坪山河水质时空演变规律研究
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摘要
城市河流水污染防治越来越受到重视,识别河流水质的主要污染物指标和时空变化规律是水质评价和水环境治理的基础性工作。以中国南方典型城市雨源型河流——深圳市坪山河流域为研究对象,利用主成分分析法评价了坪山河3个断面(碧岭、红花潭、上洋) 5年的水质现状(2011年1月至2015年12月)和时空变异特征。结果表明:上游碧岭的水质最好,介于Ⅱ类水与劣Ⅴ类水之间。中游的红花潭和下游的上洋断面水质常年为劣Ⅴ类水。通过污染程度排名,水质总体有变好趋势。坪山河主要超标污染物为氨氮、砷、硫化物和粪大肠菌群,3个断面的主要污染物成分出现了一定的变化,其中氨氮是3个断面共有的主要污染物成分。研究方法和结果可以为城市河流水系水资源保护和水环境治理、污染源防治等提供决策支持。
More and more attention has been paid to the prevention and control of urban river water pollution. It is the basic work of water quality assessment and water environment management to identify the main pollutant index and temporal and spatial change rule of river water quality. Taking Pingshan River Basin, a typical rain-source river in the south of China, as the research object, this paper used the principal component analysis method to evaluate the water quality and temporal and spatial variability characteristics of three sections( Biling, Honghuatan, Shangyang) for five years( January 2011-December 2015). The results showed that the water quality of the upper Biling was the best, between the type II water and the inferior class V water. The water quality of the Shangyang section of the middle and downstream Honghuatan was inferior to the V-type water. Through the ranking of pollution levels, the water quality was getting better on the whole. The main pollutants in Pingshan River were ammonia nitrogen, arsenic, sulfide and fecal coliforms.The main pollutants in the three sections had changed. The ammonia nitrogen was the main pollutant component shared by the three sections. Research methods and results could provide decision support for water resources protection, water environment management,pollution source prevention and control in urban river water system.
More and more attention has been paid to the prevention and control of urban river water pollution. It is the basic work of water quality assessment and water environment management to identify the main pollutant index and temporal and spatial change rule of river water quality. Taking Pingshan River Basin, a typical rain-source river in the south of China, as the research object, this paper used the principal component analysis method to evaluate the water quality and temporal and spatial variability characteristics of three sections( Biling, Honghuatan, Shangyang) for five years( January 2011-December 2015). The results showed that the water quality of the upper Biling was the best, between the type II water and the inferior class V water. The water quality of the Shangyang section of the middle and downstream Honghuatan was inferior to the V-type water. Through the ranking of pollution levels, the water quality was getting better on the whole. The main pollutants in Pingshan River were ammonia nitrogen, arsenic, sulfide and fecal coliforms.The main pollutants in the three sections had changed. The ammonia nitrogen was the main pollutant component shared by the three sections. Research methods and results could provide decision support for water resources protection, water environment management,pollution source prevention and control in urban river water system.
引文
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[21]孙玲玲,刘彬,石宝红,等.基于贝叶斯理论和主成分分析法耦合的水质评价[J].水电能源科学,2017(11):36-39.
[22]王晴晴,陈星,常进.主成分分析法与熵值法结合在水质评价中的应用[J].人民长江,2015(8):10-13.
[23]CHANG H. Spatial and temporal variations of water quality in the Han River and its tributaries,Seoul,Korea,1993–2002[J].Water,Air,and Soil Pollution,2005,161(1-4):267-284.
[24]SENER S,SENER E,DAVRAZ A. Evaluation of water quality using water quality index(WQI)method and GIS in Aksu River(SW-Turkey)[J]. Science of the Total Environment,2017,584:131-144.
[25]SINGH K P,MALIK A,MOHAN D,et al. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River(India)—a case study[J]. Water research,2004,38(18):3980-3992.
[26]张汪寿,李晓秀,王晓燕,等.北运河武清段水污染时空变异特征[J].环境科学学报,2012,32(4):836-846.
[27]拜亚丽.基于熵权的集对分析法在水环境质量评价中的应用[J].地下水,2018,40(5):70-72,195.
[2]NEMEROW N L. Scientific stream pollution analysis[M]. Washington D. C.:Scripta Book Company,1974.
[3]黄丹.基于改进型内梅罗污染指数法的玛纳斯河流域地表水水质评价[J].人民珠江,2015,36(3):103-107.
[4]余勋,粱婕.基于三角模糊数的贝叶斯水质评价模型[J].环境科学学报,2013,33(3):904-909.
[5]施云芬,刘月华,李英赞. 2001~2005年松花湖水质评价.水生态学杂志,2009,30(1):11-15.
[6]杜娟娟,李荣峰.基于单因子指数法的沁河水质评价与分析[J].山西水利科技,2015(4):62-64.
[7]邹国平,彭梅香.数据挖掘技术在水质评价中应用研究[J].计算机仿真,2012,29(1):148-151.
[8]牛红惠,尚艳玲.模糊神经网络在水质评价中的研究[J].计算机仿真,2012(4):173-176.
[9]郭劲松,霍国友,龙腾锐. BOD—DO耦合人工神经网络水质模拟的研究[J].环境科学学报,2001,21(2):140-143.
[10]莫崇勋,阮俞理,莫桂燕,等.基于T-S模糊神经网络模型的钦州市主要河流水质评价[J].人民珠江,2017,38(8):80-83.
[11]崔东文,姜敏.差分进化算法-投影寻踪模型在水质综合评价中的应用[J].人民珠江,2016,37(2):97-101.
[12]史晓新,夏军.水环境质量评价灰色模式识别模型及应用[J].中国环境科学,1997(2):32-35.
[13]杨婷,高军省.基于可拓学的护城河水环境质量评价[J].科技创新与应用,2018(33):65-67.
[14]张敏艳.物元分析法在水环境质量评价中的应用[J].绿色科技,2018(10):87-90.
[15]陈娟,余正,黄燕.水环境质量系数在水质模糊评价中的应用[J].人民珠江,2015,36(2):68-70.
[16]郜慧,金辉.模糊综合评判法在沙坪河流域水质评价中的应用[J].人民珠江,2005(6):50-51,71.
[17]盛周君,孙世群,王京城,等.基于主成分分析的河流水环境质量评价研究[J].环境科学与管理,2007,32(12):172-175.
[18]万金保,曾海燕,朱邦辉.主成分分析法在乐安河水质评价中的应用[J].中国给水排水,2009,25(16):104-108.
[19]廉铁辉,王钊,王洪翠,等.主成分分析法在海河流域多条河流水质评价分析中的应用[J].海河水利,2017(2):29-31.
[20]资惠宇,庞园,张明珠.基于主成分分析法的广州市从化区地下水质量评价[J].人民珠江,2017,38(10):72-76,99.
[21]孙玲玲,刘彬,石宝红,等.基于贝叶斯理论和主成分分析法耦合的水质评价[J].水电能源科学,2017(11):36-39.
[22]王晴晴,陈星,常进.主成分分析法与熵值法结合在水质评价中的应用[J].人民长江,2015(8):10-13.
[23]CHANG H. Spatial and temporal variations of water quality in the Han River and its tributaries,Seoul,Korea,1993–2002[J].Water,Air,and Soil Pollution,2005,161(1-4):267-284.
[24]SENER S,SENER E,DAVRAZ A. Evaluation of water quality using water quality index(WQI)method and GIS in Aksu River(SW-Turkey)[J]. Science of the Total Environment,2017,584:131-144.
[25]SINGH K P,MALIK A,MOHAN D,et al. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River(India)—a case study[J]. Water research,2004,38(18):3980-3992.
[26]张汪寿,李晓秀,王晓燕,等.北运河武清段水污染时空变异特征[J].环境科学学报,2012,32(4):836-846.
[27]拜亚丽.基于熵权的集对分析法在水环境质量评价中的应用[J].地下水,2018,40(5):70-72,195.