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基于多元统计方法的河流水质空间分析

周丰 郭怀成 黄凯 郁亚娟 郝泽嘉

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

基金项目: 国家重点基础研究(973)发展计划项目(2005CB724205)
详细信息
    作者简介:

    周丰(1981- ),男,湖南岳阳人,博士研究生,主要从事流域综合管理、环境计量学、GIS等研究.E-mail:jardon.zhou@gmail.com

  • 中图分类号: X824

Multivariate statistical technique for spatial variation in river water quality

Funds: The study is financially supported by the National Key Project for Basic Research and Development Program of China (No.2005CB724205)
  • 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。
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