{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:07:17Z","timestamp":1775869637399,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u20132020. 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.<\/jats:p>","DOI":"10.3390\/rs14030763","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T08:38:48Z","timestamp":1644223128000},"page":"763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhihua","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China"},{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources of the People\u2019s Republic of China, Hangzhou 310012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7747-3082","authenticated-orcid":false,"given":"Difeng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources of the People\u2019s Republic of China, Hangzhou 310012, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511400, China"}]},{"given":"Yinhe","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China"}]},{"given":"Fang","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources of the People\u2019s Republic of China, Hangzhou 310012, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511400, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"ref_1","first-page":"43","article-title":"Distribution of therm-water pollution of nuclear powerplant using the thermal infrared Band of HJ-IRS data-taking Daya Bay as an example","volume":"2","author":"Liang","year":"2012","journal-title":"Remote Sens. Inf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9364","DOI":"10.1021\/es102260c","article-title":"Characterization factors for thermal pollution in freshwater aquatic environments","volume":"44","author":"Verones","year":"2010","journal-title":"Environ. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1029\/JC080i036p05113","article-title":"Estimation of sea surface temperatures from two infrared window measurements with different absorption","volume":"80","author":"McMillin","year":"1975","journal-title":"J. Geophys. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MGRS.2015.2440094","article-title":"Hyperspectral pansharpening: A review","volume":"3","author":"Loncan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_5","first-page":"7","article-title":"Using MODIS imagery to map sea surface temperature","volume":"4","author":"Liu","year":"2006","journal-title":"Geospat. Inf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5768","DOI":"10.3390\/s140405768","article-title":"Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm","volume":"14","author":"Rozenstein","year":"2014","journal-title":"Sensors"},{"key":"ref_7","first-page":"45","article-title":"A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station","volume":"30","author":"Chen","year":"2018","journal-title":"Remote Sens. Land Resour."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.infrared.2019.04.022","article-title":"Sea surface temperature inversion model for infrared remote sensing images based on deep neural network","volume":"99","author":"Ai","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_9","first-page":"274","article-title":"Study on the temperature rise characteristics and influence effects of thermal discharge from coastal power plant in Xiangshan Bay","volume":"22","author":"Zhang","year":"2013","journal-title":"J. Shanghai Ocean Univ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.marpolbul.2016.07.024","article-title":"Influence of geographic setting on thermal discharge from coastal power plants","volume":"111","author":"Jia","year":"2016","journal-title":"Mar. Pollut. Bull."},{"key":"ref_11","first-page":"26","article-title":"Research on effect of water depthand flow intensity in coastal power plant outfall on warming area","volume":"38","author":"Liu","year":"2017","journal-title":"J. Waterway Harbor"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1175\/JPO2909.1","article-title":"The influence of wind forcing on the Chesapeake Bay buoyant coastal current","volume":"36","author":"Lentz","year":"2006","journal-title":"J. Phys. Oceanogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1007\/s10646-020-02229-w","article-title":"Modeling the ecosystem response of the semi-closed Daya Bay to the thermal discharge from two nearby nuclear power plants","volume":"29","author":"Jiang","year":"2020","journal-title":"Ecotoxicology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.marpolbul.2018.05.008","article-title":"Dynamics of alkaline phosphatase activity in relation to phytoplankton and bacteria in a coastal embayment Daya Bay, South China","volume":"131","author":"Zhang","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"135","DOI":"10.2112\/SI83-022.1","article-title":"Impacts of thermal discharge on phytoplankton in Daya Bay","volume":"83","author":"Ye","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1080\/01431160600580616","article-title":"Monitoring heated water pollution of the DaYaWan nuclear power plant using TM images","volume":"28","author":"Wu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, M., Yin, X., Xu, Q., Chen, Y., and Wang, B. (2020). Monitoring of fine-scale warm drain-off water from nuclear power stations in the Daya Bay based on Landsat 8 data. Remote Sens., 12.","DOI":"10.3390\/rs12040627"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106626","DOI":"10.1016\/j.ecss.2020.106626","article-title":"A split-window method to retrieving sea surface temperature from Landsat 8 thermal infrared remote sensing data in offshore waters","volume":"236","author":"Fu","year":"2020","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_19","first-page":"1351","article-title":"Thematic Mapper thermal infrared calibration","volume":"51","author":"Schott","year":"1985","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.rse.2004.02.003","article-title":"Land surface temperature retrieval from Landsat TM 5","volume":"90","author":"Sobrino","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sekertekin, A., and Bonafoni, S. (2020). Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens., 12.","DOI":"10.3390\/rs12020294"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9829","DOI":"10.3390\/rs6109829","article-title":"Land surface temperature retrieval from Landsat 8 TIRS-comparison between radiative transfer equation-based method, split window algorithm and single channel method","volume":"6","author":"Yu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1080\/01431169608948760","article-title":"Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data","volume":"17","author":"Sobrino","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1007\/s40808-020-01007-1","article-title":"A comparative assessment of the accuracies of split-window algorithms for retrieving of land surface temperature using Landsat 8 data","volume":"7","author":"Zare","year":"2021","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/0034-4257(92)90078-X","article-title":"Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm","volume":"41","author":"Kerr","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1080\/01431160010006971","article-title":"A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region","volume":"22","author":"Qin","year":"2001","journal-title":"Int. J. Rem. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/TGRS.2008.2007125","article-title":"Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval from Landsat Thermal-Infrared Data","volume":"47","author":"Cristobal","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s00376-018-8016-7","article-title":"A 31-year global diurnal sea surface temperature dataset created by an ocean mixed-Layer model","volume":"35","author":"Li","year":"2018","journal-title":"Adv. Atmos. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"544","DOI":"10.3724\/SP.J.1010.2012.00544","article-title":"Research on the distribution of temperature and drainage of nuclear power plants based on the thermal infrared band data of environmental disaster mitigation satellites","volume":"31","author":"Zhou","year":"2012","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_30","first-page":"49","article-title":"Application of temperature rise envelop in thermal discharge from nuclear power plant","volume":"32","author":"Wang","year":"2020","journal-title":"Environ. Monit. Manag. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112020","DOI":"10.1016\/j.marpolbul.2021.112020","article-title":"Quantitative estimation of sea surface temperature increases resulting from the thermal discharge of coastal power plants in China","volume":"164","author":"Lin","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1175\/1520-0426(2003)020<0159:AUGFVT>2.0.CO;2","article-title":"An unstructured grid, finite-volume, three-dimensional, primitive equations ocean model: Application to coastal ocean and estuaries","volume":"20","author":"Chen","year":"2003","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_33","first-page":"69","article-title":"Distribution and variation of warm water discharge in the coastal area of Tianwan","volume":"31","author":"Wang","year":"2013","journal-title":"Adv. Mar. Sci."},{"key":"ref_34","first-page":"55","article-title":"Effects of thermal discharge and nutrients input on size structure of phytoplankton in Daya Ba","volume":"37","author":"Xie","year":"2008","journal-title":"J. Trop. Oceanogr."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/763\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:15:20Z","timestamp":1760134520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/763"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,7]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030763"],"URL":"https:\/\/doi.org\/10.3390\/rs14030763","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,7]]}}}