Floods increasingly threaten semiarid regions, yet their long-term soil ecological impacts remain underdocumented. This study quantifies the hydrologic change and flood-induced soil transformation on the Zhabay River floodplain (Akmola, Kazakhstan) using integrated field, laboratory, and remote sensing data. Gauge records (2012–2024) were analyzed; inundation was mapped from a 0.30 m DEM (Digital Elevation Model) merging SRTM (Shuttle Radar Topography Mission), Landsat 8/Sentinel 2, and UAV (Unmanned Aerial Vehicle) photogrammetry (NDWI (Normalized Difference Water Index) > 0.28) and validated with 54 in situ depths (MAE (Mean Absolute Error) 0.17 m). Soil samples collected before and after floods were analyzed for texture, bulk density, pH, Eh, macronutrients, and heavy metals. Annual maxima increased by 0.08 m yr⁻¹, while extreme floods became more frequent. Thresholds of ≥0.5 m depth and >7 days duration marked compaction onset, whereas >1 m and ≥12 days produced maximum organic carbon loss and Zn/Ni enrichment. The combination of high-resolution DEMs, ROC (Receiver Operating Characteristic) analysis, and soil microbial monitoring provides new operational indicators of soil degradation for Central Asian steppe floodplains. Findings contribute to SDG 13 (Climate Action) and SDG 15 (Life on Land) by linking flood resilience assessment with sustainable land-use planning.

Urban air pollution caused by vehicular emissions remains one of the most pressing environmental challenges, negatively affecting both public health and climate processes. In Kokshetau, Kazakhstan, where electric vehicle (EV) adoption accounts for only 0.019% of the total fleet and charging infrastructure is nearly absent, reducing transport-related emissions requires short-term and cost-effective solutions. This study proposes an integrated approach combining urban ecology principles with computational modeling to optimize traffic signal control for emission reduction. An artificial neural network (ANN) was trained using intersection-specific traffic data to predict emissions of carbon monoxide (CO), nitrogen oxides (NO_x), sulfur dioxide (SO₂), and particulate matter (PM_2.5). The ANN was incorporated into a nonlinear optimization framework to determine traffic signal timings that minimize total emissions without increasing traffic delays. The results demonstrate reductions in emissions of CO by 12.4%, NO_x by 9.8%, SO₂ by 7.6%, and PM_2.5 by 10.3% at major congestion hotspots. These findings highlight the potential of the proposed framework to improve urban air quality, reduce ecological risks, and support sustainable transport planning. The method is scalable and adaptable to other cities with similar urban and environmental characteristics, facilitating the transition toward eco-friendly mobility and integrating data-driven traffic management into broader climate and public health policies.

The increasing amount of municipal solid waste is one of the most urgent problems for many countries in the world, including Kazakhstan. In 2021, a new environmental code was adopted in the country. In accordance with this code, some types of garbage (such as plastic, paper, cardboard or glass) are not accepted at sanitary landfills. Besides this, a separate collection system of municipal solid waste in Kokshetau is practically absent, and only a few points for the collection of secondary raw materials are currently available. The state bodies are faced with the task of introducing dual waste collection technology. This work shows the results obtained by a sociological online survey performed among the residents of the city in order to identify their attitudes with respect to the separation and classification of waste. This survey allowed us to study the level of environmental awareness and interest in solving waste-related issues in the population of Kokshetau. The results show that the majority of the population is concerned about the problem of municipal waste management and is ready to cooperate on the classification of waste at the original source. Additionally, it was revealed that the survey participants have weak environmental knowledge, so it is necessary to conduct environmental education activities for the population. The existence of a statistically significant relationship between the variables under consideration in terms of the Pearson Chi-squared criterion was observed.

In the current context of the international climate agenda, understanding both the sources of greenhouse gas (GHG) emissions and the mechanisms for their mitigation is a fundamental requirement for low-carbon development strategies. Kazakhstan has pledged to reduce its GHG emissions by 15–25% by 2030, relative to 1990 levels, and to achieve carbon neutrality by 2060. However, there is no unified methodology for comprehensively assessing the national carbon balance, particularly at the regional scale. This study addresses this gap by analyzing GHG emissions and carbon sequestration capacities across Kazakhstan’s regions using a sectoral disaggregation approach and scenario-based modeling aligned with IPCC methods. Emission hotspots were identified in the energy sector (328 MtCO₂-eq), agriculture (118 MtCO₂-eq—primarily from pasturelands), and transport (7 MtCO₂-eq). In contrast, current carbon sinks—mainly forest ecosystems and abandoned pasturelands—account for only 3.97 and 13.9 MtCO₂-eq, respectively. The research evaluates the technical potential for emissions reduction through the best available technologies (BAT), livestock management, partial transition to gas-powered vehicles, and reforestation. A geoengineering scenario combining all measures suggests that Kazakhstan could meet its 2030 climate targets, although full carbon neutrality by 2060 would remain out of reach under current policy trajectories. The Akmola region is examined as a representative case study, demonstrating a possible 35% reduction in net emissions by 2035. This work contributes a regionally nuanced, data-driven framework for integrating ecosystem services into national climate policy and identifies nature-based solutions—especially forest management—as essential components of Kazakhstan’s decarbonization pathway, offering insights for other carbon-intensive economies.

Landfills represent areas of pronounced anthropogenic disturbance, with substantial impacts on local vegetation. The composition and structure of plant communities serve as indicators of eco-system alteration and may function as reservoirs of species with potential utility in ecological restoration. This study provides the first detailed assessment of vegetation structure on a closed MSW landfill in Kokshetau (Akmola Region, northern Kazakhstan; semi-arid steppe/forest-steppe setting) and demonstrates an integrative, restoration-oriented monitoring and target-setting workflow, including a localized phytoremediation screening framework integrating field performance, ecological indicator values, and literature-based functional traits, with a risk/governance filter. A total of 76 vascular plant species were recorded during the field survey, predominantly comprising annual herbaceous taxa adapted to highly disturbed environments. The families Asteraceae and Poaceae were the most species-rich, while Chenopodiaceae and Brassicaceae were also notably represented. Meadow-steppe species constituted the majority (45.5%) of the phytosociological spectrum. Multivariate ecological and statistical analyses revealed that community composition was primarily influenced by the degree of disturbance (p = 0.016), rather than soil pH, with Cannabis sativa and Bassia scoparia emerging as key indicators of less disturbed sectors, contrasting with actively disturbed dumping areas. Consequently, restoration efforts should prioritize mesophytic species adapted to open, sunlit habitats and capable of establishing on slightly alkaline soils, while accounting for site-specific constraints to support long-term vegetation recovery. Notably, Artemisia absinthium and Bassia scoparia were identified as candidate taxa for phytoremediation-oriented restoration, based on their in situ ecological performance and literature-reported traits, albeit with limitations due to allergenic pollen and invasive tendencies, respectively. These findings support phytoremediation strategy design on disturbed landscapes by emphasizing regionally adapted species selection that balances ecological suitability with potential ecological risks.