Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field.

Coal remains a critical energy resource in China, driving rapid urbanization and industrial growth. However, extensive underground mining operations have resulted in severe environmental and structural challenges, including ground subsidence, coal-based solid waste generation, and significant CO₂ emissions. This study explores the potential of CO₂-carbonated fly ash-based backfill materials (CFBM) as a sustainable solution that integrates waste management and carbon sequestration while enhancing underground mining stability. A series of laboratory experiments evaluated the mechanical performance, microstructural evolution, and transport properties of backfill materials with varying coal fly ash (CFA) content (50 %, 60 %, 70 %, 80 %, 90 %). Results demonstrated that CO₂ carbonation significantly improved compressive strength, with CFA-rich samples exhibiting up to a 20 % increase and a CO₂ uptake capacity of 1.39 mg/g. X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) analyses confirmed the formation of calcium carbonate (CaCO₃) and calcium-silicate-hydrate (C-S-H) gel, which densified the material by filling voids and reducing porosity. Rheological analysis showed that the CFBM exhibited shear-thinning behavior, aligning with the Herschel-Bulkley model, making it suitable for pipeline transport. Numerical simulations using Flac3D validated the effectiveness of CFBM in controlling surface subsidence and improving rock strata stability. At a 60 % backfill rate, surface subsidence was reduced by 63.7 % compared to conventional caving methods. The findings highlight CO₂-carbonated backfill as a viable approach for sustainable mining, offering enhanced stability, reduced environmental impact, and contributing to the broader adoption of carbon sequestration technologies in the mining industry.

The selection of appropriate disc cutters plays a crucial role in improving the efficiency, reliability, and sustainability of tunnel boring machine (TBM) operations. In mechanized excavation projects, improper cutter selection may lead to excessive tool wear, increased energy consumption, and reduced operational performance. Therefore, intelligent decision-support approaches are required to assist engineers in selecting optimal cutting tools under uncertain geological and operational conditions. This study presents a comparative evaluation of five proximity measures within the fuzzy TOPSIS method: four similarity measures for generalized fuzzy numbers and one Gaussian kernel distance for intuitionistic fuzzy numbers. Five computational approaches are investigated, including four fuzzy similarity methods and one kernel-based distance method. The kernel distance maps intuitionistic fuzzy numbers into a higher-dimensional feature space through a Gaussian kernel, where distances are bounded and better separated. Linguistic evaluations of performance criteria are converted into trapezoidal fuzzy numbers to construct the fuzzy decision matrix, enabling the integration of expert judgment and uncertainty in the decision-making process. The proposed framework is applied to evaluate multiple cutter alternatives based on relevant mechanical, operational, and performance-related criteria. The results indicate that although all evaluated methods consistently identify the same optimal disc cutter, noticeable variations occur in the ranking of the remaining alternatives. Among the examined techniques, Chen’s similarity method and the kernel-based approach demonstrate greater sensitivity and improved discrimination capability when addressing complex nonlinear relationships among decision criteria. Furthermore, the robustness of the proposed framework is verified through sensitivity analysis using an intuitionistic fuzzy TOPSIS model with kernel distance. The findings highlight the potential of advanced fuzzy decision-making models as intelligent tools for supporting equipment selection in mechanized tunneling. By facilitating more reliable cutter selection, the proposed approach contributes to improved operational efficiency, reduced tool consumption, and more sustainable tunneling practices within modern smart mining and underground construction systems.