Light pollution can affect various groups of aquatic organisms. This effect will vary depending on the color temperature of the artificial lighting. However, at the moment, the issue of adaptation of organisms to light pollution and the influence of different color temperatures on them has not been well-studied. In this study, we decided to conduct a series of experiments with individuals of the amphipod Gmelinoides fasciatus from two populations and find out how individuals adapted to light pollution. The first group of individuals is from the population in Lake Baikal (Bolshie Koty Bay), and the second group is from the population in the Angara River (within the city of Irkutsk). The second population is significantly affected by light pollution. Since the middle of the last century, there has been an artificial barrier between these populations in the form of a hydroelectric power station. The experiments showed that G. fasciatus individuals developed different behavioral strategies in relation to artificial lighting with different color temperatures. In warm light, individuals tend to move to a shaded area, while in cold light, individuals may be attracted to artificial lighting, but only in small groups. These identified patterns may likely find practical use.

Most studies on microplastic pollution in aquatic ecosystems have focused on the quantitative and qualitative assessment of particles in surface waters. However, the highest concentrations and accumulation of microplastic particles are observed in bottom sediments. The aim of this study was to determine the concentrations of microplastic particles of different morphology in sediments in the beach and littoral zones of Lake Baikal. This study is the first in relation to Lake Baikal to focus specifically on the analysis of microplastic particles in bottom sediments. The results of the study showed that the registered values of concentration of microplastic particles do not exceed the average values for lakes around the world. The predominant type of particles in both the littoral zone and the beach is microplastic fibers. An exception is observed only for one of the locations. This exception is related to the permanent mooring of vessels in this place. Analysis of the types of artificial polymers showed that the microplastic fibers were represented by polyester, and the fragments were represented by alkyd resin (66%), polyvinyl alcohol (32%) and polyvinyl chloride (2%). Shown for the first time in this study, the presence of large numbers of microplastic particles with rare types of artificial polymers suggests that these particles may be under-reported in other studies. The underestimation of particles may be due either to the selection of sampling locations located far from heavily contaminated areas, or to the fragility of these polymers. Although the harm of these types of polymers has not yet been confirmed, the large number of these particles in local areas of lakes should be taken into account. This is due to the large number of organisms, which is usually characteristic of littoral areas, including Lake Baikal, with its diversity of fauna and flora.

Microplastic pollution of ecosystems is considered a modern problem. Freshwater ecosystems, despite the interest shown in their study, remain poorly understood. Lake Baikal (Russia) is one of the least studied freshwater ecosystems in this regard. This large lake is distinguished from others by its high level of biodiversity and clean drinking water. The aim of this study is a multi-matrix investigation of microplastic pollution in one of the lake’s bay. The following matrices are used: surface water, water column, sediment, macrophytes, macroinvertebrates, and fish, as well as ice and snow during the winter. The results show that certain locations exhibit high concentrations of microplastic particles. In some cases, this was due to the properties or characteristics of these locations (littoral zones near the water’s edge, macrophytes with mucus sheaths, ice and snow (potentially, the near-surface water layer after ice melt)), while in others, it was due to localized pollution (pier and ship mooring areas). An analysis of the polymer types of the detected microplastic particles reveals the presence of both common (polypropylene, polyethylene terephthalate, polystyrene, polyethylene, polyvinyl chloride) and rare (polyvinyl alcohol and alkyd resin). Moreover, in some locations, the latter two polymers predominate, a phenomenon rarely observed in other studies. Further research was recommended to focus on the chronic effects of microplastic particles on organisms associated with areas of elevated particle concentrations.

Light pollution is becoming more widespread every year, accompanied by the active use of LED lighting. Currently, the ability of organisms to adapt to this pollution and the potential impact of LED lighting of different color temperatures and intensities on organisms remains poorly understood. In this study, we aimed to find out how long-term light pollution affects the behavior of amphipods Gammarus lacustris, and to compare their locomotor activity under different lighting conditions, taking into account the factor of shelter from light. The response of individuals was compared in group and individual experiments under daylight, without light, warm and cold LED light up to 30 lx. The individuals were from two populations: the first is not exposed to light pollution (lake No. 14), while the second is affected (the Angara River within the city of Irkutsk). The locomotor activity of amphipods was assessed in daylight, without light, warm and cold light of 2–2.5 lx and 10–11 lx in the presence and absence of shelters from light. As a result of the experiments, adaptive changes in the reaction of G. lacustris to warm light were identified in individuals from the Angara River. The importance of LED light color temperature and warm light intensity in determining amphipod response to light was also confirmed. It was found that warm and cold light have different effects on the behavior of G. lacustris, and the presence of shelters from light can reduce the negative impact of light pollution in natural conditions.

Accurately modelling surface solar radiation (SSR) is essential for environmental research but remains a significant challenge in topographically complex regions like Lake Baikal, where ground measurements are sparse. This study evaluates the performance of various open-access cloud cover products—from satellite sensors (AVHRR, MODIS) and ground-based observations—for modelling daily SSR totals, using a physical radiation model validated against in-situ measurements from 10 coastal stations. The results demonstrate that the choice of cloud data critically impacts model accuracy. The AVHRR satellite product yields the most reliable estimates (R² = 0.54, RMSE = 4.538 MJ/m²), significantly outperforming both ground-based cloudiness observations and the ERA5 reanalysis dataset. This finding underscores that spatially continuous satellite data provide a superior representation of cloud attenuation for regional modelling than point-based ground observations or reanalysis. Consequently, a physical model driven by high-quality satellite cloud masks is recommended as an effective methodology for generating reliable SSR fields.

The aim of the study was thematic classification of mire ecosystems in Tomsk Region using Sentinel-2 Earth observation data and the K-means clustering method to improve the accuracy of area estimation and identify local spatial distribution patterns of mires. Spectral bands (2, 3, 4, 8A, 11) and indices (NDVI, NDWI, NDBI) from April to September 2024 were used as predictors and processed in Google Earth Engine. Cartographic visualization was performed in QGIS. Fourteen clusters were identified and grouped into seven classes: forested, open, and complex mires, woody and meadow-shrub vegetation, water bodies and bare soil. The total mire area was estimated as 49.7% of the region’s territory, exceeding previous estimates by 5.4%. The largest discrepancies were observed for forested mires (+9,32%) and complex mires (–5,87%); these discrepancies can be attributed to the high spatial resolution and optimized set of predictors, including the SWIR band for moisture detection. The results confirm the effectiveness of unsupervised classification for delineating homogeneous mire ecosystems. To further improve accuracy, integration of field data, time series, and additional predictors such as texture and morphometric parameters is recommended.