Chemometric evaluation of the heavy metals distribution in waters from the Dilovası region in Kocaeli, Turkey

Bingöl, Deniz; Ay, Ümit; Bozbaş, Seda Karayünlü; Uzgören, Nevin

doi:10.1016/j.marpolbul.2012.12.006

Cited by 40 publications

(21 citation statements)

References 23 publications

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“…The PM 2.5 concentration was assigned as the dependent variable while the chemical compositions were assigned as the independent variables. The number of principal components was chosen based on eigenvalues of [ 1 (Hogarh et al 2012;Bingöl et al 2013) after varimax rotation. A strong factor loading ([ 0.75) was then chosen as the baseline for classification purposes (Liu et al 2003).…”

Section: Methodsmentioning

confidence: 99%

Source Contribution of PM2.5 at Different Locations on the Malaysian Peninsula

Ee-Ling

Mustaffa

Amil

et al. 2015

Bull Environ Contam Toxicol

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This study determined the source contribution of PM2.5 (particulate matter <2.5 μm) in air at three locations on the Malaysian Peninsula. PM2.5 samples were collected using a high volume sampler equipped with quartz filters. Ion chromatography was used to determine the ionic composition of the samples and inductively coupled plasma mass spectrometry was used to determine the concentrations of heavy metals. Principal component analysis with multilinear regressions were used to identify the possible sources of PM2.5. The range of PM2.5 was between 10 ± 3 and 30 ± 7 µg m(-3). Sulfate (SO4 (2-)) was the major ionic compound detected and zinc was found to dominate the heavy metals. Source apportionment analysis revealed that motor vehicle and soil dust dominated the composition of PM2.5 in the urban area. Domestic waste combustion dominated in the suburban area, while biomass burning dominated in the rural area.

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Section: Methodsmentioning

confidence: 99%

Source Contribution of PM2.5 at Different Locations on the Malaysian Peninsula

Ee-Ling

Mustaffa

Amil

et al. 2015

Bull Environ Contam Toxicol

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“…The correlation analysis between the physico-chemical characteristics of the water in reservoir was performed to measure the relationship between variables (Table 2). Pearson's correlation coefficient (r) is the most common correlation coefficient (Bing¨ol et al, 2013;Amfo-Otu et al, 2014) and is a measure of strength and direction of association that exists between two continuous variables. A positive correlation between two variables implies that the increase of one of the variables is accompanied by the increase of the other variable while a negative correlation shows that when a variable of the considered pair of variables increases, the other one decreases.…”

Section: Dippong Et Almentioning

confidence: 99%

Assessment of Water Physicochemical Parameters in the Strîmtori‐Firiza Reservoir in Northwest Romania

Dippong

Mihali

Năsui

et al. 2018

Water Environment Research

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This article assesses the surface water quality of the Strîmtori-Firiza reservoir by analyzing 14 physicochemical parameters among 9 metals in 18 sampling points. A series of possible reservoir pollutants were identified, depending on the nature of the pollutants and their loading. The novelty of the paper consists in the broad spectrum of investigations that includes spatial distribution cluster analysis and mathematical modeling. This article presents an innovative method of assessing the distributions of the metal loads as maps of metal distribution that are in accordance with the developed mathematical models that aim to present the pollutant distribution and the degree of correlation between the physicochemical indicators. The clusters were generated using Ward's and Nearest Neighbor methods that are less used in the studies concerning the analysis of water quality. Measures for maintaining the quality of the water reservoir are proposed considering the importance of the reservoir as source for drinking/industrial water.

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“…In this case, variables number can be reduced in future work. Generally, if the correlation coefficient is bigger than 0.75, it is considered that there is a siginificant relationship between the variables (Bingol et al, 2013). In this case, the number of significant components can be determined according to the eigenvalues in the PCA (Accornero et al, 2008;Beck et al, 2013;Adams et al, 1992) The CA is a statistical data-management technique (Lyapina, 2016).…”

Section: Statistical Analytical Procedures 241 Multivariate Analysismentioning

confidence: 99%

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

2018

Global NEST Journal

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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. 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.

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Chemometric evaluation of the heavy metals distribution in waters from the Dilovası region in Kocaeli, Turkey

Cited by 40 publications

References 23 publications

Source Contribution of PM2.5 at Different Locations on the Malaysian Peninsula

Source Contribution of PM2.5 at Different Locations on the Malaysian Peninsula

Assessment of Water Physicochemical Parameters in the Strîmtori‐Firiza Reservoir in Northwest Romania

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

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