搜索

科技期刊

全部分类

在线办公

全部分类
11

基于主成分分析和水质标识指数的地下水评价—张明明,陈 刚,刘耿炜,滕祥帅,华 勇

摘要:为了解盐城市地下水水质现状及其主要污染因子,以盐城市18眼地下水为研究对象,采用主成分分析和水质标识指数法相结合评价地下水水质状况。

基于主成分分析和水质标识指数的地下水评价—张明明,陈 刚,刘耿炜,滕祥帅,华 勇

摘要:为了解盐城市地下水水质现状及其主要污染因子,以盐城市18眼地下水为研究对象,采用主成分分析和水质标识指数法相结合评价地下水水质状况。

分类:2021年第04期(总第163期)

发布: 2021-11-10 20:54:26

详情描述

张明明,陈 刚,刘耿炜,滕祥帅,华 勇

(江苏省水文水资源勘测局盐城分局, 江苏 盐城 224051)

  摘 要:为了解盐城市地下水水质现状及其主要污染因子,以盐城市18眼地下水为研究对象,采用主成分分析和水质标识指数法相结合评价地下水水质状况。结果表明,使用主成分分析可将23个水质指标综合为8个主成分进行解释,解释率为75.967%。利用主成分分析成果构建新的水质评价指标体系并用水质标识指数进行评价,综合水质标识指数表明18眼地下水水质较差,17个为Ⅳ类,1个为Ⅴ类。主要污染因子为溶解性总固体、总硬度、浊度、锰、总大肠菌群、菌落总数,在主要污染因子的确立上水质标识指数更为准确快速。2种评价方法总体趋势基本相同,排名不完全一致。2种模型结合使用比单一模型更加可靠。

  关键词:主成分分析;水质标识指数;地下水;评价;盐城

  Groundwater quality assessment based on principal component analysis and water quality identification indices

  ZHANG Mingming, CHEN Gang, LIU Gengwei, TENG Xiangshuai, HUA Yong

  (Yancheng Hydrology and Water Resources Investigation Bureau of Jiangsu Province, Yancheng 224051, China)

  Abstract: In order to study the groundwater quality and the main pollution factors of Yancheng, eighteen groundwater sites are assessed by combining Principal Component Analysis(PCA) with water quality identification index method. Results show that, by using principal component analysis, 23 water quality indices can be combined into 8 principal component, of which the interpretation rate is 75.967%. Based on PCA results, a new water quality evaluation index system is established and water quality identification index is used to evaluate the water quality. In light of the comprehensive water quality identification indices, the quality of the eighteen groundwater are seriously polluted, of which seventeen sites belongs to Ⅳ, one site belongs to Ⅴ. The main pollution factors are total dissolved solids, total hardness, turbidity, Mn, total coliforms, total bacteria. The comprehensive water quality identification indices are more accurate and quicker on distinguishing the main pollution factors. The ranking of groundwater quality predicted by PCA is not identical with that by comprehensive water quality identification indices, but the overall trend in two models is same, which indicates that combination of the two models are more reliable than that of the single predictive model.

  Key words: Principal Component Analysis(PCA); water quality identification indices; ground water; assessment; Yancheng

  • 基于主成分分析和水质标识指数的地下水评价.pdf
    下载
    下载量:0
扫一扫查看手机版
这是描述信息

水利部南京水利水文自动化研究所

电话:(025)52898300 
地址:南京市雨花台区铁心桥街95号
邮箱:
nsy@nsy.com.cn

版权所有:水利部南京水利水文自动化研究所     苏ICP备05086125号     中企动力  南京

版权所有:水利部南京水利水文自动化研究所     苏ICP备05086125号     中企动力  南京