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

GU Jialin, LOU Xiulin, ZHANG Huaguo, CAO Wenting, BAI Yaoping

Journal of Marine Sciences ›› 2024, Vol. 42 ›› Issue (4) : 123-137.

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Journal of Marine Sciences ›› 2024, Vol. 42 ›› Issue (4) : 123-137. DOI: 10.3969/j.issn.1001-909X.2024.04.011

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

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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

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GU Jialin , LOU Xiulin , ZHANG Huaguo , et al . Thermal infrared remote sensing advancements in monitoring thermal discharge from coastal power plants[J]. Journal of Marine Sciences. 2024, 42(4): 123-137 https://doi.org/10.3969/j.issn.1001-909X.2024.04.011

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [2]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [3]
[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.
Cited in this article [6]
[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.
Cited in this article [4]
[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.
Cited in this article [8]
[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.
Cited in this article [6]
[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.
Cited in this article [7]
[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.
Cited in this article [3]
[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.
Cited in this article [4]
[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.
Cited in this article [11]
[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.
Cited in this article [1]
[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.
Cited in this article [6]
[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.
Cited in this article [4]
[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
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
https://link.cnki.net/urlid/10.1759.P.20240204.1537.004
Cited in this article [2]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [2]
[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.
https://doi.org/10.11821/xb200104009
Abstract
陆地卫星 TM数据 (TM6)热波段表示地表热辐射和地表温度变化。长期以来,从 TM6数据中演算地表温度通常是通过所谓大气校正法。这一方法需要估计大气热辐射和大气对地表热辐射传导的影响,计算过程很复杂,误差也较大,在实际中应用不多。根据地表热辐射传导方程,推导出一个简单易行并且精度较高的演算方法,把大气和地表的影响直接包括在演算公式中。该算法需要用地表辐射率、大气透射率和大气平均温度 3个参数进行地表温度的演算。验证表明,该方法的地表温度演算较高。当参数估计没有误差时,该方法的地表温度演算精度达到 <0.4℃,在参数估计有适度误差时,演算精度仍达 <1.1℃。因该方法适用于仅有一个热波段的遥感数据,故称为单窗算法。
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.
https://doi.org/10.11821/xb200104009
Cited in this article [3] Abstract
Landsat TM data has a thermal band (TM6) to monitor the thermal radiance and surface temperature of the earth. For a long time, retrieval of surface temperature from TM6 data is done through the method called atmospheric correction, which requires the estimate of atmospheric thermal radiance and absorption. The computation is complicated and the accuracy is strongly subjected to the estimate, hence has few applications in real world. In this paper, a new method with simple computation and high accuracy is developed on the basis of thermal radiance transfer equation of the ground. The impacts of both atmosphere and the emitted ground on the thermal radiance transfer of the ground are directly involved into the proposed algorithm, which requires 3 essential parameters for the retrieval: emissivity, transmittance and atmospheric average temperature. Validation indicates that the accuracy of the algorithm is high up to <0.4℃ for the estimate of the essential parameters with no errors and <1.1℃ for the estimate moderate errors. Since the method is applicable for the remote sensing data with only one thermal band, it has been termed as mono window algorithm.
[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.
Cited in this article [1]
[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.
Cited in this article [2]
[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.
Cited in this article [2]
[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.
Cited in this article [1]
[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.
Cited in this article [2]
[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.
Cited in this article [1]
[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.
Cited in this article [2]
[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.
Cited in this article [2]
[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.
Cited in this article [2]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [3]
[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.
Cited in this article [10]
[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.
Cited in this article [9]
[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.
Cited in this article [3]
[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.
Cited in this article [2]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
[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.
Cited in this article [7]
[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.
Cited in this article [2]
[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.
Cited in this article [1]
[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.
Cited in this article [1]
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