Search Results (240)

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

Due to the complex structure of high-rise space buildings, traditional point fire detectors are not effective in terms of detection range and installation difficulty. Although linear beam smoke detectors are widely adopted, they still face problems such as low accuracy and false alarms caused by interference. To address these limitations, we constructed a 120 m experimental platform for analyzing smoke–light interactions. Through systematic investigation of spectral scattering phenomena, optimal operational wavelengths were identified for beam-type detection. By improving the gated recurrent unit (GRU) neural network, an algorithm combining dual-wavelength information fusion and an attention mechanism was designed. The algorithm integrates dual-wavelength information and introduces the cross-attention mechanism into the GRU network to achieve collaborative modeling of microscale scattering characteristics and macroscale concentration changes of smoke particles. The alarm strategy based on time series accumulation effectively reduces false alarms caused by instantaneous interference. The experiment shows that our method is significantly better than traditional algorithms in terms of accuracy (96.8%), false positive rate (2.1%), and response time (6.7 s). Full article

This study aims to investigate the impact of combustion chamber geometry and biodiesel on the performance of diesel engines under various load conditions. Simulations were conducted using AVL FIRE software, followed by experimental validation to compare the performance of the prototype Omega combustion chamber with the optimized TCD combustion chamber (T for turbocharger, C for charger air cooling, and D for diesel particle filter). This study utilized four types of fuels: D100, B10, B20, and B50, and was conducted under different load conditions at a rated speed of 1800 revolutions per minute (rpm). The results demonstrate that the TCD combustion chamber outperforms the Omega chamber in terms of indicated thermal efficiency (ITE), in-cylinder pressure, and temperature, and also exhibits a lower indicated specific fuel consumption (ISFC). Additionally, the TCD chamber shows lower soot and carbon monoxide (CO) emissions compared to the Omega chamber, with further reductions as the load increases and the biodiesel blend ratio is raised. The high oxygen content in biodiesel helps to reduce soot and CO formation, while its lower sulfur content and heating value contribute to a decrease in combustion temperature and a reduction in nitrogen oxide (NOx) production. However, the NOx emissions from the TCD chamber are still higher than those from the Omega chamber, possibly due to the increased in-cylinder temperature resulting from its combustion chamber structure. The findings provide valuable insights into diesel engine system design and the application of oxygenated fuels, promoting the development of clean combustion technologies. Full article

Submicron soot particles (with an aerodynamic diameter of less than 1.0 μm) are found to be one of the major factors resulting in global warming and health burdens. However, research on the biomonitoring of submicron soot particles and their associated sources using tree leaves has not been comprehensively conducted. This study investigated the seasonal trends of submicron soot particles on the leaves of seven tree types collected from four individual seasons across two years in Nanjing, in the Yangtze River Delta region of China, and performed source apportionment using stable carbon isotope analysis. Significant seasonal variations in submicron soot particles were observed on tree leaves of seven tree types, with average levels of 0.3 to 0.5 mg m⁻² during summer and 0.5 to 1.3 mg m⁻² during winter. The levels of submicron soot particles varied significantly across various tree types. In contrast, the levels of δ¹³C were not found to change significantly across different types. The levels of δ¹³C ranged from −26.3‰ to −20.9‰ in winter and from −24.0‰ to −18.1‰ in summer, with fossil fuels accounting for 56% and 78% of submicron soot in winter and summer on average, respectively. These results demonstrate that tree leaves can serve as a low-cost and effective biomonitoring tool for assessing the source status of submicron soot. Full article

Particulate matter (PM) emissions from combustion-based heating systems have been identified as a major contributor to environmental issues and human health risks. Particularly, small-scale residential combustion was responsible for 58% of the total PM_2.5 emissions in Europe in 2020, with domestic heating using wood-based fuels accounting for around 56% of soot emissions. Reducing PM_2.5 emissions has become a major goal of European environmental policies, which have included it among the key targets of the Zero Pollution Action Plan. In this framework, this study presents a performance analysis of a newly developed PM abatement system consisting of a passive cyclone abatement system (PCAS) specifically designed for small residential pellet stoves. The system was tested under steady-state and non-steady-state operating conditions. The experimental results showed that the PCAS abatement system effectively captured PM at a rate of 10.64 mg/MJ, with great efficiency in capturing particles ≥ 10 µm. The heavy metal content in the captured material was below the limit values for agricultural application-destined soil. A Life Cycle Assessment showed that the PCAS could achieve net-zero PM emissions in 1 year and 8 months. Finally, the economic analysis revealed that the PCAS is significantly more cost-effective: over a 10-year period, it could save up to €4000 in installation, maintenance, and energy costs compared to conventional active systems. These findings highlight the effectiveness of this design of PCAS as in reducing PM emissions from residential heating systems and provide valuable insights for the development of future abatement systems. Full article

This study shows how important the coordination of Al³⁺ ions in the silver support is for the overall activity in soot combustion. Five silver catalysts with a silver content of 14.7 wt.% were prepared using the following supports: α-Al₂O₃, which has only octahedrally coordinated Al³⁺, θ-Al₂O₃, which has both octahedrally and tetrahedrally coordinated Al³⁺, and zeolites, which contain only tetrahedrally coordinated Al³⁺: 10X, 13X, and 5A. The analysis of the diffraction patterns showed that silver on the surface of catalysts made with the first four supports was mainly in the metallic form, except for Ag/5A in which there was a lack of reflections from Ag⁰ in the XRD pattern. Nevertheless, the difference in the activity of the support and the catalyst as well as the EDX results indicate the presence of silver on the catalyst. The SEM-EDX analysis showed that the silver dispersion strongly depends on the support and that even the zeolites with large silver particles on the surface have silver evenly distributed across the surface. The activity of the catalysts decreased in the following series: Ag/Al 1200 > Ag/5A ≈ Ag/13X > Ag/10X ≈ Ag/Al 550. Time-of-Flight Secondary Ion Mass Spectrometry was used to delve into the reason why the catalyst with the low-surface area α-Al₂O₃ support yielded a better catalyst than that obtained using the high-surface area alumina support and showed that different ratios of secondary ions were emitted from the two surfaces. Full article

This study investigated the changes in graphitization, surface functional groups, and oxidation behavior of soot particulates along an exhaust pipe of a gasoline direct injection (GDI) engine using Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA). The main findings were as follows: The oxidation temperature of soot particulates was between 300 °C and 650 °C. The soot particulates generated for a higher engine load or near the exhaust valve tended to exhibit a lower ratio of a disordered graphite lattice and amorphous carbon. As the engine load increased, the graphitization degree of soot particulates became higher and the content of oxygen-containing functional groups and oxidation activity of soot particulates became lower, meaning that it became more difficult for the soot particulate to be oxidized. Under a light load, as the engine speed increased, the disorder of the edge array of soot particles became higher, the content of oxygen-containing functional groups and oxidation activity became higher, and the soot particles were more easily oxidized. On the other hand, with an increase in engine speed under a heavy load, the microscopic disorder of soot particulates decreased; lower contents of oxygen-containing functional groups and oxidation activity were observed and oxidation became more difficult. Moreover, with increasing transportation distance along the exhaust pipe of the GDI engine, the graphitization degree, content of surface functional groups, and oxidation behavior of soot particulate presented changes similar to the increasing engine speed under a light load, and oxidation became easier. Full article

To address the current issues with diesel vehicle exhaust after-treatment system particulate sensors—such as low accuracy and inability to perform continuous measurements of particulate mass concentration—a new sensor based on the light scattering method is proposed. During the research, it was found that the light scattering method can be affected by soot particles in the exhaust, which contaminate the optical components and reduces measurement accuracy. To solve this issue, a structure with alumina ceramic embedded lenses and optical fibers was designed, effectively improving the sensor’s resistance to contamination. The detection device is based on the principle of light scattering, and a particulate concentration measurement system with a 90° scattering angle was built. Calibration experiments were conducted using the dust particles generated by the device. The experimental results show that this sensor can measure particulate concentrations accurately, in real time, and with good stability, achieving a calibration error of less than ±5%. Full article

As the world transitions to decarbonized fuels, understanding the impact of engine oil on emissions remains crucial. Lubricant-derived particulate emissions can influence air quality and regulatory compliance in future transport. Researchers have predominantly focused on transient driving cycles to replicate real-world conditions and capture the full range of particle size. This emphasis has led to a lack of comprehensive data on oil-related particulate emissions during steady-state operations, particularly for particles smaller than 23 nm. This paper addresses this gap as upcoming regulations, such as Euro 7, are expected to impose stricter limits by extending measurement thresholds down to 10 nm. The investigation was conducted on a 1.0 L gasoline direct injection engine, assessing total particulate number (TPN) emissions using three oil formulations: a baseline oil with mid-ash content and mid-volatility, a low-ash and low-volatility oil (LoLo), and a high-ash and high-volatility oil (HiHi). A DMS500, with and without a catalytic stripper, measured particle size distribution and TPN. Two digital filters were applied to obtain particle number (PN) metrics comparable to condensation particle counters: “F1-PN > 23” with d50 = 23 nm and “F3-PN > 10” with d50 = 10 nm. Sub-23 nm particles dominated emissions, with baseline oil generally producing higher PN emissions except at low loads. Using F1-PN > 23, HiHi exhibited higher PN counts across moderate to high speeds, while F3-PN > 10 revealed lower PN emissions for HiHi at specific conditions, excluding 2250 rpm-fast idle. By a weighted arithmetic mean, HiHi’s emissions were 9.7% higher than LoLo with F1-PN > 23 and 3.6% higher with F3-PN > 10. Oil formulation did not influence nucleation mode diameter. A three-way ANOVA demonstrated that load and speed were the predominant factors affecting emissions over the entire testing map; albeit at specific operating conditions the effect of the oil is evident. This suggests that under steady-state conditions, carbon-based fuel still plays a key role in particle formation. Future work will investigate decarbonised fuels to further isolate the effect of oil on emissions. Full article

Emissions from domestic coal burning are generally recognized as the cause of the lung cancer epidemic in Xuanwei City, Yunnan Province, China. To examine the physicochemical characteristics of airborne particles emitted from burning this locally sourced coal, PM_2.5 samples were collected from Hutou village which has high levels of lung cancer, and Xize village located approximately 30 km from Hutou without lung cancer cases. Transmission Electron Microscopy-Energy Dispersive X-ray (TEM-EDX) analysis was employed to study the physiochemical features and chemistry of individual particles. Sulfur and silica are the most abundant elements found in the airborne particles in both of the two villages. Fewer elements in aerosol particles were found in Xize village compared with Hutou village. Based on the morphologies and chemical compositions, the particles in Xuanwei can be classified into five types including composite particles (38.6%); organic, soot, tar balls, and biologicals (28.3%); sulfate (14.1%); fly ash (9.8%); and minerals (9.2%). The particles in Hutou village are abundant in the size range of 0.4–0.8 μm while that in Xize is 0.7–0.8 μm. Composite particles are the most common types in all the size ranges. The percentage of composite particles shows two peaks in the small size range (0.1–0.2 μm) and the large size ranges (2–2.3 μm) in Hutou village while that shows an even distribution in all size ranges in Xize village. Core-shell particles are typical types of composite particles, with the solid ‘core’ consisting of materials such as fly ash or mineral grains, and the shell or surface layer being an adhering soluble compound such as sulfates or organics. The heterogeneous reactions of particles with acidic liquid layers produce the core-shell structures. Typically, the equivalent diameter of the core-shell particles is in the range of 0.5–2.5 μm, averaging 1.6 μm, and the core-shell ratio is usually between 0.4 and 0.8, with an average of 0.6. Regardless of the sizes of the particles, the relatively high core-shell ratios imply a less aging state, which suggests that the core-shell particles were relatively recently formed. Once the coal-burning particles are inhaled into the human deep lung, they can cause damage to lung cells and harm to human health. Full article

Purification of soot particles from automobile exhaust has closely to do with the synergistic effect between catalyst metals. Here, several binary Ni-Fe oxide catalysts were elaborately prepared via a modified solvothermal method. A non-noble-metal (Ni)-modified hexagonal Fe₂O₃ nano-sheet catalyst (Ni−Fe₂O₃) was prepared. The introduced heteroatoms replace some of the Fe atoms, which take up the surface of the [FeO₆] octahedron, and the synergistic effect formed between the heteroatoms which are on the surface and the adjacent Fe atoms promotes the formation of coordination unsaturated ions of the activated reactants. The optimal performance was obtained with the Ni-Fe₂O₃-20 composition, with catalytic soot oxidation resulting in T₅₀, SCO₂^m, E_a and TOF of 366 °C, 99.1%, 72.7 kJ mol⁻¹ and 0.156 min⁻¹ (at 310 °C), respectively. The combination of Ni and Fe₂O₃ cells increases the ratio of Fe³⁺/Fe²⁺, making the interaction among electrons between the Ni, which was proved highly dispersed over the catalyst, and the Fe₂O₃ strong. Both exist on the catalyst surface in the form of NiFe₂O₄. Ni atoms and Fe₂O₃, which demonstrate a synergistic effect, promoting the formation of coordination unsaturated ions of the activated reactants and generating more oxygen vacancies, thus promoting the adsorption of NO and accelerating the ignition of soot in O₂ at a low temperature. The novel Ni-Fe₂O₃-X oxide cocatalyst is an improved noble-free catalyst that promotes the synergistic effect between heteroatoms and metal oxides through surface regulation. This is of great significance for the further development of economic and efficient catalysts for soot particle removal from automobile exhaust. Full article

Particulate matter (PM) is a major component of ambient air pollution. PM exposure is linked to numerous adverse health effects, including chronic lung diseases. Air quality guidelines designed to regulate levels of ambient PM are currently based on the mass concentration of different particle sizes, independent of their origin and chemical composition. The objective of this study was to assess the relative hazardous effects of carbonaceous particles (soot), ammonium nitrate, ammonium sulfate, and copper oxide (CuO), which are standard components of ambient air, reflecting contributions from primary combustion, secondary inorganic constituents, and non-exhaust emissions (NEE) from vehicular traffic. Human epithelial cells representing bronchial (BEAS-2B) and alveolar locations (H441 and A549) in the airways, human lung fibroblasts (HFL-1), and rat precision-cut lung slices (PCLS) were exposed in submerged cultures to different concentrations of particles for 5–72 h. Following exposure, cell viability, metabolic activity, reactive oxygen species (ROS) formation, and inflammatory responses were analyzed. CuO and, to a lesser extent, soot reduced cell viability in a dose-dependent manner, increased ROS formation, and induced inflammatory responses. Ammonium nitrate and ammonium sulfate did not elicit any significant cytotoxic responses but induced immunomodulatory alterations at very high concentrations. Our findings demonstrate that secondary inorganic components of PM have a lower hazard cytotoxicity compared with combustion-derived and indicative NEE components, and alveolar epithelial cells are more sensitive to PM exposure. This information should help to inform which sources of PM to target and feed into improved, targeted air quality guidelines. Full article