Spatial distribution, sources, and ecological risk assessment of heavy metals in Lianyungang coastal sediments of China

IF 2.9 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES Environmental Monitoring and Assessment Pub Date : 2025-02-12 DOI:10.1007/s10661-025-13736-w
Wenying Li, Fucheng Liu, Wei Zhang, Xiaoxin Wang
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引用次数: 0

Abstract

Heavy metals are the primary pollutants in surface sediments of coastal area, and they can affect aquatic organisms and human health through accumulation and release into the environment. To investigate the spatial distribution, sources, and ecological hazards of heavy metals, this study analyzed the data on the contents of heavy metals in surface sediments of coastal areas in Lianyungang. The results indicate that significant spatial differences exist in the distribution of these metals. The average contents of As, Cd, Cr, Ni, Pb, and Zn were 5.6, 0.53, 41.4, 21.2, 12.4, and 49.6 mg/kg, respectively. Only the average value of Cd exceeded the background value, while the others were all lower than their respective background values. The ecological risk index of individual elements ranked from high to low is as follows: Cd > As > Ni > Pb > Cr > Zn. Additionally, the comprehensive ecological hazard index suggests that the ecological risk of the coastal sediments in Lianyungang is generally at a low to moderate level, with Cd being the major contributor. Numerous analyses show that Cr, Ni, and Zn mainly stem from industrial wastewater discharge, As and Cd mainly originate from agricultural and aquaculture activities, and Pb mainly comes from port traffic and atmospheric deposition.

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来源期刊
Environmental Monitoring and Assessment
Environmental Monitoring and Assessment 环境科学-环境科学
CiteScore
4.70
自引率
6.70%
发文量
1000
审稿时长
7.3 months
期刊介绍: Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.
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