Search Results (76)

Existing hydrologic landscape classifications, such as Hydrologic Landscape Regions (HLRs) and Hydrologic Response Units (HRUs), group catchments by similarity of climate, geology, and topography, but do not directly link this classification to runoff-generation function, limiting their use for ranking hydrological activity in data-sparse mountain regions. The concept of a landscape–hydrological background, integrating climatic–hydrological, soil–hydrological, and topo–hydrological components, was used as the basis for a classification framework applied to 15 model catchments in the East Kazakhstan Region. The proposed approach provides a functional representation of landscape organization by linking environmental characteristics directly to runoff-generation processes and hydrological activity. The analysis revealed two distinct hydrological domains: a runoff-generating mountain domain (Bukhtarma, Uba, and Ulba), where highly active hydrological landscapes occupy 55.2–63.9% of the catchment area, and a transitional mountain-foothill domain (Narym, Kurchum, and Kalzhyr), where their share decreases to 18.0–25.4%. The highest landscape diversity, with Shannon indices of 1.31–1.53, occurs in the transitional catchments, reflecting the most balanced combination of functional groups. This study demonstrates that hydrological activity is controlled by the combined influences of catchment size, relief differentiation, and the spatial extent of high-mountain environments rather than by elevation alone. The proposed framework has practical applications for watershed management, water resources planning, environmental monitoring, and climate change adaptation, and is particularly valuable for data-sparse mountain regions. Full article

The growing implementation of the United Nations Sustainable Development Goals (SDGs) has increased the importance of sustainable transformation in higher education institutions. Universities are increasingly expected to integrate environmental management, digital technologies, governance, education, and community engagement into unified sustainability frameworks. However, integrated sustainability management models for technical universities in emerging economies remain insufficiently explored. This study analyzes the long-term transformation of the EKTU Green Campus project at D. Serikbayev East Kazakhstan Technical University (Kazakhstan) during 2018–2025. The research is based on methodological triangulation combining institutional statistical data, official UI GreenMetric methodologies and indicators, university sustainability reports, and verified ranking results. The study examines the interaction between Smart Campus technologies, digital governance, sustainability-oriented education, environmental management, and campus infrastructure modernization. The results indicate substantial progress across multiple dimensions of institutional sustainability, including energy efficiency, digital sustainability governance, renewable energy implementation, sustainable transportation, circular economy practices, and education and research indicators. The findings suggest that Smart Campus technologies and digital monitoring systems can function as core instruments of institutional sustainability management rather than solely as technical infrastructure solutions. The EKTU Green Campus experience provides a practical reference for sustainability transformation in technical universities in Central Asia and other developing regions, while acknowledging that its applicability may depend on local institutional and socio-economic contexts. The study is limited by its single-case design and its reliance on institutional and ranking data, which may affect the generalizability of the findings. Full article

The increasing frequency and intensity of extreme weather are profoundly affecting the ecological carbon cycle in arid regions, yet there remains a lack of quantitative understanding regarding the nonlinear response of net ecosystem productivity (NEP) in Central Asia to changes in extreme weather conditions. This study focused on the five Central Asian countries and China’s Xinjiang region, where NEP was estimated using MODIS Net Primary Productivity and daily meteorological data, and 12 extreme climate indices (ECIs) were constructed. By combining the XGBoost model with the SHapley Additive exPlanations method, the key ECIs of NEP were identified for different regions, and their nonlinear responses and threshold characteristics were quantified. The results show that from 2000 to 2023, Central Asia overall acted as a weak carbon source, with NEP exhibiting a spatial pattern of increasing in the east and decreasing in the west. Extreme precipitation indices showed an overall declining trend, whereas extreme temperature indices increased significantly. There is significant spatial heterogeneity in the importance of ECIs across different regions. Specifically, the annual total precipitation (PRCPTOT) is most important in Kazakhstan, Kyrgyzstan, and Tajikistan, while the annual maximum of daily maximum temperature (TXx) shows greater importance in Turkmenistan and Xinjiang. The responses of NEP to ECIs exhibited significant nonlinear threshold characteristics. PRCPTOT showed a positive saturation effect in Kazakhstan, Kyrgyzstan, and Tajikistan, with a threshold range of 700–1000 mm in Kyrgyzstan. TXx exhibited a pronounced negative high-temperature effect in Turkmenistan and Xinjiang, with thresholds of approximately 42 °C and 30 °C, respectively. In Uzbekistan, Diurnal Temperature Range (DTR) showed a response trough near 10 °C. The study reveals the nonlinear response and spatial heterogeneity of the NEP in Central Asia to extreme weather change, offering theoretical support for ecosystem restoration and sustainable carbon management in arid regions. Full article

Hybrid renewable energy systems are regarded as one of the most promising solutions for the autonomous power supply of remote and weakly electrified sites, where diesel generation remains a costly and carbon-intensive energy source. This study presents the optimization of an off-grid PV/BESS/DGU microgrid for three representative regions of Kazakhstan—North, Central/East, and South/South-West—under different environmental scenarios. The aim of the study was to determine the optimal installed photovoltaic capacity, battery storage capacity, diesel generator rated power, and annual load coverage balance using the Generalized Reduced Gradient (GRG) method. The optimization was carried out using two objective functions: the conventional levelized cost of electricity, $LCOE$, and the environmentally adjusted cost of electricity, ${LCOE}_{\mathrm{e}\mathrm{n}\mathrm{v}}$, which includes the monetized cost of emissions associated with diesel generator operation. The model was formulated as a constrained nonlinear programming problem incorporating hourly energy balance, battery state-of-charge constraints, diesel generator operating constraints, and carbon price scenarios of 0, 25, 50, and 100 USD/tCO₂. The results show that an increase in the carbon price systematically shifts the optimum toward a higher share of photovoltaic generation and reduced diesel generator use in all regions. The strongest response is observed in the South/South-West region, followed by Central/East, whereas the North exhibits the lowest sensitivity due to the more pronounced seasonality of solar generation. Under the considered scenarios, the optimal PV capacity increases by approximately 24–28%, while the share of diesel generation in annual load coverage decreases by approximately 28% in the North, 44% in Central/East, and 61% in the South/South-West. At the same time, the rated diesel generator capacity remains unchanged in most scenarios, indicating the persistence of its backup function. The results confirm that the PV/BESS/DGU configuration constitutes a technically and economically justified baseline architecture for autonomous power supply under Kazakhstan’s conditions, while the inclusion of environmental costs supports the cost-effective displacement of diesel generation. The GRG method proved to be suitable for the transparent and efficient optimization of hybrid microgrid parameters. Full article

Population-based evidence on sleep quality in Kazakhstan remains limited. This study describes sleep quality as a multidimensional construct among adults in Kazakhstan using data collected during the first national survey wave after the adoption of a single national time zone. The survey was designed as a national post-transition baseline assessment and not as an evaluation of the causal impact of the time-zone reform. Associations with socio-demographic, behavioural, clinical, and regional factors were examined. We conducted a nationally representative cross-sectional survey of adults aged 18–69 years in Kazakhstan from May to October 2025 using a multistage stratified cluster design. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was defined as a global PSQI score > 5. Complete PSQI data were available for 5872 participants. Descriptive analyses examined the global PSQI score and the seven component scores. Survey-weighted multivariable logistic regression was used to identify factors independently associated with poor sleep quality. The weighted prevalence of poor sleep quality was 28.1%, and the weighted mean global PSQI score was 4.43. The greatest component burden was attributable to sleep latency (mean 0.87), subjective sleep quality (0.82), and sleep disturbances (0.80), whereas use of sleep medication contributed minimally (0.11). Poor sleep quality was more common among women, older adults, urban residents, and participants with diabetes, current smoking, heavy episodic drinking, and depressive symptoms. In the adjusted model, female sex (aOR 1.37, 95% CI 1.19–1.57), age 55 years or older versus 18–24 years (1.98, 1.53–2.55), diabetes (1.47, 1.22–1.78), current smoking (1.28, 1.10–1.50), heavy episodic drinking (1.43, 1.16–1.76), and depressive symptoms (4.26, 3.52–5.15) were independently associated with higher odds of poor sleep quality. Rural residence was inversely associated with the outcome (0.71, 0.61–0.84). Compared with the North, higher odds were observed in the Central region (2.00, 1.46–2.74), East (1.94, 1.48–2.53), West (1.48, 1.17–1.88), and Almaty city (2.18, 1.72–2.76). Poor sleep quality is common among adults in Kazakhstan and is characterized primarily by difficulties with sleep initiation, perceived sleep quality, and nocturnal disturbances. The findings provide national post-transition baseline evidence and suggest that sleep health surveillance in Kazakhstan should prioritize demographic, mental health, behavioural, and regional inequalities while avoiding causal interpretation of the time-zone reform itself. Full article

Industrial settlements of the East Kazakhstan Region face a persistent technogenic burden driven by the dense concentration of non-ferrous metallurgy and heat-and-power enterprises, further compounded by unfavorable pollutant dispersion conditions inherent to the region’s mountain–basin topography. This study evaluated spatiotemporal associations between annual mean concentrations of NO₂, SO₂, H₂S, and CO, the integrated air pollution index (API₅), and primary neoplasm morbidity across five settlements over the period 2014–2024. A retrospective ecological analysis was carried out for Ust-Kamenogorsk, Ridder, Altai, Shemonaikha, and the settlement of Glubokoe, incorporating Spearman’s rank correlation, lag analysis (1–3 years), and the Mann–Kendall trend test. Throughout the study period, neoplasm morbidity in the region consistently exceeded the national average by a factor of 1.3 to 2.0. In Ust-Kamenogorsk—where metallurgical SO₂ and NO₂ emissions are most heavily concentrated—strong positive associations were found in children for SO₂ (ρ = 0.791, p < 0.05) and in adolescents for NO₂ and CO, reflecting elevated inhalation exposure under conditions of chronic pollution. The negative associations with API₅ observed in Ridder and Altai, where the index showed a statistically significant downward trend, are interpreted as evidence of the inertial character of oncological processes and the lasting influence of cumulative past exposure. Across all studied settlements, SO₂ emerged as the most consistent predictor of morbidity variation. These findings support prioritizing stricter emission controls for SO₂ and NO₂ from metallurgical and energy facilities, establishing oncological screening programs for children and adolescents in chronically polluted areas, and strengthening ambient air monitoring—measures whose effective implementation will require coordinated action between public health authorities and environmental regulators. Full article

This study presents a dataset of meteorological and soil-hydrological instrumental observations collected at three agrometeorological stations in the East Kazakhstan Region during the growing seasons of 2022–2025. The dataset includes time series from automatic weather stations: WS “OCES-1” (Solnechnoe village) provides hourly data over four years (2022–2025; 14,614 records; 65 variables), while WS “OCES-2” (Lugovoe village; 203,279 records) and WS “Altyn Kazan” (Sulusary village; 207,115 records) provide minute-resolution data for 2025 (49 variables each). Measured parameters at 200 cm height include air temperature and humidity, atmospheric pressure, precipitation, wind speed and direction; soil measurements down to 100 cm depth include temperature and moisture. Also, field-based express measurements of volumetric soil moisture within a 1 m profile (every 10 cm) were collected during three campaigns (May–August 2025), resulting in a total of 253 measurements. The stations are located across steppe and forest-steppe landscapes of the transboundary Altai–Sayan mountain region on active agricultural lands under diverse soil–climatic conditions. Climate types correspond to Dfb and Dfa per the Köppen–Geiger classification. Soils are classified under WRB as Chernozems and Calcic Chernozems. The dataset is published in CSV format on Zenodo under a CC-BY 4.0 license. Full article

This study analyzed precipitation and air temperature in the Zhambyl, Almaty, Zhetysu, Abay, and East Kazakhstan regions of Kazakhstan using data from the national meteorological network of the RSE Kazhydromet. The purpose of the study was to reveal the climatic changes and their spatial distribution throughout the study area. A modified Mann–Kendall test and Sen’s Slope estimator were applied to analyze aridity conditions in combination with the drought indices SPEI and Selyaninov hydrothermal coefficient, enabling analysis of the magnitude and statistical significance of trend changes from April to September for the period 1981 to 2023. The magnitude of the observed trends of the mean growing-season temperature increased by 0.211 °C decade⁻¹, while precipitation declined by 2.074 mm decade⁻¹, which indicates a decrease in moisture availability for crops in the southeast and east of Kazakhstan. The results of this study may be of interest to agricultural specialists, ecologists, the Ministry of Emergency Situations, and hydrologists to develop activities aimed at preventing threats and mitigating the effects of climate change in Kazakhstan. The use of the above statistical methods in combination with drought indices is relevant in the context of climate change and worsening food security and can serve as a good indicator for determining when significant changes in climatic parameters occurred, which will be valuable information for making management decisions. Full article

Sustainable municipal solid waste (MSW) treatment in rapidly urbanizing secondary cities requires evidence-based, district-level prioritization of technologies. We integrate GIS hotspot mapping, Random Forest, and AnyLogic System Dynamics into a decision-support framework and apply it to Shymkent, Kazakhstan (population 1.19 million; ≈301,400 tonnes of MSW in 2025). This is the first application of such a framework to MSW management in a Kazakhstani secondary city and, to our knowledge, the first regional application across Central Asia; the integration concept has prior precedents in Latin American, South Asian, and East Asian metropolitan studies, and the present contribution lies in empirical calibration to a Central Asian upper-middle-income context using 2015–2025 morphological audits, air-quality and soil monitoring, and Sentinel-2 NDVI. Random Forest (n = 80, 9 predictors) achieved R² = 0.976 ± 0.011 under 5-fold cross-validation; a complementary GroupKFold protocol confirms the model is Shymkent-calibrated while the methodology remains transferable. AnyLogic simulation shows an Infrastructure/Waste-to-Energy pathway reduces the 2030 annual landfilled volume to ≈201 kt, environmental risk by 70%, and methane emissions by 86% (≈270 kt CO₂-eq/year) relative to the Inertial baseline. The principal deliverable is a District × Technology × Phase prioritization matrix for sequencing sustainable investment under realistic budget constraints. Full article

The Arganaty Massif in the Eastern Balkhash region (Kazakhstan) is located in a key sector of the Central Asian Orogenic Belt, but its petrogenesis and relationship to neighboring Late Palaeozoic intrusions remain poorly constrained. This study presents the first U–Pb zircon age and whole-rock geochemical data for the Arganaty granites, combined with a comparison with massifs of the Northern Balkhash region and Alatau Mountains (East Kazakhstan and Western Xinjiang, NW China). The Arganaty granites have a concordant U–Pb age of 281.5 ± 2.1 Ma. They are high-K calc-alkaline, metaluminous to slightly peraluminous I-type granites with low Mg# (0.22–0.33) and Nb/Ta ratios (10.2–17.3). Geochemical comparison indicates close affinity to the Lepsy complex intrusions and eastern plutons of Alatau mountains, rather than to the Katbar complex of Northern Balkhash. The new age and geochemical data show that the Arganaty granites formed in a post-collisional setting after the closure of the Junggar–Balkhash Ocean. Their mixed crust–mantle signature and depth estimates (~30 km) are consistent with lower crustal melting triggered by slab break-off. These results clarify the post-collisional magmatic evolution of the region and contribute to the understanding of Late Palaeozoic crustal growth in the CAOB. Full article

The depletion of global reserves of high-quality sulfide nickel deposits, coupled with the steady growth of nickel demand, has led to increased interest in the processing of oxidized (lateritic) nickel ores, including deposits with significant resource potential in the Republic of Kazakhstan. This paper provides an overview of global nickel ore reserves and their distribution, as well as the major nickel deposits in Kazakhstan, which are primarily located in the Aktobe, East Kazakhstan, Kostanay, and Pavlodar regions. Pyrometallurgical processing routes for lateritic nickel ores are also considered. Conventional production technologies, including the Rotary Kiln–Electric Furnace (RKEF), Krupp–Renn process, blast furnace smelting, Vaniukov process, and ISASMELT process, are reviewed, and their process flow diagrams are presented. These methods typically process lateritic nickel ores containing more than 1.2% Ni, whereas Kazakhstan ores are characterized by lower nickel grades, generally in the range of 0.75–1.1%. The advantages and limitations of conventional processing routes are analyzed, and the factors limiting the effective beneficiation of lateritic nickel ores using traditional methods are identified. The present study substantiates the feasibility of producing nickel-containing alloys from lateritic nickel ores using a metallothermic reduction approach. This method is based on the reduction of nickel and iron oxides using metallic reductants, which enables more selective extraction of target components and the formation of alloys with controlled composition. Metallothermic reduction is of particular interest for the processing of low-grade lateritic ores, as it allows the production of nickel-containing alloys without prior beneficiation, at lower energy consumption, and with reduced sensitivity to variations in the chemical and mineralogical composition of the raw materials. Therefore, this approach is considered a promising direction for the processing of lateritic nickel ores in Kazakhstan. Full article

Twenty-two new species of the genus Rhamphomyia Meigen (Diptera: Empididae) are described and illustrated from different parts of the Palaearctic region (mostly from East Asia): Rhamphomyia (Calorhamphomyia) iridescens sp. nov. (Mongolia, Russia); R. (Pararhamphomyia) acuticauda sp. nov. (Slovakia); R. (P.) amurensis sp. nov. (Russia); R. (P.) angustitibia sp. nov. (Russia); R. (P.) basitarsata sp. nov. (China, Russia); R. (P.) bifurcata sp. nov. (Russia); R. (P.) epandriata sp. nov. (Russia); R. (P.) globulicauda sp. nov. (Russia); R. (P.) haladai sp. nov. (Kazakhstan); R. (P.) indigirka sp. nov. (Russia); R. (P.) krivosheinae sp. nov. (Russia); R. (P.) morgunovka sp. nov. (Turkmenistan); R. (P.) norgensis sp. nov. (Norway, Russia); R. (P.) nudifemorata sp. nov. (Russia); R. (P.) plutenkoi sp. nov. (Russia); R. (P.) sausai sp. nov. (China); R. (P.) schachti sp. nov. (Spain); R. (P.) seticauda sp. nov. (Russia); R. (P.) spinicauda sp. nov. (Russia); R. (P.) spiraliseta sp. nov. (Russia); R. (P.) subcurvitibia sp. nov. (Russia); R. (P.) zeegersi sp. nov. (Russia). Full article

Mining and metallurgical activities are among the main sources of heavy metal (HM) contamination of terrestrial ecosystems and the creation of persistent technogenic pollution hotspots. This study aimed to provide a comprehensive assessment of the accumulation of zinc (Zn), cooper (Cu), cadmium (Cd) and lead (Pb) in soils and vegetation under conditions of long-term industrial impact in Ridder, East Kazakhstan Region. A total of 52 soil samples were collected from 0–5 cm and 5–20 cm depths at 26 sites, and 44 species of natural vegetation, as well as three dominant agricultural crops, were examined. Soil concentrations of Zn (4415 mg·kg⁻¹), Cu (1177 mg·kg⁻¹), Cd (179 mg·kg⁻¹), and Pb (1996 mg·kg⁻¹) were classified as extremely high. Cadmium contributed most to the potential ecological risk (Cd > Pb > Zn > Cu). The industrial zone’s vegetation cover was predominantly formed by stress-tolerant and ruderal species, including Artemisia vulgaris, Calamagrostis epigeios, Bunias orientalis, Dactylis glomerata, Convolvulus arvensis, and Urtica dioica. The agricultural crops (Helianthus annuus, Avena sativa, and Triticum aestivum) mainly accumulated HMs in their root systems, with limited translocation to their aboveground organs (TF < 1). This indicates the predominance of phytostabilisation mechanisms, and highlights the potential of locally adapted plants for managing contaminated areas. Full article

Honeybee (Apis mellifera L.) populations are a vital resource for pollination and honey production, yet their genetic diversity in Central Asia remains poorly understood. This study provides a comprehensive genetic assessment of 16 honeybee populations from Kazakhstan, with comparative samples from Russia, Georgia and Kyrgyzstan, utilizing mitochondrial COI–COII intergenic region and 12 highly polymorphic nuclear STR markers. Mitochondrial DNA analysis revealed the predominance of the Eastern European C lineage (A. m. carnica), while a few populations from East Kazakhstan and Russia attributed the M lineage (A. m. mellifera), indicating local introgression and the persistence of relict lineages. STR analyses showed high levels of polymorphism and genetic diversity, with variation in heterozygosity and inbreeding across populations. Analyses of population genetic structure delineated four principal genetic clusters shaped by regional differentiation, historical gene flow, and sporadic admixture. Concordance between mitochondrial and nuclear markers confirms the robustness of these findings. Overall, this study highlights the rich genetic diversity of honeybees from Kazakhstan and emphasizes the importance of conserving local populations and implementing selective breeding programs to sustain adaptive potential and long-term apiculture. Full article

In this work, we present the Snow Depth and Snow Water Equivalent Dataset for specific areas located in the East Kazakhstan Region that can be exploited to monitor and understand water resource dynamics in mountain regions. The present dataset represents a georeferenced collection of snow depth, snow density, and derived snow water equivalent (SWE) measurements obtained through manual snow surveys. Snow survey observations were conducted during field campaigns in the East Kazakhstan Region during the period of maximum snow accumulation from 27 February to 6 March 2025. Snow survey sites were selected to maximize coverage of diverse landscape settings and snow accumulation conditions. In total, 111 snow survey sites were established across the East Kazakhstan Region, and 2331 snow depth measurements and 555 snow density measurements were collected. In post-field (laboratory) processing, snow water equivalent (SWE) was calculated for all snow survey sites based on measured snow depth and snow density values. Full article