Assessment of water chemistry in a segment of Qua Iboe River Estuary by Principal component analysis

2021 ◽  
Vol 36 (1) ◽  
pp. 1-11
Author(s):  
I.I. Akpan
Keyword(s):  
Principal Component Analysis ◽  
Water Chemistry ◽  
River Estuary ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Total Alkalinity ◽  
Mineral Nutrient ◽  
Data Set ◽  
Physico Chemical

Assessment of water chemistry in a segment of Qua Iboe River Estuary, Niger Delta Region of Nigeria was carried out from January to December 2018 at three sampling stations. Seventeen physico-chemical parameters were analyzed using standards procedure. A total of 12 samples were collected from each station. Principal Component Analysis (PCA) was employed in the assessment of the study area. Three principal components, accounting for 99.59%, 100.01% and 100% of the total variance of information contained in the original data set for dry season were obtained. In the wet season, the components accounted for 66.0%, 69.97% and 67.50% of the total variance respectively. Results revealed that the most loading factor in the PCA when considering all the sampling stations in different seasons together in PC1, PC2 and PC3 axes were mainly sulphate, phosphate-phosphorus, calcium, potassium, temperature, total dissolved solids, total alkalinity, total hardness, dissolved oxygen, sodium, electrical conductivity, biological oxygen demand, pH, total suspended solids, A and magnesium. These loadings could be grouped into mineral/nutrient, physico-chemical, organic and domestic factors. General assessment of the study area did not indicate much deviation from prescribed standards, but sufficient to maintain a varied aquatic biodiversity.

scholarly journals Rime samples characterization and comparison using classical and fuzzy principal components analysis

2008 ◽  
Vol 6 (2) ◽  
pp. 208-215 ◽  
Author(s):  
Kamila Klimaszewska ◽  
Costel Sârbu ◽  
Żaneta Polkowska ◽  
Marek Błaś ◽  
Mieczysław Sobik ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Direct Consequence ◽  
Component Analysis ◽  
Component Model ◽  
Total Variance ◽  
Data Set ◽  
Components Analysis ◽  
Fuzzy Principal Component Analysis

AbstractThe main objective of this paper is to introduce principal component analysis and two robust fuzzy principal component algorithms as useful tools in characterizing and comparing rime samples collected in different locations in Poland (2004–2007). The efficiency of the applied procedures was illustrated on a data set containing 108 rime samples and concentration of anions, cations, HCHO, as well as pH and conductivity. The fuzzy principal component algorithms achieved better results mainly because they are more compressible than classical PCA and very robust to outliers. For example, a three component model, fuzzy principal component analysis-first component (FPCA-1) accounts for 62.37% of the total variance and fuzzy principal component analysis-orthogonal (FPCA-o) 90.11%; PCA accounts only for 58.30%. The first two principal components explain 51.41% of the total variance in the case of FPCA-1 and 79.59% in the case of FPCA-o as compared to only 47.55% for PCA. As a direct consequence, PCA showed only a partial differentiation of rime samples onto the plane or in the space described by different combination of two or three principal components, whereas a much sharper differentiation of the samples, regarding their origin and location, is observed when FPCAs are applied.

Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

Comparisons of Environmental Ordinations with Principal Component Vegetational Ordinations for Sets of Data Having Different Degrees of Complexity

1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Two Dimensional ◽  
High Complexity ◽  
Water Conductivity ◽  
Data Set ◽  
Total Data

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).

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