Appraisal of heavy metal contamination in sediments of the Shitalakhya River in Bangladesh using pollution indices, geo-spatial, and multivariate statistical analysis

2020 ◽  
Vol 13 (21) ◽  
Author(s):  
Md. Humayun Kabir ◽  
Md. Sirajul Islam ◽  
Md. Enamul Hoq ◽  
Tanmoy Roy Tusher ◽  
Md. Saiful Islam
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Pollution Indices ◽  
Multivariate Statistical

scholarly journals Evaluation of the Radiological and Chemical Risk for Public Health from Flour Sample Investigation

2021 ◽  
Vol 11 (8) ◽  
pp. 3646
Author(s):  
Francesco Caridi ◽  
Giuseppe Acri ◽  
Alberto Belvedere ◽  
Vincenza Crupi ◽  
Maurizio D’Agostino ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Metal Contamination ◽  
Inductively Coupled Plasma ◽  
Heavy Metal Contamination ◽  
Specific Activity ◽  
Single Type ◽  
Multivariate Statistical ◽  
Inductively Coupled

Flour investigation, in terms of physical and chemical pollutants and mineral content, is of great interest, in view of its high consumption for nutritional purposes. In this study, eleven types of flour (five samples for each one), coming from large retailers and employed by people for different cooking food purposes, were investigated through high-purity germanium (HPGe) gamma spectrometry, in order to estimate natural (40K) and anthropogenic (137Cs) radioisotope specific activity and thus, to assess the radiological risk due to the flour ingestion. Inductively-coupled plasma mass spectrometry (ICP-MS) and inductively-coupled plasma emission spectroscopy (ICP-OES) were also employed to evaluate any possible heavy metal contamination and the mineral composition, and to perform multivariate statistical analysis to deduce the flour authenticity. The evaluation of dose levels due to flour ingestion was performed, for the age category higher than 17 years, taking into account the average yearly consumption in Italy and assuming this need to be satisfied from a single type of flour as a precaution. All obtained results are under the allowable level set by Italian legislation (1 mSv y−1), thus excluding the risk of ionizing radiation effects on humans. As far as heavy metal contamination is concerned, Cd and Pb concentrations turned out to be lower than the threshold values, thus excluding their presence as pollutants. Finally, the multivariate statistical analysis allowed to unambiguously correlate flour samples to their botanical origin, according to their elemental concentrations.

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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