Application of multivariate statistical analysis in the pollution and health risk of traffic-related heavy metals

2017 ◽  
Vol 39 (6) ◽  
pp. 1441-1456 ◽  
Author(s):  
Mohammad Ebqa’ai ◽  
Bashar Ibrahim
Keyword(s):  
Heavy Metals ◽  
Statistical Analysis ◽  
Health Risk ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical

Seasonal and spatial distribution of heavy metals in an industrially affected river sediment and evaluation of ecological risk, health risk & pollution source

2021 ◽  
Author(s):  
Yeasmin Nahar Jolly ◽  
Atahar Rabby ◽  
Mehedi Hasan ◽  
Khan Mohammad Mamun ◽  
Shirin Akter ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Statistical Analysis ◽  
Health Risk ◽  
Ecological Risk ◽  
Multivariate Statistical Analysis ◽  
Hazard Index ◽  
Contamination Factor ◽  
Pollution Load Index ◽  
Multivariate Statistical ◽  
Sediment Samples

Abstract Present study sketched to quantify heavy metals (Cr, Mn, Ni, Cu, Zn, Pb, Cd, As) in sediment samples (Dry and Rainy season) of an industrially affected river namely Shitalakshya by Energy Dispersive X-ray fluorescence (EDXRF) technique. Different geochemical indices and multivariate statistical analysis were applied to define the accumulation, source and level of metal pollution in the sediment samples and probabilistic health risk implications due to dermal contact of sediment was also evaluated. Study revealed mean heavy metal (HM) concentrations in a sequence of Mn > Zn > Cu > Cr > Ni > Pb > As > Cd for both the seasons and almost all the elements were found within the suggestive standard value by various agencies with an exception of Cd and As. Enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF) and pollution load index (PLI) indicated a high level of contamination by HM and a moderate level of Ecological risk was assessed for both the season. Hazard Index (HI), known as non-carcinogenic health risk for all the elements studied found bellow 1, indicating no harm for health and total carcinogenic risk also revealed the safe range. Results of multivariate statistical analysis indicates, the possible sources are mostly anthropogenic which may be owing to discharge of untreated wastes from various industries, metal and waste dumping sites, oil and refinery industries, glass and ceramic industries as they are located closely to the sampling sites of the Shitalakshya river.

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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