1.226 ppm, <1.5 ppm), indicating a strong relationship between ground-based observed and retrieved values. This research contributes significantly to our understanding of the spatial characteristics of global XCO₂. Furthermore, it offers insights that can inform the analysis of differences in the inversion of carbon sources and sinks within assimilation systems when incorporating XCO₂ data from satellite observations. Full article

Carbon monoxide, XCO, and methane, XCH₄, column-averaged dry-air mole fractions (DMFs), observed by the TROPOspheric Monitoring Instrument (TROPOMI) on board Sentinel-5P (S-5P), are validated against those obtained from a Bruker ground-based low-resolution Fourier transform spectrometer, EM27/SUN, operating in the framework and according to requirements of the Collaborative Carbon Column Observing Network (COCCON), in Thessaloniki, Greece, on a mid-latitude urban site. The current operational S5P/TROPOMI observations show very good agreement with the respective FTIR measurements and capture both their seasonal variability and pollution episodes. XCO reported the highest concentrations during the fire episodes in summer 2021, when its daily mean value reached a maximum of 0.134 ± 0.015 ppm. XCH₄ shows a slight annual increase of 0.02 ppm, with the highest concentrations during early 2022 (approximately 1.92 ppm). The satellite CH₄ and CO products have been recently reprocessed with updated CH₄, CO and H₂O cross-sections, among other improvements, bringing noticeable changes in the pre-existing biases of S5P products against the FTIR ground-based data. We report that, for this mid-latitude station, mean biases and standard deviations fall well within mission requirements for XCH₄ and XCO (−0.01 ± 0.6% and 0.62 ± 4.2% for XCH₄ and XCO, respectively), underlying the significance of satellite measurements as a valuable supplement to ground-based data for the purpose of greenhouse gas monitoring. The results presented in this work for the Thessaloniki FTIR instrument are in strong agreement with FTIR locations in the middle latitudes. Full article

The issue of atmospheric pollution in urban centers has become a growing concern in recent years. The increasing levels of greenhouse gases in the atmosphere are a major contributor to atmospheric pollution, and it is imperative to monitor these gases. This study presents the measurements of column-averaged dry-air mole fractions of carbon dioxide (XCO₂) and carbon monoxide (XCO) in Thessaloniki, Greece. The measurements were taken in Thessaloniki using the Bruker EM27/SUN instrument, which was developed by Bruker and KIT and has been part of the Collaborative Carbon Column Observing Network (COCCON) since 2018. COCCON is a global network of stations around the globe and serves as an important supplement to the high-resolution Bruker IFS125 spectrometer used in the Total Carbon Column Observing Network (TCCON), and it provides an increased density of column-averaged greenhouse gas observations. In this work, a four-year analysis of column-averaged dry-air mole fractions of XCO₂ and XCO is presented, focusing on diurnal and seasonal cycles as well as on the comparison between them. The hourly time series show the expected seasonal cycle of XCO₂ with a spring maximum and late summer minimum due to photosynthesis activity, while XCO₂ presents a daily maximum of 419.987 ± 2.286 ppm and a daily minimum of 405.001 ± 3.067 ppm. The seasonal co-variability between XCO₂ and XCO reveals an interesting correlation—especially during winter (${\mathrm{R}}^2=0.841$ for 2022) and spring (${\mathrm{R}}^2=0.437 \mathrm{for} 2022$) period, when anthropogenic emission sources occur. Full article

The monitoring and analysis of the spatiotemporal distribution of anthropogenic carbon emissions is an important part of realizing China’s regional “dual carbon” goals; that is, the aim is for carbon emissions to peak in 2030 an to achieve carbon neutrality by 2060, as well as achieving sustainable development of the ecological environment. The column-averaged CO₂ dry air mole fraction (XCO₂) of greenhouse gas remote sensing satellites has been widely used to monitor anthropogenic carbon emissions. However, selecting a reasonable background region to eliminate the influence of uncertainty factors is still an important challenge to monitor anthropogenic carbon emissions by using XCO₂. Aiming at the problems of the imprecise selection of background regions, this study proposes to enhance the anthropogenic carbon emission signal in the XCO₂ by using the regional comparison method based on the idea of zoning. First, this study determines the background region based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) dataset and potential temperature data. Second, the average value of the XCO₂ in the background area was extracted and taken as the XCO₂ background. On this basis, the XCO₂ anomaly (XCO_2ano) was obtained by regional comparison method. Finally, the spatiotemporal variation characteristics and trends of XCO_2ano were analyzed, and the correlations between the number of residential areas and fossil fuel emissions were calculated. The results of the satellite observation data experiments over China from 2010 to 2020 show that the XCO_2ano and anthropogenic carbon emissions have similar spatial distribution patterns. The XCO_2ano in China changed significantly and was in a positive growth trend as a whole. The XCO_2ano values have a certain positive correlation with the number of residential areas and observations of fossil fuel emissions. The purpose of this research is to enhance the anthropogenic carbon emission signals in satellite observation XCO₂ data by combining ODIAC data and potential temperature data, achieve the remote sensing monitoring and analysis of spatiotemporal changes in anthropogenic carbon emissions over China, and provide technical support for the policies and paths of regional carbon emission reductions and ecological environmental protection. Full article

Monitoring CO₂ concentrations is believed to be an effective measure for assisting in the control of greenhouse gas emissions. Satellite measurements compensate for the sparse and uneven spatial distribution of ground observation stations, allowing for the collection of a wide range of CO₂ concentration data. However, satellite monitoring’s spatial coverage remains limited. This study fills the knowledge gaps of column-averaged dry-air mole fraction of CO₂ (XCO₂) products retrieved from the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory Satellite (OCO-2) based on the normalized output of atmospheric chemical models, WRF-Chem, in Southern China during 2010–2019. Hefei (HF)/Total Carbon Column Observing Network (TCCON), Lulin (LLN)/World Data Centre for Greenhouse Gases (WDCGG) station observations were used to validate the results of void filling with an acceptable accuracy for spatiotemporal analysis (R = 0.96, R² = 0.92, RMSE = 2.44 ppm). Compared to the IDW (inverse distance weighting) and Kriging (ordinary Kriging) interpolation methods, this method has a higher validation accuracy. In addition, spatiotemporal distributions of CO₂, as well as the sensitivity of CO₂ concentration to the urban built-up areas and urban green space areas in China’s major southern cities during 2010–2019, are discussed. The approximate annual average concentrations have gradually increased from 388.56 to 414.72 ppm, with an annual growth rate of 6.73%, and the seasonal cycle presents a maximum in spring and a minimum in summer or autumn from 2010 to 2019. CO₂ concentrations have a strong positive correlation with the impervious area to city area ratio, while anomaly values of the impervious area to urban green area ratio occurred in individual cities. The experimental findings demonstrate the viability of the study hypothesis that combines remote sensing data with the WRF-Chem model to produce a local area dataset with high spatial resolution and an extracted urban unit from statistical data. Full article

Satellites are an effective source of atmospheric carbon dioxide (CO₂) monitoring; however, city-scale monitoring of atmospheric CO₂ through space-borne observations is still a challenging task due to the trivial change in atmospheric CO₂ concentration compared to its natural variability and background concentration. In this study, we attempted to evaluate the potential of space-based observations to monitor atmospheric CO₂ changes at the city scale through simple data-driven analyses. We used the column-averaged dry-air mole fraction of CO₂ (XCO₂) from the Carbon Observatory 2 (OCO-2) and the anthropogenic CO₂ emissions provided by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) product to explain the scenario of CO₂ over 120 districts of Pakistan. To study the anthropogenic CO₂ through space-borne observations, XCO₂ anomalies (MXCO₂) were estimated from OCO-2 retrievals within the spatial boundary of each district, and then the overall spatial distribution pattern of the MXCO₂ was analyzed with several datasets including the ODIAC emissions, NO₂ tropospheric column, fire locations, cropland, nighttime lights and population density. All the datasets showed a similarity in the spatial distribution pattern. The satellite detected higher CO₂ concentrations over the cities located along the China–Pakistan Economic Corridor (CPEC) routes. The CPEC is a large-scale trading partnership between Pakistan and China and large-scale development has been carried out along the CPEC routes over the last decade. Furthermore, the cities were ranked based on mean ODIAC emissions and MXCO₂ estimates. The satellite-derived estimates showed a good consistency with the ODIAC emissions at higher values; however, deviations between the two datasets were observed at lower values. To further study the relationship of MXCO₂ and ODIAC emissions with each other and with some other datasets such as population density and NO₂ tropospheric column, statistical analyses were carried out among the datasets. Strong and significant correlations were observed among all the datasets. Full article