Hydrochemical variation during groundwater mixing: a case study with multivariate statistical approach

2013 ◽  
Vol 8 (3-4) ◽  
pp. 399-408
Author(s):  
Linhua Sun
Keyword(s):  
Cluster Analysis ◽  
Coal Mine ◽  
Multivariate Statistical ◽  
Mass Balance Calculation ◽  
Hydraulic Connection ◽  
Mixing Ratios ◽  
Groundwater Mixing ◽  
Multivariate Statistical Approach ◽  
And Cluster Analysis ◽  
Mixed Samples

Identification of groundwater mixing and calculation of the mixing ratios between aquifers are important work for hydrological studies and safety of coal mining. In this study, multivariate statistical methods including factor and cluster analysis have been presented for identification of groundwater mixing status in the Renlou coal mine, northern Anhui Province, China. The methods include three steps: identification of hydraulic connection between aquifers by using factor score plots in combination with Q-mode cluster analysis, selection of end members and mass balance calculation for revealing mixing ratios. The hydraulic connection between loose layer and limestone aquifers have been identified in the Renlou coal mine, and three representative end member water samples, as well as mixed samples have been identified. Moreover, the mixing ratios for mixed samples are also calculated. The results indicate that the methods can be used for identification of mixing and quantification of mixing ratios in groundwater systems.

scholarly journals DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2136 ◽  
Author(s):  
Patrycja Garbacz ◽  
Marek Wesolowski
Keyword(s):  
Principal Component ◽  
Water Soluble ◽  
Crystal Formation ◽  
Multivariate Statistical ◽  
Molar Ratios ◽  
Pharmaceutical Ingredients ◽  
Multivariate Statistical Approach ◽  
And Cluster Analysis ◽  
Dsc Curves ◽  
Physical Mixtures

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial.

Student Employees and Recreational Sports Administrators: A Comparison of Perceptions

2006 ◽  
Vol 30 (1) ◽  
pp. 53-69 ◽  
Author(s):  
Gary L. Miller ◽  
Thomas E. Grayson
Keyword(s):  
Recreational Sports ◽  
Multivariate Statistical ◽  
Big Ten ◽  
Work Tasks ◽  
Multivariate Statistical Approach ◽  
Student Employees ◽  
The Mean ◽  
And Cluster Analysis ◽  
The Individual ◽  
Ladder Graphs

This study evaluates the differences in perceptions between student employees and recreational sports administrators over a consistent set of work tasks and responsibilities typically done by student employees in a recreational sports setting. The focus of the study was to provide a method of improving the effectiveness and efficiency by which recreational sports programs deliver their services and programs. Nine of the 11 schools in the Big Ten Conference participated in the study with a total of eighty-five participants taking part. Concept mapping, a multivariate statistical approach using multidimensional scaling and cluster analysis was used to analyze the data. Ninety-five work tasks were sorted for similarity and rated on scales for importance toward attaining recreational sports goals and frequency of performance. Cluster maps, ladder graphs and go-to-zones were developed from the data defining the results of the analysis. Results were presented in a composite form for the nine schools participating in the study with the intent to provide comparison between individual schools and the conference composite as requested. Cluster maps illustrated the levels of importance among the six clusters, ladder graphs demonstrated the differences between the student employees and the recreational sports administrators and go-to zones broke out the individual tasks into areas of alignment, gap zones where either importance or frequency were below the mean, and a “?” zone where neither importance nor frequency rose to the mean rating on that scale. The results allow administrators now to compare, examine, and make decisions based each of the 95 work tasks in a guided manner.

scholarly journals Application of multivariate statistical analysis in the assessment of groundwater quality of Tan Thanh district, Ba Ria – Vung Tau province

2017 ◽  
Vol 1 (M2) ◽  
pp. 66-72
Author(s):  
Au Hai Nguyen ◽  
Ngan Thi Khanh Phan ◽  
Thuy Thi Thanh Hoang ◽  
Ngoc Nguyen Hong Phan
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Anthropogenic Pollution ◽  
Dry Season ◽  
Wet Season ◽  
Multivariate Statistical ◽  
Rock Interaction ◽  
And Cluster Analysis

In the present study, Multivariate Statistical Analysis (MSA) such as Principle Component Analysis (PCA) and Cluster Analysis (CA) were applied to determine the temporal and spatial variations of groundwater quality in Tan Thanh district, Ba Ria – Vung Tau province. Groundwater samples were collected from 18 monitoring wells in April (dry season) and October (wet season) during the year 2012. Fifteen parameters (pH, TH, TDS, Cl-, F-, NO3-, SO42-, Cr6+, Cu2+, Ca2+, Mg2+, Na+, K+, HCO3- and Fe2+) were selected for MSA. PCA identified a reduced number of mean three latent factors of groundwater quality. Three factors called salinization, water-rock interaction and anthropogenic pollution explanined 70,5% (dry season) and 71.28% (wet season) of the variances. Cluster analysis revealed two main different groups of similarities between the sampling sites. This study presents the necessity of MSA in order to extract more precise information from a huge minitoring data, which will be usefull to groundwater quality management.

Characteristics and evolution of hydrochemical compositions in groundwater under mining-induced disturbance: a case study

2015 ◽  
Vol 10 (3) ◽  
pp. 609-615
Author(s):  
Song Chen ◽  
Herong Gui
Keyword(s):  
Coal Mine ◽  
Statistical Approach ◽  
Chemical Compositions ◽  
Deep Groundwater ◽  
Multivariate Statistical ◽  
Carbonation Process ◽  
Groundwater Samples ◽  
Multivariate Statistical Approach ◽  
Quaternary Aquifers

To understand the hydrochemistry evolution characters of deep groundwater under the coal mine exploitation, 66 historical chemical data of groundwater samples were collected from 1997 to 2011 in Qinan coal mine, Anhui Province, China, the hydrochemical characteristics and its evolution characters were obtained by the methods such as multivariate statistical approach and conventional graphical. The results showed that the concentrations of Na+ + K+ are higher in all groundwater samples, whereas the contents of Ca2+ and Mg2+ are lower. The concentrations of Na+ + K+ were decreasing as follows: limestone aquifer < quaternary aquifers < coal bearing aquifer. The chemical compositions of groundwater collected from three aquifer were varied obviously from 1997 to 2011. Three principle component factors could be extracted through statistical approach, PC1 was affirmed the dissolution of limestone, dolomite and gypsum dissolution. PC2 could be as the carbonation process or desulfurizing process, while PC3 indicated the weathering process of feldspar minerals weathering by the carbonate acid.

scholarly journals Application of multivariate statistical methods in the assessment of mountain organic soil transformation in the central Sudetes

2017 ◽  
Vol 54 (1) ◽  
pp. 43-59
Author(s):  
Bogna Zawieja ◽  
Bartłomiej Glina
Keyword(s):  
Cluster Analysis ◽  
Statistical Methods ◽  
Science Research ◽  
Organic Soil ◽  
Soil Science ◽  
Organic Soils ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Discriminant Variable ◽  
And Cluster Analysis

Summary In studies of organic soil degradation and transformation, alongside the conventional methods used in soil science, an increase in the importance of advanced statistical methods can be observed. In this study some multivariate statistical methods were applied in an investigation of organic soil transformation in the central Sudetes. Andrews curves, linear and kernel discriminant variable analysis and cluster analysis were used. The similarities among peatland soils and their layers were determined. It can be stated that the application of statistical methods in soil science research related to organic soil transformation is a valuable tool. The use of various statistical methods (such as Andrews curves, linear and kernel discriminant variables and cluster analysis) can with high probability confirm earlier laboratory or field observations. This is particularly justified in the case of organic soils derived from varied geobotanical peat materials, different types of peatlands and water supply types, which impact the primary properties of the soil.

International Market Segmentation with the Use of CMS Method and Cluster Analysis

2019 ◽  
Vol 65 (4) ◽  
pp. 492-506
Author(s):  
Marcin Salamaga
Keyword(s):  
Cluster Analysis ◽  
Current Trend ◽  
International Exchange ◽  
Foreign Markets ◽  
Multivariate Statistical ◽  
Goods And Services ◽  
Detailed Assessment ◽  
Constant Market Share ◽  
And Cluster Analysis ◽  
A Current

The segmentation of foreign markets is currently treated as an important element of the strategy of operations of enterprises that participate in international exchange of goods and services. This paper fits squarely into a current trend in research on the matter. The article presents the possibility of combining the model of Constant Market Share developed by Leamer, Stern (1970) with cluster analysis. The CMS method allows for a detailed assessment of the sources of changes occurring in the export of compared countries, and in particular its results allow for answering the following question: To what extent may changes in export be explained by the economic situation in the world trade of individual clusters of commodities and to what extent do they result from the competitiveness of these countries? The application of the multivariate statistical methods for the designated effects will allow for the identification of the clusters of countries of the most similar position in the spatial and commodity arrangement, including countries of similar competitiveness of trade. This approach has been applied to the segmentation of EU countries’ markets.

scholarly journals Multivariate statistical characterization of groundwater quality in Fesdis, East of Algeria

2018 ◽  
Vol 37 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Safia Khelif ◽  
Abderrahmane Boudoukha
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Statistical Characterization ◽  
And Cluster Analysis

AbstractThis study is a contribution to the knowledge of hydrochemical properties of the groundwater in Fesdis Plain, Algeria, using multivariate statistical techniques including principal component analysis (PCA) and cluster analysis. 28 samples were taken during February and July 2015 (14 samples for each month). The principal component analysis (PCA) applied to the data sets has resulted in four significant factors which explain 75.19%, of the total variance. PCA method has enabled to highlight two big phenomena in acquisition of the mineralization of waters. The main phenomenon of production of ions in water is the contact water-rock. The second phenomenon reflects the signatures of the anthropogenic activities. The hierarchical cluster analysis (CA) in R mode grouped the 10 variables into four clusters and in Q mode, 14 sampling points are grouped into three clusters of similar water quality characteristics.

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 Chemical and Microbiological Characterization for PDO Labelling of Typical East Piedmont (Italy) Salami

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
V. Gianotti ◽  
S. Panseri ◽  
E. Robotti ◽  
M. Benzi ◽  
E. Mazzucco ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Biogenic Amines ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Tools ◽  
Multivariate Statistical ◽  
North West ◽  
And Cluster Analysis ◽  
Aroma Components

This study is focused on the characterisation of typical salami produced in Alessandria province (North West of Italy). Seventeen small or medium salami producers from this area were involved in the study and provided the samples investigated. The aim is double and consists in obtaining a screening of the characteristics of different products and following their evolution along ripening. The study involved five types of typical salami that were characterised for aroma components and nutritional features. This approach could provide a basis for a possible PDO or PGI label request. Principal Component Analysis and cluster analysis were used as multivariate statistical tools for data treatment. The overall results obtained point out that the products investigated do not deviate from analogous European products and show the possibility of characterising by specific parameters three main groups of samples:Salamini di Mandrogne,Muletta, andNobile Giarolo; moreover some considerations can also be drawn with respect to the nutritional characterization considering the biogenic amines profile.

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