This article aims to explore the use of machine learning (ML) methods for mapping the distribution of mercury (Hg) content in topsoil, using the city of Ufa (Russia) and adjacent areas as an example. For this purpose, a soil dataset of 250 points sampled from a 0–20 cm depth on different land uses, including residential, industrial and undisturbed (forests and parks), was used. Random Forest (RF), Extreme Gradient Boosting (XGboost), Cubist and k-Nearest Neighbor (kNN) ML techniques were employed to model and map the Hg concentrations. We used remote sensing data (RSD) and topographic attributes as explanatory variables. ML models were calibrated and validated using the leave-one-out cross-validation approach. The Hg content varied from 0.005 to 0.58 mg/kg and was characterized by very high variability. According to the MAE and RMSE metrics, the RF method resulted in the most accurate spatial prediction for the Hg content (0.029 and 0.065 mg/kg, respectively), while the XGBoost approach showed the lowest prediction efficiency (0.032 and 0.073 mg/kg, respectively). The results showed that the slope map, spectral index MSI and Sentinel-2A band B11 were the key variables in explaining the variability of Hg content. We found that higher uncertainty values of soil Hg were found in croplands, urban residential and industrial areas, which supports the view that spatial modelling of HM in urban landscapes is challenging. The present study provides insights into the potential of digital soil mapping techniques in combination with RSD and terrain variables for identifying areas at risk of Hg contamination in urban areas, which can inform land-use planning and management strategies to protect human health and the environment.

The study examined the growth characteristics of the Siberian spruce (Picea obovata Ledeb.) under the canopy of coniferous forests in early ontogeny. We revealed that spruce undergrowth in adverse conditions is suppressed, is slow to grow and has xylorhizomes. The result is a significant difference in height, age and stage of ontogeny that affects the forest’s growth dynamics. The formation of xylorhizomes changes the relative dimensions of the above-ground part and the plant’s absolute height from 42 to 75%, depending on age. We identified two periods of growth intensity (slow and stable) and the critical age for spruce undergrowth to move from one stage of ontogeny to the next. If it does not make the transition, it will die. There are two strategies for developing spruce undergrowth: a “direct” path during rapid growth and a “waiting” path when the plants are suppressed. Such growth pathways in the pre-generative stage of the Siberian spruce’s ontogeny allow the undergrowth, even in a suppressed state, to survive in an adverse environment under the forest canopy.

The object of the study was water samples, cultures of green microalgae Parachlorella kessleri U1 and Chlorella vulgaris I2, Maybalyk Lake (Astana). The aim and objectives of the study were to study the bioremediation potential of a consortium of two strains of microalgae Parachlorella kessleri U1 + Chlorella vulgaris I2 (hereinafter U1+I2) through the algolization of Lake Maibalyk according to microbiological and hydrochemical indicators. This lake is a large lake in Astana with fishery importance. The state of the reservoir is reflected in the state of the capital's air basin. A study of the water quality of Lake Maibalyk was conducted on the main morphometric, hydrochemical and hydrobiological indicators in May and June of 2020. To study the effect and determine the efficiency of using microalgae, preliminary biomass production and algolization of the U1 + I2 consortium were carried out. The introduction of a consortium of microalgae showed an improvement in the sanitary and microbiological indicators of the reservoir after just one month, due to its high antimicrobial properties and a decrease in such groups of bacteria as ammonifiers, coliforms, heterotrophic bacteria, coli group bacteria, pseudomonas, wax bacilli, staphylococcus, enterococci, enterobacteria, salmonella, fungi and yeast. After algolization for 30 days by the consortium I2+U1, an improvement was recorded not only in the organoleptic characteristics of water, but also in a number of hydrochemical parameters, including a decrease in COD, suspended solids, iron, SSAA and phosphates. . An increase in transparency, a decrease in water color and the absence of a specific odor were also noted. It was found that, although the content of BOD5, nitrates, sulfates in the original sample did not exceed the MРC values, the introduction of microalgae had a positive effect on reducing the concentration of these substances, which significantly affected the quality of the water in the reservoir. The results obtained can serve as a basis for developing environmentally friendly biotechnologies to improve water quality. In the future, it is necessary to conduct studies in triplicate, which will increase the reliability and quality of the data obtained, which will allow more effective assessment of the state of the ecosystem of the reservoir and ensure the safety of water use.

Introduction. Because of urban development the volume of municipal and industrial wastewater are growing. Along with them the amount of sewage sludge (SS) also increases. Millions of tons of SS are currently accumulated on the territory of the Russian Federation and contain various pollutants, including heavy metals (1-IM). In this regard, the search for effective methods of 55 treatment from HM is an urgent problem. The main methods of 55 treatment are drying, dehydration, thermal methods, UV and microwave wave treatment. These kinds of disinfection eliminates many pathogenic microorganisms, but they are quite expensive and not effective against HM. Reagent methods include 55 disinfection with quicklime (CaO). However, decontamination requires large doses (up to 30%) and it is also ineffective against HM. Humic-minera I reagent are more effective, they are based on crushed caustobiolites, their cleaning capacity from HM is 19-87%. Methods and materials. The authors have previously shown the effectiveness of wastewater treatment from HM using sorbents based on dolomite, quartzite, and waste from mining and processing plants. Therefore, a method for SS treatment from HM using sorbents based on dolomite, humates, and CS containing CaCO3 and humic compounds was proposed. In this regard, a method was proposed for SS treatment from HM using three types of sorbents based on: 1) waste of thermal power plants (TPP) - conditioned sludge (CS) containing CaCO3 up to 68% and humic compounds up to 12% - sorbent 1 (S1); 2) dolomite - Mg and Ca carbonate in a composition with sodium humate (25%)- sorbent 2 (52); 3) modified dolomite with sodium humate (1%) - sorbent 3 (53). Results and discussion. In laboratory experiments, the cleaning capacity of SS was studied using a dolomite-based sorbent modified with humate (1%). In field tests, a decrease in the concentration of HM in 55 was studied with the use of sorbents based on CS and the complex sorbent dolomite-humate (75:25). The cleaning capacity of SS from HM increases in the series: sorbents based on waste from TPP - CS containing CaCO3 and humates (cleaning capacity E = 4.8-48.6% for dried SS and 29.3-53.3% for dehydrated SS) < sorbent based on a composition of dolomite with humate (E = 65.1-92.1% for dried and 56.6-89.4% for dehydrated SS) < a dolomite-based sorbent modified with humate (E = 90.8-99.9%). Conclusions. The maximum cleaning capacity is shown by a dolomite-based sorbent coated with a nano- and micro- sized layer of sodium humate.