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首页> 外文期刊>The Science of the Total Environment >Evaluation of heavy metal pollution in the sediment of Poyang Lake based on stochastic geo-accumulation model (SGM)
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Evaluation of heavy metal pollution in the sediment of Poyang Lake based on stochastic geo-accumulation model (SGM)

机译：基于随机累积模型的阳湖沉积物中重金属污染评价

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摘要

The uncertainties introduced by sampling errors, measurement errors, and sediment heterogeneity in the evaluation of heavy metal pollution in the sediment of Poyang Lake are inevitable. The conventional geo-accumulation index (IGeo) cannot overcome these uncertainties. Thus, a stochastic geo-accumulation model (SGM) is established to solve this problem. In the SGM, the distribution of the heavy metal's concentration is stimulated by maximum entropy principle. Then, a membership vector is designed to quantify the pollution condition of each pollutant. Finally, a synthetic membership vector is generated to represent the comprehensive situation of heavy metal pollution in the sediment. SGM is applied in the evaluation of heavy metal pollution in four wetlands of Poyang Lake. Results show that (i) the SGM exhibits better capabilities in uncertainty analysis, risk recognition, and comprehensive pollution evaluation than the conventional IGeo and Hakanson index (HI) models. (ii) The pollution grade of heavy metals in the sediment of Longkou Wetland is "moderately contaminated," whereas the pollution categories in Nanjishan, Wucheng, and Baishazhouwetlands are "uncontaminated to moderately contaminated." (iii) Copper and lead are the key risk factors in Poyang Lake. (c) 2018 Elsevier B.V. All rights reserved.
机译：sampling阳湖沉积物中重金属污染评价中不可避免地存在采样误差，测量误差和沉积物非均质性带来的不确定性。常规的地理积累指数（IGeo）无法克服这些不确定性。因此，建立了一个随机的地质累积模型（SGM）来解决这个问题。在SGM中，最大熵原理刺激了重金属浓度的分布。然后，设计一个隶属向量来量化每种污染物的污染状况。最后，生成了一个合成隶属度向量来代表沉积物中重金属污染的综合情况。 SGM法在four阳湖四个湿地重金属污染评价中的应用。结果表明：（i）与常规的IGeo和Hakanson指数（HI）模型相比，SGM在不确定性分析，风险识别和综合污染评估方面表现出更好的功能。 （ii）龙口湿地沉积物中重金属的污染等级为“中度污染”，而南极山，Wu城和白沙洲湿地的污染类别为“未污染至中度污染”。（iii）铜和铅是Po阳湖的主要危险因素。 （c）2018 Elsevier B.V.保留所有权利。

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