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作 者:YANG Bing ZHOU Xian HU XiaoJie QIN Chao WANG Jian GAO YanZheng
出 处:《Science China(Technological Sciences)》2024年第10期3079-3090,共12页中国科学(技术科学英文版)
基 金:supported by the National Natural Science Foundation of China(Grant Nos.41925029,42377011,U22A20590,and 22161132011);Jiangsu Provincial Special Fund for S&T Innovation in Carbon Emission Peak and Neutrality(Grant No.20220013);the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20210413);the Yazhou Bay Elite Talent Science and Technology Specialized Project(Grant No.SCKJJYRC-2023-36);the Fundamental Research Funds for the Central Universities(Grant No.XUEKEN2023040).
摘 要:Microplastics have frequently been regarded as a threat to aquatic organisms due to their potential to enhance the accessibility of organic pollutants.However,studies of microplastics as“carriers”in the process of bioaccumulation have only been conducted on a few organic contaminants.In this study,we employed machine learning algorithms to predict the sorption capacity of 83 exogenous organic pollutants(in CCL4)on microplastics,the prediction model exhibits exceptional predictive performance,achieving R^(2) values of 0.8651 and 0.6962,RMSE values of 0.4625 and 0.1841 for the sorption coefficients Kf and n,respectively,and quantitatively estimated the contribution of microplastics to the bioaccumulation levels of organic pollutants in Gadus morhua.By considering both natural and microplastic-ingesting pathways,we demonstrated that the ingestion of microplastics containing organic pollutants facilitates the entry of organic pollutants into Gadus morhua.However,due to their robust sorption capacity,microplastics transport the organic pollutants accumulated through the natural pathway out of the fish,thereby preventing bioaccumulation.These findings elucidate the“cleaning”mechanism of microplastics concerning various organic pollutants in aquatic organisms,providing valuable insights for accurately assessing the risk of microplastics in aquatic environments.
关 键 词:microplastics aquatic organisms organic pollutants machine learning RISKS
分 类 号:X505[环境科学与工程—环境工程] X52
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