Search Results (413)

Gas monitoring and dust control in coal mine goafs are critical for ensuring safe and efficient production. To address the challenges posed by dust accumulation from mechanized mining and ventilation systems, this study designs a spiral-driven gas purification pipeline robot integrating a wet dust removal mechanism. The robot features a modular structure, including a spiral drive, a plugging and extraction system, and a wet dust removal unit, to enhance pipeline adaptability and dust removal performance. Dynamic modeling reveals that the robot’s speed increases with the deflection angle of the driving wheel, with optimal performance observed at a 45° angle. The analysis of the rolling friction, medium resistance, and deflection angle indicates that reducing the angle improves the obstacle-crossing ability. Numerical simulations of gas migration in the goaf identify a high dust concentration at the air outlet and show that flow velocity significantly affects dust removal efficiency. Simulation and prototype testing confirm stable robot operation at deflection angles of between 30° and 90° and effective crossing of 5 mm barriers. Optimal dust removal is achieved with a 5 m/s flow velocity, 0.6 MPa water mist pressure, and 400 mm chord grid spacing, providing both theoretical and practical guidance for gas monitoring and dust control in coal mine goafs. Full article

Xiamen, a rapidly developing coastal metropolis and tourist hub in southeastern China, faces air quality challenges due to its dense population and tourism reliance. This study investigates PM_2.5 sources and temporal variations during autumn 2013–2017 via chemical characterization, mass reconstruction, and receptor modeling. The Positive Matrix Factorization (PMF) model identified five sources: secondary sulfate (31%), coal/vehicle emissions (28%), industrial emissions with secondary organic aerosols (SOA, 20%), ship emissions (14%), and fugitive dust (7%). Interannual variations in source contributions highlighted impacts of anthropogenic activities, meteorology, power plant upgrades, and stricter vehicle standards. PM_2.5 declined 19% (2013–2017), driven by emission controls, while SOA surged 42% (2015–2017) due to VOC oxidation and lower temperatures. Backward trajectory and Potential Source Contribution Function (PSCF) analyses revealed significant regional transport from northern industrial zones (32% contribution) and maritime activities. Ship emissions, which have remained relatively stable over the years, underscore the need for stricter marine regulations. Fugitive dust peaked in 2015 (25.8% of PM_2.5), linked to urban construction. The findings emphasize the interplay of local emissions and regional transport in shaping PM_2.5 pollution, providing a scientific basis for targeted control strategies in coastal cities with similar socioeconomic and geographic contexts. Full article

Shearer drum cutting of coal seams generates over half of the coal dust in coal mines, while relevant studies focus more on micron-sized dust and much less on nano- to sub-micron-sized coal dust. Based on the self-developed experimental system for simulating dust generation from drum cutting of coal bodies, this study investigated the concentration distribution characteristics and physicochemical properties of 10 nm–10 μm coal dust generated from drum cutting of different rank coals with different cutting parameters. Results showed that the coal dust mass and number concentrations were concentrated in 2–10 μm and 10–200 nm, respectively, accounting for 90% of the total 10 nm–10 μm coal dust; the mass percentages of PM1/PM10 (PM1/PM10 = PM1 particles relative to PM10 particles, similarly hereinafter), PM1/PM2.5, and PM2.5/PM10 were 3.25–4.87%, 19.35–26.73%, and 14.82–18.81%, respectively, whereas over 99% of the total number of particles in the PM10 fraction are within the PM1 fraction (i.e., N-PM1/N-PM10 > 99%), that is, both N-PM1/N-PM2.5 and N-PM2.5/N-PM10 exceeded 99%. Lower-rank coal generates less 10 nm–10 μm coal dust, and either higher moisture content, firmness coefficient, or lower fixed carbon content of the coal can effectively reduce the 10 nm–10 μm coal dust generation. Either reduction in the tooth tip cone angle, the rotary speed, or increase in the mounting angle or the cutting depth can effectively inhibit the 10 nm–10 μm coal dust generation. Higher-rank coal dust shows fewer surface pores, smoother surfaces, larger contact angles, more hydrophobic groups, and fewer hydrophilic groups. The research results have filled the knowledge gap in the pollution characteristics of nano- to submicron-sized dust generated from shearer drum cutting of coal bodies, and can serve as an important reference for the development of dust reduction and suppression technologies in coal mining faces as well as the prevention of coal worker’s pneumoconiosis. Full article

This paper focuses on the issues of roof movement and ground pressure behavior in large-height mining extraction of shallow coal seams. By adopting a combined method of theoretical analysis and physical simulation experiments, it establishes a mechanical model for the rotational subsidence of key blocks and a physical simulation test model to conduct stability analysis on the rotational subsidence of key blocks, thereby revealing the characteristics and laws of roof movement. The findings indicate that the horizontal thrust during the rotational subsidence of key blocks increases non-linearly with the rotation angle, exhibiting a higher growth rate when the block size coefficient is less than 0.5. Two modes of instability—sliding and deformation—are observed for key blocks. To prevent sliding instability, the block size coefficient should be maintained below 0.75; however, sliding instability is likely to occur when the rotation angle exceeds 10°. Conversely, smaller rotation angles and larger block size coefficients reduce the likelihood of deformation instability. The reasonable working resistance of the support decreases with the increase in the rotation angle (it decreases sharply when the rotation angle exceeds 10°) and increases with the increase in the block size coefficient. Physical simulation indicates that roof movement is divided into three stages: immediate roof collapse, stratified fracturing and instability of the basic roof, and periodic fracturing of the basic roof. An increase in mining height accelerates the instability of the immediate roof, enlarges the opening of through-layer fissures, shortens the step distance of mining pressure, and heightens the risk of sudden pressure. The research results provide theoretical guidance for the safe and efficient mining with large mining height in shallow coal seams. Full article

Effective roadway environment sensing is critical for intelligent underground vehicle navigation. Dust pollution and complex terrain in underground roadways present key challenges for quantifying passability risks: (1) Over-filtering of dust noise in lidar point clouds can inadvertently remove valuable information. (2) The enclosed and chaotic nature of underground roadways prevents planar information from fully representing spatial constraints. To address these challenges, this paper proposes a method for constructing terrain risk voxels and assessing navigability in coal mine tunnels. First, an improved particle filter combined with image features performs two-stage dust filtering. Second, D-S theory is applied to fuse and evaluate three-dimensional tunnel risks, constructing 3D terrain risk voxels. Finally, navigable spaces are identified and their characteristics quantified to assess passage risks. Experiments show that the proposed dust filtering algorithm achieves 96.7% average accuracy in primary underground areas. The D-S theory effectively constructs roadway terrain risk voxels, enabling reliable quantitative assessment of roadway passability risks. Full article

Air pollution issues are relevant all over the world, especially in industrial areas. The main pollution of the atmosphere is caused by dust emissions from industry. This article discusses the issue of dust emission from the coal industry. The purpose of this research is a comprehensive analysis and environmental assessment of the impact of coal storage processes on the environment. The study was conducted on the example of a coal deposit in the Karaganda region of the Republic of Kazakhstan. The Karaganda region is the industrial base of Kazakhstan, and is characterized more by coal industry facilities. In addition to the impact during the mining period, coal storage is also a serious problem. The problem of storing energy coals on a large scale of their extraction and consumption has a huge impact on the environment, but it is of great economic importance for the region. In this paper, practical methods of combating weathering are considered using the example of coal: small fraction—0–50 mm; large fraction—50–300 mm; oversized—more than 300 mm. Calculations of the formation of emissions, the maximum surface concentrations of pollutants from coal depots were carried out, and plots of their dispersion were constructed. When plotting the dispersion of pollutants, it was revealed that the largest concentration of substances falls on the territory of the coal deposit. According to the data obtained, a directly proportional dependence of the amount of emissions on the volume of incoming coal and the area of the base of the coal stacks is obvious; the temperature fluctuation in the stacks during the research is in the range from 21.9 to 26.1 °C. Scientifically researched anti-emission cover (AEC) on coal stacks. AEC has advantages for a specific climate (frequent winds, dryness): preservation of properties up to 90% over their service life; resistant to environmental aggressiveness and mechanical influences. This method solves two tasks: the first task is to prevent spontaneous combustion of coal stacks, and the second task is to reduce dust emissions from coal stacks. Measures have been developed to minimize the negative impact of coal stacks on the environment. Full article

This study systematically investigated water-soluble inorganic ions (WSIIs) and their sources in PM2.5 in mountainous urban areas of Chongqing City. PM2.5 monitoring was conducted throughout 2023, spanning one year. The two districts under discussion are the Liang Jiang New Area (LJ) and He Chuan District (HC). The ion chromatography (Dionex Integrion HPIC) method was utilized to quantify eight ions (Cl⁻, SO₄²⁻, NO₃⁻, Na⁺, K⁺, Mg²⁺, Ca²⁺, NH₄⁺). The results obtained were then analyzed in conjunction with the EPA PMF 5.0 source apportionment model. The following key findings are presented: the data demonstrate that there is significant seasonal fluctuation in PM2.5 concentrations. The mean winter concentration (64 ± 27 μg/m³) was found to be 3.25 times higher than the mean summer concentration (19.7 ± 2 μg/m³). These fluctuations were primarily influenced by basin topography and unfavorable meteorological conditions. The proportion of PM2.5 mass attributable to WSII ranges from 31 to 33 percent, with the majority of this mass being attributed to secondary inorganic aerosols (SNA: SO₄²⁻, NO₃⁻, NH₄⁺; accounting for 47–85% WSII). The annual NO₃⁻/SO₄²⁻ ratio (0.69–0.80, <1) indicates that fixed sources (coal/industry) dominate, but a winter ratio >1 suggests increased contributions from mobile sources under low-temperature conditions. The sulfur oxidation rate (SOR: 0.35–0.37) is significantly higher than the nitrogen oxidation rate (NOR: 0.08–0.13), reflecting the efficient conversion of SO₂ through wet, low-temperature pathways. PMF identified six sources, with secondary formation (43.8–44.3%) being the primary contributor to the overall process. In urban LJ, transportation (26.1%) and industry (13.6%) have been found to contribute significantly, while in suburban HC, combustion (15.4%) and dust (8.8%) have been determined to have notable impacts. This study recommends the implementation of synergistic control of SNA precursors (SO₂, NOx, NH₃), the strengthening of transportation and industrial management in LJ, and the enhancement of biomass combustion and dust control in HC. Full article

Accurate boundary detection is critical for autonomous trackless rubber-wheeled vehicles in underground coal mines, as it prevents lateral collisions with tunnel walls. Unlike open-road environments, underground tunnels suffer from poor illumination, water mist, and dust, which degrade visual imaging. To address these challenges, this paper proposes a navigable area computation for underground autonomous vehicles via ground feature and boundary processing, consisting of three core steps. First, a real-time point cloud correction process via pre-correction and dynamic update aligns ground point clouds with the LiDAR coordinate system to ensure parallelism. Second, corrected point clouds are projected onto a 2D grid map using a grid-based method, effectively mitigating the impact of ground unevenness on boundary extraction; third, an adaptive boundary completion method is designed to resolve boundary discontinuities in junctions and shunting chambers. Additionally, the method emphasizes continuous extraction of boundaries over extended periods by integrating temporal context, ensuring the continuity of boundary detection during vehicle operation. Experiments on real underground vehicle data validate that the method achieves accurate detection and consistent tracking of dual-sided boundaries across straight tunnels, curves, intersections, and shunting chambers, meeting the requirements of underground autonomous driving. This work provides a rule-based, real-time solution feasible under limited computing power, offering critical safety redundancy when deep learning methods fail in harsh underground environments. Full article

In underground coal mines, narrow and irregular tunnels, dust, and gas hazards pose significant challenges to robotic path planning, particularly for shotcrete operations. The traditional A* algorithm has the limitations of limited safety, excessive node expansion, and insufficient dynamic obstacle avoidance capabilities. To address these, a hybrid algorithm integrating adaptive A*, Q-learning, and the Dynamic Window Approach (DWA) is proposed. The A* component is enhanced through improvements to its evaluation function and node selection strategy, incorporating dynamically adjustable neighborhood sampling to improve search efficiency. Q-learning re-plans unsafe trajectories in complex environments using a redesigned reward mechanism and an adaptive exploration strategy. The DWA module facilitates real-time obstacle avoidance in dynamic scenarios by optimizing both the velocity space and the trajectory evaluation process. The simulation results indicate that the proposed algorithm reduces the number of path nodes by approximately 30%, reduces the computational time by approximately 20% on 200 × 200 grids, and increases the path length by only 10%. These results demonstrate that the proposed approach effectively balances global path optimality with local adaptability, significantly improving the safety and real-time performance in complex underground environments. Full article

Environmental perception is crucial for achieving autonomous driving of auxiliary haulage vehicles in underground coal mines. The complex underground environment and working conditions, such as dust pollution, uneven lighting, and sensor data abnormalities, pose challenges to multimodal fusion perception. These challenges include: (1) the lack of a reasonable and effective method for evaluating the reliability of different modality data; (2) the absence of in-depth fusion methods for different modality data that can handle sensor failures; and (3) the lack of a multimodal dataset for underground coal mines to support model training. To address these issues, this paper proposes a coal mine underground BEV multiscale-enhanced fusion perception model based on dynamic weight adjustment. First, camera and LiDAR modality data are uniformly mapped into BEV space to achieve multimodal feature alignment. Then, a Mixture of Experts-Fuzzy Logic Inference Module (MoE-FLIM) is designed to infer weights for different modality data based on BEV feature dimensions. Next, a Pyramid Multiscale Feature Enhancement and Fusion Module (PMS-FFEM) is introduced to ensure the model’s perception performance in the event of sensor data abnormalities. Lastly, a multimodal dataset for underground coal mines is constructed to provide support for model training and testing in real-world scenarios. Experimental results show that the proposed method demonstrates good accuracy and stability in object-detection tasks in coal mine underground environments, maintaining high detection performance, especially in typical complex scenes such as low light and dust fog. Full article

To address the research gap on water-soluble heavy metals (WSHMs) in Taiyuan, China, we conducted a winter campaign (18–29 January 2019) at an urban site to measure fifteen WSHMs (Zn, Fe, Mn, Ba, Cu, Se, As, Sb, Sn, Pb, Ni, V, Ti, Cd, and Co). The mean concentration of total WSHMs (∑WSHMs) in PM_2.5 was 209.17 ± 187.21 ng m⁻³. Notably, the mass concentrations of ∑WSHMs on heavy pollution days (291.01 ± 170.64 ng m⁻³) were 224.8% higher than those on mild pollution days (89.61 ± 55.36 ng m⁻³). Principal component analysis (PCA) was applied in combination with absolute principal component score–multiple linear regression (APCS-MLR) to analyze pollution sources and their contributions. The results showed that the main sources of pollution were coal combustion and vehicle emissions (42.50%), along with the metallurgical industry and natural dust (34.47%). The carcinogenic and non-carcinogenic risks of WSHMs were assessed for both adults and children based on the United States Environmental Protection Agency’s (U.S. EPA) assessment guidelines and the International Agency for Research on Cancer (IARC) database. Children faced higher non-carcinogenic risks (hazard index = 2.37) than adults (hazard index = 0.30), exceeding the safety threshold (hazard index = 1). The total carcinogenic risk reached 2.20 × 10⁻⁵, exceeding the threshold value (1 × 10⁻⁶) for carcinogenic risk. Water-soluble arsenic (As) dominated both carcinogenic and non-carcinogenic risks in winter and was the riskiest element. These findings provide an essential basis for controlling PM_2.5-bound WSHMs in industrialized areas. Full article

Background: Occupational bronchopulmonary diseases (OBPDs)—including pneumoconiosis, silicosis, and occupational COPD—remain a pressing public health issue, especially in regions with intensive mining, metallurgy, and construction industries. Caused by chronic inhalation of fibrogenic dusts, these conditions are often diagnosed at late stages, resulting in irreversible lung damage and diminished work capacity. Methods: A scoping review was performed using the Arksey and O’Malley framework, with methodological refinements from the Joanna Briggs Institute. Following PRISMA-ScR guidelines, we searched PubMed, Scopus, and gray literature for publications from 2014 to 2024. After screening 1761 records and full-text review, nine studies were included in the final synthesis, comprising two systematic reviews, two narrative literature reviews, and five observational studies. Results: Key risk factors identified included prolonged exposure to silica and coal dust, tobacco use, and genetic susceptibility. Diagnostic delays were attributed to the underuse of high-resolution CT and exhaled nitric oxide analysis. Several studies highlighted the diagnostic value of oxidative stress and inflammatory markers (e.g., IL-6, TNF-α). Nutritional rehabilitation and polyphenol-enriched herbal therapies were associated with improved respiratory function and quality of life. However, these strategies remain underutilized, particularly in low-resource settings. Conclusions: A coordinated, biomarker-driven approach integrating early diagnosis, dust exposure control, and tailored rehabilitation is urgently needed. Multidisciplinary models may reduce the clinical and socioeconomic burden of OBPDs. Full article

Carbonaceous aerosols exert significant impacts on human health and climate systems. This study investigates the seasonal variations of carbonaceous components in fine particulate matter (PM_2.5) in Qinhuangdao, a coastal city in northern China, throughout 2023. The mass concentrations of organic carbon (OC) and elemental carbon (EC) averaged 9.44 ± 4.57 μg m⁻³ and 0.84 ± 0.33 μg m⁻³, contributing 26.49 ± 8.74% and 2.81 ± 1.56% to total PM_2.5, respectively. OC exhibited a distinct seasonal trend: winter (12.02 μg m⁻³) > spring (11.96 μg m⁻³) > autumn (8.15 μg m⁻³) > summer (5.71 μg m⁻³), whereas EC followed winter (1.31 μg m⁻³) > autumn (0.73 μg m⁻³) > spring (0.70 μg m⁻³) > summer (0.63 μg m⁻³). Both OC and EC levels were elevated at night compared to daytime. Secondary organic carbon (SOC), estimated via the EC-tractor method, constituted 37.94 ± 14.26% of total OC. A positive correlation between SOC/OC ratios and PM_2.5 concentrations suggests that SOC formation critically influences haze events. In autumn and winter, SOC formation was higher at night, likely driven by aqueous-phase reactions, whereas in summer SOC formation was more pronounced during the day, likely due to enhanced photochemical reactions. Source apportionment analysis revealed that gasoline and diesel vehicles were major contributors to carbonaceous aerosols, accounting for 27.35–29.06% and 14.97–31.83%, respectively. Coal combustion contributed less (10.51–21.55%), potentially due to strict regulations prohibiting raw coal use for domestic heating in surrounding regions. Additionally, fugitive dust was found to have a high contribution to carbonaceous aerosols during spring and summer. Full article

The geometric symmetry of the pipeline constitutes a critical determinant in regulating the energy propagation dynamics during the explosion process. In the present study, a transparent plexiglass pipe experimental system incorporating a range of angles (30° to 150°) was meticulously constructed. Leveraging high-frequency pressure sensors in conjunction with high-speed camera technology, this investigation examines the influence of the pipe angle, which disrupts geometric symmetry, on the coupling explosion of gas and coal dust. The experimental findings illustrate that an increase in the pipeline turning angle significantly enhances the velocity of the explosion flame front (with the maximum velocity escalating from 97.92 m/s to 361.28 m/s) and concurrently reduces the total propagation time (from 71 ms to 56.5 ms). Moreover, there is a notable reduction in the duration of the explosion flame, decreasing from 240.5 ms to 64.17 ms at the coal dust deposition point. The peak overpressure of the shock wave exhibits a significant increase with the augmentation of the turning angle (rising from 7.07 kPa at 30° to 88.40 kPa at 150°). Furthermore, the overpressure in the fore section of the turning is amplified, attributable to the superimposition of reflected waves and turbulent effects. This study elucidates critical mechanisms including turbulence-enhanced combustion, secondary dust generation from coal dust, and energy dissipation resulting from abrupt alterations in pipeline geometry, thereby offering a theoretical framework for the prevention and effective emergency management of coal mine explosion disasters. Full article

Objectives: This study assessed the effectiveness of a cognitive behavioral therapy (CBT)-based fine dust education program, grounded in the Health Belief Model (HBM), on elementary students’ fine dust knowledge, related behaviors, and mental health (depression, anxiety, stress, sleep quality). Methods: From September to November 2024, 95 students (grades 4–6) living near a coal-fired power plant in midwestern South Korea were assigned to either an intervention group (n = 44) or a control group (n = 51). The intervention group completed a three-session CBT-based education program; the control group received stress management education. Assessments were conducted at weeks 1, 2, 4, and 8 using standardized mental health and behavior scales (PHQ: Patient Health Questionnaire, GAD: Generalized Anxiety Disorder Assessment, PSS: Perceived Stress Scale, ISI: Insomnia Severity Index). Results: A chi-square test was conducted to compare pre- and post-test changes in knowledge and behavior related to PM2.5. The intervention group showed significant improvements in seven fine dust-related knowledge and behavior items (e.g., PM2.5 awareness rose from 33.3% to 75.0%; p < 0.05). The control group showed limited gains. Regarding mental health, based on a mixed-design ANCOVA, anxiety scores significantly declined over time in the intervention group, with group and interaction effects also significant (p < 0.05). Depression scores showed time effects, but group and interaction effects were not significant. No significant changes were observed for stress, sleep, or group × PM2.5 interactions. Conclusions: The CBT-based education program effectively enhanced fine dust knowledge, health behaviors, and reduced anxiety among students. It presents a promising, evidence-based strategy to promote environmental and mental health in school-aged children. Full article