乌江流域水环境质量评价及污染源解析
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摘要
为全面了解乌江流域贵州段的水质污染状况,根据2016年乌江流域贵州段丰水期、平水期、枯水期的水质监测数据,采用单因子和综合水质标识指数对其水质状况进行了评价,并用绝对主成分多元线性回归分析(APCS-MLR)量化不同主成分对各污染物的贡献率。结果表明:溶解氧、总磷在丰水期和平水期水质标识指数高于枯水期,高锰酸盐指数、化学需氧量、五日生化需氧量、氨氮在各水期变化并不明显;氨氮、总磷是乌江流域水环境的主要污染因子,其次为化学需氧量、五日生化需氧量和溶解氧。乌江流域内各小流域之间差异显著,清水河流域水质最差,三水期平均样点超标率高达62%,湘江流域有20%样点超标严重,超标样点均劣于V类水,乌江干流中游水质次之,其余流域样点均处于Ⅱ类以上,且无样点超标;总体上乌江流域丰水期水质受农业面源影响略低于平水期和枯水期。根据PCA(主成分分析)和APCS-MLR分析结果,丰水期第一主成分与氨氮呈显著相关,对其贡献率为45.99%,表明丰水期氨氮为主要污染物,平水期第一主成分主要与化学需氧量、五日生化需氧量、氨氮呈显著相关,对其贡献率分别117.88%,117.39%,118.38%,表明化学需氧量、五日生化需氧量、氨氮为平水期主要污染物,枯水期第一主成分与化学需氧量、五日生化需氧量、氨氮、总磷相关性较高,对其贡献率分别为6.38%,6.08%,6.21%,6.26%,表明这几个因子是枯水期主要污染物。研究表明,污染物主要来源于流域内沿岸乡镇、村寨、部分市县生活污水、生活垃圾排放以及农业面源和磷化工企业的废水排放,水质最差的清水河流域和湘江流域的J33样点受城市点源污染主导。
In order to fully understand the water pollution situation in Guizhou section of the Wujiang River Basin. According to the water quality monitoring data of high water period in Guizhou section of Wujiang River Basin in median water period, low water period of 2016, the water quality status was evaluated by single factor and comprehensive water quality marking index. And absolute principal component multiple linear regression analysis(APCS-MLR) was used to quantify the contribution of different principal components to each pollutant. The results showed that the water quality indexes of dissolved oxygen and total phosphorus in high water period were higher than those in dry water period, permanganate index, chemical oxygen demand, 5 days biochemical oxygen demand and ammonia nitrogen did not change obviously in each water period; total phosphorus was the main pollution factor of water environment, followed by chemical oxygen demand, 5 days biochemical oxygen demand and dissolved oxygen; the water quality of Qingshuihe River Basin was the worst, the average sample point exceeding standard rate is 62% in the three water period, 20% of the sample points in Xiangjiang River Basin exceeded the standard seriously, the exceeding standard point was inferior to water, the water quality of the middle reaches of Wujiang River main stream was the second, and the water quality of the middle reaches of Wujiang River was inferior to that of the water in the middle reaches of Wujiang River. The other watershed samples were above II and no samples exceeded the standard, and the water quality of Wujiang River Basin in the high water period was slightly lower than that in the plain and dry water periods by agricultural non-point sources. According to the results of principal component analysis) and APCS-MLR analysis, the first principal component was significantly correlated with ammonia nitrogen in high water period, and the contribution rate was 45.99, which indicated that ammonia nitrogen was the main pollutant in high water period, and the first principal component was mainly chemical oxygen demand in normal water period. 5 days biochemical oxygen demand and ammonia nitrogen were significantly correlated, and their contribution rates were 117.88%,117.39% and 118.38%, respectively, indicating that chemical oxygen demand, five days′ biochemical oxygen demand, ammonia nitrogen were the main pollutants in the normal water period; the first principal component was correlated to chemical oxygen demand, five days′ biochemical oxygen demand, ammonia nitrogen, and total phosphorus in dry season, respectively, the contribution rates of chemical oxygen demand, five days′ biochemical oxygen demand, ammonia nitrogen, and total phosphorus were 6.38%,6.08%,6.21% and 6.26%, respectively, indicating that these factors were the main pollutants in the dry season. The results show that the pollutants mainly came from the domestic sewage, domestic refuse discharge, agricultural non-point source and phosphorus chemical industry wastewater discharge from towns, villages, and some cities and counties in the river basin. The worst water quality in Qingshui River Basin and Xiangjiang River Basin on J33 testing station was dominated by urban point source pollution.
In order to fully understand the water pollution situation in Guizhou section of the Wujiang River Basin. According to the water quality monitoring data of high water period in Guizhou section of Wujiang River Basin in median water period, low water period of 2016, the water quality status was evaluated by single factor and comprehensive water quality marking index. And absolute principal component multiple linear regression analysis(APCS-MLR) was used to quantify the contribution of different principal components to each pollutant. The results showed that the water quality indexes of dissolved oxygen and total phosphorus in high water period were higher than those in dry water period, permanganate index, chemical oxygen demand, 5 days biochemical oxygen demand and ammonia nitrogen did not change obviously in each water period; total phosphorus was the main pollution factor of water environment, followed by chemical oxygen demand, 5 days biochemical oxygen demand and dissolved oxygen; the water quality of Qingshuihe River Basin was the worst, the average sample point exceeding standard rate is 62% in the three water period, 20% of the sample points in Xiangjiang River Basin exceeded the standard seriously, the exceeding standard point was inferior to water, the water quality of the middle reaches of Wujiang River main stream was the second, and the water quality of the middle reaches of Wujiang River was inferior to that of the water in the middle reaches of Wujiang River. The other watershed samples were above II and no samples exceeded the standard, and the water quality of Wujiang River Basin in the high water period was slightly lower than that in the plain and dry water periods by agricultural non-point sources. According to the results of principal component analysis) and APCS-MLR analysis, the first principal component was significantly correlated with ammonia nitrogen in high water period, and the contribution rate was 45.99, which indicated that ammonia nitrogen was the main pollutant in high water period, and the first principal component was mainly chemical oxygen demand in normal water period. 5 days biochemical oxygen demand and ammonia nitrogen were significantly correlated, and their contribution rates were 117.88%,117.39% and 118.38%, respectively, indicating that chemical oxygen demand, five days′ biochemical oxygen demand, ammonia nitrogen were the main pollutants in the normal water period; the first principal component was correlated to chemical oxygen demand, five days′ biochemical oxygen demand, ammonia nitrogen, and total phosphorus in dry season, respectively, the contribution rates of chemical oxygen demand, five days′ biochemical oxygen demand, ammonia nitrogen, and total phosphorus were 6.38%,6.08%,6.21% and 6.26%, respectively, indicating that these factors were the main pollutants in the dry season. The results show that the pollutants mainly came from the domestic sewage, domestic refuse discharge, agricultural non-point source and phosphorus chemical industry wastewater discharge from towns, villages, and some cities and counties in the river basin. The worst water quality in Qingshui River Basin and Xiangjiang River Basin on J33 testing station was dominated by urban point source pollution.
引文
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[25]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.
[26]Gulgundi M S,Shetty A.Identification and apportionment of pollution sources to groundwater quality[J].Environmental Processes,2016,3(2):1-11.
[27]张嘉嘉,赵景波,董雯,等.关中平原近十年来渭河水环境演变研究[J].干旱区资源与环境,2007,21(1):68-72.
[2]邵田.中国东部城市水环境代谢研究:以上海市为例[D].上海:复旦大学,2008.
[3]Lermontov A,Yokoyama L,Lermontov M,et al.River quality analysis using fuzzy water quality index:Ribeira do Iguape river watershed,Brazil[J].Ecological Indicators,2009,9(6):1188-1197.
[4]武玮,徐宗学,于松延.渭河流域水环境质量评价与分析[J].北京师范大学学报:自然科学版,2013,49(2/3):275-281.
[5]郑倩玉,刘硕,万鲁河,等.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.
[6]Ip W C,Hu B Q,Wong H,et al.Applications of grey relational method to river environment quality evaluation in China[J].Journal of Hydrology,2009,379(3):284-290.
[7]杨振华,苏维词,吴克华,等.基于级别特征值的岩溶含水层水质模糊综合评价修正[J].中国岩溶,2015,34(6):551-559.
[8]宋焱,徐颂军,刘贤赵,等.南沙红树林湿地公园水环境质量时空差异分析:基于改进后倍斜率聚类分析的视角[J].地理科学,2016,36(2):303-311.
[9]郑德凤,张卓,姜俊超.基于熵权的模糊物元模型在水环境质量评价中的应用研究[J].环境科学与管理,2016,41(6):184-187.
[10]李名升,张建辉,梁念,等.常用水环境质量评价方法分析与比较[J].地理科学进展,2012,31(5):617-624.
[11]Singh K P,Malik A,Sinha S.Water quality assessment and apportionment of pollution sources of Gomti river(India)using multivariate statistical techniques:Acase study[J].Analytica Chimica Acta,2005,538(1):355-374.
[12]徐祖信.我国河流单因子水质标识指数评价方法研究[J].同济大学学报:自然科学版,2005,33(3):321-325.
[13]徐祖信.我国河流综合水质标识指数评价方法研究[J].同济大学学报:自然科学版,2005,33(4):482-488.
[14]Parinet B,Lhote A,Legube B.Principal component analysis:an appropriate tool for water quality evaluation and management-application to a tropical lake system[J].Ecological Modelling,2004,178(3/4):295-311.
[15]Liu R X,Kuang J,Gong Q,et al.Principal component regression analysis with SPSS[J].Computer Methods&Programs in Biomedicine,2003,71(2):141-147.
[16]杨学福,王蕾,关建玲,等.基于多元统计分析的渭河西咸段水质评价[J].环境工程学报,2016,10(3):1560-1565.
[17]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.
[18]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.
[19]Yang L,Mei K,Liu X,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 International,2013,20(8):5341-5352.
[20]王庆鹤.典型自然河道形态结构差异对水体自净作用的关系[D].贵阳:贵州大学,2016.
[21]Shrestha S,Kazama F.Assessment of surface water quality using multivariate statistical techniques:A case study of the Fuji river basin,Japan[J].Environmental Modelling&Software,2007,22(4):464-475.
[22]Thurston G D,Spengler J D.A qualitative assessment of source contribution to inhalable particulate matter pollution in metropolitan Boston[J].Atmospheric Environment,1985,19(1):9-25.
[23]Miller S L,Anderson M J,Daly E P,et al.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.
[24]Li Q,Shang L,Gao T,et al.Use of principal component scores in multiple linear regression models for simulation of chlorophyll-a and phytoplankton abundance at a karst deep reservoir,southwest of China[J].Acta Ecologica Sinica,2014,34(1):72-78.
[25]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.
[26]Gulgundi M S,Shetty A.Identification and apportionment of pollution sources to groundwater quality[J].Environmental Processes,2016,3(2):1-11.
[27]张嘉嘉,赵景波,董雯,等.关中平原近十年来渭河水环境演变研究[J].干旱区资源与环境,2007,21(1):68-72.