长江流域资源与环境 >> 2015, Vol. 24 >> Issue (05): 824-831.doi: 10.11870/cjlyzyyhj201505015

• 生态环境 • 上一篇    下一篇

盲数优化地积累模型评价长江中下游湖泊沉积物重金属污染

弓晓峰1,2, 孙明哲2, 陈春丽1,2, 王佳佳2, 刘春英2,3, 杨菊云2, 向洪锐2, 方亮2   

  1. 1. 南昌大学鄱阳湖环境与资源利用教育部重点实验室, 江西 南昌 330047;
    2. 南昌大学资源环境与 化工学院, 江西 南昌 330031;
    3. 江西财经大学旅游和城市管理学院, 江西 南昌 330032
  • 收稿日期:2014-04-14 修回日期:2014-05-29 出版日期:2015-05-20
  • 作者简介:弓晓峰(1962~),女,教授,博士,主要从事重金属污染与植物修复研究.E-mail:xfgong@ncu.edu.cn
  • 基金资助:
    国家自然科学基金项目资助(41261097,21067008);江西省教育厅基金项目(GJJ14206);鄱阳湖环境与资源教育部重点实验室(南昌大学)开放基金(SKLURE2008-1-4);南昌大学分析测试基金(2008ZX07209-010)

EVALUATION OF HEAVY METAL POLLUTION IN THE SEDIMENTS OF THE LAKES FROM THE MIDDLE AND LOWER REACHES OF YANGTZE RIVER BY OPTIMIZING GEO-ACCUMULATION MODEL BASED ON BLIND NUMBER THEORY

GONG Xiao-feng1,2, SUN Ming-zhe2, CHEN Chun-li1,2, WANG Jia-jia2, LIU Chun-ying2,3, YANG Ju-yun2, XIANG Hong-rui2, FANG Liang2   

  1. 1. Key Laboratory of Poyang Lake Environment and Resource Utilization of Ministry of Education; Nanchang University; Nanchang 330047, China;
    2. School of Resource Environment and Chemical Engineering, Nanchang University, Nanchang 330031, China;
    3. School of the Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330032, China
  • Received:2014-04-14 Revised:2014-05-29 Online:2015-05-20
  • Contact: 陈春丽 E-mail:hnclchen@163.com

摘要: 盲数优化地积累模型是基于最大隶属度原则和隶属度加权相结合的一种不确定性评价方法。鉴于污染评价系统多种不确定信息共存的特点, 将盲数优化地积累模型应用于长江中下游不同类型湖泊沉积物中重金属污染程度评价中。根据计算出的可能值区间及可信度看空间分布的均匀性, 以及评价等级的可信度水平, 辨识污染程度和等级, 减小局部污染对区域整体污染程度的影响。结果表明:象湖、鄱阳湖、洞庭湖3个湖泊表层沉积物中的重金属的空间分布都不均匀, 象湖表层沉积物中重金属的污染情况为:Pb > Cu > Zn, 其中Pb为中度污染, Cu为轻度污染, Zn为清洁;鄱阳湖沉积物中重金属污染评价结果为Cu﹥Pb﹥Zn, 其中Cu为中度污染, Pb为偏中度污染, Zn为偏重污染;洞庭湖沉积物中重金属污染评价结果为Cu≈Pb≈Zn, 且均为轻度污染。盲数优化地积累模型方法可行, 与定性评价结果基本一致, 但在对污染等级判定上更真实可靠。弥补了传统确定性方法的不足, 更真实、更客观地表征了评价区域沉积物重金属的富集污染程度。

关键词: 盲数, 地累积指数, 沉积物, 重金属

Abstract: Currently, there are many methods for evaluating heavy metal pollution, and most of these methods are based on deterministic approaches rather than uncertainty evaluation methods. Recently, the Blind Number Theory is attracting more and more international attention for the evaluation of heavy metal pollution. This theory is based on an uncertainty point of view. Optimizing geo-accumulation model based on the Blind Number Theory is an uncertainty evaluation method, and it combines the principle of maximum degree of membership and the weighting degree of membership. Application of the Blind Number Theory in the area of pollution evaluation was different from the traditional deterministic pollution assessment method. The Blind Number Theory is suitable for the coexistence and uncertainty complex systems, especially suitable for evaluation of heavy metal contamination. Considering that the pollution assessment system involves many uncertain characteristics of information, the blind number optimization geo-accumulation model was applied to evaluate the heavy metal pollution of the sediments in the different types of lakes from the middle and lower reaches of Yangtze River based on the Blind Number Theory. Three typical heavy metals including Copper (Cu), lead (Pb) and zinc (Zn) were selected as the main indicators for heavy metals contamination evaluation. According to the equality of the spatial distribution from the computed possible value and reliability as well as the rating reliability level, the pollution degree and level were identified, and the influence of the local pollution on overall regional pollution was reduced. The results showed that the spatial distributions of heavy metals from the surface sediments of Xianghu Lake, Poyang Lake, Dongting Lake were not uniform. The heavy metal pollution of the surface sediments from Xiang Lake showed a trend of Pb > Cu > Zn. Pb was a moderate pollution, and Cu was slightly polluted, but Zn was clean. The heavy metal pollution in the sediments of Poyang Lake showed a trend of Cu > Pb > Zn. Cu was a moderate pollution, Pb was a partially moderate pollution, and Zn was a partially heavy pollution. The heavy metal pollution from the sediments of Dongting Lake was Cu ≈ Pb ≈ Zn, at a level of slight pollution. The blind number optimization geo-accumulation model was a feasible method, which was basically the same as the qualitative evaluation results, but more reliable to the determination of the pollution level. Moreover, the optimizing geo-accumulation model based on the Blind Number Theory could make the deficiencies of the traditional deterministic method and it could demonstrate the heavy metal accumulation pollution degree of the sediments in the evaluated regions more authentically and objectively. This method could provide new ideas for the methods of heavy metal pollution evaluation, and it can also provide a more reliable basis for environmental management and decision-making.

Key words: blind theory, index of geo-accumulation, sediment, heavy metal

中图分类号: 

  • X820.4
[1] MULLER G.Index of geoaccumulation in sediments of the Rhine River[J].Geojournal, 1969, 2(3):108-118.
[2] HAKANSON L.An ecological risk index for aquatic pollution control-A sediment logical approach[J].Water Research, 1980, 14(8):975-1001.
[3] CHERNOFF H.The use of faces to represent points in K-dimensional space graphically[J].Journal of the American Statistical Association, 1973, 68(342):361-368.
[4] HILTON J, DAVISON W, OCHSENBEIN U.A mathematical model for analysis of sediment core data:implications for enrichment factor calculations and trace-metal transport mechanisms[J].Chemical Geology, 1985, 48(1-4):281-291.
[5] LI W, ZHANG X, WU B, et al.A comparative analysis of environmental quality assessment methods for heavy metal-contaminated soils[J].Pedospher, 2008, 18(3):344-352.
[6] 刘开第, 吴和琴, 庞彦军, 等.盲数可信度的概念及BM模型[J].运筹与管理, 1998, 7(4):43-50.
[7] 李如忠, 潘成荣, 陈婧, 等.基于盲数理论的城市表土与灰尘重金属污染健康风险评价模型[J].环境科学学报, 2013, 33(1):276-285.
[8] 庞彦军.盲数理论在煤矿开采中的应用[J].数量经济技术经济研究, 2000, 17(2):7-10.
[9] 李如忠, 钱家忠, 汪家权, 等.基于盲数理论的河流水质未确知风险分析初探[J].水电能源科学, 2003, 21(1):18-21.
[10] 蔡臣, 黄涛, 贺玉龙, 等.基于盲数理论的湖泊富营养化总磷平衡浓度模型应用研究[J].安全与环境学报, 2012, 12(5):130-133.
[11] 唐晓娇, 黄瑾辉, 李飞, 等.基于盲数理论的水体沉积物重金属污染评价模型[J].环境科学学报, 2012, 32(5):1104-1112.
[12] 尹念辅, 李铁松, 左云霞, 等.基于盲数理论的四川省西充河水环境容量研究[J].水土保持通报, 2012, 32(1):233-237.
[13] 陈剑勇, 蔡红梅.苏浩益.盲数理论在发电系统可靠性评估中的应用[J].电力系统保护与控制, 2012, 40(13):74-77, 83.
[14] 孟衡, 李建林.基于盲数理论的关键块体稳定性分析研究[J].水电能源科学, 2009, 27(3):117-119.
[15] 王磊, 张明文, 王秋莎, 等.基于盲数理论的输电系统可靠性评估[J].电力科学与工程, 2010, 26(8):4-9.
[16] 赵志峰, 徐卫亚.基于盲数理论的边坡安全稳定分析研究[J].岩土力学, 2007, 28(11):2401-2404.
[17] 弓晓峰, 陈春丽, 周文斌, 等.鄱阳湖底泥中重金属污染现状评价[J].环境科学, 2006, 27(4):732-736.
[18] 李鸣, 刘琪璟.鄱阳湖水体和底泥重金属污染特征与评价[J].南昌大学学报(理科版), 2010, 34(5):486-489, 494.
[19] 朱志军, 聂逢君, 胡青华, 等.江西省德兴矿区土壤重金属污染的综合评价分析[J].东华理工学院学报, 2007, 30(4):332-336.
[20] FÖRSTNER U.Lecture Notes in Earth Science(contaminated sediments)[M].Berlin:Springer Verlag, 1989: 107-109.
[21] 刘开第, 吴和琴, 庞彦军, 等.不确定性信息数学处理及应用[M].北京:科学出版社, 1999:160-183.
[22] 姚志刚, 鲍征宇, 高璞, 等.洞庭湖沉积物重金属环境地球化学[J].地球化学, 2006, 35(6):629-638.
[1] 夏芳, 王秋爽, 蔡立梅, 杨超, 冯志州, 唐翠华, 卫瀛海, 许振成. 有色冶金区土壤-蔬菜系统重金属污染特征及健康风险分析[J]. 长江流域资源与环境, 2017, 26(06): 865-873.
[2] 张玉柱, 黄春长, 庞奖励, 查小春, 周亚利, 石彬楠, 李晓刚. 基于HEC-RAS模型的汉江上游旬阳西段超长尺度古水文演化重建[J]. 长江流域资源与环境, 2017, 26(05): 755-764.
[3] 李杨, 李海东, 施卫省, 何俊德, 胡亚文. 基于神经网络的土壤重金属预测及生态风险评价[J]. 长江流域资源与环境, 2017, 26(04): 591-597.
[4] 王秀, 王振祥, 潘宝, 周春财, 刘桂建. 南淝河表层水中重金属空间分布、污染评价及来源[J]. 长江流域资源与环境, 2017, 26(02): 297-303.
[5] 党丽娜, 梅杨, 廖祥森, 刘颖颖. 城市不同交通圈(带)土壤重金属多元统计分析及空间分布研究——以武汉市为例[J]. 长江流域资源与环境, 2016, 25(06): 925-931.
[6] 朱强, 杨世伦, 孟翊, 杨海飞, 吴创收, 史本伟. 近期长江口南港河槽沉积地貌变异及其可能原因[J]. 长江流域资源与环境, 2016, 25(04): 560-566.
[7] 赵丽, 姜霞, 王雯雯, 王书航, 常乐, 陈俊伊. 丹江口水库表层沉积物不同形态氮的赋存特征及其生物有效性[J]. 长江流域资源与环境, 2016, 25(04): 630-637.
[8] 赵敏, 张丽旭. 长江口海域表层沉积物环境质量的综合评价[J]. 长江流域资源与环境, 2016, 25(02): 284-291.
[9] 陆亚萍, 姚敏. 龙感湖表层沉积硅藻探究[J]. 长江流域资源与环境, 2015, 24(12): 2047-2053.
[10] 李正阳, 袁旭音, 王欢, 许海燕, 陈海龙, 鲁朝朋. 西苕溪干流水体、悬浮物和表层沉积物中营养盐分布特征与水质评价[J]. 长江流域资源与环境, 2015, 24(07): 1150-1156.
[11] 蒋豫, 刘新, 高俊峰, 蔡永久. 江苏省浅水湖泊表层沉积物中重金属污染特征及其风险评价[J]. 长江流域资源与环境, 2015, 24(07): 1157-1162.
[12] 柳云龙, 章立佳, 庄腾飞, 韩晓非, 卢小遮. “城郊乡”梯度下土壤Cu、Zn、Pb含量的空间变异特征[J]. 长江流域资源与环境, 2015, 24(07): 1207-1213.
[13] 余光辉, 云琨, 翁建兵, 朱佳文, 张勇. 湘潭锰矿重金属环境安全及植物耐性研究[J]. 长江流域资源与环境, 2015, 24(06): 1046-1051.
[14] 孙婷婷, 唐涛, 申恒伦, 张长群, 孙美琴, 李斌, 蔡庆华. 香溪河流域不同介质中碳、氮、磷的分布特征及相关性研究[J]. 长江流域资源与环境, 2015, 24(05): 853-859.
[15] 刘足根, 彭昆国, 方红亚, 李惠民, 廖 兵. 江西大余县荡坪钨矿尾矿区自然植物组成及其重金属富集特征[J]. 长江流域资源与环境, 2010, 19(2): 220-.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 李 娜,许有鹏, 陈 爽. 苏州城市化进程对降雨特征影响分析[J]. 长江流域资源与环境, 2006, 15(3): 335 -339 .
[2] 张 政, 付融冰| 杨海真, 顾国维. 水量衡算条件下人工湿地对有机物的去除[J]. 长江流域资源与环境, 2007, 16(3): 363 .
[3] 孙维侠, 赵永存, 黄 标, 廖菁菁, 王志刚, 王洪杰. 长三角典型地区土壤环境中Se的空间变异特征及其与人类健康的关系[J]. 长江流域资源与环境, 2008, 17(1): 113 .
[4] 许素芳,周寅康. 开发区土地利用的可持续性评价及实践研究——以芜湖经济技术开发区为例[J]. 长江流域资源与环境, 2006, 15(4): 453 -457 .
[5] 郝汉舟, 靳孟贵, 曹李靖, 谢先军. 模糊数学在水质综合评价中的应用[J]. 长江流域资源与环境, 2006, 15(Sup1): 83 -87 .
[6] 刘耀彬, 李仁东. 现阶段湖北省经济发展的地域差异分析[J]. 长江流域资源与环境, 2004, 13(1): 12 -17 .
[7] 陈永柏,. 三峡工程对长江流域可持续发展的影响[J]. 长江流域资源与环境, 2004, 13(2): 109 -113 .
[8] 时连强,李九发,应 铭,左书华,徐海根. 长江口没冒沙演变过程及其对水库工程的响应[J]. 长江流域资源与环境, 2006, 15(4): 458 -464 .
[9] 翁君山,段 宁| 张 颖. 嘉兴双桥农场大气颗粒物的物理化学特征[J]. 长江流域资源与环境, 2008, 17(1): 129 .
[10] 王书国,段学军,姚士谋. 长江三角洲地区人口空间演变特征及动力机制[J]. 长江流域资源与环境, 2007, 16(4): 405 .