On the Implementation of POSEIDON-R to the northwestern Pacific region with application to Fukushima-Daiichi accident

DC Field Value Language
dc.contributor.author R. Bezhenar -
dc.contributor.author V. Maderich -
dc.contributor.author Brovchenko -
dc.contributor.author 정경태 -
dc.contributor.author 명정구 -
dc.contributor.author 조양기 -
dc.contributor.author F. Qiao -
dc.date.accessioned 2020-07-16T08:50:04Z -
dc.date.available 2020-07-16T08:50:04Z -
dc.date.created 2020-02-11 -
dc.date.issued 2013-06-14 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/26918 -
dc.description.abstract The compartment model POSEIDON-R was applied after some modifications to the Northwestern Pacific and adjacent seas to simulate the transport and fate of radioactivity in the period 1945-2010 and to perform a radiological assessment on the releases of radioactivity due to the Fukushima Dai-ichi accident for the period 2011-2040. The model predicts the dispersion of radioactivity in the water column and in sediments, and the transfer of radionuclides throughout the marine food web, and the subsequent doses to the population due to the consumption of marine products. A generic predictive dynamical food-chain model is used instead of the biological concentration factor (BCF) approach. The model was validated on observation data on 137Cs for the period 1955-2010. Calculated concentrations of 137Cs in the water, bottom sediments and marine organisms before and after the Fukushima Dai-ichi accident are close to measurements from the Japan national agencies. The dynamical food web model predicts that due to the delay of the transfer throughout the food web, the concentration values of 137Cs for non-piscivorous and piscivorous fishes return to background level only in 2015 and 2017, respectively, while it takes place in 2012 according to the prediction by BCF approach. Following the Fukushima Dai-ichi accident the collective dose rate due to ingestion of marine products for Japan increased in 2011 with a factor more than thrreleases of radioactivity due to the Fukushima Dai-ichi accident for the period 2011-2040. The model predicts the dispersion of radioactivity in the water column and in sediments, and the transfer of radionuclides throughout the marine food web, and the subsequent doses to the population due to the consumption of marine products. A generic predictive dynamical food-chain model is used instead of the biological concentration factor (BCF) approach. The model was validated on observation data on 137Cs for the period 1955-2010. Calculated concentrations of 137Cs in the water, bottom sediments and marine organisms before and after the Fukushima Dai-ichi accident are close to measurements from the Japan national agencies. The dynamical food web model predicts that due to the delay of the transfer throughout the food web, the concentration values of 137Cs for non-piscivorous and piscivorous fishes return to background level only in 2015 and 2017, respectively, while it takes place in 2012 according to the prediction by BCF approach. Following the Fukushima Dai-ichi accident the collective dose rate due to ingestion of marine products for Japan increased in 2011 with a factor more than thr -
dc.description.uri 1 -
dc.language English -
dc.publisher FIO-KIOST -
dc.relation.isPartOf 2nd China-Korea Workshop on oceanic monitoring and development of prediction system of radionuclides for nuclear safety -
dc.title On the Implementation of POSEIDON-R to the northwestern Pacific region with application to Fukushima-Daiichi accident -
dc.type Conference -
dc.citation.conferencePlace CC -
dc.citation.endPage 21 -
dc.citation.startPage 21 -
dc.citation.title 2nd China-Korea Workshop on oceanic monitoring and development of prediction system of radionuclides for nuclear safety -
dc.contributor.alternativeName 정경태 -
dc.contributor.alternativeName 명정구 -
dc.identifier.bibliographicCitation 2nd China-Korea Workshop on oceanic monitoring and development of prediction system of radionuclides for nuclear safety, pp.21 -
dc.description.journalClass 1 -
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