Chemometric Assessment of Soil Pollution and Pollution Source Apportionment for an Industrially Impacted Region around a Non-Ferrous Metal Smelter in Bulgaria.

The present study deals with the assessment of pollution caused by a large industrial facility using multivariate statistical methods. The primary goal is to classify specific pollution sources and to apportion their involvement in the formation of the total concentration of the chemical parameters being monitored. This aim is accomplished by intelligent data analysis based on cluster analysis, principal component analysis and principal component regression analysis. Five latent factors are found to explain over 80% of the total variance of the system being conditionally named "organic", "non-ferrous smelter", "acidic", "secondary anthropogenic contribution" and "natural" factor. The apportionment models designate the contribution of the identified sources quantitatively and help in the interpretation of risk assessment and management actions. Since the study takes into account pollution uptake from soil to a cabbage plant, the data interpretation could help in introducing biomonitoring aspects of the assessment. The chemometric expertise helps in revealing hidden relationships between the objects and the variables involved to achieve a better understanding of specific pollution events in the soil of a severely industrially impacted region.

Chemometrics Expertise in the Links Between Ecotoxicity and Physicochemical Features of Silver Nanoparticles: Environmental Aspects.

Studies of the ecotoxicological aspects of nanomaterials in aquatic environments are scarce. Given the growing variety of nanoparticles (NPs), along with the diversity of aquatic species and environments, the key to promoting sound risk assessment in nanoecotoxicology is understanding the mechanisms that govern the fate of NPs in aquatic environments and their behavior at the NP-biota interface. In this paper, data collected from the literature on ecotoxicological effects observed in aquatic species is discussed and analyzed using multivariate statistics techniques. We expand the knowledge of the environmental impact of silver NPs (AgNPs) by testing the acute toxicity of 47 AgNPs on crustacean eukaryotic organisms (Daphnia magna, Thamnocephalus platyurus, and D. galeata). Physicochemical properties, stabilization agents, toxicological end points, and test media were monitored as adding-outcome factors for the evaluation of environmental effects due to exposure to NPs. The chemometrics expertise performed by the use of hierarchical and nonhierarchical cluster analysis and principal component analysis revealed specific links between the ecotoxicology and the physicochemical features of NPs and helped in creating specific patterns of NPs discriminated by ecotoxicity levels and physicochemical parameters.

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.

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.