In this study, the authors obtained samples of biological fertilizer by thermophilic fermentation of substrates of different compositions. Two types of effluent were studied in the experiment: food plant waste with the addition of cattle manure and liquid pig manure with litter cattle manure. To...

This article examines the impact of climate change on the hydrometeorological indicators of some lakes and reservoirs in the Akmola and North Kazakhstan regions. Two meteorological variables’ annual and seasonal trends at three weather stations in 1986–2023 were analyzed. The non-parametric Mann–Kendall and Sen’s slope methods were used to determine the presence of a positive or negative trend in weather data and their statistical significance. Hydrometric indicators were studied using the ArcGIS 10.8 program from 1995 to 2023. The results indicate an increasing average spring air temperature, with an annual rise of 0.08–0.09 °C. A significant trend in increasing average annual precipitation was observed in Saumalkol, with a rise of 4.7 mm per year. In contrast, no significant trends were found in the annual and seasonal precipitation data for Sergeyevka. It was also found that the area of Lake Saumalkol increased by 1.6% due to a rise in annual precipitation. In contrast, the area of Lake Kopa decreased by 6.04% because of an increase in the annual average temperature.

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.

The necessity to optimize feed crop cultivation in Kazakhstan's steppe zone is underscored by evolving climatic conditions and sustainable agriculture demands. This study, conducted from 2021 to 2023 in the Akmola region, evaluated the nutritional value and production efficiency of annual and perennial grass mixtures. A randomized complete block design was utilized for annual grasses, while a sequential scheme was applied for perennial grasses, each with three replications per plot. Statistical data processing was employed to analyze the outcomes. Results indicated that mixed-feed crops exhibited superior nutritional composition and energy value. Specific combinations of annual grasses, such as oats with peas or oats, peas, and vetch, alongside multicomponent mixtures incorporating legumes for perennial grasses, demonstrated optimal results. The ideal harvest timings for these grass mixtures were also established. It is recommended to cultivate combinations like Sudan grass with peas and vetch, or oats with peas and vetch for green feed, and for hay and pasture, combinations of red fescue, bluegrass, wheatgrass, and alfalfa, as well as red fescue, bluegrass, brome, sainfoin, and alfalfa. This research emphasizes the importance of diverse crop mixtures to enhance feed nutritional value, thereby contributing to sustainable agricultural practices, food security, and environmental resilience amid climate change.