Probabilistic ecological risk assessment of heavy metals in sediments from China’s major aquatic bodies

被引:1
|
作者
Changsheng Qu
Bing Li
Haisuo Wu
Shui Wang
Fengying Li
机构
[1] Jiangsu Academy of Environmental Sciences,Key Laboratory of Environmental Engineering
[2] Nanjing University of Information Science and Technology,School of Environmental Science and Engineering
关键词
Heavy metal; Sediment; Risk assessment; Joint approach; Monte Carlo;
D O I
暂无
中图分类号
学科分类号
摘要
An overall and comparative ecological risk assessment of heavy metals (including Cd, Cr, Cu, Pb, Zn, Hg and As) in surface sediments from China’s eight major aquatic bodies was conducted to better understand their potential risks on a national scale. By applying the joint approach of Hakanson risk index (RI) and Monte Carlo simulation, ecological risk in this work is expressed as probability distribution of RI values instead of single point calculations to reflect the uncertainties in risk assessment process. The results show that the highest ecological risks posed by heavy metals existed in Xiangjiang River and Dianchi Lake. Although only a slim margin of high risk (651.88/600 = 1.08 and 700.61/600 = 1.17) was identified based on average RI values, the probabilities of high risk level derived from Monte Carlo simulation reached as high as 56.7 and 52.9 % in these two aquatic bodies, respectively. And the probability of low risk level was less than 1.6 %. Furthermore, the risk was mainly contributed by Hg and Cd, discharged through local intensive mining and industrial activities. The findings indicate that rigid control and effective management measures to prevent heavy metal pollution are urgently needed in China, especially for the high-risk aquatic bodies. This study shows that the joint approach can be used to identify the high risk water bodies and the major metal pollutants. It may avoid overestimating or underestimating the ecological risk and provide more decision-making support for risk alleviation in the polluted aquatic bodies.
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页码:271 / 282
页数:11
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