Simulating sulfur dioxide plume dispersion and subsequent deposition downwind from a stationary point source: a model

Dispersion and subsequent deposition of SO(2) downwind from a stationary point source are affected by several transport processes: buoyancy at the source, advection, and air turbulence en route from the source to the area of impact. In this paper, SO(2) transport processes are simulated by way of Lagrangian air parcel trajectory simulations. In these simulations, the source releases air parcels in puffs. The calculations cover both daytime and night-time conditions and take into account: (i) solar geometry, (ii) diurnal variations of wind speed and air turbulence, (iii) resistance to the transfer of SO(2) from the air to the land, and (iv) flat terrain. Deposition to the forest is determined by calculating the rate of SO(2) flux from individual air parcels to the land according to the parcel's velocity and an assumed air-to-surface SO(2) transfer coefficient. Daily cumulative SO(2) deposition rates are calculated by summing the simulated diffusional fluxes of SO(2) from air to land over each simulated time step. Daily cumulative SO(2) amounts are calculated for downwind distances from 0 to 42 km, for smokestack heights from 30 to 200 m, and for each day of the year according to historical year-round and local weather patterns representative of days with neutral conditions and days with transitions from stable to unstable conditions. Annual per hectare rates of SO(2) deposition are calculated by way of Monte Carlo simulations, according to historical patterns for daily wind, atmospheric stability, and precipitation. These simulations are calibrated for the area surrounding a coal-burning power generator at Grand Lake in south-central New Brunswick, Canada. Calculated concentrations for SO(2) were similar to those obtained with a mobile SO(2) detection unit and a SO(2)-monitoring unit 42 km NE from the emission source. Cumulative SO(2) deposition rates were reasonably similar to those obtained with PbO(2) sulfation plates. A detailed comparison revealed topography was an important factor in modifying actual cumulative SO(2) deposition rates.     

Fluorescent Tracer Studies of Pollutant Transport in the San Francisco Bay Area

To simulate the transport and diffusion of airborne contaminants across a metropolitan region, point-source releases of fluorescent tracer material were made near various urban centers and some 50 samplers were arrayed in expected downwind directions. The effects of land-water, hill-valley, and urban-rural differences on airflow and diffusion were observed in their existing interrelationships during these experiments. Since the tracer could be assessed with high sensitivity over great distances, tracer results provided a quantitative indicator of pollutant dispersion across an extensive metropolitan complex.

From July 1967 through June 1968, the test series included typical seasonal weather patterns, with emphasis on those conducive to the travel and accumulation of pollutants. In each test about 15 kilograms of tracer material were released during two-hour periods, and significant dosages were found at downwind distances up to 80 kilometers. All tests were conducted during daylight hours, to coincide better with the oxidant-type pollution important in this region.

Dispersion characteristics showed much greater complexity than predictable from classical models, thus limiting the applicability of such models in this region. Built-up urban areas increased the initial dispersion rates of tracer clouds, and travel over water tended to decrease them. Hilly terrain resulted in increased dispersion, but channeling associated with such terrain caused locally higher concentrations. The complex horizontal dosage patterns obtained did confirm previously observed airflow patterns as aids in predicting pollutant distributions.     

Investigation of air pollution distribution in Linz: case studies to evaluate a K-type diffusion model coupled with a mass-consistent wind model

An Eulerian diffusion model coupled with a refined mass consistent wind model is developed for the operational forecasting of pollution distribution in complex terrain. The model is evaluated for a city situated in complex terrain. The study is carried out for a 20×20 km² area surrounding Linz, one of the industrial cities of Austria. The models are initialized with routinely measured meteorological parameters and topology information derived from the Geographical Information Systems (GIS). Four case studies, representative for major pollution episodes, are presented for model evaluation. The case studies include presence of a thermally induced wind system, presence of cold front an easterly southeasterly flow and a westerly–northwesterly flow. In presence of thermally induced wind systems, the flow field is most complex and existence of a shallow mixed layer with residual layer aloft enhances the pollution levels inside the city. Second case is used to study the development of pollution distribution inside the city in presence of low-level inversions and associated with low surface wind speeds. The low wind speeds in the surface layer lead to less mechanical generation of turbulence and lateral transport. The case studies of easterly and westerly flow fields are carried out to assess the capability of model under most frequently observed meteorological situations. The model is able to reproduce the pollution distribution near the slopes. There were over prediction inside the city in presence of thermally induced wind systems and is attributed to inadequate model physics during convective case. The present model setup is found to be a useful tool for the routine forecasting of pollution and could also be tested for other complex terrains.     

A comparison of model performance between AERMOD and AUSTAL2000

In this study the performance of the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD), a Gaussian plume model, is compared in five test cases with the German Dispersion Model according to the Technical Instructions on Air Quality Control (Ausbreitungsmodell gem?beta der Technischen Anleitung zur Reinhaltung der Luft) (AUSTAL2000), a Lagrangian model. The test cases include different source types, rural and urban conditions, flat and complex terrain. The predicted concentrations are analyzed and compared with field data. For evaluation, quantile-quantile plots were used. Further, a performance measure is applied that refers to the upper end of concentration levels because this is the concentration range of utmost importance and interest for regulatory purposes. AERMOD generally predicted concentrations closer to the field observations. AERMOD and AUSTAL2000 performed considerably better when they included the emitting power plant building, indicating that the downwash effect near a source is an important factor. Although AERMOD handled mountainous terrain well, AUSTAL2000 significantly underestimated the concentrations under these conditions. In the urban test case AUSTAL2000 did not perform satisfactorily. This may be because AUSTAL2000, in contrast to AERMOD, does not use any algorithm for nightly turbulence as caused by the urban heat island effect. Both models performed acceptable for a nonbuoyant volume source. AUSTAL2000 had difficulties in stable conditions, resulting in severe underpredictions. This analysis indicates that AERMOD is the stronger model compared with AUSTAL2000 in cases with complex and urban terrain. The reasons for that seem to be AUSTAL2000's simplification of the meteorological input parameters and imprecision because of rounding errors.     

Applied dispersion modelling based on meteorological scaling parameters

A method for calculating the dispersion of plumes in the atmospheric boundary layer is presented. The method is easy to use on a routine basis. The inputs to the method are fundamental meteorological parameters, which act as distinct scaling parameters for the turbulence. The atmospheric boundary layer is divided into a number of regimes. For each scaling regime we suggest models for the dispersion in the vertical direction. The models directly give the crosswind-integrated concentrations at the ground, x_y, for nonbuoyant releases from a continuous point source. Generally the vertical concentration profile is proposed to be other than Gaussian. The lateral concentration profile is always assumed to be Gaussian, and models for determining the lateral spread σ_y are proposed. The method is limited to horizontally homogeneous conditions and travel distances less than 10km. The method is evaluated against independent tracer experiments over land. The overall agreement between measurements and predictions is very good and better than that found with the traditional Gaussian plume model.     

Deposition and removal of fugitive dust in the arid southwestern United States: measurements and model results

This work was motivated by the need to better reconcile emission factors for fugitive dust with the amount of geologic material found on ambient filter samples. The deposition of particulate matter with aerodynamic diameter less than or equal to 10 microm (PM10), generated by travel over an unpaved road, over the first 100 m of transport downwind of the road was examined at Ft. Bliss, near El Paso, TX. The field conditions, typical for warm days in the arid southwestern United States, represented sparsely vegetated terrain under neutral to unstable atmospheric conditions. Emission fluxes of PM10 dust were obtained from towers downwind of the unpaved road at 7, 50, and 100 m. The horizontal flux measurements at the 7 m and 100 m towers indicated that PM10 deposition to the vegetation and ground was too small to measure. The data indicated, with 95% confidence, that the loss of PM10 between the source of emission at the unpaved road, represented by the 7 m tower, and a point 100 m downwind was less than 9.5%. A Gaussian model was used to simulate the plume. Values of the vertical standard deviation sigma(z) and the deposition velocity Vd were similar to the U.S. Environmental Protection Agency (EPA) ISC3 model. For the field conditions, the model predicted that removal of PM10 unpaved road dust by deposition over the distance between the point of emission and 100 m downwind would be less than 5%. However, the model results also indicated that particles larger than 10 microm (aerodynamic diameter) would deposit more appreciably. The model was consistent with changes observed in size distributions between 7 m and 100 m downwind, which were measured with optical particle counters. The Gaussian model predictions were also compared with another study conducted over rough terrain and stable atmospheric conditions. Under such conditions, measured PM10 removal rates over 95 m of downwind transport were reported to be between 86% and 89%, whereas the Gaussian model predicted only a 30% removal. One explanation for the large discrepancy between measurements and model results was the possibility that under the conditions of the study, the dust plume was comparable in vertical extent to the roughness elements, thereby violating one of the model assumptions. Results of the field study reported here and the previous work over rough terrain bound the extent of particle deposition expected to occur under most unpaved road emission scenarios.     

Evaluation of low wind modeling approaches for two tall-stack databases

The performance of the AERMOD air dispersion model under low wind speed conditions, especially for applications with only one level of meteorological data and no direct turbulence measurements or vertical temperature gradient observations, is the focus of this study. The analysis documented in this paper addresses evaluations for low wind conditions involving tall stack releases for which multiple years of concurrent emissions, meteorological data, and monitoring data are available. AERMOD was tested on two field-study databases involving several SO₂ monitors and hourly emissions data that had sub-hourly meteorological data (e.g., 10-min averages) available using several technical options: default mode, with various low wind speed beta options, and using the available sub-hourly meteorological data. These field study databases included (1) Mercer County, a North Dakota database featuring five SO₂ monitors within 10 km of the Dakota Gasification Company’s plant and the Antelope Valley Station power plant in an area of both flat and elevated terrain, and (2) a flat-terrain setting database with four SO₂ monitors within 6 km of the Gibson Generating Station in southwest Indiana. Both sites featured regionally representative 10-m meteorological databases, with no significant terrain obstacles between the meteorological site and the emission sources. The low wind beta options show improvement in model performance helping to reduce some of the overprediction biases currently present in AERMOD when run with regulatory default options. The overall findings with the low wind speed testing on these tall stack field-study databases indicate that AERMOD low wind speed options have a minor effect for flat terrain locations, but can have a significant effect for elevated terrain locations. The performance of AERMOD using low wind speed options leads to improved consistency of meteorological conditions associated with the highest observed and predicted concentration events. The available sub-hourly modeling results using the Sub-Hourly AERMOD Run Procedure (SHARP) are relatively unbiased and show that this alternative approach should be seriously considered to address situations dominated by low-wind meander conditions.

Implications: AERMOD was evaluated with two tall stack databases (in North Dakota and Indiana) in areas of both flat and elevated terrain. AERMOD cases included the regulatory default mode, low wind speed beta options, and use of the Sub-Hourly AERMOD Run Procedure (SHARP). The low wind beta options show improvement in model performance (especially in higher terrain areas), helping to reduce some of the overprediction biases currently present in regulatory default AERMOD. The SHARP results are relatively unbiased and show that this approach should be seriously considered to address situations dominated by low-wind meander conditions.     

Episodes of high SO2 concentration in The Netherlands

In episodes of high daily mean SO₂ concentrations in the atmosphere there is a very extensive concentration field over The Netherlands caused by influx from both the Ruhr area and Central Europe. The meteorological conditions for such episodes range from very stable to stable with a boundary layer height between 100 and 450 m, wind speed at 200 m between 0 and 18 m s⁻¹ and at 20 m between 0 and 9 m s⁻¹. The last figures indicate that transport, as well as transport combined with stagnation, causes the high concentrations. The episodes nearly all occur in December, January and February, with easterly winds. In two thirds of the cases a local snow cover is present. This cover is more extensive in the east, so probably in all cases there is reduced dry deposition of SO₂. The turbulence is very low with hardly any vertical dispersion at higher altitudes, except around noon. In consequence of the wind direction variations over a day and the wind direction shear, the daily horizontal dispersion is normal or larger than normal. Therefore the contribution of a local high point source in The Netherlands to the ground level concentration will be small during air pollution episodes in winter.     

CFD modelling of small particle dispersion: The influence of the turbulence kinetic energy in the atmospheric boundary layer

When considering the modelling of small particle dispersion in the lower part of the Atmospheric Boundary Layer (ABL) using Reynolds Averaged Navier Stokes simulations, the particle paths depend on the velocity profile and on the turbulence kinetic energy, from which the fluctuating velocity components are derived to predict turbulent dispersion. It is therefore important to correctly reproduce the ABL, both for the velocity profile and the turbulence kinetic energy profile.For RANS simulations with the standard k–ε model, Richards and Hoxey (1993. Appropriate boundary conditions for computational wind engineering models using the k–ε turbulence model. Journal of Wind Engineering and Industrial Aerodynamics 46–47, 145–153.) proposed a set of boundary conditions which result in horizontally homogeneous profiles. The drawback of this method is that it assumes a constant profile of turbulence kinetic energy, which is not always consistent with field or wind tunnel measurements. Therefore, a method was developed which allows the modelling of a horizontally homogeneous turbulence kinetic energy profile that is varying with height.By comparing simulations performed with the proposed method to simulations performed with the boundary conditions described by Richards and Hoxey (1993. Appropriate boundary conditions for computational wind engineering models using the k–ε turbulence model. Journal of Wind Engineering and Industrial Aerodynamics 46–47, 145–153.), the influence of the turbulence kinetic energy on the dispersion of small particles over flat terrain is quantified.     

A Comparison of Model Performance between AERMOD and AUSTAL2000

ABSTRACT

In this study the performance of the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD), a Gaussian plume model, is compared in five test cases with the German Dispersion Model according to the Technical Instructions on Air Quality Control (Ausbreitungsmodell gemäβ der Technischen Anleitung zur Reinhaltung der Luft) (AUSTAL2000), a Lagrangian model. The test cases include different source types, rural and urban conditions, flat and complex terrain. The predicted concentrations are analyzed and compared with field data. For evaluation, quantile-quantile plots were used. Further, a performance measure is applied that refers to the upper end of concentration levels because this is the concentration range of utmost importance and interest for regulatory purposes. AERMOD generally predicted concentrations closer to the field observations. AERMOD and AUSTAL2000 performed considerably better when they included the emitting power plant building, indicating that the downwash effect near a source is an important factor. Although AERMOD handled mountainous terrain well, AUSTAL2000 significantly underestimated the concentrations under these conditions. In the urban test case AUSTAL2000 did not perform satisfactorily. This may be because AUSTAL2000, in contrast to AERMOD, does not use any algorithm for nightly turbulence as caused by the urban heat island effect. Both models performed acceptable for a nonbuoyant volume source. AUSTAL2000 had difficulties in stable conditions, resulting in severe underpredictions. This analysis indicates that AERMOD is the stronger model compared with AUSTAL2000 in cases with complex and urban terrain. The reasons for that seem to be AUSTAL2000's simplification of the meteorological input parameters and imprecision because of rounding errors.

IMPLICATIONS This study contributes to the understanding of dispersion modeling and demonstrates the advantage of detailed meteorological data. It also helps air quality regulators and planners to identify the most appropriate model to use. It is indicated that AERMOD is more suitable for air quality planning and regulation, when all required meteorological information is available, because its predictions are mostly closer to field observations. Furthermore AUSTAL2000 predicted concentrations that showed a narrow range and triggered far less impacts from the source.     

A complex terrain dispersion model for regulatory applications

This paper demonstrates the development of a model designed to estimate concentrations associated with a source situated in complex terrain. The model is designed to provide estimates of concentration distributions and is thus primarily suitable for regulatory applications. The model assumes that the concentration at a receptor is a combination of concentrations caused by two asymptotic states: the plume remains horizontal, and the plume climbs over the hill. The factor that weights the two states is a function of the fractional mass of the plume above the dividing streamline height. The model has been evaluated against data from four complex terrain sites. The evaluation shows that the model performs at least as well as CTDMPLUS (Perry, S.G., 1992. CTDMPLUS, a dispersion model for sources near complex topography. Part I: technical formations. Journal of Applied Meteorology 31, 633–645), a more comprehensive model designed for complex terrain applications.     

Computational Fluid Dynamics (CFD) Simulations on the Effect of Rough Surface on Atmospheric Turbulence Flow Above Hilly Terrain Shapes

The behavioral distribution of the atmospheric turbulence flow over the terrain with changes in a rough surface has become one of the most important topics of air pollution research, among such other topics as transportation and dispersion pollutants. In this study, a computational model on atmospheric turbulence flow over a terrain hill shaped with rough surface was investigated under neutral atmospheric conditions. The flow was assumed to be 2D and modeled using computational fluid dynamics (CFD) models, which were numerically solved using Reynolds-averaged Navier-Stokes equations. Rough surface conditions were modeled using a number of windbreak fences regularly spaced on the hill. The mean velocity and turbulent structures such as turbulence intensity and turbulent kinetic energy were investigated in the upwind and downwind regions over the hill, and the numerical models were validated against the wind-tunnel results to optimize the turbulence model. The computational results agreed well with the results obtained from the wind tunnel experiments. The computational results indicate that the mean velocity was observed to increase dramatically around the crest of the upwind slope of the hill. A thick internal boundary layer was observed with a fence on the crest and downwind region of the hill. The reversed flow and recirculation zone were formed in the wake region behind the hill. It was thus determined that turbulent kinetic energy decreases as the mean velocity increases.     

Evaluation of a Puff Dispersion Model in Complex Terrain

California's Pacific Gas and Electric Company has many power plant operations situated in complex terrain, prominent examples being the Geysers geothermal plant in Lake and Sonoma Counties, and the Diablo Canyon nuclear plant in San Luis Obispo County. Procedures ranging from plant licensing to emergency response require a dispersion modeling capability in a complex terrain environment. This paper describes the performance evaluation of such a capability, the Pacific Gas and Electric Company Modeling System (PGEMS), a fast response Gaussian puff model with a three-dimensional wind field generator.

Performance of the model was evaluated for ground level and short stack elevated release on the basis of a special intensive tracer experiment in the complex coastal terrain surrounding the Diablo Canyon Nuclear Power Plant in San Luis Obispo County, California. The model performed well under a variety of meteorological and release conditions within the test region of 20-kilometer radius surrounding the nuclear plant, and turned in a superior performance in the wake of the nuclear plant, using a new wake correction algorithm for ground level and roof-vent releases at that location.     

Transport and Diffusion Calculations during Time- and Space-Varying Meteorological Conditions Using a Microcomputer

Small computers can now solve complex transport and diffusion problems. Making such calculations rapidly and on the spot would be very useful for decision makers during emergencies. This paper describes a microcomputer model that can simulate nonsteady state transport and diffusion and calculate mass-consistent flow fields in uneven terrain. Transport and diffusion calculations use a Puff model approach to simulate dispersion from a continuous point source. Three-dimensional wind fields are generated from linear combinations of solutions that are obtained ahead of time on a larger machine. A computer code has been written in BASIC and successfully run on an Apple II personal computer.     

A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA

A method based on a statistical approach of estimating uncertainty in simulating the transport and dispersion of atmospheric pollutants is developed using observations and modeling results from a tracer experiment in the complex terrain of the southwestern USA. The method takes into account the compensating nature of the error components by representing all terms, except dispersion error and variance of stochastic processes. Dispersion error and the variance of the stochastic error are estimated using the maximum likelihood estimation technique applied to the equation for the fractional error. Mesoscale Model 5 (MM5) and a Lagrangian random particle dispersion model with three optional turbulence parameterizations were used as a test bed for method application. Modeled concentrations compared well with the measurements (correlation coefficients on the order of 0.8). The effects of changing two structural components (the turbulence parameterization and the model grid vertical resolution) on the magnitude of the dispersion error also were examined. The expected normalized dispersion error appears to be quite large (up to a factor of three) among model runs with various turbulence schemes. Tests with increased vertical resolution of the atmospheric model (MM5) improved most of the dispersion model statistical performance measures, but to a lesser extent compared to selection of a turbulence parameterization. Method results confirm that structural components of the dispersion model, namely turbulence parameterizations, have the most influence on the expected dispersion error.     

Numerical study of atmospheric dispersion of a substance released from an industrial complex in the southern coast of Korea

Diurnal variations of wind field and pollutant dispersion in a complex terrain with a shoreline were investigated under the insolation conditions of summer and winter. The area is located in the south of the Korean Peninsula and includes a large petrochemical industrial complex. The Regional Atmospheric Modeling System (RAMS) was used in the simulation study. Initially, horizontally homogeneous wind fields were assumed on the basis of sounding data at the nearby upper-air station for days with morning wind speeds below 2 m s⁻¹. On these days, the sea breeze prevailed in summer while the land breeze lasted for a few hours in the morning; the effect of synoptic winds was strong in winter with some inclusion of wind variations owing to the interaction between sea and land. The predicted wind direction at the location of the weather station captured an important change of the sea/land breeze of the observed one. In the morning, both in summer and winter, complicated wind fields with low wind speeds resulted in high pollutant concentrations almost all over the area. On the other hand, in the afternoon, the wind field was rather uniform and the terrain effects were not significant even in the mountainous area with the development of a mixing layer.     

A Lagrangian stochastic model for predicting concentration fluctuations in urban areas

A combined Lagrangian stochastic model with a micromixing sub-model is used to estimate the fluctuating concentrations observed in two wind tunnel experiments. The Lagrangian stochastic model allows fluid trajectories to be simulated in the inhomogeneous flow, while the mixing model accounts for the dissipation of fluctuations using the interaction by exchange with the mean (IEM) mechanism. The model is first tested against the open terrain, wind tunnel data of Fackrell, J.E. and Robins, A.E. [1982. Concentration fluctuations and fluxes in plumes from point sources in a turbulent boundary layer. Journal of Fluid Mechanics 117, 1–26] and shows good agreement with the observed mean concentrations and fluctuation intensities. The model is then compared with the wind tunnel simulation of a two-dimensional street canyon by Pavageau, M. and Schatzmann, M. [1999. Wind tunnel measurements of concentration fluctuations in an urban street canyon. Atmospheric Environment 33, 3961–3971]. Despite the limitations of the k–ε turbulence scheme and the IEM mixing mechanism, the model reproduces the fluctuation intensity pattern within the canyon well. Overall, the comparison with both sets of wind tunnel experiments are encouraging, and the simplicity of the model means that predictions in a complex, three-dimensional geometry can be produced in a practicable amount of time.     

Modelling local and synoptic scale influences on ozone concentrations in a topographically complex region of Southern Italy

A modelling study with the on-line coupled Eulerian chemical-weather model WRF/Chem for the Southern Italian region around Cosenza (Calabria) was conducted to identify the influences of synoptic scale meteorology, local scale wind systems and local emissions on ozone concentrations in this orographically complex region. Four periods of 5–7 days were chosen, one from each season, which had wind pattern characteristics representative of typical local climatological conditions, in order to study the local versus non-local impacts on ozone transport and formation. To account for the complex terrain, the horizontal resolution of the smallest modelling domain was 3 km. Model results were compared with measurements to demonstrate the capability of the model to reproduce ozone concentrations in the region. The comparison was favourable with a mean bias of ?1.1 ppb. The importance of local emissions on ozone formation and destruction was identified with the use of three different emission scenarios. Generally the influence of regional emissions on the average ozone concentration was small. However during periods when mountain-sea wind systems were well developed and synoptic scale winds were weak, the influence of local emissions from the urban area was at its greatest. The maximum influence of local emissions on ozone concentrations was 18 ppb.