Atmospheric Pollutant Dispersion Research Articles

CFD-DPM Simulation on the Atmospheric Pollutant Dispersion in Industrial Park

In order to mitigate the impact of particulate pollutants in Nanjing Sample Industrial Park, it is imperative to simulate the wind field and pollutant dispersion inside the park. Therefore, a CFD-DPM study was employed to simulate the wind field and pollutant dispersion with an accurate landform model. A large eddy simulation was utilized for calculating wind flow distribution inside the park, which is more suitable than Reynolds-Averaged Navier–Stokes Equations (RANS). The physical model of the plant canopy was incorporated to assess its influence on the wind field and particulate pollutants through drag, buoyancy, and deposition effects. Using this method, the distributions of the wind field, and contaminant and the sensitivity tests were obtained by means of calculating a number of research cases under different meteorological conditions. In the numerical results, the wind field was obstructed by the plant canopy, resulting in near-ground uniformity under unstable weather conditions. The distribution of particulate pollutants was influenced not only by the drag and buoyancy effects but also by deposition, which caused an accumulation of particulate pollutants on the windward side of the canopy under unstable weather conditions. The sensitivity tests were performed by comparing the concentrations of particulate pollutants under various conditional settings. The canopy regions can remove the particulate pollutant by 50% under stable weather conditions. The deposition effect is enhanced by larger particle density and diameter and is also influenced by leaf area density.

An equilibrium model of city with atmospheric pollution dispersion

We propose a spatial model of city coupling a labour market, a residential market, and air pollution resulting from commuter traffic. The city can be of any shape. Agents choose where to work and live in order to maximize their utility, by consuming goods, residential surface and by valuing air quality. Pollution dispersion is described by an advection–diffusion equation. We prove existence of equilibria, and uniqueness when the number of job locations is finite. We obtain analytical and numerical results emphasizing the combined role of economic and meteorological factors on urban air quality.

Investigating seasonal air quality variations consequent to the urban vegetation in the metropolis of Faisalabad, Pakistan

Urban atmospheric pollution is global problem and and have become increasingly critical in big cities around the world. Issue of toxic emissions has gained significant attention in the scientific community as the release of pollutants into the atmosphere rising continuously. Although, the Pakistani government has started the Pakistan Clean Air Program to control ambient air quality however, the desired air quality levels are yet to be reached. Since the process of mapping the dispersion of atmospheric pollutants in urban areas is intricate due to its dependence on multiple factors, such as urban vegetation and weather conditions. Therefore, present research focuses on two essential items: (1) the relationship between urban vegetation and atmospheric variables (temperature, relative humidity (RH), sound intensity (SI), CO, CO2, and particulate matter (PM0.5, PM1.0, and PM2.5) and (2) the effect of seasonal change on concentration and magnitude of atmospheric variables. A geographic Information System (GIS) was utilized to map urban atmospheric variables dispersion in the residential areas of Faisalabad, Pakistan. Pearson correlation and principal component analyses were performed to establish the relationship between urban atmospheric pollutants, urban vegetation, and seasonal variation. The results showed a positive correlation between urban vegetation, metrological factors, and most of the atmospheric pollutants. Furthermore, PM concentration showed a significant correlation with temperature and urban vegetation cover. GIS distribution maps for PM0.5, PM1.0, PM2.5, and CO2 pollutants showed the highest concentration of pollutants in poorly to the moderated vegetated areas. Therefore, it can be concluded that urban vegetation requires a rigorous design, planning, and cost–benefit analysis to maximize its positive environmental effects.

Numerical study on the gaseous radioactive pollutant dispersion in urban area from the upstream wind: Impact of the urban morphology

The radioactive pollutant could migrate to the downstream urban area under the action of atmospheric dispersion due to the turbulent mixing under actual pollution accidents. A scenario in which radioactive contaminants from the upstream (for example, a nearshore nuclear power plant accident) migrates to the downstream urban blocks have been considered in this study. Numerical simulations using computational fluid dynamics (CFD) are then conducted to investigate the effects of the urban morphology (building packing density and layout) on the atmospheric dispersion of radioactive pollutants in this scenario. The building packing density and structure can significantly affect urban areas' mean flow pattern and the turbulent kinetic energy (TKE). The flow pattern and the TKE distribution influence the radioactive pollution dispersion. It is found that the radioactive pollution at the urban canyons is significantly affected by the vertical transport at the canyon. A comparison of the distributions of radioactive and traditional non-radioactive pollutants is also provided.

CFD simulation of pollutant dispersion using anisotropic models: Application to an urban like environment under neutral and stable atmospheric conditions

Modeling the atmospheric dispersion of pollutants emitted from different types of sources under various atmospheric conditions is an essential prerequisite for risk assessment studies and emergency preparedness. In this study, we evaluate the performance of different turbulence models and velocity-scalar correlation models implemented in the Code_Saturne. The evaluation is done with observations of four trial cases of the Mock Urban Setting Test (MUST) campaign in an urban type environment and in neutral and stable atmospheric conditions. For all of the trials studied, the CFD model with a first-order closure model (k−ɛ) predicts 61.1% of the concentrations within a factor of two of the observations, which is higher than the percentage of predicted points (58.8%) when a second-order closure model (Rij−ɛ) is used. Overall, the CFD model underestimates observed concentrations, regardless of the turbulence model used. For the trial with slightly stable conditions, the results show that the k−ɛ model combined with an algebraic SGDH model predicts 75% concentrations within a factor of two of the observations. The performance of the k−ɛ model is compared to that of the Rij−ɛ model when used with the algebraic SGDH, GGDH models and with the scalar flux transport equation (DFM model). Under slightly stable atmospheric conditions, the DFM model predicts 69% of concentrations within a factor of two of observations, showing promise for modeling under these conditions, despite its relatively high computational cost.

Effects of Inversion Layer on The Atmospheric Pollutant Dispersion from A High Chimney

Effects of Inversion Layer on The Atmospheric Pollutant Dispersion from A High Chimney

Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes

Abstract. Super-resolution atmospheric modelling can be used to interpret and optimize environmental observations during top-down emission rate retrieval campaigns (e.g. aircraft-based) by providing complementary data that closely correspond to real-world atmospheric pollution transport and dispersion conditions. For this work, super-resolution model simulations with large-eddy-simulation sub-grid-scale parameterization were developed and implemented using WRF-ARW (Weather Research and Forecasting - Advanced Research WRF). We demonstrate a series of best practices for improved (realistic) modelling of atmospheric pollutant dispersion at super-resolutions. These include careful considerations for grid quality over complex terrain, sub-grid turbulence parameterization at the scale of large eddies, and ensuring local and global tracer mass conservation. The study objective was to resolve small dynamical processes inclusive of spatio-temporal scales of high-speed (e.g. 100 m s−1) airborne measurements. This was achieved by downscaling of reanalysis data from 31.25 km to 50 m through multi-domain model nesting in the horizontal and grid-refining in the vertical. Further, WRF dynamical-solver source code was modified to simulate the release of passive tracers within the finest-resolution domain. Different meteorological case studies and several tracer source emission scenarios were considered. Model-generated fields were evaluated against observational data (surface monitoring network and aircraft campaign data) and also in terms of tracer mass conservation. Results indicated agreement between modelled and observed values within 5 ∘C for temperature, 1 %–25 % for relative humidity, and 1–2 standard deviations for wind fields. Model performance in terms of (global and local) tracer mass conservation was within 2 % to 5 % of model input emissions. We found that, to ensure mass conservation within the modelling domain, tracers should be released on a regular-resolution grid (vertical and horizontal). Further, using our super-resolution modelling products, we investigated emission rate estimations based on flux calculation and mass-balancing. Our results indicate that retrievals under weak advection conditions (horizontal wind speeds < 5 m s−1) are not reliable due to weak correlation between the source emission rate and the downwind tracer mass flux. In this work we demonstrate the development of accurate super-resolution model simulations useful for planning, interpreting, and optimizing top-down retrievals, and we discuss favourable conditions (e.g. meteorological) for reliable mass-balance emission rate estimations.

A Lattice-Boltzmann-based modelling chain for traffic-related atmospheric pollutant dispersion at the local urban scale

Urban traffic-related air pollution is a major source of environmental and health damage and is difficult to quantify due to its inherent physical complexity. We construct a CFD-based simulation framework coupling an efficient numerical method for turbulent fluid flows with a microscopic traffic model and an emissions model to simulate road transport pollutant dispersion at the urban microscale. We improve the open-source Lattice-Boltzmann based CFD software OpenLB to overcome its original stability deficiencies for high Reynolds number flows. A stable recursive regularization procedure with a double distribution function approach is proposed to solve an advection diffusion equation for passive scalar transport at high Reynolds number. The code is successfully validated on three reference cases of increasing complexity and the traffic model SUMO along with a physical engine emissions model are coupled with OpenLB to simulate traffic-induced pollution from a road network in a realistic complex geometry. Transient flow features are analysed and the time-averaged concentration levels in different neighbourhoods of the considered geometry are evaluated: high concentration levels are observed close to the streets but also inside specific building infrastructures due to complex wind dynamics. Analyses of altitudinal concentration variations show that flow recirculations located close to traffic lights can drive pollutant over the buildings and increase concentration levels inside inner courtyards. Time-averaged concentration maps are constructed using both spatially uniform and non-uniform line sources and it is shown that using uniform sources leads to up to 20% local concentration overestimations inside the urban canopy.

A System Coupled GIS and CFD for Atmospheric Pollution Dispersion Simulation in Urban Blocks

Atmospheric pollution is a critical issue in public health systems. The simulation of atmospheric pollution dispersion in urban blocks, using CFD, faces several challenges, including the complexity and inefficiency of existing CFD software, time-consuming construction of CFD urban block geometry, and limited visualization and analysis capabilities of simulation outputs. To address these challenges, we have developed a prototype system that couples 3DGIS and CFD for simulating, visualizing, and analyzing atmospheric pollution dispersion. Specifically, a parallel algorithm for coordinate transformation was designed, and the relevant commands were encapsulated to automate the construction of geometry and meshing required for CFD simulations of urban blocks. Additionally, the Fluent-based command flow was parameterized and encapsulated, enabling the automatic generation of model calculation command flow files to simulate atmospheric pollution dispersion. Moreover, multi-angle spatial partitioning and spatiotemporal multidimensional visualization analysis were introduced to achieve an intuitive expression and analysis of CFD simulation results. The result shows that the constructed geometry is correct, and the mesh quality meets requirements with all values above 0.45. CPU and GPU parallel algorithms are 13.3× and 25× faster than serial. Furthermore, our case study demonstrates the developed system’s effectiveness in simulating, visualizing, and analyzing atmospheric pollution dispersion in urban blocks.

Investigation of the accuracy of the transient wind forcing model applied for a shear-driven LES

We examine the effectiveness of the Transient Wind Forcing (TWF), previously validated with field and wind tunnel experiments, using LES in large-scale and small-scale cyclic domains. The small domain includes only a part of the atmospheric boundary-layer height, so the effect of wind shear is modeled near its upper boundary with the TWF coupling method. It is essentially a time-dependent volume source of momentum, which is controlled using velocity time series extracted from a point in the large domain. Surface roughness is explicitly modeled in both domains. The dispersion of atmospheric pollutants is investigated by the Lagrange method. The model error of TWF coupling is quantitatively evaluated in the case of atmospheric boundary layers with different Coriolis parameters, atmospheric stability, and surface roughness. The results of the small model driven by the TWF method showed good agreement with the reference results obtained from the large domain based on the average wind speed and turbulence profiles between and directly above the buildings, as well as the concentration of air pollutants emitted at ground level. For this, it was also necessary to consider the pressure gradient corresponding to the geostrophic wind and the effect of the Coriolis force in the small domain.

Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion

Mapping near-field pollutant concentration is essential to track plume dispersion in urban areas. By solving most of the turbulence spectrum, large-eddy simulations (LES) can accurately represent concentration spatial variability. Synthesizing this large amount of information to improve the accuracy of lower-fidelity operational models is particularly appealing. Due to the computational cost of LES, it becomes a challenge in multi-query contexts, to quantify how tracer dispersion changes with atmospheric and source parametric variations. We propose a data-driven reduced-order model (ROM) combining proper orthogonal decomposition (POD) and probabilistic Gaussian process regression (GPR) to efficiently predict tracer concentration field first- and second-order statistics. The novelty of this work lies in the original approach used to efficiently tune the GPR based on the available reduced-basis information and properties. In practice, the GPR hyperparameters are optimized through the hierarchy of POD modes thanks to Bayesian optimization informed by POD. The robustness of the approach is also addressed under small data constraint. A detailed analysis of our model performance is carried out in the case of a turbulent atmospheric boundary-layer flow over a surface-mounted obstacle for which the convective character of the system together with the uncertainty of the pollutant source location make it very challenging for ROM. The model is able to capture the wide range of spatial scales across the POD modes. The ROM accuracy remains acceptable for a minimal budget of about one hundred LES snapshots, providing a first estimation moving towards more realistic atmospheric dispersion applications.

ANALYSIS OF SEASONAL VARIATION AND DISPERSION PATTERN OF AMBIENT AIR POLLUTANTS IN AN URBAN ENVIRONMENT

<p>The level of air pollution increased in urban areas due to local atmospheric conditions and dispersion of various air pollutants. In this study, the air quality index was carried out in four places in the urban zone and investigated the seasonal variation of PM2.5, PM10, SO2, NO2, and O3 over one year (2021) by considering the meteorological parameters in Coimbatore. Furthermore, fluidyn-PANACHE, a CFD tool, has been used to simulate the dispersion pattern of gaseous pollutants in the selected locations. To track the model, the concentration details of gaseous pollutants were collected and pollutant dispersion under numerous atmospheric conditions (Humidity, Temperature, Pressure) was identified using fluidyn-PANACHE software. From the collected air samples and dispersion pattern, SO2 and O3 show higher concentrations in summer. Particulate matter and NO2 show higher concentrations in winter due to seasonal variations in energy use and atmospheric constancy. Pearson correlation coefficients indicate most pollutants are correlated in Coimbatore except Ozone. Compared to the AQI category given by CPCB, the selected four locations are in the moderate category. However, increased PM10 levels were observed at all the selected zones in winter due to the process of inversion and environmental conditions.</p>

Indigenous Knowledge of seasons delivers a new way of considering annual cycles in atmospheric dispersion of pollutants

Poor air quality is recognised as the most important environmental health issue of our time. Meteorological variables like temperature and wind speed can strongly influence air quality and these variables often show clear annual cycles. It is therefore common to analyse atmospheric pollutants within a seasonal framework. However, the commonly used seasons in Australia do not align well with all of the most important annual weather patterns that influence air quality in the Sydney Basin. We used Indigenous perspectives on ‘seasons’ as identified by the co-authors and combined these with statistical analysis of the local climatology. This enabled us to create a set of locally informed ‘quasi-seasons’ that we named IKALC-seasons (Indigenous Knowledge Applied to Local Climatology). Engaging with the IKALC-seasons improved our understanding of temporal variability of air pollution in western Sydney, mainly due to a better identification of the time of year when cold, still weather conditions result in higher levels of fine particulate pollution, carbon monoxide and nitrogen oxides. Although the IKALC seasons identified in this study are intrinsically local in nature, the methodology developed has broadscale application. This approach can be used to identify the times of year when micrometeorological conditions are most likely to drive poor air quality thereby helping to inform effective decision-making about emission controls.

Criteria for the Assessment of Health Risk from a Waste Gasification Plant

Abstract In mountainous regions, the atmospheric dispersion of air pollutants is limited by complex terrain morphology. Trentino (a province located in the Italian Alps) is characterized by a high population density, highly developed agriculture and farming, and the presence of civil and industrial activities, whose emissions tend to be trapped inside the valleys. These conditions translate into exposure levels that are potentially higher than other geographical contexts (e.g., flat or coastal regions). The academic research carried out in Trentino since 2005 has produced a series of publications on topics related to the risk for health derived from waste management and other activities. These publications has focused on: 1) the evaluation of the emissions of organic pollutants from the waste sector; 2) the monitoring of persistent organic pollutants (POPs) in various environmental matrices; 3) a methodology to carry out a comprehensive health risk assessment considering all the relevant exposure routes in an area; 4) the role of the local diet in the exposure of a population to POPs; 5) methodologies to define new regulatory limit values for dioxin and hexavalent chromium; and 6) the role of dispersion modeling in assessing the exposure to air pollutants. The present paper will review these publications and provide a reference document to evaluate new project proposals that require a health risk assessment and facilitate the work of proponents, clarifying the requirements of a health risk assessment. The paper will refer to waste gasification, but the considerations expressed in this paper may apply to other activities.

Оцінка екосистемних послуг зі зниження рівнів пилового забруднення атмосферного повітря міста вздовж автошляхів

Air pollution by particulate matter PM10, PM2.5 is one of the aspects that determines the quality of the environment in cities. The general trend today is an increase in the share of road dust among anthropogenic sources of pollution. Removing dust from the air is one of the ecosystem services (ES) provided by urban green spaces (UGS). Currently, there is a lack of methods and technologies that would make it easy to determine the volume of ES both for a particular UGS and for the entire urban blue-green infrastructure (UGBI). The goal of the study is to develop a methodology for assessing of ES in reducing the levels of dust pollution in the city’s atmospheric air along roads. The assessment should take into account the condition of UGS and their effectiveness in air purification. The main factors that determine the differences in ES indicators are the power of the emission source, the specific features of PM redistribution in the air, and the characteristics of the green space. Therefore, the algorithm for assessing the volume of ES is to establish: the parameters of the primary pollution field PM2.5 and PM10; the effectiveness of the UGS in reducing pollution; and the volume of ES for air purification from dust. The main research method is geoinformation modelling, in particular, the processes of atmospheric dispersion of pollutants (based on LEDI). The source materials are: the boundaries of the UGS and roads extracted from the OpenStreetMap database; ESA WorldCover 2020 and Copernicus Land Cover, from which the qualitative characteristics of the UGS were obtained; aerological sounding data (University of Wyoming). A geodatabase was created in the study. The calculated indicators included in the database are the average annual values of PM10 and PM2.5 coming from roads, meteorological parameters of their redistribution, and the coefficient of air purification from dust (Idust_cleaning). The characteristics of the primary pollution field – Contpm10, Contpm2.5 and the efficiency of the function of reducing dust pollution of the UGS – Еdust_cleaning(2.5), Еdust_cleaning(10) were determined. Normalisation of efficiency values according to the Harrington desirability scale allowed us to calculate the volumes of the ES of cleaning urban air from dust (ESdust_cleaning). This assessment can become a tool for urban planning decisions, as it allows to identify the UGS that require priority actions to improve their dust removal capabilities.

Modelling the atmospheric dispersion of ammonia in an industrial area in northern Greece

Air pollution is one of the greatest challenges for ecosystems globally, as it adversely affects not only human health, but also water resources, land, animals and plants. The composition of atmospheric air is changing due to urbanization, industry, land surface alteration, climate change and global population increase. Studying the dispersion of atmospheric pollutants is a crucial issue for better urban, regional and global predictions of air quality. Dispersion models have proven a valuable tool in estimating the concentration of air pollutants produced by industrial facilities, as well as predicting future concentrations under specific scenarios (e.g., changes in emission sources or emission rates). The objective of this research was to examine a number of hypothetical industrial accident scenarios, including the local spread of ammonia from an industrial zone located 5 km east of the coastal city of Kavala in northern Greece. For that, the Atmospheric Dispersion Modeling System 5 (ADMS-5) was used as a safety and emergency planning tool, in order to model instantaneous releases (puffs) of ammonia and to investigate changes in ground-level concentration. To determine the structure of the atmospheric boundary layer, seven different meteorological conditions which correspond to the Pasquill-Gifford stability categories A-G were considered. The maximum ground-level concentration (1400 - 2400 μg/m3) occurred in slightly stable and moderately stable conditions (categories E and F) very close to the source, while in extremely stable conditions (category G) the puff remained stagnant for over 600 s after release. The lowest ground-level concentration (300 μg/m3) occurred with slightly unstable conditions (category C). The obtained results can be used for safety and emergency planning reasons and highlight the importance of atmospheric stability on the fate of accidental industrial releases.

Neural stochastic differential equations for particle dispersion in large-eddy simulations of homogeneous isotropic turbulence

In dilute turbulent particle-laden flows, such as atmospheric dispersion of pollutants or virus particles, the dynamics of tracer-like to low inertial particles are significantly altered by the fluctuating motion of the carrier fluid phase. Neglecting the effects of fluid velocity fluctuations on particle dynamics causes poor prediction of particle transport and dispersion. To account for the effects of fluid phase fluctuating velocity on the particle transport, stochastic differential equations coupled with large-eddy simulation are proposed to model the fluid velocity seen by the particle. The drift and diffusion terms in the stochastic differential equation are modeled using neural networks (“neural stochastic differential equations”). The neural networks are trained with direct numerical simulations (DNS) of decaying homogeneous isotropic turbulence at low and moderate Reynolds numbers. The predictability of the proposed models is assessed against DNS results through a priori analyses and a posteriori simulations of decaying homogeneous isotropic turbulence at low-to-high Reynolds numbers. Total particle fluctuating kinetic energy is under-predicted by 40% with no model, compared to the DNS data. In contrast, the proposed model predictions match total particle fluctuating kinetic energy to within 5% of the DNS data for low- to high-inertia particles. For inertial particles, the model matches the variance of uncorrelated particle velocity to within 10% of DNS results, compared to 60%–70% under-prediction with no model. It is concluded that the proposed model is applicable for flow configurations involving tracer and inertial particles, such as transport and dispersion of pollutants or virus particles.

AIR QUALITY INFLUENCE OF SELECTED POLLUTANTS EMITTED FROM A FIRE OF 300 MG OF MUNICIPAL WASTE ON AMBIENT AIR QUALITY: AN EXAMPLE OF MODELLING WITH HYSPLIT

Reducing the amount of waste disposed of in landfills requires treatment in specialised facilities. Potential threats that can arise both in waste processing and in its storage comprise a fire hazard. Waste fires hold a significant share of the total number of the largest fires in Poland. Fires in landfills and their treatment facilities are related to air quality since during a fire a large number of various pollutants is emitted into the atmosphere in a relatively short time. The paper discusses the impact of a fire taking place at a waste treatment facility on air quality, in which 300 Mg of municipal waste was burnt. Using the HYSPLIT package, the atmospheric dispersion of 23 selected organic and inorganic pollutants was determined and then analysed in GIS software. The concentrations were compared with reference values specified in relevant legal regulations. Of the 23 substances analyzed, the concentration levels were exceeded only for 6 substances at a distance of more than half a kilometre from the fire: benzo(a)pyrene, benzene, styrene, acetaldehyde, PM10, and NO2. The substance with the greatest range as regards the reference concentration levels was benzo(a)pyrene, a compound with a proven carcinogenic effect, which indicates the importance of reducing landfill fires in the context of public health.

Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety

Modern society is confronted with emerging threats from chemical, biological, and radiological (CBR) hazardous substances, which are intensively utilized in the chemical, medical, and energy industries. The atmospheric dispersion of released CBR hazardous pollutants can influence a large percentage of the population owing to their rapid process with extensive spatial coverage. It is important to comprehensively understand the behaviors of the released CBR pollutants in the atmosphere to fully evaluate the risks and protect public safety. In this study, we reviewed the advancements in the atmospheric transport of CBR pollutants, including the urban atmospheric boundary layer, unique concepts, and models for CBR pollutants. We underlined the development of innovative methodologies (e.g., inverse estimation and data assimilation methods) for the atmospheric transport of accidentally released CBR pollutants to reduce uncertainties in emissions and accumulated errors during dispersion by combining numerical models with monitoring data. Finally, we introduced progress in quantitative risk assessment, including exposure assessment and dose-response relationships for CBR hazardous pollutants. A framework, source, assimilation, fundamentals, exposure, and risk (SAFER), has been proposed to integrate the key components in the risk assessment of airborne CBR hazardous pollutants. These methods and models can contribute to effective risk preparedness, prevention, evidence-based policymaking, and emergency response to airborne CBR pollutants.

Fractional Atmospheric Pollutant Dispersion Equation in a Vertically Inhomogeneous Planetary Boundary Layer: an Analytical Solution Using Conformable Derivatives

Fractional Atmospheric Pollutant Dispersion Equation in a Vertically Inhomogeneous Planetary Boundary Layer: an Analytical Solution Using Conformable Derivatives