Multivariate statistical analysis of heavy metal concentration in soils of Yelagiri Hills, Tamilnadu, India – Spectroscopical approach

2015 ◽  
Vol 137 ◽  
pp. 589-600 ◽  
Author(s):  
A. Chandrasekaran ◽  
R. Ravisankar ◽  
N. Harikrishnan ◽  
K.K. Satapathy ◽  
M.V.R. Prasad ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Concentration ◽  
Multivariate Statistical Analysis ◽  
Heavy Metal Concentration ◽  
Multivariate Statistical

Risk estimation and multivariate statistical analysis of the heavy metal content of drinking water samples

2018 ◽  
Vol 34 (10) ◽  
pp. 714-725
Author(s):  
Rajan Jakhu ◽  
Rohit Mehra
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Drinking Water ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Inductively Coupled Plasma ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Drinking Water Samples

Drinking water samples of Jaipur and Ajmer districts of Rajasthan, India, were collected and analyzed for the measurement of concentration of heavy metals. The purpose of this study was to determine the sources of the heavy metals in the drinking water. Inductively coupled plasma mass spectrometry was used for the determination of the heavy metal concentrations, and for the statistical analysis of the data, principal component analysis and cluster analysis were performed. It was observed from the results that with respect to WHO guidelines, the water samples of some locations exceeded the contamination levels for lead (Pb), selenium (Se), and mercury (Hg), and with reference to the EPA guidelines, the samples were determined unsuitable for drinking because of high concentrations of Pb and Hg. Using multivariate statistical analysis, we determined that copper, manganese, arsenic, Se, and Hg were of anthropogenic origin, while Pb, copper, and cadmium were of geogenic origin. The present study reports the dominance of the anthropogenic contributions over geogenics in the studied area. The sources of the anthropogenic contaminants need to be investigated in a future study.

scholarly journals Determination of Spatiotemporal Variations in Heavy Metal Concentration through Orontes River

2017 ◽  
Vol 5 (9) ◽  
pp. 1086 ◽  
Author(s):  
Ece Kılıç ◽  
Mehmet Fatih Can
Keyword(s):  
Heavy Metal ◽  
Metal Concentration ◽  
Heavy Metal Concentration ◽  
Trend Test ◽  
Multivariate Statistical ◽  
Quality Of Water ◽  
Important Concern ◽  
Study Results ◽  
Adequate Quality

Water quality in surface waters is an important concern since it directly affects public health. Therefore, to provide adequate quality of water, monitoring studies have been conducted all around the world. In this study, multivariate statistical methods were used to understand the spatial and temporal changes in heavy metal concentration (including As, Cd, Cr, Cu, Fe, Hg, Mn, Pb, Zn) through Orontes River. Monitoring study results belonging to 5 different stations in Orontes River was obtained from State of Hydraulic Works and evaluated according to Box and Whisker plots, two-way PERMONAVA analysis and Mann-Kendall trend test. Results indicated that heavy metal concentrations has been increasing year by year. However, there is no significant spatial change in heavy metal concentration and rather homogenous distribution was observed through Orontes River. On the other hand, Iron (Fe) concentration in river was seasonal depended with no significant trend.

Heavy Metal Pollution of Soil around the Gangue Hill: A Case Study from Zhuxiangzhuang Coal Mine, Northern Anhui Province, China

2011 ◽  
Vol 356-360 ◽  
pp. 114-118
Author(s):  
Xiang Hong Liu ◽  
Lin Hua Sun ◽  
Song Chen
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Statistical Analysis ◽  
Coal Mine ◽  
Multivariate Statistical Analysis ◽  
Anhui Province ◽  
Multivariate Statistical ◽  
And Cluster Analysis ◽  
Cu And Zn

Heavy metal concentrations of soils around two gangue hills from Zhuxiangzhuang coal mine, northern Anhui province, China had been determined by using X-Ray Fluorescence, and the calculation of enrich factor and index of geo-accumulation, as well as multivariate statistical analysis (including principle component analysis and cluster analysis) had been brought out to light: V, Cr, Fe, Cu and Zn of soils are unpolluted when normalize to soil environmental background value of China. However, when normalized to their minimum concentrations, Zn is light pollution. Two sources of heavy metals have been identified by using multivariate statistical analysis, including lithogenic (V and Fe) and anthropogenic (Cr, Cu and Zn). The soils from the area between two gauge hills have the highest degrees of heavy metals pollution relative to other areas, implying that the method in the Zhuxianzhuang coal mine is useful for environmental protection.

Sign in / Sign up

Export Citation Format

Share Document