Lagrangian Particle Research Articles

Observed decade-long improvement of combustion efficiency in the Yangtze River Delta region in China

The ΔCO/ΔCO2 ratio is a good indicator of the combustion efficiency of carbon-containing fuels, and can be useful to assess the combustion efficiency on a regional scale. In this study, we analyzed in-situ observations of CO2 and CO concentrations from 2011 to 2021 at the Station for Observing Regional Processes of the Earth System (SORPES), in the Yangtze River Delta (YRD) region of eastern China, and calculated the ΔCO/ΔCO2 ratio to investigate the combustion efficiency in the YRD region. Furthermore, we used a Lagrangian particle dispersion model WRF-FLEXPART to evaluate the contribution of each emission sources to the observed ΔCO/ΔCO2 ratio. We found that the observed ΔCO/ΔCO2 ratio showed a persistent decreasing trend of 1.0 ppb/ppm per year and decreased ∼47.9% during this period, illustrating an evident improvement in the combustion efficiency in the YRD region. The improvement of the combustion efficiency is a result of China’s Air Pollution Prevention and Control Action Plan announced in 2013. However, the decrease of ΔCO/ΔCO2 ratio slowed down from 1.3 ppb ppm−1 per year during 2011–2016 to 0.6 ppb ppm−1 per year during 2017–2021. The simulation results reveal that the slowdown of the decrease in the ΔCO/ΔCO2 ratios can be explained by the slowing improvement of combustion efficiency in steel source in the industry sector. Our results verify the effectiveness of emission reduction efforts in the YRD region and highlight the necessity of long-term observations of CO2 and CO.

Flow over traveling and rotating cylinders using a hybrid Eulerian–Lagrangian solver

Hybrid Eulerian–Lagrangian solvers have gained increasing attention in the field of external aerodynamics, particularly when dealing with strong body–vortex interactions. This approach effectively combines the strengths of the Eulerian component, which accurately resolves boundary layer phenomena, and the Lagrangian component, which efficiently evolves the wake downstream. This study builds on our team’s previous work by enhancing the capabilities of a two-dimensional hybrid Eulerian–Lagrangian solver. We aim to upgrade our solver which was initially designed for static cases, to now also simulate cases involving moving objects. To ensure the reliability and applicability of a new solver, it is essential to validate its performance in complex cases. Here, the solver is validated across the case of a traveling cylinder and the case of a rotating cylinder in two different rotational speeds at low Reynolds numbers. In the realm of Eulerian solvers, such as OpenFOAM (utilized for the Eulerian component of this hybrid approach), traditional techniques include the use of morphing meshes, overset meshes, and Arbitrary Mesh Interfaces (AMI) to model body motion. The proposed methodology involves extending the Eulerian mesh up to a short distance from the solid boundary and moving it entirely as a solid entity. Then the Lagrangian solver is responsible for calculating the updated boundary conditions, thereby completing the hybrid solver’s functionality. This approach is very similar to the overset mesh technique. However, unlike the traditional method where an Eulerian mesh moves on top of a static one, our method involves the motion of an Eulerian mesh over a Lagrangian grid. We compared the results from our hybrid solver with those from a purely Eulerian solver, specifically OpenFOAM. The comparison demonstrates that our solver can replicate OpenFOAM’s results with high accuracy. Another interesting point highlighted in this study is the presence of high-frequency oscillations in the body forces in hybrid solvers that incorporate the redistribution of Lagrangian particles and do not utilize surface elements such as vortex panels, specifically when dealing with dynamic mesh simulations. When the Eulerian mesh travels on top of the Lagrangian grid of particles, the positions of the particles with respect to the Eulerian mesh continuously change. This results in a constant shift of particles near the solid body, where the highest vorticity is observed. Particles that are close to the solid boundary at one time step may find themselves inside the boundary at the next time step, leading to their removal. This pattern continuously changes during the simulation, causing fluctuations in the boundary conditions of the Eulerian solver and manifesting as oscillations in the forces acting on the body. It is shown that this issue can be alleviated either by increasing the spatial resolution of the Lagrangian solver or by synchronizing the movement of the Lagrangian grid with the motion of the Eulerian mesh. The results of the study make the solver trustworthy and pave the way for more demanding external aerodynamic simulations.

Computational fluid dynamics simulation of passive downstream fish passage over a weir with OpenFOAM®

Many fish species are dependent on migration through river systems. Weirs are an obstacle to downstream migration, which can lead to obstruction as well as injury and mortality. The current level of knowledge about downstream migration over weirs is limited, and the current assessment of this is uncertain. This study uses a computational fluid dynamics (CFD) method for assessing the damage potential of the downstream migration over the weir, evaluating the effectiveness of the current recommendations. Two test cases were simulated using OpenFOAM® and an interFoam-based solver, extended with Lagrangian particles to represent passively transported fish. The results of the simulations show that adherence to the recommendation for a necessary tailwater level has no significant impact on the relevant hydraulic stressors, shear stress, pressure changes and collisions in certain situations. A thick nappe, however, leads to a significant reduction in collisions, especially of particles initialized near the surface. The CFD method used in this study proves to be suitable for evaluating downstream fish passage with certain limitations. This means that qualitative comparisons of different situations are possible, but not the determination of the damage potential of a specific situation. Further investigations are necessary to identify factors for safe downstream fish passage.

Spatial distribution of small microplastics in the Norwegian Coastal Current

High concentrations of microplastic (MP) particles have been reported in the Arctic Ocean. However, studies on the high-resolution lateral and vertical transport of MPs from the European waters to the Arctic are still scarce. Here, we provide information about the concentrations and compositions of MPs in surface, subsurface, and deeper waters (< 1 m, ∼ 4 m, and 17–1679 m) collected at 18 stations on six transects along the Norwegian Coastal Current (NCC) using an improved Neuston Catamaran, the COntinuos MicroPlastic Automatic Sampling System (COMPASS), and in situ pumps, respectively. FTIR microscopy and spectroscopy were applied to measure MP concentration, polymer composition, and size distribution. Results indicate that the concentrations of small microplastics (SMPs, <300 μm) varied considerably (0–1240 MP m−3) within the water column, with significantly higher concentrations in the surface (189 MP m−3) and subsurface (38 MP m−3) waters compared to deeper waters (16 MP m−3). Furthermore, the average concentration of SMPs in surface water samples was four orders of magnitude higher than the abundance of large microplastics (LMPs, >300 μm), and overall, SMPs <50 μm account for >80 % of all detected MPs. However, no statistically significant geographical patterns were observed in SMP concentrations in surface/subsurface seawaters between the six sampling transects, suggesting a relatively homogeneous horizontal distribution of SMPs in the upper ocean within the NCC/Norwegian Atlantic Current (NwAC) interface. The Lagrangian particle dispersal simulation model further enabled us to assess the large-scale transport of MPs from the Northern European waters to the Arctic.

Climate‐influenced phenology of larval fish transport in a large lake

AbstractElucidating physical transport phenologies in large lakes can aid understanding of larval recruitment dynamics. Here, we integrate a series of climate, hydrodynamic, biogeochemical, and Lagrangian particle dispersion models to: (1) simulate hatch and transport of fish larvae throughout an illustrative large lake, (2) evaluate patterns of historic and potential future climate‐induced larval transport, and (3) consider consequences for overlap with suitable temperatures and prey. Simulations demonstrate that relative offshore transport increases seasonally, with shifts toward offshore transport occurring earlier during relatively warm historic and future simulations. Intra‐ and inter‐annual trends in transport were robust to assumed pelagic larval duration and precise location and timing of hatching. Larvae retained nearshore generally encountered more favorable temperatures and zooplankton densities compared to larvae transported offshore. Larval exploitation of nearshore resources under climate change may depend on a concomitant shift to earlier spawning and hatch times in advance of earlier offshore transport.

Flow around a pair of 2D cylinders using a hybrid Eulerian-Lagrangian solver

The field of external aerodynamics encompasses various engineering disciplines with a significant impact on wind energy technology. Aerodynamic investigations provide insights not only into the characteristics of individual blades or standalone wind turbines but also into entire wind farms. As advancements in wind turbine design continue, understanding the interactions between turbines in close proximity becomes crucial, presenting a multi-body problem. Researchers require efficient and accurate tools to comprehensively study such dynamics. This paper presents a hybrid Eulerian-Lagrangian solver designed to leverage the strengths of Eulerian solvers in resolving boundary layers and Lagrangian solvers in convecting wakes downstream without introducing significant numerical diffusion. The solver adeptly handles multi-body simulations, allowing the construction of independent Eulerian meshes that communicate seamlessly through Lagrangian particles. In this way, the computational study of multibody problems does not require very large and dense meshes. Validation in single-body cases has already been conducted, with this paper demonstrating the solver’s application to a pair of cylinders in different configurations. A comparative performance analysis is carried out against pure Eulerian solvers. The results highlight that the hybrid solver efficiently reproduces the accuracy of the Eulerian solver, demonstrating its effectiveness in handling complex aerodynamic simulations.

LES on wind turbines by comparison of Vortex Particle Method and Finite Volume Method codes

The aim of this paper is to compare two different computational methods that analyse the flow around wind turbine using Large-Eddy-Simulations (LES). The first method uses a three-dimensional unsteady Lagrangian Vortex Particle method (VP) associated to a lifting-line (LL) approach providing radial loads, integrated thrust and power coefficients, circulations and angle of attack across the turbine blades. The second method solves the incompressible Navier-Stokes equations with the classical Finite Volume (FV) method coupled to the Actuator Line (AL) method to model the rotor blades. Both methods are compared by means of a benchmark consisting in a single full-scale NREL5MW wind turbine and the results are thus compared to Martínez-Tossas et al (2018). The comparisons involve loads, angle of attack and velocity along the blade. Wakes downstream of the turbine are analysed via the evolution of flow fields as mean velocity and vorticity. Results are found to be in good agreement with the verification case. A comparison is also performed on the evaluation of the numerical cost, precision and efficiency of both computational methods.

Influence of wakes interaction and upstream turbulence on three tidal turbines behaviour

The current study presents numerical results on three tidal turbine models (two in front, one downstream) interacting in a turbulent upstream flow. The numerical results come from a lifting-line (LL) embedded in a Lagrangian vortex particle (VP) solver: Dorothy LL-VP. The objective is to assess the extent to which this numerical tool is suited to reproduce accurately wakes interaction as well as fluctuating loads perceived by the downstream turbine. To this aim, the numerical set-up reproduces an experimental campaign led at IFREMER’s wave and current flume tank. The downstream turbine is placed at different positions to change the wakes interaction. Two upstream turbulence intensities (TI) experimentally tested are reproduced numerically using the synthetic eddy method (SEM). Favourable comparisons are obtained between numerical and experimental wakes, including velocity profiles. Preliminary results suggest that the downstream turbine performance decrease is numerically well captured. More investigations are needed on the loads fluctuations with longer computation time, and adding an angular velocity controller as well as hub modelling to Dorothy LL-VP.

Seasonal and Interannual Salish Sea Inflow Origins Using Lagrangian Tracking

AbstractThe Salish Sea is a semi‐enclosed sea between Vancouver Island and the coast of British Columbia and Washington State, invaluable from both an economic and ecologic perspective. Here we explore the contribution of Pacific water masses to the flow through Juan de Fuca Strait (JdF), the Salish Sea's primary connection to the Pacific Ocean. Quantitative Lagrangian particle tracking within Ariane, an offline Lagrangian tool capable of volume transport calculations, was applied to two numerical ocean models to track the paths and physical properties of water parcels before entering JdF (CIOPS) and within the Salish Sea (SalishSeaCast). During summer upwelling, flow from the north shelf and offshore dominate Pacific inflow, while during winter downwelling, flow from the south shelf and surface flow from the Columbia River plume are the dominant Pacific sources. A weaker and less consistent estuarine flow regime in the winter leads to less Pacific inflow overall and a smaller percentage of said inflow reaching the Salish Sea's inner basins than in the summer. Nevertheless, it was found that winter dynamics are a large driver of interannual variability. This analysis extends the knowledge on the dynamics of Pacific inflow to the Salish Sea and highlights the importance of winter inflow to interannual variability.

The Mixed Layer Salinity Balance in the Western Arctic Ocean

AbstractIn this study, we explore the mixed layer salinity (MLS) balance in the western Arctic Ocean based on the Arctic Subpolar gyre sTate Estimate (ASTE) results. The key components of the MLS budgets and their variabilities in response to the Beaufort Gyre (BG) spin‐up are identified. Seasonally, the surface forcing (brine rejection plus freshwater input) is the most important dominant contributor to the MLS balance. On the other hand, the entrainment dominates the interannual variability of MLS tendency inside the BG, while the advection dominates that in the Beaufort Sea. The sensitivity test of increased river discharge revealed a greater role of the advection term, along with weakened contributions from the surface forcing and entrainment, in determining the interannual variability of MLS balance. In contrast, the seasonal variabilities remained largely unchanged. The Lagrangian particle tracking reveals that the majority of BG freshwater within the mixed layer exits through the Canadian Archipelago prior to the BG spin‐up (2002–2006) and during its relaxation (2012–2017). We found a reduction in mixed layer freshwater sources from the external BG that could feed the gyre during its spin‐up (2007–2011), with the major contributions coming from the Beaufort Sea and the BG region itself through Ekman convergence. The mixed layer freshwater pathways are similar in the two versions of ASTE, but with noticeable proportion changes with the increasing river discharge.

A Numerical Study of Smoke Concentration Distribution near Low-Intensity Fires: Sensitivity to Vertical Canopy Structure and Fire Heat Source Strength

Abstract Broadly speaking, prediction of the negative impacts of prescribed fire on air quality is limited by gaps in our understanding of the underlying fire, fuels, and atmospheric processes. These knowledge gaps hinder our ability to accurately predict smoke concentration distributions, leading to unintended smoke intrusions into nearby communities and subsequent threats to public health and safety. In this study, numerical simulations are performed using the Flexible Particle Weather Research and Forecasting (FLEXPART-WRF) Model, a Lagrangian particle dispersion model, with particle motion driven by output from a full-physics atmospheric model with a forest canopy submodel and 10-m horizontal grid spacing [Advanced Regional Prediction System (ARPS)-CANOPY], to address two research questions. First, what is the relationship between near-fire (within ∼50–150 m of fire) smoke concentration distribution and (i) vertical canopy structure and (ii) fire heat source strength? Second, what roles do mean transport (i.e., transport by the mean wind) and turbulent diffusion play in shaping the near-fire smoke concentration distribution? To address these questions, simulations are run with 25 combinations of plant area density profile and fire sensible heat flux magnitude, and smoke is represented by particles with diameters ≤ 2.5 μm (PM2.5). Results show that near-fire PM2.5 concentration distribution is primarily controlled by vertical canopy structure, with fire heat source strength primarily controlling the PM2.5 concentration magnitude. Analysis of the underlying ARPS-CANOPY variables driving the FLEXPART-WRF particle dispersion helps elucidate the roles of mean transport and turbulent diffusion. In total, the study findings suggest that the vertical distribution of canopy vegetation and fire heat source strength are important factors influencing PM2.5 dispersion and concentration distribution near low-intensity fires.

Applicability Study of Euler–Lagrange Integration Scheme in Constructing Small-Scale Atmospheric Dynamics Models

The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler background field approach to construct a small-scale atmospheric flow field model. The model streamlines the modeling process by combining the benefits of the Lagrangian dynamics model and the Eulerian integration scheme. To verify the effectiveness of the Euler–Lagrange hybrid model, experiments using the Fluent wind field model were conducted for comparison. The results show that both models have their advantages in handling terrain-induced wind fields. The Fluent model excels in simulating the general characteristics of wind fields under specific terrain, while the Euler–Lagrange hybrid model is better at capturing the upstream and downstream disturbances of the terrain on the atmospheric flow field. These findings provide powerful tools for in-depth diagnostic analysis of atmospheric flow simulation and convective precipitation processes. Notably, the Euler–Lagrange hybrid model demonstrates excellent computational efficiency, with an average computation time of approximately 2 s per time step in a Python environment, enabling rapid simulation of 40 time steps within approximately 90 s.

Role of Japan Sea Throughflow in the spatial variability of the long-term sea surface temperature trend

The Japan Sea shows a much stronger warming of long-term sea surface temperature (SST) than surrounding oceans. The warming trend possesses a meridionally alternating zonal band pattern, with weak trends along the paths of the Japan Sea Throughflow and strong trends in the remaining interior region. Using idealized models of the Japan Sea Throughflow and atmospheric heating, this study examines the process behind the formation of such spatial patterns in the SST trend. We find that zonal band structures form in a flat rectangular coastline model, and heat budget analysis shows that horizontal heat transport, due to throughflow, reduces the warming effect created by the surface heat flux. A weak SST trend appears around the jet, while a strong SST trend appears elsewhere. Bathymetric effects are also examined using a model with realistic coastline settings. The location of the western boundary current stabilizes, and the coastal branch begins to disconnect from the Japanese coastline toward the north, allowing a more stable SST warming region to form in the southern interior region. Lagrangian particle tracking experiments confirm that a weak (strong) SST trend corresponds to a short (long) residence time, and eddies in the Japan Sea prolong the residence time in interior regions. The model results suggest that the accumulation time of surface heating is essential to the spatial distribution of the long-term SST warming trend.

Euphotic Zone Residence Time of Antarctic Bottom Water

AbstractAntarctic Bottom Water (AABW) transports surface nutrients deep into the ocean, sequestering them for centuries. Enhancing its biological carbon fixation could augment carbon sinks and reduce atmospheric CO2. Yet, it is uncertain if AABW receives enough light for significant photosynthesis before subducting. Using Lagrangian particle tracking in an ocean sea‐ice model, we found: (a) 70% (66%–73%, depending on euphotic zone (Zeu) criteria) nutrient rich circumpolar deep water never enters the Zeu before becoming AABW. This percentage varies from 70% to 91%, depending on the mixed layer (ML) parameterization adjustments; (b) The remaining particles in the euphotic zone have 39 (34–49, depending on Zeu criteria) days average residence time. The time is not sensitive to ML parameterization strength but turning it off doubles the time; and (c) AABW is mainly exposed to enough light on the Antarctic continental shelf during spring and summer. We conclude that the potential of biotic carbon sequestration in AABW is highly limited by light energy.

Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6

Abstract. Lagrangian particle dispersion models are indispensable tools for the study of atmospheric transport processes. However, Lagrangian transport simulations can become numerically expensive when large numbers of air parcels are involved. To accelerate these simulations, we made considerable efforts to port the Massive-Parallel Trajectory Calculations (MPTRAC) model to graphics processing units (GPUs). Here we discuss performance optimizations of the major bottleneck of the GPU code of MPTRAC, the advection kernel. Timeline, roofline, and memory analyses of the baseline GPU code revealed that the application is memory-bound, and performance suffers from near-random memory access patterns. By changing the data structure of the horizontal wind and vertical velocity fields of the global meteorological data driving the simulations from structure of arrays (SoAs) to array of structures (AoSs) and by introducing a sorting method for better memory alignment of the particle data, performance was greatly improved. We evaluated the performance on NVIDIA A100 GPUs of the Jülich Wizard for European Leadership Science (JUWELS) Booster module at the Jülich Supercomputing Center, Germany. For our largest test case, transport simulations with 108 particles driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, we found that the runtime for the full set of physics computations was reduced by 75 %, including a reduction of 85 % for the advection kernel. In addition to demonstrating the benefits of code optimization for GPUs, we show that the runtime of central processing unit (CPU-)only simulations is also improved. For our largest test case, we found a runtime reduction of 34 % for the physics computations, including a reduction of 65 % for the advection kernel. The code optimizations discussed here bring the MPTRAC model closer to applications on upcoming exascale high-performance computing systems and will also be of interest for optimizing the performance of other models using particle methods.

Role of electrostatic perturbation on kinetic resistive wall mode with application to spherical tokamak

A more complete non-perturbative magnetohydrodynamic (MHD)-kinetic hybrid formulation is developed by including the perturbed electrostatic potential δφ in the particle Lagrangian. The fluid-like counter-parts of the hybrid equations, in the Chew-Goldberger-Low high-frequency limit, are also derived and utilized to test the new toroidal implementation in the MARS-K code. Application of the updated non-perturbative hybrid model for a high-β spherical tokamak plasma in MAST finds that the perturbed electrostatic potential generally plays a minor role in the n = 1 (n is the toroidal mode number) resistive wall mode instability. The effect of δφ is largely destabilizing, with the growth rate of the instability increased by several (up to 20) percent as compared to the case without including δφ. A similar relative change is also obtained for the kinetic-induced resonant field amplification effect at high-β in the MAST plasma considered. The updated capability of the MARS-K code allows quantitative exploration of drift kinetic effects on various MHD instabilities and the antenna-driven plasma response where the electrostatic perturbation, coupled to magnetic perturbations, may play important roles.

Bayesian Inference for Multiple Datasets

Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here, we use the AMIS approach to fit a von Mises distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from 3D meteorological data. A posterior distribution of parameters can help to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and the pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. The novelty of our study is in testing the performance of the method on a very large number of datasets (&gt;11,000). Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

Particle Bounce Stick Behavior in the Rotating Frame of Reference

Abstract Particle deposition is a major damage mechanism for gas turbine components, especially in the secondary air system. Predicting the transport and deposition of ingested atmospheric contaminants is of great interest in component-level simulations. Bounce stick models predict deposition upon collision with a wall during Lagrangian particle tracking; they consider a range of physical phenomena, including van der Waals forces and plastic deformation. The effect of the rotating frame of reference on particle collision physics has thus far been neglected in the literature despite the significant centrifugal (CF) loads experienced by many components of interest for deposition studies, for example, turbine blades. The collision physics of the rotating frame are discussed here using low-order models and Monte Carlo style simulations. The present work aims to provide a conceptual framework for the inclusion of CF forces in collision physics. No significant effect was found on the rebound velocities of particles experiencing “worst case” CF forces. However, differences were observed in the sticking probability between concave and convex rotating surfaces, where the CF forces act in opposing directions to either push the particle into the surface or detach it. A force-based analogy of the popular critical velocity model was developed to study the phenomenon. It was used in a Monte Carlo style simulation of a rotor disk, finding that the variation of critical velocity due to CF forces was likely to bias deposition toward concave surfaces. The collision physics of the rotating frame were found to be unintuitive, with complex implications for modeling deposition in gas turbine components. However, they were consequential in determining the distribution of deposition through the system, hence should be included in particle deposition simulations in computational fluid dynamics (CFD).

A physical simulation-machine learning model for optimal process schemes in laser-based directed energy deposition process

The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

Application of Effective Conversion Rates between NO and NO2 in a Standard Airport Dispersion Model System

The NO/NO2/O3 reaction mechanism of the standard VDI 3783 Part 19 was coupled to the Lagrangian particle model LASAT and quasi-stationary, individual plumes were calculated for a point source under various conditions. First-order conversion rates between NO and NO2 were derived by fitting to these plumes and further simplified to sets of categorized conversion rates which depend on background NO2 concentration, atmospheric stability and time of the day. The rates were applied in the standard airport dispersion model system LASPORT and compared to measured NO2 concentrations at Los Angeles International Airport. The agreement between modelled and measured NO2 concentrations (weekly averages) and ratios NO2 over NOx at monitor stations dominated by airport emissions was in most cases better than a factor of 2 with a Pearson correlation coefficient of about 0.9 or above.