Relationship Between Major And Trace Elements In Ulaanbaatar Soils: A Study Based On Multivariate Statistical Analysis

2019 ◽  
Vol 12 (3) ◽  
pp. 199-212 ◽  
Author(s):  
Elena V. Shabanova ◽  
Ts. Byambasuren ◽  
G. Ochirbat ◽  
Irina E. Vasil'eva ◽  
B. Khuukhenkhuu ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Analysis ◽  
Principal Components ◽  
Multivariate Statistical Analysis ◽  
Urban Soils ◽  
Thermal Power ◽  
Principal Component ◽  
Major Elements ◽  
Multivariate Statistical ◽  
Data Set

This article focuses on the relationships between major (Si, Al, Mg, Fe, Ca, Na, K, S, P and Ti) and potentially toxic trace (Ag, As, B, Ba, Bi, Co, Cd, Cr, Cu, F, Ge, Mo, Mn, Li, Ni, Pb, Sb, Sn, Sr, Tl, V and Zn) elements in Ulaanbaatar surface soils and also sources of the trace elements in the soils distinguished by the methods of multivariate statistical analysis. Results of exploratory data analysis of 325 Ulaanbaatar soil samples show the accumulation of Ca, S, B, Bi, Cu, Mo, Pb, Sb, Sn, Sr and Zn in urban soils. The major elements were grouped by cluster analysis in tree associations characterizing main soil fractions: sandy P-(K-Na-Si), clayey (Mg-Ti-Fe-Al) and silty (S-Ca). The factor analysis shows that silty fraction is enriched in major elements of both natural and anthropogenic origin. The principal component analysis from 32 variables extracted nine principal components with 82.49% of the cumulative explained variance. The results of cluster and factor analyses well agree and reaffirm the enrichment causes of potentially toxic elements are a coal combustion at thermal power stations (B, Bi, Ca, Mo, S and Sr) and traffic emissions (Cu, Pb, Sn and Zn). Spatial distributions of trace elements in the districts of Ulaanbaatar city were obtained by ordinary kriging. It is illustrated that the different principal components define the various origins and patterns of accumulation of trace elements in soils. The supplementation of data set by the concentration of organic carbon and the species of elements could help to identify the sources of such elements as P, Ni, Al, Fe, Ca, Ba, Bi, Cr, Zn, Sr and Sb in urban soils more completely.

scholarly journals The distribution of the trace element contents in lignite and ash from Drama lignite deposit, using multivariate statistical analysis

2001 ◽  
Vol 34 (3) ◽  
pp. 1255
Author(s):  
S. PANILAS ◽  
G. HATZIYANNIS
Keyword(s):  
Trace Elements ◽  
Rare Earth ◽  
Statistical Analysis ◽  
Rare Earth Elements ◽  
Multivariate Statistical Analysis ◽  
Major And Trace Elements ◽  
Major Elements ◽  
Geochemical Data ◽  
Multivariate Statistical ◽  
Inorganic Matter

Multivariate statistical analysis was used on existing geochemical data of the Drama lignite deposit, eastern Macedonia, Greece. Factor analysis with varimax rotation technique was applied to study the distribution of major, trace and rare earth elements in the lignite and 850°C lignitic ash, to find a small set of factors that could explain most of the geochemical variability. The study showed that major elements AI, Na, Κ, contained in the lignite samples, presented high correlation with most of the trace and rare earth elements. In 850°C lignitic ashes major and trace elements present different redistribution. Only Al remained correlated with the trace elements Co, Cr, Rb, Ta, Th, Ti, Sc and rare earths related with inorganic matter in the lignite beds. Trace elements Fe, Mo, U, V, W, and Lu were associated with organic matter of lignite and had also been affected by the depositional environment.

Trace metals concentrations in schoolyard and playground soils in Coronel city, Chile

2020 ◽  
Author(s):  
Pedro Tume ◽  
Viviana Acevedoa ◽  
Núria Roca ◽  
Jaume Bech
Keyword(s):  
Trace Elements ◽  
Statistical Analysis ◽  
Soil Sample ◽  
Multivariate Statistical Analysis ◽  
Urban Soils ◽  
Anthropogenic Sources ◽  
World Population ◽  
Sampling Point ◽  
Ecological Indices ◽  
Multivariate Statistical

<p>An urban world population of 0.75 billion in 1950 and expected 6 billion in 2050 shows the tremendous potential growth of urban areas. The urban soil fulfills the role of the reactor for the physical, chemical and biological transformations of matter but also covers such functions as reduction of air pollution, regulation of climate elements in cities, source of biodiversity and formation of areas for ornamental and recreation purposes. As a usual part of urban ecosystems, urban soils in general have high concentrations of trace elements derive from human activities. The objectives of this work were (1) to quantify the concentrations and establish background levels of Ba, Co, Cr, Cu, Mn, Pb, Ni, V and Zn in soils of Coronel city; (2) to assess the degree of pollution and identify local sources of pollution and (3) to assess the health risks of TE in soils of Coronel city. From Coronel city were collected 129 samples from 43 sites located in schoolyards and playground areas. At each sampling point were taken three samples: topsoil sample (TS) (0-10 cm), subsoil sample (SS) (10-20 cm) and deep soil sample (DS) (150 cm). Multivariate statistical analysis and depth ratios were used to distinguish the source. Ecological indices were implemented to evaluate the degree of contamination. The median and (range) of the trace elements (TE) in TS were Ba 38 mg kg<sup>-1</sup> (12-147 mg kg<sup>-1</sup>), 38 mg kg<sup>-1</sup>; Co 4-40 mg kg<sup>-1</sup>; 15 mg kg<sup>-1</sup>; Cr 10-35 mg kg<sup>-1</sup>, 18 mg kg<sup>-1</sup>; Cu 12-70 mg kg<sup>-1</sup>, 22 mg kg<sup>-1</sup>; Mn 167-950 mg kg<sup>-1</sup>, 536 mg kg<sup>-1</sup>; Ni 11-115 mg kg<sup>-1</sup>, 35.5 mg kg<sup>-1</sup>; Pb 1.5-115 mg kg<sup>-1</sup>, 6 mg kg<sup>-1</sup>; V 52-528 mg kg<sup>-1</sup>, 94 mg kg<sup>-1</sup>; Zn 42-373 mg kg<sup>-1</sup>, 65 mg kg<sup>-1</sup>. Depth ratios and multivariate statistical analysis suggested that Co, Ni and Mn have principal contribution of geogenic sources and Ba, Cr, Cu, Pb, V and Zn anthropogenic sources. The upper limit of background values estimated with median absolute deviation (MAD) method and DS samples were Ba 30 mg kg<sup>-1</sup>,  Co 24 mg kg<sup>-1</sup>,  Cr 22 mg kg<sup>-1</sup>,  Cu 24 mg kg<sup>-1</sup>,  Mn 662 mg kg<sup>-1</sup>,  Ni 66 mg kg<sup>-1</sup>,  Pb 1.5 mg kg<sup>-1</sup>,  V 108 mg kg<sup>-1</sup>,  and Zn 52 mg kg<sup>-1</sup>. Contamination factor showed that some soil sample were categorized as considerable contamination to very high contamination for Pb, Zn and V. Both Hazard index and cancer risk indicated no adverse health effects.</p><p><strong>Keywords</strong>: Heavy metals, urban soils, ecological indices, health risk assessment</p>

Evaluation of Groundwater Quality at an Industrial Park Site Zone Using Statistical Analyses: A Case Study in Taiwan

2013 ◽  
Vol 457-458 ◽  
pp. 1581-1584
Author(s):  
Bo Ming Yang ◽  
Zong Han Yang ◽  
Jong Kang Liu ◽  
Hui Yu Lee ◽  
Chih Ming Kao
Keyword(s):  
Statistical Analysis ◽  
Groundwater Quality ◽  
Quality Indicators ◽  
Principal Components ◽  
Multivariate Statistical Analysis ◽  
Organic Pollutant ◽  
Principal Component ◽  
Original Data ◽  
Industrial Park ◽  
Multivariate Statistical

Multivariate statistical analysis explains the huge and complicated current situation of the original data efficiently, concisely, and explicitly. It simplifies the original data into representative factors, or bases on the similarity between data to cluster and identify clustering outcome. In this study, the statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate characteristics of groundwater quality at an industrial park site located in Kaohsiung, Taiwan. Results from the principal component analysis (PCA) and factor analyses (FA) show that seven principal components could be compiled from 20 groundwater quality indicators obtained from groundwater analyses, which included background factor, salt residua factor, hardness factor, ethylene chloride factor, alkalinity factor, organic pollutant factor, and chloroform factor. Among the seven principal components, the major influencing components were salinization factor and acid-base factor. Results show that the seven principal component factors were able to represent 89.6% of the total variability for 20 different groundwater quality indicators. Groundwater monitoring wells were classified into seven groups according to the partition of homogeneity and similarity using the two-phase cluster analysis (CA). The clustering results indicate that chlorides such as 1,1-dichloroethylene, 1,1-dichloroethane, and cis-1,2-dichloroethylene had the highest concentrations among the clusters. This indicates that groundwater at nearby areas may be polluted by chlorinated organic compounds. Results from the correlation analysis by Fisher coefficient formula show that the cluster results of seven groups of groundwater wells had 100 and 80% accuracies using discriminant and cross-validation analyses, respectively. This implies that high accuracy can be obtained when discriminant and cluster analyses are applied for data evaluation.

scholarly journals Multivariate Statistical Analysis of Spectrum Lines and Images

1997 ◽  
Vol 3 (S2) ◽  
pp. 931-932 ◽  
Author(s):  
Ian M. Anderson ◽  
Jim Bentley
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quantitative Imaging ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Recent Developments ◽  
Data Files

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

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 Seasonal distribution and population dynamics of limnic microalgae and their association with physico-chemical parameters of river Noyyal through multivariate statistical analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pandian Suresh Kumar ◽  
Jibu Thomas
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Sampling Site ◽  
Principal Component ◽  
Environmental Parameters ◽  
Diversity Indices ◽  
Population Diversity ◽  
Chemical Parameters ◽  
Multivariate Statistical ◽  
Physico Chemical

Abstract The present investigation embarks on understanding the relationship between microalgal species assemblages and their associated physico-chemical parameter dynamics of the catchment region of river Noyyal. Totally, 142 microalgae cultures belonging to 10 different families were isolated from five different sites during four seasons and relative percentage distribution showed that Scenedesmaceae (36.6%) and site S1 (26.4%) with predominant microalgae population. Diversity indices revealed that microalgae communities were characterized by high Hʹ index, lower Simpson dominance, and Margalef index value with indefinite patterns of annual variations. Results showed that variation in the physico-chemical parameters in each sampling site has its impact on the microalgae population during each season. Multivariate statistical analysis viz., Karl Pearson’s correlation coefficient, principal component analysis, and canonical correspondence analysis were applied on microalgae species data, to evaluate the seasonal relationship between microalgae and physico-chemical parameters. The findings of our study concluded that the physico- chemical parameters influenced the dominant taxa of microalgae Chlorellaceae, Scenedesmaceae and Chlorococcaceae in river Noyyal and gives a base data for the seasonal and dynamic relationship between environmental parameters and microalgae population.

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