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1
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Long-Term Statistical Assessment of the Water Quality of Tundja River

100%
Papazova P., Simeonova P.
Ecological Chemistry and Engineering S
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2012
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vol. 19
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issue 2
213-226
EN
Two major environmetric methods (Cluster analysis (CA) and Principal components analysis (PCA)) were applied for statistical assessment of the water quality of trans-border river Tundja. The study used long-term monitoring data from 26 sampling sites characterized by 12 physicochemical parameters. Clustering of chemical indicators results in 3 major clusters: the first one shows the impact of anthropogenic sources, the second - the impact of agriculture and farming activities and the last one describes the role of the physical parameters on the water quality and also the impact of urban wastes. For better assessment of the monitoring data, PCA was implemented, which identified four latent factors. Two of them - "urban wastes" factor and "agriculture" factor correspond almost entirely to clusters 3 and 2 from the previous statistical analysis. The third one, named "industrial wastes" factor, reveals a specific seasonal behavior of the river system. The last latent factor describes the active reaction of the water body and is determined as "acidity" factor. The linkage of the sampling sites along the river flow by CA formed two clusters with the spatial "upstream-downstream" separation. The apportionment model of the pollution determined the contribution of each one of identified pollution factors to the total concentration of each one of the water quality parameters.
PL
Dwie główne metody analizy danych środowiskowych (analiza skupień (CA) i analiza składowych głównych (PCA)) zastosowano do statystycznej oceny jakości wód transgranicznej rzeki Tundja. W badaniach wykorzystano dane otrzymane z monitoringu długookresowego. Próbki pobrano w 26 miejscach i scharakteryzowano za pomocą 12 parametrów fizykochemicznych. Pogrupowanie tych parametrów ze względu na 3 wskaźniki chemiczne pozwoliło na zbudowanie 3 głównych klastrów: pierwszy z nich pokazuje wpływ źródeł antropogennych, drugi - wpływ rolnictwa i działalności rolniczej, a trzeci opisuje rolę parametrów fizycznych i zanieczyszczeń środowiska miejskiego na jakość wody. W celu lepszej oceny danych monitoringowych zastosowano PCA, co pozwoliło na identyfikację czterech ukrytych czynników. Dwa z nich - czynnik "miejskie odpady" i czynnik "rolnictwo" - odpowiadają niemal w całości klastrom 3 i 2 z poprzedniej analizy statystycznej. Trzeci czynnik, nazwany "odpadami przemysłowymi", ukazuje specyficzne zmiany sezonowe w systemie rzecznym. Ostatni czynnik opisuje reakcję wody i jest określany jako czynnik "kwasowość". Powiązania pomiędzy miejscami pobierania próbek wzdłuż przepływu oceniono za pomocą CA. Wskazano istnienie dwóch klastrów z separacją przestrzenną "upstream-downstream". Model podziału zanieczyszczeń określał wkład każdego ze zidentyfikowanych czynników zanieczyszczeń w całkowitym stężeniu każdego z parametrów jakościowych wody.
2
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Statistical Calibration of Model Solution of Analytes

81%
Simeonov D., Spasov L., Simeonova P.
Ecological Chemistry and Engineering S
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2012
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vol. 19
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issue 1
67-75
EN
A new method based on spectrophotometric-partial least-squares procedure was proposed for simultaneously determination of thorium and zirconium using SPADNS (4,5-Dihydroxy-3-(p-sulfophenylazo)-2,7-naphthalene disulfonic acid, trisodium salt) as a color reagent. Absorbance measurements were made in the range of γ = 541÷620 nm with 1.0 nm steps in buffered solutions at pH 3.5. The linear ranges were obtained for 0.5÷11.5 and 1.5÷14.5 μg cm-3 for Th4+ and Zr4+ ions, respectively. The limits of detection were determined 0.4 and 1.2 μg cm-3 for thorium and zirconium, respectively. The standard deviation (n = 3) and recovery percent of 10 samples in the prediction set were obtained in the amplitude 0.22÷0.38 μg cm-3 and 91.3÷109.2, respectively. The proposed method was used for simultaneously determination of mentioned ions in spiked real water samples and wastewater. The results show that the method is applicable for the analysis of samples with similar matrix.
PL
Nowa metoda opiera się na procedurze spektrofotometrycznej - najmniejszych kwadratów, która została zaproponowana do równoczesnego oznaczania toru i cyrkonu z wykorzystaniem SPADNS (kwas 4,5-Dihydroksy-3-(p-sulfofenylazo)-2,7-naftaleno disulfonowy, sól trisodowa) jako odczynnika koloru. Pomiarów absorbancji dokonano w zakresie λ = 541÷620 nm co 1,0 nm w roztworach buforowych o pH 3,5. Liniowy zakres uzyskano przy stężeniach jonów Th4+ i Zr4+ odpowiednio 0,5÷11,5 i 1,5÷14,5 μg cm-3. Granice wykrywalności dla toru i cyrkonu wynosiły odpowiednio 0,4 i 1,2 μg cm-3. Wyznaczono odchylenie standardowe (n = 3) i procent odzysku w serii 10 próbek odpowiednio w zakresie 0,22÷0,38 i 91,3÷109,2 μg cm-3. Proponowana metoda została zastosowana do równoczesnego oznaczania wymienionych jonów w wzbogaconych próbkach rzeczywistych wody i ścieków. Wyniki pokazują, że ta metoda może być wykorzystywana do analizy próbek o podobnej matrycy.
3
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Chemometrics as an option to assess clinical data from diabetes mellitus type 2 patients

81%
Nikolov M., Simeonova P., Simeonov V.
Open Medicine
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2009
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vol. 4
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issue 4
433-443
EN
The present study deals with the application of two major multivariate statistical approaches - Cluster Analysis (CA) and Principal Components Analysis (PCA) as an option for assessment of clinical data from diabetes mellitus type 2 patients. One hundred clinical cases of patients are considered as object of the statistical classification and modeling, each one of them characterized by 34 various clinical parameters. The goal of the study was to find patterns of similarity, both between the patients and the clinical tests. Each group of similarity is interpreted revealing at least five clusters of correlated parameters or five latent factors, which determine the data structure. Relevant explanation of the clustering is found based on the pattern of similarity like glucose level, anthropometric data, enzyme level, liver function, kidney function etc. It is assumed that this classification could be of help in optimizing the performance of clinical test for this type of patients and for designing a pattern for the role of the different groups of test in determining the metabolic syndrome of the patients.
4
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An advanced multivariate statistical approach to study coastal sediment data

71%
Simeonova P., Sarbu C., Spanos T., Simeonov V., Tsakovski S.
Open Chemistry
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2006
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vol. 4
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issue 1
68-80
EN
The present paper deals with the application of classical and fuzzy principal components analysis to a large data set from coastal sediment analysis. Altogether 126 sampling sites from the Atlantic Coast of the USA are considered and at each site 16 chemical parameters are measured. It is found that four latent factors are responsible for the data structure (“natural”, “anthropogenic”, “bioorganic”, and “organic anthropogenic”). Additionally, estimating the scatter plots for factor scores revealed the similarity between the sampling sites. Geographical and urban factors are found to contribute to the sediment chemical composition. It is shown that the use of fuzzy PCA helps for better data interpretation especially in case of outliers.
5
Content available remote

Statistical interpretation of medical data of patients with alcohol abuse

61%
Bardarov V., Simeonova P., Neikova L., Bardarov K., Simeonov V., Tsakovski S., Kanev K.
Open Medicine
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2012
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vol. 7
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issue 4
465-474
EN
An attempt is made to assess a set of biochemical, kinetic and anthropometric data for patients suffering from alcohol abuse (alcoholics) and healthy patients (non-alcoholics). The main goal is to identify the data set structure, finding groups of similarity among the clinical parameters or among the patients. Multivariate statistical methods (cluster analysis and principal components analysis) were used to assess the data collection. Several significant patterns of related parameters were found to be representative of the role of the liver function, kinetic and anthropometric indicators (conditionally named “liver function factor”, “ethanol metabolism factor”, “body weight factor”, and “acetaldehyde metabolic factor”). An effort is made to connect the role of kinetic parameters for acetaldehyde metabolism with biochemical, ethanol kinetic and anthropometric data in parallel.
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