Platinum group metals (PGMs) assume an important role within the chemistry and chemical engineering due to their exceptional chemical stability in high temperatures and various environmental conditions. Their unique attributes make them highly demanded materials across an array of industries. Nevertheless, the gradual depletion of PGM reserves underscores necessitates of recycling PGM-containing waste as a means to ensure the reasonable utilization of resources. Recycling of catalytic waste, in particular, presents a more cost-effective and environmentally sustainable approach acquiring these metals, in contrast to the conventional practice of mining from natural ores. Of particular importance are spent automotive catalysts, which represent a valuable source of platinum group metals, featuring substantially higher PGM concentrations than their naturally occurring counterparts. Conventionally, the recovering of PGMs from waste materials predominantly employs hydrometallurgical and pyrometallurgical processes. Unfortunately, these established techniques entail the utilization of potent oxidizing acidic solutions, including aqua regia and hydrochloric acid with chlorine gas, which exert adverse ecological consequences. In recent years, there has been a growing focus on the development of alternative methodologies that are both environmentally friendly and economically viable for the recovery of PGMs from spent catalysts. Notable among these emerging techniques are solvometallurgy, molecular recognition technology, and magnetic separation. This comprehensive review endeavors to study and assess the latest advancements in the recovery of platinum group metals from spent catalysts, meticulously evaluating their respective advantages and disadvantages. Through an analysis, this review aspires to identify the most promising method - one that combines environmental friendliness and economic feasibility.

The given research employs high-resolution air quality monitoring and contemporary statistical methods to address gaps in understanding the urban air pollution in Pavlodar, a city with a significant industrial presence and promising touristic potential. Using mobile air quality sensors for detailed spatial data collection, the research aims to quantify concentrations of particulate matter (PM_2.5, PM₁₀), carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ground-level ozone (O₃); assess their distribution; and identify key influencing factors. In this study, we employed Geographic Information Systems (GISs) for spatial analysis, integrating multi-level B-spline interpolation to model spatial variability. Correlation analysis and structural equation modeling were utilized to explore the relationships between variables, while regression analysis was conducted to quantify these relationships. These techniques were crucial for accurately mapping and interpreting spatial patterns and their underlying factors. The study identifies PM_2.5 and NO₂ as the primary contributors to air pollution in Pavlodar, with NO₂ exceeding the 24 h threshold in 87.38% of locations and PM_2.5 showing the highest individual air quality index (AQI) in 75.7% of cases. Correlation analysis reveals a positive association between PM_2.5 and AQI and a negative correlation between NO₂ and AQI, likely due to the dominant influence of PM_2.5 in AQI calculations. Structural equation modeling (SEM) further underscores PM_2.5 as the most significant impactor on AQI, while NO₂ shows no significant direct impact. Humidity is positively correlated with AQI, though this relationship is context-specific to seasonal patterns observed in May. The sectoral analysis of landscape indices reveals weak correlations between the green space ratio (GSR) and air quality, indicating that while vegetation reduces pollutants, its impact is minimal due to urban planting density. The road ratio (RR) lacks sufficient statistical evidence to draw conclusions about its effect on air quality, possibly due to the methodology used. Spatial variability in pollutant concentrations is evident, with increasing PM_2.5, PM₁₀, and AQI towards the east-northeast, likely influenced by industrial activities and prevailing wind patterns. In contrast, NO₂ pollution does not show a clear geographic pattern, indicating vehicular emissions as its primary source. Spatial interpolation highlights pollution hotspots near industrial zones, posing health risks to vulnerable populations. While the city’s overall AQI is considered “moderate”, the study highlights the necessity of implementing measures to improve air quality in Pavlodar. This will not only enhance the city’s attractiveness to tourists but also support its sustainable development as an industrial center.

The aim of this research is to investigate the phase composition and structural peculiarities of complex metamorphic manganese ores from Central Kazakhstan before and after sintering in the temperature range of 600–1200 °C in an air atmosphere. X-ray diffraction, X-ray fluorescence, scanning electron microscopy, and optical microscopy were used to analyze changes in elemental and phase composition. In their initial state, according to XRF analysis, the Bogach ore was manganese-rich, with a manganese content of 60.77 wt.%, while the Zhaksy ore contained manganese (44.88 wt.%), silicon (20.85 wt.%), and iron (6.14 wt.%) as its main components. In the Bogach ore samples, manganese content increased from 60.77% to 65.7% as the sintering temperature rose to 1100 °C, while the hausmannite phase (Mn₃O₄) emerged as the dominant phase, comprising 95.77% of the crystalline component at 1200 °C. Conversely, the Zhaksy ore samples displayed a sharp increase in braunite-phase (Mn₇O₁₂Si) content, reaching 83.81% at 1100 °C, alongside significant quartz amorphization. The degree of crystallinity in Bogach ore peaked at 56.2% at 900 °C but declined at higher temperatures due to amorphous phase formation. A surface morphology analysis revealed the transformation of dense, non-uniform particles into porous, granular structures with pronounced recrystallization as the temperature increased. In the Bogach samples, sintering at 900 °C resulted in elongated, needle-like crystalline formations, while at 1200 °C, tetragonal crystals of hausmannite dominated, indicating significant grain growth and recrystallization. For Zhaksy samples, sintering at 1100 °C led to a porous morphology with interconnected grains and microvoids, reflecting enhanced braunite crystallization and quartz amorphization. These findings provide quantitative insights into optimizing manganese oxide phases for industrial applications, such as catalysts and pigments, and emphasize the impact of thermal treatment on phase stability and structural properties. This research contributes to the development of efficient processing technologies for medium-grade manganese ores, aligning with Kazakhstan’s strategic goals in sustainable resource utilization.

This study assesses heavy metal (HM) contamination in soils of an urban industrial zone using statistical and spatial analysis methods. Concentrations of 12 key HMs, including Zn, Pb, Cu, and Ni, were measured using X-ray fluorescence (XRF), with values exceeding background levels several times in certain areas. Pollution indices such as the Pollution Load Index (PLI) and Total Pollution Indicator (Zc) revealed moderate to high contamination levels, with PLI values ranging from 1.05 to 3.38 and Zc values between 0.67 and 51.34. Health risk assessments indicated that the hazard quotients (HQ) exceeded safe thresholds in hotspots, highlighting potential risks. Spatial distribution maps identified industrial activities as the primary source of contamination. Specifically, according to the PLI, approximately 93.757% of the studied area is classified as moderately contaminated, while 0.702% is considered significantly contaminated. These findings provide a baseline for monitoring and mitigating soil pollution in industrial regions while offering insights for sustainable land management.

This study addressed the critical challenge of filling gaps in PM2.5 time series data from Pavlodar, Kazakhstan. We developed and evaluated a comprehensive hierarchy of 46 gap-filling methods across five representative gap lengths (5–72 hours), introducing dynamic models capable of adapting to gaps of variable duration. Tree-based models with bidirectional sequence-to-sequence architectures delivered superior performance, with XGB Seq2Seq achieving a mean absolute error of 5.231 ± 0.292 μg/m³ for 12-hour gaps, representing a 63% improvement over basic statistical methods. The advantage of multivariate models incorporating meteorological variables increased substantially with gap length, from modest improvements of 2–3% for 5-hour gaps to significant enhancements of 16–18% for 48–72 hour gaps. Dynamic multivariate models demonstrated remarkable operational flexibility by successfully processing real-world gaps ranging from 1 to 191 hours despite being trained on maximum lengths of 72 hours. Analysis of the reconstructed complete time series revealed that 61.2% of monitored hours exceeded the WHO daily threshold of 15 μg/m³, with strong seasonal patterns and pronounced diurnal cycles. This research advances environmental monitoring capabilities by providing robust methodological tools for addressing data continuity challenges that currently limit the utility of PM2.5 measurements for public health applications and scientific analysis.

This study focused on predicting the spatial distribution of environmental risk indicators using mathematical modeling methods including machine learning. The northern industrial zone of Pavlodar City in Kazakhstan was used as a model territory for the case. Nine models based on the methods kNN, gradient boosting, artificial neural networks, Kriging, and multilevel b-spline interpolation were employed to analyze pollution data and assess their effectiveness in predicting pollution levels. Each model tackled the problem as a regression task, aiming to estimate the pollution load index (PLI) values for specific locations. It was revealed that the maximum PLI values were mainly located to the southwest of the TPPs over some distance from their territories according to the average wind rose for Pavlodar City. Another area of high PLI was located in the northern part of the studied region, near the Hg-accumulating ponds. The high PLI level is generally attributed to the high concentration of Hg. Each studied method of interpolation can be used for spatial distribution analysis; however, a comparison with the scientific literature revealed that Kriging and MLBS interpolation can be used without extra calculations to produce non-linear, empirically consistent, and smooth maps.