衛星ベースの手法で石炭火力発電所のCO₂排出推定精度を向上(Researchers Develop Satellite-Based Method to Boost Accuracy of Coal Plant CO2 Emission Estimates)

2025-07-11 中国科学院(CAS)

中国科学院の程天海教授率いる研究チームが、衛星データを活用し石炭火力発電所のCO2排出量推定精度を向上させる手法「PCSM」を開発。NOxとCO2の安定した排出比を利用し、TROPOMIとOCO-2衛星データを組み合わせて日次排出量を高精度で算出。米国15発電所で従来手法に比べ誤差を45.8%から13.0%に削減。世界38施設での検証でも、既存インベントリに見られる大小発電所の系統的な誤差を是正した。パリ協定に向けた排出監視手段として有望。

<関連情報>

環境汚染と炭素の相乗効果により、宇宙から発電所のCO2排出量を追跡する能力が大幅に向上 Pollution-Carbon Synergy Significantly Enhances the Capability of Tracking Power Plants’ CO2 Emissions from Space

Donghao Fan,Tianhai Cheng,Hao Zhu,Xiaotong Ye,Tao Tang,Haoran Tong,Xingyu Li,and Lili Zhang
Environmental Science & Technology  Published: May 29, 2025
DOI:https://doi.org/10.1021/acs.est.5c01100

Abstract

衛星ベースの手法で石炭火力発電所のCO₂排出推定精度を向上(Researchers Develop Satellite-Based Method to Boost Accuracy of Coal Plant CO2 Emission Estimates)

The potential of satellite-based CO2 emission estimation from power plants is gaining increasing attention. However, the limited spatiotemporal coverage of current satellite-derived XCO2 data poses significant challenges to tracking CO2 variations on a large scale and over extended periods. In view of this, this study uses satellite-derived NO2 data as a suitable proxy and tracks CO2 emissions from 38 selected power plants globally by integrating near-synchronously observed TROPOMI NO2 data and OCO-2 XCO2 data. The results show that our method significantly increases the effective observation frequency by almost 200 times compared to using OCO-2 data alone. Compared to the emissions reported by the power plants, the correlation coefficient of the method used in this study (0.78) is higher than that of the emission inventory estimates (0.43–0.62), resulting in an accuracy improvement of approximately 1.8–2.3 Mt/yr per power plant. The use of satellite-derived NO2 data significantly enhances the ability to remotely estimate CO2 emissions from power plants, which gives us confidence in studying anthropogenic point-source CO2 emissions across different spatial and temporal scales. This enhances the understanding of their variability and mitigation potential, supporting the development of refined carbon inventories and advanced carbon cycle assimilation systems.

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