Underground mining environments pose significant challenges for automated hazard detection due to low illumination, restricted visibility, and the absence of Global Navigation Satellite System (GNSS) coverage. These factors limit situational awareness and delay inspection efforts, particularly after disruptive events when rapid assessment is essential for safety. This study addresses this problem by developing a dual-pipeline framework for 2D–3D detection that uses 360° imaging and LiDAR-based machine learning to identify people, vehicles, and positional changes in underground settings without requiring personnel to re-enter hazardous areas. The objective was to create a system capable of recognizing objects and monitoring spatial changes under real underground mine conditions. The 2D component used a Ricoh Theta Z1 camera to collect panoramic images, and a YOLO (You Only Look Once) v8n model was fine-tuned using datasets representing low light, shadowed underground scenes. The 3D component employed an Ouster OS1-070-64 LiDAR sensor, and point clouds were processed through denoising, ICP alignment, surface reconstruction, manual annotation, and 2D projection. A YOLO-based model was then trained to detect objects and measure displacement between LiDAR scans. Results demonstrated strong performance for both components. The fine-tuned YOLOv8n model reliably detected personnel and vehicles despite challenging lighting and visual clutter, while the 3D pipeline localized objects in the registered LiDAR frame and quantified vehicle displacement between consecutive scans by comparing 3D bounding-box centroids after ICP alignment (displacement vector and magnitude). These findings indicate that the combined 2D–3D system can effectively support automated hazard recognition and environmental monitoring in GNSS-denied underground spaces.

Occupational exposure to respirable crystalline silica (RCS) remains a significant health concern in metal and nonmetal (MNM) mining operations, contributing to the development of silicosis, lung cancer, and other chronic respiratory con-ditions. This review examines the prevalence and effects of RCS exposure in MNM mining environments, the toxicity of silica dust, and the effectiveness of regulatory interventions aimed to control exposure and mitigate health hazards. Key factors influencing RCS concentrations, including mine type, size, and geographic location, are analyzed, with particu-lar focus on the impact of recent regulatory updates from the Mine Safety and Health Administration (MSHA). Ad-vanced characterization techniques, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS), are critically assessed for their applica-tion in dust monitoring and composition analysis. Our analysis of nearly 60,000 samples collected between 2000 and 2023 revealed that RCS concentrations frequently exceed permissible exposure limits, particularly in large surface mines and facilities located in the western United States. Surface mines exhibited higher dust concentrations than under-ground mines, and metal and nonmetal mines showed greater silica hazards compared to sand & gravel, and stone op-erations. Statistical analysis confirmed significant differences in RCS exposure levels based on mine type, size, and geo-graphic location.. While regulatory initiatives, including MSHA's new action-level plan, represent a step forward, their success hinges on rigorous enforcement and the adoption of comprehensive dust control strategies. This review high-lights the urgent need for an integrated approach, combining advanced monitoring technologies, effective dust suppres-sion methods, and targeted worker training programs, to mitigate RCS exposure and improve occupational health out-comes in the mining industry.

This study aims to improve the understanding of respirable coal mine dust (RCMD) particle deposition in the lungs to enhance health and safety measures in coal mining environments. Using the Mobile Aerosol Lung Deposition Apparatus (MALDA) and a wind tunnel, experiments were conducted under simulated mining conditions to evaluate how particle characteristics influence deposition patterns during airflow and activity similar to that of mine workers. The experimental results followed a trend similar to the conventional International Commission on Radiological Protection (ICRP) lung deposition curve but showed higher particle deposition in the gas exchange region, likely due to differences between moving and still air conditions. Irregular coal dust particle shapes may also contribute to the observed discrepancies. The study confirms that smaller particles tend to penetrate deeper into the alveolar region, increasing the risk of developing coal mine dust lung diseases (CMDLDs), including coal workers' pneumoconiosis (CWP). These findings provide valuable insights into lung deposition dynamics under realistic mine-like airflow conditions.