Study on the migration of radioactive nuclides Uranium (U) and Thorium (Th) and the properties of solid waste in coal-fired power plants with zero liquid discharge of desulfurization wastewater
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Changan Kang
, Min Liu
, Qiang Yao
Abstract
In order to investigate the migration and distribution characteristics of radioactive nuclides Uranium (U) and Thorium (Th) in coal-fired power plant with zero liquid discharge of desulfurization wastewater, the fly ash, slag, gypsum and particulate matter from a 1000 MW coal-fired power plant were tested and leaching toxicity assessment of fly ash, slag and gypsum was performed.(1)Studies on the migration of radionuclides U and Th show that: U and Th radionuclides introduced by coal burning are mainly enriched in fly ash, U constitutes 88.8 % of the total U content, Th comprises 92.2 % of the total Th content.(2)The enrichment degree of U and Th in solid waste is related to the particle size of solid waste, the content of U and Th in fly ash is 1.67 and 2.19 times that of U and Th in slag. U and Th are more enriched in fine particulate matter, and the enrichment level of Th shows a greater dependence on particle size.(3)Testing of the leaching toxicity of U and Th in solid waste was conducted to evaluate the environmental risks associated with U and Th contaminants, the results show that among all solid waste leachates, the concentration of U in gypsum leachate is the highest, and the concentration of Th in slag leachate is the highest, this suggests that gypsum facilitates U leaching, while slag enhances Th mobility. Gypsum and slag are more likely to impact the environment when exposed. (4) By calculating the input and output of U and Th, verification of the U and Th mass balance was performed. The mass balance rates of U and Th are 128.5 % and 121.4 %, respectively, indicating that the detection data are reliable and acceptable.
Funding source: Hebei Natural Science Foundation
Award Identifier / Grant number: B2022407001
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Research ethics: Not applicable.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author state no conflict of interest.
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Research funding: Supported by Hebei Natural Science Foundation, B2022407001.
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Data availability: Not applicable.
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