周丰, 郭怀成, 黄凯, 郁亚娟, 郝泽嘉. 基于多元统计方法的河流水质空间分析[J]. 水科学进展, 2007, 18(4): 544-551.
引用本文: 周丰, 郭怀成, 黄凯, 郁亚娟, 郝泽嘉. 基于多元统计方法的河流水质空间分析[J]. 水科学进展, 2007, 18(4): 544-551.
ZHOU Feng, GUO Huai-cheng, HUANG Kai, YU Ya-juan, HAO Ze-jia. Multivariate statistical technique for spatial variation in river water quality[J]. Advances in Water Science, 2007, 18(4): 544-551.
Citation: ZHOU Feng, GUO Huai-cheng, HUANG Kai, YU Ya-juan, HAO Ze-jia. Multivariate statistical technique for spatial variation in river water quality[J]. Advances in Water Science, 2007, 18(4): 544-551.

基于多元统计方法的河流水质空间分析

Multivariate statistical technique for spatial variation in river water quality

  • 摘要: 基于聚类分析和判别分析探讨了河流水质空间分析方法,旨在识别采样点的空间相似性与差异性,从而为水质监测网络优化提供支持。该方法首先利用kurtosis和Skewness检验数据分布特征和进行数据对数转化与标准化处理;然后利用聚类分析进行空间相似性分析,确定空间尺度分类情况;最后利用判别分析识别显著性污染指标,以此反映上述空间尺度分类的差异性。以香港后海湾水质管制区为例,结果表明:①通过对数转化显著改善数据分布特征,使绝大部分污染指标呈正态或接近正态分布;②该区域采样点在个案链锁距离与最大链锁距离之比(Dlink/Dmax)×100<35处明显分为3类,它们分别代表轻度、中度、重度污染3种类型,且后两者属于采样点主要属于营养盐和重金属污染类型,需要控制其生活污水、畜牧污染、工业污染和地表径流污染;③后退式判别分析具有良好的指标降维能力,仅需7个显著性污染指标(pH,NH3-N,NO3-N,F.coil,Fe,Ni和Zn)可以反映整体水质的空间差异性,且具有90.65%的正确判别能力;④归纳起来,从3类采样点中选择一个或多个、监测7个显著性污染指标即可全面反映后海湾水质管制区的水质空间特征,实现水质监测网络优化。

     

    Abstract: This paper proposes an integrated approach for the spatial variation in water quality based on the multivariate statistical analysis,i.e.,the cluster analysis (CA) and the discriminant analysis (DA),aimning at identifying the spatial similarity and differences between sampling sites and optimizing the monitoring network.The main procedures of this approach include: (1) checking the normality of all parameter's distribution with the kurtosis and skewness tests,then log-transforming the original data of all parameters,(2) grouping the sampling sites based on CA which was performed on the standardized log-transformed data,and (3) recognizing the discriminant parameters based on DA which can account for most of the expected spatial variation in water quality.The proposed approach is applied to deep bay water control zone in northern Hong Kong,and the results demonstrate that: (1) the distribution of original data is improved after log-transformation,and all parameters are close to the normal distribution; (2) the sampling sites are classified into 3 clusters at (Dlink/Dmax)×100 < 25,i.e,the low,moderate,and highly polluted.Moreover,the later two clusters belong to the nutrient and heavy metal pollutions,thus the domestic wastewater,livestock pollution,industrial pollution and surface runoff should be controlled; (3) in backward stepwise DA on the original data,only seven discriminant parameters (pH,NH3-N,NO3-N,F.coil,Fe,Ni and Zn) in spatial variation are identified and the correct assignations are 90.65% for three cluster sites; and (4) based on spatial variations in water quality,it is possible to optimize the monitoring strategy in future with only one of 3 sampling sites and seven discriminant parameters,which can decrease the number of sampling stations and corresponding costs.

     

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