Neural Network Based Estimation of Regional Scale Anthropogenic CO2 Emissions Using OCO-2 Dataset Over East and West Asia.

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]