The climatic regions of New Brunswick: a multivariate analysis of meteorological data

1984 ◽  
Vol 14 (3) ◽  
pp. 389-394 ◽  
Author(s):  
H. van Groenewoud
Keyword(s):  
New Brunswick ◽  
Classification Scheme ◽  
Meteorological Data ◽  
Principal Component ◽  
Site Classification ◽  
Forest Site ◽  
Multivariate Statistical ◽  
Climatic Regions ◽  
Geological Maps ◽  
And Cluster Analysis

New Brunswick was divided into 11 climatic regions by means of three multivariate statistical analyses (principal component analysis, R and Q type, and cluster analysis) of data on precipitation, various temperature parameters, elevation, latitude, and longitude for 76 climatological stations. These regions form the first-level division for a forest site classification scheme being implemented in New Brunswick. Comparison of the climatic and geological maps of New Brunswick with the plant community distribution shows that either climatic or geologic parameters may control the distribution of the vegetation.

scholarly journals Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

2018 ◽  
Vol 20 (1) ◽  
pp. 161-168 ◽  
Keyword(s):  
Anthropogenic Impact ◽  
Sediment Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Cluster Number ◽  
Multivariate Statistical ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

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.

scholarly journals Lithofacies Identification and Multivariate Analysis of Groundwater Chemistry in Coastal Aquifers in Koko Area of the Western Niger Delta

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 31 ◽  
Author(s):  
Oghenero Ohwoghere-Asuma ◽  
Kizito Aweto ◽  
Chukwuma Ugbe
Keyword(s):  
Groundwater Quality ◽  
Principal Component ◽  
Organic Pollution ◽  
Clayey Sand ◽  
Multivariate Statistical ◽  
Subsurface Geology ◽  
Dissolved Ions ◽  
Considerable Impact ◽  
And Cluster Analysis

Understanding aquifer lithofacies and depth of occurrence, and what factors influence its quality and chemistry are of paramount importance to the management of groundwater resource. Subsurface lithofacies distribution was characterized by resistivity and validated with available subsurface geology. Resistivity values varied from less than 100 Ωm to above 1000 Ωm. Lithofacies identified includes clay, clayey sand, sand and peat. Shallow unconfined and confined aquifers occurred at depths ranging from 0 to 12 m and 18 to 63 m, respectively. Geochemistry and multivariate statistical analysis consisting of principal component analysis (PCA) and cluster analysis (CA) were used for the determination of quality and groundwater evolution. Groundwater types depicted by Piper plots were Ca3+, Cl− and Na+, Cl−, which was characterized by low dissolved ions, slightly acidic and Fe2+. The dominant variables influencing groundwater quality as returned by PCA were organic pollution resulting from swampy depositional environment, anthropogenic effects resulting from septic and leachates from haphazard dumpsites mixing with groundwater from diffuse sources. In addition, the weathering and dissolution of aquifer sediments rich in feldspar and clay minerals have considerable impact on groundwater quality. CA depicted two distinct types of groundwater that are significantly comparable to those obtained from Piper plots.

Characterization of Balsamic Vinegar by Multivariate Statistical Analysis of Trace Element Content

1998 ◽  
Vol 81 (5) ◽  
pp. 1087-1095 ◽  
Author(s):  
Antonella Del Signore ◽  
Barbara Campisi ◽  
Franco Di Giacomo
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Trace Element Content ◽  
The Other ◽  
Statistical Techniques ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Balsamic Vinegar ◽  
And Cluster Analysis

Abstract To characterize vinegars according to the types prescribed by Italian regulations, 8 trace elements (Cr, Mn, Co, Ni, Cu, Zn, Cd, and Pb) were determined. The data collected were successively elaborated by 3 statistical techniques: linear principal component analysis (LPCA), linear discriminant analysis (LDA), and cluster analysis (CA). LDA and LPCA best classified and discriminated the 3 types of vinegar under study, separating traditional balsamic vinegars from the other 2 types, nontraditionally aged balsamic vinegars and common vinegars. The latter 2 types were appreciably distinguished only by LDA through bidimensional analysis of discriminant scores

scholarly journals AMSM Summary Papers- (Chapter One- Introduction)

2018 ◽  
Author(s):  
Mohammed R. Dahman
Keyword(s):  
Linear Regression ◽  
Structural Equation ◽  
Principal Component ◽  
Descriptive Statistics ◽  
Basic Knowledge ◽  
Equation Modeling ◽  
Multivariate Statistical ◽  
Statistics And Probability ◽  
Prerequisite Skills ◽  
And Cluster Analysis

In the upcoming some 40 summary papers I will demonstrate a comprehensive view of Applied Multivariate Statistical Modeling. First, I will start with a thorough introduction of AMSM. Then, I will explain the univariate descriptive statistics, sampling distribution, estimation, in addition to hypothesis testing. After that, I will do a comprehensive review of multivariate descriptive statistics, the normal distribution of it, and the inferential statistics. Having we accomplished that, it will be the time to discuss some various models: ANOVA, MANOVA, Multiple Linear Regression, and Multivariate Linear Regression. Furthermore, we will discuss, Principal Component analysis, Factor Analysis, and Cluster Analysis. At the end of this series of summaries, some intro to structural equation modeling (SEM), and correspondence analysis will be discussed. Prerequisite skills are, of which readers must have, basic knowledge of statistics and probability, in addition to some advanced knowledge of linear algebra. I have published summary papers in both disciplines, see the reference page.

scholarly journals Habitat Preferences of PokeA (Batissa violacea var. celebensis von Martens, 1897) Basic on Substrat Characterisation in Pohara River Southeast Sulawesi

2020 ◽  
Vol 4 (2) ◽  
pp. 74-82
Author(s):  
Bahtiar Bahtiar ◽  
Muh. Fajar Purnama
Keyword(s):  
Habitat Preferences ◽  
Principal Component ◽  
Coarse Sand ◽  
Sediment Texture ◽  
Chi Square ◽  
Multivariate Statistical ◽  
Entire Cross Section ◽  
Lack Of Information ◽  
And Cluster Analysis ◽  
Sand And Gravel

This research is motivated by the lack of information about the habitat preferences of pokea clams in Pohara River, Southeast Sulawesi. This study aims to determine the density, distribution pattern and habitat preferences of pokea clams in the Pohara River, Southeast Sulawesi. This research was conducted for 6 months (April-September 2011). Sampling of pokea, water quality, and sediment texture was carried out in the Pohara River and analyzed at the FPIK UHO Laboratory. The density and distribution of pokea were calculated using a standard formula and analyzed using Mann Whitney and Chi Square respectively, while the habitat preferences based on different substrate textures were analyzed using Principal Component Analysis (PCA) and Cluster Analysis (CA) in the Multivariate Statistical Package (MVSP). The results showed that the density of pokea clams ranged from 117±96.78-816±594.84 ind/m2 which was distributed in cluster over the entire cross-section of the river. Pokea clams were found in all substrate textures from gravel to clay. The habitat preference of pokea clams indicated by the highest density was found in the clay texture. Pokea clams relatively do not like the habitat of coarse sand and gravel texture which is characterized by pokea population with the lowest density

scholarly journals Forest site classification in New Brunswick

1992 ◽  
Vol 68 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Colin Bowling ◽  
Vincent Zelazny
Keyword(s):  
New Brunswick ◽  
Assessment Tool ◽  
Vegetation Type ◽  
Soil Characteristics ◽  
Site Classification ◽  
Forest Site ◽  
Treatment Unit ◽  
Field Guides ◽  
Natural Stands ◽  
Forest Site Classification

Six site classification field guides covering nine site regions were published in March, 1989. They completed the design phase of a province-wide site classification program begun in 1981. The site classification system is designed as an on-site, preharvest assessment tool for use in mature and overmature natural stands. It incorporates easily recognizable vegetation and soil characteristics to classify each stand into a Vegetation Type (VT), a Soil Type (ST), and a Treatment Unit (TU). Forest management and silvicultural interpretations are given for each TU, with the primary interpretation being site productivity.

Rapid Discrimination of Indonesian Herbal Medicines by Using Electronic Nose Based on Array of Commercial Gas Sensors

2015 ◽  
Vol 771 ◽  
pp. 209-212 ◽  
Author(s):  
Fajar Hardoyono ◽  
Kuwat Triyana ◽  
Bambang Heru Iswanto
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Herbal Medicines ◽  
Principal Component ◽  
Statistical Analyses ◽  
Local Market ◽  
Multivariate Statistical ◽  
Good Repeatability ◽  
And Cluster Analysis ◽  
Rapid Discrimination

The aim of this study is to discriminate herbal medicines (here after referred to as herbals) by an electronic nose (e-nose) based on an array of eight commercially gas sensors and multivariate statistical analyses. Seven kinds of herbal essential oils purchased from local market in Yogyakarta Indonesia, including zingiberofficinale (ZO), kaempferiagalanga (KG), curcuma longa (CL), curcuma zedoaria (CZ), languasgalanga (LG), pogostemoncablin (PO), and curcuma xanthorrizharoxb (CX) were measured by using this e-nose consecutively. Due to the use of dynamic headspace in this e-nose, data for one cycle (sampling and purging) were recorded every five second for 10 cycles. Each kind of herbals was analyzed for five replications and relative amplitude of the responses was extracted as a feature. The statistical analyses of principal component analysis (PCA) and cluster analysis (CA) were used for discriminating samples. The PCA score plot shows that these 35 essential oil samples were separated into 7 groups based on similarity of patterns. The first two components, PC1 and PC2, capture 96.2% of data variance. Meanwhile, by using 80% similarity, the CA clusters 7 herbals into 3 classes. In this case, the first class consists of ZO and CZ and the second class consists of KG, CL, LG and CX, while the PO sample is clustered in the third class. These classes need to be validated using a standard analytical instrument such as GC/MS. The technique shows some advantages including easy in operation because of without any sample preparation, rapid detection, and good repeatability.

The Status of Surface Water in West Tripura District, India: An Approach by Using Water Quality Index and Multivariate Statistical Technique

2021 ◽  
Vol 18 (2) ◽  
pp. 27-36
Author(s):  
Biplab Roy ◽  
Ajay Kumar Manna
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Statistical Technique ◽  
Total Hardness ◽  
Multivariate Statistical ◽  
And Cluster Analysis ◽  
Entropy Weighted

The present investigation provides a better interpretation of surface water (rivers, ponds, bills, lakes, etc.) quality utilising entropy weighted water quality index (EWWQI) and different multivariate statistical techniques. Eleven physicochemical parameters including alkalinity, dissolved oxygen (DO), pH, total dissolved solids (TDS), electrical conductivity (EC), calcium (Ca), turbidity, magnesium (Mg), total hardness (TH), chloride (Cl-), and iron (Fe) were analysed and monitored at 23 sampling sites (in December 2018) of West Tripura district. Experimental outcomes of turbidity followed by Fe contamination exceeded recommended WHO standard limit. The maximum values of Fe and turbidity were estimated as 8.745 mg/L and 797.7 NTU, respectively. WQI values confirmed that most of the monitoring locations had poor water quality except three reported areas (S7, S14, and S15) but without Fe and turbidity, estimated WQI confirmed drinkable water condition for entire samples. Multivariate statistical approaches like correlation analysis, principal component analysis (PCA) and cluster analysis (CA) were applied to explore water quality. PCA outcomes recognised three principal factors explaining almost 85% of the total variance. CA investigated three major clusters of 23 sampling sites namely less polluted, highly polluted and moderately polluted zone. Confirming all above, the surface water at the monitoring locations is a major concern which may lead to serious health issues in local people.

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.

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