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Article

Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China

1
School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China
2
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources of the People’s Republic of China, Hangzhou 310012, China
3
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511400, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(3), 763; https://doi.org/10.3390/rs14030763
Submission received: 3 January 2022 / Revised: 29 January 2022 / Accepted: 1 February 2022 / Published: 7 February 2022
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation)

Abstract

Thermal discharge (i.e., warm water) from nuclear power plants (NPPs) in Daya Bay, China, was analyzed in this study. To determine temporal and spatial patterns as well as factors affecting thermal discharge, data were acquired by the Landsat series of remote-sensing satellites for the period 1993–2020. First, sea surface temperature (SST) data for waters off NPPs were retrieved from Landsat imagery using a radiative transfer equation in conjunction with a split-window algorithm. Then, retrieved SST data were used to analyze seasonal and interannual changes in areas affected by NPP thermal discharge, as well as the effects of NPP installed capacity, tides, and wind field on the diffusion of thermal discharge. Analysis of interannual changes revealed an increase in SST with an increase in NPP installed capacity, with the area affected by increased drainage outlet temperature increasing to different degrees. Sea surface temperature and NPP installed capacity were significantly linearly related. Both flood tides (peak spring and neap) and ebb tides (peak spring and neap) affected areas of warming zones, with ebb tides having greater effects. The total area of all warming zones in summer was approximately twice that in spring, regardless of whether winds were favorable (i.e., westerly) or adverse (i.e., easterly). The effects of tides on areas of warming zones exceeded those of winds.
Keywords: Daya Bay Nuclear Power Plants; thermal discharge; long-term changes; Landsat; radiative transfer equation; split-window algorithm; power plant installed capacity; flood tide; ebb tide; wind field Daya Bay Nuclear Power Plants; thermal discharge; long-term changes; Landsat; radiative transfer equation; split-window algorithm; power plant installed capacity; flood tide; ebb tide; wind field

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MDPI and ACS Style

Zhang, Z.; Wang, D.; Cheng, Y.; Gong, F. Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China. Remote Sens. 2022, 14, 763. https://doi.org/10.3390/rs14030763

AMA Style

Zhang Z, Wang D, Cheng Y, Gong F. Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China. Remote Sensing. 2022; 14(3):763. https://doi.org/10.3390/rs14030763

Chicago/Turabian Style

Zhang, Zhihua, Difeng Wang, Yinhe Cheng, and Fang Gong. 2022. "Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China" Remote Sensing 14, no. 3: 763. https://doi.org/10.3390/rs14030763

APA Style

Zhang, Z., Wang, D., Cheng, Y., & Gong, F. (2022). Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China. Remote Sensing, 14(3), 763. https://doi.org/10.3390/rs14030763

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