Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haz...Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haze,remains very scarce.This study used single-particle aerosol mass spectrometry to investigate IM-containing particles in Chengdu,one of the megacities in the SCB,during summer and winter before and after implemen-tation of the Three-year Action Plan to Win the Blue-Sky Defense War(BSDW).We found that IM-containing particles accounted for 1.2%–12.0%of all detected particles,and they highly mixed with carbonaceous com-ponents,secondary inorganic species,and organic nitrogen.From before to after the BSDW,the proportion of IM-containing particles decreased by 1.8%in summer,but increased by 9.6%in winter.Ammonium/amines and carbonyl compounds were closely related to IM-containing particles;the highest proportion of IM-containing particles occurred in particles mixed with amines and carbonyls.The number fraction of IM-containing particles in all seasons was higher at night than during daytime.The potential source areas of IM-containing particles showed notable narrowing after the BSDW,and the high-value areas were found distributed closer to Chengdu and its surrounding areas.In the winter before the BSDW,most IM-containing particles(>70%)were mixed with organic carbon(OC)particles,and the contributions of OC and mixed organic–elemental carbon(OC-EC)particles increased with aggravation of pollution,whereas OC-EC and Metal particles played a more crucial role in the winter after the BSDW.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this...During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.展开更多
In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the...In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability.展开更多
Biocompatible amphiphilic nanoparticles(NPs)with tunable particle morphology and surface property are important for their applications as functional materials.However,previously developed methods to prepare amphiphili...Biocompatible amphiphilic nanoparticles(NPs)with tunable particle morphology and surface property are important for their applications as functional materials.However,previously developed methods to prepare amphiphilic NPs generally involve several steps,especially an additional step for surface modification,greatly hindering their largescale production and widespread applications.Here,a versatile one-step strategy is developed to prepare biocompatible amphiphilic dimer NPs with tunable particle morphology and surface property.The amphiphilic dimer NPs,which consist of a hydrophobic shellac bulb and a hydrophilic poly(lactic acid)(PLA)bulb with PLA-poly(ethylene glycol)(PEG)on the bulb surface,are prepared in a single step by controlled co-precipitation and self-assembly.Amphiphilic PLA-PEG/shellac dimer NPs demonstrate excellent tunability in particle morphology,thus showing good performances in controlling the interfacial curvature and emulsion type.In addition,temperatureresponsive PLA-poly(N-isopropyl acrylamide)(PNIPAM)/shellac dimer NPs are prepared following the same method and emulsions stabilized by them show temperature-triggered response.The applications of PLA-PEG-folic acid(FA)/shellac dimer NPs for drug delivery have also been demonstrated,which show a very good performance.The strategy of preparing the dimer NPs is green,scalable,facile and versatile,which provides a good platform for the design of dimer NPs with tunable particle morphology and surface property for diverse applications.展开更多
Accurately acquiring catalyst size and morphology is essential for supporting catalytic reaction process design and optimal control. We report an intelligent catalyst sizing and morphological classification method bas...Accurately acquiring catalyst size and morphology is essential for supporting catalytic reaction process design and optimal control. We report an intelligent catalyst sizing and morphological classification method based on the Mask-RCNN framework. A dataset of 9880 high-resolution images was captured by using a self-made fiber-optic endoscopic system for 13 kinds of silicoaluminophosphate-34 (SAPO-34) catalyst samples with different coke. Then there were approximately 877881 individual particles extracted from this dataset by our AI-based particle recognition algorithm. To clearly describe the morphology of irregular particles, we proposed a hybrid classification criterion that combines five different parameters, which are deformity, circularity, roundness, aspect ratio, and compactness. Therefore, catalyst morphology can be classified into two categories with four types. The first category includes regular types, such as the spherical, ellipsoidal, and rod-shaped types. And all the irregular types fall into the second category. The experimental results showed that a catalyst particle tends to be larger when its coke deposition increased. Whereas particle morphology remained primarily spherical and ellipsoidal, the ratio of each type varied slightly according to its coke. Our findings illustrate that this is a promising approach to be developing intelligent instruments for catalyst particle sizing and classification.展开更多
Brake wear particle(BWP)emissions are considered one of the dominant sources of particulate matter pollution in urban environments.BWP emissions have increased significantly under high-temperature conditions,emerging ...Brake wear particle(BWP)emissions are considered one of the dominant sources of particulate matter pollution in urban environments.BWP emissions have increased significantly under high-temperature conditions,emerging as a focal point of research interest.This study investigates the effect of brake temperatures on BWP emissions.The brake pad materials undergo violent decomposition and oxidation reactions and generate large amounts of incompletely oxidized organic products at temperatures above 475℃.These organic products cause particles below 200 nm to proliferate,and nanoparticles below 40 nm account for the largest contribution of total BWPs.When the friction surface temperature exceeds 475℃,the high-concentration BWPs below 200 nm will agglomerate into larger particles.High temperatures also cause the brake pad surface to delaminate and fragment into particles above 2.5μm.In addition,when the initial brake speed is above 160 km/h,or the brake pressure is above 7 bar,there is a sharp increase in particles below 200 nm.The results suggest that a significant number of nanoparticles below 40 nm are inferred to be generated as the flash temperature of the friction surface reaches the violent reaction temperature.This study provides guidelines for designing low-emission brake pads,as improving the high-temperature resistance of brake pad material components possibly reduces BWP generation.展开更多
In the field of discretization-based meshfree/meshless methods,the improvements in the higher-order consistency,stability,and computational efficiency are of great concerns in computational science and numerical solut...In the field of discretization-based meshfree/meshless methods,the improvements in the higher-order consistency,stability,and computational efficiency are of great concerns in computational science and numerical solutions to partial differential equations.Various alternative numerical methods of the finite particle method(FPM)frame have been extended from mathematical theories to numerical applications separately.As a comprehensive numerical scheme,this study suggests a unified resolved program for numerically investigating their accuracy,stability,consistency,computational efficiency,and practical applicability in industrial engineering contexts.The high-order finite particle method(HFPM)and corrected methods based on the multivariate Taylor series expansion are constructed and analyzed to investigate the whole applicability in different benchmarks of computational fluid dynamics.Specifically,four benchmarks are designed purposefully from statical exact solutions to multifaceted hydrodynamic tests,which possess different numerical performances on the particle consistency,numerical discretized forms,particle distributions,and transient time evolutional stabilities.This study offers a numerical reference for the current unified resolved program.展开更多
1. Introduction High-speed gas-particle flows are crucial in engineering applications and natural phenomena, such as volcanic eruptions,combustion, and hypersonic flight. These flows involve complex gas-particle inter...1. Introduction High-speed gas-particle flows are crucial in engineering applications and natural phenomena, such as volcanic eruptions,combustion, and hypersonic flight. These flows involve complex gas-particle interactions, posing significant challenges for simulations and experiments. This research highlight summarizes recent advancements in gas-particle dynamics under compressible conditions, covering key findings, numerical and experimental progress, and future directions. Details can be found in the work of Capecelatro and Wagner (Gas-particle dynamics in high-speed flows. Annual Review of Fluid Mechanics 2024;56:379–403).展开更多
Particle settling in narrow rough fractures is a common but poorly understood phenomenon during hydraulic fracturing.This study first constructs a large slot with two rough surfaces to simulate rock fractures and empl...Particle settling in narrow rough fractures is a common but poorly understood phenomenon during hydraulic fracturing.This study first constructs a large slot with two rough surfaces to simulate rock fractures and employs the particle image velocimetry to measure particle settling.Results show that particle settling in the rough slot is more complex than in the smooth slot.Rough pathways significantly change particle settling characteristics.The rough-walled slot alters the classic settling process by creating preferential pathways,localized trapping,and vortex-driven redistribution.Particles settle along preferential pathways,increasing settling velocity.The wall retardation effect becomes more prominent for large particles,reducing the settling velocity.Particle settling induces vortices throughout the rough surface,affecting particle behavior.Higher particle volume fractions increase settling nonuniformity,leading to unstable fluid flow within fractures,characterized by high vorticity and upward flow.The frequent interplay between particles and particle-walls,and fluid resistance complicates particle trajectories and settling behavior.Fluid viscosity significantly changes settling patterns and promotes particle clusters,forming chain-like and curtain-like clusters in rough fractures.An innovative model is proposed to predict settling velocity in rough fractures.展开更多
Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV pred...Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R^(2)enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications.展开更多
Particle morphology is critical in affecting the crushing behavior of rockfill materials.In contrast,most current single particle simulations lack satisfactory morphology accuracy,and the resulting crushing modes devi...Particle morphology is critical in affecting the crushing behavior of rockfill materials.In contrast,most current single particle simulations lack satisfactory morphology accuracy,and the resulting crushing modes deviate from observations to some extent.Therefore,we reconstruct the real particle morphology with the spherical harmonic(SH)method and employ the finite-discrete element method(FDEM)to simulate the one-dimensional(1D)compressive crushing process of basalt particles commonly used in rockfill.The influences of four main morphological parameters,i.e.sphericity,aspect ratio,roundness,and convexity,on the single particle strength and the crushing modes are discussed.The results show that with the SH degree set to 15 and a mesh number of 20,480,the FDEM models of reconstructed particles achieve sufficient morphology accuracy and high computational efficiency.Based on the model,the simulation results demonstrate that the aspect ratio has the most significant impact on single particle strength,followed by sphericity.In contrast,roundness and convexity have a weaker effect than the above two parameters.Also,it is revealed that single particle strength decreases with increasing aspect ratio and sphericity,while it increases with higher roundness and convexity.Furthermore,aspect ratio significantly changes the initial crushing position,sphericity dominates post-crushing fragment size and quantity,and roundness mainly affects post-crushing morphology.The model results have been employed in establishing a support vector regression(SVR)-based predicted model,exhibiting good predictive performance and advantages for the optimization of rockfill particles in engineering.展开更多
The energy spectrum of energetic particles in space often shows a non-thermal spectral shape with two spectral transitions/breaks over a wide energy range, carrying crucial information about their acceleration, releas...The energy spectrum of energetic particles in space often shows a non-thermal spectral shape with two spectral transitions/breaks over a wide energy range, carrying crucial information about their acceleration, release and transportation process. To self-consistently characterize the spectral features of energetic particles, here we propose a novel extended pan-spectrum(EPS) formula to fit the particle energy-flux spectrum, which has the merit that can incorporate many commonly used spectrum functions with one spectral transition, including the pan-spectrum, double-power-law, Kappa, Ellison-Ramaty(ER) functions, etc. The formula can also determine the spectral shape with two spectral transitions, including the triple-power-law function, Kappa distribution(at low energy)plus power law(at high energy), power law(at low energy) plus ER function, etc. Considering the uncertainties in both J and E, we can fit this EPS formula well to the representative energy spectra of various particle phenomena in space, including solar energetic particles(electrons, protons, ~3He and heavier ions), anomalous cosmic rays, solar wind suprathermal particles(halo and superhalo electrons;pick-up ions and the suprathermal tail), etc. Therefore, the EPS fitting can help us self-consistently determine the spectral features of different particle phenomena, and improve our understanding of the physical nature of the origin, acceleration, and transportation of energetic particles in space.展开更多
The three-dimensional particle electrode system exhibits significant potential for application in the treatment of wastewater.Nonetheless,the advancement of effective granular electrodes characterized by elevated cata...The three-dimensional particle electrode system exhibits significant potential for application in the treatment of wastewater.Nonetheless,the advancement of effective granular electrodes characterized by elevated catalytic activity and minimal energy consumption continues to pose a significant challenge.In this research,Fluorine-doped copper-carbon(F/Cu-GAC)particle electrodes were effectively synthesized through an impregnationcalcination technique,utilizing granular activated carbon as the carrier and fluorinedoped modified copper oxides as the catalytic agents.The particle electrodes were subsequently utilized to promote the degradation of 2,4,6-trichlorophenol(2,4,6-TCP)in a threedimensional electrocatalytic reactor(3DER).The F/Cu-GAC particle electrodes were polarized under the action of electric field,which promoted the heterogeneous Fenton-like reaction in which H2O2 generated by two-electron oxygen reduction reaction(2e-ORR)of O_(2) was catalytically decomposed to·OH.The 3DER equipped with F/Cu-GAC particle electrodes showed 100%removal of 2,4,6-TCP and 79.24%removal of TOC with a specific energy consumption(EC)of approximately 0.019 kWh/g·COD after 2 h of operation.The F/Cu-GAC particle electrodes exhibited an overpotential of 0.38 V and an electrochemically active surface area(ECSA)of 715 cm^(2),as determined through linear sweep voltammetry(LSV)and cyclic voltammetry(CV)assessments.These findings suggest a high level of electrocatalytic performance.Furthermore,the catalytic mechanism of the 3DER equipped with F/Cu-GAC particle electrodes was elucidated through the application of X-ray photoelectron spectroscopy(XPS),electron spin resonance(ESR),and active species capture experiments.This investigation offers a novel approach for the effective degradation of 2,4,6-TCP.展开更多
Studying the contribution of regional transport to ultrafine particles(UFPs)and the deposition effect of nanoscale particles in human respiratory system is conducive to exploring the impact of atmospheric particles on...Studying the contribution of regional transport to ultrafine particles(UFPs)and the deposition effect of nanoscale particles in human respiratory system is conducive to exploring the impact of atmospheric particles on the environment and human health.Based on the data set of number concentration spectrum in the particle size range of 5.6–560 nm in the spring of Hefei,the Yangtze River Delta region obtained by a fast mobility particle sizer,the explosive growth characteristics,potential source identification and deposition flux analysis of UFPs were systematically studied.The results showed that the frequency of new particle formation(NPF)events during spring was 31.5%.SO_(2) and O_(3) contribute to NPF events.Daytime,higher temperature,stronger solar radiation and lower humidity were more conducive to the explosive growth of UFPs.In addition,regional transport of pollutants from the cities around Hefei played an important role in the accumulation mode particles,which were mainly affected by the land-source air mass from northwest Jiangsu(23.64%)and the sea-source air mass from the Yellow Sea(23.99%).It was worth noting that approximately 10,406 ng of UFPs enters the human respiratory system every day.Themain deposition area of 5.6–560 nm nanoscale particles was alveolar,5.6–400 nm is more likely to be deposited on alveolar,while nanoscale particles with particle size between 400 and 560 nm is more likely to be deposited on head airways.This study identified the deposition risk of nanoscale particles in the respiratory system under different particle sizes.展开更多
The high stress levels in tall tailings dams can lead to particle crushing.Understanding the compressibility and breakage characteristics of tailings particles will contribute to the advancement to the design and cons...The high stress levels in tall tailings dams can lead to particle crushing.Understanding the compressibility and breakage characteristics of tailings particles will contribute to the advancement to the design and construction processes of high-rise tailings dams,as well as the accurate evaluation of the stability of tailings storage facilities(TSFs).This paper presents the results of a series of detailed one-dimensional oedometer compression tests conducted to investigate the compression behavior and particle breakage of iron ore tailings(IOTs)collected from two typical TSFs,with different initial particle size distributions and a wide range of initial specific volumes,under effective vertical stresses of up to 4.8 MPa.The results show that the compression paths of the IOTs were slowly convergent,and this nontransitional mode of compression behavior experienced a significant amount of particle breakage.The relative breakage(Br)was used to quantify the amount of breakage and the input specific work(W)was adopted to evaluate the factors influencing Br.The initial breakage stress of the IOTs was less than 0.2 MPa.For the finer tailings,Br increased with increasing vertical stresses until it reached a threshold,after which Br tended to remain constant.However,coarser IOTs continued to experience crushing even at 4.8 MPa.The particle breakage in the coarser IOTs is much more significant than it in the finer IOTs overall.It was also observed that the tailings grains within the loose specimens broke more easily than those within the dense specimens.Additionally,three types of particle crushing modes were identified for IOTs under one-dimensional compression,namely,abrasion,chipping,and splitting.展开更多
Aerosol particle pollution has become an increasing serious environmental problem,and urban vegetation plays a long-lasting and positive role in mitigating it.This study compared the particle capture abilities of tree...Aerosol particle pollution has become an increasing serious environmental problem,and urban vegetation plays a long-lasting and positive role in mitigating it.This study compared the particle capture abilities of trees,shrubs,and herbs,and examined the compositions and influence of aerosol particles accumulated on leaf functional traits.Retained particles primarily contained Ca^(2+),K^(+),SO_(4)^(2-),NO_(3)^(-)and NH_(4)^(+),indicating their anthropogenic origins.The leathery-leaved tree Osmanthus fragrans and the papery-leaved herb Alternanthera sessilis demonstrated the higher competence in particle accumulation than other plants,and leaf morphologic structures(e.g.,leaf grooves,trichomes,waxy layers,and stomata characteristics)were closely associated with particle capture by plant species.Particle retention negatively impacted stomata,impeding photosynthesis,and reducing transpiration.In response to particle accumulation,plants tended to decrease specific leaf area and adjust stomatal conductance.Both growth form and leaf texture significantly influenced the particle capture abilities of different plant species.The substantial contribution of plants,particularly herbs in the lower vegetation strata,to particle removal should not be overlooked.Vegetation with a tree-shrub-herb configuration excels at particle capture,offering potential advantages in mitigating particle pollution and enhancing ecological benefits.展开更多
The COVID-19 lockdown was a typical example of extreme emission reduction,providing an opportunity to study the impact of lockdown measures on air pollution.Particle number concentrations(PNC)originate from direct emi...The COVID-19 lockdown was a typical example of extreme emission reduction,providing an opportunity to study the impact of lockdown measures on air pollution.Particle number concentrations(PNC)originate from direct emissions or through new particle formation events.However,their variations during the lockdown period are under investigation.This study focuses on Luohe,a city on the southern edge of the North China Plain,analyzing the changes in PNC and its sources before,during,and after the COVID-19 lockdown.From March 25^(th)to May 31^(st),2022,real-time PNC measurements were conducted using a Scanning Mobility Particle Sizer for particle size.Results showed an 11.2%decrease in PNC during the lockdown compared to pre-lockdown and a 3.6%decrease compared to post-lockdown,indicating reduced local emissions and weakened regional transportation during the lockdown.Positive Matrix Factorization analysis identified six sources contributing to the total PNC,including photochemical nucleation,aged photochemical nucleation,gasoline vehicle emissions,diesel vehicle emissions,coal and biomass combustion,and secondary aerosols.The significant changes in source emissions indicate a substantially reduced traffic volume after the implementation of lockdown measures(2644.8#/cm^(3),2202.2#/cm^(3),2792.7#/cm^(3)).Concurrently,photochemical nucleation(310.1#/cm^(3),306.3#/cm^(3),393.1#/cm^(3))and photochemical nucleation aging(592.8#/cm^(3),744.1#/cm^(3),810.7#/cm^(3))exhibited increasing trends,while coal/biomass combustion(1656.6#/cm^(3),1586.2#/cm^(3),980.0#/cm^(3))and secondary sources(999.4#/cm^(3),791.1#/cm^(3),804.1#/cm^(3))showed decreasing trends.In summary,the contributions of traffic emissions to PNC highlight the potential for targeted traffic management strategies to improve urban air quality.展开更多
基金supported by Sichuan Science and Technology Program(No.2024NSFSC0060)the National Natural Science Foundation of China(No.U23A2030)the Basic Research Cultivation Support Plan of Southwest Jiaotong University(No.2682023ZTPY016).
文摘Imidazole(IM)particles in the atmosphere affect climate,atmospheric chemical reactions,and human health.However,research on IM particles in the Sichuan Basin(SCB),one of the areas of China affected most heavily by haze,remains very scarce.This study used single-particle aerosol mass spectrometry to investigate IM-containing particles in Chengdu,one of the megacities in the SCB,during summer and winter before and after implemen-tation of the Three-year Action Plan to Win the Blue-Sky Defense War(BSDW).We found that IM-containing particles accounted for 1.2%–12.0%of all detected particles,and they highly mixed with carbonaceous com-ponents,secondary inorganic species,and organic nitrogen.From before to after the BSDW,the proportion of IM-containing particles decreased by 1.8%in summer,but increased by 9.6%in winter.Ammonium/amines and carbonyl compounds were closely related to IM-containing particles;the highest proportion of IM-containing particles occurred in particles mixed with amines and carbonyls.The number fraction of IM-containing particles in all seasons was higher at night than during daytime.The potential source areas of IM-containing particles showed notable narrowing after the BSDW,and the high-value areas were found distributed closer to Chengdu and its surrounding areas.In the winter before the BSDW,most IM-containing particles(>70%)were mixed with organic carbon(OC)particles,and the contributions of OC and mixed organic–elemental carbon(OC-EC)particles increased with aggravation of pollution,whereas OC-EC and Metal particles played a more crucial role in the winter after the BSDW.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by the National Natural Science Foundation of China(No.52175414)the Natural Science Foundation of Jiangsu Province of China(No.BK20220134)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.NE2023002)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(No.KYCX24_0559)。
文摘During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.
基金Supported by the Guangxi Special Program for Technological Innovation Guidance(No.GuiKeAC25069006).
文摘In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability.
基金supported by National Natural Science Foundation of China(No.22278352)National Key Research and Development Program of China(No.2021YFC3001100)+3 种基金Longyan City Science and Technology Plan Project(No.2020LYF17043)Longyan City Science and Technology Plan Project(No.2020LYF17042)ARC Discovery Project(No.DP200101238)and NHMRC Investigator Grant(No.APP2008698)supported by the Harvard Materials Research Science and Engineering Center(No.DMR2011754)。
文摘Biocompatible amphiphilic nanoparticles(NPs)with tunable particle morphology and surface property are important for their applications as functional materials.However,previously developed methods to prepare amphiphilic NPs generally involve several steps,especially an additional step for surface modification,greatly hindering their largescale production and widespread applications.Here,a versatile one-step strategy is developed to prepare biocompatible amphiphilic dimer NPs with tunable particle morphology and surface property.The amphiphilic dimer NPs,which consist of a hydrophobic shellac bulb and a hydrophilic poly(lactic acid)(PLA)bulb with PLA-poly(ethylene glycol)(PEG)on the bulb surface,are prepared in a single step by controlled co-precipitation and self-assembly.Amphiphilic PLA-PEG/shellac dimer NPs demonstrate excellent tunability in particle morphology,thus showing good performances in controlling the interfacial curvature and emulsion type.In addition,temperatureresponsive PLA-poly(N-isopropyl acrylamide)(PNIPAM)/shellac dimer NPs are prepared following the same method and emulsions stabilized by them show temperature-triggered response.The applications of PLA-PEG-folic acid(FA)/shellac dimer NPs for drug delivery have also been demonstrated,which show a very good performance.The strategy of preparing the dimer NPs is green,scalable,facile and versatile,which provides a good platform for the design of dimer NPs with tunable particle morphology and surface property for diverse applications.
基金supported by the National Natural Science Foundation of China(22308348)the Natural Science Foundation of Liaoning Province of China(2024-MSBA-65)+1 种基金the Qin Chuangyuan Project for Introducing High-Level Innovative and Entrepreneurial Talents(QCYRCXM-2023-024)the Energy Revolution S&T Program of Yulin Innovation Institute of Clean Energy(E201041206).
文摘Accurately acquiring catalyst size and morphology is essential for supporting catalytic reaction process design and optimal control. We report an intelligent catalyst sizing and morphological classification method based on the Mask-RCNN framework. A dataset of 9880 high-resolution images was captured by using a self-made fiber-optic endoscopic system for 13 kinds of silicoaluminophosphate-34 (SAPO-34) catalyst samples with different coke. Then there were approximately 877881 individual particles extracted from this dataset by our AI-based particle recognition algorithm. To clearly describe the morphology of irregular particles, we proposed a hybrid classification criterion that combines five different parameters, which are deformity, circularity, roundness, aspect ratio, and compactness. Therefore, catalyst morphology can be classified into two categories with four types. The first category includes regular types, such as the spherical, ellipsoidal, and rod-shaped types. And all the irregular types fall into the second category. The experimental results showed that a catalyst particle tends to be larger when its coke deposition increased. Whereas particle morphology remained primarily spherical and ellipsoidal, the ratio of each type varied slightly according to its coke. Our findings illustrate that this is a promising approach to be developing intelligent instruments for catalyst particle sizing and classification.
基金supported by the National Natural Science Foundation of China(Nos.52172337 and 52272342)the Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(No.GZB20240352)the Shuimu Tsinghua Scholar Program of Tsinghua University(No.2023SM230)。
文摘Brake wear particle(BWP)emissions are considered one of the dominant sources of particulate matter pollution in urban environments.BWP emissions have increased significantly under high-temperature conditions,emerging as a focal point of research interest.This study investigates the effect of brake temperatures on BWP emissions.The brake pad materials undergo violent decomposition and oxidation reactions and generate large amounts of incompletely oxidized organic products at temperatures above 475℃.These organic products cause particles below 200 nm to proliferate,and nanoparticles below 40 nm account for the largest contribution of total BWPs.When the friction surface temperature exceeds 475℃,the high-concentration BWPs below 200 nm will agglomerate into larger particles.High temperatures also cause the brake pad surface to delaminate and fragment into particles above 2.5μm.In addition,when the initial brake speed is above 160 km/h,or the brake pressure is above 7 bar,there is a sharp increase in particles below 200 nm.The results suggest that a significant number of nanoparticles below 40 nm are inferred to be generated as the flash temperature of the friction surface reaches the violent reaction temperature.This study provides guidelines for designing low-emission brake pads,as improving the high-temperature resistance of brake pad material components possibly reduces BWP generation.
基金supported by the National Natural Science Foundation of China(No.12002290)。
文摘In the field of discretization-based meshfree/meshless methods,the improvements in the higher-order consistency,stability,and computational efficiency are of great concerns in computational science and numerical solutions to partial differential equations.Various alternative numerical methods of the finite particle method(FPM)frame have been extended from mathematical theories to numerical applications separately.As a comprehensive numerical scheme,this study suggests a unified resolved program for numerically investigating their accuracy,stability,consistency,computational efficiency,and practical applicability in industrial engineering contexts.The high-order finite particle method(HFPM)and corrected methods based on the multivariate Taylor series expansion are constructed and analyzed to investigate the whole applicability in different benchmarks of computational fluid dynamics.Specifically,four benchmarks are designed purposefully from statical exact solutions to multifaceted hydrodynamic tests,which possess different numerical performances on the particle consistency,numerical discretized forms,particle distributions,and transient time evolutional stabilities.This study offers a numerical reference for the current unified resolved program.
文摘1. Introduction High-speed gas-particle flows are crucial in engineering applications and natural phenomena, such as volcanic eruptions,combustion, and hypersonic flight. These flows involve complex gas-particle interactions, posing significant challenges for simulations and experiments. This research highlight summarizes recent advancements in gas-particle dynamics under compressible conditions, covering key findings, numerical and experimental progress, and future directions. Details can be found in the work of Capecelatro and Wagner (Gas-particle dynamics in high-speed flows. Annual Review of Fluid Mechanics 2024;56:379–403).
基金supported by the National Natural Science Foundation of China(Grant No.52274035)。
文摘Particle settling in narrow rough fractures is a common but poorly understood phenomenon during hydraulic fracturing.This study first constructs a large slot with two rough surfaces to simulate rock fractures and employs the particle image velocimetry to measure particle settling.Results show that particle settling in the rough slot is more complex than in the smooth slot.Rough pathways significantly change particle settling characteristics.The rough-walled slot alters the classic settling process by creating preferential pathways,localized trapping,and vortex-driven redistribution.Particles settle along preferential pathways,increasing settling velocity.The wall retardation effect becomes more prominent for large particles,reducing the settling velocity.Particle settling induces vortices throughout the rough surface,affecting particle behavior.Higher particle volume fractions increase settling nonuniformity,leading to unstable fluid flow within fractures,characterized by high vorticity and upward flow.The frequent interplay between particles and particle-walls,and fluid resistance complicates particle trajectories and settling behavior.Fluid viscosity significantly changes settling patterns and promotes particle clusters,forming chain-like and curtain-like clusters in rough fractures.An innovative model is proposed to predict settling velocity in rough fractures.
基金supported by the National Natural Science Foundation of China(Grant No.52409143)the Basic Scientific Research Fund of Changjiang River Scientific Research Institute for Central-level Public Welfare Research Institutes(Grant No.CKSF2025184/YT)the Hubei Provincial Natural Science Foundation of China(Grant No.2022CFB673).
文摘Accurately forecasting peak particle velocity(PPV)during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses.This research introduces an approach to PPV prediction by combining conventional empirical equations with physics-informed neural networks(PINN)and optimizing the model parameters via the Particle Swarm Optimization(PSO)algorithm.The proposed PSO-PINN framework was rigorously benchmarked against seven established machine learning approaches:Multilayer Perceptron(MLP),Extreme Gradient Boosting(XGBoost),Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting Decision Tree(GBDT),Adaptive Boosting(Adaboost),and Gene Expression Programming(GEP).Comparative analysis showed that PSO-PINN outperformed these models,achieving RMSE reductions of 17.82-37.63%,MSE reductions of 32.47-61.10%,AR improvements of 2.97-21.19%,and R^(2)enhancements of 7.43-29.21%,demonstrating superior accuracy and generalization.Furthermore,the study determines the impact of incorporating empirical formulas as physical constraints in neural networks and examines the effects of different empirical equations,particle swarm size,iteration count in PSO,regularization coefficient,and learning rate in PINN on model performance.Lastly,a predictive system for blast vibration PPV is designed and implemented.The research outcomes offer theoretical references and practical recommendations for blast vibration forecasting in similar engineering applications.
基金financial support to this study from the National Natural Science Foundation of China,NSFC(Grant No.52278367).
文摘Particle morphology is critical in affecting the crushing behavior of rockfill materials.In contrast,most current single particle simulations lack satisfactory morphology accuracy,and the resulting crushing modes deviate from observations to some extent.Therefore,we reconstruct the real particle morphology with the spherical harmonic(SH)method and employ the finite-discrete element method(FDEM)to simulate the one-dimensional(1D)compressive crushing process of basalt particles commonly used in rockfill.The influences of four main morphological parameters,i.e.sphericity,aspect ratio,roundness,and convexity,on the single particle strength and the crushing modes are discussed.The results show that with the SH degree set to 15 and a mesh number of 20,480,the FDEM models of reconstructed particles achieve sufficient morphology accuracy and high computational efficiency.Based on the model,the simulation results demonstrate that the aspect ratio has the most significant impact on single particle strength,followed by sphericity.In contrast,roundness and convexity have a weaker effect than the above two parameters.Also,it is revealed that single particle strength decreases with increasing aspect ratio and sphericity,while it increases with higher roundness and convexity.Furthermore,aspect ratio significantly changes the initial crushing position,sphericity dominates post-crushing fragment size and quantity,and roundness mainly affects post-crushing morphology.The model results have been employed in establishing a support vector regression(SVR)-based predicted model,exhibiting good predictive performance and advantages for the optimization of rockfill particles in engineering.
基金supported in part by NSFC under contracts 42225404, 42127803, 42150105by National Key R&D Program of China No. 2021YFA0718600by ISSI-BJ through the international teams Nos. 23-581 and 56。
文摘The energy spectrum of energetic particles in space often shows a non-thermal spectral shape with two spectral transitions/breaks over a wide energy range, carrying crucial information about their acceleration, release and transportation process. To self-consistently characterize the spectral features of energetic particles, here we propose a novel extended pan-spectrum(EPS) formula to fit the particle energy-flux spectrum, which has the merit that can incorporate many commonly used spectrum functions with one spectral transition, including the pan-spectrum, double-power-law, Kappa, Ellison-Ramaty(ER) functions, etc. The formula can also determine the spectral shape with two spectral transitions, including the triple-power-law function, Kappa distribution(at low energy)plus power law(at high energy), power law(at low energy) plus ER function, etc. Considering the uncertainties in both J and E, we can fit this EPS formula well to the representative energy spectra of various particle phenomena in space, including solar energetic particles(electrons, protons, ~3He and heavier ions), anomalous cosmic rays, solar wind suprathermal particles(halo and superhalo electrons;pick-up ions and the suprathermal tail), etc. Therefore, the EPS fitting can help us self-consistently determine the spectral features of different particle phenomena, and improve our understanding of the physical nature of the origin, acceleration, and transportation of energetic particles in space.
基金supported by Guangxi Science and Technology Major Program(No.AA23073008)Hubei Key Laboratory of Water System Science for Sponge City Construction(Wuhan University)(No.2023–05)Nanning Innovation and Entrepreneur Leading Talent Project(No.2021001).
文摘The three-dimensional particle electrode system exhibits significant potential for application in the treatment of wastewater.Nonetheless,the advancement of effective granular electrodes characterized by elevated catalytic activity and minimal energy consumption continues to pose a significant challenge.In this research,Fluorine-doped copper-carbon(F/Cu-GAC)particle electrodes were effectively synthesized through an impregnationcalcination technique,utilizing granular activated carbon as the carrier and fluorinedoped modified copper oxides as the catalytic agents.The particle electrodes were subsequently utilized to promote the degradation of 2,4,6-trichlorophenol(2,4,6-TCP)in a threedimensional electrocatalytic reactor(3DER).The F/Cu-GAC particle electrodes were polarized under the action of electric field,which promoted the heterogeneous Fenton-like reaction in which H2O2 generated by two-electron oxygen reduction reaction(2e-ORR)of O_(2) was catalytically decomposed to·OH.The 3DER equipped with F/Cu-GAC particle electrodes showed 100%removal of 2,4,6-TCP and 79.24%removal of TOC with a specific energy consumption(EC)of approximately 0.019 kWh/g·COD after 2 h of operation.The F/Cu-GAC particle electrodes exhibited an overpotential of 0.38 V and an electrochemically active surface area(ECSA)of 715 cm^(2),as determined through linear sweep voltammetry(LSV)and cyclic voltammetry(CV)assessments.These findings suggest a high level of electrocatalytic performance.Furthermore,the catalytic mechanism of the 3DER equipped with F/Cu-GAC particle electrodes was elucidated through the application of X-ray photoelectron spectroscopy(XPS),electron spin resonance(ESR),and active species capture experiments.This investigation offers a novel approach for the effective degradation of 2,4,6-TCP.
基金supported by the National Natural Science Foundation of China(Nos.U21A2027,42207113,and 42407141)。
文摘Studying the contribution of regional transport to ultrafine particles(UFPs)and the deposition effect of nanoscale particles in human respiratory system is conducive to exploring the impact of atmospheric particles on the environment and human health.Based on the data set of number concentration spectrum in the particle size range of 5.6–560 nm in the spring of Hefei,the Yangtze River Delta region obtained by a fast mobility particle sizer,the explosive growth characteristics,potential source identification and deposition flux analysis of UFPs were systematically studied.The results showed that the frequency of new particle formation(NPF)events during spring was 31.5%.SO_(2) and O_(3) contribute to NPF events.Daytime,higher temperature,stronger solar radiation and lower humidity were more conducive to the explosive growth of UFPs.In addition,regional transport of pollutants from the cities around Hefei played an important role in the accumulation mode particles,which were mainly affected by the land-source air mass from northwest Jiangsu(23.64%)and the sea-source air mass from the Yellow Sea(23.99%).It was worth noting that approximately 10,406 ng of UFPs enters the human respiratory system every day.Themain deposition area of 5.6–560 nm nanoscale particles was alveolar,5.6–400 nm is more likely to be deposited on alveolar,while nanoscale particles with particle size between 400 and 560 nm is more likely to be deposited on head airways.This study identified the deposition risk of nanoscale particles in the respiratory system under different particle sizes.
基金supported by the National Natural Science Foundation of China(Grant Nos.41630640,41790445)the National Key Research and Development Program of China(Grant No.2022YFC3003205).
文摘The high stress levels in tall tailings dams can lead to particle crushing.Understanding the compressibility and breakage characteristics of tailings particles will contribute to the advancement to the design and construction processes of high-rise tailings dams,as well as the accurate evaluation of the stability of tailings storage facilities(TSFs).This paper presents the results of a series of detailed one-dimensional oedometer compression tests conducted to investigate the compression behavior and particle breakage of iron ore tailings(IOTs)collected from two typical TSFs,with different initial particle size distributions and a wide range of initial specific volumes,under effective vertical stresses of up to 4.8 MPa.The results show that the compression paths of the IOTs were slowly convergent,and this nontransitional mode of compression behavior experienced a significant amount of particle breakage.The relative breakage(Br)was used to quantify the amount of breakage and the input specific work(W)was adopted to evaluate the factors influencing Br.The initial breakage stress of the IOTs was less than 0.2 MPa.For the finer tailings,Br increased with increasing vertical stresses until it reached a threshold,after which Br tended to remain constant.However,coarser IOTs continued to experience crushing even at 4.8 MPa.The particle breakage in the coarser IOTs is much more significant than it in the finer IOTs overall.It was also observed that the tailings grains within the loose specimens broke more easily than those within the dense specimens.Additionally,three types of particle crushing modes were identified for IOTs under one-dimensional compression,namely,abrasion,chipping,and splitting.
基金supported by the National Natural Science Foundation of China(No.31700475).
文摘Aerosol particle pollution has become an increasing serious environmental problem,and urban vegetation plays a long-lasting and positive role in mitigating it.This study compared the particle capture abilities of trees,shrubs,and herbs,and examined the compositions and influence of aerosol particles accumulated on leaf functional traits.Retained particles primarily contained Ca^(2+),K^(+),SO_(4)^(2-),NO_(3)^(-)and NH_(4)^(+),indicating their anthropogenic origins.The leathery-leaved tree Osmanthus fragrans and the papery-leaved herb Alternanthera sessilis demonstrated the higher competence in particle accumulation than other plants,and leaf morphologic structures(e.g.,leaf grooves,trichomes,waxy layers,and stomata characteristics)were closely associated with particle capture by plant species.Particle retention negatively impacted stomata,impeding photosynthesis,and reducing transpiration.In response to particle accumulation,plants tended to decrease specific leaf area and adjust stomatal conductance.Both growth form and leaf texture significantly influenced the particle capture abilities of different plant species.The substantial contribution of plants,particularly herbs in the lower vegetation strata,to particle removal should not be overlooked.Vegetation with a tree-shrub-herb configuration excels at particle capture,offering potential advantages in mitigating particle pollution and enhancing ecological benefits.
基金supported by the National Research Program for Key Issues in Air Pollution Control in China(No.DQGG202137)the National Natural Science Foundation of China(No.42277429)。
文摘The COVID-19 lockdown was a typical example of extreme emission reduction,providing an opportunity to study the impact of lockdown measures on air pollution.Particle number concentrations(PNC)originate from direct emissions or through new particle formation events.However,their variations during the lockdown period are under investigation.This study focuses on Luohe,a city on the southern edge of the North China Plain,analyzing the changes in PNC and its sources before,during,and after the COVID-19 lockdown.From March 25^(th)to May 31^(st),2022,real-time PNC measurements were conducted using a Scanning Mobility Particle Sizer for particle size.Results showed an 11.2%decrease in PNC during the lockdown compared to pre-lockdown and a 3.6%decrease compared to post-lockdown,indicating reduced local emissions and weakened regional transportation during the lockdown.Positive Matrix Factorization analysis identified six sources contributing to the total PNC,including photochemical nucleation,aged photochemical nucleation,gasoline vehicle emissions,diesel vehicle emissions,coal and biomass combustion,and secondary aerosols.The significant changes in source emissions indicate a substantially reduced traffic volume after the implementation of lockdown measures(2644.8#/cm^(3),2202.2#/cm^(3),2792.7#/cm^(3)).Concurrently,photochemical nucleation(310.1#/cm^(3),306.3#/cm^(3),393.1#/cm^(3))and photochemical nucleation aging(592.8#/cm^(3),744.1#/cm^(3),810.7#/cm^(3))exhibited increasing trends,while coal/biomass combustion(1656.6#/cm^(3),1586.2#/cm^(3),980.0#/cm^(3))and secondary sources(999.4#/cm^(3),791.1#/cm^(3),804.1#/cm^(3))showed decreasing trends.In summary,the contributions of traffic emissions to PNC highlight the potential for targeted traffic management strategies to improve urban air quality.
