Pollutant dispersion in urban street canopies

The effects of building configurations on pollutant dispersion around street canopies were studied numerically. The dispersion of pollutants emitted from ground sources was simulated by continuously discharging large number of particles into the computation domain. The mean wind velocities at each time-step were firstly computed by solving the time-dependent incompressible Navier–Stokes equations, while the fluctuated velocities were determined using a statistical procedure. The trajectories of the discharged particles were obtained from a Lagrangian particle model. Three categories of numerical simulation were conducted to study the effect of different canopy geometries on the pollutant dispersion. The computed wind field data were consistent with the wind field characteristics described in the previous wind tunnel studies. A counter-clockwise vortex was found resulting in high pollutant concentration at the windward side of the downstream building of the street canopy and low pollutant concentration at the leeward side of the upstream building. The increase in height of the urban roughness buildings would facilitate the pollutant dispersion in urban street canopy under certain building configurations. Two or more vortices stacked vertically in a street canopy were found when height of the upstream and downstream buildings of a street canopy was increased, preventing pollutants from escaping out of the canopy.     

Evaluation of a Gaussian and a Lagrangian model against a roadside data set,with emphasis on low wind speed conditions

The evaluation of the high percentiles of concentration distributions is required by most national air quality guidelines, as well as the EU directives. However, it is problematic to compute such high percentiles in stable, low wind speed or calm conditions. This study utilizes the results of a previous measurement campaign near a major road at Elimäki in southern Finland in 1995, a campaign specifically designed for model evaluation purposes. In this study, numerical simulations were performed with a Gaussian finite line source dispersion model CAR-FMI and a Lagrangian dispersion model GRAL, and model predictions were compared with the field measurements. In comparison with corresponding results presented previously in the literature, the agreement of measured and predicted data sets was good for both models considered, as measured using various statistical parameters. For instance, considering all NO_x data (N=587), the so-called index of agreement values varied from 0.76 to 0.87 and from 0.81 to 1.00 for the CAR-FMI and GRAL models, respectively. The CAR-FMI model tends to slightly overestimate the NO_x concentrations (fractional bias FB=+14%), while the GRAL model has a tendency to underestimate NO_x concentrations (FB=−16%). The GRAL model provides special treatment to account for enhanced horizontal dispersion in low wind speed conditions; while such adjustments have not been included in the CAR-FMI model. This type of Lagrangian model therefore predicts lower concentrations, in conditions of low wind speeds and stable stratification, in comparison with a standard Lagrangian model. In low wind speed conditions the meandering of the flow can be quite significant, leading to enhanced horizontal dispersion. We also analyzed the difference between the model predictions and measured data in terms of the wind speed and direction. The performance of the CAR-FMI model deteriorated as the wind direction approached a direction parallel to the road, and for the lowest wind speeds. However, the performance of the GRAL model varied less with wind speed and direction; the model simulated better the cases of low wind speed and those with the wind nearly parallel to the road.     

Dispersion of Traffic and Space Heating Emissions In Urban Settings

A long-term dispersion model is presented for traffic and space heating emissions in urban areas, allowing fast assessment of the spatial-averaged and center-maximum pollutant concentrations.

The assumption of study areas with circular shape and normal emissions density profiles is made for the purpose of streamlining model inputs with the inventory data normally available. In addition, the rather typical assumptions of Gaussian dispersion, narrow plume, flat or gently rolling terrain, homogeneous wind field and nonreactive pollutants are made. Values of σz from Briggs correlation are used with an initial value of 30 to account for building effects.

Meterological data inputs are reduced to six parameters, inventory data inputs to two, while computations are simplified to a degree that use of a digital computer is not required.

The model is well suited to yield separate assessments for individual types of sources and control measures, as well as to reveal sensitivities from parameters such as city size, or emission density levels and distribution patterns. Its predictions are virtually identical to those of the CDM-2 UNAPMAR model for study areas with circular shape and normal emissions density profiles, and as results do not appear overly sensitive to shape and distribution patterns, the model is believed to be valid for most urban areas.     

Evaluation of traffic-producing turbulence schemes within Operational Street Pollution Models using roadside measurements

Low wind scenarios are associated with the worst air pollution episodes in urban street canyons. Under these conditions, operational dispersion models often over-predict pollutant concentration. Traffic-producing turbulence (TPT) becomes dominant in mixing and diluting traffic-related pollutants under low wind speed conditions. Determining the TPT effect on the flow and dispersion patterns within urban street canyons is crucial for the development of detailed operational dispersion models for assessing urban air quality. Several spatially averaged TPT formulations have been recently proposed in the literature. However, only a few attempts have been made so far to incorporate different TPT schemes into operational dispersion models and evaluate their performance using measurements.In this paper, several TPT schemes presented in literature were evaluated. Two TPT schemes were implemented in the well-validated Windows version of the Danish Operational Street Pollution Model (WinOSPM). Both formulations were evaluated using six independent datasets of roadside CO concentrations collected in European cities. Statistical and sensitivity analyses were undertaken to test the performance of the different formulations. The results showed that the overall model performance was significantly sensitive to the TPT schemes adopted. The model performance improved when a detailed characterisation of the TPT, depending on the density of road traffic, was used.     

Numerical simulation of flow and dispersion around an isolated cubical building: The effect of the atmospheric stratification

The aim of this work is to investigate atmospheric flow and dispersion of contaminants in the vicinity of single buildings under different stability conditions. The mathematical model used is based on the solution of equations of conservation of mass, linear momentum and energy with the use of a non-standard κ–? turbulence model. The modifications proposed in the κ–? model are the inclusion of the Kato and Launder correction in the production of turbulent kinetic energy and the use of a modified wall function. Results are presented of numerical simulations of dispersion around a cubical obstacle, under neutral, stable and unstable atmospheric conditions. Experimental data from wind tunnel and field trials obtained by previous authors are used to validate the numerical results. The numerical simulation results show a reasonable level of agreement with field and wind tunnel concentration data. The deviation between model results and field experimental data is of the same order as the deviation between field and wind tunnel data.     

Modeling of dispersion near roadways based on the vehicle-induced turbulence concept

A mathematical model is developed for dispersion near roadways by incorporating vehicle-induced turbulence (VIT) into Gaussian dispersion modeling using computational fluid dynamics (CFD). The model is based on the Gaussian plume equation in which roadway is regarded as a series of point sources. The Gaussian dispersion parameters are modified by simulation of the roadway using CFD in order to evaluate turbulent kinetic energy (TKE) as a measure of VIT. The model was evaluated against experimental carbon monoxide concentrations downwind of two major freeways reported in the literature. Good agreements were achieved between model results and the literature data. A significant difference was observed between the model results with and without considering VIT. The difference is rather high for data very close to the freeways. This model, after evaluation with additional data, may be used as a framework for predicting dispersion and deposition from any roadway for different traffic (vehicle type and speed) conditions.     

Assimilating concentration observations for transport and dispersion modeling in a meandering wind field

Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.     

Improving pollutant source characterization by better estimating wind direction with a genetic algorithm

In homeland security applications, it is often necessary to characterize the source location and strength of a potentially harmful contaminant. Correct source characterization requires accurate meteorological data such as wind direction. Unfortunately, available meteorological data is often inaccurate or unrepresentative, having insufficient spatial and temporal resolution for precise modeling of pollutant dispersion. To address this issue, a method is presented that simultaneously determines the surface wind direction and the pollutant source characteristics. This method compares monitored receptor data to pollutant dispersion model output and uses a genetic algorithm (GA) to find the combination of source location, source strength, and surface wind direction that best matches the dispersion model output to the receptor data. A GA optimizes variables using principles from genetics and evolution.The approach is validated with an identical twin experiment using synthetic receptor data and a Gaussian plume equation as the dispersion model. Given sufficient receptor data, the GA is able to reproduce the wind direction, source location, and source strength. Additional runs incorporating white noise into the receptor data to simulate real-world variability demonstrate that the GA is still capable of computing the correct solution, as long as the magnitude of the noise does not exceed that of the receptor data.     

Development of a livestock odor dispersion model: part I. Model theory and development

A livestock odor dispersion model (LODM) was developed to predict mean odor concentration, odor frequency, instantaneous odor concentration, and peak odor concentration from livestock operations. This model is based on the Gaussian fluctuating plume model and has the ability to consider the instantaneous concentration fluctuations and the differences between odor and traditional air pollutants. It can predict odor frequency from the routine hourly meteorological data input and deal with different types of sources and multiple sources. Also, the relationship between odor intensity and odor concentration was incorporated into the model.     

Simulation of short-range diffusion experiment in low-wind convective conditions

A previously obtained analytical solution to model the short-range dispersion of pollutants in low winds from surface releases has been used to simulate diffusion tests conducted during winter in weakly convective conditions at the Indian Institute of Technology (IIT) Delhi. The turbulence parameterization based on friction velocity has been tested to simulate diffusion experiment. Such a parameterization in this study is considered justifiable on two counts: (1) prevailing meteorological and dispersion conditions have been generally of weakly unstable type as indicated by values of Monin–Obukhov length and bulk Richardson number, (2) uncertainties associated with the application of convective velocity based similarity parameterization to simulation of diffusion experiment at IIT Delhi, resulting in significant underprediction in most of the cases (Atmos. Environ. 30 (1996a) 1137). With this parameterization, the model simulations have improved considerably and compare reasonably well with the observations. Further, the results from a simple Gaussian model have been included for comparison. This study is in continuation of the work done earlier to simulate near-source dispersion in weak winds.     

Unsteady characteristics of the dispersion process in the vicinity of a pig barn. Wind tunnel experiments and comparison with field data

Two complementary methods, field experiments and physical modelling in a wind tunnel, have been used to investigate the dispersion of tracer-gas released from the ventilation system of a pig barn, under near-neutral stability conditions. In both cases, concentration fluctuations were measured and the deduced statistical results were compared. The choice of data processing applied to the time series of concentration was motivated by special issues in the assessment of odour annoyances: “where, how often, how long and how strong does it smell?” These features were described by the mean concentration distribution, the intermittency factor, the persistence and the 90-percentile. The good agreement between field and wind tunnel data confirmed the ability to replicate in wind tunnel the unsteady properties of a dispersion process, if the unsteady turbulent behaviour of the atmospheric boundary layer was properly modelled.A parametrical study of the influence on the dispersion process of the ratio between the exhaust velocity from the stack and the wind speed was then performed in wind tunnel. The fundamental outcome was that the near-field dispersion process under neutral stability conditions, despite the strong influence of the building wake, was for the most part driven by the meandering behaviour of the plume and not so much by the diffusion process.This study was also focused on the influence of the averaging time on the statistical results. The scatter generated by using dimensionless averaging times 200<T_a^*<400 (used during field experiments) instead of T_a^*→∞ (averaging time to ensure reproducible statistic results) was quantified in the wind tunnel. A degree of representativity of the results obtained from short-term samples, compared to fully converged statistical results was therefore assessed.     

An Inversion Algorithm for Determining Area-Source Emissions from Downwind Concentration Measurements

Measuring emissions from nonuniform area sources, such as waste repository sites, has been a difficult problem. A simple but reliable method is not available. An objective method of inverting downwind concentration measurements, utilizing an assumed form of atmospheric dispersion to reconstruct total emission rate and distribution, is described in this study. The Gaussian dispersion model is compared to a more realistic model based on K-theory and similarity expressions. A sensitivity analysis is presented indicating the atmospheric conditions under which a successful application of the method could be anticipated. Field releases of sulfur hexaf luoride (SF₆) from a simulated area source in flat terrain were conducted to check the method,ability to reconstruct source distribution, and total emission rate. The sensitivity analysis and the field study confirm that a few ground-level concentration measurements and a simple determination of the atmospheric dispersion characteristics are sufficient, under neutral to stable conditions, to obtain the total emission rate accurately. Reconstruction of the spatial pattern of the source is possible by utilizing concentration information from samplers located on two separate ground-level receptor lines, if a shift in the wind direction occurs and if it can be assumed that the total emission rate is time invariant. A method of cross-checking the accuracy of the reconstruction, using a simultaneous tracer release, is presented.     

Traffic and Meteorological Impacts on Near-Road Air Quality: Summary of Methods and Trends from the Raleigh Near-Road Study

Abstract

A growing number of epidemiological studies conducted worldwide suggest an increase in the occurrence of adverse health effects in populations living, working, or going to school near major roadways. A study was designed to assess traffic emissions impacts on air quality and particle toxicity near a heavily traveled highway. In an attempt to describe the complex mixture of pollutants and atmospheric transport mechanisms affecting pollutant dispersion in this near-highway environment, several real-time and time-integrated sampling devices measured air quality concentrations at multiple distances and heights from the road. Pollutants analyzed included U.S. Environmental Protection Agency (EPA)-regulated gases, particulate matter (coarse, fine, and ultrafine), and air toxics. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices to provide continuous and integrated assessments of the variation of near-road air pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road. Measurement results demonstrated the temporal and spatial impact of traffic emissions on near-road air quality. The distribution of mobile source emitted gas and particulate pollutants under all wind and traffic conditions indicated a higher proportion of elevated concentrations near the road, suggesting elevated exposures for populations spending significant amounts of time in this microenvironment. Diurnal variations in pollutant concentrations also demonstrated the impact of traffic activity and meteorology on near-road air quality. Time-resolved measurements of multiple pollutants demonstrated that traffic emissions produced a complex mixture of criteria and air toxic pollutants in this microenvironment. These results provide a foundation for future assessments of these data to identify the relationship of traffic activity and meteorology on air quality concentrations and population exposures.     

BTX concentrations near a stage II implemented petrol station

A combined monitoring and dispersion modelling methodology was applied for assessing air quality at three different levels of proximity to the selected service station: (I) next to the fuel pumps, (II) in the surrounding environment, and (III) in the background. Continuous monitoring and passive sampling were used for achieving high temporal and spatial resolution, respectively. A Gaussian dispersion model (CALINE4) was used for assessing the road traffic contribution to the local concentrations under different meteorological conditions. It was established that Stage 2 vapour recovery reduces BTX concentrations not only near the pumps, but also in their surrounding environment. However, there is evidence that the efficiency of the system is wind speed dependent. The modelling simulation of the worst case wind scenario revealed the significance of local traffic emissions. It was shown that the traffic contribution even from a single road in the vicinity of the station can, under certain conditions, be higher than the contribution of the station itself to the local BTX levels. Finally, after comparison with previous studies, the concentrations measured near the service station (which was situated in a rural environment) appear to be lower than those observed in busy street canyons in city centres. It can be concluded, although Stage 2 recovery system effectively reduces working VOC losses in service stations, that it will only have a limited positive impact on local air quality if the service station is located in a heavily polluted area.     

Traffic and meteorological impacts on near-road air quality: summary of methods and trends from the Raleigh Near-Road Study

A growing number of epidemiological studies conducted worldwide suggest an increase in the occurrence of adverse health effects in populations living, working, or going to school near major roadways. A study was designed to assess traffic emissions impacts on air quality and particle toxicity near a heavily traveled highway. In an attempt to describe the complex mixture of pollutants and atmospheric transport mechanisms affecting pollutant dispersion in this near-highway environment, several real-time and time-integrated sampling devices measured air quality concentrations at multiple distances and heights from the road. Pollutants analyzed included U.S. Environmental Protection Agency (EPA)-regulated gases, particulate matter (coarse, fine, and ultrafine), and air toxics. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices to provide continuous and integrated assessments of the variation of near-road air pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road. Measurement results demonstrated the temporal and spatial impact of traffic emissions on near-road air quality. The distribution of mobile source emitted gas and particulate pollutants under all wind and traffic conditions indicated a higher proportion of elevated concentrations near the road, suggesting elevated exposures for populations spending significant amounts of time in this microenvironment. Diurnal variations in pollutant concentrations also demonstrated the impact of traffic activity and meteorology on near-road air quality. Time-resolved measurements of multiple pollutants demonstrated that traffic emissions produced a complex mixture of criteria and air toxic pollutants in this microenvironment. These results provide a foundation for future assessments of these data to identify the relationship of traffic activity and meteorology on air quality concentrations and population exposures.     

Validation of the lagrangian particle dispersion model FLEXPART against large-scale tracer experiment data

A comprehensive validation of FLEXPART, a recently developed Lagrangian particle dispersion model based on meteorological data from the European Centre for Medium-Range Weather Forecasts, is described in this paper. Measurement data from three large-scale tracer experiments, the Cross-Appalachian Tracer Experiment (CAPTEX), the Across North America Tracer Experiment (ANATEX) and the European Tracer Experiment (ETEX) are used for this purpose. The evaluation is based entirely on comparisons of model results and measurements paired in space and time. It is found that some of the statistical parameters often used for model validation are extremely sensitive to small measurement errors and should not be used in future studies. 40 cases of tracer dispersion are studied, allowing a validation of the model performance under a variety of different meteorological conditions. The model usually performs very well under undisturbed meteorological conditions, but it is less skilful in the presence of fronts. The two ETEX cases reveal the full range of the model’s skill, with the first one being among the best cases studied, and the second one being, by far, the worst. The model performance in terms of the statistical parameters used stays rather constant with time over the periods (up to 117 h) studied here. It is shown that the method used to estimate the concentrations at the receptor locations has a significant effect on the evaluation results. The vertical wind component sometimes has a large influence on the model results, but on the average only a slight improvement over simulations which neglect the vertical wind can be demonstrated. Subgrid variability of mixing heights is important and must be accounted for.     

Numerical modeling on air quality in an urban environment with changes of the aspect ratio and wind direction

Due to heavy traffic emissions within an urban environment, air quality during the last decade becomes worse year by year and hazard to public health. In the present work, numerical modeling of flow and dispersion of gaseous emissions from vehicle exhaust in a street canyon were investigated under changes of the aspect ratio and wind direction. The three-dimensional flow and dispersion of gaseous pollutants were modeled using a computational fluid dynamics (CFD) model which was numerically solved using Reynolds-averaged Navier–Stokes (RANS) equations. The diffusion flow field in the atmospheric boundary layer within the street canyon was studied for different aspect ratios (W/H?=?1/2, 3/4, and 1) and wind directions (θ?=?90°, 112.5°, 135°, and 157.5°). The numerical models were validated against wind tunnel results to optimize the turbulence model. The numerical results agreed well with the wind tunnel results. The simulation demonstrated that the minimum concentration at the human respiration height within the street canyon was on the windward side for aspect ratios W/H?=?1/2 and 1 and wind directions θ?=?112.5°, 135°, and 157.5°. The pollutant concentration level decreases as the wind direction and aspect ratio increase. The wind velocity and turbulence intensity increase as the aspect ratio and wind direction increase.     

Evaluation of weather conditions for methyl bromide ambient air monitoring in Monterey, Santa Cruz, and Kern Counties in the year 2000

Weather condition is one of the most important factors affecting spatial and temporal distributions of air pollutants, especially for short-term air dispersion. Abnormal weather conditions might lead to higher or lower ambient air concentrations than they would be under normal weather conditions. Therefore, testing for normality of weather conditions during the air monitoring period is an essential step for evaluating ambient air monitoring results. In this paper, a distance method was used to select a most representative weather station from the available candidates. An array of meteorological elements were identified that affect air dispersion and transportation. A statistical method was used to determine whether the weather conditions during the air monitoring period were significantly different from that of previous years. Using methyl bromide ambient air monitoring as a case study, this paper documents the methods, procedures, and results of weather analysis for Monterey, Santa Cruz, and Kern Counties during ambient air monitoring periods for methyl bromide in the year 2000. With a few exceptions, the meteorological elements and atmospheric stability factors, such as wind speeds, wind directions, and stability classes, during the monitoring period were in the normal range. Although there were higher frequencies of stable atmospheric conditions in Monterey/Santa Cruz Counties than in Kern County, weather conditions during the monitoring period were not significantly different from normal local weather conditions of previous years. Consequently, the subchronic air concentrations observed during the ambient air monitoring periods for methyl bromide in the year 2000 was taken under typical weather conditions of those areas at that time of the year.     

An atmospheric dispersion model for the environmental impact assessment of thermal power plants in Japan--a method for evaluating topographical effects

An atmospheric dispersion model was developed for the environmental impact assessment of thermal power plants in Japan, and a method for evaluating topographical effects using this model was proposed. The atmospheric dispersion model consists of an airflow model with a turbulence closure model based on the algebraic Reynolds stress model and a Lagrangian particle dispersion model (LPDM). The evaluation of the maximum concentration of air pollutants such as SO2, NOx, and suspended particulate matter is usually considered of primary importance for environmental impact assessment. Three indices were therefore estimated by the atmospheric dispersion model: the ratios (alpha and beta, respectively) of the maximum concentration and the distance of the point of the maximum concentration from the source over topography to the respective values over a flat plane, and the relative concentration distribution [gamma(x)] along the ground surface projection of the plume axis normalized by the maximum concentration over a flat plane. The atmospheric dispersion model was applied to the topography around a power plant with a maximum elevation of more than 1,000 m. The values of alpha and beta evaluated by the atmospheric dispersion model varied between 1 and 3 and between 1 and 0.4, respectively, depending on the topographical features. These results and the calculated distributions of y(x) were highly similar to the results of the wind tunnel experiment. Therefore, when the slope of a hill or mountain is similar to the topography considered in this study, it is possible to evaluate topographical effects on exhaust gas dispersion with reasonable accuracy using the atmospheric dispersion model as well as wind tunnel experiments.