Protected natural areas of Kazakhstan, particularly national parks, occupy a unique role in developing ecological tourism. One way to ensure biodiversity conservation, environmental preservation, and regional sustainability is through promoting ecotourism in national parks. For the “Altynemel” National Park, ecotourism should be regarded as a priority type of tourism. The article considers the essential technologies and principles of implementation of ecological tourism in the National Park “Altynemel”. Identified and justified the uniqueness of this protected area—the equal value of natural and cultural heritage on its territory. We revealed the problems and constraints of ecotourism development in the national park and offered the ways of their solution. The article is written based on field research materials, methods of a local population survey, interviews, and focus group discussions. In this article, 201 representative families were interviewed using questions on a 5-point Likert scale, including 159 residents of the village of Basshi and 42 residents of the village of Nurym. The results show that the overall assessment of sustainability is mild in the two communities. As a result, we claim that sustainable development in the “Altynemel” National Park is far from perfect. Sustainable development in the “Altynemel” National Park could be better. In particular, tourism development’s positive economic and political impact has yet to be apparent.

Seasonal snow cover in mountainous regions represents a critical natural freshwater reserve for arid and semi-arid areas of Central Asia. This study evaluates the long-term (2000–2024) spatiotemporal dynamics of snow cover in the Arys River basin, located within the Western Tien Shan. The research utilizes daily satellite data from MODIS Terra and Aqua, along with data from the MODSNOW automated processing system. Terra-Aqua composite imagery was employed to minimize cloud cover effects. Satellite-derived estimates were validated against observational data from five meteorological stations of the Republican State Enterprise (RSE) “Kazhydromet”. The results indicate significant interannual variability in snow cover extent: the snow-covered area during the cold season ranged from 16.2% to 54.1%, with a mean value of 34.4%. Trend analysis revealed a weak negative trend, while Sen’s slope estimator showed an average annual reduction in snow cover area of 0.37% per year. The most pronounced decline in snow accumulation was observed in mid-elevation mountain zones. These findings suggest potential increased risks to seasonal water availability in the Arys River basin and, more broadly, across the Syr Darya basin under ongoing climate change conditions. The results provide a scientific basis for quantifying climate impacts and developing adaptation strategies for integrated water resources management in Central Asia.

Heavy metal (HM) contamination in urban soils is a pressing global issue, particularly in rapidly industrializing regions like Kazakhstan, where anthropogenic activities such as transportation, energy production, and manufacturing exacerbate accumulation in ecosystems. In Almaty, the largest city in Kazakhstan, urban expansion and legacy pollution pose risks to soil functions, biodiversity, and public health through bioaccumulation and migration pathways. This study evaluates the spatial distribution and ecological impacts of total heavy metal concentrations (HMs) (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) in Almaty’s soils to inform remediation strategies. Soil samples (n = 73) were collected using a systematic grid sampling method across urban, industrial, and peri-urban zones in Almaty. HM concentrations were determined via X-ray fluorescence spectrometry (XRF) following GOST 33850-2016 standards. Pollution indices (contamination factor K_c and integrated pollution index Z_c) were calculated relative to Kazakhstani permissible limits (PDK RK) and Russian approximate permissible concentrations (ODK RF). Statistical analyses included Spearman’s correlation, boxplots, and coefficient of variation. Morphological, physicochemical (pH, humus content), and biological assessments evaluated degradation. Spatial interpolation via GIS mapped the hotspots. HM distributions showed significant variability, with As, Zn, and Ni exceeding norms in >90% of samples (median K_c ≈ 5 for As). Z_c classified >70% of sites as hazardous or extremely hazardous (Z_c > 32), with hotspots in central-eastern districts (Z_c 90–145). Strong correlations (ρ ≥ 0.6) identified a technogenic group (Pb–Zn–Cu–Ni) from traffic and industry, contrasting predominantly geogenic elements with possible anthropogenic contribution (As–Co–Mo–Mn). Pollution induced soil compaction, reduced humus/pH, and disrupting biogeochemical cycles. Local exceedances were noted near TECs, factories, and transport hubs. Almaty’s soils exhibit pervasive technogenic HM pollution, driven by urban sources, leading to ecosystem degradation and health risks. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models. Prioritized monitoring and phytoremediation in hotspots are recommended to enhance resilience, aligning with UN SDGs for sustainable cities and ecosystems. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models.

Pasture ecosystems in the arid regions of Kazakhstan are highly vulnerable to the combined effects of climatic variability and increasing grazing pressure, while long-term spatial assessments of degradation remain limited. This study develops an integrative remote sensing-based framework for assessing pasture degradation in Atyrau Oblast by combining long-term NDVI time series (2000–2023) with grazing pressure indicators ($K_{sust}$ and LIPS), field observations, and climatic data. The results show that 49.3% of pasturelands are degraded, with statistically significant negative NDVI trends observed across most administrative districts. Areas experiencing pasture overload ($K_{sust}$ > 1.2) spatially coincide with persistent vegetation decline, and significant negative relationships between NDVI and livestock numbers are identified in several districts. The analysis also reveals spatial heterogeneity and lagged responses of vegetation dynamics to grazing pressure under varying climatic conditions. The proposed approach provides a novel integrative framework that links spectral vegetation indicators with climate-adjusted grazing metrics, enabling the identification of degradation hotspots and supporting spatially differentiated pasture management. This framework can be applied in regional land monitoring systems to improve decision-making for sustainable rangeland use under climate change.