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
<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 <spa...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/7cbe48370549423984c27d8f5621e557 |
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| Sumario: | <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> |
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