Assessment of eutrophication in the Yangtze River estuary and its adjacent waters
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摘要: 根据2004年4个季度2、5、8、11月的调查资料,选择化学耗氧量、溶解氧、活性磷酸盐、溶解无机氮和叶绿素a作为评价因子,利用人工神经网络模型对长江口及其邻近海域的营养水平进行评价。结果表明,富营养化区域主要集中在口门附近,富营养化程度由口门向东和东北方向递减。富营养化范围几乎均分布在盐度小于20的一侧,并随着长江冲淡水的变化而发生季节性的变化。5月和8月的富营养化比较严重,且都有转向东北的趋势,可能与5月进入丰水期长江冲淡水转向有关。断面分布表明,各个季度月由河口向东,富营养化评价等级由高到低,垂直方向则呈现复杂的变化。分析表明影响富营养化的主要评价指标是溶解无机氮。Abstract: According to the investigations of four mid-months (Feb.,May,Aug.,and Nov.)of seasons in 2004,we choose COD,DO,PO4-P,DIN,Chl-a as assessing indexes,and use the artificial neural network to assess eutrophication in the Yangtze River estuary and its adjacent waters.The results show that the waters of eutrophication mainly concentrate round the rivermo uth,and the extent of eutrophication gradually decreases from the rivermouth to the east and northeast.The areas of eutrophication are almost distributed in the side of salinity bellowed 20 and have seasonal variations along with the changes of Yangtze River diluted water.The phenomenon of eutrophication is more serious in May and August than in other months,and both have the trend of turning around to the northeast,which may be caused by the diluted water turnaround with the flood coming in May.The transection distributions of eutrophication in each rmnth indicate that the grades of eutrophication change from high to low level,from rivermouth to the east,and it has complex variations in the vertical direction.The analysis shows that DIN is the main control factor for the assessment results of eutrophication.
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Key words:
- the Yangtze River estuary /
- eutrophication /
- artificial neural network /
- assessment /
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[1] 钟霞芸,杨鸿山,赵立清,等.长江口水域氮、磷的变化及其影响[J].中国水产科学,1999,6(5):6-9. [2] 舒金华.我国湖泊富营养化程度评价方法的探讨[J].环境污染与防治,1990,12(5):2-7. [3] 龚绍琦,黄家柱,李云梅,等.基于GIS下的太湖水质富营养化模糊综合评价[J].环境科学,2005,26(5):34-37. [4] 胡守仁,余少波,戴葵.神经网络导论[M].长沙:国防科学技术大学出版社,1993. [5] 张立明.人工神经网络的模型及其应用[M].上海:复旦大学出版社,1993. [6] 周继成,周青山,韩飘扬.人工神经网络——第六代计算机的实现[M].北京:科学普及出版社,1993. [7] 王冬云,黄焱歆.海水富营养化评价的人工神经网络方法[J].河北建筑科技学院学报,2001,18(4):27-29. [8] 楼文高.湖库富营养化人工神经网络评价模型[J].水产学报,2001,25(5):474-478. [9] 任黎,董增川,李少华.人工神经网络模型在太湖富营养化评价中的应用[J].河海大学学报,2004,32(2):147-150. [10] 全为民,沈新强,韩金娣,等.长江口及邻近水域富营养化现状及变化趋势的评价与分析[J].海洋环境科学,2005,24(3):13-16. [11] 杨红,李曰嵩.长江口水质人工神经网络模型的建立及现状评价[J].上海水产大学学报,2002,11(1):32-36. [12] 王伟.人工神经网络原理[M].北京:北京航空航天大学出版社,1995.20-76. [13] 姚云,沈志良.胶州湾海水富营养化水平评价[J].海洋科学,2004,28(6):14-22. [14] 阮仕平,党志良,胡晓寒,等.人工神经网络在综合水质评价中的应用[J].水资源研究,2004,25(2):21-23. [15] 叶仙森,张勇,项有堂.长江口海域营养盐的分布特征及其成因[J].海洋通报,2000,19(1):89-92. [16] 潘玉球,王康,黄树生.长江冲淡水输运和扩散途径的分析[J].东海海洋,1997,15(2):25-34. [17] 冈市友利.浅海的污染与赤潮的发生,内湾赤潮的发生机制[R].东京:日本水产资源保护协会,1972.58-76. [18] 彭云辉,王肇鼎.珠江河口富营养化水平评价[J].海洋环境科学,1991,10(3):7-13. [19] 薛建军,姚桂基.人工神经网络在湟水水质综合评价中的应用[J].青海环境,1997,7(1):25-29. -
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