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利用卫星、无人机和数值模拟技术对核电站热排放进行综合监测和预测。

Integrated monitoring and prediction of thermal discharge from nuclear power plants using satellite, UAV, and numerical simulation.

机构信息

School of Resources and Environmental Engineering, Ludong University, Yantai, 264025, China.

Institute of Coastal Research, Ludong University, Yantai, 264025, China.

出版信息

Environ Monit Assess. 2024 Jul 15;196(8):736. doi: 10.1007/s10661-024-12890-x.

Abstract

Global nuclear power is surging ahead in its quest for global carbon neutrality, eyeing an anticipated installed capacity of 436 GW for coastal nuclear power plants by 2040. As these plants operate, they emit substantial amounts of warm water into the ocean, known as thermal discharge, to regulate the temperature of their nuclear reactors. This discharge has the potential to elevate the temperature of the surrounding seawater, potentially influencing the marine ecosystem in the discharge vicinity. Therefore, our study area is on the Qinshan and Jinqimen Nuclear Power Plants in China, employing a blend of Landsat 8/9, and unmanned aerial vehicle (UAV) imagery to gather sea surface temperature (SST) data. In situ measurements validate the temperature data procured through remote sensing. Leveraging these SST observations alongside hydrodynamic and meteorological data from field measurements, we input them into the MIKE 3 model to prognosticate the three-dimensional (3D) spatial distribution and temperature elevation resulting from thermal discharge. The findings reveal that (1) satellite remote sensing can instantly acquire the horizontal distribution of thermal discharge, but with a spatial resolution much lower than that of UAV. The spatial resolution of UAV is higher, but the imaging efficiency of UAV is only 1/40,000 of that of satellite remote sensing. (2) Numerical simulation models can predict the 3D spatial distribution of thermal discharge. Although UAV and satellite remote sensing cannot directly obtain the 3D spatial distribution of thermal discharge, using remotely sensed SST as the temperature field input for the MIKE 3 model can reduce the quantity of measured temperature data and lower the cost of numerical simulation. (3) In the process of monitoring and predicting the thermal discharge of nuclear power plants, achieving an effective balance between monitoring accuracy and cost can be realized by comprehensively considering the advantages and costs of satellite, UAV, and numerical simulation technologies.

摘要

全球核能在追求全球碳中和的道路上高歌猛进,预计到 2040 年,沿海核电站的预计装机容量将达到 436GW。这些核电站在运行时会向海洋排放大量温水,称为热排放,以调节其核反应堆的温度。这种排放有可能使周围海水温度升高,从而影响排放附近的海洋生态系统。因此,我们的研究区域是中国的秦山和金崎核电厂,采用了 Landsat 8/9 和无人机 (UAV) 图像相结合的方法来收集海面温度 (SST) 数据。现场测量验证了通过遥感获取的温度数据。利用这些 SST 观测值以及现场测量的水动力和气象数据,我们将其输入 MIKE 3 模型,以预测热排放引起的三维 (3D) 空间分布和温度升高。研究结果表明:(1)卫星遥感可以即时获取热排放的水平分布,但空间分辨率远低于无人机。无人机的空间分辨率较高,但无人机的成像效率仅为卫星遥感的 1/40000。(2)数值模拟模型可以预测热排放的三维空间分布。虽然无人机和卫星遥感不能直接获取热排放的三维空间分布,但将遥感 SST 用作 MIKE 3 模型的温度场输入可以减少测量的温度数据量,并降低数值模拟的成本。(3)在监测和预测核电厂热排放的过程中,可以通过综合考虑卫星、无人机和数值模拟技术的优势和成本,实现监测精度和成本之间的有效平衡。

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