Simulation of dispersion of a power plant plume using an adaptive grid algorithm

A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme—the piecewise parabolic method (PPM)—for computing advective solution fields; a weight function capable of promoting grid node clustering by moving grid nodes; and a conservative interpolation equation using PPM for redistributing the solution field after movement of grid nodes. Applications of the algorithm to a model problem, in which emissions from a point source disperse through the atmosphere in time, reflect that the algorithm is able to capture not only the regional ozone plume distribution, but also the small-scale plume structure near the source. In contrast, the small-scale plume structure was not captured in the corresponding static grid solution. Performance achieved in model problem simulations indicates that the algorithm has the potential to provide accurate air quality modeling solutions at costs that may be significantly less than those incurred in obtaining equivalent static grid solutions.     

Performance Evaluation of Integral and Analytical Plume Rise Algorithms

The purpose of this study was to evaluate the performance of current regulatory algorithms for predicting plume rise for refinerytype sources (short stacks and a wide range of source conditions) and the performance of new or alternate algorithms which may provide better estimates. To meet the objectives, five plume rise algorithms were statistically evaluated against ten field and laboratory plume rise data bases. Two forms of the Briggs plume rise equations were tested because they are almost exclusively used in current EPA regulatory models. Two modified Briggs equations were tested to assess how simple modifications can Improve the accuracy of the estimates. The fifth algorithm was a numerical solution to the basic equations for conservation of mass, momentum, and energy often referred to as an Integral plume rise algorithm. This algorithm was selected because It handles the wide range of source and atmospheric boundary-layer conditions that affect trajectories of plumes from refinery stacks.

Ten independent plume rise data bases were assembled that covered a wide range of source and meteorological conditions. From the data bases, a total of 107 different data sets were obtained and each data set included plume rise observations versus downwind distance for one source and meteorological condition. Each model was run for each data set and the root-mean-square and mean error between model and observation was computed for use in statistically evaluating model performance.

The statistical evaluation of the algorithms showed that the rms error (considering all data bases) for the Integral plume rise algorithm was approximately 30 percent less than the errors for all other algorithms tested. This difference was significant at the 95 percent confidence level. The results suggest that improved plume rise estimates in regulatory models applied to refineries and other appropriate sources could be achieved to reduce costs and improve ambient air quality estimates through the use of an integral plume rise algorithm.     

Dispersion over and around a steep obstacle for varying thermal stratification—numerical simulations

A rather steep dump of an open-cast working area for lignite is situated close to the nuclear research installation of Juelich. This dump is more than 200 m high; under light wind conditions the air flow goes around this obstacle during night-time (stable stratification) and over it in the early morning hours when the stratification is destabilized. This air flow is simulated by the non-hydrostatic mesoscale numerical model FITNAH; for the time 0500–0900 LST the concentration field (of a point source) is simulated with a Lagrangian particle model. The computed flow behaviour causes a bifurcation of the plume during night while after 0900 LST dispersion is nearly unaffected by topography.     

A comparison of operational Lagrangian particle and adaptive puff models for plume dispersion forecasting

Transport and dispersion of pollutants in the lower atmosphere are predicted by using both a Lagrangian particle model (LPM) and an adaptive puff model (APM2) coupled to the same mesoscale meteorological prediction model PMETEO. LPM and APM2 apply the same numerical solutions for plume rise; but, for advection and plume growth, LPM uses a stochastic surrogate to the pollutant conservation equation, and APM2 applies interpolated winds and standard deviations from the meteorological model, using a step-wise Gaussian approach. The results of both models in forecasting the SO₂ ground level concentration (glc) around the 1400 MWe coal-fired As Pontes Power Plant are compared under unstable conditions. In addition, meteorological and SO₂ glc numerical results are compared to field measurements provided by 17 fully automated SO₂ glc remote stations, nine meteorological towers and one Remtech PA-3 SODAR, from a meteorological and air quality monitoring network located 30 km around the power plant.     

Inherent uncertainty in air quality modeling

This paper summarizes the discussions of a working group that was charged with the task of examining inherent uncertainty in air quality modeling. The major topics of the paper are:

• 1.1. Definition of inherent uncertainty in air quality models;
• 2.2. Determination of inherent uncertainty;
• 3.3. Role of inherent uncertainty in model evaluation.

The concepts introduced here are illustrated through a numerical simulation with Gifford's fluctuating plume model.     

Simulation of Sulfate and Nitrate Chemistry in Power Plant Plumes

ABSTRACT

The rate of formation of secondary particulate matter (PM) in power plant plumes varies as the plume material mixes with the background air. Consequently, the rate of oxidation of sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) to sulfate and nitric acid, respectively, can be very different in plumes and in the background air (i.e., air outside the plume). In addition, the formation of sulfate and nitric acid in a power plant plume is a strong function of the chemical composition of the background air and the prevailing meteorological conditions.

We describe the use of a reactive plume model, the Reactive and Optics Model of Emissions, to simulate sulfate and nitrate formation in a power plant plume for a variety of background conditions. We show that SO₂ and NO₂ oxidation rates are maximum in the background air for volatile organic compound (VOC)-limited airsheds but are maximum at some downwind distance in the plume when the background air is nitrogen oxide (NO_x)-limited. Our analysis also shows that it is essential to obtain measurements of background concentrations of ozone, aldehydes, peroxyacetyl nitrate, and other VOCs to properly describe plume chemistry.     

An experimental verification of a theoretical model for the dispersion of a stack plume heavier than air

Experiments were carried out in a windtunnel to test the theoretical model for the dispersion of a stack plume heavier than air developed by Ooms et al. (1974, First Int. Symp. on Loss Prevention and Safety Promotion in the Process Industries, The Hague). Particular attention was paid to the initial conditions which have to be supplied in order to make model calculations possible. A good agreement between experimental results and model predictions was found for the plume path and the density distribution along the plume axis. The velocity distribution inside the plume was less well predicted.     

A trajectory plume model for simulating air pollution transients

An air quality simulation model that is simple, yet capable of accurately estimating concentrations under unsteady meteorological conditions, has been developed. This trajectory plume model uses the Gaussian plume equation, but has an applicability that is approximately as wide as the Lagrangian puff model. The plume axis is represented by a series of straight-line plume segments. The performance of this model was evaluated by comparing it with other diffusion models. A comparison between simulation results using the present model and those using integrated puff and Eulerian diffusion models for three different metropolitan areas (one in Japan and two in the U.S.) has indicated that a simple trajectory plume model performs as well as the two other more complex models in simulating pollutant dispersion under complicated meteorological conditions such as those which occur during the transition period from a sea breeze to a land breeze.     

Improving Plume Rise Prediction Accuracy for Stable Atmospheres with Complex Vertical Structure

Accurately predicting the rise of a buoyant exhaust plume is difficult when there are large vertical variations in atmospheric stability or wind velocity. Such conditions are particularly common near shoreline power plants. Simple plume rise formulas, which employ only a mean temperature gradient and a mean wind speed, cannot be expected to adequately treat an atmosphere whose lapse rate and wind velocity vary markedly with height. This paper tests the accuracy of a plume rise model which is capable of treating complex atmospheric structure because it integrates along the plume trajectory. The model consists of a set of ordinary differential equations, derived from the fluid equations of motion, with an entralnment parameterization to specify the mixing of ambient air into the plume. Comparing model predictions of final plume rise to field observations yields a root mean square difference of 24 m, which is 9 % of the average plume rise of 267 m. These predictions are more accurate than predictions given by three simpler models which utilize variants of a standard plume rise formula, the most accurate of the simpler models having a 12% error.     

A fluctuating plume model for concentration fluctuations in a plant canopy

A one-particle Lagrangian model for continuous releases in the non-Gaussian inhomogeneous turbulence of a canopy layer is derived based on the fluctuating plume model of Franzese [2003. Lagrangian stochastic modeling of a fluctuating plume in the convective boundary layer. Atmos. Environ. 37, 1691–1701.]. The model equations are filtered by a time-dependent low-pass filter applied to the turbulent kinetic energy in order to obtain a fluctuating plume model able to simulate the vertical meandering of the cloud centroid through non-stationary Lagrangian equations. The model satisfies the well-mixed condition. The relative dispersion of particles and the concentration fluctuation statistics of a passive tracer inside a modeled vegetal canopy are studied. The probability density function of the concentration relative to the plume centroid is parameterized and the mean and variance fields of concentration are simulated and compared with wind-tunnel data and numerical simulations. A skewed, reflected probability density function for the vertical position of the plume centroid is considered.     

Prediction of cloud effects in chimney plumes

A numerical model to predict the occurrence of visible condensation and cloud length in chimney plumes is tested against observations of the plume from a 500 MW natural gas-fired power station at Melbourne, Australia. The model employs two entrainment parameters selected from the literature on plume trajectories and not fitted to the observations reported here. A criterion for a plume to be visible is developed for droplets around 5 μm radius and plume distribution of condensed water determined from Gaussian two-dimensionalization of temperature and total water. The model correctly discriminates between cloudy and non-cloudy chimney plumes, between occasions of persistent cloud that extends into the far-field and when cloud terminates in the coherent-plume-phase and reliably forecasts cloud length in this latter case. The results emphasize that good meteorological data are required for accurate predictions.     

On plume meandering in unstable stratification

Vertical plume meandering of gaseous pollutant is commonly experienced in the daytime atmospheric boundary layer (also know as convective boundary layer, CBL) that arose from the complicated interaction between buoyancy-generated turbulence and gravitational force. It leads to rapid pollutant mixing that cannot be accurately modeled by conventional Gaussian plume model. In the light of explaining the mechanism of plume rises and descents in CBLs, this study employs a direct numerical simulation (DNS) technique to compute the plume behaviors for pollutant emitted from line sources placed parallel to the spanwise direction in an unstably stratified turbulent open channel flow. The DNS results show that the plume meandering is due to the domination of uni-directional mean vertical pollutant fluxes above and below the mean plume height.     

Water tank measurements of buoyant plume rise and structure in neutral crossflows

In this paper, an experimental study of the rise and development of a single buoyant plume and a pair of in-line buoyant plumes is presented. The investigations were carried out at small scale in a water filled towing tank using both quantitative flow visualisation and local concentration measurements. The measured plume trajectories for a single plume were compared with the Briggs plume rise equation and predictions from a numerical integral model. Plume trajectories were studied for twin in-line plumes, with particular attention to changes in the plume trajectory, especially any additional rise that resulted from the interaction between the two plumes. Concentration field distributions in cross-sections through both single and interacting twin plumes were obtained from the local concentration measurement system. These showed how the interaction affected the plume structure, notably the double vortex system that occurs in a fully developed plume.     

Integral model of dense gas plume dispersion

The injection of a dense gas stream at ground level into a flowing turbulent atmosphere produces a wide, flat plume that entrains air primarily through its upper surface. A quasi-one-dimensional flow model of an isothermal dense gas plume is developed for the purpose of comparing experiments in wind tunnels and water flumes and field tests in the atmosphere. Comparisons are made for plume width, including the width at the source, and centerline ground plane source gas concentration. All published data are used in this comparison, which cover a factor of about 100 in plume length scale and Reynolds number. Tests conducted by different experimenters were found not to be dynamically similar. Dimensionless model parameters, all of order unity, are selected to give the best agreement among all the experimental data. The dependence of entrainment rate on the plume Richardson number, a key feature of the model, is confirmed in the comparison. The entrainment rate parameter is found to be larger for the field tests than for the laboratory experiments, reflecting the much higher Reynolds number of the former.     

Remote monitoring of air pollutant emissions from point sources by a mobile lidar/sodar system

This paper describes remote monitoring of air pollutant emissions by a mobile lidar (light detection and ranging)/ sodar (sound detection and ranging) system. First, measurements are carried out in the flue gas plume of a public power plant. The investigations focus mainly on quantifying SO2 emissions, but the uncertainties of such measurements are also emphasized. Furthermore, an example providing valuable data sets for the development and validation of plume dispersion models is outlined with measurements of the dilution of SO2 along the plume axis. Series of repeated determinations of SO2 emissions show a large variation in the obtained flux values, with moderate margins of error. Incomplete recording of the plume within the individual lidar scans, induced by strong looping movements of the flue gas plume, predominantly causes the variations of flux values. Therefore, the highest flux values determined are considered to be the most exact. This is verified by a comparison of measured fluxes with in situ measurements made by the plant operators. The results further indicate that lidar measurements illustrate the location and dimension of aerosol plumes better than the location and dimension of the plumes of gaseous compounds. The wind direction affecting the plume at any moment can be determined faster by lidar than by sodar because the latter requires much longer time intervals of signal averaging. Measurements show higher concentrations of SO2 compared with results from a Gaussian plume model for periods of less than 5 min after dispersion. The findings emphasize the suitability of remote sensing for detecting emissions and for investigating the propagation and dilution of air pollutant plumes.     

Simulation of the evolution of particle size distributions in a vehicle exhaust plume with unconfined dilution by ambient air

Over the past several years, numerous studies have linked ambient concentrations of particulate matter (PM) to adverse health effects, and more recent studies have identified PM size and surface area as important factors in determining the health effects of PM. This study contributes to a better understanding of the evolution of particle size distributions in exhaust plumes with unconfined dilution by ambient air. It combines computational fluid dynamics (CFD) with an aerosol dynamics model to examine the effects of different streamlines in an exhaust plume, ambient particle size distributions, and vehicle and wind speed on the particle size distribution in an exhaust plume. CFD was used to calculate the flow field and gas mixing for unconfined dilution of a vehicle exhaust plume, and the calculated dilution ratios were then used as input to the aerosol dynamics simulation. The results of the study show that vehicle speed affected the particle size distribution of an exhaust plume because increasing vehicle speed caused more rapid dilution and inhibited coagulation. Ambient particle size distributions had an effect on the smaller sized particles (approximately 10 nm range under some conditions) and larger sized particles (>2 microm) of the particle size distribution. The ambient air particle size distribution affects the larger sizes of the exhaust plume because vehicle exhaust typically contains few particles larger than 2 microm. Finally, the location of a streamline in the exhaust plume had little effect on the particle size distribution; the particle size distribution along any streamline at a distance x differed by less than 5% from the particle size distributions along any other streamline at distance x.     

The dispersion of the Buncefield oil fire plume: An extreme accident without air quality consequences

The dispersion of pollutants from the huge Buncefield oil depot fire that occurred on 11 December 2005 is simulated using a regional Eulerian chemistry-transport model. We analyse the transport and mixing of the fire plume. We show that the hot plume never reached the ground. Instead, it pierced the thin wintertime boundary layer and was injected into the free troposphere at higher altitudes. This is in agreement with data from many air quality stations. This high injection was fortunate because the fine aerosol particles (PM₁₀) mass column generated by the fire smoke exceeded that of ordinary pollution by an order of magnitude. Our regional chemistry-transport modelling is able to predict the early development of the plume dispersion, as shown by a qualitative comparison between simulated PM₁₀ columns and a satellite image obtained by the EOS-TERRA-MODIS sensor.If the accident had occurred in summer when boundary layers are much deeper and convective, a severe degradation in air quality due to PM₁₀ could have occurred, as shown by a sensitivity simulation assuming a similar fire during one of the hottest days of August 2003. The modelled impact of the fire on regional and European air quality levels strongly depends on the altitude reached by the buoyant plume, as shown by a set of sensitivity simulations with variable injection heights. However, in all cases we found that the fire only affected surface aerosol concentrations without increasing photochemical pollution.     

Development and evaluation of the PRIME plume rise and building downwash model

A new Gaussian dispersion model, the Plume Rise Model Enhancements (PRIME), has been developed for plume rise and building downwash. PRIME considers the position of the stack relative to the building, streamline deflection near the building, and vertical wind speed shear and velocity deficit effects on plume rise. Within the wake created by a sharp-edged, rectangular building, PRIME explicitly calculates fields of turbulence intensity, wind speed, and streamline slope, which gradually decay to ambient values downwind of the building. The plume trajectory within these modified fields is estimated using a numerical plume rise model. A probability density function and an eddy diffusivity scheme are used for dispersion in the wake. A cavity module calculates the fraction of plume mass captured by and recirculated within the near wake. The captured plume is re-emitted to the far wake as a volume source and added to the uncaptured primary plume contribution to obtain the far wake concentrations. The modeling procedures currently recommended by the U.S. Environmental Protection Agency (EPA), using SCREEN and the Industrial Source Complex model (ISC), do not include these features. PRIME also avoids the discontinuities resulting from the different downwash modules within the current models and the reported overpredictions during light-wind speed, stable conditions. PRIME is intended for use in regulatory models. It was evaluated using data from a power plant measurement program, a tracer field study for a combustion turbine, and several wind-tunnel studies. PRIME performed as well as or better than ISC/SCREEN for nearly all of the comparisons.     

Development and Evaluation of the PRIME Plume Rise and Building Downwash Model

ABSTRACT

A new Gaussian dispersion model, the Plume Rise Model Enhancements (PRIME), has been developed for plume rise and building downwash. PRIME considers the position of the stack relative to the building, streamline deflection near the building, and vertical wind speed shear and velocity deficit effects on plume rise. Within the wake created by a sharp-edged, rectangular building, PRIME explicitly calculates fields of turbulence intensity, wind speed, and streamline slope, which gradually decay to ambient values downwind of the building. The plume trajectory within these modified fields is estimated using a numerical plume rise model. A probability density function and an eddy diffusivity scheme are used for dispersion in the wake. A cavity module calculates the fraction of plume mass captured by and recirculated within the near wake. The captured plume is re-emitted to the far wake as a volume source and added to the uncaptured primary plume contribution to obtain the far wake concentrations.

The modeling procedures currently recommended by the U.S. Environmental Protection Agency (EPA), using SCREEN and the Industrial Source Complex model (ISC), do not include these features. PRIME also avoids the discontinuities resulting from the different downwash modules within the current models and the reported overpredictions during light-wind speed, stable conditions. PRIME is intended for use in regulatory models. It was evaluated using data from a power plant measurement program, a tracer field study for a combustion turbine, and several wind-tunnel studies. PRIME performed as well as or better than ISC/SCREEN for nearly all of the comparisons.     

Numerical simulation of air pollution in urban areas: model performance

The numerical model developed in the first part of this investigation is applied to assess the behavior of sulfur dioxide and sulfate concentration distributions in an urban area using the St Louis Regional Air Pollution Study (RAPS) data. Statistical techniques chosen to determine the accuracy and uncertainty associated with the numerical model results include paired analysis and resampling analysis. The results of the numerical model are also compared with those of RAM, a Gaussian plume model. Finally, the behavior of point and area emission sources in an urban area is assessed to provide an insight into the complex interrelationships between the emissions and meteorological conditions which determine the distribution of ground level concentrations.