A substantial amount of bauxite tailings (BTs) at abandoned mine sites have been stored in waste reservoirs for long periods, leading to significant land occupation and environmental degradation. Although many studies of the resource utilization of BTs were conducted to address this challenge, there is still a lack of efforts to systematically review the state of the art in BTs. In the present paper, a systematic literature review was carried out to summarize and analyze the properties, treatment, and resource utilization of BTs. Physical characteristics and the mineral and chemical composition of BTs are introduced. The efficacy of physical, chemical, and microbial treatment methods for BTs in terms of dehydration are outlined, and their respective benefits and limitations are discussed. Moreover, the extraction process of valuable elements (e.g., Si, Al, Fe, Li, Na, Nd, etc.) from BTs is examined, and the diverse applications of BTs in adsorption materials, ceramic materials, cementitious materials, lightweight aggregates, foamed mixture lightweight soil, among others, are studied. Finally, an efficient and smart treatment strategy for BTs was proposed. The findings of the present review provide a scientific basis and reference for future research focusing on the treatment and resource utilization of BTs.

Elastic modulus, crucial for assessing material stiffness and structural deformation, has recently gained popularity in predictions using data-driven methods. However, research systematically comparing different machine learning models under the same conditions, especially for ultra-high-performance concrete (UHPC), remains limited. In this study, 10 different machine learning models were evaluated for their capacity to predict the elastic modulus of UHPC. The results showed that XGBoost demonstrated the highest accuracy in predictions with large training datasets, followed by KNNs. For smaller training datasets, Decision Tree exhibited the greatest accuracy, while XGBoost was the second-best performing model. Linear regression displayed the lowest accuracy. XGBoost demonstrated the most potential for accurately predicting the elastic modulus of UHPC, particularly when a comprehensive dataset is available for model training. The optimized XGBoost exhibited better predictive performance than fitting equations for different UHPC formulations. The findings of this study provide valuable insights for researchers and engineers working on the data-driven design and characterization of UHPC.

Sewerage systems-related solid waste accumulates in considerable quantities in urban water systems, including rainwater drainage pipes, pumping stations, grease traps, grit chambers, and septic tanks. Traditional management methods, such as sanitary landfilling, incineration, and composting, not only endanger the environment but also consume a significant amount of land. To address this problem, a variety of waste was collected from a terminal and different facilities in sewerage systems, and the characteristics of solid waste were tested and analyzed. The corresponding appropriate approaches to resource recovery strategies were proposed in detail. The solid waste in the wastewater treatment plant (WWTP) contains low organic matter content and a certain quantity of heavy metals, making recycling difficult. Before the solid waste enters the WWTP, the strategy of pre-sorting, treating, and recycling the solid waste is worth recommending. The waste was divided into three categories based on its nature, and corresponding resource utilization strategies were proposed. A small part of solid waste that is not suitable for pre-recycling can be discharged normally and enter the WWTP for treatment. This paper provides a scientific basis for the green resource utilization of solid waste in the field of sewerage systems in developing countries.

Red mud (RM) and electrolytic manganese residue (EMR) possess inherently complementary acid–base characteristics, and their synergistic utilization offers a promising approach to simultaneously enhance mechanical performance and mitigate environmental risks. However, the environmental behavior of RM-EMR composites, particularly in terms of pH buffering, ammonium nitrogen (NH₄⁺-N) control, and heavy metal mobility, remains inadequately understood. In this study, a composite cementitious paste was developed using RM and EMR, and the effects of the RM-to-EMR ratio (1: 3, 2: 3, 1: 1, and 3: 2), alkali activator dosage (30%, 40%, 50% and 60% by weight), and curing time (3 day, 7 day, 14 day, and 28 day) under standard curing conditions on unconfined compressive strength (UCS) were systematically evaluated. Leaching tests were conducted to assess pH evolution, NH₄⁺-N release, and heavy metal migration. The results showed that the optimal 28-day UCS of 35 MPa was achieved with an RM-to-EMR mass ratio of 2:3 and an activator dosage of 60%. EMR contributed to NH₄⁺-N leaching concentrations as high as 302 mg/L; however, under alkaline conditions (pH > 11), over 50% of the block samples met regulatory limits due to the transformation of NH₄⁺ into gaseous NH₃. Furthermore, Mn and Cd were effectively immobilized. In contrast, Al and Se exhibited elevated leaching, with Al showing particularly high concentrations under water leaching conditions. These results underscore the importance of raw material pretreatment and system optimization. Overall, this study provides new insights into the environmental behavior and safe resource utilization of RM and EMR in cementitious systems.