文章摘要
吴先亮,黄先飞,全文选,胡继伟,秦樊鑫,唐凤华.黔西煤矿区周边土壤重金属形态特征、污染评价及富集植物筛选[J].水土保持通报,2018,38(5):313~321
黔西煤矿区周边土壤重金属形态特征、污染评价及富集植物筛选
Chemical Forms and Risk Assessment of Heavy Metals in Soils and Selected Hyper- tolerant Plants Around a Coal Mining Area in Western Guizhou Province
投稿时间:2018-04-06  修订日期:2018-05-13
DOI:10.13961/j.cnki.stbctb.2018.05.050
中文关键词: 重金属  潜在生态风险  模糊数学法  风险评估编码法  生物富集系数
英文关键词: heavy metals  potential ecological risk assessment  fuzzy mathematics  weighted average model  bio-concentration factors
基金项目:贵州省林业厅项目“废弃煤矿区植被恢复树种选择及适应性研究”(黔林科合[2016]09);贵州省科技厅联合基金(LH[2016]7203);国家111项目(D17016)
作者单位E-mail
吴先亮 贵州师范大学 贵州省山地环境信息系统与生态环境保护重点实验室, 贵州 贵阳 550001  
黄先飞 贵州师范大学 贵州省山地环境信息系统与生态环境保护重点实验室, 贵州 贵阳 550001 hxfswjs@gznu.edu.cn 
全文选 贵州师范大学 贵州省山地环境信息系统与生态环境保护重点实验室, 贵州 贵阳 550001  
胡继伟 贵州师范大学 贵州省山地环境信息系统与生态环境保护重点实验室, 贵州 贵阳 550001  
秦樊鑫 贵州师范大学 贵州省山地环境信息系统与生态环境保护重点实验室, 贵州 贵阳 550001  
唐凤华 贵州师范大学 贵州省山地环境信息系统与生态环境保护重点实验室, 贵州 贵阳 550001  
摘要点击次数: 63
全文下载次数: 65
中文摘要:
      [目的]研究黔西某煤矿区周边土壤重金属污染情况、重金属形态潜在风险及其周边重金属富集植物,为当地的重金属污染防治提供科学依据。[方法]采用潜在生态风险评价及模糊数学法的两种评价方法(单因素决定模型和加权平均模型)对煤矿区及非煤矿区土壤进行重金属生态风险评价,对影响土壤肥力的土壤理化指标进行检测,利用风险评估编码法对重金属形态进行分析,并采用生物富集系数法对煤矿区周边富集重金属植物进行筛选。[结果]煤矿区Hg, Cd, As, Zn, Cr及Ni平均值含量分别是背景值的2.47, 3.65, 2.00, 1.23, 1.74, 1.69倍。煤矿区潜在生态危害趋势为:Cd > Hg > As > Ni > Cr > Pb > Zn。模糊数学法单因素决定模型评价显示,非煤矿区污染大于煤矿区,加权平均模型则反之。煤矿区Cd, Cr, Cu, Mn, Ni, Pb及Zn潜在风险指数分别为69.17%, 7.97%, 8.24%, 40.10%, 45.29%, 53.70%及29.90%。蜈蚣草对As富集系数大于1.00,火棘、构树、盐肤木、马桑、凤尾蕨及金丝梅等对Cd富集系数大于1.00,马桑及白蒿对Pb富集系数大于1.00。[结论]煤矿区存在重金属污染,以Cd, As, Hg较为严重。煤矿区周边土壤中重金属对环境构成的潜在风险顺序为:Cd > Pb > Ni > Mn > Zn > Cu > Cr。对当地而言,蜈蚣草可作为煤矿区周边修复As污染的先行植物,凤尾蕨可作为修复Cd污染的先行植物,马桑可作为修复Pb污染的先行植物。
英文摘要:
      [Objective] To investigate the heavy metal pollution and chemical forms and identify the hypertolerant plants in Western Guizhou Province in order to provide a scientific basis for preventing and controlling heavy metals pollution in the area.[Methods] The ecological risks of heavy metals in mining areas and non-mining areas were evaluated using potential ecological risk and fuzzy mathematic assessment models (the single factor deciding and the weighted average models). The physical and chemical indexes affecting soil fertility were tested. The chemical forms of heavy metals in soil samples were analyzed by risk assessment code. Bio-concentration factors were used to select plants with high tolerance to heavy metals around the coal mining area.[Results] The average concentrations of Hg, Cd, As, Zn, Cr and Ni in coal mining areas were 3.37, 1.11, 1.50, 1.63, 1.23 and 1.73 times higher than the background values. The potential ecological risk of studied heavy metals in coal mining area followed the order of:Cd > Hg > As > Ni > Cr > Pb > Zn. The single factor deciding model of the fuzzy mathematic assessment showed that the pollution of non-mining area was higher than that of mining area, but the weighted average model was opposite. The potential risk indexes of Cd, Cr, Cu, Mn, Ni, Pb and Zn in coal mining area were 69.17%, 7.97%, 8.24%, 40.10%, 45.29%, 53.70% and 29.90%, respectively. The As enrichment coefficient of Pteris vittata was greater than 1.00. The Cd enrichment coefficient of Pyracantha fortuneana, Broussonetia papyrifera, Rhus chinensis, Coriaria nepalensis, P. cretica and Hypericum patulum were greater than 1.00. The Pb enrichment coefficient of C. nepalensis and Artemisia stelleriana were greater than 1.00.[Conclusion] There existed more serious pollution in mining areas, especially Hg, Cd and As pollution. The potential risk of heavy metals in soils around the coal mining area is in the order of Cd > Pb > Ni > Mn > Zn > Cu > Cr. In conclusion, P. vittata can be used as the primary plant for remediation of As pollution surrounding the coal mining area. In addition, P. cretica could be used as the primary plant to repair the Cd pollution, and C. nepalensis could be used as the primary plant to repair the Pb pollution.
查看全文   查看/发表评论  下载PDF阅读器
关闭