Development,Evaluation, and Application of a Primary Aerosol Model

ABSTRACT

The Segmented-Plume Primary Aerosol Model (SPPAM) has been developed over the past several years. The earlier model development goals were simply to generalize the widely used Industrial Source Complex Short-Term (ISCST) model to simulate plume transport and dispersion under light wind conditions and to handle a large number of roadway or line sources. The goals have been expanded to include development of improved algorithm for effective plume transport velocity, more accurate and efficient line and area source dispersion algorithms, and recently, a more realistic and computationally efficient algorithm for plume depletion due to particle dry deposition. A performance evaluation of the SPPAM has been carried out using the 1983 PNL dual tracer experimental data. The results show the model predictions to be in good agreement with observations in both plume advection-dispersion and particulate matter (PM) depletion by dry deposition. For PM_2.5 impact analysis, the SPPAM has been applied to the Rubidoux area of California. Emission sources included in the modeling analysis are: paved road dust, diesel vehicular exhaust, gasoline vehicular exhaust, and tire wear particles from a large number of roadways in Rubidoux and surrounding areas. For the selected modeling periods, the predicted primary PM_2.5 to primary PM10 concentration ratios for the Rubidoux sampling station are in the range of 0.39–0.46. The organic fractions of the primary PM_2.5 impacts are estimated to be at least 34–41%. Detailed modeling results indicate that the relatively high organic fractions are primarily due to the proximity of heavily traveled roadways north of the sampling station. The predictions are influenced by a number of factors; principal among them are the receptor locations relative to major roadways, the volume and composition of traffic on these roadways, and the prevailing meteorological conditions.     

Validation of a hybrid forecasting system for the ozone concentrations over the Paris area

A simplified hybrid statistical-deterministic chemistry-transport model, is used in real time for the prediction of ozone in the area of Paris during Summer 1999. We present here a statistical validation of this experiment. We distinguish the forecasts in the urban area from forecasts in the pollution plume downwind of the city. The validation of model forecasts, up to 3 days ahead, is performed against ground based observations within and up to 50 km outside of Paris. In the urban area, ozone levels are fairly well forecast, with correlation coefficients between forecast and observations ranging between 0.7 and 0.8 and root mean square errors in the range 15–20 μg m⁻³ at short lead times. While the bias of urban forecast is very low, the largest peaks are somehow underestimated. The ozone plume amplitude is generally well reproduced, even at long lead times (root mean square errors of about 20–30 μg m⁻³), while the direction of the plume is only captured at short lead times (about 70% of the time). The model has difficulties in forecasting the direction of the plume under stagnant weather conditions. We estimate the model ability to forecast concentrations above 180 μg m⁻³, which are of practical relevance to air quality managers. It is found that about 60% of these events are well forecast, even at long lead times, while the exact monitoring station where the exceedance is observed can only be forecast at short lead times. Finally, we found that about half of the forecast error is due to the error in the estimation of the boundary conditions, which are forecast by a simple linear regression model here.     

Evaluation of the reactive and optics model of emissions (ROME)

The reactive and optics model of emissions (ROME) is a reactive plume visibility model that simulates the potential atmospheric impacts of stack emissions. We present here an evaluation of the ability of ROME to simulate several plume physical and chemical variables, using an experimental data base that consists of a total of 40 case studies from four field programs. The evaluation variables include plume height, horizontal width, NO_x and SO₂ maximum concentrations, NO₂/NO_x concentration ratio at the plume centerline, and plume-to-sky radiance ratios. Three algorithms used to simulate plume dispersion in ROME were compared: (1) the empirical Pasquill–Gifford–Turner (PGT) scheme, (2) a first-order closure (FOC) algorithm and (3) a second-order closure (SOC) algorithm that simulates the instantaneous plume dimensions.The plume height results show a correlation of 0.82 between simulated and measured values and a gross error that is 13% of the mean measured value. For plume horizontal dispersion, the second-order closure algorithm produces a moderate correlation (0.54) and a small bias (5% of the mean measured value) in comparison with the field data. Although the PGT scheme also demonstrates moderate correlation with the measurements, it produces a negative bias by significantly underestimating plume horizontal dispersion. The first-order closure algorithm overestimates plume width and shows the least correlation (with the measurements) of the three dispersion algorithms.For the NYSEG data set where coordinated measurements of stack emissions, meteorology at plume height and plume characteristics were available, the SOC algorithm provides better correlations for NO_x concentrations, NO₂/NO_x ratios and plume visibility than the FOC and PGT algorithms. For plume visibility, the SOC algorithm shows a correlation of 0.96 at 405 nm, the wavelength where the plume was visible, and it simulates no visible plume at the other wavelengths (550 and 700 nm).A comparison of ROME simulations with those of the plume visibility model PLUVUE II shows that ROME, with the SOC algorithm, performs better for all variables.     

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.     

Theoretical study of the impact of particulate matter gravitational settling on ambient coarse particulate matter monitoring for agricultural emissions

The particle size distributions (PSDs) of particulate matter (PM) in the downwind plume from simulated sources of a cotton gin were analyzed to determine the impact of PM settling on PM monitoring. The PSD of PM in a plume varies as a function of gravitational settling. Gravitational settling has a greater impact on the downwind PSD from sources with PSDs having larger mass median diameters (MMDs). The change in PSD is a function of the source PSD of emitted PM, wind speed, and downwind distance. Both MMD and geometric standard deviation (GSD) in the downwind plume decrease with an increase in downwind distance and source MMD. The larger the source MMD, the greater the change in the downwind MMD and GSD. Also, the greater the distance from the source to the sampler, the greater the change in the downwind MMD and GSD. Variations of the PSD in the downwind plume significantly impact PM10 sampling errors associated with the U.S. Environmental Protection Agency (EPA) PM10 samplers. For the emission sources with MMD > 10 microm, the PM10 oversampling rate increases with an increase in downwind distance caused by the decrease of GSD of the PSD in the downwind plume. Gravitational settling of particles does not help reduce the oversampling problems associated with the EPA PM10 sampler. Furthermore, oversampling rates decrease with an increase of the wind speed.     

Environmental tobacco smoke particles in multizone indoor environments

Environmental tobacco smoke (ETS) is a major source of human exposure to airborne particles. To better understand the factors that affect exposure, and to investigate the potential effectiveness of technical control measures, a series of experiments was conducted in a two-room test facility. Particle concentrations, size distributions, and airflow rates were measured during and after combustion of a cigarette. Experiments were varied to obtain information about the effects on exposure of smoker segregation, ventilation modification, and air filtration. The experimental data were used to test the performance of an analytical model of the two-zone environment and a numerical multizone aerosol dynamics model. A respiratory tract particle deposition model was also applied to the results to estimate the mass of ETS particles that would be deposited in the lungs of a nonsmoker exposed in either the smoking or nonsmoking room. Comparisons between the experimental data and model predictions showed good agreement. For time-averaged particle mass concentration, the average bias between model and experiments was less than 10%. The average absolute error was typically 35%, probably because of variability in particle emission rates from cigarettes. For the conditions tested, the use of a portable air filtration unit yielded 65–90% reductions in predicted lung deposition relative to the baseline scenario. The use of exhaust ventilation in the smoking room reduced predicted lung deposition in the nonsmoking room by more than 80%, as did segregating the smoker from nonsmokers with a closed door.     

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.     

Sources and concentration of nanoparticles (<10 nm diameter) in the urban atmosphere

Whilst limited information on particle size distributions and number concentrations in cities is available, very few data on the very smallest of particles, nanoparticles, have been recorded. Measurements in this study show that road traffic and stationary combustion sources generate a significant number of nanoparticles of diameter <10 nm. Measurements at the roadside (4 m from the kerb) and downwind from the traffic (more than 25 m from the kerb) show that nanoparticles (<10 nm diameter) accounted for more than 36–44% of the total particle number concentrations. Measurements designed to sample the plume of individual vehicles showed that both a diesel- and a petrol-fuelled vehicle generated nanoparticles (<10 nm diameter). The fraction of nanoparticles was even greater in a plume 350 m downwind of a stationary combustion source. On a few occasions, a temporal association between nanoparticles in the size range 3–7 nm and solar radiation was observed in urban background air at times when no other local sources were influential, which suggests that homogeneous nucleation can also be an important source of particles in the urban atmosphere.     

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.     

A Wind Tunnel Study of Gaseous Pollutants in City Street Canyons

Steady state mean concentrations of tracer gas were measured in a 400:1 scale model of an idealized city with variable geometry placed within a wind tunnel at various orientations to the mean flow for a free stream velocity of 6.8 ft/sec. The tracer gas was released from two parallel line sources to simulate lanes of traffic in an effort to quantify the persistence of pollution as well as the mean values realized at street levels. An aerodynamically rough turbulent boundary layer of neutral thermal stratification was employed to simulate the atmosphere. Values of concentration measured in the model city were converted to prototype concentrations in ppm and compared to National Ambient Air Quality Standards. It was shown that single isolated structures may cause favorable mixing of pollution downwind but very high concentrations exist in the immediate leeward vicinity of the building. Two favorable geometries for city blocks tested were found to reduce pedestrian exposure to pollution both near heavy traffic congestion and downwind. It was concluded that the pollutant dilution was controlled by the mean flow rather than by turbulent diffusion and that the lateral spread of the plume was slight as one proceeded downwind of the line source. The combination of favorable geometry and higher dilution velocities may bring pollution levels down to existing Air Quality Standards. The body of information presented in this paper should interest city planners and air quality monitoring personnel, as well as those researchers attempting to study and model flow in city street canyons. It provides order of magnitude estimates on pedestrian and office worker exposure to pollutants under a wide range of conditions.     

Selection of an appropriate model to predict plume dispersion in coastal areas

In order to suggest a new methodology for selecting an appropriate dispersion model, various statistical measures having respective characteristics and recommended value ranges were integrated to produce a new single index by using fuzzy inference where eight statistical measures for various model results, including fractional bias (FB), normalized mean square error (NMSE), geometric bias mean (MG), geometric bias variance (VG), within a factor of two (FAC2), index of agreement (IOA), unpaired accuracy of the peak concentration (UAPC), and mean relative error (MRE), were taken as premise part variables. The new methodology using a single index was applied to the prediction of ground-level SO₂ concentration of 1-h average in coastal areas, where eight modeling combinations were organized with fumigation models, σ_y schemes for pre-fumigation, and modification schemes for σ_y during fumigation. As a result, the fumigation model of Lyons and Cole was found to have better predictability than the modified Gaussian model assuming that whole plume is immerged into the Thermal Internal Boundary Layer (TIBL). Again, a better scheme of σ_y (fumigation) was discerned. This approach, which employed the new integrated index, appears to be applicable to model evaluation or selection in various areas including complex coastal areas.     

Air Quality Model Evaluation and Uncertainty

The many advances made in air quality model evaluation procedures during the past ten years are discussed and some components of model uncertainty presented. Simplified statistical procedures for operational model evaluation are suggested. The fundamental model performance measures are the mean bias, the mean square error, and the correlation. The bootstrap resampling technique is used to estimate confidence limits on the performance measures, In order to determine if a model agrees satisfactorily with data or if one model is significantly different from another model. Applications to two tracer experiments are described.

It is emphasized that review and evaluation of the scientific components of models are often of greater Importance than the strictly statistical evaluation. A necessary condition for acceptance Of a model should be that it is scientifically correct. It Is shown that even in research-grade tracer experiments, data Input errors can cause errors In hourly-average model predictions of point concentrations almost as large as the predictions themselves. The turbulent or stochastic component of model uncertainty has a similar magnitude. These components of the uncertainty decrease as averaging time increases.     

Modeling of Potential Power Plant Plume Impacts on Dallas-Fort Worth Visibility

ABSTRACT

During wintertime, haze episodes occur in the Dallas-Ft. Worth (DFW) urban area. Such episodes are characterized by substantial light scattering by particles and relatively low absorption, leading to so-called “white haze.” The objective of this work was to assess whether reductions in the emissions of SO₂ from specific coal-fired power plants located over 100 km from DFW could lead to a discernible change in the DFW white haze. To that end, the transport, dispersion, deposition, and chemistry of the plume of a major power plant were simulated using a reactive plume model (ROME). The realism of the plume model simulations was tested by comparing model calculations of plume concentrations with aircraft data of SF₆ tracer concentrations and ozone concentrations. A second-order closure dispersion algorithm was shown to perform better than a first-order closure algorithm and the empirical Pasquill-Gifford-Turner algorithm. For plume impact assessment, three actual scenarios were simulated, two with clear-sky conditions and one with the presence of fog prior to the haze. The largest amount of sulfate formation was obtained for the fog episode. Therefore, a hypothetical scenario was constructed using the meteorological conditions of the fog episode with input data values adjusted to be more conducive to sulfate formation. The results of the simulations suggest that reductions in the power plant emissions lead to less than proportional reductions in sulfate concentrations in DFW for the fog scenario. Calculations of the associated effects on light scattering using Mie theory suggest that reduction in total (plume + ambient) light extinction of less than 13% would be obtained with a 44% reduction in emissions of SO₂ from the modeled power plant.     

Efficient model calibration method based on phase experiments for anaerobic–anoxic/nitrifying (A2N) two-sludge process

A systematic calibration and validation procedure for the complex mechanistic modeling of anaerobic–anoxic/nitrifying (A2N) two-sludge system is needed. An efficient method based on phase experiments, sensitivity analysis, and genetic algorithm is proposed here for model calibration. Phase experiments (anaerobic phosphorus release, aerobic nitrification, and anoxic denitrifying phosphate accumulation) in an A2N sequencing batch reactor (SBR) were performed to reflect the process conditions accurately and improve the model calibration efficiency. The calibrated model was further validated using 30 batch experiments and 3-month dynamic continuous flow (CF) experiments for A2N-SBR and CF-A2N process, respectively. Several statistical criteria were conducted to evaluate the accuracy of model predications, including the average relative deviation (ARD), mean absolute error (MAE), root mean square error (RMSE), and Janus coefficient. Visual comparisons and statistical analyses indicated that the calibrated model could provide accurate predictions for the effluent chemical oxygen demand (COD), ammonia nitrogen (NH₄ ⁺-N), total nitrogen (TN), and total phosphorus (TP), with only one iteration.

     

Modeling of potential power plant plume impacts on Dallas-Fort Worth visibility

During wintertime, haze episodes occur in the Dallas-Ft. Worth (DFW) urban area. Such episodes are characterized by substantial light scattering by particles and relatively low absorption, leading to so-called "white haze." The objective of this work was to assess whether reductions in the emissions of SO2 from specific coal-fired power plants located over 100 km from DFW could lead to a discernible change in the DFW white haze. To that end, the transport, dispersion, deposition, and chemistry of the plume of a major power plant were simulated using a reactive plume model (ROME). The realism of the plume model simulations was tested by comparing model calculations of plume concentrations with aircraft data of SF6 tracer concentrations and ozone concentrations. A second-order closure dispersion algorithm was shown to perform better than a first-order closure algorithm and the empirical Pasquill-Gifford-Turner algorithm. For plume impact assessment, three actual scenarios were simulated, two with clear-sky conditions and one with the presence of fog prior to the haze. The largest amount of sulfate formation was obtained for the fog episode. Therefore, a hypothetical scenario was constructed using the meteorological conditions of the fog episode with input data values adjusted to be more conducive to sulfate formation. The results of the simulations suggest that reductions in the power plant emissions lead to less than proportional reductions in sulfate concentrations in DFW for the fog scenario. Calculations of the associated effects on light scattering using Mie theory suggest that reduction in total (plume + ambient) light extinction of less than 13% would be obtained with a 44% reduction in emissions of SO2 from the modeled power plant.     

Simulation of the evolution of particle size distributions containing coarse particulate in the atmospheric surface layer with a simple convection-diffusion-sedimentation model

The Fugitive Dust Model (FDM) and Industrial Source Complex (ISC), widely used coarse particulate dispersion models, have been shown inaccurate due to the neglect of vertical variations in atmospheric wind speed and turbulent diffusivity (Vesovic et al., 2001), omission of the gravitational advection velocity, and an underestimation of the ground deposition velocity (Kim and Larson, 2001). A simple, transient two-dimensional convection-diffusion-sedimentation model is proposed to simulate the evolution in particle size distribution of an aerosol ‘puff’ containing coarse particulate in the atmospheric surface layer. Monin-Okhubov similarity theory, accompanied by empirical observations made by Businger et al. (1971), is adopted to characterize the surface layer wind speed and turbulent diffusivity profiles over a wide range of atmospheric conditions. A first order analysis of the crossing trajectories effect suggests simulation data presented here are not significantly affected by particle inertia. The model is validated against Suffield experimental data in which coarse particulate deposition was measured out to a distance of 800 m from the source (Walker, 1965). Good agreement is found for the decay in ground deposits with distance from the source for stable atmospheres. Deposition data was also simulated for unstable atmospheric stratification and the current model was determined to modestly underestimate the peak concentration with increasing accuracy further downwind of the release. The current model's effective deposition velocity was compared to that suggested by Kim et al. (2000) and shows improvement with respect to FDM. Lastly, the model was used to simulate the dispersion of nine lognormal aerosol puffs in the lowest 50 m of the atmospheric surface layer for four classes of atmospheric stability. The simulated mass median aerodynamic diameters (MMAD) at multiple downwind sampling locations were calculated and plotted with distance from the source. The first 50 m from the source was found to have a substantial impact on the evolution of MMAD for stable atmospheric conditions. Away from the source, it was observed that particle size distributions were truncated by removal of all particles larger than about 60 μm. A particle Peclet number was also defined to quantify the relative importance of turbulent dispersion and sedimentation on particle motion in the vertical direction.     

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.     

Demonstration of a global modeling methodology to determine the relative importance of local and long-distance sources

A global three-dimensional (3D) transport–dispersion model was used to simulate Krypton-85 (⁸⁵Kr) background concentrations at five sampling locations along the US east coast during 1982–1983. The samplers were established to monitor the ⁸⁵Kr plume downwind of the Savannah river plant (SRP), a nuclear fuel reprocessing facility. The samplers were located 300–1000 km downwind of the SRP. In the original analyses of the measurements, a constant background concentration, representing an upper-limit and different for each sampling station, was subtracted from the measurements to obtain the part of the measurement representing the SRP plume. The use of a 3D global model, which includes all major ⁸⁵Kr sources worldwide, was able to reproduce the day-to-day concentration background variations at the sampling locations with correlation coefficients of 0.36–0.46. These 3D model background predictions, without including the nearby SRP source, were then subtracted from the measured concentrations at each sampler, the result representing the portion of the measurement that can be attributed to emissions from the SRP. The revised plume estimates were a factor of 1.3–2.4 times higher than from the old method using a constant background subtraction. The greatest differences in the SRP plume estimates occurred at the most distant sampling stations.     

The evolution of photochemical smog in a power plant plume

The evolution of photochemical smog in a plant plume was investigated with the aid of an instrumented helicopter. Air samples were taken in the plume of the Cumberland Power Plant, located in central Tennessee, during the afternoon of 16 July 1995 as part of the Southern Oxidants Study – Nashville Middle Tennessee Ozone Study. Twelve cross-wind air sampling traverses were made at six distance groups from 35 to 116 km from the source. During the sampling period the winds were from the west–northwest and the plume drifted towards the city of Nashville TN. Ten of the traverses were made upwind of the city, where the power plant plume was isolated, and two traverses downwind of the city when the plumes were possibly mixed. The results revealed that even six hours after the release, excess ozone production was limited to the edges of the plume. Only when the plume was sufficiently dispersed, but still upwind of Nashville, was excess ozone (up to 109 ppbv, 50–60 ppbv above background levels) produced in the center of the plume. The concentrations image of the plume and a Lagrangian particle model suggests that portions of the power plant plume mixed with the urban plume. The mixed urban power plant plume began to regenerate O₃ that peaked at 120 ppbv at a short distance (15–25 km) downwind of Nashville. Ozone productivity (the ratio of excess O₃ to NO_y and NO_z) in the isolated plume was significantly lower compared with that found in the city plume. The production of nitrate, a chain termination product, was significantly higher in the power plant plume compared to the mixed plume, indicating shorter chain length of the photochemical smog chain reaction mechanism.     

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.