Search Results (137)

Infectious diseases can spread through virus-laden respiratory droplets exhaled into the air. Ventilation systems are crucial in indoor settings as they can dilute or eliminate these droplets, underscoring the importance of understanding their efficacy in the management of indoor infections. Within the field of fluid dynamics methods, the dispersed droplets may be approached through either a Lagrangian framework or an Eulerian framework. In this study, various Eulerian methodologies are systematically compared against the Eulerian–Lagrangian (E-L) approach across three different scenarios: the pseudo-single-phase model (PSPM) for assessing the transport of gaseous pollutants in an office with displacement ventilation (DV), stratum ventilation (SV), and mixing ventilation (MV); the two-fluid model (TFM) for evaluating the transport of non-evaporating particles within an office with DV and MV; and the two-fluid model-population balance equation (TFM-PBE) approach for analyzing the transport of evaporating droplets in a ward with MV. The Eulerian and Lagrangian approaches present similar agreement with the experimental data, indicating that the two approaches are comparable in accuracy. The computational cost of the E-L approach is closely related to the number of tracked droplets; therefore, the Eulerian approach is recommended when the number of droplets required by the simulation is large. Finally, the performances of DV, SV, and MV are presented and discussed. DV creates a stratified environment due to buoyant flows, which transport respiratory droplets upward. MV provides a well-mixed environment, resulting in a uniform dispersion of droplets. SV supplies fresh air directly to the breathing zone, thereby effectively reducing infection risk. Consequently, DV and SV are preferred to reduce indoor infection. Full article

This study investigates the potential Kadomtsev–Petviashvili equation incorporating a power-type nonlinearity (PKPp), a model that features prominently in various nonlinear phenomena encountered in physics and applied mathematics. A complete Noether symmetry classification of the PKPp equation is conducted, revealing four distinct scenarios based on different values of the exponent $p$, namely, the general case where $p\ne 1,-1,-2,$ and three special cases where $p=1,$$p=-1,$ and $p=-2.$ Corresponding to each case, conservation laws are derived through a second-order Lagrangian framework. Furthermore, Lie group analysis is employed to reduce the nonlinear partial differential Equation (NLPDE) to ordinary differential Equations (ODEs), thereby enabling the effective application of the Kudryashov method and direct integration techniques to construct exact solutions. In particular, exact solutions of of the considered nonlinear partial differential equation are obtained for the cases $p=1$ and $p=2,$ illustrating the practical implementation of the proposed approach. The solutions obtained include solitary wave, periodic, and rational-type solutions. These results enhance the analytical understanding of the PKPp equation and contribute to the broader theory of nonlinear dispersive equations. Full article

Especially in real-world circumstances with uneven road surfaces and impulsive shocks, nonlinear dynamic effects in vehicle systems can greatly skew biometric data utilized to track passenger and driver physiological states. By creating a thorough multibody human–seat–chassis model, this work tackles the effect of vehicle-induced vibrations on the accuracy and dependability of biometric measures. The model includes external excitation from road-induced inputs, nonlinear damping between structural linkages, and vertical and angular degrees of freedom in the head–neck system. Motion equations are derived using a second-order Lagrangian method; simulations are run using representative values of a typical car and human body segments. Results show that higher vehicle speed generates more vibrational energy input, which especially in the head and torso enhances vertical and angular accelerations. Modal studies, on the other hand, show that while resonant frequencies stay constant, speed causes a considerable rise in amplitude and frequency dispersion. At speeds ≥ 50 km/h, RMS and VDV values exceed ISO 2631 comfort standards in the body and head. The results highlight the need to include vibration-optimized suspension systems and ergonomic design approaches to safeguard sensitive body areas and preserve biometric data integrity. This study helps to increase comfort and safety in both traditional and autonomous car uses. Full article

CAMx-LPiG (Comprehensive Air Quality Model with Extensions—Linear Plume in Grid) is an online hybrid model based on the Chemistry and Transport Model (CTM) CAMx, which includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work, the model is presented and evaluated against measured NO₂ concentrations for the city of Milan for the month of January 2017. The model can introduce road traffic-induced gradient in NO₂ concentration at sub-grid resolution. Moreover, CAMx-LPiG has been shown to reduce bias compared to CAMx stand-alone simulations. Full article

In this study, based on high-resolution online monitoring data of CO₂ concentration in Nanning City in the spring of 2024, we analyzed the characteristics of diurnal and monthly changes of CO₂ concentration in Nanning City and explored the influencing factors through the background sieving method and Lagrangian Particle Dispersion Model (LPDM) traceability simulations combined with meteorological factor analysis. The results demonstrates that the diurnal variation of CO₂ concentration in Nanning City exhibits a bimodal pattern of peak in the afternoon and trough in the early morning, with a mean concentration of 460 ± 15 ppm. Transportation emissions were identified as the dominant source of this variation. The trend of monthly concentration changes was first increasing and then decreasing, with an increase in February–March and a decrease in April, indicating that it was affected by the combined effect of vegetation photosynthesis and urban human activities. The results of the background sieving method and traceability simulation analysis showed that the CO₂ concentration in Nanning City was more affected by local emission sources than sinks, and the industrial sources and transportation sources in the north–south direction had a significant effect on the CO₂ concentration. This research provides critical data support for formulating carbon reduction strategies and coordinated atmospheric environment management in subtropical cities. Full article

Water residence time (WRT) is a crucial parameter for evaluating the rate of water exchange and it serves as a timescale for elucidating hydrodynamic processes, pollutant dispersion, and biogeochemical cycling in coastal waters. This study investigates the tidal-driven WRT patterns in the Bohai and Yellow Seas (collectively known as BYS) by employing a tidal model in conjunction with an adjoint WRT diagnostic model and explores the influence of tidal constituents on WRT. The findings indicate that the tidal-driven WRT in the BYS is approximately 2.11 years, exhibiting a significant spatially heterogeneous distribution. The WRT pattern shows a strong correlation with the pattern of tidal-driven Lagrangian residual currents (LRCs). Semidiurnal tides have a more pronounced effect on WRT than diurnal tides. Semidiurnal tides significantly reduce WRT across the entire BYS, while diurnal tides predominantly influence WRT in the Bohai Sea (BS). The M₂ tidal constituent is the most influential in decreasing WRT and enhancing water exchange, owing to its dominant energy contribution within the tidal system. In contrast, the S₂ tidal constituent has a minimal effect; however, its interaction with the M₂ tidal constituent plays a significant role in reducing the WRT. The K₁ and O₁ constituents exert more localized effects on WRT, particularly in the central BS, where their energy ratios relative to M₂ are relatively high. Although the amplitude of the S₂ constituent exceeds that of K₁ and O₁, its contribution to LRC—and consequently to WRT—is limited due to the overlapping tidal wave with M₂. This research contributes to a deeper understanding of the influence of tidal dynamics on long-term water transport and associated timescales, which are vital for enhancing predictions of material transport and ecosystem dynamics in tidal-dominated environments. Full article

The effects of substrate roughness and impact angle on the spreading behavior of Polyvinylidene Fluoride (PVDF) slurry droplets during the spraying process using a dispersing disk are investigated, aiming to enhance the quality of lithium-ion battery separators. In this study, through theoretical modeling and simulation analysis, mathematical expressions for the maximum spreading coefficient and the final shrinking coefficient of the droplets are derived. A simulation model for droplet impact and diffusion on the substrate surface is established based on the Lattice Boltzmann Method (LBM) and the Lagrangian function. Simulation results indicate that the maximum spreading coefficient of the droplet decreases with increasing substrate roughness and impact angle, while the final shrinking coefficient increases with substrate roughness but decreases as the impact angle increases. Finally, spray coating experiments for lithium-ion battery separators are conducted, and the results show that as the surface roughness and impact angle of the substrate increase, the average diameter of the droplets decreases, thereby validating the accuracy of the simulation results. Full article

The paper analyzes the processes of emission and dispersion of particulate contaminants from a large point source emitter: a hard coal-fired power plant. Reference is made to the European Green Deal and its main objective of reducing anthropogenic particulate and greenhouse gas emissions. CHPP, Krakow Combined Heat and Power Plant, Poland, as described in the article, has a strong impact on the mechanisms that shape the microclimatic factors of the Krakow agglomeration. This combined heat and power plant provides heat and electricity for the city, while simultaneously emitting significant amounts of suspended particulate matter into the atmosphere. Due to the adverse impact of non-conventional energy sources on the natural environment and the increasing effects of climate warming, radical changes need to be implemented. The HYSPLIT (Hybrid Single-Particles Lagrangian Integrated Trajectories) model was used to track the movement of contaminated air masses. A 5-day episode of increased hourly concentrations of PM2.5 particulate matter contamination was selected to analyze the backward trajectories of air mass displacement. From 15 August 2022 to 19 August 2022, high 24-h particulate matter concentrations were recorded, measuring around 20 µg/m³. The HYSPLIT model, a unique tool in the precise identification of point sources of pollution and their impact on the air quality of the region, was used to analyze the influx of polluted air masses. A 5-day episode of increased hourly concentrations of PM2.5 pollutants was selected for the study, with values of approximately 20 µg/m³. It was found that low-pressure systems over the North Atlantic brought wet and variable weather conditions, while high-pressure systems in southern and eastern Europe, including Poland, provided stable and dry weather conditions. The simulation results were verified by analyzing synoptic maps of the study area. The image of the displacement of contaminated air masses obtained from the HYSPLIT model was found to be consistent with the synoptic maps, confirming the accuracy of the applied model. This means that the HYSPLIT model can be used to create maps of contaminant dispersion directions. Consequently, it was confirmed that modeling using the HYSPLIT model is an effective method for predicting the displacement directions of particulate contamination originating from coal combustion in a combined heat and power plant. Identifying circulation patterns and front zones during episodes of increased contaminant concentrations is strategic for effective weather monitoring, air quality management, and alerting the public to episodes of increased health risk in a large agglomeration. Full article

In this paper, droplet vaporization dynamics in a heated bubbling fluidized bed was studied. A volatile hydrocarbon liquid jet comprising acetone was injected into a hot bubbling fluidized bed of Geldart A-type glass ballotini particles heated at 150 °C, well above the saturation temperature of acetone (56 °C). Intense interactions were observed among the evaporating droplets and hot particles during contact with the re-suspension of particles due to a release of vapour. A non-intrusive schlieren imaging method was used to track the hot air and vapour mixture plume in the freeboard region of the bed and the acetone vapour fraction therein was mapped. The jet vaporization dynamics in the bubbling fluidized bed was modelled in a Eulerian–Lagrangian CFD (computational fluid dynamics) modelling framework involving heat and mass transfer sub models. The CFD model indicated a dispersion of the vapour plume from the evaporating droplets which was qualitatively compared with the schlieren images. Further, the CFD simulation predicted a significant reduction (~60 °C) in the local bed temperature at the point of the jet injection, which was indirectly confirmed in an experiment by the presence of particle agglomerates. Full article

A major challenge in accidental or unregulated releases is the ability to identify the pollutant source, especially if the location is in a large industrial area. Usually in such cases, only a few sensors provide non-zero signal. A crucial issue is therefore the ability to use a small number of sensors in order to identify the source location and rate of emission. The general problem of characterizing source parameters based on real-time sensors is known to be a difficult task. As with many inverse problems, one of the main obstacles for an accurate estimation is the non-uniqueness of the solution, induced by the lack of sufficient information. In this study, an efficient method is proposed that aims to provide a quantitative estimation of the source of hazardous gases or breathable aerosols. The proposed solution is composed of two parts. First, the physics of the atmospheric dispersion is utilized by a well-established Lagrangian stochastic model propagated backward in time. Then, a new algorithm is formulated for the prediction of the spacial expected uncertainty reduction gained by the optimal placement of an additional sensor. These two parts together are used to construct an adaptive decision support system for the dynamical deployment of detectors, allowing for an efficient characterization of the emitting source. This method has been tested for several scenarios and is shown to significantly reduce the uncertainty that stems from the insufficient information. Full article

Livestock farms are recognized sources of ammonia emissions, impacting nearby regions’ fine dust particle concentrations, though the full extent of this impact remains uncertain. Air dispersion models, commonly employed to estimate particulate matter (PM) levels, are heavily reliant on data quality, resulting in varying levels of accuracy. This study compares the performance of both air dispersion models and spatiotemporal deep learning models in estimating PM concentrations in Republic of Korea’s livestock-farming areas. Hourly PM concentration data, alongside temperature, humidity, and air pressure, were collected from seven monitoring stations across the study area. Using a 200 m × 200 m prediction grid, forecasts were generated for both 1 h and 24 h intervals using the Graz Lagrangian model (GRAL) and a one-dimensional convolutional neural network combined with the long short-term memory algorithm (1DCNN-LSTM). Results highlight the potential of the deep learning model to enhance PM prediction, indicating its promise as an effective alternative or supplement to conventional air dispersion models, particularly in data-scarce areas such as those surrounding livestock farms. Gaining a comprehensive understanding and evaluating the advantages and disadvantages of each approach would offer valuable scientific insights for monitoring atmospheric pollution levels within a specific area. Full article

As the wealth of evidence grows as to the negative impact of indoor air quality on human health, it has become increasingly urgent to investigate and characterise sources of air pollution within the home. Fine particulate matter with a diameter of 2.5 µm or less (PM_2.5) is a key cause for concern, and cooking is known to be one of the most significant sources of domestic PM_2.5. In this study, the aim was to demonstrate the efficacy of combining experimental techniques and cutting-edge High-Performance Computing (HPC) to characterise the dispersion of PM_2.5 during stir-frying within a kitchen laboratory. This was carried out using both experimental measurement with low-cost sensors and high-fidelity Computational Fluid Dynamics (CFD) modelling, in which Lagrangian Stochastic Methods were used to model particle dispersion. Experimental results showed considerable spatio-temporal variation across the kitchen, with PM_2.5 mass concentrations in some regions elevated over 1000 μg ${\mathrm{m}}^{-3}$ above the baseline. This demonstrated both the impact that even a short-term cooking event can have on indoor air quality and the need to factor in such strong spatio-temporal variations when assessing exposure risk in such settings. The computational results were promising, with a reasonable approximation of the experimental data shown at the majority of monitoring points, and future improvements to and applications of the model are suggested. Full article

Rapid industrialization and the influx of human resources have led to the establishment of industrial complexes near urban areas, exposing residents to various air pollutants. This has led to a decline in air quality, impacting neighboring residential areas adversely, which highlights the urgent need to monitor air pollution in these areas. Recent advancements in technology, such as Solar Occultation Flux (SOF) and Sky Differential Optical Absorption Spectroscopy (SkyDOAS) used as remote sensing techniques and mobile extraction Fourier Transform Infrared Spectrometry (MeFTIR) used as an in situ technique, now offer enhanced precision in estimating the pollutant emission flux and identifying primary sources. In a comprehensive study conducted in 2020 in the Sinpyeong Jangrim Industrial Complex in Busan City, South Korea, a mobile laboratory equipped with SOF, SkyDOAS, and MeFTIR technologies was employed to approximate the emission flux of total alkanes, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), formaldehyde (HCHO), and methane (CH₄). Using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) diffusion model, pollutant dispersion to residential areas was simulated. The highest average daily emission flux was observed for total alkanes, with values of 69.9 ± 71.6 kg/h and 84.1 ± 85.8 kg/h in zones S₁ and S₂ of the Sinpyeong Jangrim Industrial Complex, respectively. This is primarily due to the prevalence of metal manufacturing and mechanical equipment industries in the area. The HYSPLIT diffusion model confirmed elevated pollution levels in residential areas located southeast of the industrial complex, underscoring the influence of the dominant northwesterly wind direction and wind speed on pollutant dispersion. This highlights the urgent need for targeted interventions to address and mitigate air pollution in downwind residential areas. The total annual emission fluxes were estimated at 399,984 kg/yr and 398,944 kg/yr for zones S₁ and S₂, respectively. A comparison with the Pollutant Release and Transfer Registers (PRTRs) survey system revealed that the total annual emission fluxes in this study were approximately 24.3 and 4.9 times higher than those reported by PRTRs. This indicates a significant underestimation of the impact of small businesses on local air quality, which was not accounted for in the PRTR survey system. Full article

Understanding how ocean currents influence larval dispersal and measuring its magnitude is critical for conservation and sustainable exploitation, especially in the Tropical Eastern Pacific (TEP), where the larval transport of rocky reef fish remains untested. For this reason, a lagrangian simulation model was implemented to estimate larval transport pathways in Northwestern Mexico and TEP. Particle trajectories were simulated with data from the Hybrid Ocean Coordinate Model, focusing on three simulation scenarios: (1) using the occurrence records of Lutjanus peru and L. argentiventris as release sites; (2) considering a continuous distribution along the study area, and (3) taking the reproduction seasonality into account in both species. It was found that the continuous distribution scenario largely explained the genetic structure previously found in both species (genetic brakes between central and southern Mexico and Central America), confirming that the ocean currents play a significant role as predictors of genetic differentiation and gene flow in Northwestern Mexico and the TEP. Due to the oceanography of the area, the southern localities supply larvae from the northern localities; therefore, disturbances in any southern localities could affect the surrounding areas and have impacts that spread beyond their political boundaries. Full article

As atmospheric CO₂ emissions and the trend of urbanization both increase, the ability to accurately assess the CO₂ budget from urban environments becomes more important for effective CO₂ mitigation efforts. This task can be difficult for complex areas such as the urban–coastal Mediterranean region near Marseille, France, which contains the second most populous city in France as well as a broad coastline and nearby mountainous terrain. In this study, we establish a CO₂ modeling framework for this region for the first time using WRF-Chem and demonstrate its efficacy through comparisons against cavity-ringdown spectrometer measurements recorded at three sites: one 75 km north of the city in a forested area, one in the city center, and one at the urban/coastal border. A seasonal CO₂ analysis compares Summertime 2016 and Wintertime 2017, to which Springtime 2017 is also added due to its noticeably larger vegetation uptake values compared to Summertime. We find that there is a large biogenic signal, even in and around Marseille itself, though this may be a consequence of having limited fine-scale information on vegetation parameterization in the region. We further find that simulations without the urban heat island module had total CO₂ values 0.46 ppm closer to the measured enhancement value at the coastal Endoume site during the Summertime 2016 period than with the module turned on. This may indicate that the boundary layer on the coast is less sensitive to urban influences than it is to sea-breeze interactions, which is consistent with previous studies of the region. A back-trajectory analysis with the Lagrangian Particle Dispersion Model found 99.83% of emissions above 100 mol km⁻² month⁻¹ captured in Summer 2016 by the three measurement towers, providing evidence of the receptors’ ability to constrain the domain. Finally, a case study showcases the model’s ability to capture the rapid change in CO₂ when transitioning between land-breeze and sea-breeze conditions as well as the recirculation of air from the industrial Fos region towards the Marseille metroplex. In total, the presented modeling framework should open the door to future CO₂ investigations in the region, which can inform policymakers carrying out CO₂ mitigation strategies. Full article