An Assessment of Anthropogenic CO₂ Emissions by Satellite-Based Observations in China.

Shaoyuan Yang,Hui Zhong,Zhaocheng Zeng,Liping Lei,Zhonghua He

doi:10.3390/s19051118

Abstract

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations.

Highlights

Atmospheric carbon dioxide (CO2 ) is the most significant anthropogenic greenhouse gas (GHG)and its concentration in atmosphere has been increasing from 280 ppm since the preindustrial era to a level higher than 400 ppm at present at a global scale [1]
We introduce therefrom an interannual variability by removing the regional background signal and calculating their annual mean to enhance the signals of CO2 from anthropogenic emission as following equation proposed by Hakkarainen et al [17]: dXCO2 = XCO2 − MXCO2 (t) where dXCO2 indicates the deviation from regional background for each grid at a specific time unit t where t is the 3-day unit of used mapping-XCO2 data; XCO2 is XCO2 for each grid at time t from mapping-XCO2 data; MXCO2 (t) is median of XCO2 for all girds in the study region at time unit/period
The an anthropogenic emission estimation method using a machine learning technique is applied to model is verified by estimating results in 2015 and comparing with the ODIAC emissions

Summary

Introduction

Atmospheric carbon dioxide (CO2 ) is the most significant anthropogenic greenhouse gas (GHG). Its concentration in atmosphere has been increasing from 280 ppm since the preindustrial era to a level higher than 400 ppm at present at a global scale [1]. Anthropogenic CO2 emissions, 70% of which come from fossil fuel combustion and industrial activities [2], are the main driver of the atmospheric CO2 concentration increase. If atmospheric CO2 concentration continues to increase at the current rate, 1.5 ◦ C of global warming will be reached between 2030 and 2052, which will cause more climate extremes [3]. Atmospheric CO2 concentration, will be continually increasing as the rapid development of industrialization requires enormous energy around the world. In order to slow down the increase of atmospheric CO2

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Conclusion