Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan
Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan
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DOI：
10.1016/j.envsoft.2006.02.001
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发表时间：
2007-04-01
影响因子：
4.9
通讯作者：
Kazama, F.
中科院分区：
文献类型：
--
作者：
Shrestha, S.;Kazama, F.
采用聚类分析（CA）、主成分分析（PCA）、因子分析（FA）和判别分析（DA）等多元统计方法，对富士河流域8年（1995-2002年）13个站点（14976个观测值）12个参数的大型复杂水质数据集进行了时空变化评价和解释。基于水质特征的相似性，分层聚类分析将13个采样点分为相对低污染（LP）、中度污染（MP）和高度污染（HP） 3类。对聚类分析得到的三组数据集进行因子分析/主成分分析，得到5个、5个和3个潜在因子分别解释了LP、MP和HP地区水质数据集总方差的73.18%、77.61%和65.39%。因子分析得到的变异因子表明，在污染相对较少的地区，影响水质变化的参数主要与排放和温度（自然）、有机污染（点源：生活污水）有关；中等污染地区的有机污染（点源：生活废水）和营养物（非点源：农业和果园种植园）；有机污染物和营养物（点源：生活污水、污水处理厂和工业）在流域高污染地区。判别分析在空间分析和时间分析中均获得最佳结果。它提供了重要的数据简化，因为它仅使用6个参数（放电、温度、溶解氧、生化需氧量、电导率和硝酸盐氮），在时间分析中提供了85%以上的正确率，在空间分析中提供了81%以上的正确率。盆地三个不同采样点的照片。因此，数据分析可以降低大型数据集的维数，描绘出导致水质大变化的几个指标参数。因此，本研究说明了多元统计技术在分析和解释复杂数据集、水质评估、污染源/因素识别和了解水质时空变化方面的有用性，从而实现有效的河流水质管理。(c) 2006 Elsevier Ltd.版权所有。
Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the Fuji river basin, generated during 8 years (1995-2002) monitoring of 12 parameters at 13 different sites (14 976 observations). Hierarchical cluster analysis grouped 13 sampling sites into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, based on the similarity of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the three different groups obtained from cluster analysis, resulted in five, five and three latent factors explaining 73.18, 77.61 and 65.39% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The varifactors obtained from factor analysis indicate that the param eters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point source: domestic wastewater) in relatively less polluted areas; organic pollution (point source: domestic wastewater) and nutrients (non-point sources: agriculture and orchard plantations) in medium polluted areas; and organic pollution and nutrients (point sources: domestic wastewater, wastewater treatment plants and industries) in highly polluted areas in the basin. Discriminant analysis gave the best results for both spatial and temporal analysis. It provided an important data reduction as it uses only six parameters (discharge, temperature, dissolved oxygen, biochemical oxygen demand, electrical conductivity and nitrate nitrogen), affording more than 85% correct assignations in temporal analysis, and seven parameters (discharge, temperature, biochemical oxygen demand, pH, electrical conductivity, nitrate nitrogen and ammonical nitrogen), affording more than 81% correct assignations in spatial analysis, of three different sampling sites of the basin. Therefore, DA allowed a reduction in the dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management. (c) 2006 Elsevier Ltd. All rights reserved.