Dispersion Model Research Articles

Environmental exposures associated with early childhood recurrent wheezing in the mother and child in the environment birth cohort: a time-to-event study

BackgroundAntenatal factors and environmental exposures contribute to recurrent wheezing in early childhood.AimTo identify antenatal and environmental factors associated with recurrent wheezing in children from birth to 48 months in the...

A Python toolkit for integrating geographic information system into regulatory dispersion models for refined pollution modeling

AERMOD is designated as U.S. Environmental Protection Agency (EPA)'s preferred air dispersion model for refined transportation project hot-spot analyses beginning in 2020. One of the key challenges in its modeling process is spatially encoding roadway geometry, especially when simulating highways with complex geometric designs. This research proposed an open-source Python package, GTA, which enables conversion of publicly available roadway Geographic Information System (GIS) layers into defined sources, and source-based emission rates from MOtor Vehicle Emissions Simulator (MOVES) output for AERMOD modeling. The research selected a suburban area in Atlanta, and conducted a comprehensive analysis in terms of annual PM2.5 concentration results and the speed of preparing AERMOD input files for highway network modeling both manually and using software developed based on the proposed methodology. The results prove that the proposed methodology significantly expedites the AERMOD input preparation process, and facilitates convenient testing of multiple modeling configurations for multi-scenario or sensitivity analysis.

Optimization of damping function parameters for -D3 and -D4 dispersion models for Hartree-Fock based symmetry-adapted perturbation theory.

Symmetry-adapted perturbation theory (SAPT) directly computes intermolecular interaction energy in terms of electrostatics, exchange-repulsion, induction/polarization, and London dispersion components. In SAPT based on Hartree-Fock ("SAPT0") or based on density functional theory, the most time-consuming step is the computation of the dispersion terms. Previous work has explored the replacement of these expensive dispersion terms with simple damped asymptotic models. We recently examined [Schriber et al. J. Chem. Phys. 154, 234107 (2021)] the accuracy of SAPT0 when replacing its dispersion term with Grimme's popular -D3 correction, reducing the computational cost scaling from O(N5) to O(N3). That work optimized damping function parameters for SAPT0-D3/jun-cc-pVDZ using estimates of the coupled-cluster complete basis set limit [CCSD(T)/CBS] on a 8299 dimer dataset. Here, we explore the accuracy of SAPT0-D3 with additional basis sets, along with an analogous model using -D4. Damping parameters are rather insensitive to basis sets, and the resulting SAPT0-D models are more accurate on average for total interaction energies than SAPT0. Our results are surprising in several respects: (1) improvement of -D4 over -D3 is negligible for these systems, even charged systems where -D4 should, in principle, be more accurate; (2) addition of Axilrod-Teller-Muto terms for three-body dispersion does not improve error statistics for this test set; and (3) SAPT0-D is even more accurate on average for total interaction energies than the much more computationally costly density functional theory based SAPT [SAPT(DFT)] in an aug-cc-pVDZ basis. However, SAPT0 and SAPT0-D3/D4 interaction energies benefit from significant error cancellation between exchange and dispersion terms.

Thermal-induced buoyant water jet discharge under shallow coastal water conditions

A novel discharge dispersion model is developed to simulate the complex three-dimensional flow behaviour of thermal-induced buoyant water jets under current-wave coexisting conditions. The model solved the governing fluid flow and energy equations for two immiscible and incompressible phases (water and air) which were weakly coupled by applying the Boberbeck-Boussinesq approximation. Different turbulence models, such as k−ε multiphase, k-ω SST, k-ω SST-multiphase, k-ω SST-stable, and realizable k−ε were applied. Extensive verification of the model's performance is conducted by comparing the developed model results against a diverse range of analytical and experimental data. First, a series of simulations are carried out to evaluate the performance of the model in reproducing the results of the wave hydrodynamic and interactions with the submerged trapezoid bar. This is followed by numerically replicating the experimental results of a vertical non-buoyant submerged jet under current-only and current-wave environments. Finally, the potency of the coupled hydro-thermal algorithm is assessed by validating against different thermal-induced buoyant submerged jet experimental tests. For this purpose, numerical prediction of the developed model is tested against physical experiments for a series of tests for thermal-induced buoyant submerged horizontal jets in stationary water and inclined thermal-induced buoyant water jet under the influence of current-wave environments. Results showed that the k-ω SST-multiphase provides the best agreement with the laboratory measured data in terms of flow, temperature distribution field, plume trajectory and dilution. The findings confirmed that the developed model can be used as a reliable tool in precisely modelling characteristic of thermal-induced buoyant water jet in shallow coastal waters.

Solute Dispersion in Casson Blood Flow through a Stenosed Artery with the Effect of Temperature and Electric Field

This study develops a mathematical model to address solute dispersion in arterial blood flow, particularly considering the effects of temperature and electric fields using the Casson fluid model. Given the physiological importance of drug delivery within the human vascular system, understanding these dynamics is crucial, especially in the presence of cardiovascular diseases (CVD). The Generalized Dispersion Model (GDM) is employed to simulate the dispersion function. Results demonstrate that elevated temperatures and electric fields enhance drug uptake and distribution by increasing blood flow. The model accurately predicts drug behavior in narrowed arteries, optimizing dosage regimens and improving therapeutic outcomes. Visual representations and detailed discussions of these findings are presented in the subsequent sections. The results are validated against a previous study that did not consider the effects of stenosis height, temperature and electric field. The findings suggest a strong agreement between the two studies. Additionally, an increase in mean absorption leads to an increase in velocity. When mean absorption increases, the functions of steady dispersion and overall dispersion decrease, while the function of unsteady dispersion increases. The reverse behavior is observed for the electric field. The study concludes that integrating temperature and electric field effects into drug delivery systems can significantly enhance treatment efficacy and reduce side effects, providing a valuable tool for advanced targeted therapies in CVD and cancer management.

Effect of Circadian Rhythm Modulated Blood Flow on Nanoparticle based Targeted Drug Delivery in Virtual In Vivo Arterial Geometries.

A novel integration of nanoparticle-based drug delivery framework with shear-rate dependent blood flow model is presented. The framework is comprised of a unique combination of mechanics-based dispersion model, an asperity model for endothelium surface roughness, and a stochastic nanoparticle-endothelial cell adhesion model. Simulations of MRI based in vivo carotid artery system showcase the effects of vessel geometry on nanoparticle adhesion and retention at the targeted site. Vessel geometry and target site location impact nanoparticle adhesion; curved and bifurcating regions favor local accumulation of drug. It is also shown that aligning drug administration with circadian rhythm and sleep cycle can enhance the efficacy of drug delivery processes. These simulations highlight the potential of the computational modeling for exploring circadian rhythm modulated blood flow for targeted drug delivery while minimizing the in vivo experimentation.

Numerical study on the behavior of a polymeric MHD nanofluid: entropy optimization and thermal analysis

PurposeAnalyzing and reducing entropy generation is useful for enhancing the thermodynamic performance of engineering systems. This study aims to explore how polymers and nanoparticles in the presence of Lorentz forces influence the fluid behavior and heat transfer characteristics to lessen energy loss and entropy generation.Design/methodology/approachThe dispersion model is initially used to examine the behavior of polymer additives over a magnetized surface. The governing system of partial differential equations (PDEs) is subsequently reduced through the utilization of similarity transformation techniques. Entropy analysis is primarily performed through the implementation of numerical computations on a non-Newtonian polymeric FENE-P model.FindingsThe numerical simulations conducted in the presence of Lorentz forces provide significant insights into the consequences of adding polymers to the base fluid. The findings suggest that such an approach minimizes entropy in the flow region. Through the utilization of polymer-MHD (magnetohydrodynamic) interactions, it is feasible to reduce energy loss and improve the efficiency of the system.Originality/valueThis study’s primary motivation and novelty lie in examining the significance of polymer additives as agents that reduce entropy generation on a magnetic surface. The author looks at how nanofluids affect the development of entropy and the loss of irreversibility. To do this, the author uses the Lorentz force, the Soret effect and the Dufour effect to minimize entropy. The findings contribute to fluid mechanics and thermodynamics by providing valuable insights for engineering systems to increase energy efficiency and conserve resources.

Computational fluid dynamics simulation of ammonia leakage scenarios during ship-to-ship bunkering

Ammonia is emerging as a potential marine fuel, yet its toxicity and risk of accidental leaks pose significant safety challenges compared to other alternatives. This study focuses on the critical ship-to-ship bunkering process using Computational Fluid Dynamics (CFD) simulations to analyse diverse ammonia leakage scenarios considering various leak orientations relative to the wind and the ships. The numerical setup was initially validated against controlled experiments, demonstrating satisfactory agreement. Results indicate that atmospheric ammonia dispersion is significantly influenced by obstacles like the superstructure and hull in the near field, with diminishing effect in the far field. Conventional Gaussian plume models are inadequate within the first few hundred metres from the release source due to the complex near-field flow patterns and increased turbulence generated by the wakes of the hull and superstructure. These findings underscore the necessity for improved ship-scale dispersion models to accurately evaluate the impact of obstacles on ammonia dispersion. The consequence analysis reveals that upward leaks create larger risk zones with potential for irreversible health effects, while horizontal leaks may expose personnel to life-threatening concentrations. Vertical leaks in crosswind configurations generate lethal zones covering tens of metres on deck. These results highlight the importance of considering the exact location of the transfer hose relative to the ship and all potential wind orientations in risk assessments. Given the extensive range of potential dispersion patterns and the inadequacy of conventional Gaussian dispersion models in this context, we strongly recommend vessel-specific CFD simulations before certifying ammonia bunkering operations as safe. This approach is crucial for accurate risk assessment at the ship scale, where complex geometries and multi-directional flows invalidate the assumptions of traditional point-source, uni-directional dispersion models.

SAPERI: An Emergency Modeling Chain for Simulating Accidental Releases of Pollutants into the Atmosphere

Timely forecast of atmospheric pollutants fallout due to accidental fires can provide decision-makers with useful information for effective emergency response, for planning environmental monitoring and for conveying essential alerts to the population to minimize health risks. The SAPERI project (Accelerated simulation of accidental releases in the atmosphere on heterogeneous platforms—from its Italian initials) implements a modeling chain to quickly supply evidence about the dispersion of pollutants accidentally released in the atmosphere, even in the early stages of the emergency when full knowledge of the incident details is missing. The SAPERI modeling chain relies on SPRAY-WEB, a Lagrangian particle dispersion model openly shared for research purposes, parallelized on a GPU to take advantage of local or cloud computing resources and interfaced with open meteorological forecasts made available by the Meteo Italian SupercompuTing PoRtAL (MISTRAL) consortium over Italy. The operational model provides a quantitative and qualitative estimate of the impact of the emergency event by means of a maximum ground level concentration and a footprint map. In this work, the SAPERI modeling chain is tested in a real case event that occurred in Beinasco (Torino, Italy) in December 2021, mimicking its use with limited or missing local input data as occurs when an alert message is first issued. An evaluation of the meteorology forecast is carried out by comparing the wind and temperature fields obtained from MISTRAL with observations from weather stations. The concentrations obtained from the dispersion model are then compared with the observations at three air quality monitoring stations impacted by the event.

Using the dispersion model of a chiral metamaterial to calculate the characteristics of homogeneous and heterogeneous reflective and waveguide structures

Currently, metamaterials are actively used in the creation of microwave and optic devices for various purposes. This is due to their non-traditional properties, compared to dielectric, conductive and other materials. One type of metamaterial is chiral media, which contain conductive microelements of mirror asymmetric shape. Such metamaterials have dispersion and, as a consequence, it is necessary to take it into account when developing various microwave structures. The work demonstrates taking into account the dispersion of the dielectric constant and the chirality parameter for calculating waveguide and reflecting structures with homogeneous and inhomogeneous chiral layers. The work carried out an electrodynamic analysis of the reflective properties of an inhomogeneous chiral layer and a rectangular waveguide, with a homogeneous layer of chiral metamaterial located in the transverse plane. The work has developed a method for calculating the reflection and transmission coefficients of an optical wave from the inhomogeneous chiral structure under consideration, which is based on the use of the differential sweep method. When constructing a mathematical model, we took into account the cross-polarization of the optical wave field, which consists in the appearance of orthogonal components during the interaction of the field with a chiral metamaterial. The solution to the problem was reduced to a matrix differential equation for the unknown reflection coefficients of the main and cross-polarized components of the optical field. The work examined chiral metamaterials with linear and parabolic inhomogeneity profiles. The results of the work can be applied in the design of new microwave and optics devices with layers of metamaterial, which can expand their functionality.

Comparing Large-Eddy Simulation and Gaussian Plume Model to Sensor Measurements of an Urban Smoke Plume

The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a ‘normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.

Assessing urban air pollution dynamics: the impact of traffic emissions and urban morphology in Lecce and Bari, Italy

Urban air pollution, exacerbated by vehicular emissions, poses a significant health threat, especially in densely populated areas. This study, part of the PNRR Italian National Center for Sustainable Mobility (MOST), aims to assess the impact of vehicular pollutants in urban environments. Focusing on Lecce and Bari (Italy), the research integrates data from dispersion models and meteorological analyses to evaluate pollutant concentrations. Preliminary analysis of eight urban areas in Lecce and six in Bari reveals trends in PM10 concentrations influenced by urban morphology and wind dynamics. These findings underscore the essential role of sustainable mobility strategies in mitigating urban air pollution and promoting healthier urban environments.

Optimizing Medical Care during a Nerve Agent Mass Casualty Incident Using Computer Simulation

IntroductionChemical mass casualty incidents (MCIs) pose a substantial threat to public health and safety, with the capacity to overwhelm healthcare infrastructure and create societal disorder. Computer simulation systems are becoming an established mechanism to validate these plans due to their versatility, cost-effectiveness and lower susceptibility to ethical problems.MethodsWe created a computer simulation model of an urban subway sarin attack analogous to the 1995 Tokyo sarin incident. We created and combined evacuation, dispersion and victim models with the SIMEDIS computer simulator. We analyzed the effect of several possible approaches such as evacuation policy (‘Scoop and Run’ vs. ‘Stay and Play’), three strategies (on-site decontamination and stabilization, off-site decontamination and stabilization, and on-site stabilization with off-site decontamination), preliminary triage, victim distribution methods, transport supervision skill level, and the effect of search and rescue capacity.ResultsOnly evacuation policy, strategy and preliminary triage show significant effects on mortality. The total average mortality ranges from 14.7 deaths in the combination of off-site decontamination and Scoop and Run policy with pretriage, to 24 in the combination of onsite decontamination with the Stay and Play and no pretriage.ConclusionOur findings suggest that in a simulated urban chemical MCI, a Stay and Play approach with on-site decontamination will lead to worse outcomes than a Scoop and Run approach with hospital-based decontamination. Quick transport of victims in combination with on-site antidote administration has the potential to save the most lives, due to faster hospital arrival for definitive care.

Modeling Intermolecular Interactions with Exchange-Hole Dipole Moment Dispersion Corrections to Neural Network Potentials.

Neural network potentials (NNPs) are an innovative approach for calculating the potential energy and forces of a chemical system. In principle, these methods are capable of modeling large systems with an accuracy approaching that of a high-level ab initio calculation, but with a much smaller computational cost. Due to their training to density-functional theory (DFT) data and neglect of long-range interactions, some classes of NNPs require an additional term to include London dispersion physics. In this Perspective, we discuss the requirements for a dispersion model for use with an NNP, focusing on the MLXDM (Machine Learned eXchange-Hole Dipole Moment) model developed by our groups. This model is based on the DFT-based XDM dispersion correction, which calculates interatomic dispersion coefficients in terms of atomic moments and polarizabilities, both of which can be approximated effectively using neural networks.

Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine

Hydrogen fueled gas turbine is an efficient and environmentally friendly energy conversion equipment. However, it is prone to gas leakages from corrosion-prone components and pipe connections. In order to address gas leakage problems, source term estimation (STE) can be applied to estimate the location and intensity of the leakage sources. Traditional STE methods are based on an optimization procedure using an atmospheric transport and dispersion model, which requires considerable computation efforts and cannot meet the need of real-time implementation. The complex flow field around the gas turbine, the possible existence of multiple leakage sources, and the high-dimensional time series data further increase the difficulty of the STE problem. To address these challenges, in the present study, a deep learning based STE approach is proposed. The basic idea is to use long short-term memory auto-encoder (LSTM-AE) network to extract the encoded features of multi-sensor data and then use a deep neural network (NN) to establish the relationship between the encoded features and the source term parameters of hydrogen leakages. The multi-sensor data are generated using computational fluid dynamics (CFD) analysis to simulate relevant hydrogen leakage scenarios of concern. The results show that compared with other machine learning algorithms, the proposed approach has an overall best performance in terms of the STE accuracy. Specifically, its hydrogen leakage source localization accuracy is 0.9798, and the leakage strength estimation R-squared (R2) can reach 0.9632. When the training data proportion is only 20–30%, the proposed approach can still perform well, indicating the ability of the model to effectively predict the location and strength of the leakage source even with relatively less training data.

How dependent are quantitative volcanic ash concentration and along‐flight dosage forecasts to model structural choices?

AbstractProducing quantitative volcanic ash forecasts is challenging due to multiple sources of uncertainty. Careful consideration of this uncertainty is required to produce timely and robust hazard warnings. Structural uncertainty occurs when a model fails to produce accurate forecasts, despite good knowledge of the eruption source parameters, meteorological conditions and suitable parameterizations of transport and deposition processes. This uncertainty is frequently overlooked in forecasting practices. Using a Lagrangian particle dispersion model, simulations with varied output spatial resolution, temporal averaging period and particle release rate are performed to quantify the impact of these structural choices. This experiment reveals that, for the 2019 Raikoke eruption, structural choices give measurements of peak ash concentration spanning an order of magnitude, significantly impacting decision‐relevant thresholds used in aviation flight planning. Conversely, along‐flight dosage estimates exhibit less sensitivity to structural choices, suggesting it is a more robust metric to use in flight planning. Uncertainty can be reduced by eliminating structural choices that do not result in a favourable level of agreement with a high‐resolution reference simulation. Reliable forecasts require output spatial resolution 80 km, temporal averaging periods 3 h and particle release rates 5000 particles/h. This suggests that simulations with relatively small numbers of particles could be used to produce a large ensemble of simulations without significant loss of accuracy. Comparison with previous Raikoke simulations indicates that the uncertainty associated with these constrained structural choices is smaller than those associated with satellite constrained eruption source parameter and internal model parameter uncertainties. Thus, given suitable structural choices, other epistemic sources of uncertainty are likely to dominate. This insight is useful for the design of ensemble methodologies which are required to enable a shift from deterministic to probabilistic forecasting. The results are applicable to other long‐range dispersion problems and to Eulerian dispersion models.

Lignite reduces odour from broiler farms without reducing production performance

An unavoidable consequence of chicken meat production is the generation of substantial quantities of spent litter. This poses several environmental and social challenges, as broiler farms become hotspots for odour emissions. The main source of odour from broiler litter is the microbial decomposition of organic material. Thus, lignite's previously shown capacity to reduce microbial activity in broiler litter was expected to reduce odour emissions from broiler housing. The effect of lignite on odour emission rate (OER) (ou/s) was investigated using dynamic olfactometry over two consecutive summer broiler grow-out cycles in Victoria, Australia, with lignite applied at 3.5 kg/m2. The air quality dispersion model, AERMOD, was used to investigate how lignite's effect on OER influenced the predicted odour dispersion distances and concentrations in the context of various Australian regulatory requirements. Additionally, the effect of lignite on bird production performance was also determined. This study showed that lignite reduced the OER from commercial broiler housing by an average of 56% over both grow-outs. This effect was observed for the duration of the trial. The observed reduction in OER reduced the required separation distances by 44–53% according to the state odour criteria for Victoria, New South Wales, Queensland and South Australia. There were no observed differences in feed conversion ratio, bird live weight or mortality throughout either grow-out. This study demonstrated the capacity of lignite to reduce odour emissions from commercial broiler housing without reducing bird production performance.

Patterns of cerebral damage in multiple sclerosis and aquaporin-4 antibody-positive neuromyelitis optica spectrum disorders-major differences revealed by non-conventional imaging.

Multiple sclerosis and aquaporin-4 antibody neuromyelitis optica spectrum disorders are distinct autoimmune CNS disorders with overlapping clinical features but differing pathology. Multiple sclerosis is primarily a demyelinating disease with the presence of widespread axonal damage, while neuromyelitis optica spectrum disorders is characterized by astrocyte injury with secondary demyelination. Diagnosis is typically based on lesion characteristics observed on standard MRI imaging and antibody testing but can be challenging in patients with in-between clinical presentations. Non-conventional MRI techniques can provide valuable diagnostic information by measuring disease processes at the microstructural level. We used non-conventional MRI to measure markers of axonal loss in specific white matter tracts in multiple sclerosis and neuromyelitis optica spectrum disorders, depending on their relationship with focal lesions. Patients with relapsing-remitting multiple sclerosis (n = 20), aquaporin-4 antibody-associated neuromyelitis optica spectrum disorders (n = 20) and healthy controls (n = 20) underwent a 3T brain MRI, including T1-, T2- and diffusion-weighted sequences, quantitative susceptibility mapping and phase-sensitive inversion recovery sequence. Tractometry was used to differentiate tract fibres traversing through white matter lesions from those that did not. Neurite density index was assessed using neurite orientation dispersion and density imaging model. Cortical damage was evaluated using T1 relaxation rates. Cortical lesions and paramagnetic rim lesions were identified using phase-sensitive inversion recovery and quantitative susceptibility mapping. In tracts traversing lesions, only one out of 50 tracts showed a decreased neurite density index in multiple sclerosis compared with neuromyelitis optica spectrum disorders. Among 50 tracts not traversing lesions, six showed reduced neurite density in multiple sclerosis (including three in the cerebellum and brainstem) compared to neuromyelitis optica spectrum disorders. In multiple sclerosis, reduced neurite density was found in the majority of fibres traversing (40/50) and not traversing (37/50) white matter lesions when compared to healthy controls. A negative correlation between neurite density in lesion-free fibres and cortical lesions, but not paramagnetic rim lesions, was observed in multiple sclerosis (39/50 tracts). In neuromyelitis optica spectrum disorders compared to healthy controls, decreased neurite density was observed in a subset of fibres traversing white matter lesions, but not in lesion-free fibres. In conclusion, we identified significant differences between multiple sclerosis and neuromyelitis optica spectrum disorders corresponding to their distinct pathologies. Specifically, in multiple sclerosis, neurite density reduction was widespread across fibres, regardless of their relationship to white matter lesions, while in neuromyelitis optica spectrum disorders, this reduction was limited to fibres passing through white matter lesions. Further studies are needed to evaluate the discriminatory potential of neurite density measures in white matter tracts for differentiating multiple sclerosis from neuromyelitis optica spectrum disorders.

Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows

Abstract. VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NOx concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment.

Atmospheric dynamics and fire-induced phenomena: Insights from a comprehensive analysis of the Sertã wildfire event

The Sertã wildfire started on October 15th, 2017, in a mountainous area in central Portugal, on the hottest day of an exceptionally dry month compared to the previous two decades. This dry spell was attributed to a persistent heatwave from October 1st to 16th, which impacted most of mainland Portugal. Due to the critical weather and vegetation conditions, the fire was very intense, generating a thermal plume that reached high altitudes. The main purpose of this work is to analyze and discuss the impact of the Sertã wildfire on the dynamics of atmospheric mountain circulation. To accomplish this, a numerical modeling system integrating meteorological, fire spread, and smoke dispersion models was employed. The results indicate that the interaction between the wildfire and the local mountain breeze in central Portugal, under the synoptic weather conditions in Europe, which included the Hurricane Ophelia over the Atlantic Ocean and a warm-sector polar frontal depression in northern Europe, led to a locally disturbed weather situation. The consistent, efficient, and uniform local transport of atmospheric properties at lower levels was disrupted, intensifying wind shear and developing more turbulent advection patterns. Moreover, the fire's impact contributed to the development of a baroclinic wave at 500 hPa over mass centers, which settled at the surface and enhanced cloud development over the region. This phenomenon aligns with the atmospheric circulation effects studied in the Bjerknes and Rossby theories.