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In this work, we extracted the near-surface CO2 concentration from the Greenhouse gases Observing SATellite (GOSAT) and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker model datasets for a temporal period of eight years from 2010 to 2017 to study the spatiotemporal distribution of near-surface CO2 and the factors affecting it over five regions of Asia, including Central Asia, East Asia, South Asia, Southeast Asia, and West Asia. The near-surface CO2 datasets from both satellite and model were first validated against the ground-based CO2 observations obtained from the World Data Center for Greenhouse Gases (WDCGG) stations located in Asia to confirm their applicability, and the results showed a good agreement between the datasets with significant correlations. The results from the time-series analyses showed a gradual increase in the near-surface CO2 with significant monthly and seasonal variations over all the regions. To study the factors affecting the spatial distribution of near-surface CO2, we investigated the relationship of near-surface CO2 with the anthropogenic CO2 emissions, terrestrial ecosystem, and winds. The results showed that, over Asia, the anthropogenic CO2 emissions and winds primarily controlled the spatial distribution of near-surface CO2. However, in the areas where anthropogenic emissions were lower, the terrestrial ecosystem and winds affected the near-surface CO2 distribution. To study the factors controlling the temporal distribution of near-surface CO2, the relationship of near-surface CO2 with vegetation, precipitation, and relative humidity was investigated. The results showed an inverse relationship between near-surface CO2 and normalized difference vegetation index (NDVI), precipitation, and relative humidity over monsoon-influenced regions, i.e., East Asia, South Asia, and Southeast Asia. However, a positive relation of near-surface CO2 was observed with precipitation and relative humidity over arid and semiarid regions, i.e., Central Asia and West Asia. The results were also verified by determining the correlations among these variables. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2 , i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2 , MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas and its concentration is continuously increasing mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and designing mitigation strategies aimed at stabilizing the CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of column-averaged dry-air mole fraction of CO2 i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory 2 (OCO-2) to estimate the anthropogenic CO2 emissions using Generalized Regression Neural Network over East and West Asia. OCO-2 XCO2 and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015-2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variabilities. Then the XCO2 anomaly and ODIAC emission datasets from 2015 to 2018 were used to train the GRNN model, and finally, the anthropogenic CO2 emissions were estimated for 2019 based on the XCO2 anomalies derived for the same year. The XCO2-based estimated and the ODIAC actual CO2 emissions were compared and the results showed a good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional scale anthropogenic CO2 emissions and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the carbon budget on global and regional scales is critically important to design mitigation strategies aimed at stabilizing the atmospheric carbon dioxide (CO2) emissions. For a better understanding of CO2 variation trends over Asia, in this study, the column-averaged CO2 dry air mole fraction (XCO2) derived from the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker (CT) was compared with that of Greenhouse Gases Observing Satellite (GOSAT) from September 2009 to August 2019 and with Orbiting Carbon Observatory 2 (OCO-2) from September 2014 until August 2019. Moreover, monthly averaged time-series and seasonal climatology comparisons were also performed separately over the five regions of Asia; i.e., Central Asia, East Asia, South Asia, Southeast Asia, and Western Asia. The results show that XCO2 from GOSAT is higher than the XCO2 simulated by CT by an amount of 0.61 ppm, whereas, OCO-2 XCO2 is lower than CT by 0.31 ppm on average, over Asia. The mean spatial correlations of 0.93 and 0.89 and average Root Mean Square Deviations (RMSDs) of 2.61 and 2.16 ppm were found between the CT and GOSAT, and CT and OCO-2, respectively, implying the existence of a good agreement between the CT and the other two satellites datasets. The spatial distribution of the datasets shows that the larger uncertainties exist over the southwest part of China. Over Asia, NOAA CT shows a good agreement with GOSAT and OCO-2 in terms of spatial distribution, monthly averaged time series, and seasonal climatology with small biases. These results suggest that CO2 can be used from either of the datasets to understand its role in the carbon budget, climate change, and air quality at regional to global scales. [ABSTRACT FROM AUTHOR]