Preprints
https://doi.org/10.5194/amt-2021-222
https://doi.org/10.5194/amt-2021-222

  11 Aug 2021

11 Aug 2021

Review status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

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

Farhan Mustafa1, Lingbing Bu1, Qin Wang1, Na Yao1, Muhammad Shahzaman2, Muhammad Bilal3, Rana Waqar Aslam4, and Rashid Iqbal5 Farhan Mustafa et al.
  • 1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-5 Precipitation of China Meteorological Administration, Key Laboratory of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
  • 2School of Atmospheric Sciences (SAS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
  • 3School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
  • 4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
  • 5Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

Abstract. 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.

Farhan Mustafa et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-222', Anonymous Referee #1, 30 Aug 2021
    • AC1: 'Reply on RC1', Farhan Mustafa, 14 Oct 2021
  • RC2: 'Comment on amt-2021-222', Anonymous Referee #2, 08 Sep 2021
    • AC2: 'Reply on RC2', Farhan Mustafa, 14 Oct 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-222', Anonymous Referee #1, 30 Aug 2021
    • AC1: 'Reply on RC1', Farhan Mustafa, 14 Oct 2021
  • RC2: 'Comment on amt-2021-222', Anonymous Referee #2, 08 Sep 2021
    • AC2: 'Reply on RC2', Farhan Mustafa, 14 Oct 2021

Farhan Mustafa et al.

Farhan Mustafa et al.

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Short summary
A neural network-based approach was suggested to estimate the CO2 emissions using satellite-based XCO2 retrievals. XCO2 anomalies were calculated for each year using OCO-2 retrievals. Then a GRNN model was built and XCO2 anomalies and ODIAC CO2 emissions from 2015 to 2018 were used as a training dataset and finally, CO2 emissions were predicted for 2019 based on XCO2 anomalies calculated for the same year.