The results of a study of the hydrochemical and production characteristics of Artemia spp. in eight different types of lakes in southern Western Siberia are presented. The research was conducted from July to October 2024. All of the studied lakes are shallow, with the maximum depth recorded in Lake Bolshoye Yarovoye (7.4 m) and Lake Maloye Yarovoye (5.0 m). They are located in the arid and semi-arid zones of the Kulunda Plain. The salinity of the studied lakes in the Altai Krai varied greatly, ranging from 41.2 ± 1.3 (Lake Maloe Shklo) to 251 ± 36 g/L (Lake Kuchuk). Based on the results of hydrochemical analysis, three types of lakes were identified: hydrocarbonate (Lake Tanatar III) with a dominant bisexual population of Artemia sp. Kazakhstan, sulphate (Lake Mormyshanskoe) with a dominant parthenogenetic population of A. parthenogenetica, and chloride (lakes Kuchuk, Kulunda, Maloye Yarovoye, Bolshoye Yarovoye, Maloye Shklo, Malinovoye) – represented by both populations (parthenogenetic and bisexual). We studied 817 females of Artemia parthenogenetic and bisexual populations for morphometric analysis. The overall fertility of Artemia sp. Kazakhstan and A. parthenogenetica females does not differ significantly. However, a significant difference was found in the fertility of female crustaceans inhabiting chloride and sulphate lakes (Kruskal – Wallis test H = 18.13; p < 0.01) and hydrocarbonate lakes (H = 16.40; p < 0.01). No significant difference was found between females from sulphate and hydrocarbonate lakes (p > 0.05). Thus, females from hyperhaline chloride lakes are characterised by the lowest fertility. The most informative morphometric indicators for assessing productivity are the dimensional characteristics of the ovary. Females of the chloride-type lake crustacean had the smallest ovary length and width, which is one of the reasons for their lowest fertility.
The aim of this work is a comprehensive assessment of the Vychegda River water quality at the sites of Syktyvkar water intake facilities (Station 1 – Krasnaya Gora and Station 2 – Ezhvinskiy District). The study is based on data from a two-year (2022–2023) monthly monitoring program conducted at two stations located in the areas of active water intakes. The main hydrochemical (pH, color, turbidity, total dissolved solids, hardness, BOD5, COD, total Fe, Mn, Zn, As, Al, Pb, pesticides, phenols, formaldehyde, Cl–, SO42–, F–) and microbiological (total coliform bacteria, TCB) parameters were analyzed. The river’s hydrochemical regime has pronounced seasonal dynamics, with peak values of color, turbidity, and organic indicators (COD) during the spring flood period. The river water is classified as low-mineralized. A key finding is the identification of consistent exceedance of hygienic standards (maximum permissible concentrations, MPC) for total Fe (up to 2.7 mg/dm3) and Mn (up to 0.9 mg/dm3) throughout the studied section at both stations, as well as periodic significant exceedance for color (up to 160°), COD (up to 58 mgO2/dm3), and microbiological indicators (TCB up to 700 CFU/100 cm3). The obtained data indicate that the main deterioration in water quality is due to natural factors (humic runoff, metal leaching); however, the identified spatial differences point to the presence of localized anthropogenic impacts. It is concluded that a differentiated approach to water treatment at the intakes is necessary, with mandatory removal of Fe, Mn, and organic compounds, as well as enhanced control during the flood period.
In recent years, scientific research has seen the rapid development of methods based on artificial intelligence (AI) and large language models (LLMs), which are capable of processing and interpreting significant volumes of text and structured information, including tables, numerical and categorical data, and sensor data and other heterogeneous information, using specialized and multimodal AI systems. Soil ecology, as a field characterized by the rapid accumulation of heterogeneous data, including the results of metagenomic analyses of soil microbiomes, can potentially benefit significantly from the application of such tools. The aim of this review is to identify, analyze, and summarize current approaches to the use of AI and LLM technologies in soil and environmental research. The review examines the main areas of AI application, including scientific literature search and analysis, semantic search for relevant publications, citation mapping, text summarization, and the use of generative AI assistants. Particular attention is paid to the application of AI methods in the analysis of soil microbiomes and to the potential use of metagenomic data for predicting soil fertility and agricultural productivity. The challenges associated with adapting AI methods originally developed for biomedical research to the specific characteristics of soil and environmental datasets are also considered. The key advantages of these approaches, their limitations, and potential methodological risks, including those related to data quality and the reliability of AI-generated information, are discussed. Despite the need for further improvement and optimization of currently available AI and LLM technologies, the literature review demonstrates their significant potential for advancing soil and environmental research and supporting practical applications in agriculture.
The variability in toxicity of the same hazardous substance across different environments due to the influence of additional factors is a key challenge in bioassay. This study aimed to evaluate the effect of water mineralization and organic matter content on copper toxicity to the cladoceran Ceriodaphnia dubia Richard, 1894. Test organisms were exposed to waters amended with CuSO4·5H2O (0.005, 0.01 and 0.025 mg/L Cu2+). Sodium chloride (0.5, 1.0 and 2.0 g/L) and potassium humate (0.5, 1.0 and 2.5 %) were added to the above solutions to assess the effect of mineralization and organic matter. The effect of mineralization on copper toxicity was twofold. A moderate increase in mineralization (0.5 g/L NaCl) significantly reduced copper toxicity, as evidenced by decreased C. dubia mortality at 0.005 and 0.025 mg/L Cu2+ (p < 0.05). Conversely, high mineralization (2.0 g/L NaCl) combined with a high copper content (0.025 mg/L) resulted in 100 % mortality. Furthermore, C. dubia fertility showed a consistent negative correlation with increasing mineralization. Organic matter addition reduces mortality and enhances reproductive output in C. dubia. This effect was most pronounced at the highest Cu2+ concentration (0.025 mg/L). Specifically, Ceriodaphnia brood size in treatments supplemented with 0.5 %, 1 % and 2.5 % potassium humate was 6.9, 6.7 and 3.2 times higher than in the corresponding control, respectively.
Comprehensive wastewater treatment from various pollutants is an urgent problem, and the development of new sorbents is of great scientific and practical importance. Sorbents based on sapropel were obtained: thermally oxidized sapropel, humic acids isolated from sapropel, thermally oxidized sapropel modified with humic acids, organomineral composite material based on thermally oxidized sapropel and bentonite. Resorcinol adsorption from model solutions with concentrations from 1 · 10-5 to 1 · 10-2mol/dm3 onsapropel-based sorbents was carried out under static conditions at a ratio of 1 g of sorbent per 200 cm3 of solution. It is shown that the limiting stage of resorcinol adsorption onsapropel-based sorbents is mixed diffusion. The adsorption theories of Langmuir, Freundlich, Brunauer–Emmett–Teller (BET) and Dubinin–Radradkevich are applicable to describe the resorcinol adsorption onsapropel-based sorbents. Taking into account the values of the activation energy and thermodynamic parameters of resorcinol adsorption, the obtained sapropel-based sorbents can be considered as the mechanism of resorcinol adsorption on the surface and physical adsorption due to volumetric filling of micro- and mesopores, as well as cavities. Resorcinol can also form covalent and hydrogen bonds with silicate and aluminosilicate groups, and hydrogen bonds with amino groups with sapropel-based sorbents. Therefore, it is quite difficult to separate the contribution of physical adsorption and possible chemical interactions of functional groups of sapropel-based sorbents with resorcinol. Limiting values of resorcinol adsorption onsapropel-based sorbents calculated by the BET equation (0.25–1.00 mmol/ g) and the Dubinin–Radushkevich equation (0.52–2.60 mmol/g), comparable to other developed sorbents.
