当前位置: X-MOL 学术Environ. Geochem. Health › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A multivariate statistical approach for monitoring of groundwater quality: a case study of Beri block, Haryana, India.
Environmental Geochemistry and Health ( IF 4.2 ) Pub Date : 2020-07-05 , DOI: 10.1007/s10653-020-00654-8
Vishal Panghal 1 , Rachna Bhateria 1
Affiliation  

The present research deals with assessment of groundwater quality of Beri block, Jhajjar district, Haryana, India and its nearby villages. Multivariate statistics is an efficient technique to display relationship between different limiting factors. Around 24 groundwater samples were collected. A total of 16 variables were analysed: pH, potassium, total dissolved solids (TDS), hardness (calcium, magnesium and total), sulphate, sodium, electrical conductivity and phosphate, chloride (Cl) and heavy metals, namely iron, chromium, lead and zinc. Principal component analysis is one of the commonly used tools in water quality assessment because it effectively reduces number of variables. Multivariate statistical tools “principal component analysis (PCA)” and “cluster analysis” were used to set up relationship among the studied parameters. PCA showed the existence of up to five significant PCs which account for 80.35% of the variance. Few parameters such as pH, sodium, potassium, sulphate, phosphate and zinc were found to be well within limits as approved by WHO and BIS, whereas parameters such as chloride, alkalinity, hardness, total dissolved solids and metals (Pb, Cr and Fe) were found to go beyond the prescribed limits. High levels of hardness, total dissolved solids and chlorides are responsible for saline behaviour of water. The correlation matrices for 16 parameters were executed. EC, TDS, chloride and total hardness were significantly and positively correlated with each other. pH and phosphate (PO42−) were negatively correlated with majority of the physicochemical variables. After studying the physiochemical properties of groundwater samples, it is recommended that water quality parameters should be analysed periodically to conserve the water resources and emphasis should be laid on water quality management practices.



中文翻译:

监测地下水水质的多元统计方法:以印度哈里亚纳邦贝里区为例。

本研究致力于评估印度哈里亚纳邦贾哈杰尔地区贝里区的地下水水质及其附近村庄。多元统计是一种显示不同限制因素之间关系的有效技术。收集了大约24个地下水样品。总共16个变量进行分析:pH值,钾,总溶解固体(TDS),硬度(钙,镁和总计),硫酸根,钠,导电性和磷酸盐,氯化物(氯-)和重金属,即铁,铬,铅和锌。主成分分析是水质评估中常用的工具之一,因为它可以有效减少变量数量。多元统计工具“主成分分析(PCA)”和“聚类分析”用于建立研究参数之间的关系。PCA显示存在多达五台有效PC,占差异的80.35%。发现很少的参数(例如pH,钠,钾,硫酸盐,磷酸盐和锌)完全在WHO和BIS批准的范围内,而参数例如氯化物,碱度,硬度,总溶解固体和金属(铅,铬和铁) )被发现超出规定的限制。高水平的硬度,总溶解的固体和氯化物是水的盐水行为的原因。执行了16个参数的相关矩阵。EC,TDS,氯化物和总硬度之间呈显着正相关。pH和磷酸盐(PO4 2−)与大多数物理化学变量呈负相关。在研究了地下水样品的理化特性后,建议定期分析水质参数以节约水资源,并应将重点放在水质管理实践上。

更新日期:2020-07-05
down
wechat
bug