This study assesses heavy metal contamination in the snow cover of northeastern Kazakhstan by analyzing both the melted filtrated water and the solid sediment after filtration near various pollution sources. The research examines the impact of oil refining, thermal power plants (northern industrial zone), aluminum production (eastern industrial zone), and transportation on heavy metal dispersion. Results indicate that Zn, Cr, and Pb concentrations in the solid phase of snow in residential areas exceed those in industrial zones, reaching 436.6, 259.1, and 218.6 mg/kg, respectively. The highest overall concentrations were found for barium (949.4 mg/kg) and manganese (638.1 mg/kg). In the liquid fraction (meltwater), Zn (58.6 μg/l) and Sr (34.8 μg/l) were predominant, while Mn (28.3 μg/l) was the main pollutant in the eastern industrial zone. Dust load values in the snow cover ranged from 42.3 to 418.5 mg/m²/day, with the highest pollution load observed for Cd, Pb, and Mo. Despite variations in dust load across the city (135.5 mg/m²/day in the northern industrial zone, 152.3 mg/m²/day in the eastern industrial zone, and 147.1 mg/m²/day in residential areas), the overall dust pollution level remains low. However, a sanitary-hygienic assessment revealed that most heavy metal concentrations in snow exceed maximum permissible levels for soil in areas influenced by industrial facilities and transportation, except for Mo, V, and Mn. The ecological risk index of snow pollution in Pavlodar was calculated at 192.13, indicating a high potential ecological risk. These findings highlight the importance of snow as an indicator of environmental pollution and the need for continuous monitoring to assess urban contamination trends.

This study aims to assess the environmental sustainability and carbon footprint of tourism in Bayanaul State National Park (Kazakhstan) using the GSTC criteria and the Protocol on Greenhouse Gas Emissions. As part of the work, surveys and interviews were conducted with representatives of the tourism industry, administration officials and environmental organizations to analyze awareness of the principles of sustainable tourism, existing barriers and opportunities to increase sustainability. The results showed that the main contribution to the carbon footprint (530 tons of CO₂-eq.) is made by indirect emissions (57%) related to procurement and logistics as well as coal heating (20.5%). Based on the expert assessment method, key factors were identified, such as increased energy efficiency, the introduction of renewable energy sources, separate waste collection and recycling, the transparency of financial management and educational programs for tourists and employees. Based on these factors, a model of sustainable tourism management has been developed, adapted to regional peculiarities. This model provides for the active involvement of the state, business and the local community, and introduces mechanisms to reduce the carbon footprint and increase environmental sustainability. The theoretical contribution of the study is the adaptation of GSTC standards to the conditions of Kazakhstan and the development of a sustainable tourism management model that takes into account local characteristics. The results obtained provide a practical basis for reducing the negative impact of tourism on the environment, improving environmental sustainability and creating conditions for further research and the introduction of sustainable practices.

This article investigates the extent of heavy metal pollution in both urban and rural gardens in Pavlodar, which cultivate potatoes and tomatoes. As a city of industrialization, Pavlodar is exposed to emissions from industrial enterprises, transport and stove heating. The city also has the highest incidence of environmental diseases among the population. This study examines the accumulation of heavy metals and metalloid in the snow, their migration into the soil and their accumulation in plants, and assesses the non-cancer and cancer health risks of consuming these vegetables. The results show that the concentrations of trace elements in the solid phase of snow decrease in the following order: Fe (26,000) > Mn (592.5) > Cr (371.3) > Zn (338.8) > Pb (161.9) > Cu (142.5) > Ni (30.9) > As (15.1) > Co (12.1) > Cd (2.6). In soils, the concentrations of elements decrease in the following order: Mn (22,125) > Fe (20,375) > Zn (246.9) > Cr (109.5) > Cu (39.3) > Pb (25.6) > Ni (22.4) > As (9) > Co (6.6) > Cd (0.2). In urban gardens, the snow pollution coefficient was the highest. In rural gardens, the contamination index varied from 0.3 (Cr) to 5.3 (Cd). Magnesium in the soil exceeds the maximum allowable concentration (MPC) by 28.6–35.7 times, and zinc by 1.6–10.9 times. Only zinc and copper exceed the MPC for vegetables. Nickel in potatoes exceeds MPC by a factor of 6 and in tomatoes by a factor of 4.4. The cobalt content in tomatoes exceeds the background value by 2.2 times, with a maximum value of 5.3 times. The risk assessment showed that the non-carcinogenic and carcinogenic risks associated with potato and tomato consumption were low. However, these risks are higher in urban areas than in rural areas.

The ribbon-like pine forests of North Kazakhstan represent the principal territorial intrazonal and azonal biotopes. Integrated bio-geographic studies of the pine forests’ status were performed in the Beskaragai and Chaldai Nature Reserves in the Pri-Irtysh River basin within, at present, the climate–change most susceptible transitional parkland-steppe zone of Central Asia, adjoining the West Siberian Lowland. The investigations followed the regional topographic gradient with a series of mapped sites characterizing the spatial relief patterns of the pristine forest distribution and the associated phytocenoses. The results revealed marked natural arboreal cover restoration differences between the geographically close upland and lowland forest ecosystems. The regional tree growth dynamics show the varying intensity of the pine seedlings’ succession, the tree stands’ biomass productivity and the environmental stability, weakened by the extreme continentality and progressing aridification along with adverse anthropogenic ecological impacts. The specific geomorphic, soil and hydrological conditions are the principal determining factors. The more vital plain and lowland pine forests host the floristically richer fescue-dominated communities compared to the more fragile and precipitation-poorer upland pine settings. The latter forest ecosystems display a higher vulnerability to the current climate change, generating tree drying, forest fires, and to modern human activities such as logging, herding and recreation. The research conclusions provide new insights on the natural ribbon-like pine forests’ sustainability and adaptation to the ongoing continental warming triggering fundamental environmental transformations in Central Asia’s parklands.

Two saxaul species - black saxaul (Haloxylon aphyllum Minkw.) and white saxaul (Haloxylon persicum Bunge) - constitute the principal arboreal cover of the cold continental deserts of Central Asia. While the latter is a rain-fed shrub distributed on sand dunes, the former is a ground-water phreatophyte mainly found on alluvial terraces. Saxaul has played an important role as a fodder plant also used as firewood by local herders. Due to over-grazing and over-exploitation for fuel during the past fifty years, the oncedominant saxaul vegetation has considerably degraded. Important growth characteristics at the present plantations (such as height, and basal trunk and crown diameters) show a direct quantitative relationship between the plants' age up to the 25-year lifetime and the total tree biomass reduced by natural degradation. Annual productivity largely depends on the overall vegetation density that reflects specific environmental conditions at particular locations. The recommended harvest rate, ...

The spectral characteristics of harmful insect pests in wheat fields were characterised using hyperspectral imaging for the first time. The analysis of spectral profiles revealed that reflectance is determined by the structure of the insect’s chitin and the colouration of its body surface. Insects with lighter or more vivid colours (white, yellow, or green) showed higher reflectance values compared to those with predominantly dark pigmentation. Reflectance was also influenced by the presence of wings, surface roughness, and the age of the insect. Each species exhibited distinct spectral patterns that allowed for differentiation not only from other insect species but also from the plant background. A classification model using PLS-DA was developed and demonstrated high accuracy in identifying 12 pest species, confirming the strong potential of hyperspectral imaging for species-level classification. The results validate the PLS-DA method for differentiating insects based on spectral characteristics and underscore the reliability of this approach for automated monitoring systems to detect phytophagous pests in crop fields. This technology could reduce insecticide use by 30–40% through targeted application. The research has both scientific and economic significance, laying the groundwork for integrating machine learning and computer vision into agricultural monitoring. It supports the advancement of precision farming and contributes to improved global food security.