Search Results (251)

A total of three variants of pellets from field bean and potato waste mixtures were investigated and analyzed. Their physical, mechanical and chemical properties were examined, and the burning emissions of pellets were measured. An experimental study was performed to determine the dependence of density and compressive strength. An empirical linear regression model was developed, allowing calculation of the density and compressive strength dependence of pellets with 84–91% accuracy. The lower calorific value of the dry waste pellets was similar across all tested samples. The chemical properties of pellet ash were determined, and its suitability for use as a fertilizer was assessed. The pellet density ranged from 1269.24 kg m⁻³ (potato pellets) to 1369.89 kg m⁻³ (potato and bean MIX DC pellets). The moisture content of the pellets varied from 6.14% to 7.37%. The moisture content of the pellets increased with the proportion of potato waste meal in the mixture. The ash contains elements magnesium, iron, calcium, potassium, silicon, sodium and phosphorus. Potassium in potato pellet ash was found to have the highest concentration (60.42%), which has a direct effect on boiler slag. The share of potato waste in biofuels helps to remove soot from the chimney in the form of solid particles. It can therefore be stated that bean and potato waste can be used in the production of solid biofuels and that pellets produced with a 20% (1:4 ratio) potato waste fraction obtain the best properties. Full article

The object of the study is a single heated carbonaceous particle of relatively small size, 0.003 to 0.01 m. Main hypothesis: The formation of soot particles and black carbon particles is caused by the thermochemical destruction of dry organic matter of forest fuel and the mechanical fragmentation of coke residue. The aim of the study is to conduct numerical simulations of heat and mass transfer in a single heated carbonaceous particle, taking into account the soot formation process and assessing its fragmentation with regard to heat exchange with the external environment in a 2D setting. As part of this study, a new model of heat and mass transfer in a pyrolyzed carbonaceous particle was developed, taking into account its step-by-step fragmentation (fragmentation tree model with four secondary particle formations from the initial particle). The calculations resulted in the distributions of temperature and volume fractions of phases in the carbonaceous particle across various scenarios. Scenarios of surface fires (initial temperatures of 900 K and 1000 K), crown fires (1100 K), and a firestorm (1200 K) for typical vegetation (pine, spruce, birch) are considered. Cubic carbonaceous particles are considered in the approximation of a 2D mathematical model. To describe heat and mass transfer in the structure of the carbonaceous particle, a differential equation of thermal conductivity with corresponding initial and boundary conditions of the third type is used, taking into account the gross reaction in the kinetic scheme of pyrolysis and soot formation. Differential analogues of partial differential equations are solved using the finite difference method of second-order approximation. Options for using the developed mathematical model and probabilistic fragmentation criterion for assessing aerosol emissions are proposed. Recommendations: The suggested mathematical model must be incorporated with mathematical models of forest fire plume and aerosol transport in the upper layers of the atmosphere. Moreover, probabilistic criteria for health assessment must be developed for the practical use of the suggested mathematical model. Full article

Against the backdrop of a low-carbon economy, the control of soot emissions from combustion processes is of paramount importance. In this study, the effects of CO₂ dilution on soot formation in ethylene laminar diffusion flames are investigated through a combination of experimental measurements and numerical simulations. In addition, a virtual species, denoted as F_xCO₂, is introduced to progressively decouple the individual mechanisms by which different effects suppress soot formation. The results indicate that increasing the CO₂/N₂ dilution ratio leads to reductions in both the peak flame temperature and the soot volume fraction, with CO₂ exhibiting a more pronounced inhibitory effect than N₂. The decoupling analysis reveals that the dilution effect and the chemical effect are the dominant contributors to flame temperature reduction. The soot-inhibiting effectiveness of the individual effects follows the order: dilution effect > thermal effect > chemical effect > density effect > transport effect. With respect to their influence on C₂H₂ concentration, the effects are ranked as: dilution effect > chemical effect > transport effect > thermal effect > density effect. The chemical effect suppresses the formation of OH radicals, thereby reducing the flame temperature and H radical concentration. In contrast, the dilution effect enhances soot oxidation by increasing the OH radical concentration, effectively inhibiting soot particle formation. Full article

Firefighters are exposed to complex combustion products and to contaminants carried on personal protective equipment (PPE). Occupational exposure as a firefighter is classified as carcinogenic. This review summarizes the current evidence on exposure environments, routes of uptake, contamination and secondary exposure from PPE, and the effectiveness and limits of decontamination approaches. Across incident types, smoke composition varies with the fuels and combustion conditions, but fine and ultrafine particles and semi-volatile organic chemicals are common. Biomonitoring confirms uptake after incidents. Self-contained breathing apparatus reduces inhalation exposure during active suppression, yet exposures persist through dermal absorption at ensemble interfaces and post-incident tasks. Protective ensembles can retain soot-bound polycyclic aromatic hydrocarbons, additive chemicals, and metals; volatiles and particles resuspension in vehicles and stations can extend exposure. Studies show that on-scene preliminary exposure reduction and laundering can lower contaminant burdens on PPE; however, removal remains incomplete and decreases when cleaning is delayed or when gear is aged. Emerging evidence raises additional concern for per- and polyfluoroalkyl substances from foams and coating materials, with limited data on exposure metrics and removability. The field lacks standardized, realistic contamination platforms and a dose-based definition of clean PPE. Integrated intervention studies linking exposure, secondary exposure pathways, biomarkers, and decontamination methods are needed to set performance-based targets and evaluate emerging hazards. Full article

The global effort to mitigate hazardous particulate matter (PM) emissions from diesel engines relies significantly on advances in separations technologies. The diesel particulate filter (DPF) is a critical component designed to trap soot and ash from diesel engine exhaust, ensuring cleaner emissions and compliance with environmental regulations. In the current paper, a gas-particle two-phase flow model in the microchannels of a DPF is developed. A novel statistical approach based on probability sampling is proposed aimed at generating a particle ensemble that adheres to the real-world soot particle size distribution (PSD). The Eulerian-Lagrangian approach is employed to model the soot-laden gas flow, where the gas phase flow field is solved in the Eulerian framework, while the particle phase motion is tracked in the Lagrangian framework. The results demonstrate that the through-wall velocity plays a predominant role in the overall deposition behavior of the mixed-sized particle group. Increasing upstream velocity shifts initial particle deposition positions further from the channel inlet and enhances mass accumulation at the channel’s terminal section. Reduced filtration wall permeability promotes the uniformity of soot deposition along the channel. A permeability of 5 × 10⁻¹³ m² is identified as the critical threshold, below which the soot deposition distribution approaches near-complete uniformity. Full article

Black crusts are multilayered alteration products that develop on historic masonry exposed to urban pollution. This study investigates the west enclosure wall of the XVII^th-century Golia Monastery in Iași, Romania—located along a busy traffic corridor—and presents multi-analytical results on two lime-based mortar fragments exhibiting well-developed blackened surface layers. Both the exposed (blackened) finishes and protected verso areas were analyzed using portable X-ray fluorescence (pXRF), scanning electron microscopy with energy-dispersive X-ray analysis (SEM–EDX), micro-FTIR spectroscopy, X-ray diffraction (XRD), CIE Lab colorimetry and optical microscopy (OM). The data reveal gypsum-rich surface layers enriched in traffic-derived particles, including metal oxides and soot, with marked contrasts relative to the minimally altered verso. Handheld XRF and SEM–EDX indicate elevated sulfur and associated traffic-related elements within porous gypsum matrices, while FTIR and XRD consistently identify calcium sulfate as the dominant secondary phase. Colorimetric measurements additionally document pronounced lightness loss and visible darkening on exposed surfaces. These results demonstrate the onset of directional sulfation and black crust formation on mortars under urban pollution pressure and establish an integrated analytical protocol for diagnosing black crusts on historic lime mortars in urban heritage settings. Full article

Soot particle deposition is a common form of surface contamination in enclosed architectural environments and can significantly alter the optical appearance of painted surfaces. In the Zhaomiao temple halls of Inner Mongolia, long-term exposure to soot generated by butter lamps and incense burning has led to pronounced color darkening of mural pigments. To clarify the mechanisms by which soot deposition affects pigment optical behavior, this study investigates the surface deposition-induced color degradation of mineral pigment coatings, using Zhaomiao temple murals as a representative application context. Thirty-six typical mineral pigments were prepared as standardized coating specimens, and controlled soot deposition experiments were conducted to simulate progressive particulate accumulation on pigment surfaces. Variations in Commission Internationale de l’Éclairage (CIE) XYZ tristimulus values, luminance, and color difference (ΔE) were quantitatively analyzed under different soot-loading conditions. The results show systematic luminance attenuation and chromatic compression with increasing soot deposition, indicating that optical degradation is primarily governed by surface absorption and scattering effects introduced by carbonaceous particles. These results establish a quantitative framework based on measurable optical parameters—rather than a single absolute value—for evaluating particulate-induced optical degradation of pigment coatings. This study provides a quantitative basis for evaluating particulate-induced optical degradation of pigment coatings and supports surface condition assessment and digital reconstruction of soot-contaminated painted surfaces in architectural contexts such as the Zhaomiao temples. 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 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

This study investigates the relationship between exhaust gas composition, particle number (PN) emissions, and soot microstructure of a 1.9 L compression-ignition engine operated under six controlled steady-state load regimes at 2000 min⁻¹. Unlike standardized transient procedures (e.g., WLTP), the steady-state approach enables isolation and quantification of fundamental thermochemical processes governing soot formation and NO_x production, providing engine-out data highly relevant for understanding Euro 7 emission behavior at the source. The novel contributions of this study include (i) a combined macroscopic–microscopic analysis linking PN emissions with SEM/EDS-based soot morphology; (ii) distribution-based estimation of soot mass concentration using experimentally derived primary particle sizes; and (iii) an experimental demonstration of the NO_x–soot trade-off across increasing load, supported by microstructural evidence of soot oxidation and agglomeration. The results show a clear decrease in PN concentrations with increasing load (from 1.31 × 10⁷ to 6.4 × 10⁶ cm⁻³), accompanied by a marked rise in NO_x emissions and exhaust temperature. SEM analysis confirms a transition from fine, weakly agglomerated soot structures at low load to more compact, oxidized aggregates at high load. Distribution-based particle sizing (20–80 nm, average ~45 nm) yields soot mass estimates that are consistent with theoretical trends and more accurate than fixed-radius approaches. These findings provide experimentally supported insights into engine-out particulate behavior that complements regulatory PN metrics in Euro 7, offering a mechanistic basis for improved emission control strategies and for interpreting PN-focused regulatory thresholds under real-world operating conditions. Full article

An integrated approach for enhancing microparticle separation efficiency in acoustofluidic lab-on-a-chip systems is presented, combining numerical modeling in COMSOL 6.2 Multiphysics^® with reinforcement learning techniques implemented in Python 3.10.14. The proposed method addresses the limitations of traditional parameter tuning, which is time-consuming and computationally intensive. A simulation framework based on LiveLink™ for COMSOL–Python integration enables the automatic generation, execution, and evaluation of particle separation scenarios. Reinforcement learning algorithms, trained on both successful and failed experiments, are employed to optimize control parameters such as flow velocity and acoustic frequency. Experimental data from over 100 numerical simulations were used to train a neural network, which demonstrated the ability to accurately predict and improve sorting efficiency. The results confirm that incorporating failed outcomes into the reward structure significantly improves learning convergence and model accuracy. This work contributes to the development of intelligent microfluidic systems capable of autonomous adaptation and optimization for biomedical and analytical applications, such as label-free separation of microplastics from biological fluids, selective sorting of soot and ash particles for environmental monitoring, and high-precision manipulation of cells or extracellular vesicles for diagnostic assays. Full article

Traditional pulsed laser detection systems predominantly utilize threshold-based decision methods that rely on the peak voltage of echo signals for target recognition. However, in environments with soot (smoke and dust) interference, strong backscattering effects lead to severe signal distortion, resulting in false alarms or missed detections, thereby significantly degrading recognition accuracy. To overcome this limitation, through comprehensive simulations and experimental trials, we propose a novel pulse-width comparison method that utilizes the characteristic broadening of echoes induced by soot particles. The method exploits the differences in pulse width between target and soot-induced echoes and achieves effective interference suppression. The target-detection accuracy increases from 71.25% to 93.75% in environments with different concentrations (0–350 mg/${\mathrm{m}}^3$), corresponding to a 31.58% relative improvement. This approach leverages pulse-width differences between soot-induced and target echoes to enhance anti-interference capability in dusty environments. Full article

To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direct injection (GDI) engines using advanced machine learning methods. Three machine learning approaches, namely, support vector regression (SVR), deep neural network (DNN), and a Stacking integration model of SVR and DNN, were employed, respectively, to predict the soot mass concentration at the GPF inlet. The input data includes engine speed, torque, ignition timing, throttle valve opening angle, fuel injection pressure, and pulse width. Exhaust gas soot mass concentration at the three-way catalyst (TWC) outlet is obtained by an engine bench test. The results show that the correlation coefficients (R²) of SVR, DNN, and Stacking integration model of SVR and DNN are 0.937, 0.984, and 0.992, respectively, and the prediction ranges of soot mass concentration are 0–0.038 mg/s, 0–0.030 mg/s, and 0–0.07 mg/s, respectively. The distribution, median, and data density of prediction results obtained by the three machine learning approaches fit well with the test results. However, the prediction result of the SVR model is poor when the soot mass concentration exceeds 0.038 mg/s. The median of the prediction result obtained by the DNN model is closer to the test result, specifically for data points in the 25–75% range. However, there are a few negative prediction results in the test dataset due to overfitting. Integrating SVR and DNN models through stacked models extends the predictive range of a single SVR or DNN model while mitigating the overfitting of DNN models. The results of the study can serve as a reference for the development of accurate prediction algorithms to estimate soot loads in GPFs, which in turn can provide some basis for the control of the particulate mass and particle number (PN) emitted from GDI engines. Full article

Compression–ignition engines emit particulate matter (PM) (soot), prompting the widespread use of diesel particulate filters (DPFs) in the automotive sector. An alternative method for PM reduction involves the use of ultrasonic waves to disperse and modify the structure of exhaust particles. This article presents experimental results of the effects of ultrasonic emitter parameters, including the number, arrangement, and power, along with the engine speed, on the exhaust smoke density. Tests were conducted on a laboratory prototype equipped with six ultrasonic emitters spaced 0.17 m apart. The exhaust source was a diesel engine from a construction excavator, based on the MTZ-80 tractor design, delivering 80 HP and a displacement of 4750 cm³. A regression model was developed to describe the relationship between the engine speed, emitter power and spacing, and smoke density. The optimal configuration was found to involve an emitter power of 319.35 W and a spacing of 1.361 m for a given engine speed. Under the most effective conditions—an engine speed of 1500 rpm, six active emitters, and a total power of 600 W—smoke emissions were reduced by 18%. These findings support the feasibility of using ultrasonic methods as complementary or alternative exhaust gas filtration techniques for non-road diesel engines. Full article

Understanding the chemical composition and mixing states of individual particles in indoor/outdoor environments is important for assessing daily human exposure. In this study, the chemical composition and mixing states of micron-sized individual particles in university classrooms, dwellings, and corresponding outdoor atmospheres collected between November 2024 and January 2025 were analyzed using micro-Raman spectroscopy. Inorganics and carbonaceous matter were identified in the individual particles; inorganics included CaCO₃, CaMg(CO₃)₂, Ca(NO₃)₂, CaSO₄, CaSO₄•2H₂O, Mg(NO₃)₂, Na₂SO₄, SiO₂, NH₄NO₃, and (NH₄)₂SO₄, and carbonaceous matter included soot and organics. This study found significant differences in the chemical composition of indoor and outdoor particles. For example, the percentage of particles containing CaSO₄ was higher in university classrooms than in corresponding outdoor atmospheres, which may be related to the use of chalk. Particles containing organics in the dwelling accounted for more than 80% of the total, which was significantly higher than those found in the corresponding outdoor atmospheres. This may be due to indoor cooking and cleaning activities. Internally mixed CaSO₄/NH₄NO₃ particles and internally mixed CaSO₄•2H₂O/NH₄NO₃/(NH₄)₂SO₄ particles were identified in the indoor atmospheres, indicating the complexity of indoor particle formation. In addition, soot and organics were primarily internally mixed with inorganics in individual particles in both indoor and outdoor atmospheres. This study offers new insights for understanding the formation mechanisms and sources of individual atmospheric particles. Full article