Search Results (590)

Pulverized coal explosions pose significant hazards during pneumatic conveying and handling in coal preparation and mining. To investigate flame propagation and pressure evolution under conditions representative of vertical conveying pipelines, explosion experiments were conducted using pulverized coal with defined particle sizes and dust concentrations. Flame propagation and pressure dynamics were synchronously captured via high-speed imaging and dynamic pressure measurements. Results showed that flame height exhibited a Logistic growth pattern, whereas flame propagation velocity followed an inverted parabolic trend, reaching a maximum value of 14.5 m s⁻¹ at approximately 20 ms after ignition. Significant flame-front wrinkling, distortion, and oscillatory propagation were observed during explosion development, reflecting increasingly complex flame evolution within the confined vertical pipeline. Increasing dust concentration from 0.3 to 0.5 kg m⁻³ promoted flame acceleration and pressure development. For 45 μm particles, the maximum explosion pressure increased from 0.710 to 0.948 MPa. At a constant concentration, decreasing particle size enhanced both flame propagation and explosion severity. Under 0.5 kg m⁻³, the maximum pressure increased from 0.788 MPa for 200 μm particles to 0.948 MPa for 45 μm particles. The enhanced explosion intensity at higher concentrations and smaller particle sizes is attributed to accelerated heat and mass transfer together with more efficient combustion under confined conditions. These findings provide new insight into the coupled evolution of flame propagation and pressure development and contribute to explosion risk assessment in pulverized coal conveying systems. Full article

Airborne fine particulate matter (PM_2.5) has been extensively identified as a major contributor to adverse health outcomes, including respiratory illnesses, cardiovascular diseases, and premature mortality. PM_2.5 poses an even greater threat due to the presence of environmentally persistent free radicals (EPFRs). It is, thus, important to study human exposure to EPFRs. Dust samples from residential areas in Al Ain, UAE, were tested, and free radicals were detected and quantified. ESR measurements showed distinct signals with g-factors around 2.004–2.006, typically associated with oxygen-centered EPFRs. Spin concentrations were quantified at approximately 2.64 × 10¹⁶ spins/g in Dust Sample 1 (DS1) and 2.36 × 10¹⁷ spins/g in Dust Sample 2 (DS2), values comparable to those reported in other regions. DS1 and DS2 were collected from two different locations in inhabited areas of the city of Al Ain. Fourier Transform InfraRed (FTIR) spectra revealed possible functional groups such as catechols and hydroquinones, while X-ray diffraction (XRD) confirmed mineral oxides that could stabilize these radicals. However, the spectra also displayed features consistent with the six-line hyperfine splitting of Mn. These findings confirm the presence of environmentally relevant paramagnetic species in dust, which have implications for human health. Full article

Blasting is an essential operation in surface mining, but it can generate high particulate matter concentrations within a short period. Accurate prediction of blast-induced dust concentration is useful for air quality management, worker protection, and dust control planning. In this study, an interpretable machine learning framework was developed to predict particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀) and total suspended particulate matter (TSP) concentrations induced by blasting in a large surface coal mine. The dataset was derived from published field monitoring records and included blasting design parameters, monitoring distance, material-related variables, and measured dust concentrations. A total of 148 valid samples were used for model development and evaluation. Six tree-based ensemble models, including Extra Trees Regression, Random Forest, Gradient Boosting Regression Trees, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were established and compared. Bayesian optimization was used for hyperparameter tuning, and model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Under the adopted 80:20 hold-out validation scheme, CatBoost achieved the best test performance for PM₁₀ prediction, with a test R² of 0.9555, RMSE of 444.87 μg/m³, and MAE of 343.48 μg/m³. Extra Trees Regression performed best for TSP prediction, with a test R² of 0.8803, RMSE of 3899.04 μg/m³, and MAE of 3036.27 μg/m³. Residual analysis further indicated that the optimal models had no obvious systematic bias. Shapley additive explanations (SHAP) analysis, supported by within-model feature importance rankings, showed that explosive quantity, number of blastholes, and monitoring distance were the dominant variables affecting PM₁₀ and TSP predictions. The explosive quantity and number of blastholes mainly increased the predicted dust concentration, whereas the monitoring distance generally reduced it. The proposed framework may provide useful support for blast parameter optimization, monitoring point arrangement, and dust control decision-making in surface mines, but further validation using larger multi-site datasets is still needed. Full article

Construction dust is a major contributor to urban inhalable particulate matter (PM₁₀) pollution, posing severe respiratory and cardiovascular health risks to construction workers and nearby residents, severely undermining urban environmental sustainability. Construction fences are widely adopted as a primary dust mitigation measure, yet their underlying dispersion mechanisms and comprehensive impacts on vertical air quality remain poorly understood due to the limitations of traditional field monitoring and empirical models, creating critical barriers to site-level pollution control and long-term urban sustainability. In this study, a reliable computational fluid dynamics (CFD) method was developed to investigate the spatial distribution of construction dust and quantify the dust suppression performance of fences with heights ranging from 0 to 3 m. Three mainstream k-ε turbulence models (Standard, RNG, and Realizable) were evaluated using on-site measurement data, and the RNG k-ε model was found to provide the best agreement with field observations, with statistical metrics of q = 1, FB = 0.052, and NMSE = 0.028. The results show that construction fences effectively reduce dust dispersion into the surrounding environment, particularly in the pedestrian breathing zone (z < 1.5 m). Increasing the fence height from 1.5 m to 3 m improves the breathing-zone dust reduction rate from 39% to 55%, with the most significant mitigation effect observed within 50 m downwind of the fence. However, a critical dual effect was identified: while fences suppress near-ground pollution, they induce strong upward airflow and turbulence, leading to elevated dust concentrations in the upper part of the near-ground region (z = 1.5–9 m), a phenomenon absent in the no-fence scenario. These findings provide practical implications for urban construction site management, suggesting that fence height and configuration should be carefully designed not only to reduce pedestrian-level exposure but also to avoid unintended pollutant accumulation aloft, thereby improving overall air quality control strategies and delivering balanced, long-term environmental sustainability at construction sites. Full article

In response to ecological degradation caused by uncontrolled dust emissions from open-pit mines, this study selected the Hequ open-pit coal mine as the study area and established a monitoring system to collect data on TSP, PM10, PM2.5, and environmental indicators across three zones: the mining pit, the main haul road, and the coal yard. The necessity of zoning was validated using the least significant difference (LSD) method. Pollutant correlations were examined using the individual air quality index (IAQI), Pearson correlation matrix analysis, and grey relational analysis. Univariate models, multiple linear regression (MLR), and principal component analysis–multiple linear regression (PCA–MLR) were applied to quantitatively analyze dust evolution patterns and the influence of environmental factors, with model accuracy verified by the mean relative error (MRE) method. The results showed significant differences in dust concentrations among the three zones. Dust concentrations of all particle sizes in the mining pit and coal yard exceeded the secondary standard limit, whereas those on the haul road only exceeded the primary limit, with pollution intensity ranked as mining pit > coal yard > haul road and PM2.5 identified as the core pollutant in all zones. Linear relationships were significant in univariate models, and multivariate fitting outperformed univariate fitting, with MLR prediction accuracy ranked as coal yard (3.02%) > haul road (9.46%) > mining pit (10.75%). In the mining pit, TSP and PM10 exhibited a strong positive correlation with atmospheric pressure, while PM2.5 showed a strong negative correlation with relative humidity. On the haul road, all particle size fractions displayed strong negative correlations with temperature and wind speed. In the coal yard, only a strong negative correlation with temperature was observed. The PCA–MLR model improved prediction accuracy by 56.63% and 13.41% compared to the direct MLR model. Comprehensive analysis indicates that the atmospheric environment of the Hequ open-pit mine urgently requires proactive restoration measures to optimize the sustainability of the ecological environment. Full article

Enclosed transport environments are becoming an integral part of compact urban structures; however, their air quality is monitored less systematically than that of the outdoor urban environment. This study evaluates particulate matter concentrations (PM₁, PM_2.5, PM₁₀) in two urban microenvironments in Banská Bystrica (Slovakia): an underground parking garage (representing private urban mobility) and an underground bus station (representing public transport). Continuous measurements using an enviDUST monitoring device were analysed in relation to occupancy rates and transport intensity. The results showed a dominance of the PM₁₀ fraction in both environments, suggesting the importance of non-exhaust sources and dust resuspension in enclosed urban transport spaces. In the parking facility, the immediate relationship between occupancy and PM concentrations was weak; however, a time lag effect was observed, indicating particle accumulation. The bus station exhibited higher average concentrations (PM₁: 8.67; PM_2.5: 14.12; PM₁₀: 34.12 µg/m³), while peak levels during nighttime highlighted the critical role of air stagnation and ventilation regimes. The study emphasizes the need to perceive such transport nodes as semi-public urban spaces requiring the integration of intelligent air quality management within sustainable urban development. Full article

Ultra-Wideband (UWB) positioning is a promising technique for underground mine localization, but its accuracy is strongly affected by complex tunnel topology, multipath propagation, and non-line-of-sight (NLOS) ranging errors. To address these challenges, this paper proposes a three-dimensional UWB positioning algorithm based on a Multi-head Attention Feature Fusion Graph Neural Network (MAFF-GNN). The localization problem is formulated as a graph-based node position regression task, where anchors and tags are represented as nodes and UWB ranging links are represented as edges. The proposed model integrates graph message passing, multi-head attention-based feature fusion, global skip connections, and geometry-constrained regularization to learn reliability-aware spatial representations from noisy ranging measurements. A physics-guided simulated underground mine environment is constructed by considering tunnel geometry, wall roughness, coal dust concentration, humidity attenuation, and controlled NLOS conditions. Three mine-like corridor topologies are generated, with 10,500 localization samples in total. Experimental results under controlled simulation conditions show that MAFF-GNN achieves an RMSE of 0.323 ± 0.093 m, an MAE of 0.274 ± 0.024 m, and a P90 error of 0.480 ± 0.038 m. Compared with weighted least squares and support vector regression, the proposed method reduces RMSE by 74.16% and 40.30%, respectively. Robustness tests under different simulated NLOS ratios further indicate that the proposed graph-attention framework maintains a gradual error-growth trend as NLOS severity increases. These results indicate the potential of attention-enhanced graph learning for UWB localization in a challenging mine-like environment. Full article

With increasingly strict air quality standards and growing concerns about air pollution, fine and ultrafine particulate matter remains a major challenge for conventional air cleaning technologies. Due to their small size, these particles are difficult to remove using traditional filtration and separation methods. Acoustic agglomeration can be used as a pre-treatment technology to increase particle size in a high-intensity acoustic field and improve the efficiency of particle removal. This study investigates acoustic-induced agglomeration of solid aerosol particles in a dynamic airflow system. The effects of acoustic frequency were evaluated at 3, 5.5, 7.5, and 15 kHz under a sound pressure level of 135 dB and at two airflow velocities: 0.75 m/s and 1.5 m/s. These velocities corresponded to different particle residence times in the acoustic field. Arizona test dust was used as the test aerosol, and particle-number concentration and particle-size distribution were measured before and after the acoustic field. The results showed that acoustic agglomeration of fine and ultrafine particles was strongly affected by both acoustic frequency and particle residence time. The highest agglomeration efficiency, reaching up to 42%, was obtained at 3 kHz, 135 dB, and longer particle residence time. These findings indicate that acoustic agglomeration can promote particle-size redistribution in moving airflow and may be used as a pre-treatment method for improving particulate matter removal in air quality control systems. Full article

Cooking fumes are an important source of indoor and outdoor air pollution. Containing potentially carcinogenic particles and toxic chemical components, they pose significant health threats, making precise detection essential for risk prevention and the formulation of emission standards. This study used the manual gravimetric analysis as the benchmark reference method to systematically evaluate the performance of three alternative measurement techniques: (1) an optical particle counter (Promo 3000, Palas GmbH, Karlsruhe, Germany), (2) a photometer (DustTrak 8533, TSI, Shoreview, MN, USA), and (3) infrared spectrophotometry. A data correction model was constructed by simulating typical cooking conditions. Results indicate that the manual gravimetric analysis yielded the highest particulate concentrations. Infrared spectrophotometry measured only 75.6% of the benchmark value, mainly because of its selectivity toward oil-derived organic components and possible sampling losses. The optical particle counter, influenced by particle light-scattering properties and density differences, underestimated the mass concentration of particles larger than 0.3 μm, capturing only 46.9% of the benchmark value. Conversely, the photometer exhibited the smallest deviation, with readings closest to the gravimetric reference under the tested cooking fumes conditions. Theoretical derivation and data fitting determined correction factors of 1.32 for infrared spectrophotometry, 2.13 for the optical particle counter for particles larger than 0.3 μm, and 0.97 for the photometer. This correction system enables the standardization of data across different detection methods, allowing on-site monitoring data to be calibrated against the gravimetric reference method. Full article

Waste aluminum dust generated from aluminum refining and machining processes contains high fractions of Al₂O₃ and SiO₂, making it a potential secondary aluminosilicate resource for zeolite synthesis. In this study, waste aluminum dust was converted into functional zeolite materials through dry fusion purification, NaOH-assisted hydrothermal synthesis, acid purification, Si/Al ratio control, and cation-exchange modification. The raw dust was subjected to dry fusion at 1600 °C under an Ar atmosphere to remove metallic impurities and obtain an aluminosilicate precursor. Na-type zeolite was then synthesized using 50 wt.% NaOH solution at 90 °C for 24 h. The as-synthesized Na-type zeolite exhibited an estimated chemical purity of 97.501 wt.% based on measured residual impurities, with Mg, Ca, K, and Ti remaining as major impurities. HCl leaching at 0.25 M for 24 h increased the estimated chemical purity based on measured residual impurities to 98.469 wt.% while retaining the major zeolitic diffraction features. The Si/Al ratio was further controlled using water glass, and the maximum Si/Al ratio of 1.77 was obtained at a Na-type zeolite-to-water-glass mass ratio of 1:2 after reaction at 90 °C for 6 h. The purified and composition-controlled zeolite was subsequently modified with Mg²⁺ and K⁺ ions to prepare Mg-modified and K-modified zeolites. Under fixed batch conditions using a relatively high zeolite dosage and a single initial concentration, Mg-modified zeolite reduced Pb, Hg, Cr(VI), and Cd concentrations from 100 ppm to 0.004, 0.00059, 0.018, and 0.004 ppm, respectively, while K-modified zeolite reduced the total hardness of synthetic hard water from 308.3 to 40.13 ppm as CaCO₃. These results should be interpreted as preliminary batch-performance results under the tested conditions rather than as maximum adsorption capacities or a complete adsorption-mechanism evaluation. Overall, this study demonstrates the feasibility of valorizing waste aluminum dust into purified and cation-modified zeolite materials for potential water-treatment applications. Further adsorption isotherm, kinetic, dosage-dependent, BET surface area, pore-volume, pore-size distribution, and quantitative phase analyses are required to evaluate adsorption capacity, adsorption mechanism, true zeolite phase purity, and framework–performance relationships. Full article

Beach sediments may contain natural radionuclides, fallout-derived radionuclides, naturally occurring radioactive material (NORM) or technologically enhanced naturally occurring radioactive material (TENORM), but their radiological significance depends on sediment dynamics and exposure scenario as much as on bulk activity concentration. RadSed-INT is introduced as a tiered field–laboratory protocol for assessing radioactivity in dynamic beach sediments. The protocol links in situ gamma screening, statistically designed transects, primary/confirmatory A/B sampling, high-purity germanium (HPGe) gamma-ray spectrometry, grain-size and heavy-mineral partitioning, vertical mini-core radiostratigraphy, and triggered dust, radon/thoron and ingestion pathway modules. Its central requirement is radiometric mass closure between directly measured bulk activity and the mass-weighted reconstruction from sediment fractions. External and internal doses are evaluated only for explicitly defined material status, exposure pathways, occupancy assumptions and regulatory domains; construction-material and NORM indices are retained as context-specific comparators, not universal beach-sediment limits. RadSed-INT defines escalation triggers from reconnaissance to confirmatory spatial, grain-size, vertical or aerosol investigations. This article is methodological and does not report new primary field data. Extended notation, a date-sensitive regulatory matrix, confounder checklists, implementation tables and a minimal worked example are included to support transparent and reproducible application of the core protocol logic. Full article

Informal electronic-waste (e-waste) dismantling can mobilize mercury (Hg) from Hg-containing components and contaminated dust, while local diet can contribute methylmercury (MeHg), creating a mixed environmental exposure setting for human biomonitoring. This pilot study integrated paired biomarkers, local foods, work and indoor dust, Hg speciation and hair Hg isotope signatures among 18 e-waste dismantling workers in Qingyuan, South China. Total Hg (THg), MeHg and inorganic Hg (IHg) were measured in hair, blood and foods; urine and dust were analyzed for THg; and hair δ²⁰²Hg, Δ¹⁹⁹Hg and Δ²⁰¹Hg were determined. Median THg was 403 μg/kg in hair, 1.60 μg/L in blood and 0.438 μg/L in urine. Fish showed the highest food THg, whereas work and indoor dust had the highest matrix concentrations. Hair was MeHg-dominant but contained substantial IHg, and Δ¹⁹⁹Hg values were positive but modest. The integrated patterns support mixed dietary MeHg and non-dietary IHg contributions and identify exposure-pathway priorities for future biomonitoring and source-focused studies. Full article

The risk of exposure to mycotoxins in livestock farming is still poorly characterized, particularly in Italy where human biomonitoring data are scarce. Livestock farms represent a high-risk setting due to frequent handling of contaminated feed and dust-generating activities. This pilot study applied a human biomonitoring approach to assess internal exposure to multiple mycotoxins among pig farmers in Southern Italy. Urinary biomarkers of aflatoxin B1 (AFB1), aflatoxin M1 (AFM1), ochratoxin A (OTA), and fumonisin B1 (FB1), together with oxidative stress biomarkers (8-oxo-7,8-dihydroguanine (8-oxoGua), 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodGuo), 8-oxo-7,8-dihydroguanosine (8-oxoGuo), 3-nitrotyrosine (3-NO₂Tyr), and 5-methylcytidine (5-MeCyt)), were measured in urine samples from 35 workers and 30 non-exposed controls. A sensitive and validated HPLC–MS/MS multi-mycotoxin method was developed and applied. Biomonitoring results were also discussed in relation to previous environmental monitoring. AFM1 emerged as the most frequently detected biomarker in the exposed group, with concentrations above the limit of detection (LOD) in 22.8% of samples; 11.4% exceeded the limit of quantification (LOQ). In contrast, only 10% of the control samples had values above the LOD and none exceeded the LOQ, suggesting a possible contribution linked to occupational tasks. This study provides original biomonitoring evidence of low-dose, mixed mycotoxin exposure among Italian swine farmers and highlights the value of integrating environmental and biological monitoring to improve occupational exposure assessment in livestock production systems. Full article

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM_2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM_2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks. Full article

This study focused on the spatial and temporal changes in photosynthetic pigment concentrations in the leaves of Ginkgo biloba and their integration into a new bioindicator index, the Pigment Integrity-to-Dust Ratio (PIDR), to assess urban air pollution stress on trees in Budapest, Hungary. High levels of chlorophyll and carotenoids in early summer indicated greater pigment integrity at the moderate-traffic site, whereas there were clear indications of reductions in the high-traffic area. The control site represented a low-traffic, pollution-free baseline. Chlorophyll concentrations dropped in the traffic-exposed leaves, and there were increased levels in the formation of pheophytin. It is thought that these reductions were caused by city stress. Responses of pigments were also variable at the moderate site, perhaps due to some form of recovery or adjustment in the study’s time frame. The observed negative relationships between selected pollutants and PIDR suggested that pollutant exposure was associated with pigment degradation and foliar dust deposition, although these associations should be interpreted as exploratory. The Air Pollution Tolerance Index (APTI) was significantly different between the pollution-exposed sites and the control, reflecting physiological tolerance in chronically exposed trees rather than directly measuring pigment damage. Therefore, the APTI and PIDR provide complementary information. Overall, the PIDR appears to be a promising exploratory bioindicator of physiological stress response, based on pigment concentration changes and dust deposition. Full article