Water quality sample collection, data treatment and results presentation for principal components analysis – literature review and Illinois River watershed case study

<p class="Abstract">This paper illustrates a case study related to the characterisation of corrosion products present on recently excavated artefacts. The archaeological findings, from the Rakafot 54 site (Beer-Sheva, Israel), consist of 23 coins and a pendant, all dating back to the Roman period. Raman spectroscopy was used to identify the corrosion products that compose the patina covering the objects. To facilitate and support their identification, spectra were then processed using principal components analysis. This chemometric technique allowed the identification of two main compounds, classified as atacamite and clinoatacamite, which formed the main components of the patinas. The results of this investigation can help in assessing the conservation state of artefacts and defining the correct restoration strategy.</p>

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Multivariate Statistical Analysis Applied to Water Quality of a Tropical Coastal Lagoon, Cartagena, Colombian Caribbean

Indonesian Journal of Chemistry ◽

10.22146/ijc.43035 ◽

2019 ◽

Vol 20 (1) ◽

pp. 141

Author(s):

Ildefonso Baldiris-Navarro ◽

Juan Carlos Acosta-Jimenez ◽

Angel Dario Gonzalez-Delgado ◽

Alvaro Realpe-Jimenez ◽

Juan Gabriel Fajardo-Cuadro

Keyword(s):

Water Quality ◽

Principal Components Analysis ◽

Principal Components ◽

Coastal Lagoon ◽

Coastal Lagoons ◽

Oxygen Demand ◽

Habitat Destruction ◽

Environmental Data ◽

Fecal Coliforms ◽

Components Analysis

Coastal lagoons are one of the most threatened ecosystems in the world, because of population growth, habitat destruction, pollution, wastewater, overexploitation and invasive species which are the main causes of their degradation. The objective of this paper was to evaluate the water quality behavior in a stressed coastal lagoon in Cartagena, Colombian Caribbean. Environmental data was analyzed using hypothesis testing, confidence intervals, and also Principal components analysis (PCA). The study was focused on water parameters such as dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (COD), salinity, pH, total dissolved solids, total coliforms (TC), Fecal coliforms (FC), ammonium (NH4+) and total phosphorus (TP). The analysis was conducted in line with the Colombian national water standard. Results showed that BOD5, COD, phosphorus, and coliforms are out of the limits for these variables in Colombia and are reaching levels that may be a threat to human health. Principal components analysis detected five components that explained 79.4% of the variance of data and showed that anthropogenic and temporal factors might be affecting the variation of the parameters.

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Using multivariate factorial kriging for multiscale ordination: a case study

Canadian Journal of Forest Research ◽

10.1139/x05-211 ◽

2005 ◽

Vol 35 (12) ◽

pp. 2860-2874 ◽

Cited By ~ 16

Author(s):

Nikos Nanos ◽

Fernando Pardo ◽

Jesus Alonso Nager ◽

José Alberto Pardos ◽

Luis Gil

Keyword(s):

Principal Components Analysis ◽

Principal Components ◽

Multiple Scale ◽

Analysis Data ◽

Vegetation Pattern ◽

Soil Conditions ◽

Linear Model Of Coregionalization ◽

Factorial Kriging ◽

Components Analysis

Vegetation ordination is usually based on classical data reduction techniques such as principal components analysis, correspondence analysis, or multidimensional scaling. The usual methods do not account for multiscale correlations among species. In this paper, we use a geostatistical method, known as multivariate factorial kriging, for studying multiple-scale correlations. The case study was carried out in a mixed broadleaf forest of central Spain. Six tree species were included in the analysis. Data analysis included (i) experimental variogram calculation and modeling with the use of the linear model of coregionalization, (ii) principal components analysis, and (iii) cokriging. The results indicate that correlations among species are different depending on the spatial scale. We conclude that competition for light is the main factor controlling the spatial distribution of species at the plot-level scale of variation. At larger scales of variation, soil conditions and (or) human intervention are the key factors in determining the observed vegetation pattern. Based on the factor scores for the largest scale of variation, we conducted a cluster analysis to identify plots with similar characteristics. The resulting clusters have the remarkable property of being spatially continuous.

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