Environmental Analytical Chemistry.

Environmental analytical chemistry has evolved into a well-established interdisciplinary field (analytical chemistry, pollution chemistry, chemical engineering, etc [...].

Multivariate Exploratory Analysis of the Bulgarian Soil Quality Monitoring Network.

The goal of the present study is to assess the soil quality in Bulgaria using (i) an appropriate set of soil quality indicators, namely primary nutrients (C, N, P), acidity (pH), physical clay content and potentially toxic elements (PTEs: Cu, Zn, Cd, Pb, Ni, Cr, As, Hg) and (ii) respective data mining and modeling using chemometrical and geostatistical methods. It has been shown that five latent factors are responsible for the explanation of nearly 70% of the total variance of the data set available (principal components analysis) and each factor is identified in terms of its contribution to the formation of the overall soil quality-the mountain soil factor, the geogenic factor, the ore deposit factor, the low nutrition factor, and the mercury-specific factor. The obtained soil quality patterns were additionally confirmed via hierarchical cluster analysis. The spatial distribution of the patterns throughout the whole Bulgarian territory was visualized via the mapping of the factor scores for all identified latent factors. The mapping of identified soil quality patterns was used to outline regions where additional measures for the monitoring of the phytoavailability of PTEs were required. The suggested regions are located near to thermoelectric power plants and mining and metal production facilities and are characterized by intensive agricultural activity.

Emissions and toxic units of solvent, monomer and additive residues released to gaseous phase from latex balloons.

This study describes the VOCs emissions from commercially available latex balloons. Nine compounds are determined to be emitted from 13 types of balloons of different colors and imprints in 30 and 60 °C. The average values of total volatile organic compounds (TVOCs) emitted from studied samples ranged from 0.054 up to 7.18 µg g-1 and from 0.27 up to 36.11 µg g-1 for 30 °C and 60 °C, respectively. The dataset is treated with principal component analysis (PCA) and multiple curve resolution (MCR) to characterize its internal patterns. Here two groups on compounds are recognized - the first one related to balloon material, the second one being emissions of compounds previously adsorbed on balloon material. The toxicity assessment of MCR modeled balloons' emissions was performed by toxic unit (TU) approach. The obtained TUs were summed to give toxicity emission assessment. The incorporation of TUs allows to identify the balloons with the most toxic emissions- imprinted ones in 60 °C. The compounds of the highest TUs are hexanal and benzene. FTIR analysis shows that all balloons are made of the same polymeric material - isoprene, so all differences in emissions are related to different additives like pigments, imprints or these responsible for opaqueness. Analyzing the obtained research results it was noticed that latex balloons might be considered as an important source of emission of aliphatic and monoaromatic hydrocarbons to the gaseous phase.

Sediment Assessment of the Pchelina Reservoir, Bulgaria.

The temporal dynamics of anthropogenic impacts on the Pchelina Reservoir is assessed based on chemical element analysis of three sediment cores at a depth of about 100-130 cm below the surface water. The 137Cs activity is measured to identify the layers corresponding to the 1986 Chernobyl accident. The obtained dating of sediment cores gives an average sedimentation rate of 0.44 cm/year in the Pchelina Reservoir. The elements' depth profiles (Ti, Mn, Fe, Zn, Cr, Ni, Cu, Mo, Sn, Sb, Pb, Co, Cd, Ce, Tl, Bi, Gd, La, Th and Unat) outline the Struma River as the main anthropogenic source for Pchelina Reservoir sediments. The principal component analysis reveals two groups of chemical elements connected with the anthropogenic impacts. The first group of chemical elements (Mn, Fe, Cr, Ni, Cu, Mo, Sn, Sb and Co) has increasing time trends in the Struma sediment core and no trend or decreasing ones at the Pchelina sampling core. The behavior of these elements is determined by the change of the profile of the industry in the Pernik town during the 1990s. The second group of elements (Zn, Pb, Cd, Bi and Unat) has increasing time trends in Struma and Pchelina sediment cores. The increased concentrations of these elements during the whole investigated period have led to moderate enrichments for Pb and Unat, and significant enrichments for Zn and Cd at the Pchelina sampling site. The moderately contaminated, according to the geoaccumulation indexes, Pchelina Reservoir surface sediment samples have low ecotoxicity.

Chemometric Assessment of Bulgarian Wastewater Treatment Plants' Effluents.

Surface water quality strongly depends on anthropogenic activity. Among the main anthropogenic sources of this activity are the wastewater treatment plant (WWTP) effluents. The discharged loads of nutrients and suspended solids could provoke serious problems for receiving water bodies and significantly alter the surface water quality. This study presents inventory analysis and chemometric assessment of WWTP effluents based on the mandatory monitoring data. The comparison between the Bulgarian WWTPs and previously reported data from other countries reveals that discharged loads from investigated WWTPs are lower. This is particularly valid for total suspended solids (TSS). The low TSS loads are the reason for the deviations of the typical calculated WWTP effluent ratios of Bulgarian WWTPs compared to the WWTPs worldwide. The performed multivariate analysis reveals the hidden factors that determine the content of WWTP effluents. The source apportioning based on multivariate curve resolution analysis provides detailed information for source contribution profiles of the investigated WWTP effluent loads and elucidate the difference between WWTPs included in this study.

Influence of Storage Time and Temperature on the Toxicity, Endocrine Potential, and Migration of Epoxy Resin Precursors in Extracts of Food Packaging Materials.

The aim of the present study was to establish a standard methodology for the extraction of epoxy resin precursors from several types of food packages (cans, multi-layered composite material, and cups) with selected simulation media (distilled water, 5% ethanol, 3% dimethyl sulfoxide, 5% acetic acid, artificial saliva) at different extraction times and temperatures (factors). Biological analyses were conducted to determine the acute toxicity levels of the extracts (with Vibrio fischeri bacteria) and their endocrine potential (with Saccharomyces cerevisiae yeasts). In parallel, liquid chromatography-tandem mass spectrometry was performed to determine levels of bisphenol A diglycidyl ether (BADGE), bisphenol F diglycidyl ether (mixture of isomers, BFDGE), ring novolac glycidyl ether (3-ring NOGE), and their derivatives. The variation induced by the different experimental factors was statistically evaluated with analysis of variance simultaneous component analysis (ASCA). Our findings demonstrate the value of using a holistic approach to best partition the effects contributing to the end points of these assessments, and offer further guidance for adopting such a methodology, thus being a broadly useful reference for understanding the phenomena related to the impacts of food packaging materials on quality for long- and short-term storage, while offering a general method for analysis.

Assessment of the Bulgarian Wastewater Treatment Plants' Impact on the Receiving Water Bodies.

Deterioration of water quality is a major problem world widely according to many international non-governmental organizations (NGO). As one of the European Union (EU) countries, Bulgaria is also obliged by EU legislation to maintain best practices in assessing surface water quality and the efficiency of wastewater treatment processes. For these reasons studies were undertaken to utilize ecotoxicological (Microtox®, Phytotoxkit FTM, Daphtoxkit FTM), instrumental (to determine pH, electrical conductivity (EC), chemical oxygen demand, total suspended solids (TSS), total nitrogen (N) and phosphorus (P), chlorides, sulphates, Cr, Co, Cu, Cd, Ba, V, Mn, Fe, Ni, Zn, Se, Pb), as well as advanced chemometric methods (partial least squares-discriminant analysis (PLS-DA)) in data evaluation to comprehensively assess wastewater treatment plants' (WWTPs) effluents and surface waters quality around 21 major Bulgarian cities. The PLS-DA classification model for the physicochemical parameters gave excellent discrimination between WWTP effluents and surface waters with 93.65% correct predictions (with significant contribution of EC, TSS, P, N, Cl, Fe, Zn, and Se). The classification model based on ecotoxicological data identifies the plant test endpoints as having a greater impact on the classification model efficiency than bacterial, or crustaceans' endpoints studied.

Modeling and MANOVA studies on toxicity and endocrine potential of packaging materials exposed to different extraction schemes.

The stability of the linings of packaging that is in contact with the goods stored has been of major concern during decades of the development of packaging materials. In this work, an attempt was undertaken to assess the applicability of using two bioassays (Microtox® and XenoScreen YES/YAS) in estimating the stability of packaging (cans, caps, multilayer material) and the impact of their degradation on the toxicity of some simulated media. The assessment of the impact of packaging storage conditions (temperature, disinfection, preservation, extracting and washing solvents) was planned and performed with i) regression modeling of the experimental effects on the ecotoxicity readings, ii) ANOVA and MANOVA estimation of the experimental conditions as significant factors affecting the toxicity results and iii) FTIR analysis of the packages. It is shown that the effects of temperature and extraction solvents could be quantitatively assessed by the agreement between all methods applied. It can be stated that temperature and acidity as well as the alcohol content in the sensitive media have the greatest impact on the toxicity of the extract and thus on the stability of the internal lining and the extractability of xenobiotics.

Phytoavailability of potentially toxic elements from industrially contaminated soils to wild grass.

Topsoil and grass samples from 14 sites located in different distances from three copper mining factories and a copper smelter were collected in Srednogorie, Bulgaria. The paper discusses results of studies on the mobility of potentially toxic elements (As, Cd, Cr, Cu, Mn, Ni, Pb, Zn) from contaminated soils to wild grass Cynodon dactylon in terms of transfer (TF: soil to plant) and phytoavailability (PF: EDTA (ethylenediaminetetraacetic acid)-soluble to plant) factors. Soils located close to mine and smelter factories were heavily contaminated by As and Cu reaching levels up to 500 mg/kg and 2400 mg/kg, respectively for As and Cu. At seven sites arsenic and copper in grass were over the normal levels (1 mg/kg As; 20 mg/kg Cu). The content of Cu in grass averaged 63 mg/kg exceeding the maximum allowable for sheep. Relationship between PF and TF values was found for As, Cd, Cu, Ni, Pb, Zn but not for Mn and Cr. To some extent the soil pH, TOM (total organic matter) and CaCO3 content are related to the phytoavailability of As, Cd and Cu since for Zn and Pb this relation is attributed to Al- and Fe-content of soil. Statistically significant correlation coefficients were determined between the EDTA-soluble fraction of soil and content in grass in the case of As, Cd, Cr, Cu, Mn and Pb indicating the suitability of the short procedure for phytoavailability studies of those elements. The application of hierarchical cluster analysis and self-organizing maps of Kohonen made it possible to reveal specific hidden relationships between the soil variables and transfer factors as well as between the sampling locations. This additional information helps in more detailed interpretation of phytoavailability and transfer processes in the region of interest. The novelty in this study is achieved by careful consideration of the possibility of using EDTA extracts of the toxic metals in investigating of the phytoavailability and transfer processes soil/plant. Additionally, chemometric expertise used makes it possible to differentiate the behavior of each toxic metal in the processes studied. The combination of easy option for rapid extraction and intelligent data analysis gives a new perspective for contributions in explanation of the complex interactions between soils and plants when assessing pollution events in a certain environment.

Assessment of ecotoxicity and total volatile organic compound (TVOC) emissions from food and children's toy products.

The development of new methods for identifying a broad spectrum of analytes, as well as highly selective tools to provide the most accurate information regarding the processes and relationships in the world, has been an area of interest for researchers for many years. The information obtained with these tools provides valuable data to complement existing knowledge but, above all, to identify and determine previously unknown hazards. Recently, attention has been paid to the migration of xenobiotics from the surfaces of various everyday objects and the resulting impacts on human health. Since children are among those most vulnerable to health consequences, one of the main subjects of interest is the migration of low-molecular-weight compounds from toys and products intended for children. This migration has become a stimulus for research aimed at determining the degree of release of compounds from popular commercially available chocolate/toy sets. One of main objectives of this research was to determine the impact of time on the ecotoxicity (with Vibrio fischeri bioluminescent bacteria) of extracts of products intended for children and to assess the correlation with total volatile organic compound emissions using basic chemometric methods. The studies on endocrine potential (with XenoScreen YES/YAS) of the extracts and showed that compounds released from the studied objects (including packaging foils, plastic capsules storing toys, most of toys studied and all chocolate samples) exhibit mostly androgenic antagonistic behavior while using artificial saliva as extraction medium increased the impact observed. The impact of time in most cases was positive one and increased with prolonging extraction time. The small-scale stationary environmental test chambers - µ-CTE™ 250 system was employed to perform the studies aimed at determining the profile of total volatile organic compounds (TVOCs) emissions. Due to this it was possible to state that objects from which the greatest amounts of contaminants are released are plastic containers (with emission rate falling down from 3273 to 2280 ng/g of material at 6 h of conditioning in elevated temperature).

Hasse diagram as a green analytical metrics tool: ranking of methods for benzo[a]pyrene determination in sediments.

This study presents an application of the Hasse diagram technique (HDT) as the assessment tool to select the most appropriate analytical procedures according to their greenness or the best analytical performance. The dataset consists of analytical procedures for benzo[a]pyrene determination in sediment samples, which were described by 11 variables concerning their greenness and analytical performance. Two analyses with the HDT were performed-the first one with metrological variables and the second one with "green" variables as input data. Both HDT analyses ranked different analytical procedures as the most valuable, suggesting that green analytical chemistry is not in accordance with metrology when benzo[a]pyrene in sediment samples is determined. The HDT can be used as a good decision support tool to choose the proper analytical procedure concerning green analytical chemistry principles and analytical performance merits.

Urban air quality assessment using monitoring data of fractionized aerosol samples, chemometrics and meteorological conditions.

The present article deals with assessment of urban air by using monitoring data for 10 different aerosol fractions (0.015-16 µm) collected at a typical urban site in City of Thessaloniki, Greece. The data set was subject to multivariate statistical analysis (cluster analysis and principal components analysis) and, additionally, to HYSPLIT back trajectory modeling in order to assess in a better way the impact of the weather conditions on the pollution sources identified. A specific element of the study is the effort to clarify the role of outliers in the data set. The reason for the appearance of outliers is strongly related to the atmospheric condition on the particular sampling days leading to enhanced concentration of pollutants (secondary emissions, sea sprays, road and soil dust, combustion processes) especially for ultra fine and coarse particles. It is also shown that three major sources affect the urban air quality of the location studied-sea sprays, mineral dust and anthropogenic influences (agricultural activity, combustion processes, and industrial sources). The level of impact is related to certain extent to the aerosol fraction size. The assessment of the meteorological conditions leads to defining of four downwind patterns affecting the air quality (Pelagic, Western and Central Europe, Eastern and Northeastern Europe and Africa and Southern Europe). Thus, the present study offers a complete urban air assessment taking into account the weather conditions, pollution sources and aerosol fractioning.

Prediction of class membership of biodiesels using chemometrics.

Recently, serious scientific and technological attention is paid to creation of alternative energy sources, including biofuels. The assessment of the quality of the biofuels produced and of the raw materials needed for the production technology is an important scientific challenge. One of the major sources for biodiesel production is plant oils material (sunflower, rapeseed, palm, soya etc.). Since plants are complex system from the biota it is not easy to find specific chemical components responsible for their ability to serve as biodiesels. The characterization and classification of plant sources as biofuel material could be reliably estimated only by the use of multivariate statistical approaches (chemometrics). The chemometric expertise makes it possible not only to classify different biofuel sources into similarity classes but also to predict the membership of unknown by origin chemically analyzed samples to already existing classes. The present study deals with the prediction of the class membership of several unknown by origin samples, which are included in a large data set with FAME profiles of biodiesel plant sources. Using a data set from chromatographic analysis of fatty acid methyl esters profiles (FAME) of different plant biodiesel sources and applying the chemometric technique know as partial least squares-discriminant analysis (PLS - DA) a pattern recognition procedure is developed to: I. Model classes of similarity of biodiesel plant sources using their FAME profiles not taking into account the samples with unknown origin; II. Classify correctly the samples with unknown origin to the previously defined classes of biodiesel sources (palm oil, soybean oil, peanut oil, rapeseed oil, sunflower oil and maize oil). The prediction is successfully achieved for all samples with previously unknown origin. This pattern recognition approach is applied for the first time in the field of biodiesel classification and modeling tasks.

Novel approach to ecotoxicological risk assessment of sediments cores around the shipwreck by the use of self-organizing maps.

Marine and coastal pollution plays an increasingly important role due to recent severe accidents which drew attention to the consequences of oil spills causing widespread devastation of marine ecosystems. All these problems cannot be solved without conducting environmental studies in the area of possible oil spill and performing chemometric evaluation of the data obtained looking for similar patterns among pollutants and optimize environmental monitoring during eventual spills and possible remediation actions - what is the aim of the work presented. Following the chemical and ecotoxicological studies self-organising maps technique has been applied as a competitive learning algorithm based on unsupervised learning process. Summarizing it can be stated that biotests enable assessing the impact of complex chemical mixtures on the organisms inhabiting particular ecosystems. Short and simple application of biotests cannot easily explain the observable toxicity without more complex chemometric evaluation of datasets obtained describing dependence between xenobiotics and toxicological results.

Identification of metabolic pathways in Daphnia magna explaining hormetic effects of selective serotonin reuptake inhibitors and 4-nonylphenol using transcriptomic and phenotypic responses.

The molecular mechanisms explaining hormetic effects of selective serotonin reuptake inhibitors (SSRIs) and 4-nonylphenol in Daphnia magna reproduction were studied in juveniles and adults. Transcriptome analyses showed changes in mRNA levels for 1796 genes in juveniles and 1214 genes in adults (out of 15000 total probes) exposed to two SSRIs (fluoxetine and fluvoxamine) or to 4-nonylphenol. Functional annotation of affected genes was improved by assuming the annotations of putatively homologous Drosophila genes. Self-organizing map analysis and partial least-square regression coupled with selectivity ratio procedures analyses allowed to define groups of genes with specific responses to the different treatments. Differentially expressed genes were analyzed for functional enrichment using Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes databases. Serotonin metabolism, neuronal developmental processes, and carbohydrates and lipid metabolism functional categories appeared as selectively affected by SSRI treatment, whereas 4-nonylphenol deregulated genes from the carbohydrate metabolism and the ecdysone regulatory pathway. These changes in functional and metabolic pathways are consistent with previously reported SSRIs and 4-nonylphenol hormetic effects in D. magna, including a decrease in reserve carbohydrates and an increase in respiratory metabolism.

Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review.

The evaluation of the spatial and temporal distribution of pollutants is a crucial issue to assess the anthropogenic burden on the environment. Numerous chemometric approaches are available for data exploration and they have been applied for environmental health assessment purposes. Among the unsupervised methods, Self-Organizing Map (SOM) is an artificial neural network able to handle non-linear problems that can be used for exploratory data analysis, pattern recognition, and variable relationship assessment. Much more interpretation ability is gained when the SOM-based model is merged with clustering algorithms. This review comprises: (i) a description of the algorithm operation principle with a focus on the key parameters used for the SOM initialization; (ii) a description of the SOM output features and how they can be used for data mining; (iii) a list of available software tools for performing calculations; (iv) an overview of the SOM application for obtaining spatial and temporal pollution patterns in the environmental compartments with focus on model training and result visualization; (v) advice on reporting SOM model details in a paper to attain comparability and reproducibility among published papers as well as advice for extracting valuable information from the model results is presented.

Statistical modeling of air pollution.

The present communication deals with the application of several chemometric methods (principal components analysis, source apportioning on absolute principal components scores, chemical mass balance, self-organizing maps) to various aerosol data collections from different regions in Europe. It is shown that different latent factors explaining over 75 % of the total variance are responsible for the data structure and could be reliable identified and interpreted. Further, the contribution of each identified source to the formation of the particle total mass and chemical compounds total concentration is calculated. Thus, a reliable assessment of the air quality in the respective region is done. Classification by self-organizing maps makes it possible to better understand the role of different discriminating tracers in the air pollution. The use of chemical mass balance approach ensures a sound modeling of the pollution sources. The requirements of the sustainability concept for ecological indicators in this case is easily transformed to a multivariate statistical problem taking into account not separate indicators but the specific multivariate nature of the aerosol pollution.

Classification and modeling of different fractions of aerosol monitoring data.

The present study deals with the application of self-organizing maps (SOM) of Kohonen and four-way Tucker method for the classification and modeling, respectively, of aerosol monitoring data sets from two sampling points (Arnoldstein and Unterloibach) located close to the border, between Austria and Slovenia. The goal of the chemometric data treatment was to find some specific patterns in the classification maps and in the four-way model of complexity [2422] for 5 different aerosol fractions collected in 4 different seasons of the year. The results indicated a distinct separation of the ultrafine particles (PM 0.01-PM 0.4) from the other fractions, which underlines their specific effect on human health. Seasonal separation (but only between summer and winter sampling) is also observed.

Geostatistical and multivariate statistical analysis of heavily and manifoldly contaminated soil samples.

The surroundings of the former Kremikovtzi steel mill near Sofia (Bulgaria) are influenced by various emissions from the factory. In addition to steel and alloys, they produce different products based on inorganic compounds in different smelters. Soil in this region is multiply contaminated. We collected 65 soil samples and analyzed 15 elements by different methods of atomic spectroscopy for a survey of this field site. Here we present a novel hybrid approach for environmental risk assessment of polluted soil combining geostatistical methods and source apportionment modeling. We could distinguish areas with heavily and slightly polluted soils in the vicinity of the iron smelter by applying unsupervised pattern recognition methods. This result was supported by geostatistical methods such as semivariogram analysis and kriging. The modes of action of the metals examined differ significantly in such a way that iron and lead account for the main pollutants of the iron smelter, whereas, e.g., arsenic shows a haphazard distribution. The application of factor analysis and source-apportionment modeling on absolute principal component scores revealed novel information about the composition of the emissions from the different stacks. It is possible to estimate the impact of every element examined on the pollution due to their emission source. This investigation allows an objective assessment of the different spatial distributions of the elements examined in the soil of the Kremikovtzi region. The geostatistical analysis illustrates this distribution and is supported by multivariate statistical analysis revealing relations between the elements.

Self-organizing maps classification of epidemiological data and toenail selenium content monitored on cancer and healthy patients from Poland.

This paper deals with epidemiological multivariate statistical analysis of cancer and health patients from Pomeranian and Lubuskie Voivodships, Poland. The anthropometric and epidemiologic data include 8 parameters: toenail selenium concentration, sex, age, body mass index (BMI), smoking status, taking of Se supplements, health state, and family history of cancer. The self-organizing maps (SOM) are used for simultaneous classification of parameters and patients with relation to cancer diagnosis. Three different patterns (groups) of patients with cancer diagnosis are outlined: (i) older, smoking men with low toenail selenium concentration; (ii) older smoking women with family relation to cancer and toenail selenium deficiency; (iii) middle, aged nonsmokers with high level of selenium toenail concentration. The simultaneous classification of parameters and patients makes it possible to determine discriminating parameters for each pattern and relations between parameters. The relation of each parameter to cancer disease is discussed as special attention is paid to toenail selenium deficiency. More than 80% of patients with cancer diagnosis possess toenail selenium deficiency, accompanied by old age and smoking.