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The removal of trace metals (TM), dissolved organic carbon (DOC), mineral nitrogen (Nmin.), and polycyclic aromatic hydrocarbons (PAHs) from the water of Lake Baikal and its tributaries was evaluated. The contaminant removal rate (CRR) and the contaminant removal capacity (CRC) were used as water self-purification parameters. The CRR was calculated as the difference between contaminant mass flow rates at downstream and upstream gauging stations. The CRC was calculated as the quotient of the CRR and the change in water discharge between downstream and upstream gauging stations. Whether the CRR and CRC have positive or negative values depends on whether contaminant release or removal occurs in the water body. The CRR depends on the size of the water body. The lowest and the highest CRRs observed for Baikal were equal to −15 mg/s (PAHs) to −7327 g/s (DOC), whereas the highest PAH and DOC removal rates observed for Selenga River (the major Baikal tributary) in summer were equal to −9 mg/s and −3190 g/s correspondingly. The highest PAH and DOC removal rates observed for small tributaries were equal to 0.0004 mg/s and −0.7 g/s respectively. The amplitude of annual CRR oscillations depends on contaminant abundance. The highest amplitude was typical for most abundant contaminants such as Nmin. and DOC. In unpolluted sections of the Selenga River the highest rates of N and C removal (−85 g/s and −3190 g/s, respectively) were observed in summer and the lowest rates (4 g/s and 3869 g/s, respectively) were observed in the spring. The lowest amplitude was typical for PAHs and some low-abundance TM such as V and Ni. The highest summer rates of V and Ni removal were equal to −378 mg/s and −155 mg/s respectively, whereas lowest spring rates are equal to 296 mg/s and 220 mg/s. The intermediate CRR amplitudes were typical for most abundant TM such as Sr, Al, and Fe. The spatial CRR variability depends on water chemistry and the presence of pollution sources. The lowest (up to 38 g/s) rates of Nmin. removal was observed for polluted lower Selenga sections characterized by low water mineralization and high DOC concentrations. The highest rates (−85 g/s) were observed for unpolluted upper sections. Seepage loss from the river to groundwater was also recognized as an important means of contaminant removal. The CRC values depend mostly on water residence time. The DOC removing capacity value of Baikal (−26 g/m3) were lower than those of Selenga in summer (−35 g/m3) but higher than the CRCs of all tributaries during the other seasons (from 30 mg/m3 to −10 g/m3).

The aim of this study was to select chemical species characterized by distinctly different proportions in natural and anthropogenic particulate matter that could be used as tracers for air pollutant sources. The end-member mixing approach, based on the observation that the chemical species in snow closely correlated with land use are those that exhibit differences in concentrations across the different types of anthropogenic wastes, was used for source apportionment. The concentrations of Si and Fe normalized to Al were used as tracers in the mixing equations. Mixing diagrams showed that the major pollution sources (in descending order) are oil, coal, and wood combustion. The traces of several minor sources, such as aluminum production plants, pulp and paper mills, steel rust, and natural aluminosilicates, were also detected. It was found that the fingerprint of diesel engines on snow is similar to that of oil combustion; thus, future research of the role of diesel engines in air pollution will be needed. The insufficient precision of source apportionment is probably due to different combinations of pollution sources in different areas. Thus, principles for the delineation of areas affected by different source combinations should be the subject of further studies.

Critical source areas (CSAs) are the areas prone to generating runoff and are characterized by a high level of soil pollution. CSAs may accumulate and release soil pollutants emitted by primary emission sources (industrial and municipal enterprises) into the surface water during storm events. The aim of this study was to identify CSAs and their pollution sources and to assess the level of soil pollution in CSAs with polycyclic aromatic hydrocarbons (PAH) and trace metals (TM). CSAs were identified using a geospatial data model (GIS), and primary emission sources were identified using a positive matrix factorization (PMF) model. It was found that the soils of CSAs were characterized by higher pollution levels than soils outside the CSAs. Pollution levels were highly variable among the identified CSAs due to the different capacities of the plants located in those areas. Due to high variability of TM concentrations in preindustrial soils, the pollution level of PAHs and the pollution level of TMs in CSA soils did not correlate with each other. The PAH composition of bottom sediments was different from that of soils, whereas the TM compositions of the soils and bottom sediments were similar. It was proved that the main sources of PAHs and TMs in CSA soils were traffic emissions and central heating boilers.

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