This study investigated the macro- and microelement profiles and bioactive substances (BAS) in the herbaceous species of the genus Rheum collected from two villages near Almaty, Kazakhstan, and the accumulation of heavy metals (HMs) in wild plants of Eastern Kazakhstan. Representative zonal species from the steppes and desert–steppes (Eastern Kazakhstan) were analyzed—totaling one hundred samples across 18 species from six families. According to the research, different plant species have a selective ability to accumulate heavy metals, even when growing in the same type of soil. Long-term observations reveal dominant patterns of HM (Cu, Zn, Mn, Co, Pb, Cd) occurrence in dry-steppe vegetation. It was found that Cu and Co demonstrate moderate bioadsorption with minimal accumulation, while Zn, Mn, and Pb show strong biological enrichment. Cadmium falls under elements with pronounced biological retention. Copper and cobalt are classified as moderately absorbed with limited buildup. Native plants in this region contain substantially greater amounts of lead relative to cobalt, and reflect cadmium–zinc geochemical specificity: Cd 3.85, Zn 0.15, Mn 0.10, Pb 0.09, Co 0.07, Cu 0.04, per Clark’s concentration index. Samples of collected Rheum tataricum L. in the village of Miyaly were found to have higher levels of flavonoids, phenolic compounds, and organic acids, while raw materials from the village of Bakbakty showed increased accumulation of anthracene derivatives and tannins. Flavonoids constituted the largest proportion of the studied groups of biologically active substances in both samples, confirming the high biological and pharmacological value of the plant material studied. The results obtained during the research can be successfully applied to the development of a strategy for the conservation of plant biodiversity in the studied areas and the sustainable management of plant resources.

Knowledge of the heterogeneous composition of mineral substances and technogenic materials is vital in the development of technological designs and in environmental impact assessments. However, the simultaneous application of analytical methods often reveals discrepancies between different methods that cannot be explained by the commonly used scalar agreement metrics. The current study suggests the possibility of evaluating inter-methodical agreement in the compositional data of heterogeneous substances using the example of lead smelting slag compositional data. The compositional data of 96 samples of lead smelting slag were analyzed using independent methods of XRF, ICP-OES and AAS. The agreement between the obtained results was evaluated at three levels of hierarchy: element-wise bias and dispersion, structured variability between methods and preservation of covariance structure; PCA was used as the tool for the evaluation. High agreement between the ICP-OES and AAS methods was found for the transition metals (r ≈ 0.97–0.99), with negligible bias. The presence of increased dispersion and deviations in the inter-methodical agreement was found in the case of the methods compared with the XRF method, especially for the matrix components such as Si (r ≈ 0.92). Coefficients of variation for metallic elements stay between 12 and 15%, but XRF has shown consistently higher variability for certain elements. PCA results show that despite local differences, the main covariance structure was kept across methods, with the first two components explaining about 40–45% of total variance. These results clearly indicate that high correlation does not necessarily ensure methodological interchangeability. The hierarchical framework proposed here will provide a reproducible basis for cross-method validation and support reliable data integration in complex mineral systems.

Molybdenum is one of the most important uranium-related metals. In the ores of many deposits, the molybdenum content is comparable to the uranium content. The processing of complex uranium-molybdenum ores is based on the methods of acid and soda autoclave leaching, in which uranium and molybdenum pass into solution. At the present time, the associated extraction of molybdenum by the sorption method has been developed and introduced. In this case, the capacity of ion exchangers with respect to uranium and molybdenum substantially depends on the salt composition of the medium. The aim of this work is to study the sorption of molybdenum from a carbonate leach solution on BD-301G-I anion exchange resin in the presence of the most common anionic impurities in industrial solutions. Studies of the sorption capacity of anion exchange resin with respect to molybdenum were carried out under static conditions with stirring of the solution with the sorbent during eight hours. Sorption of molybdenum was carried out from carbonate solutions with the corresponding given concentrations of impurity components. According to the results of studies, it was found that, according to the strength of the depressing effect, the considered anions arranged in the following row: S2O32- > NO3- > Cl- > SO42- > HCO3- > CO32- > PO43-. The results of this study can be used to further study the sorption of molybdenum from real industrial solutions of carbonate leaching.

Mining and metallurgical wastes contain reactive Fe-, Al-, Ca-, Mg-, and Mn-bearing phases. These phases can be used for water treatment. Their performance depends on mineral accessibility, processing history, and water chemistry rather than waste identity alone. This review compares tailings, slags, red mud, and leaching residues for inorganic and organic contaminant removal, focusing on mechanisms, modification intensity, and validation. Cations are removed by ion exchange, surface complexation, and precipitation, whereas anion removal relies on Fe/Al sites, Ca-mediated mineral formation, and redox reactions. Organic treatment proceeds through adsorption or catalytic transformation and generally requires activation or additional functional phases. Red mud and blast-furnace slag showed the broadest applicability across both pollutant classes, although no unchanged formulation was validated for both. Under favorable conditions, waste-derived sorbents can achieve pollutant-removal efficiencies approaching 100% in synthetic solutions. However, their performance generally decreases in real wastewater and under continuous or cyclic operating conditions, with reported efficiency losses reaching approximately 25 percentage points. Deployment and machine-learning-assisted selection require application-matched processing, real-water and static/dynamic validation, and standardized reporting of separation, regeneration, cyclic stability, mass loss, secondary leaching, and spent-material fate.

Mining and metallurgical wastes contain potentially valuable mineral resources but pose growing environmental risks. This review proposes a mineralogy-guided framework for converting these wastes into low-carbon binders and construction materials. Rather than classifying residues by origin or bulk oxide composition, it distinguishes them according to the reaction roles of their mineral phases. The framework first distinguishes self-sufficient systems, in which activation unlocks the intrinsic mineral inventory of the waste sufficiently for matrix or product formation, from mineralogically compensated systems, in which additional functional mineral solids are required to supply deficient reactive Si, Al, Ca, sulfate, alkalinity, or other phase-forming components. Binary and multicomponent formulations are then interpreted according to the specific mineralogical functions supplied by the complementary solids and the resulting changes in reaction pathways and products. Thermal, hydrothermal, mechanochemical, alkaline, and carbonation-based processing routes are compared in relation to phase composition, co-precursor function, and the mechanical, thermal, and service properties of concretes, foams, and backfill materials. Evidence indicates that performance depends more on chemical complementarity than on maximizing waste content. Environmental benefits must also account for contaminant immobilization, carbon dioxide binding, and the energy and reagent demand of pretreatment. Mineralogy-informed machine-learning models may support inverse mixture design, but their transferability remains constrained by feedstock heterogeneity and limited standardized data. The framework links mineralogy, processing, reaction products, performance, and scale-up.