X-ray fluorescence analysis was applied to assess the ecological state of the area potentially polluted by emissions of the aluminum industry and heat power engineering. Soil and pine needle samples were collected in areas with industrial activity and analyzed using wavelength-dispersive X-ray fluorescence (WDXRF) and total-reflection X-ray fluorescence (TXRF) techniques. Both techniques were validated using the matrix-matched certified reference materials. Different sample preparation procedures, such as fusion and pressing for WDXRF and acid digestion and suspensions for TXRF as well as quantification approaches (the external calibration for WDXRF and the internal standard for TXRF) were applied according to the features of the analyzed samples. The rock-forming oxides (Na₂O, MgO, Al₂O₃, SiO₂, P₂O₅, K₂O, CaO, TiO₂, MnO, and Fe₂O₃) and trace elements (V, Cr, Ni, Cu, Zn, Rb, Sr, Y, Ba, and Pb) were quantified in soils, as well as several elements (Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Br, Rb, Sr, Pb, and Ba) were quantified in pine needles. Comparing the results of soils and pine needles analysis with the regional background values indicated a significant contamination pollution level of the studied area.

The provenance study of archaeological materials is an important step in understanding the cultural and economic life of ancient human communities. One of the most popular approaches in provenance studies is to obtain the chemical composition of material and process it with chemometric methods. In this paper, we describe a combination of the total-reflection X-ray fluorescence (TXRF) method and chemometric techniques (PCA, k-means cluster analysis, and SVM) to study Neolithic ceramic samples from eastern Siberia (Baikal region). A database of ceramic samples was created and included 10 elements/indicators for classification by geographical origin and ornamentation type. This study shows that PCA cannot be used as the primary method for provenance purposes, but can show some patterns in the data. SVM and k-means cluster analysis classified most of the ceramic samples by archaeological site and type with high accuracy. The application of chemometric techniques also showed the similarity of some samples found at sites located close to each other. A database created and processed by SVM or k-means cluster analysis methods can be supplemented with new samples and automatically classified.