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基于地面遥感的火电厂 CO 排放量量化性能评估和测量不确定性。

Assessment of thermal power plant CO emissions quantification performance and uncertainty of measurements by ground-based remote sensing.

机构信息

Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; University of Science and Technology of China, Hefei, 230026, China; Key Laboratory of General Optical Calibration and Characterization Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.

Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; University of Science and Technology of China, Hefei, 230026, China; Key Laboratory of General Optical Calibration and Characterization Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.

出版信息

Environ Pollut. 2024 Nov 15;361:124886. doi: 10.1016/j.envpol.2024.124886. Epub 2024 Sep 6.

DOI:10.1016/j.envpol.2024.124886
PMID:39245203
Abstract

Thermal power plants serve as significant CO sources, and accurate monitoring of their emissions is crucial for improving the precision of global carbon emission estimates. In this study, a measurement method based on measuring point source plumes was employed in ground-based remote sensing experiments at the thermal power plant. By simulating CO plumes, we analyzed the impact of surrounding urban structures, the geometric relationship between measurement points and plumes, and the influence on measurement points selection. We also assessed the capability and uncertainties in quantifying CO emissions. For the Hefei power plant, CO emission estimates were on average 7.98 ± 10.01 kg/s higher with surface buildings compared to scenarios without buildings (approximately 4.09% error). By selectively filtering discrete data, the emission estimation errors were significantly reduced by 7.31 ± 7.13 kg/s compared to pre-filtered data. Regarding the relationship between observation paths and plume geometry, simulation studies indicated that the ability to estimate CO emissions varied for near and middle segment observations. The lowest emission rate error was found in the mid-segment near 1.5-2.0 km, reaching 7.13 ± 5.39 kg/s. CO distribution at the mid-segment position becomes more uniform relative to the near segment, making it more suitable for meeting emission estimation requirements. Optimizing measurement schemes by considering environmental factors and precisely selecting measurement points significantly enhances emission estimation accuracy, providing crucial technical support for top-down estimates of anthropogenic CO emissions.

摘要

火力发电厂是重要的 CO 排放源,准确监测其排放量对于提高全球碳排放量估计的精度至关重要。本研究在火力发电厂进行了基于测量点源羽流的地面遥感实验,采用测量方法。通过模拟 CO 羽流,分析了周围城市结构、测量点与羽流的几何关系以及对测量点选择的影响。评估了定量 CO 排放的能力和不确定性。对于合肥电厂,与没有建筑物的情景相比,有建筑物时 CO 排放估算值平均高出 7.98 ± 10.01 kg/s(约 4.09%的误差)。通过选择性地过滤离散数据,与预过滤数据相比,排放估算误差显著降低了 7.31 ± 7.13 kg/s。关于观测路径和羽流几何形状的关系,模拟研究表明,近段和中段观测的 CO 排放估算能力不同。在近 1.5-2.0 km 的中段,发现最低的排放率误差为 7.13 ± 5.39 kg/s。与近段相比,中段位置的 CO 分布更加均匀,更适合满足排放估算要求。通过考虑环境因素优化测量方案并精确选择测量点,显著提高了排放估算的准确性,为自上而下估算人为 CO 排放提供了关键的技术支持。

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