Neural-network-based estimation of regional-scale anthropogenic CO2 emissions using an Orbiting Carbon Observatory-2 (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 <spa...

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Autores principales:	F. Mustafa, L. Bu, Q. Wang, N. Yao, M. Shahzaman, M. Bilal, R. W. Aslam, R. Iqbal
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
Acceso en línea:	https://doaj.org/article/7cbe48370549423984c27d8f5621e557
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spelling	oai:doaj.org-article:7cbe48370549423984c27d8f5621e5572021-11-18T12:01:09ZNeural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia10.5194/amt-14-7277-20211867-13811867-8548https://doaj.org/article/7cbe48370549423984c27d8f5621e5572021-11-01T00:00:00Zhttps://amt.copernicus.org/articles/14/7277/2021/amt-14-7277-2021.pdfhttps://doaj.org/toc/1867-1381https://doaj.org/toc/1867-8548<p>Atmospheric carbon dioxide (<span class="inline-formula">CO<sub>2</sub></span>) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of <span class="inline-formula">CO<sub>2</sub></span> is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing <span class="inline-formula">CO<sub>2</sub></span> emissions. Satellites provide the most effective way to monitor the concentration of <span class="inline-formula">CO<sub>2</sub></span> in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of <span class="inline-formula">CO<sub>2</sub></span>, i.e., <span class="inline-formula">XCO<sub>2</sub></span> retrieved from a <span class="inline-formula">CO<sub>2</sub></span> 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 <span class="inline-formula">CO<sub>2</sub></span> emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 <span class="inline-formula">XCO<sub>2</sub></span>, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) <span class="inline-formula">CO<sub>2</sub></span> emission datasets for a period of 5 years (2015–2019) were used in this study. The annual <span class="inline-formula">XCO<sub>2</sub></span> anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background <span class="inline-formula">CO<sub>2</sub></span> concentrations and seasonal variability. The <span class="inline-formula">XCO<sub>2</sub></span> anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic <span class="inline-formula">CO<sub>2</sub></span> emissions were estimated for 2019 based on the NPP and <span class="inline-formula">XCO<sub>2</sub></span> anomalies derived for the same year. The estimated and the ODIAC <span class="inline-formula">CO<sub>2</sub></span> emissions were compared, and the results showed good agreement in terms of spatial distribution. The <span class="inline-formula">CO<sub>2</sub></span> emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and <span class="inline-formula">XCO<sub>2</sub></span> anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based <span class="inline-formula">XCO<sub>2</sub></span> retrievals can be used to estimate the regional-scale anthropogenic <span class="inline-formula">CO<sub>2</sub></span> emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more <span class="inline-formula">CO<sub>2</sub></span> emission and concentration datasets.</p>F. MustafaL. BuQ. WangN. YaoM. ShahzamanM. BilalR. W. AslamR. IqbalCopernicus PublicationsarticleEnvironmental engineeringTA170-171Earthwork. FoundationsTA715-787ENAtmospheric Measurement Techniques, Vol 14, Pp 7277-7290 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
spellingShingle	Environmental engineering TA170-171 Earthwork. Foundations TA715-787 F. Mustafa L. Bu Q. Wang N. Yao M. Shahzaman M. Bilal R. W. Aslam R. Iqbal Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia
description	<p>Atmospheric carbon dioxide (<span class="inline-formula">CO<sub>2</sub></span>) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of <span class="inline-formula">CO<sub>2</sub></span> is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing <span class="inline-formula">CO<sub>2</sub></span> emissions. Satellites provide the most effective way to monitor the concentration of <span class="inline-formula">CO<sub>2</sub></span> in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of <span class="inline-formula">CO<sub>2</sub></span>, i.e., <span class="inline-formula">XCO<sub>2</sub></span> retrieved from a <span class="inline-formula">CO<sub>2</sub></span> 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 <span class="inline-formula">CO<sub>2</sub></span> emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 <span class="inline-formula">XCO<sub>2</sub></span>, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) <span class="inline-formula">CO<sub>2</sub></span> emission datasets for a period of 5 years (2015–2019) were used in this study. The annual <span class="inline-formula">XCO<sub>2</sub></span> anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background <span class="inline-formula">CO<sub>2</sub></span> concentrations and seasonal variability. The <span class="inline-formula">XCO<sub>2</sub></span> anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic <span class="inline-formula">CO<sub>2</sub></span> emissions were estimated for 2019 based on the NPP and <span class="inline-formula">XCO<sub>2</sub></span> anomalies derived for the same year. The estimated and the ODIAC <span class="inline-formula">CO<sub>2</sub></span> emissions were compared, and the results showed good agreement in terms of spatial distribution. The <span class="inline-formula">CO<sub>2</sub></span> emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and <span class="inline-formula">XCO<sub>2</sub></span> anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based <span class="inline-formula">XCO<sub>2</sub></span> retrievals can be used to estimate the regional-scale anthropogenic <span class="inline-formula">CO<sub>2</sub></span> emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more <span class="inline-formula">CO<sub>2</sub></span> emission and concentration datasets.</p>
format	article
author	F. Mustafa L. Bu Q. Wang N. Yao M. Shahzaman M. Bilal R. W. Aslam R. Iqbal
author_facet	F. Mustafa L. Bu Q. Wang N. Yao M. Shahzaman M. Bilal R. W. Aslam R. Iqbal
author_sort	F. Mustafa
title	Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia
title_short	Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia
title_full	Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia
title_fullStr	Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia
title_full_unstemmed	Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia
title_sort	neural-network-based estimation of regional-scale anthropogenic co<sub>2</sub> emissions using an orbiting carbon observatory-2 (oco-2) dataset over east and west asia
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/7cbe48370549423984c27d8f5621e557
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Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

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