scholarly journals Exploration for Fe-Mn Oxides Using Geochemical Signatures in Soil: A Case Study of Part of Northwestern Nigeria

Geochemistry ◽  
2021 ◽  
Author(s):  
Olufemi Sijuade Bamigboye
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Elemental Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Samples ◽  
Pca Analysis ◽  
Manganese Mineralization ◽  
Mn Oxides

Part of northwestern Nigeria was investigated with the aim of delineating concealed mineralization using geochemical signatures in soils. To achieve this, 30 selected soil samples were analysed geochemically. The result of the elemental analysis was subjected to Principal Component Analysis (PCA) and isograde plotting, while selected elements were correlated. From the geochemical result, most of the analysed elements have anomalous value in the southern part of the area, while the least values are in the southwestern. From the PCA analysis, six factor groups were distinct. The factor groups were interpreted geochemical to fingerprint mineralization in the area. The result of correlation analysis shows that Fe is negatively correlated with most of the correlated elements. The study concluded that the central part of the study area is mineralized with both manganite and goethite. In addition, manganese mineralization is indicated by elemental association: Zn+As+Be+Bi+Co+Nb+Ni+CsP+Al+Ca+Cd+Li+K, while iron mineralization is indicated with Zr+Th+Pd+Mo+V+Sn+Cr+Ce+InSc+P+Pb association.

Application of the principal component analysis on geochemical data: a case study in the basement complex of Southern Ilesa area, Nigeria

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geochemical Data ◽  
Basement Complex

scholarly journals Assessment of heavy metals in soils of a vineyard region with the use of principal component analysis

2009 ◽  
Vol 66 (3) ◽  
pp. 361-367 ◽  
Author(s):  
Gustavo Souza Valladares ◽  
Otávio Antônio de Camargo ◽  
José Ruy Porto de Carvalho ◽  
Alessandra Maria Cia Silva
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Agricultural Soils ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Samples ◽  
Metal Concentrations ◽  
Zn Content ◽  
Heavy Metals In Soils ◽  
Cu And Zn

Agricultural management with chemicals may contaminate the soil with heavy metals. The objective of this study was to apply Principal Component Analysis and geoprocessing techniques to identify the origin of the metals Cu, Fe, Mn, Zn, Ni, Pb, Cr and Cd as potential contaminants of agricultural soils. The study was developed in an area of vineyard cultivation in the State of São Paulo, Brazil. Soil samples were collected and GPS located under different uses and coverings. The metal concentrations in the soils were determined using the DTPA method. The Cu and Zn content was considered high in most of the samples, and was larger in the areas cultivated with vineyards that had been under the application of fungicides for several decades. The concentrations of Cu and Zn were correlated. The geoprocessing techniques and the Principal Component Analysis confirmed the enrichment of the soil with Cu and Zn because of the use and management of the vineyards with chemicals in the preceding decades.

Monitoring bioreactors using principal component analysis: production of penicillin G acylase as a case study

2009 ◽  
Vol 33 (5) ◽  
pp. 557-564 ◽  
Author(s):  
Edson Romano Nucci ◽  
Antonio J. G. Cruz ◽  
Roberto C. Giordano
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Penicillin G Acylase ◽  
Penicillin G

Bootstrap based confidence limits in principal component analysis — A case study

2013 ◽  
Vol 120 ◽  
pp. 97-105 ◽  
Author(s):  
Hamid Babamoradi ◽  
Frans van den Berg ◽  
Åsmund Rinnan
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Confidence Limits

scholarly journals Groundwater Quality Assessment: An Improved Approach to K-Means Clustering, Principal Component Analysis and Spatial Analysis: A Case Study

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 437 ◽  
Author(s):  
Ana Marín Celestino ◽  
Diego Martínez Cruz ◽  
Elena Otazo Sánchez ◽  
Francisco Gavi Reyes ◽  
David Vásquez Soto
Keyword(s):  
Principal Component Analysis ◽  
Spatial Analysis ◽  
Groundwater Quality ◽  
Quality Assessment ◽  
Principal Component ◽  
Component Analysis ◽  
Groundwater Quality Assessment

Principal Component Analysis of Hydrological Data

2010 ◽  
pp. 364-388
Author(s):  
Petr Praus
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Ground Water ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Hydrological Data ◽  
First Case

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.

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