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湛江湾红树林湿地表层沉积物重金属形态特征、生态风险评价及来源分析
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  • 英文篇名:Speciation, Ecological Risk Assessment and Source Analysis of Heavy Metals in the Surface Sediments of Mangrove Wetland in Zhanjiang Bay
  • 作者:罗松英 ; 邢雯淋 ; 梁绮霞 ; 邓子艺 ; 刘显兰 ; 全晓文
  • 英文作者:LUO Songying;XING Wenlin;LIANG Qixia;DENG Ziyi;LIU Xianlan;QUAN Xiaowen;Department of Geography, Lingnan Normal University;
  • 关键词:重金属 ; 形态特征 ; 风险评价 ; 因子分析 ; 湛江湾
  • 英文关键词:heavy metal;;speciation characteristics;;risk assessment;;factor analysis;;Zhanjiang Bay
  • 中文刊名:生态环境学报
  • 英文刊名:Ecology and Environmental Sciences
  • 机构:岭南师范学院地理系;
  • 出版日期:2019-02-18
  • 出版单位:生态环境学报
  • 年:2019
  • 期:02
  • 基金:国家自然科学基金项目(41606053);; 广东省自然科学基金项目(2016A030310364);; 广东省教育厅青年创新人才项目(2016KQNCX098);; 岭南师范学院人才引进专项项目(ZL1607)
  • 语种:中文;
  • 页:138-148
  • 页数:11
  • CN:44-1661/X
  • ISSN:1674-5906
  • 分类号:X53;X826
摘要
随着工业化与城市化进程加快,湛江湾重金属污染物入海量逐年增加,沿岸红树林湿地承受着愈来愈大的环境压力。为了解湛江湾红树林表层(0-15 cm)沉积物重金属污染状况,对湾内沉积物中8种重金属元素(As、Cd、Cr、Cu、Hg、Ni、Pb、Zn)的质量分数、赋存形态特征及潜在生态风险进行了研究。采用改进BCR提取法分析重金属形态特征,运用基于总质量分数的潜在生态风险指数法和基于形态学的次生相与原生相分布比值法与风险评估编码法进行风险评价,并结合多元统计方法对重金属来源进行解析。结果表明,(1)沉积物中除Cr、Pb外,其余6种重金属的质量分数平均值均超过雷州半岛土壤环境背景值;8种重金属空间分布差异明显,变异系数在37.24%-108.19%之间。(2)As、Hg以残渣态为主要赋存形态,Cd、Zn以酸提取态为主要赋存形态,Cu、Ni、Pb以可还原态为主要赋存形态,Cr、Ni以可氧化态为主要赋存形态。(3)8种重金属元素非残渣态所占比例排序依次为Cd>Cu>Ni>Zn>Pb>Cr>As>Hg,其中Cd、Cu、Ni、Zn、Pb、Cr的非残渣态所占比例均超过70%,说明重金属元素具有极强的迁移性和生物有效性,再次释放风险较大;非残渣态质量分数高值区出现在NHD和GHCL站位,说明这两个区域红树林沉积物重金属活性较高,重金属污染问题应引起重视。(4)湛江湾红树林表层沉积物中重金属RI均值为273.076,为中等潜在生态风险;而总体RSP值为5.79,表现为重度污染。从空间分布看,各站位重金属元素为中度至重度污染;其中NHD站位重金属污染程度最高,RSP值为11.97,与非残渣态空间分布特征一致。(5)多元统计结果表明,湾内红树林表层沉积物中重金属来源主要与生活排污、养殖排污和工业排污有关,其次为自然风化产物的输入。
        With the acceleration of industrialization and urbanization, more and more heavy metal pollutants are discharged into Zhanjiang Bay, and mangrove wetlands along the coast are under increasing environmental pressure. To investigate the pollution condition of heavy metals in surface sediments(0-15 cm) of mangrove wetland in Zhanjiang Bay, the mass fraction, speciation characteristics and potential ecological risks of 8 heavy metals(As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) in the sediments were studied.The improved BCR extraction method was used to analyze the speciation characteristics of heavy metals. The potential ecological risk index based on total mass fraction, the ratio of secondary phase to primary phase distribution and risk assessment coding method based on morphology were used to evaluate the risk. Also, the sources of heavy metals were analyzed by multivariate statistical method. Five results were obtained. First, except for Cr and Pb, the average mass fraction of the other six heavy metals in sediments exceeded the background value of soil environment in Leizhou Peninsula. The spatial distributions of 8 heavy metals were significantly different, with the coefficient of variation ranging from 37.24% to 108.19%. Second, As and Hg were mainly in residual state, Cd and Zn were mainly in acid extractable state, Cu, Ni and Pb were mainly in reducible state, and Cr and Ni were mainly in oxidable state. Third, the proportion of non-residual state of the 8 heavy metals was Cd>Cu>Ni>Zn>Pb>Cr>As>Hg, and the proportion of non-residual state of Cd, Cu, Ni, Zn, Pb, Cr exceed 70%, indicating that the heavy metals had strong migration and bioavailability, and the risk of re-release was high. The high value area of non-residual mass fraction was mainly distributed in the NHD and GHCL stations, indicating that the heavy metal activity of mangrove sediments in these two areas was high, and heavy metal pollution problems should be paid attention. Forth, heavy metal pollution in the surface sediments of mangrove forests in Zhanjiang Bay was medium potential ecological risk, with the average RI value of 273.076. The total RSP value of heavy metals was5.79, which was a serious pollution. According to the spatial distribution characteristics, the heavy metal pollution in each station reached moderate to severe. Herein, the NHD station had the highest degree of heavy metal pollution, with a RSP value of 11.97,which was consistent with the spatial distribution characteristics of non-residual state. Fifth, the results of multivariate statistics showed that the heavy metals mainly sourced from sewage discharges from domestic pollutant, aquaculture and industry. Weathering also contributed to the input of heavy metals.
引文
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