Thermal infrared remote sensing advancements in monitoring thermal discharge from coastal power plants

doi:10.3969/j.issn.1001-909X.2024.04.011

Abstract

Abstract:

Accurate monitoring and assessment of the temperature rise intensity and spatio-temporal distribution of thermal discharge from power plants are of great significance for ensuring safe operations of the power plants and protecting the ecological environment of surrounding marine areas. Based on thermal infrared remote sensing observation technology, information such as the spatial distribution, temperature rise intensity, and temporal variation of thermal discharge from coastal power plants can be obtained, making it an important means for monitoring thermal discharge from these plants. The current research status and progress in monitoring thermal discharge from coastal power plants using thermal infrared remote sensing technology are systematically reviewed, including remote sensing data sources, water temperature retrieval, thermal discharge background temperature information extraction, spatio-temporal statistics of thermal discharge, and analysis of influencing factors. Furthermore, the paper proposes future directions for remote sensing research on thermal discharge from coastal power plants.

Key words: coastal power plants, thermal discharge, thermal infrared remote sensing, background temperature, influencing factors, remote sensing monitoring, thermal pollution, sea surface temperature

CLC Number:

GU Jialin, LOU Xiulin, ZHANG Huaguo, CAO Wenting, BAI Yaoping. Thermal infrared remote sensing advancements in monitoring thermal discharge from coastal power plants[J]. Journal of Marine Sciences, 2024, 42(4): 123-137.

Figures/Tables 11

Tab.1 Information of thermal infrared remote sensing sensor and platform for thermal discharge monitoring

卫星	有效载荷	传感器工作时间	空间分辨率/ m	重访周期/ d	波段/ μm	研究案例	主要结论
NOAA	AVHRR	1979年至今	1 100	1	10.5~12.5	大亚湾核电厂^[11] 永光核电厂^[12]	空间分辨率低,难以获取温排水温升细节
AQUA/TERRA	MODIS	2002年至今/ 2000年至今	1 000	1	10.8~12.3	大亚湾核电厂^[13-14] 田湾核电厂^[21] 红沿河核电厂^[15] 乐清湾火电厂^[16]	空间分辨率低,导致混合像元效应
HJ-1B	IRS	2008年至今	300	4	10.5~12.5	大亚湾核电厂^[13-14,18] 田湾核电厂^[17,21-22] 红沿河核电厂^[15] 乐清湾火电厂^[16]	时间分辨率较高,但还不足以反映精细的温排水温升等级及范围
AQUA/TERRA	ASTER	2002年至今/ 2000年至今	90	16	10.3~11.7	希舍姆核电厂^[19] 塞兹韦尔核电厂^[19]	温排水信息丰富,但晴空数量少,不适合用于温排水日常监测
CBERS-04	IRS	2014年至今	80	26	10.4~12.5	田湾核电厂^[21]	温排水信息与MODIS一致,但条带噪声限制了其数据使用
						永光核电厂^[12] 大亚湾核电厂^[14,18] 田湾核电厂^[21-22] 红沿河核电厂^[15] 乐清湾火电厂^[16] 希舍姆核电厂^[19] 塞兹韦尔核电厂^[19] 秦山核电站^[23]	可以反映更为精细的温度场边缘,监测到的最高温升级别更高,能更准确地描述温排水对周边海域热影响的分布状况
Landsat-5	TM	1984—2012年	120	16	10.4~12.5
Landsat-7	ETM+	1999年至今	60	16	10.4~12.5
Landsat-8	TIRS	2013年至今	100	16	10.6~11.2
Landsat-9	TIRS2	2021年至今	100	16	10.6~11.2

Fig.1 Distribution of temperature rise of thermal discharge from Daya Bay Nuclear Power Plant based on the average temperature correction method (Figure was redrawn from the reference [14].)

Fig.2 Distribution of temperature rise of thermal discharge from Tianwan Nuclear Power Plant based on the adjacent-zone substitution method (Figure was redrawn from the reference [47].)

Fig.3 Reference zone in Daya Bay using adjacent-zone substitution method (Figure was redrawn from the reference [48].)

Fig.4 Radius selection of background areas in Daya Bay-Lingao Nuclear Power Plant (White triangle represents outlet, white circles represent different background areas, and red dashed circle is the final background areas.) (Figure was redrawn from the reference [49].)

Fig.5 Sea surface temperature profile on the same latitude as the Tianwan Nuclear Power Plant (Figure was redrawn from the reference [50].)

Fig.6 Statistical distribution of areas with different temperature rise grades of thermal discharge from Tianwan Nuclear Power Plant from 2001 to 2020 (Figure was redrawn from the reference [57].)

Fig.7 Seasonal variation of temperature rise envelope of thermal discharge from Daya Bay Nuclear Power Plant from 1993 to 2020 (Figure was redrawn from the reference [48].)

Fig.8 Probability density map of thermal discharge from Heysham Nuclear Power Plant in the UK from 2000 to 2019 (Figure was redrawn from the reference [19].)

Fig.9 High-resolution true color images of thermal discharge from deep and shallow outlet types (Figure was redrawn from the reference [49].)

Fig.10 Seasonal distribution of thermal drainage from Tianwan Nuclear Power Plant (Figure was redrawn from the reference [57].)

References 60

[1]	李慧鹏. 火电厂冷却技术改造的成本效益分析:以江苏为例[D]. 南京: 南京大学, 2017.
	LI H P. Cost-benefit analysis of cooling technology transfor-mation in thermal power plant—Taking Jiangsu as an example[D]. Nanjing: Nanjing University, 2017.
[2]	曾江宁. 滨海电厂温排水对亚热带海域生态影响的研究[D]. 杭州: 浙江大学, 2008.
	ZENG J N. Study on the ecological impact of warm drainage from coastal power plants on subtropical waters[D]. Hangzhou: Zhejiang University, 2008.
[3]	黄晓琛, 陈雪初, 彭欣, 等. 滨海电厂温排水对海洋环境的影响研究进展[J]. 海洋环境科学, 2014, 33(6):972-976.
	HUANG X C, CHEN X C, PENG X, et al. The research progress of the impact of thermal discharge from coastal power plants on marine environment[J]. Marine Environmental Science, 2014, 33(6): 972-976.
[4]	陈惠泉, 许玉麟, 贺益英. 火/核电厂冷却水试验研究50年的进展和体验[J]. 中国水利水电科学研究院学报, 2008, 6(4):288-298.
	CHEN H Q, XU Y L, HE Y Y. Progress and experience of 50 years’ study on cooling water circulation of thermal/nuclear power plants[J]. Journal of China Institute of Water Resources and Hydropower Research, 2008, 6(4): 288-298.
[5]	程杭平, 韩曾萃. 热污染的一、二维耦合模型及其应用[J]. 水动力学研究与进展:A辑, 2002, 17(6):647-655.
	CHENG H P, HAN Z C. 1-D and 2-D coupling modeling for thermal pollution and application[J]. Journal of Hydrodynamics, 2002, 17(6): 647-655.
[6]	ZENG G G, GUAN W B, ZENG J N, et al. 3D modeling of the thermal effluents dispersion from power plants in the Xiangshan Bay[J]. Energy Education Science and Technology Part A: Energy Science and Research, 2011, 28(1): 71-82.
[7]	许静. 基于HJ-1B红外相机监测宁德核电温排水研究[D]. 北京: 中国地质大学, 2014.
	XU J. The study on monitoring thermal discharge of Ningde nuclear power plant with HJ-1B infrared camera data[D]. Beijing: China University of Geosciences, 2014.
[8]	滨海核电厂温排水卫星遥感监测技术规范(试行):HJ 1213—2021[S]. 2022.
	Technical specification to thermal discharge monitoring for coastal nuclear power plants based on satellite remote sensing(on trial): HJ 1213—2021[S]. 2022.
[9]	朱利, 吴国增, 张爱玲, 等. 我国滨海核电厂温排水遥感监测与评价技术研究及应用[J]. 中国科技成果, 2014(9):21-23.
	ZHU L, WU G Z, ZHANG A L, et al. Research and application of remote sensing monitoring and evaluation technology for thermal discharge of coastal nuclear power plants in China[J]. China Science and Technology Achieve-ments, 2014(9): 21-23.
[10]	李嵘. 遥感技术在水环境监测中的应用研究[J]. 江西化工, 2005(4):49-51.
	LI R. Application of remote sensing technology in water environment monitoring[J]. Jiangxi Chemical Industry, 2005(4): 49-51.
[11]	TANG D L, KESTER D R, WANG Z D, et al. AVHRR satellite remote sensing and shipboard measurements of the thermal plume from the Daya Bay, nuclear power station, China[J]. Remote Sensing of Environment, 2003, 84(4): 506-515.
[12]	AHN Y H, SHANMUGAM P, LEE J H, et al. Application of satellite infrared data for mapping of thermal plume contamination in coastal ecosystem of Korea[J]. Marine Environmental Research, 2006, 61(2): 186-201.
[13]	梁珊珊, 张兵, 李俊生, 等. 环境一号卫星热红外数据监测核电站温排水分布——以大亚湾为例[J]. 遥感信息, 2012, 27(2):41-46.
	LIANG S S, ZHANG B, LI J S, et al. Distribution of therm-water pollution of nuclear power plant using the thermal infrared band of HJ-IRS data-taking Daya Bay as an example[J]. Remote Sensing Information, 2012, 27(2): 41-46.
[14]	张彩, 朱利, 贾祥, 等. 不同空间分辨率热红外数据在近海核电厂温排水监测一致性研究[J]. 遥感信息, 2015, 30(2):71-76.
	ZHANG C, ZHU L, JIA X, et al. Consistency of different spatial resolution thermal infrared data in monitoring thermal discharge of nuclear power plant in coastal area[J]. Remote Sensing Information, 2015, 30(2): 71-76.
[15]	WANG X, WANG X X, FAN J C, et al. Comparison of different spatial resolution thermal infrared data in monitoring thermal plume from the Hongyanhe nuclear power plant[C]// 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 10-15, 2016, Beijing, China. IEEE, 2016: 4649-4652.
[16]	MA P, DAI X Y, GUO Z Y, et al. Detection of thermal pollution from power plants on China’s eastern coast using remote sensing data[J]. Stochastic Environmental Research and Risk Assessment, 2017, 31(8): 1957-1975.
[17]	朱利. 基于环境一号红外相机的田湾核电厂温排水遥感监测研究[C]// 第十六届中国环境遥感应用技术论坛论文集, 2012:84-89.
	ZHU L. Remote sensing monitoring of thermal discharge from Tianwan nuclear power plant based on HJ-1 infrared camera[C]// Proceedings of the 16th China environmental remote sensing application technology forum, 2012: 84-89.
[18]	许静, 朱利, 姜建, 等. 基于HJ-1B与TM热红外数据的大亚湾核电基地温排水遥感监测[J]. 中国环境科学, 2014, 34(5):1181-1186.
	XU J, ZHU L, JIANG J, et al. Monitoring thermal discharge in Daya Bay plant based on thermal infrared band of HJ-1B and TM remote sensing[J]. China Environmental Science, 2014, 34(5): 1181-1186.
[19]	FAULKNER A, BULGIN C E, MERCHANT C J. Coastal tidal effects on industrial thermal plumes in satellite imagery[J]. Remote Sensing, 2019, 11(18): 2132.
[20]	ABRAMS M, HOOK S J. Simulated ASTER data for geologic studies[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(3): 692-699.
[21]	石海岗, 梁春利, 张建永, 等. 基于CBERS-04星田湾核电温排水遥感监测研究[J]. 地理空间信息, 2019, 17(12):75-79,10.
	SHI H G, LIANG C L, ZHANG J Y, et al. Remote sensing monitoring of thermal water discharge in Tianwan nuclear power plant based on CBERS-04[J]. Geospatial Information, 2019, 17(12): 75-79, 10.
[22]	石海岗, 张春雷, 张建永, 等. Landsat8和HJ-1B海面温度监测可靠性和一致性研究[J]. 地理空间信息, 2017, 15(11):67-70,87,10.
	SHI H G, ZHANG C L, ZHANG J Y, et al. Research on reliability and consistency of the sea temperature monitoring based on Landsat8 and HJ-1B[J]. Geospatial Information, 2017, 15(11): 67-70, 87, 10.
[23]	石海岗, 梁春利, 薛庆, 等. 基于卫星遥感的秦山核电周边海域温度分布研究[J/OL]. 自然资源遥感. https://link.cnki.net/urlid/10.1759.P.20240204.1537.004.
	SHI H G, LIANG C L, XUE Q, et al. Study on tempera-ture distribution in the sea area around Qinshan nuclear power plant based on satellite remote sensing[J/OL]. Remote Sensing for Natural Resources. https://link.cnki.net/urlid/10.1759.P.20240204.1537.004
[24]	甘甫平, 陈伟涛, 张绪教, 等. 热红外遥感反演陆地表面温度研究进展[J]. 国土资源遥感, 2006, 18(1):6-11.
	GAN F P, CHEN W T, ZHANG X J, et al. The progress in the study of thermal infrared remote sensing for retrieving land surface temperature[J]. Remote Sensing for Land & Resources, 2006, 18(1): 6-11.
[25]	祝善友, 张桂欣, 尹球, 等. 地表温度热红外遥感反演的研究现状及其发展趋势[J]. 遥感技术与应用, 2006, 21(5):420-425.
	ZHU S Y, ZHANG G X, YIN Q, et al. Actualities and development trends of the study on land surface temperature retrieving from thermal infrared remote sensing[J]. Remote Sensing Technology and Application, 2006, 21(5): 420-425.
[26]	陈瀚阅. 劈窗算法陆表温度反演精度比较与敏感性分析[D]. 福州: 福建师范大学, 2010.
	CHEN H Y. Accuracy comparison and sensitivity analysis of land surface temperature inversion based on split window algorithm[D]. Fuzhou: Fujian Normal University, 2010.
[27]	覃志豪, ZHANG M H, KARNIELI A, 等. 用陆地卫星TM6数据演算地表温度的单窗算法[J]. 地理学报, 2001, 56(4):456-466. DOI
	QIN Z H, ZHANG M H, KARNIELI A, et al. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data[J]. Acta Geographica Sinica, 2001, 56(4): 456-466. DOI
[28]	覃志豪, KARNIELI A. 用NOAA-AVHRR热通道数据演算地表温度的劈窗算法[J]. 国土资源遥感, 2001, 13(2):33-42.
	QIN Z H, KARNIELI A. Split window algorithms for retrieving land surface temperature from NOAA-AVHRR data[J]. Remote Sensing for Land & Resources, 2001, 13(2): 33-42.
[29]	谷洪钦, 邹国良, 马进荣. Landsat-8 TIRS数据在某核电站温排水监测中应用探讨[J]. 海洋科学, 2016, 40(10):76-81.
	GU H Q, ZOU G L, MA J R. Thermal plume monitoring of nuclear power plant based on Landsat-8 TIRS data[J]. Marine Sciences, 2016, 40(10): 76-81.
[30]	熊攀, 朱利, 顾行发, 等. 基于HJ-1B星热红外数据的大亚湾海温反演模型及其应用[J]. 国土资源遥感, 2014, 26(1):132-138.
	XIONG P, ZHU L, GU X F, et al. Sea water temperature retrieval model for Daya Bay based on HJ-1B thermal infrared remote sensing data and its application[J]. Remote Sensing for Land & Resources, 2014, 26(1): 132-138.
[31]	JIMÉNEZ-MUÑOZ J C, SOBRINO J A. A generalized single-channel method for retrieving land surface tempera-ture from remote sensing data[J]. Journal of Geophysical Research: Atmospheres, 2003, 108(D22): 4688.
[32]	BONANSEA M, FERRERO S, FERRAL A, et al. Assessing water surface temperature from Landsat imagery and its relationship with a nuclear power plant[J]. Hydrological Sciences Journal, 2021, 66(1): 50-58.
[33]	QIN Z, KARNIELI A, BERLINER P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region[J]. International Journal of Remote Sensing, 2001, 22(18): 3719-3746.
[34]	LIN Y, GUO Z Y, KAN P P, et al. Research on thermal discharge pollution of Xiangshan power plant based on remote sensing[J]. Advanced Engineering Forum, 2012, 6/7: 128-134.
[35]	纪成, 蒋廷臣, 罗文奇. 基于Landsat-8数据的田湾核电站温排水分布研究[J]. 海洋测绘, 2020, 40(2):57-60.
	JI C, JIANG T C, LUO W Q. Study on the distribution of thermal drainage in Tianwan nuclear power station based on landsat-8 data[J]. Hydrographic Surveying and Charting, 2020, 40(2): 57-60.
[36]	徐进, 王海霞, 曲颖丽, 等. 核电厂温排水遥感定量监测方法研究[J]. 地理信息世界, 2019, 26(1):82-86.
	XU J, WANG H X, QU Y L, et al. Research on thermal effluent quantitative remote sensing monitoring method for nuclear power station[J]. Geomatics World, 2019, 26(1): 82-86.
[37]	王祥, 苏岫, 王新新, 等. 基于Landsat-8卫星数据的红沿河核电站温排水监测[J]. 红外, 2015, 36(8):22-27.
	WANG X, SU X, WANG X X, et al. Thermal plume monitoring of Hongyanhe Nuclear Power Plant based on Landsat-8 satellite data[J]. Infrared, 2015, 36(8): 22-27.
[38]	SUNDER S, RAMSANKARAN R, RAMAKRISHNAN B. Machine learning techniques for regional scale estimation of high-resolution cloud-free daily sea surface temperatures from MODIS data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 166: 228-240.
[39]	LINS I D, ARAUJO M, DAS CHAGAS MOURA M, et al. Prediction of sea surface temperature in the tropical Atlantic by support vector machines[J]. Computational Statistics & Data Analysis, 2013, 61: 187-198.
[40]	AI B, WEN Z, JIANG Y C, et al. Sea surface temperature inversion model for infrared remote sensing images based on deep neural network[J]. Infrared Physics & Technology, 2019, 99: 231-239.
[41]	ZHU L, YIN S J, WU C Q, et al. Remote sensing monitoring of thermal discharge in Daya Bay Nuclear Power Station based on HJ-1 infrared camera[C]// Ocean Remote Sensing and Monitoring from Space, Beijing, China. SPIE, 2014: 182-187.
[42]	CHEN C Q, SHI P, MAO Q W. Application of remote sensing techniques for monitoring the thermal pollution of cooling-water discharge from nuclear power plant[J]. Journal of Environmental Science and Health Part A, Toxic/Hazardous Substances & Environmental Engineering, 2003, 38(8): 1659-1668.
[43]	张春雷, 高丽辉, 石海岗, 等. 田湾核电温排水遥感监测与数模一致性分析[J]. 环境监测管理与技术, 2018, 30(3):67-71.
	ZHANG C L, GAO L H, SHI H G, et al. Consistency analysis on remote sensing monitoring and numerical simulation of cooling water from Tianwan nuclear power plant[J]. The Administration and Technique of Environmental Monitoring, 2018, 30(3): 67-71.
[44]	成丰, 朱利, 吴传庆, 等. 基于遥感数据的核电站温排水季节性分布监测分析:以阳江核电站为例[J]. 环境保护, 2017, 45(4):44-48.
	CHENG F, ZHU L, WU C Q, et al. Application of remote sensing data for monitoring seasonal distribution of thermal plume from nuclear power plant: A case study of Yangjiang nuclear power plant[J]. Environmental Protection, 2017, 45(4): 44-48.
[45]	韩涛, 张阔, 王方, 等. 基于Landsat8的海阳核电厂周边海域水温监测[J]. 地理空间信息, 2022, 20(9):81-85,94.
	HAN T, ZHANG K, WANG F, et al. Sea surface tempera-ture monitoring around Haiyang nuclear power plant based on Landsat8[J]. Geospatial Information, 2022, 20(9): 81-85, 94.
[46]	王虎. 基于卫星热红外数据的田湾核电温排水监测[J]. 地理空间信息, 2022, 20(5):48-53.
	WANG H. Thermal discharge monitoring of Tianwan nuclear power plant based on satellite thermal infrared data[J]. Geospatial Information, 2022, 20(5): 48-53.
[47]	石海岗, 王虎, 章新益, 等. 基于遥感技术的田湾核电周边海域环境监测[J]. 地理空间信息, 2023, 21(1):94-100.
	SHI H G, WANG H, ZHANG X Y, et al. Sea region environment monitoring surrounding Tianwan nuclear power plant based on remote sensing technology[J]. Geospatial Information, 2023, 21(1): 94-100.
[48]	ZHANG Z H, WANG D F, CHENG Y H, et al. Long-term changes and factors that influence changes in thermal discharge from nuclear power plants in Daya Bay, China[J]. Remote Sensing, 2022, 14(3): 763.
[49]	WEI J W, FENG L, TONG Y, et al. Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning[J]. Remote Sensing of Environ-ment, 2023, 295: 113707.
[50]	DAI X Y, GUO Z Y, LIN Y, et al. Application of satellite remote sensing data for monitoring thermal discharge pollution from Tianwan nuclear power plant in Eastern China[C]// 2012 5th International Congress on Image and Signal Processing, October 16-18, 2012, Chongqing, China. IEEE, 2012: 1019-1023.
[51]	周颖, 巩彩兰, 匡定波, 等. 基于环境减灾卫星热红外波段数据研究核电厂温排水分布[J]. 红外与毫米波学报, 2012, 31(6):544-549.
	ZHOU Y, GONG C L, KUANG D B, et al. Distribution of thermal discharge from a power plant: Analysis of thermal infrared data from the environmental mitigation satellite[J]. Journal of Infrared and Millimeter Waves, 2012, 31(6): 544-549.
[52]	ROY P, RAO I N, MARTHA T R, et al. Discharge water temperature assessment of thermal power plant using remote sensing techniques[J]. Energy Geoscience, 2022, 3(2): 172-181.
[53]	MUTHULAKSHMI A L, NATESAN U, FERRER V A, et al. A novel technique to monitor thermal discharges using thermal infrared imaging[J]. Environmental Science Processes & Impacts, 2013, 15(9): 1729-1734.
[54]	LIU Y L, HUANG S T, YI P Y, et al. The thermal discharge retrieve and analysis of Hongyanhe district with Landsat-8 and GF-5 image[J]. IOP Conference Series: Earth and Environmental Science, 2020, 569(1): 012062.
[55]	HUANG S J, LIN J T, LO Y T, et al. The coastal sea surface temperature changes near the nuclear power plants of northern Taiwan province observed from satellite images[C]// OCEANS 2014-TAIPEI, April 7-10, 2014, Taipei, China. IEEE, 2014: 1-5.
[56]	WANG D F, PAN D L, WEI J A, et al. Monitoring thermal discharge from a nuclear plant through Landsat 8[C]// Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2016, Edinburgh, United Kingdom. SPIE, 2016: 99991E.
[57]	NIE P J, WU H, XU J, et al. Thermal pollution monitoring of Tianwan nuclear power plant for the past 20 years based on Landsat remote sensed data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 6146-6155.
[58]	王任飞, 杨红艳, 朱利, 等. 温升包络线在核电站温排水监测中的应用[J]. 环境监测管理与技术, 2020, 32(1):49-52.
	WANG R F, YANG H Y, ZHU L, et al. Application of temperature rise envelop in thermal discharge from nuclear power plant[J]. The Administration and Technique of Environmental Monitoring, 2020, 32(1): 49-52.
[59]	YAN E P, LIN H, WANG G X, et al. Multi-resolution mapping and accuracy assessment of forest carbon density by combining image and plot data from a nested and clustering sampling design[J]. Remote Sensing, 2016, 8(7): 571.
[60]	张琨, 张爱玲, 覃春丽, 等. CORMIX与二维数模、物模在滨海核电厂温排水近区模拟中的比对研究[J]. 海洋科学进展, 2017, 35(1):117-123.
	ZHANG K, ZHANG A L, QIN C L, et al. Comparison between CORMIX and traditional models on the simulation of coastal nuclear power plant water discharge[J]. Advances in Marine Science, 2017, 35(1): 117-123.