辽河水环境质量评价及其污染源解析
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摘要
为全面了解松辽河河流水体的污染状况,根据辽河的水质监测数据,采用主成分分析(PCA)对水质污染现状进行综合评价,并在主成分分析计算的相关结果之上进一步进行绝对主成分多元线性回归分析(APCS-MLR),量化了每个主成分对各污染物的贡献率。结果表明:辽河水体的主要污染物包括高锰酸盐指数、COD_(Cr)、BOD_5、NH_3-N和石油类。丰水期第一主成分对NH_3-N、石油类的贡献率分别为49.09%和24.71%;平水期第一主成分对高锰酸盐指数、COD_(Cr)、BOD_5和NH_3-N的贡献率为30.48%、56.16%、26.93%和160.89%;枯水期第一主成分对高锰酸盐指数、COD_(Cr)、BOD_5、NH_3-N和石油类的贡献率分别为100.25%、101.26%、128.36%、93.71%和54.33%。辽河整体水质为Ⅳ类或Ⅴ类水质,在7个监测断面中,双台子河闸水质最差。研究表明,污染物主要受到沿岸城镇居民生活和石油化工企业废水的排放以及农业和畜牧业等面源污染的影响。
In order to fully understand the water pollution status of Liao River, Principal component analysis( PCA) is used to evaluate the status of water pollution according to the water quality monitoring data in 2015. Based on the correlation results of principal component analysis, the absolute principal component scores multiple linear regression analysis(APCS-MLR) is performed to quantify the source contribution rate of each pollution factor. The results showed that the main pollutants in Liao River were permanganate index, COD_(Cr), BOD_5, NH_3-N and Petroleum. The contribution rates of the first principal component to NH_3-N and petroleum in the wet period is 49.09% and 24.71%. The contribution rates of the first principal component to permanganate index, COD_(Cr), BOD_5 and NH_3-N during the flow period were 30.48%, 56.16%, 26.93% and 160.89%, respectively. The contribution rates of the first principal component to permanganate index, COD_(Cr), BOD_5, NH_3-N and petroleum in the dry period were 100.25%, 101.26%, 128.36%, 93.71% and 54.33%, respectively. The overall water quality of the Liao River is class Ⅳ or Ⅴ. Among the seven monitoring sections, the water quality of Shuangtaizi is worst. The research indicates that pollutants are mainly affected by the discharge of wastewater from coastal towns and petrochemical enterprises and non-point source pollution such as agriculture and animal husbandry.
In order to fully understand the water pollution status of Liao River, Principal component analysis( PCA) is used to evaluate the status of water pollution according to the water quality monitoring data in 2015. Based on the correlation results of principal component analysis, the absolute principal component scores multiple linear regression analysis(APCS-MLR) is performed to quantify the source contribution rate of each pollution factor. The results showed that the main pollutants in Liao River were permanganate index, COD_(Cr), BOD_5, NH_3-N and Petroleum. The contribution rates of the first principal component to NH_3-N and petroleum in the wet period is 49.09% and 24.71%. The contribution rates of the first principal component to permanganate index, COD_(Cr), BOD_5 and NH_3-N during the flow period were 30.48%, 56.16%, 26.93% and 160.89%, respectively. The contribution rates of the first principal component to permanganate index, COD_(Cr), BOD_5, NH_3-N and petroleum in the dry period were 100.25%, 101.26%, 128.36%, 93.71% and 54.33%, respectively. The overall water quality of the Liao River is class Ⅳ or Ⅴ. Among the seven monitoring sections, the water quality of Shuangtaizi is worst. The research indicates that pollutants are mainly affected by the discharge of wastewater from coastal towns and petrochemical enterprises and non-point source pollution such as agriculture and animal husbandry.
引文
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[19] 黄旻旻,毛献忠,陶益.东引运河排涝对东江东莞段取水口水质的影响研究[J].水电能源科学,2012,(11):25-28.
[2] SINGH K P,MALIK A,SINHA S.Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques—a case study[J].Analytica Chimica Acta,2005,538(1):355-374.
[3] 郑倩玉.松花江哈尔滨段水环境质量评价及污染源解析[J].环境科学研究,2018,31(3):507-513.
[4] 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.
[5] 马腾飞,陈文波,黄莹波.主成分分析法在高州水库水质评价中的应用[J].四川环境,2018,37(1):65-71.
[6] CAMDEVREN H D N,KANIK A,ET AL.Use of principal component scores in multiple linear regression models for prediction of Chlorophyll in reservoirs[J].Ecological Modelling,2005,181(4):581-589.
[7] ZOU R,LUNG W S,GUO H.Neural Network Embedded Monte Carlo Approach for Water Quality Modeling under Input Information Uncertainty[J].Journal of Computing in Civil Engineering,2002,16(2):135-142.
[8] 颜小品,李玉照,刘永,等.基于结构方程模型的滇池叶绿素a与关键影响因子关系识别[J].北京大学学报(自然科学版),2013,49(6):1031-1039.
[9] 瞿明凯,李卫东,张传荣,等.基于受体模型和地统计学相结合的土壤镉污染源解析[J].中国环境科学,2013,33(5):854-860.
[10] ZHANG Y,GUO F,MENG W,et al.Water quality assessment and source identification of Daliao River Basin using multivariate statistical methods[J].Environmental Monitoring & Assessment,2009,152(1-4):105-121.
[11] ZHOU F,HUANG G H,GUO H,et al.Spatio-temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong[J].Water Research,2007,41(15):3429-3439.
[12] YANG,LIPING,LIU,et al.Spatial distribution and source apportionment of water pollution in;different administrative zones of Wen-Rui-Tang (WRT) river watershed,China[J].Environmental Science & Pollution Research,2013,20(8):5341-5352.
[13] 邹海明,蒋良富,李粉茹.基于主成分分析的水质评价方法[J].数学的实践与认识,2008,38(8):85-90.
[14] 杨学福,王蕾,关建玲,等.基于多元统计分析的渭河西成段水质评价[J].环境工程学报,2016,10(3):1560-1565.
[15] GULGUNDI M S,SHETTY A.Identification and Apportionment of Pollution Sources to Groundwater Quality[J].Environmental Processes,2016,3(2):1-11.
[16] NAZEER S,ALI Z,MALIK R N.Water Quality Assessment of River Soan (Pakistan) and Source Apportionment of Pollution Sources Through Receptor Modeling[J].Archives of Environmental Contamination & Toxicology,2016,71(1):1-16.
[17] MJ M S A,DALY E P,MILFORD J B.Source apportionment of exposures to volatile organic compounds.I.Evaluation of receptor models using simulated exposure data[J].Atmospheric Environment,2002,36(22):3629-3641.
[18] 李义禄,张玉虎,贾海峰,等.苏州古城区水体污染时空分异特征及污染源解析[J].环境科学学报,2014,34(4):1032-1044.
[19] 黄旻旻,毛献忠,陶益.东引运河排涝对东江东莞段取水口水质的影响研究[J].水电能源科学,2012,(11):25-28.
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