Search Results (5,478)

Collisions between over-height vehicles and low-clearance bridges cause infrastructure damage and pose safety risks. Existing detection systems rely primarily on optical sensors, which suffer from performance degradation in adverse weather conditions. This paper presents an alternative approach based on a 94 GHz millimeter-wave radar that achieves velocity-independent height measurement. The proposed technique exploits the ratio of Doppler shifts from two scattering centers on a vehicle, specifically the roof and the wheel–road interface. This ratio depends only on the measurement geometry, as the unknown vehicle velocity cancels algebraically, enabling direct height computation without speed measurement. The paper provides a closed-form height estimation model, analyzes the trade-off between frequency resolution and geometric constancy during integration, and presents experimental validation using a scaled laboratory testbed. An optical tracking system is used solely for ground-truth validation in the laboratory and is not required for operational deployment. Results across six test cases with heights ranging from 20 cm to 46 cm demonstrate an average absolute error of 0.60 cm and relative errors below 3.3 percent. A scaling analysis for representative full-scale geometries indicates that at highway speeds of 80 km/h, integration times in the millisecond range (approximately 3–18 ms for representative 20–50 m measurement standoff) are feasible; warning distance can be extended independently by upstream radar placement. The expected advantage in fog, rain, and dust is based on established W-band propagation characteristics; dedicated adverse-weather and full field validation (including multipath, clutter, and multi-vehicle scenarios) remain future work. Full article

Textile wastewater pollution is a global issue that requires attention due to its potential negative environmental impacts. Therefore, in the present investigation, Inga feuilleei seed (IFS) powder was used as a potential adsorbent of Direct Red 80 (DR80) dye in synthetic water. The results showed that the highest removal efficiency was achieved at pH = 2 and an IFS powder particle size of 150 μm. ART-FTIR analysis of IFS dust showed the presence of -OH, C=C, and C-H groups. The kinetic study revealed a better fit of the experimental adsorption data to the pseudo-second-order kinetics, suggesting that the mechanism governing the adsorption process is chemisorption. The maximum adsorption capacity of DR80 on IFS powder was 133.11, 146.78, 152.78, and 183.51 mg/g at 20, 30, 40, and 50 °C, respectively. According to the thermodynamic study, the process is endothermic and spontaneous. Finally, the use of IFS powder is a cost-effective alternative since the project approach for the development of the production process of this adsorbent is feasible and profitable. In conclusion, IFS powder is a cost-effective adsorbent for removing DR80 in water, and the process could be scaled up in removal studies on textile wastewater. Full article

This study examines the carbonation behavior and CO₂ storage potential of a Ca-rich alkali-activated binder produced entirely from industrial residues-ladle furnace slag (LFS), coal ash (CA), and cement kiln dust (CKD). The system was designed as a one-part alkali-activated material (AAM), with CKD acting as an internal activator, and subjected to ambient curing, water curing, and accelerated CO₂ curing at ambient pressure. Phase evolution, microstructural development, and pore-structure characteristics were investigated using X-ray diffraction, FTIR spectroscopy, DSC–TG analysis, scanning electron microscopy, and X-ray micro-computed tomography, together with measurements of density, water absorption, and compressive strength. Loss-on-ignition measurements combined with chemical analysis were further used to quantify CO₂ uptake and evaluate the degree of carbonation of the binder system. CO₂ curing fundamentally altered the reaction pathway of the binder, shifting it from hydration-dominated to carbonation-controlled phase evolution, leading to the decomposition of calcium-bearing hydrates and complete carbonation of non-hydraulic γ-belite with the formation of vaterite, aragonite, and calcite. These transformations induced pronounced microstructural densification, reflected in a near-doubling of compressive strength (>48 MPa), increased apparent density, reduced water absorption, and simplified pore-network topology. A preliminary carbon footprint assessment indicates that the production of 1 m³ of the developed LFS–CA–CKD concrete generates about 14.36 kg CO₂-eq, while the carbonation process enables significant CO₂ sequestration, resulting in a net negative carbon balance. The results demonstrate that controlled carbonation is an effective post-treatment strategy for waste-derived alkali-activated binders, enabling simultaneous performance enhancement and permanent CO₂ sequestration. Full article

This study presents the engineering design and system-level modeling of a high-frequency power supply architecture for electrostatic precipitators intended to improve particulate removal efficiency and operational stability. Atmospheric air pollution by fine particulate matter (PM2.5) remains one of the most critical challenges in environmental protection and public health. Although electrostatic precipitators (ESPs) are widely used for industrial gas cleaning, the efficiency and stability of conventional 50 Hz power supplies are limited under conditions of strongly nonlinear corona discharge and high-resistivity dust. This paper presents the development and investigation of an advanced high-frequency power supply system for electrostatic precipitators based on a coupled electrical–electrophysical mathematical model. The work follows an engineering design methodology that integrates converter topology selection, electrophysical modeling of corona discharge, and control-oriented system optimization. The proposed model provides a unified description of electric field formation, space charge accumulation, ion transport, and particle motion in the corona discharge region. The simulation results show that in the operating voltage range of 10–100 kV, the electric field strength reaches (2–5)·10⁶ V/m, the ion concentration stabilizes in the range of 10¹³–10¹⁵ m⁻³, and the particle drift velocity increases from approximately 0.05 to 0.3 m/s, leading to an increase in collection efficiency from about 55% to 93%. It is demonstrated that the proposed system ensures stable output voltage regulation within ±2.5–5% even under strongly nonlinear load conditions. The use of an LC output filter (C = 1–10 nF, L = 10–100 mH) reduces the voltage ripple from about 14% to 1.4–4.8% and significantly improves the transient response. In addition, adaptive adjustment of the pulse repetition frequency in the range of 10–200 kHz makes it possible to reduce energy consumption by 12–18% while simultaneously increasing the collection efficiency by 8–15%. The obtained results confirm that the proposed high-frequency power supply architecture provides a physically well-founded and energy-efficient solution for improving the environmental performance and operational stability of electrostatic precipitators. Full article

Industrialization has caused extensive environmental change, including a global surge in plastic production and pollution. This has resulted in the accumulation of microplastics (MPs; <5 mm) and nanoplastics (NPs; <1 μm) in ecosystems worldwide. MPs originate from both primary sources, such as cosmetics and industrial applications, and secondary sources, through the degradation of larger plastic debris. As a result, MPs and NPs have become ubiquitous contaminants, posing significant toxicological risks to living organisms. These persistent pollutants are diverse polymers that vary in size, shape, and chemical composition, making their impacts on organism physiology complex and difficult to disentangle. Plastic pollution is particularly severe in aquatic environments, where particles accumulate from terrestrial sources such as urban dust, agricultural runoff, industrial discharges, and wastewater effluents. Although most research has centered on marine ecosystems, emerging evidence indicates that freshwater environments may contain comparable or even higher concentrations of MPs. Once inside the body, MPs can translocate into tissues and exert toxic effects on multiple organ systems. Collectively, plastic pollution poses not only physiological but also neurological and behavioral risks to aquatic life, with potential consequences for ecosystem stability and trophic interactions. Both MPs and NPs are sufficiently small to cross the blood–brain barrier, raising concerns about their potential impacts on the nervous system by interfering with neuronal function and brain development. Plastic particles can accumulate in neural tissues, inducing oxidative stress, neuroinflammation, and disruption of neurotransmitter signaling. Such neurotoxic effects are linked to altered locomotion, feeding, predator avoidance, and social behaviors across multiple species. This review examines current evidence on the neurotoxic effects of plastic pollution in aquatic organisms and underscores the urgent need for further research and action to mitigate its impact. In light of escalating plastic production and inadequate waste management, the growing evidence that MPs and NPs disrupt aquatic nervous systems, behavior, and ecosystem stability underscores an urgent need for intensified research, improved mitigation strategies, particularly for nanoplastics, and the accelerated development of truly safe and sustainable alternatives. Full article

Existing intelligent monitoring methods are limited by insufficient training samples and target-feature degradation in complex environments. To address these issues, an industrial visual inspection scheme with dual verification is proposed for material sheds. The scheme integrates sample enhancement preprocessing based on a Dynamic Enhanced Generative Adversarial Network (DEGAN) with an Attention-Enhanced YOLO-SLOWFAST (AE-YOLO-SLOWFAST) model for target and behavior detection, enabling feature enhancement, real-time dust monitoring, and timely dust suppression. A dynamic enhancement module is first introduced into a GAN, creating DEGAN to generate high-quality samples and augment the training dataset. An AE-YOLO model is then developed to improve static feature extraction under low illumination and enhance small-target detection. The objective function is refined to improve recognition of hard-to-distinguish samples during training. AE-YOLO is combined with SLOWFAST to recognize vehicle behaviors. Dual verification is performed using dust and vehicle detection results together with action recognition outputs, enabling precise control of dust suppression equipment for targeted water mist spraying. The improved AE-YOLO model achieves an mAP@50 of 94.4%. The proposed method delivers a vehicle–dust association matching accuracy of up to 97.2%, which enables all-weather, intelligent, traceable dust suppression in material sheds, reduces false recognition interference, and ensures timely suppression in areas where vehicles are operating. Full article

Open-air asphalt milling generates hazardous respirable crystalline silica (RCS), posing severe risks to operators of legacy machines lacking enclosed cabs. This study evaluates a novel, standalone retrofit water spray system designed to intercept fugitive dust. Field validation across 11 road maintenance sites involved particle characterization and paired system-off/on exposure monitoring. Results indicated a Mass Median Aerodynamic Diameter (MMAD) of 6.12 µm, confirming the efficacy of fine-atomizing nozzles (0.3 mm) for capturing respirable fractions. The system achieved RCS suppression efficiencies ranging from 60% to over 85% under low-to-moderate wind conditions (<2.5 m/s). A comparative analysis revealed no significant performance gain from larger 0.5 mm nozzles, supporting the use of smaller orifices for optimal water conservation. However, suppression efficacy degraded significantly when crosswinds exceeded 2.5 m/s, indicating a potential operational boundary. This retrofit solution provides a scientifically validated, cost-effective engineering control for reducing occupational silica exposure in aging road maintenance fleets. Full article

Dust pollution has emerged as a critical issue in a wide range of industrial applications, creating an urgent demand for effective strategies to mitigate particle deposition. Recent experimental studies have demonstrated that superhydrophobic coatings represent a promising class of self-cleaning materials, primarily attributed to their hierarchical rough structures and intrinsically low surface energy. Nevertheless, the underlying self-cleaning mechanisms of superhydrophobic surfaces have not yet been fully elucidated. This work examines particle deposition on superhydrophobic surfaces featuring stochastic roughness distributions through computational modeling. Surface topographies were generated using Fast Fourier Transform techniques. An integrated lattice Boltzmann–discrete element method (LBM–DEM) framework simulated particle transport in superhydrophobic-coated channels. Particle–fluid coupling was achieved via the immersed moving boundary approach, while particle–surface interactions employed a modified Johnson–Kendall–Roberts (JKR) adhesion model. Parametric studies quantified effects of particle size, interfacial energy, flow Reynolds number, and topographical statistics on deposition dynamics. Experimental validation demonstrates good agreement between numerical predictions and measurements. Smaller particles exhibit a lower tendency to deposit on superhydrophobic surfaces, whereas increasing surface energy significantly enhances particle deposition due to stronger adhesion forces and the suppression of particle resuspension. In addition, higher Reynolds numbers effectively reduce particle deposition. The revealed self-cleaning mechanisms provide theoretical guidance for the design of high-performance self-cleaning coatings, and the identified effects of particle and surface parameters offer practical insights for anti-pollution engineering applications. Full article

Phosphate mining companies in Morocco pose many environmental and occupational safety risks, especially through the release of airborne particulates, gas pollutants, and heavy metals. While there is increased implementation of monitoring systems within industrial mining contexts, current methodologies are still predominantly founded on rule-based systems or classical statistics that presume linearity in relationships between an arbitrary set of environmental parameters and the likelihood of an incident. Conversely, mining operations are characterized by intricately dynamic nonlinear combinations of numerous environmental and operational variables. As a result, a potential research opportunity exists for the application of sophisticated machine learning techniques that provide the ability to detect various levels of operational risk within phosphate mining scenarios. This study has three objectives. First, to examine the mining environmental and operational data from the phosphate mining sites to determine the mining operational conditions that present the highest risk. Second, to create a machine learning classification model which utilizes a Feedforward Neural Network (FNN) to identify operational states that are prone to incidents based on multivariate sensor data. Third, to assess the validity and reliability of the model using machine learning validity and reliability evaluation techniques along with statistical validation methods. In this study, an artificial intelligence-based approach for AI-based safety monitoring was proposed by using a Feedforward Neural Network (FNN) on a detailed data set of 1536 hourly measurements, directly recorded onsite at OCP plants in Benguerir and Khouribga. Environmental and industrial parameters (dust concentration, gas emissions, temperature, and toxic metal content) were measured using industrial-grade sensors certified for such a type of application. By means of training the proposed FNN model with adaptive gradient descent and dropout regularization with early stopping, a test mean squared error of 0.057 and over 85% accuracy on incident detection were obtained. Gradient tracking and m-adaptive validation proved the stability and convergence of the model. Emissions and dust were identified as the main risk classifiers in a variable importance analysis. The findings demonstrate that the mining sector may move from reactive to proactive safety management and validate the incorporation of AI into a real-time monitoring infrastructure inside the OCP ecosystem. Practical concerns of industrial data gathering, model interpretability, and the moral application of AI in high-risk settings are also addressed by the study. Full article

Roadside green spaces function as critical ecological barriers in urban environments, and their plant communities play a key role in improving regional air quality. This study investigates typical roadside plant communities in southern Xinjiang, a region characterized by extreme aridity and frequent dust storms. By quantifying indicators such as dust retention capacity at both individual and community levels, together with leaf surface microstructural characteristics, we evaluate the comprehensive dust retention performance of different community configuration patterns. The results show that: (1) Among the studied species, Juniperus chinensis ‘Kaizuca’ exhibited the highest dust retention capacity per unit leaf area, followed by Juniperus chinensis L. and Rosa rugosa Thunb. Among trees, Platanus acerifolia (Aiton) Willd showed the greatest dust retention capacity per individual plant; among shrubs, Rosa rugosa Thunb. performed strongly, and among herbaceous species, Lolium perenne L. exhibited relatively high dust retention capacity. (2) Leaf dust retention is governed by the synergistic effects of multiple traits, including leaf aspect ratio, stomatal aspect ratio, stomatal protrusion, stomatal density, wax layer characteristics, and surface roughness. Leaf aspect ratio exerts a significant positive direct effect on dust retention, whereas stomatal aspect ratio shows a significant negative direct effect. (3) At the community level, the multi-layered tree–shrub–herbaceous configuration dominated by Platanus acerifolia (Aiton) Willd exhibited the strongest dust retention capacity, making it the most effective configuration for roadside green spaces. Overall, this study provides a robust theoretical framework and empirical evidence for the scientific selection and optimized configuration of roadside vegetation in arid regions, thereby supporting the sustainable improvement of urban roadside air quality in southern Xinjiang. Full article

Because dust-emission processes driven by local, small-scale winds (e.g., terrain-induced winds) are difficult to accurately capture with mesoscale or larger-scale predictive models, this study employed a CFD-Lagrangian particle-tracking approach to numerically simulate near-surface dust release and transport under different atmospheric stability conditions in the same local flow field. The novelty of this work was the integration of MOST-based stable/neutral/unstable inflow construction with Lagrangian particle tracking, enabling a consistent comparison of stability effects within one framework. This framework is useful for assessing local blowing-sand impacts on short-range receptors. A near-surface source term was specified for PM10-class mineral dust, and particles were emitted using a vertically exponential allocation. Simulations were conducted over a kilometer-scale flow domain containing an idealized cosine hill, and the low-level concentration patterns and dispersion-height variations in the resulting dust cloud were analyzed. Compared with neutral conditions, stable stratification produced higher near-surface concentrations and a lower dispersion height, whereas unstable stratification yielded lower near-surface concentrations and a higher dispersion height; as the $\left|L\right|$ increased, the unstable cases gradually approached the neutral state. The influence of reference wind speed exhibited clear stability dependence: under stable conditions, stronger winds intensified the buoyancy-related suppression of dust dispersion, while under unstable conditions, stronger winds inhibited the vertical spreading of the dust cloud. In addition, reduced air density representative of plateau environments resulted in lower dust-cloud concentrations and higher dispersion heights. These findings highlight the coupled effects of stratification and wind speed on near-field dust dispersion and provide a reference for assessing local dust emissions over complex terrain. Full article

Dust and sand storms occurring in northern China are strongly controlled by near-surface aerodynamics, yet the spatial heterogeneity of these processes remains poorly understood. We obtained field measurements of the wind above gobis, sandy surfaces, and dry lakebeds in the Hexi Corridor Desert and Heihe River Basin, and sandy surfaces in northern China. First, the slope of wind profile (a₁) reveals distinct drag reversal with increasing wind speed: under low winds, a₁ increases from sandy to dry lakebed to gobi surfaces, whereas under high winds, actively saltating sandy surfaces exhibit the highest a₁, surpassing gobi and dry lakebed. Second, the dynamic feedback between sediment transport and aerodynamics is clear: at below-threshold winds, friction velocity ($u_{*}$) and aerodynamic roughness length (z₀) are lowest for sand; however, as wind speed increases to initiate significant saltation, the sandy surface develops the highest $u_{*}$ and z₀, highlighting the dominant role of grain-borne roughness. Third, the focal height (z_f) shows regional disparity, varying by up to two orders of magnitude for both sandy and gobi surfaces, with a strong correlation to local gravel coverage. This work provides spatially explicit parameterizations of surface type, offering a physical basis for modeling dust emission and transport in northern China and similar arid regions globally. Such parameterizations are essential for developing reliable early warning systems and evidence-based land management strategies. These advances contribute directly to ecosystem sustainability and community resilience in vulnerable arid and semi-arid regions under climate change. Full article

Welding fumes in the shipbuilding industry severely threaten workers’ health. This study systematically investigated welding fume exposure in a Chinese shipyard, analyzing mass concentration, particle size distribution, and harmful metal content using data from 2015. Differences were observed across welding sites and processes. Confined spaces and gas metal arc welding (GMAW) were associated with significantly higher exposure levels. Welding fumes were dominated by particles smaller than 1.00 μm, a distribution influenced by welding site, distance from the welding spot, and process. Iron (Fe) and manganese (Mn) were the predominant metal components, with concentrations significantly higher in respirable dust than in total dust. Risk assessment indicated minimal non-cancer hazards for Fe, zinc, and copper. However, Mn posed the predominant risk (Hazard Quotient >> 1), while nickel (Ni) and chromium (Cr) also exceeded safety thresholds at most points. Consequently, confined spaces and GMAW should be prioritized as key control targets in shipyards, as respirable dust rich in metal-bearing particles poses greater health risks. Therefore, China urgently requires the establishment of specific occupational exposure limits for respirable welding fumes. Additionally, personal sampling is more focused and efficient than area sampling for precise occupational health risk assessment due to the greater mobility of welding operations. Full article

This study addresses the problem of efficient utilization of aspiration dust (AD) generated during crushing of high-carbon ferrochrome (HCFeCr). To solve this issue, a briquetting technology was proposed, involving aspiration dust blended with dry gas-cleaning dust (20 wt.% as filler) and an organic polymer binder (3 wt.%). The produced briquettes demonstrated high mechanical strength (average 195 kg per briquette in splitting strength and 98% drop resistance), ensuring maximum integrity during transportation and handling. Pilot-industrial remelting of 35 tons of briquettes in a 1.8 MVA direct current electric arc furnace (DC EAF) confirmed the effectiveness of the proposed technology for HCFeCr production. Chromium recovery into the alloy reached 94%, which is 3–4% higher compared to remelting of loose dust. The specific electric energy consumption was 1600 kWh/t, representing a 29% reduction compared to loose dust processing. The produced metal met commercial grades FeCr800–FeCr900 specifications. Additional advantages included elimination of dust formation, reduction in fines generation during crushing of the final metal to 15%, and improved environmental performance. The developed technology represents an economically and environmentally viable solution for comprehensive recycling of ferroalloy dust waste. Full article

Dust generated during coal mining and transportation poses serious threats to miners’ health, operational safety, and the surrounding environment. However, comprehensive review studies on dust suppression in coal mines remain limited, particularly those integrating dust movement laws with numerical simulation approaches. This review presents a systematic and reproducible analysis of dust control methods in coal mines with a particular focus on numerical simulation. Current research progress and development trends are summarized from three aspects: structural optimization of dust suppression devices, optimization of operating conditions, and ventilation system design. Existing studies indicate that structural improvements mainly concentrate on nozzle geometry, diameter, installation position, and spraying distance, while operating condition optimization primarily involves pressure regulation. Due to the complexity and high cost of full-scale experimental platforms, ventilation system optimization is largely achieved through numerical simulation, supplemented by field measurements. Studies based purely on numerical simulations remain limited in addressing the chemical modification of dust removers; however, with the advancement of molecular dynamics techniques, this area may represent a promising direction for future research. Full article