Environmetrical Treatment of Water Quality Survey Data from Yantra River, Bulgaria

 The environmetrical analysis carried out has indicated that the short-term water quality survey may give a very important information on the latent factors influencing the water quality of Yantra river basin. The principal components analysis carried out reveals that at least four principal components are necessary for multivariate statistical modeling of the water quality – combination of natural and anthropogenic influences (“mixed” factor) reflecting parameters such as water hardness, marine influence, organic pollution; typical anthropogenic influences (“anthropogenic” factor) explaining the metal contamination of the river water; everyday wastes, usually N-containing pollutants such as nitrates, nitrites or ammonia, form the “N-containing wastes” factor and a “temperature” factor formed by typical physical parameters such as water and air temperature. The formation of these special features of the river waters from Yantra basin is also confirmed by the results of cluster analysis (variable clustering) where the content of the significant clusters of the variables is the same as the content of the principal components modeling over 75% of the total variance of the system. Additionally, the cluster analysis of the objects has proved that the water quality during both sampling traverses is very stable and reproducible. Few exceptions are observed due to momentary local pollution in an industrial area along the river stream. Comparison with standard requirements for water quality has indicated that the Yantra river waters are of high quality and could be used after minor pretreatment as potable water sources. The environmetrical approaches applied reveal a specific information concerning the river water quality. In this way the ecological problem treated has not a local importance but suggests a strategy for estimation of similar ecosystems in global sense. Received July 30, 1998. Revision June 1, 1999.     

Danube River Water Data Modelling by Multivariate Data Analysis

 A data set (48×19) consisting of Danube river water analytical data collected at Galati site, Romania, during a four-year period has been treated by principal components analysis (PCA). The PCA indicated that seven latent factors (“hardness”, “biochemical”, “waste inlets”, “turbidity”, “acidity”, “soil extracts” and “organic wastes”) are responsible for the data structure and explain over 80 % of the total variance of the system. Its complexity is further proved by the application of multiple linear regression analysis on the absolute principal components scores (APCS) where the contribution of each natural or anthropogenic sources in the factor formation is shown. The apportioning makes clear that each variable participates to a different extent to each source and, in this way, no pure natural or pure anthropogenic influence could be determined. No specific seasonality for the variables in consideration is found. Received January 24, 2001. Revision July 6, 2001.     

An advanced multivariate statistical approach to study coastal sediment data

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.     

Multivariate statistical assessment of polluted soils

This study deals with the application of several multivariate statistical methods (cluster analysis, principal components analysis, multiple regression on absolute principal components scores) for assessment of soil pollution by heavy metals. The sampling was performed in a heavily polluted region and the chemometric analysis revealed four latent factors, which describe 84.5 % of the total variance of the system, responsible for the data structure. These factors, whose identity was proved also by cluster analysis, were conditionally named “ore specific”, “metal industrial”, “cement industrial”, and “steel production” factors. Further, the contribution of each identified factor to the total pollution of the soil by each metal pollutant in consideration was determined.     

Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties

The ability of multivariate analysis methods such as hierarchical cluster analysis, principal component analysis and partial least squares-discriminant analysis (PLS-DA) to achieve olive oil classification based on the olive fruit varieties from their triacylglycerols profile, have been investigated. The variations in the raw chromatographic data sets of 56 olive oil samples were studied by high-temperature gas chromatography with (ion trap) mass spectrometry detection. The olive oil samples were of four different categories (“extra-virgin olive oil”, “virgin olive oil”, “olive oil” and “olive-pomace” oil), and for the “extra-virgin” category, six different well-identified olive oil varieties (“hojiblanca”, “manzanilla”, “picual”, “cornicabra”, “arbequina” and “frantoio”) and some blends of unidentified varieties. Moreover, by pre-processing methods of chemometric (to linearise the response of the variables) such as peak-shifting, baseline (weighted least squares) and mean centering, it was possible to improve the model and grouping between different varieties of olive oils. By using the first three principal components, it was possible to account for 79.50% of the information on the original data. The fitted PLS-DA model succeeded in classifying the samples. Correct classification rates were assessed by cross-validation.     

Chemometrics in the assessment of the sustainable development rule implementation

The sustainable development rule implementation is tested by the application of chemometrics in the field of environmental pollution. A data set consisting of Cd, Pb, Cr, Zn, Cu, Mn, Ni, and Fe content in bottom sediment samples collected in the Odra River (Germany/Poland) is treated using cluster analysis (CA), principal component analysis (PCA), and source apportionment techniques. Cluster analysis clearly shows that pollution on the German bank is higher than on the Polish bank. Two latent factors extracted by PCA explain over 88 % of the total variance of the system, allowing identification of the dominant “semi-natural” and “anthropogenic” pollution sources in the river ecosystem. The complexity of the system is proved by MLR analysis of the absolute principal component scores (APCS). The apportioning clearly shows that Cd, Pb, Cr, Zn and Cu participate in an “anthropogenic” source profile, whereas Fe and Mn are “semi-natural”. Multiple regression analysis indicates that for particular elements not described by the model, the amounts vary from 4.2 % (Mn) to 13.1 % (Cr). The element Ni participates to some extent to each source and, in this way, is neither pure “semi-natural” nor pure “anthropogenic”. Apportioning indicates that the whole heavy metal pollution in the investigated river reach is 12510.45 mg·kg⁻¹. The contribution of pollutants originating from “anthropogenic sources” is 9.04 % and from “semi-natural” sources is 86.53 %.     

Archaeometric Analysis of Ancient Copper Artefacts by Laser-Induced Breakdown Spectroscopy Technique

Twelve archaeological copper objects from the burial site of “Fontino” cave, near Grosseto, (around 2500–2000 B.C.) were analysed using laser-induced breakdown spectroscopy. Qualitative results and a preliminary study of the samples’ composition are reported and used to make a quantitative estimate; based on these results, the samples were classified using principal components statistical analysis. The perspectives of using laser-induced breakdown spectroscopy for archaeometric analysis are also discussed.     

Environmetric modeling and interpretation of river water monitoring data

This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut. For a period of one year of observation between September 1993 and August 1994 a data set from twelve campaigns at twenty-nine sampling sites from the Saale river and six campaigns from the river Ilm at seven sampling sites and from river Unstrut at ten sampling sites was collected. Twenty-seven chemical and physicochemical properties were measured to estimate the water quality. The application of cluster analysis, principal components analysis, and apportioning modeling on absolute principal components scores revealed important information about the ecological status of the region of interest:identification of two separate patterns of pollution (upper and lower stream of the rivers);identification of six latent factors responsible for the data structure with different content for the two identified pollution patterns; anddetermination of the contribution of each latent factor (source of emission) to the formation of the total concentration of the chemical burden of the river water.As a result more objective ecological policy and decision making is possible.     

Distribution of selected trace elements in samples of sediment,suspension and water taken along the Isar River,Bavaria

The three phases “dissolved solids”, “suspended solids” and “sediment” of four sampling sites along the river Isar were analysed by INAA. In these as well as in the different grain-size fractions between<2 and 63 μm 17 trace elements were determined. Compared with the values of other rivers in Middle Europe the river Isar is still below the levels of significant pollution.     

Chemometric study of soil analysis data

The present paper deals with chemometric interpretation of soil analysis data collected from 31 sampling sites in the region of Kavala and Drama, Northern Greece. The determination of 16 different chemical and physicochemical characteristics is principally needed for prognosis of the land treatment and fertilizing. The study carried out indicates that the application of multivariate statistical approaches could reveal new and specific information about sampling sites. It has been found that they could be divided into four general patterns: pattern 1 contains dominantly inorganic and alkaline soil samples from semi-mountainous regions in close proximity to the seacoast; pattern 2 indicates the same soil sample type and regional location as pattern 1 but is far from the coastal line; pattern 3 includes samples from sites from the plains with organic and alkaline soils with close proximity to the coast; pattern 4 resembles pattern 3 as soil type but involves samples from sites far from the shore. Further, six latent factors were identified, conditionally named “structural”, “acidic”, “nutritional”, “salt”, “microcomponents” and “organic”. Finally, an apportioning procedure was carried out to find the source contributions in the measured analytical values. In this way the routine estimation of the soil quality could be improved.     

Multivariate statistical analysis of coastal sediment data

Multivariate statistical analysis of sediment data (information matrix 123 × 16) from the Gulf of Mexico, USA shows that the data structure is defined by four latent factors conditionally called “inorganic natural”, “inorganic anthropogenic”, “bioorganic” and “organic anthropogenic” explaining 39.24%, 23.17%, 10.77% and 10.67% of the total variance of the data system, respectively. The receptor model obtained by the application of the PCR approach makes it possible to apportion the contribution of each chemical component for the latent factor formation. A separation of the contribution of each chemical parameter is achieved within the frames of “natural” and “anthropogenic” origin of the respective heavy metal or organic matter to the sediment formation process. This is a new approach as compared to the traditional “one dimensional” search with a limited number of preliminary selected tracer components. The model suggested divides natural from anthropogenic influences and allows in this way each participant in the sediment formation process to be used as marker of either natural or anthropogenic effects. Received: 20 March 1999 / Revised: 1 June 1999 / Accepted: 3 June 1999     

Multivariate statistical analysis of coastal sediment data

Multivariate statistical analysis of sediment data (information matrix 123 × 16) from the Gulf of Mexico, USA shows that the data structure is defined by four latent factors conditionally called “inorganic natural”, “inorganic anthropogenic”, “bioorganic” and “organic anthropogenic” explaining 39.24%, 23.17%, 10.77% and 10.67% of the total variance of the data system, respectively. The receptor model obtained by the application of the PCR approach makes it possible to apportion the contribution of each chemical component for the latent factor formation. A separation of the contribution of each chemical parameter is achieved within the frames of “natural” and “anthropogenic” origin of the respective heavy metal or organic matter to the sediment formation process. This is a new approach as compared to the traditional “one dimensional” search with a limited number of preliminary selected tracer components. The model suggested divides natural from anthropogenic influences and allows in this way each participant in the sediment formation process to be used as marker of either natural or anthropogenic effects. Received: 20 March 1999 / Revised: 1 June 1999 / Accepted: 3 June 1999     

A bidimensional simulation of particle-cluster aggregation with variable active sites particles

 In this work a simple program has been developed which simulates the process of particle– cluster aggregation limited by diffusion. All the simulation have been carried out using 2d square lattices with square “particles” having a variable number of active inter-action sites (from 3 to 8) for each particle in order to analyze the effect of such limitation on the fractal dimension of the aggregates. The fractal dimension of such aggregates was calculated by the so-called “box counting” method. It has been shown that there is no change in the value of the fractal dimension (1.70) as the active site number is increased. Instead it appears that there is an average number of active sites of about 2.3 for all the structures no matter how many active interaction sites the particles have. This appears as an interesting result in connection with the aggregation of particles such as renneted casein micelles, which could present differences in the surface density of active sites. Received: 11 February 1997 Accepted: 8 January 1998     

Spectroscopic Study of Cerium-Doped Silica Gel Monoliths and Their Densified Derivatives

Cerium is exploited as a probe cation for elucidating the structure of an alkoxide-derived silica gel and its progressive evolution to a glass network as a function of heat-treatment up to 1000°C. At intermediate temperatures, the host structure exhibits inhomogeneity due to insufficient formation of siloxane bonds, which is reflected by at least two different sites and co-ordination spheres (termed “high” and “low” water ligation) for cerium. This is proved by the response of the gels heated up to 700°C to rehydration. Further formation of Si−O−Si network (900°C) leads to the destruction of the “high water” sites of cerium and progression towards a glassy structure. It is, however, only after heat-treatment at 1000°C that a dense silica glass network, not responding to rehydration, is finally obtained with cerium ions embedded in it.     

Surface functionalization via in situ interaction of plasma-generated free radicals with stable precursor-molecules on cellulose

The surface functionalization process was accomplished in a consecutive 3 step process including: (1) Argon- and oxygen-plasma enhanced generation of free radical sites on cellophane surfaces; (2) “In situ” gas phase derivatization in the absence of plasma using hydrazine, ethylene diamine, or propylene diamine; (3) Second “in situ”, gas phase derivatization in the absence of plasma using oxallyl chloride or “ex situ” derivatization in the presence of glutaraldehyde. The presence of free radical sites on the plasma exposed cellophane surfaces was demonstrated using “in situ” sulfur dioxide and nitric oxide labeling techniques. It was shown that the free radical sites readily react under “in situ” conditions with the stable chain-precursor components and generate the desired spacer-chain molecules. ESCA, ATR-FTIR analysis and dying techniques were used to monitor the cellophane surface changes. A factorial design was used for selecting the optimal plasma parameters. Functionalized cellophane substrates were used for immobilization of α-chymotrypsin in the presence of spacer-chain molecules. The activity of the immobilized α-chymotrypsin was found to be lower in comparison to the activity of the free enzyme and the presence of virgin cellophane in the free enzyme solution also reduced significantly the activity of the enzyme. It is suggested that the swollen state of the cellophane plays a significant role in the decrease of the immobilized enzyme activity.     

Heme Binding in Gas-Phase Holo-Myoglobin Cations: Distal Becomes Proximal?

His64 and His93 are the two well-known sites of heme binding in water-dissolved holo-myoglobin, with His93 being a proximal, strongly binding partner, while the distal His64 weakly coordinates to the heme through a small-molecule ligand, e.g., water or O₂. The heme bonding scheme in a water-free environment is as yet unclear. Here we employed electron transfer dissociation tandem mass spectrometry to study the preferential attachment site of the ferri-heme (Fe³⁺) in electrospray-produced 12+, 14+, and 16+ holo-myoglobin ions. Contrary to expectations, in lower-charge complexes that should have a structure resembling that in solution, the heme seems to be preferentially attached to the “distal” histidine. In contrast, in the highest studied charge state, the “proximal” histidine is the site of preferential attachment; the 14+ charge state is an intermediate case. This surprising finding raises a question of heme coordination in proteins transferred to water-free environment, as well as the effect of the protonation sites on heme bonding.     

Multivariate standard addition method solved by net analyte signal calculation and rank annihilation factor analysis

This article describes the use of the net analyte signal (NAS) concept and rank annihilation factor analysis (RAFA) for building two different multivariate standard addition models called “SANAS” and “SARAF.” In the former, by the definition of a new subspace, the NAS vector of the analyte of interest in an unknown sample as well as the NAS vectors of samples spiked with various amounts of the standard solutions are calculated and then their Euclidean norms are plotted against the concentration of added standard. In this way, a simple linear standard addition graph similar to that in univariate calibration is obtained, from which the concentration of the analyte in the unknown sample and the analytical figures of merit are readily calculated. In the SARAF method, the concentration of the analyte in the unknown sample is varied iteratively until the contribution of the analyte in the response data matrix is completely annihilated. The proposed methods were evaluated by analyzing simulated absorbance data as well as by the analysis of two indicators in synthetic matrices as experimental data. The resultant predicted concentrations of unknown samples showed that the SANAS and SARAF methods both produced accurate results with relative errors of prediction lower than 5% in most cases.     

Heavy-metal distribution in river waters and sediments around a "firefly village", Shikoku, Japan: application of multivariate analysis.

River water and sediment samples were collected at the same site in a vicinity of an abandoned mine, and the concentrations of major elements and heavy metals were determined. The chemical correlations were observed by principal component analysis (PCA), and the samples were classified by cluster analysis (CA) based on the PCA scores. The PCA results presented a macroscopic viewpoint of covariance structure, i.e., the chemical elements could be classified into three groups: 1) major elements and heavy metals in the river water, 2) Cd, Fe and Mn in the sediments, and 3) Cu and Zn in the sediments. The CA results implied a similarity of chemical compositions in most parts of the study area, except the ranges close to the abandoned copper mine. At the mixing location of mining water with natural river water, major elements and cadmium showed simple physical mixing (conservative mixing). Other heavy metals (Cu, Fe, Mn and Zn) showed the massive precipitation at the mixing event. The PCA structure was mainly interpreted in terms of the mixing process between mining water and diluted natural river water.     

Small-angle X-ray scattering study of a poly(oxyphenylethylene)–poly(oxyethylene) diblock copolymer gel under shear flow

 The structure and flow behaviour of a micellar “cubic” phase is studied, using small-angle X-ray scattering (SAXS) and constant stress rheometry on a poly(oxyphenylethylene)–poly(oxyethylene) diblock copolymer in water. The predominant structure is a face-centred cubic (fcc) array of spherical micelles, which under shear undergoes layer sliding to give a scattering pattern from stacked hexagonal close-packed layers. A detailed analysis of the SAXS data indicates the presence of a fraction of grains with a structure distorted from a fcc phase. The additional reflections that characterize this structure can be indexed to a rhombohedral unit cell, space group R3ˉm, with the same volume as the fcc unit cell. The rhombohedral unit cell corresponds to a cubic cell that has been “stretched” along a [111] direction, and it is suggested that such a structure results from the gradient in shear velocity in the Couette cell employed. Shearing at high shear rates leads to a “smearing out” of the reflections, but upon cessation of shear under these conditions a highly oriented SAXS pattern is obtained, which confirms the persistence of rhombohedral ordering. The shear-induced changes in orientation are correlated to a plateau observed in the stress plotted against shear rate, such a plateau being a sign of inhomogeneous flow. Received: 8 September 2000 Accepted: 29 November 2000     

Structures of deoxypeganine salts

The structures of deoxypeganine (DOP) hydrochloride and oxalate were solved by x-ray structure analysis. An infinite chain along the crystallographic c axis was formed in the crystal structure of DOP oxalate. A molecular framework consisting of Cl anions and DOP cation protonated at N1 was found in the structure of unhydrated DOP hydrochloride. The molecular packing of the “host” (DOP cation) was pseudoisostructural in the studied ion-molecular crystals but differed from other known DOP salts. The “guest” molecules (acid anions) in the studied and known DOP salts formed different intermolecular contacts. __________ Translated from Khimiya Prirodnykh Soedinenii, No. 3, pp. 280–283, May–June, 2006.