Assessment of heavy metal(loid)s in groundwater by multivariate statistical analysis and metals pollution indices: a case study of Çarşamba coastal aquifer, North Turkey

2021 ◽  
Vol 14 (23) ◽  
Author(s):  
Abdourazakou Maman Hassan ◽  
Arzu Fırat Ersoy ◽  
Nazlı Ayyildiz Turan
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Coastal Aquifer ◽  
Pollution Indices ◽  
Multivariate Statistical ◽  
Metals Pollution

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.

scholarly journals Data analysis of heavy metal content in riverwater: multivariate statistical analysis and inequality expressions

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yasemin Leventeli ◽  
Fusun Yalcin
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Statistical Methods ◽  
Multivariate Statistical Analysis ◽  
Metal Content ◽  
Applied Mathematics ◽  
Heavy Metal Content ◽  
Pollution Indices ◽  
Multivariate Statistical ◽  
The Mediterranean

AbstractThe purpose of this paper is to use multivariate statistical methods with asymmetric distributions approach, chemical analysis, and inductively coupled plasma–mass spectrometry (ICP-MS) device. We investigate data of heavy metal content from Akcay Riverwater to the Mediterranean involving Finike sea coast at Turkey. We determine the chemical content, origin of heavy metals of the surface water in Akcay River, which flows into the Mediterranean realted to the above-mentioned region by multivariate statistical analysis, pollution indices, and density maps involving numerical comments by numbers. With the help of special numbers represented by special chemical components and simmetric statistical methods given above, in this paper, we obtain many new relations and results. Furhermore, we give some comments, observations, and remarks about the results of this paper. These results have a high potential to be used not only in engineering fields and health sciences, but also in applied mathematics, statistics, and other fields.

Assessment of Heavy Metals Content in the Proximity of a Contaminated Site From Cluj-Napoca (Romania)

2020 ◽  
Vol 65 (2) ◽  
pp. 17-37
Author(s):  
Georgiana Grosu ◽  
◽  
Carmen Andreea Roba ◽  
Ramona Bălc ◽  
Maria Lucia Bizău-Cârstea ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Sediment Contamination ◽  
Contaminated Site ◽  
Pollution Indices ◽  
Chemical Parameters ◽  
Multivariate Statistical ◽  
Water And Sediment ◽  
Physico Chemical

The present study was conducted in the proximity of a contaminated site from Cluj-Napoca city (Cluj County, Romania), where metal processing activities have been carried out for decades. Metal content and physico-chemical parameters were analyzed in soil, water and sediment samples, while organic matter (OM) and total organic carbon (TOC) was additionally analyzed for the soil samples. The sources of heavy metals were evaluated based on multivariate statistical analysis, while the soil and sediment contamination degree was assessed based on specific pollution indices. The calculated indices indicated a significant pollution with Cd and Pb, which may represent a risk if the area would become a residential area. Keywords: heavy metals, contaminated site, soil pollution indices, multivariate statistical analysis, Cluj-Napoca

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.

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