An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China
<p>Number of available retrievals and temporal changes of collected XCO<sub>2</sub> data: (<b>a</b>) the number of XCO<sub>2</sub> data points within 1° × 1° grid for 6 years from 2010 to 2015; (<b>b</b>) the temporal variation of XCO<sub>2</sub> and the monthly averages.</p> "> Figure 2
<p>Flowchart of estimating anthropogenic emission using XCO<sub>2</sub> data obtained by GOSAT observations. It consists of three major steps, firstly enhancing the signals of CO<sub>2</sub> from anthropogenic emission in XCO<sub>2</sub>; secondly establishing GRNN model using the training datasets; the last estimating the anthropogenic emissions and validating the result.</p> "> Figure 3
<p>Schematic diagram of the generalized regression neural network architecture based on Cigizolu and Alp [<a href="#B30-sensors-19-01118" class="html-bibr">30</a>].</p> "> Figure 4
<p>The anthropogenic CO<sub>2</sub> emissions in 2015 in China: (<b>a</b>) CO<sub>2</sub> emission estimated using GRNN based on the annual dXCO<sub>2</sub> in 2015 from GOSAT observations. The Tibet area shown as blank is filtered due to their high uncertainty XCO<sub>2</sub> retrievals; (<b>b</b>) the CO2 emission from ODIAC in 2015.</p> "> Figure 5
<p>(<b>a</b>) The difference between the estimated CO<sub>2</sub> emission using mapping-XCO<sub>2</sub> in 2015 and ODIAC emission in 2015; (<b>b</b>) Histogram comparison of the estimated and the ODIAC CO<sub>2</sub> emission (in unit of Ton/year) in 2015; (<b>c</b>) Land use of China in 2010.</p> "> Figure 6
<p>Scatterplot between estimated CO<sub>2</sub> emission using mapped-XCO<sub>2</sub> data and the actual ODIAC emission in 2015, in which red line is the regression line between estimated emission and ODIAC emission, and black dotted line is the 1:1 line.</p> "> Figure 7
<p>(<b>a</b>) Segment of ODIAC emissions, where the data are binned by every 0.3 t/yr of lgE using mean emission calculated from annual emission during 2010–2015; (<b>b</b>) correlation between mean ODIAC CO<sub>2</sub> emissions and mean dXCO<sub>2</sub> calculated from annual dXCO<sub>2</sub> during 2010–2015 for each segment, where red line is the regression line between dXCO<sub>2</sub> and ODIAC CO<sub>2</sub> emissions with emission lager than 10<sup>4</sup> t/yr.</p> "> Figure 8
<p>(<b>a</b>) the mean of dXCO<sub>2</sub> from the annual dXCO<sub>2</sub> during 2010 to 2015 overlaid with CARMA power plants locations; (<b>b</b>) the correlation between emission of CARMA power plants within 1° × 1°grid which are binned by every 0.3 t/yr of lgE and the corresponding mean dXCO<sub>2</sub>, different color dots represent different segment of CARMA power plants emissions.</p> ">
Abstract
:1. Introduction
2. Data and Methodology
2.1. XCO2 Retrievals and Mapping XCO2 Dataset
2.2. Anthropogenic Emission Data
2.3. Methodology
2.3.1. Variable of XCO2 Used for Estimation of Anthropogenic Emission
2.3.2. Estimation of Anthropogenic CO2 Emission by Neural Network Development
3. Results and Discussion
3.1. Estimated Anthropogenic Emissions by GRNN
3.2. Discussion of Correlation between Retrieved XCO2 and Anthropogenic Emissons
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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ODIAC | CARMA | |
---|---|---|
Grid, timely unit/period | 1° × 1°, Month/2010–2015 | Points/2009 |
Unit | Ton | Ton |
Statistical sectors | Point sources non-point sources | - |
Cement production | ||
Gas flaring | ||
International aviation and marine bunker | ||
Used data sources | Fuel statistic data published as united nation energy statistics database BP statistical review of world energy 2017 | The environmental protection agency and department of energy International atomic energy agency |
Producer | Center for global environment research, national institute for environment studies | Center for Global Development |
Oda, T et al. [25] | Wheeler, D et al. [26] |
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Yang, S.; Lei, L.; Zeng, Z.; He, Z.; Zhong, H. An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China. Sensors 2019, 19, 1118. https://doi.org/10.3390/s19051118
Yang S, Lei L, Zeng Z, He Z, Zhong H. An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China. Sensors. 2019; 19(5):1118. https://doi.org/10.3390/s19051118
Chicago/Turabian StyleYang, Shaoyuan, Liping Lei, Zhaocheng Zeng, Zhonghua He, and Hui Zhong. 2019. "An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China" Sensors 19, no. 5: 1118. https://doi.org/10.3390/s19051118
APA StyleYang, S., Lei, L., Zeng, Z., He, Z., & Zhong, H. (2019). An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China. Sensors, 19(5), 1118. https://doi.org/10.3390/s19051118