Search Results (698)

Forest canopies effectively remove airborne particles, reducing the frequency of atmospheric haze and improving air quality as well as playing a crucial role in maintaining human health. In this study, we examined the retention of particulate matter by Picea crassifolia Kom. (P. crassifolia) needles using an aerosol regenerator in two typical catchments, while the concentrations of dissolved trace elements (Na, Zn, Pb, and Cd) were determined only in the Tianlaochi catchment. The results showed that the retention of airborne particles was lower in the Tianlaochi catchment (e.g., total suspended particles (TSP): 0.0049 μg cm⁻² in summer) than in the Sancha catchment (e.g., TSP: 0.0145 μg cm⁻²) in summer and autumn, while the opposite trend was found in winter and spring, with Tianlaochi catchment reaching higher TSP levels (0.0230 μg cm⁻² in spring) compared to Sancha catchment (0.0205 μg cm⁻²). The big tree exhibited the highest particulate retention, with a maximum flux of 84.870 μg cm⁻², indicating it was the most effective at particle trapping. The highest Na, Zn, Cd, and Pb values absorbed by the needle samples were 1.794 mg L⁻¹, 11.345 μg L⁻¹, 0.081 μg L⁻¹, and 4.316 μg L⁻¹, respectively. P. crassifolia needles absorbed more Na, Zn, and Cd in July and August than in June. The absorption capacity of the needles decreased in the order Na > Zn > Pb > Cd. P. crassifolia forest can effectively reduce airborne particulate matter. Our study provides a theoretical foundation to examine the role of forest ecosystems in the retention of atmospheric particulate matter in the Qilian Mountains region. Full article

This study evaluated the influence of various surface treatments on the bonding performance and mechanical behavior of zirconia, with particular emphasis on the effect of laser surface texturing (LST) compared with conventional 10-methacryloyloxydecyl dihydrogen phosphate (10-MDP) and airborne particle abrasion (APA) methods. Two zirconia compositions, 3 mol% yttria-stabilized tetragonal zirconia polycrystal (3Y-TZP) and 5 mol% partially stabilized zirconia (5Y-PSZ), were subjected to four surface treatment protocols: as-milled, 10-MDP, APA, and LST (n = 12). Shear bond strength (SBS) to titanium and biaxial flexural strength (BFS) of zirconia were measured. Surface morphology, failure mode, and phase composition were analyzed using scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and X-ray diffraction (XRD). Data were analyzed with two-way ANOVA and Tukey’s post hoc test (α = 0.05), and the reliability of flexural strength was assessed using Weibull analysis. Surface treatment significantly affected SBS (p < 0.05). The LST groups exhibited the highest SBS values and a higher proportion of mixed failures, whereas other groups predominantly showed adhesive failures. However, LST-treated specimens, particularly 5Y-PSZ, showed reduced BFS. XRD confirmed phase stability, although localized microstructural changes were observed after LST. LST enhanced the zirconia–titanium interfacial bond strength and promoted mixed failure modes; however, this improvement was accompanied by a reduction in flexural strength, particularly in 5Y-PSZ. Full article

Accurately assessing collapse risks of high-elevation, concealed rock mass blocks within the steep cliffs of Bijiashan, Three Gorges Reservoir Area, is challenging. This study employed a multidimensional approach—integrating airborne Light Detection and Ranging (LiDAR), the transient electromagnetic method (TEM), close-range photogrammetry, horizontal drilling, and borehole optical imaging—to characterize the rock mass structure of the WD1 cliff belt and delineate 52 individual blocks. Stability analysis incorporated stereographic projection for macro-scale assessment and employed mechanical models specific to three primary failure modes (toppling, sliding, falling). Finite element strength reduction quantified the stress–strain response of a representative block under natural and rainstorm conditions. Particle Flow Code (PFC) simulated dynamic instability of the exceptionally large block W1-37. Results indicate the WD1 rock mass is highly fractured, with base sections prone to weakness. Toppling failure dominates (90.4%). Under rainstorm conditions, the average Factor of Safety (FOS) decreased by 14.7%, and 73.1% of the blocks that were stable under natural conditions were destabilized—specifically transitioning to marginally stable or substable states—often triggering chain-reaction instability characterized by “crack propagation—base buckling”. W1-37 exhibited staged failure under rainstorm: “strain localization at fissure tips—penetration of basal cracks—overturning of the upper rock mass”. Its frontal rock reached a peak sliding velocity of 15.17 m/s, indicative of base-breaking toppling. The integrated “multi-technology survey—multi-method evaluation—multi-scale simulation” framework provides a quantitative basis for risk assessment of rock mass disasters in the Three Gorges Reservoir Area and offers a technical paradigm for similar high-steep canyon regions. Full article

Airborne micro- and nanoplastic particles (MNPs) are increasingly recognized as a potential occupational exposure hazard, yet substance-specific workplace data remain limited. This study quantified airborne MNP concentrations during polyester microfiber production using a correlative SEM–Raman approach that enabled chemical identification and size-resolved particle characterization. The aerosol mixture at the workplace was dominated by sub-micrometer particles, with PET—handled onsite—representing the main process-related MNP type, and black tire rubber (BTR) forming a substantial background contribution. Across both sampling periods, total MNP particle number concentrations ranged between 6.2 × 10⁵ and 1.2 × 10⁶ particles/m³, indicating consistently high particle counts. In contrast, estimated MNP-related mass concentrations were much lower, with PM₁₀ levels of 12–15 µg/m³ and PM_2.5 levels of 1.3–1.6 µg/m³, remaining well below applicable occupational exposure limits and near or below 8 h-equivalent WHO guideline values. Comparison with earlier workplace and indoor studies suggests that previously reported concentrations were likely underestimated due to sampling strategies with low efficiency for small particles. Moreover, real-time optical measurements substantially underestimated particle number and mass in this study, reflecting their limited suitability for aerosols dominated by small or dark particles. Overall, the data show that workplace MNP exposure at the investigated site is driven primarily by very small particles present in high numbers but low mass. The findings underscore the need for substance-specific, size-resolved analytical approaches to adequately assess airborne MNP exposure and to support future development of MNP-relevant occupational health guidelines. Full article

Optical image stabilization (OIS) is crucial for improving airborne opto-electronic imaging performance under dynamic conditions. This study presents a two-dimensional piezoelectric-driven OIS platform capable of compensating linear image shift errors. A motion platform integrating a bridge amplification mechanism and right-angle guiding beams was developed, and its theoretical model was validated through finite element analysis (FEA). To enhance the platform’s repeatability, the hysteresis of the piezoelectric actuator was described using the Bouc-Wen model, and was optimized using a Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO). Experimental results demonstrated that the platform achieves a workspace of 53.92 μm × 53.76 μm, a motion resolution of 30 nm, a maximum coupling error of 2.28%, and a first-order resonant frequency of 356.69 Hz. A composite controller incorporating HGAPSO attained submicron tracking accuracy, with errors of 0.43 μm and 0.47 μm along the X and Y axes, respectively. Strong agreement among theoretical analysis, FEA, and experimental results confirms the platform’s precision and effectiveness meeting the requirements of the OIS. This work provides valuable guidance for the development of high-frequency OIS systems in highly dynamic operational environments. Full article

Background/Objectives: The clinical application of CAD/CAM restorative materials continues to evolve due to increasing demand for aesthetic, durable, and minimally invasive indirect restorations. Hybrid nanoceramics, such as Grandio disc (VOCO GmbH, Cuxhaven, Germany), are increasingly used in indirect restorative dentistry due to their favourable combination of mechanical strength, polishability, wear resistance, and bonding potential. One challenge associated with adhesive protocols for CAD/CAM materials lies in achieving durable bonds with resin cements. Extensive post-polymerization during fabrication reduces the number of unreacted monomers available for chemical interaction, thereby limiting the effectiveness of traditional adhesive strategies and necessitating specific surface conditioning approaches. This study aimed to evaluate, in a preliminary, non-inferential manner, the influence of several combined conditioning protocols on surface micromorphology, elemental composition, and descriptive SBS trends of a CAD/CAM hybrid nanoceramic. This work was designed as a preliminary pilot feasibility study. Due to the limited number of specimens (two discs per protocol, each providing two independent enamel bonding measurements), all bond strength outcomes were interpreted descriptively, without inferential statistical testing. This in vitro study investigated the effects of various surface conditioning protocols on the adhesive performance of CAD/CAM hybrid nanoceramics (Grandio disc, VOCO GmbH, Cuxhaven, Germany) to dental enamel. Hydrofluoric acid (HF) etching was performed to improve adhesion to indirect resin-based materials using two commercially available gels: 9.5% Porcelain Etchant (Bisco, Inc., Schaumburg, IL, USA) and 4.5% IPS Ceramic Etching Gel (Ivoclar Vivadent, Schaan, Liechtenstein), in combination with airborne-particle abrasion (APA), silanization, and universal adhesive application. HF may selectively dissolve the inorganic phase, while APA increases surface texture and micromechanical retention. However, existing literature reports inconsistent results regarding the optimal conditioning method for hybrid composites and nanoceramics, and the relationship between micromorphology, elemental surface changes, and adhesion remains insufficiently clarified. Methods: A total of ten composite specimens were subjected to five conditioning protocols combining airborne-particle abrasion with varying hydrofluoric acid (HF) concentrations and etching times. Bonding was performed using a dual-cure resin cement (BiFix QM) and evaluated by shear bond strength (SBS) testing. Surface morphology was examined through environmental scanning electron microscopy (ESEM), and elemental composition was analyzed via energy-dispersive X-ray spectroscopy (EDS). Results: indicated that dual treatment with HF and sandblasting showed descriptively higher SBS, with values ranging from 5.01 to 6.14 MPa, compared to 1.85 MPa in the sandblasting-only group. ESEM revealed that higher HF concentrations (10%) created more porous and irregular surfaces, while EDS indicated an increased fluorine presence trend and silicon reduction, indicating deeper chemical activation. However, extending HF exposure beyond 20 s did not further improve bonding, suggesting the importance of protocol optimization. Conclusions: The preliminary observations suggest a synergistic effect of mechanical and chemical conditioning on hybrid ceramic adhesion, but values should be interpreted qualitatively due to the pilot nature of the study. Manufacturer-recommended air abrasion alone may provide limited adhesion under high-stress conditions, although this requires confirmation in studies with larger sample sizes and ageing simulations. Future studies should address long-term durability and extend the comparison to other hybrid CAD/CAM materials and to other etching protocols. Full article

This study assessed the spatial distribution and composition of airborne particulate matter within a 10 km long urban green corridor in Zacatecas, Mexico, using magnetic biomonitoring with Tillandsia recurvata and SEM-EDS particle characterization. A total of 44 samples were collected from distinct urban park contexts (e.g., commercial zones, malls, bus stops), revealing mass-specific magnetic susceptibility χ values ranging from −6.71 to 61.1 × 10⁻⁸ m³ kg⁻¹. Three compositional groups were identified based on a PCA performed using elemental concentrations from SEM-EDS and magnetic data, which are associated with traffic emissions and industrial inputs. SEM-EDS images confirmed abundant magnetite-like particles (1–8 μm) with hazardous metals including Pb (up to 5.6 wt.%), Ba (up to 67.6 wt.%), and Cr (up to 31.5 wt.%). Wind direction data indicated predominant SSW–NNE transport, correlating with hotspots in central and northeastern park areas. Overall, vegetated zones exhibited markedly lower magnetic loads (mean χ = 8.84 × 10⁻⁸ m³ kg⁻¹) than traffic-exposed sites (mean χ = 17.27 × 10⁻⁸ m³ kg⁻¹), representing an approximate 50% reduction in magnetic particle accumulation, which highlights the effective role of continuous vegetation cover as a functional green barrier that attenuates the lateral transport and deposition of airborne particulate matter within the park. This research highlights the applicability of combined magnetic and microscopic techniques for evaluating the dynamics of airborne pollution in urban parks and supports their use for identifying both pollution hotspots and mitigation zones, reinforcing the role of urban green spaces as biofunctional filters in cities facing vehicular air pollution. Full article

Airborne dust emissions from bulk cargo handling in port terminals can degrade local air quality, but traditional dispersion models are often too slow or coarse to support rapid operational decisions. There is thus a pressing need for efficient tools that retain the spatial detail of CFD while enabling near-real-time scenario evaluation. In this work, we develop and test a hybrid framework that couples an RANS-based CFD model of dust dispersion with a neural network surrogate to rapidly predict exposure patterns for a bulk terminal under variable wind and operational conditions. The ML surrogate model, based on a decoder-style Multilayer Perceptron (MLP) architecture, processes two-dimensional slices of dispersion fields across particle diameter classes, enabling predictions in milliseconds with an acceleration factor of approximately $8\times {10}^6$ over traditional CFD while preserving high fidelity, as validated by performance metrics such as the F₁ score and precision values exceeding $0.8$ and $0.76$, respectively. This approach not only addresses computational inefficiencies but also lays the groundwork for real-time air-quality monitoring and sustainable urban planning, potentially integrating with digital twins fed by live weather data. Full article

Brake particulate matter (PM) represents a significant portion of the non-exhaust related soot emissions from all forms of transport, posing significant environmental and health concerns. Euro 7 standards only regulate road automotive emissions, while no regulation covers train transportation. This study compares two brake PM samples from rail and automotive applications. Rail brake PM was generated from composite brake pads subjected to real-world urban rapid transit braking conditions, while automotive brake PM was generated using ECE brake pads and discs under World Harmonized Light-Duty Test Cycle (WLTC) conditions. Transmission electron microscopy (TEM) and energy-dispersive X-ray (EDX) analyses were performed to assess PM morphology and composition. Both samples showed PM in coarse (10–2.5 µm), fine (2.5–0.1 µm), and ultrafine (<0.1 µm) size ranges, with angular flakes in automotive PM and rounded particles in rail PM. The rail PM exhibited a uniform size distribution, with a mean Feret diameter of 1 µm. In contrast, the automotive PM shifted toward larger particles, with ultrafine PM representing only 4% of the population. Excluding carbon and oxygen, automotive PM was dominated by iron (6 at.%) and magnesium (1 at.%). Rail PM showed lower iron (0.6 at.%) and higher aluminium (0.7 at.%) and calcium (0.8 at.%), with a broader non-C/O composition. This study tackles source-specific PM features, thereby supporting safer and more efficient non-exhaust emissions regulations. Full article

The oxidative potential (OP) of airborne particulate matter (PM) has emerged as a promising metric to assess the capacity of particles to induce oxidative stress and related health effects. Thus, ensuring the reliability and comparability of OP measurements is essential for accurate environmental and toxicological assessment. This study aimed to develop and evaluate a quality control approach for the dithiothreitol (DTT) assay used in OP determination. The DTT assay provides an estimation on how harmful PM can be to human health through oxidative stress, relating it to the consumption of DTT during the test period. Two experiments were conducted using the Standard Reference Material (SRM) 1648–Urban Particulate Matter (NIST, USA). The first assessed the effect of trichloroacetic acid (TCA) addition order and the feasibility of using SRM 1648 as a reference material. The second evaluated the stability of the SRM solution over a 63-day period. Statistical analysis (Mann–Whitney test) indicated that the order of TCA addition did not significantly affect OP values (p > 0.05). SRM 1648 solution determination showed high reproducibility (mean DTTₘ = 14.6 ± 2.4 pmol·min⁻¹·µg⁻¹), although a gradual increase in DTT metrics was observed over time, consistent with progressive dissolution. The results support the application of SRM 1648 as a reference material for DTT assay quality control, supporting methodological harmonization in OP determination, provided that a freshly prepared solution is used. Full article

Airborne magnetic particles (AMPs) are associated with potentially toxic elements, and their size, mineralogy, and concentration can significantly impact both the environment and human health. However, their spatial analysis is often limited by small datasets, non-normality, and pronounced local variability. In this work, two sites with distinct demographic and geographic characteristics, the city of Mar del Plata (Argentina) and the Aburrá Valley region (Colombia), were analyzed using the fuzzy Magnetic Pollution Index (IMC) as an indicator of the concentration of AMPs. Moreover, an original methodological framework that explicitly incorporates measurement uncertainty through fuzzy numbers, combined with an approach modeling fuzzy semivariances via α-cuts, performs spatial prediction via ordinary kriging. This study produces maps that simultaneously reflect the magnitude of IMC and its associated uncertainty. Unlike classical geostatistics, the fuzzy-based model captures the inherent imprecision of magnetic measurements and reveals spatial patterns where uncertainty becomes informative about the type and origin of pollution. In particular, this approach demonstrates that areas with higher IMC levels are associated with high anthropic activity (near industrial zones, main avenues, slow traffic). In contrast, lower values were found in residential areas. Overall, the fuzzy-driven approach provides an additional layer of information not accessible through traditional methods, improving spatial interpretation and supporting the identification of priority areas for environmental monitoring. Full article

Bioaerosols are a major source of airborne microbial contamination in intensive poultry production systems. Their concentration and community structure can profoundly influence animal health, public health, and the overall safety of the farming environment. However, the dynamic characteristics of bacterial aerosols in enclosed poultry houses during winter remain insufficiently studied. Using Taihang chickens as a model, this study investigated three key production stages—brooding (15 days), growing (60 days), and laying (150 days)—under winter cage-rearing conditions. A six-stage Andersen sampler was employed alongside culture-dependent enumeration and 16S rRNA high-throughput sequencing to analyze variations in bacterial aerosol concentration, particle size distribution, and community succession patterns. The results revealed a significant increase in the concentration of culturable airborne bacteria with bird age, rising from 8.98 × 10³ colony-forming unit (CFU)/m³ to 2.89 × 10⁴ CFU/m³ (p < 0.001). The particle size distribution progressively shifted from larger, settleable particles (≥4.7 μm) toward smaller, respirable particles (<4.7 μm). Microbial sequencing indicated a continuous increase in bacterial alpha diversity across the three stages (Chao1 and Shannon indices, p < 0.05), while beta diversity exhibited stage-specific clustering, reflecting clear differences in community assembly. The composition of dominant bacterial genera transitioned from potentially pathogenic taxa such as Acinetobacter and Corynebacterium during the brooding stage to a greater abundance of beneficial genera, including Bacteroides, Lactobacillus, and Ruminococcus, in later stages. This shift suggests a potential ecological link between aerosolized bacterial communities and host development, possibly related to the aerosolization of gut microbiota. Notably, several zoonotic bacterial species were detected in the poultry house air, indicating potential public health and occupational exposure risks under winter confinement conditions. This study is the first to elucidate the ecological succession patterns of airborne bacterial aerosols in Taihang chicken houses across different growth stages during winter. The findings provide a scientific basis for optimizing winter ventilation strategies, implementing stage-specific environmental controls, and reducing pathogen transmission and occupational hazards. Full article

Erosion of the leading edge is one of the most severe forms of damage in wind turbine blades, particularly in offshore wind farms. This degradation, mainly caused by rain, sand, and airborne particles through droplet impingement wear, significantly decreases blade aerodynamic efficiency and power output. Since blades, typically made of fiber-reinforced polymer composites, are the most expensive components of a turbine, developing protective coatings is essential. In this study, polyurethane (PU) composite coatings reinforced with titanium dioxide (TiO₂) particles were added on glass fiber substrates by spray coating. The incorporation of TiO₂ improved the mechanical and electrochemical performance of the PU coatings. FTIR and XRD confirmed that low TiO₂ loadings (1 and 3 wt%) were well dispersed within the PU matrix due to hydrogen bonding between TiO₂ –OH groups and PU –NH groups. The PU/TiO₂ 3% coating exhibited ~61% lower corrosion current density (I_corr) compared to neat PU, indicating superior corrosion resistance. Furthermore, uniform TiO₂ dispersion resulted in statistically significant improvements (p < 0.05) in hardness, yield strength, elastic modulus, and adhesion strength. Overall, the PU/TiO₂ coatings, particularly at 3 wt% loading, show strong potential as protective materials for wind turbine blades, given their enhanced mechanical integrity and corrosion resistance. Full article

Unsupervised learning methods significantly benefit various practical applications by effectively identifying intrinsic patterns within unlabelled data. However, inherent data noise and uncertainties often compromise model reliability, result interpretability, and the overall effectiveness of unsupervised learning strategies, particularly in complex fields such as biomedical, engineering, and physics research. To address these critical challenges, this study proposes SUN (Stochastic UNsupervised learning), a novel approach that integrates probabilistic unsupervised techniques—specifically Gaussian Mixture Models—into the RUN-ICON unsupervised learning algorithm to achieve optimal clustering, systematically reduce data noise, and quantify inherent uncertainties. The SUN methodology strategically leverages probabilistic modelling for robust classification and detection tasks, explicitly targeting particle dispersion scenarios related to environmental pollution and airborne viral transmission, with implications for minimising public health risks. By combining advanced uncertainty quantification methods and innovative unsupervised denoising techniques, the proposed study aims to provide more reliable and interpretable insights than conventional methods while alleviating issues such as computational complexity and reproducibility that limit traditional mathematical modelling. This research contributes to enhanced trustworthiness and interpretability of unsupervised learning systems, offering a robust methodological framework for handling significant uncertainty in complex real-world data environments. Full article

The COVID-19 pandemic emerging in early 2020 triggered global responses. In China, stringent lockdown measures were implemented to suppress the rapid spread of infection, resulting in substantial reductions in anthropogenic emissions. However, several atmospheric haze episodes still occurred. Previous studies have investigated the cause of these haze events predominantly based on the average concentration obtained from bulk analysis, while the micro-scale structure and composition of the haze particles remain poorly understood. In this study, we analyzed the morphology and elemental composition of individual airborne particles collected from an urban area of Beijing in early 2020 using high-resolution transmission electron microscopy equipped with Energy Dispersive X-ray Spectroscopy. The results show that sulfur-dominant, ultrafine, and mixed particles were the most abundant types during the pollution process. Reduced human activities corresponded with a lower percentage of anthropogenic-derived soot, organic particles, and metal-containing particles. Atmospheric aging analysis demonstrated that secondary aerosols were the most significant component during the haze events. The proportion of core–shell particles increased with the intensification of the pollution, while the core/shell ratio of the particles decreased, suggesting a substantial contribution of secondary aerosols to the haze formation. Despite reductions in anthropogenic emissions, larger proportions of secondary aerosol formation enhanced aerosol aging and thereby caused episodic haze pollution during the lockdown period. Full article