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1

Evaluation of water treatment plants quality in Basrah Province, by factor and cluster analysis

Al Saad Zainb A.A., Hamdan Ahmed N.A.

Journal of Water and Land Development

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2020

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no. 46

10--19

EN

The Shatt Al Arab River (SAAR) is a major source of raw water for most water treatment plants (WTP’s) located along with it in Basrah province. This study aims to determine the effects of different variables on water quality of the SAAR, using multivariate statistical analysis. Seventeen variables were measured in nine WTP’s during 2017, these sites are Al Hussain (1), Awaissan (2), Al Abass (3), Al Garma (4), Mhaigran (5), Al Asmaee (6), Al Jubaila (7), Al Baradia (8), Al Lebani (9). The dataset is treated using principal component analysis (PCA) / factor analysis (FA), cluster analysis (CA) to the most important factors affecting water quality, sources of contamination and the suitability of water for drinking and irrigation. Three factors are responsible for the data structure representing 88.86% of the total variance in the dataset. CA shows three different groups of similarity between the sampling stations, in which station 5 (Mhaigran) is more contaminated than others, while station 3 (Al Abass) and 6 (Al Asmaee) are less contaminated. Electrical conductivity (EC) and sodium adsorption ratio (SAR) are plotted on Richard diagram. It is shown that the samples of water of Mhaigran are located in the class of C4-S3 of very high salinity and sodium, water samples of Al Abass station, are located in the class of C3-S1 of high salinity and low sodium, and others are located in the class of C4-S2 of high salinity and medium sodium. Generally, the results of most water quality parameters reveal that SAAR is not within the permissible levels of drinking and irrigation.

2

DOC and POC in the southern Baltic Sea. Part II – Evaluation of factors affecting organic matter concentrations using multivariate statistical methods

Maciejewska A., Pempkowiak J.

Oceanologia

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2015

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No. 57 (2)

168--176

EN

This study is a continuation of the research on organic matter sources, distribution and dynamics in the southern Baltic Sea described in detail by Maciejewska and Pempkowiak (2014). In this paper, cluster analysis, principal component analysis and segment analysis were used to assess relations among factors influencing organic matter concentration in the Baltic sea-water. The following sea-water properties, salinity (Sal), temperature (Temp), pH, concentrations of chlorophyll a (Chla) and phaeopigment a (Feo), were assessed, while dissolved (DOC) and particulate (POC) organic carbon were used as organic matter measures. Water samples were collected in the course of a three-year study (2009–2011) from the Gdańsk Deep, the Gotland Deep and the Bornholm Deep (Southern Baltic). As a result, relations among both DOC and POC and the measured water properties were revealed. The cluster analysis leads to the discovery of the following structure of the analyzed water properties: DOC-pH, POC-Chla, without providing interpretation why the structure exists. Using the principal component analysis, factors influencing DOC and POC concentrations were classified as plankton activity and the inflows of saline and freshwater water masses as the study area. Segment regression analysis revealed that organic matter consists of labile and stable fractions and led to the quantification of relations between DOC and the measured sea-water properties. The following contributions to the DOC fluctuations were calculated: salinity – 11%, chlorophyll a – 26%, phaeopigment a – 26%, POC – 38% in the growing season and 31%, 33%, 21% and 22% respectively in the non-growing season.

3

Monsoon-driven Dynamics of water quality by multivariate statistical methods in Daya Bay, South China Sea

Wu M-L., Wang Y-S., Sun C-C., Sun F-L., Cheng H., Wang Y-T., Dong J-D., Wu J.

Oceanological and Hydrobiological Studies

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2012

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Vol. 41, No. 4

66-76

EN

Eleven physicochemical parameters of data collected from 12 stations in Daya Bay in 2003 were analyzed by multivariate statistical analysis. Cluster analysis (CA) grouped data from 4 seasons into two groups, the northeast and southwest monsoon periods, representing different natural processes. During the northeast monsoon period, principal component analysis (PCA) and CA group the 12 monitoring sites into Cluster DA1 (S1, S2 and S6) and Cluster DA2 (S3-S5 and S7-S12). During the southwest monsoon period, PCA and CA group the 12 monitoring sites into Cluster WB1 (S1, S2, S7, S9 and S11) and Cluster WB2 (S3-S6, S8, S10, S11 and S12). The spatial heterogeneity within the bay was defined by different hydrodynamic conditions and human activities. These results may be valuable for achieving sustainable use of the coastal ecosystems in Daya Bay.

4

Assessment of the influence of soil properties on 137Cs accumulation in Of horizon in forest soils

Ziembik Z., Dołhańczuk-Śródka A., Wacławek M.

Nukleonika

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2010

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Vol. 55, No. 2

205-212

EN

The work focuses on assessment of soil physicochemical parameters influence on 137Cs accumulation in Of soil horizon. Besides organic matter content and pH, the parameters related to sorption properties and mobile ions concentration were considered. The data were transformed using Box-Cox formula. To find mutual relationships between variables cluster analysis (CA) and principal components analysis (PCA) were used. It was found that the transformed physicochemical parameters in Of horizon are more or less related with each other but no linear or nearly linear relationships between 137Cs activity and physicochemical parameters were found.

5

Metody eksploracji danych, analiza skupień i analiza głównych składowych w badaniach monitoringowych wód powierzchniowych

Cieszyńska M., Wesołowski M.

Wiadomości Chemiczne

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2009

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[Z] 63, 7-8

575-600

EN

The paper presents overview of literature pointing up usefulness of two chemometric methods of data exploration: cluster analysis (CA) and principal component analysis (PCA) for interpretation of multidimensional data obtained during surface water monitoring programme. The exceptional importance of fresh water quality rests on the fact that it determines condition and development of all human beings as well as animals and plants. In sections 1 and 3 a concise description of the two methods of pattern recognition was presented. CA provides visual presentation of clustering mode of samples or variables in the form of tree-like scheme called dendrogram [3, 9]. Samples which exhibit considerable similarity fall into one cluster and at the same time they differ to the greatest extent from samples belonging to other clusters [1, 70-74]. PCA enables significant reduction of data matrix dimension without an excessive loss of information [5, 6, 82]. In addition, PCA provides intelligible graphic visualization of the data structure by scattering samples, described by many variables, on plane formed by the following principal components [74]. In CA methods of calculation the distance between different clusters or objects in a hierarchical dendrogram were also mentioned [75]. They define whether two clusters or objects are sufficiently similar to be joined together into one cluster. It was also denoted that among most commonly applied methods Ward's clustering method turns out to be the best and most effective for surface water monitoring results as it gives the largest number of correctly classified observations. The sections 2 and 4 present several examples of the two multidimensional chemometric techniques application in assessment of surface water quality. It was perceived that CA and PCA allow classification of sampling stations in relation to chemical composition of water and therefore permit to pinpoint areas with similar water quality [5, 82]. CA and PCA also help to indicate areas with distinctive chemical composition of water [6, 73, 90, 105]. They also help to identify factors which determine water quality at different regions [4, 5, 88, 113]. When applied for the results acquired from one watercourse at different sites CA and PCA enable to examine changes of water quality downstream [73, 80, 114]. The two chemometric methods are also useful tools for determination if season of sampling or specific weather conditions can influence variation of physicochemical parameters of water [90, 95, 103]. Other chemometric methods applied for monitoring results evaluation are mentioned in section 5 [74, 75, 116].

6

Using chemometrics to identify water quality in Daya Bay, China

Wu M-L., Wang Y-S., Sun C-C., Wang H., Lou Z-P., Dong J-D.

Oceanologia

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2009

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No. 51 (2)

217-232

EN

In this paper, chemometric approaches based on cluster analysis, classical and robust principal component analysis were employed to identify water quality in Daya Bay (DYB), China. The results show that these approaches divided water quality in DYB into two groups: stations S3, S8, S10 and S11 belong to cluster A, which lie in Dapeng Cove, Aotou Harbor and the north-eastern part of DYB, where water quality is related mainly to anthropogenic activities. The other stations belong to cluster B, which lie in the southern, central and eastern parts of DYB, where the quality is related mainly to water exchange with the South China Sea. Cluster analysis yields good results as a first exploratory method for evaluating spatial difference, but it fails to demonstrate the relationship between variables and environmental quality on the one hand and the untreated data on the other. However, with the aid of suitable chemometric approaches, the relationship between samples or variables can be investigated. Classical and robust principal component analysis can provide a visual aid for identifying the water environment in DYB, and then extracting specific information about relationships between variables and spatial variation trends in water quality.