scholarly journals Multivariate statistical analysis applied to assess the dispersion of contaminants in a mining tailings basin in the semiarid region of Bahia – Brazil

2020 ◽  
Vol 15 (5) ◽  
pp. 1
Author(s):  
Nelize Lima Santos ◽  
Maria Da Conceição Rabelo Gomes ◽  
José Ângelo Sebastião Araújo dos Anjos ◽  
Fernanda Gonçalves Cunha
Keyword(s):  
Environmental Management ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Semiarid Region ◽  
Natural Background ◽  
Multivariate Statistical ◽  
Sediment Samples ◽  
Analysis Techniques ◽  
Main Factors ◽  
Street Sediment

This study employed multivariate analysis techniques to identify and evaluate the chemical variables responsible for the contamination of the urban area of Boquira, Bahia, due to the abandonment of the tailings basin of Pb-Zn mining, in order to assist in the environmental management of the area. Factor analysis was performed on main and grouping components. The factor analysis allowed grouping the variables into two main factors for street sediment samples, adding up to 72% of the total accumulated variance, and three factors for house dust samples, which explained 77% of the total variance. The variables have a strong correlation with the composition of the tailings basin. Cluster analysis classified the samples according to the concentration of metals in the area, where the influence of the tailings basin and the natural background of the region's rocks in the contamination distribution can be identified.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

scholarly journals Research of personality types of employees of internal affairs agencies with negative mental states

2020 ◽  
Vol 5 (3) ◽  
pp. 121-132
Author(s):  
Dar'ya Ruseckaya ◽  
Igor' Lebedev
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Police Officers ◽  
Mental States ◽  
Personality Types ◽  
Professional Activity ◽  
Sample Population ◽  
Psychological Experiment ◽  
Main Factors ◽  
Internal Affairs

The article deals with the problem of negative mental States that occur in employees of internal Affairs bodies when performing professional tasks. The analysis of modern scientific research on the studied problem is carried out. As part of this study, a psychodiagnostic tool was developed and tested to study negative mental States in police officers. The authors studied the negative mental States of employees of internal Affairs agencies and based on the data obtained, conducted a factor analysis that allowed us to determine the main factors for the occurrence of these conditions. Next, a cluster analysis was conducted, which revealed three types of personality of ATS employees with negative mental States: resistant to the effects of negative mental States; little exposed to the effects of negative mental States; exposed to negative mental States. Thanks to the data obtained in the course of the pilot study, the prospects for studying this problem are determined. The set of research methods corresponds to the tasks set; the choice of mathematical statistics methods corresponds to the characteristics of the sample population and takes into account the features of the psychological experiment. The value of the research is to update the study of the problem of negative mental States as a factor of maladaptation of professional activity of police officers.

scholarly journals Evaluation of disparities in living standards of regions of the Czech Republic

2012 ◽  
Vol 60 (4) ◽  
pp. 407-414
Author(s):  
Hana Vostrá Vydrová ◽  
Zuzana Novotná
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Czech Republic ◽  
Analysis Of Variance ◽  
Risk Groups ◽  
Environmental Variable ◽  
Living Standards ◽  
Standard Of Living ◽  
Multivariate Statistical ◽  
The Czech Republic

This paper focuses on regional differences between the regions of the Czech Republic. We will focus on observation of inequalities between indicators of living in different regions of the Czech Republic. The indicators are evaluated at NUTS 3 (regions), using multivariate statistical techniques - factor analysis and cluster analysis. We have identified the twelve indicators of living standards. Base data was reduced using factor analysis on the three emerging factors: 1) basic characteristics, 2) risk groups, 3) environmental variable. Cluster analysis was compiled groups of regions with similar characteristics. Cluster analysis of the breakdown of the county into three clusters based on selected indicators of living standards. They can be described as a group with higher average and lower standard of living. In the first cluster are only two regions (Liberec Region and Karlovy Vary), the third cluster is composed of Prague and the second cluster includes all other regions of the Czech Republic. To verify the evidence of differences between clusters were calculated by multivariate analysis of variance for the various indicators of living standards. An analysis of variance indicates that significant differences between clusters are caused by the standard of living indicators: GDP (regional), the average wage of women, medical equipment, culture entertainment and recreation, higher education, the disabled handicapped and older people. The data were processed in the program STATISTICA 10th.

scholarly journals Multivariate statistical analysis applied to the evaluation of groundwater quality in the central-southern portion of the state of Bahia - Brazil

2020 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
Maria Da Conceição Rabelo Gomes ◽  
José Ângelo Sebastião Araújo dos Anjos ◽  
Rafael Ribeiro Daltro
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Water Quality Monitoring ◽  
The State ◽  
Factorial Analysis ◽  
Semiarid Region ◽  
Multivariate Statistical ◽  
Management Actions ◽  
Quality Of Waters ◽  
And Cluster Analysis

 The objective of this study was to identify and evaluate the variables responsible for contributing to possible natural and/or human contamination in groundwater of the semiarid region of the state of Bahia, seeking to subsidize water quality monitoring and management actions in the area. To do so, multivariate analysis techniques regarding factorial analysis in principal components and cluster analysis were used. The factorial analysis allowed the grouping of variables into two principal factors that explained 93% of total accumulated variance. Variables were strongly related to concentrations of metals and salinity in the water. The cluster analysis was used to classify water sources according to the quality of waters into three clusters in each factor. The natural background of the rocks of the municipality of Boquira was shown to influence water resources. A continuous (during dry and rainy seasons) monitoring of water quality from wells and springs located upstream and downstream from contamination sources is recommended, even if these waters are not used for public supply, to determine possible contamination plumes from contaminated material.

scholarly journals Analysis of Factors Affecting Consumers in UHT Milk Consumption: The Case Study of Erzurum

2018 ◽  
Vol 6 (10) ◽  
pp. 1485
Author(s):  
Ahmet Semih Uzundumlu ◽  
Avni Birinci ◽  
Seval Kurtoğlu
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Consumer Preferences ◽  
Primary Data ◽  
Milk Consumption ◽  
Factor Scores ◽  
Factors Affecting ◽  
Uht Milk ◽  
Main Factors

The primary purpose of this study was to determine factors influencing consumer preferences for UHT milk consumption in Erzurum province. The primary data used in this research was derived from Palandoken, Yakutiye and Aziziye districts of Erzurum province in 2010. The factor analysis was used to find out the factors affecting consumer preferences for UHT milk and to reduce these factors. As for the segmentation of consumers and bringing out the profile of each segment, cluster analysis was used. According to the results, 95.00% of households consumed UHT milk. 18 factors that are affecting the consumption of UHT milk were reduced to five main factors with factor analysis. The factor scores which determined with factor analysis were divided into three clusters by cluster analysis. UHT milk for consumers entering the first cluster has because of homogenous and packaging as well as intrinsic and extrinsic properties for advertising and price advantage is preferred. UHT milk for consumers entering the second cluster has ease of preparation and transportation, and confidential properties are preferred by reason. On the contrary, consumers entering the third cluster prefer to UHT milk for a good diet product.

scholarly journals Desenvolvimento Socioeconômico nas microrregiões do Paraná: uma análise multivariada

2012 ◽  
Vol 38 (2) ◽  
Author(s):  
Vinícius Gonçalves Vidigal ◽  
Isis De Castro Amaral ◽  
Glauber Flaviano Silveira
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Regional Differences ◽  
Industrial Development ◽  
Socioeconomic Development ◽  
Housing Conditions ◽  
Multivariate Statistical ◽  
Group 2 ◽  
Development Levels ◽  
The One

Este trabalho teve como objetivo avaliar as diferenças de nível de desenvolvimento socioeconômico entre as microrregiões do Estado do Paraná, bem como hierarquizá-las e, posteriormente, agrupá-las de acordo com suas principais características. Utilizou-se para tanto a análise estatística multivariada. Os principais resultados demonstraram a existência de disparidades regionais, a partir de indicadores de condições de moradia, de precariedade dos serviços de saúde e de desenvolvimento industrial, obtidos a partir da análise fatorial. A análise de cluster distribuiu as microrregiões em cinco grupos, sendo que o Grupo 2, formado pelos municípios de Cerro Azul e Pitanga, foi aquele com as piores condições de desenvolvimento. Todos os demais grupos apresentaram resultado negativo em apenas um dos três indicadores. Portanto, observa-se que coexistem carências extremamente importantes e que afetam milhares de famílias em todo o Estado do Paraná.Abstract: This study had as objective to evaluate the differences in socioeconomic development levels among regions of Paraná State, as well as rank and then groupthem according to their main characteristics. The methodological tools refer to techniques of multivariate statistical analysis which were factor and clusteranalysis. The main results showed the existence of great regional differences, considering indicators of housing conditions, precarious health services and industrial development, all obtained from the factor analysis. The cluster analysis distributed the regions in five groups, in which Group 2 was the one with the worst conditions of development. All other groups showed a negative result in only one of the three indicators. Therefore, it is observed that exist extremely important needs that affect thousands of families throughout the state of Paraná.

scholarly journals Highway tunnel air quality assessment using multivariate statistical classification on factor, cluster, and discriminant analysis

2018 ◽  
Vol 7 (2.13) ◽  
pp. 287
Author(s):  
Shu Lung Kuo ◽  
Edward Ming-Yang Wu
Keyword(s):  
Air Pollution ◽  
Factor Analysis ◽  
Air Quality ◽  
Quality Management ◽  
Air Pollutants ◽  
Management Control ◽  
Air Quality Management ◽  
Multivariate Statistical ◽  
Main Factors ◽  
Northern Taiwan

The air quality monitoring points set up at the existing 10 tunnels (a total of 20 tunnel entrances) on Formosa Freeway in Northern Taiwan were used in this study to investigate the correlation among various types of air pollutants measured at these 20 tunnel entrances via a multivariate statistical analysis. This study aimed to determine the main factors that affect the extent of air pollution along the Formosa Freeway and its vicinity, and explored the interrelationships among various air pollutants to reflect the differences among the air pollutants found along the Formosa Freeway in Northern Taiwan, as well as to establish an evaluation mode for types and characteristics of air pollutants after their quantification. Two main factors were obtained from the factor analysis: the “photochemical reaction pollution factor” and the “vehicle fuel factor.” Cluster analysis is used to classify air quality in Formosa Freeway in Northern Taiwan into five clusters to present different characteristics and pollution degrees of air quality in this study. The more that the air pollutants and samples are used when performing a factor analysis, the more effective the validity and reliability of the factor analysis! This research results can serve as a reference for those involved in the review of air quality management effectiveness and/or the enactment of management control strategies. In addition, it is also a good method to use in an air quality management program and it is expected that the results can serve as a reference for the management to prevent and control air pollution in the future. 

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