用深度学习融合卫星数据，精准估算火电厂碳排放
Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated Data
- 融合哨兵-5号NO2与OCO-2/3观测，生成连续XCO2地图
- 在71个数据稀缺地区实现排放率精度显著提升
- 适合气候政策制定者和环境监管机构使用
火电厂作为主要碳排放源，其准确量化对气候应对至关重要。尽管基于卫星烟羽反演的方法有潜力，但受限于数据不足和大气条件复杂。本研究通过（a）整合哨兵-5号的NO2数据生成连续XCO2图谱，并结合OCO-2/3真实观测数据覆盖71个数据匮乏地区的火电厂；（b）采用定制化U-Net模型处理多时空分辨率数据，实现排放速率估计。结果表明，相比以往方法，排放率估算精度显著提高。该方法可支持近实时、高精度的大型碳排放源监测，助力环境保护行动并为监管框架提供依据。
原文摘要 · Abstract (English)
CO2 emissions from power plants, as significant super emitters, contribute substantially to global warming. Accurate quantification of these emissions is crucial for effective climate mitigation strategies. While satellite-based plume inversion offers a promising approach, challenges arise from data limitations and the complexity of atmospheric conditions. This study addresses these challenges by (a) expanding the available dataset through the integration of NO2 data from Sentinel-5P, generating continuous XCO2 maps, and incorporating real satellite observations from OCO-2/3 for over 71 power plants in data-scarce regions; and (b) employing a customized U-Net model capable of handling diverse spatio-temporal resolutions for emission rate estimation. Our results demonstrate significant improvements in emission rate accuracy compared to previous methods. By leveraging this enhanced approach, we can enable near real-time, precise quantification of major CO2 emission sources, supporting environmental protection initiatives and informing regulatory frameworks.
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