A Simple Technique for Determining The Maximum Ground Level Concentration of An Elevated Gaseous Release

A screening technique has been developed to determine the maximum one-hour ground level concentration of a gaseous emission from a stack located In flat terrain. The method does not require the use of a computer and eliminates the usual trial and error calculations. An infinite mixing height is assumed. It involves a linear or quadratic solution of the gaussian plume diffusion as a function of the effective stack height and a linear approximation of the Briggs plume rise equation. The linear approximation of the former gives results that are within 5 % of the gaussian plume results for stability criteria A, B, and C. For stability criteria D, the difference can be as great as 80%. If a quadratic estimation Is used, the differences are less than 3% for stability criteria A, B, and C, and are within 18% for stability criteria D. A linear approximation is used for the Briggs plume rise equation. This gives results within 4% of the Briggs equation. Overall, this is a simple straightforward approximation which gives results which can be used to determine if more sophisticated procedures are necessary.     

Plume Rise Determination-A New Technique Without Equations

Information on plume rise is important in determining the resulting concentrations of a pollutant on the ground. Practical use of plume rise values may be made in connection with stack design, the use of urban air pollution models, and in evaluating the hazards to a population complex.

This paper presents a new equationless technique for estimating plume rise as well as a comparison of seventeen commonly used plume rise formulas. Data from 10 sets of experiments, involving 615 observations and 26 different stacks, were used to study the relation between plume rise and related meteorological and stack parameters.

An independent data set was used to test the derived methods for determining plume rise. These data were obtained by Bringfelt of Sweden and contained measurements from stacks smaller than that at the Argonne National Laboratory to those approaching the TVA stacks.

A significant improvement in the prediction of plume rise from meteorological and stack parameters resulted from the use of a new technique called the Tabulation Prediction Technique. This is a method whereby an estimate of the value of a dependent variable may be obtained from information on the independent variables. Combinations of the independent variables—wind speed, heat emission rate, momentum rate, and stability—are arranged in an ordered sequence. For each combination of independent variables, the cumulative percentile frequency distribution of the dependent variable based on past measurements is given along with other statistics such as the mean, standard deviation, and interquartile range, i.e., the difference in plume rise between the 75th and 25th percentile values. Thus, one may look up the combination of independent variables just as one looks up words in a dictionary to obtain the percentile frequency distribution of the dependent variable. The mean, for each combination of independent variables may be considered as the best estimate for the given conditions.     

A Correlation of Field Observations of Plume Rise

Data from 137 sets of plume observations, comprising nearly 1 500 data points, are correlated with two simple formulae. These formulae, one for the buoyancy-dominated rise region and the other for the stratification-dominated levelled-off region of a plume, represent an approximate form of the entrainment theory of Hoult, et al. (1968)¹ for the case of uniform atmospheric stratification and zero wind shear. The observations, which are those of the Tennessee Valley Authority and of Bringfelt (1968),⁶ were made of plumes whose source strengths ranged from 0.4 to 111 Mw and which were emitted from stacks of heights between 21 and 183 m. The two formulae are found to correlate the data equally well over all values of the stack exit and meteorological parameters, provided only that the bulk mean velocity of the stack gases exceeds the mean wind speed by at least 20%. The ratio of observed to calculated plume rise is found to be distributed log normally about the mean value.

The median rise at large distances downstream was found to differ insignificantly from that given by the effective stack height formula recommended recently¹¹ for large buoyant plumes. Based upon the correlation, two formulae are recommended for computing median plume rise at all distances downstream of the stack. The formulae include an estimate of the expected uncertainty in the predicted rise.     

A Theory of Plume Rise Compared with Field Observations

A theory for the rise of a plume in a horizontal wind is proposed in which it is assumed that, for some distance downwind of a high stack, the effects of atmospheric turbulence may be ignored in comparison with the effects of turbulence generated by the plume. The theory, an extension of the local similarity ideas used by Morton, Taylor, and Turner,¹ has two empirical parameters which measure the rate that surrounding fluid is entrained into the plume. Laboratory measurements of buoyant plume motion in laminar unstratified cross flow are used to estimate the empirical parameters. Using this determination of the parameters in the theory, the trajectories of atmospheric plumes may be predicted. To make such a prediction, the observed wind velocity and temperature as functions of altitude, and flow conditions at the stack orifice, are used in numerically integrating the equations. The resulting trajectories are compared with photographs, made by Leavitt, et al.,² of TVA, of plumes from 500 to 600 ft high stacks. Within 10 stack heights downwind of the stack, the root mean square discrepancy between the observed height of the trajectory above ground level and the theoretical value is 14%, which is about the uncertainty in the observed height. The maximum plume rise within the field of observation is within 15% of that predicted by the present theory.     

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.     

Full-Scale Study of Plume Rise At Large Coal-Fired Electric Generating Stations

This paper reports on the plume rise research project conducted by TVA under sponsorship of the U. S. Public Health Service. Plume rise data were collected at six coal-fired, steam-electric generating stations within the TVA system over a 2-year period. Unit ratings ranged from 173 to 704 Mw with stack heights varying from 250 to 600 ft. An instrumented helicopter and special photographic equipment were used to obtain 1580 separate plume observations and significant related meteorological parameters during stable, neutral, and slightly unstable conditions. The 1580 observations were resolved and consolidated into 133 composite observation periods covering 30 to 120 min. Meteorological parameters and other compiled input data were entered into four principal equations for calculation of plume rise, and calculated plume rise values were compared with observed values. Most equations overestimated plume rise in low wind speed. For moderately high wind speeds, the Carson and Moses and the Concawe equations gave best fit.     

高架连续点源落地浓度预测中风速的确定

高架连续点源污染物排放落地浓度是大气环境影响预测的主要内容。由于大气污染物扩散明显受气象条件尤其是风速的影响,而现有预测模型中对于风速的取值都是按经验值来确定的。通过分析在不同气象和烟源条件下,平均风速的计算方法对烟羽抬升高度以及最大落地浓度产生的影响,与实测值相比较确定了风速取值的合理方法,缩小了预测偏差。     

Panel Discussion

A stack design procedure is developed which accounts for the effect of plume interception by downwind buildings, and which provides information on effluent concentrations in a form useful to planning authorities. The information presented in this paper is directed to engineers carrying out stack designs for locations where downwind buildings are of comparable height to the stack. A wind tunnel investigation using tracer gas techniques indicates that, for a plume at building height, downwash on the upwind face of a building causes the high concentrations observed near the roof to be transported to ground level. The effect of a plume on elevated points is determined by the concept of the minimum descent height of the maximum allowable ambient concentration isopleth. This minimum descent height, computed using Gaussian plume dispersion theory, defines a building height below which pollutant concentrations will always lie within safe limits. A case study is presented for the use of the design procedure for a small thermal power plant in an urban area.     

Water flume study of the enhancement of buoyant rise in pairs of merging plumes

When multiple stacks are grouped or ganged together at a site, the effluent plumes are often observed to merge downwind, forming a single buoyant plume whose rate of rise is enhanced relative to the rise of the plumes individually. The magnitude of this rise enhancement depends on many factors, and the few available models for rise enhancement do not always agree with one another. In the present study the rise behaviour of pairs of merging, buoyant plumes was studied by physical modelling in a water flume at 1:500 scale. The experiments were conducted at several stack separation distances and various exit velocity ratios for stack pairs aligned with, or perpendicular to, the ambient flow. Limited experiments were also done with the stacks aligned at other angles to the flow. The stack releases were made buoyant by heating the source water, and the resulting plumes were measured with an array of sensitive temperature probes. From these measurements it was possible to determine the plume structure and rise rates. For small stack separations when the stacks are aligned with the ambient flow, the experimental results show that the enhanced rise is close to, and sometimes above, the maximum theoretical rise enhancement factor of 2^1/3. For the perpendicular orientation there is little or no rise enhancement. The rise enhancement for other stack orientations is somewhere between these two extremes. A plausible physical explanation for the observed behaviour is given, based on initial momentum shielding and line vortex dynamics in the merging plumes.     

Pseudo-source parameters for flares: Derivation,implementation, and comparison

The United States Environmental Protection Agency (US EPA) flare pseudo-source parameters are over 30 years old and few dispersion modellers understand their basis and underlying assumptions. The calculation of plume rise from the user inputs of pseudo-stack diameter, temperature and velocity have the most influence on air dispersion model predictions of ground-level concentrations. Regulatory jurisdictions across Canada, the United States and around the world have adopted their own approach to pseudo-source parameters for flares; all relate buoyancy flux to the heat release rate, none consider momentum flux and flare tip downwash as adopted by the Alberta Energy Regulator (AER). This paper derives the plume buoyancy flux for flares burning a gas in terms of combustion variables readily known or calculated without simplifying assumptions. Dispersion model prediction sensitivity to flared gas composition, temperature and velocity, and ambient conditions are now correctly handled by the AER approach. The AER flare pseudo-source parameters are based on both the buoyancy and momentum flux, thus conserving energy and momentum. The AER approach to calculate the effective source height for flares during varying wind speeds is compared to the US EPA approach. Instead of a constant source for all meteorological conditions, multiple co-located sources with varying effective stack height and diameter are used. AERMOD is run with the no stack tip downwash option as flare stack tip downwash is accounted for in the effective stack height rather than the AERMOD model calculating the downwash incorrectly using the pseudo-source parameters. The modelling approaches are compared for an example flare. Maximum ground level predictions change, generally increasing near the source and decreasing further away, with the AER flare pseudo-source parameters. It's time to update how we model flares.

Implications: What are the implications of continuing to model flare source parameters using the overly simplified US EPA approach? First, the regulators perpetuate the myths that the flare source height, temperature, diameter and velocity are constant for all wind speeds and ambient temperatures. Second, that it is acceptable to make simplifying assumptions that violate the conservation of momentum and energy principles for the sake of convenience. Finally, regulatory decisions based on simplified source modelling result in predictions that are not conservative (or realistic). The AER regulatory approach for flare source parameters overcomes all of these shortcomings. AERflare is a publicly available spreadsheet that provides the “correct” inputs to AERMOD.     

Simulation of stack plume opacity

The visual impact of primary particles emitted from stacks is regulated according to stack opacity criteria. In-stack monitoring of the flue gas opacity allows plant operators to ensure that the plant meets U.S. Environmental Protection Agency opacity regulations. However, the emission of condensable gases such as SO3 (that hydrolyzes to H2SO4), HCl, and NH3, which may lead to particle formation after their release from the stack, makes the prediction of stack plume opacity more difficult. We present here a computer simulation model that calculates the opacity due to both primary particles emitted from the stack and secondary particles formed in the atmosphere after the release of condensable gases from the stack. A comprehensive treatment of the plume rise due to buoyancy and momentum is used to calculate the location at which the condensed water plume has evaporated (i.e., where opacity regulations apply). Conversion of H2SO4 to particulate sulfate occurs through nucleation and condensation on primary particles. A thermodynamic aerosol equilibrium model is used to calculate the amount of ammonium, chloride, and water present in the particulate phase with the condensed sulfate. The model calculates the stack plume opacity due to both primary and secondary particles. Examples of model simulations are presented for three scenarios that differ by the emission control equipment installed at the power plant: (1) electrostatic precipitators (ESP), (2) ESP and flue gas desulfurization, and (3) ESP and selective catalytic reduction. The calculated opacity is most sensitive to the primary particulate emissions. For the conditions considered here, SO3 emissions showed only a small effect, except if one assumes that most H2SO4 condenses on primary particles. Condensation of NH4Cl occurs only at high NH3 emission rates (about 25 ppm stack concentration).     

Control of Asphaltic Concrete Plants in Los Angeles County

Characteristics of maximum short-term ground level concentrations from an elevated point source, namely, the effective plume height, the critical wind speed, the distance to the point of maximum concentration, and the maximum concentration, are derived from the gaussian plume model. Both phases of plume development—before and after it has reached its final height—are considered. The plume rise treatment includes both thermal buoyancy and momentum effects. Certain limitations on critical wind speed are discussed. The dispersion model whose basis is established in this paper should be especially useful in applications where on site meteorological data are unavailable.     

Stable plume rise in a shear layer

Solutions are given for plume rise assuming a power-law wind speed profile in a stably stratified layer for point and finite sources with initial vertical momentum and buoyancy. For a constant wind speed, these solutions simplify to the conventional plume rise equations in a stable atmosphere. In a shear layer, the point of maximum rise occurs further downwind and is slightly lower compared with the plume rise with a constant wind speed equal to the wind speed at the top of the stack. If the predictions with shear are compared with predictions for an equivalent average wind speed over the depth of the plume, the plume rise with shear is higher than plume rise with an equivalent average wind speed.     

Observations Less than the Analytical Limit of Detection: A New Approach

Abstract

Solutions are given for plume rise assuming a power-law wind speed profile in a stably stratified layer for point and finite sources with initial vertical momentum and buoyancy. For a constant wind speed, these solutions simplify to the conventional plume rise equations in a stable atmosphere. In a shear layer, the point of maximum rise occurs further downwind and is slightly lower compared with the plume rise with a constant wind speed equal to the wind speed at the top of the stack. If the predictions with shear are compared with predictions for an equivalent average wind speed over the depth of the plume, the plume rise with shear is higher than plume rise with an equivalent average wind speed.     

Establishing the propensity for dioxin formation using a plume temperature model for medical waste incinerator emissions in developing countries

Air pollution control devices (APCDs) are not compulsory for medical waste incinerators (MWIs) in developing countries. In South Africa, combustion gases are usually vented directly to the atmosphere at temperatures greater than the formation temperature of dioxin. The possibility of dioxin formation outside the incinerator stack has been hypothesized. A plume model has been developed and tested in the wind tunnel with a scale model of an incinerator stack. The plume temperature and trajectory predictions of the plume model were verified within a +/- 3% experimental accuracy. Using South African data, the plume model predicts that the residence time of gases in the temperature range of 150-450 degrees C in a plume is 1.3 sec on average for 5% of a year (18 days) at meteorological conditions resulting in wind speeds of less than 1 m/sec. Two published dioxin formation models were used to assess the probability of dioxin formation in the plume. The formation models predict that the average polychlorinated dibenzodioxins/furans (PCDD/Fs) formed in the plume will exceed the stack emission regulations in South Africa of 0.2 ng/Nm3 toxic equivalent quotient (TEQ) by between 2 and 40 times. The calculated concentrations do not include additional gaseous PCDD/F compounds that may be formed at high-temperature post-combustion zones through pyrosynthesis mechanisms.     

Principal Plume Dispersion Models: TVA Power Plants

The body of information presented in this paper is directed to those individuals who may be concerned with principal plume dispersion models at coal-burning power plants. About 20 years of comprehensive field surveillance and documentation of dispersion of power plant emissions for a varied range of unit sizes, stack heights, and meteorological conditions have determined the Tennessee Valley Authority’s interpretation of principal plume dispersion models. TVA’s experience indicates that as unit sizes are increased and taller stacks are constructed, the plume dispersion model associated with maximum surface concentrations changes. Maximum surface concentrations for principal plume dispersion models were approximately equal for the early small plants. However, the coning model was considered the critical plume dispersion model because the frequency of recurrence of surface concentrations from this model was appreciably greater than other models.

There were progressive changes because of an increase in unit sizes and stack heights; the magnitude of maximum surface concentrations from the coning model decreased, and the magnitude (relative to the coning model) of concentrations from the inversion breakup model increased. However, with plumes from newer and larger units with higher stacks, the trapping dispersion model became prominent. Finally, by the time unit size had increased to 900 mw and stack height to about 245 meters, as at Bull Run Power Plant, the magnitude of surface concentrations associated with trapping had increased to such a degree that it became the critical dispersion model identified with power plants of this size.     

Modelling plume rise and dispersion from pool fires

This paper describes an investigation into the behaviour of smoke plumes from pool fires, and the subsequent generation of empirical models to predict plume rise and dispersion from such a combustion source. Synchronous video records of plumes were taken from a series of small-scale (0.06–0.25m²) outdoor methanol/toluene pool fire experiments, and used to produce sets of images from which plume dimensions could be derived. Three models were used as a basis for the multiple regression analysis of the data set, in order to produce new equations for improved prediction. Actual plume observations from a large (20.7 m×14.2 m) aviation fuel pool fire were also used to test the predictions. The two theoretically based models were found to give a better representation of plume rise and dispersion than the empirical model based on measurements of small-scale fires. It is concluded that theoretical models tested on small-scale fires (heat output ≈70 kW) can be used to predict plume behaviour from much larger combustion sources (heat output ≈70 MW) under near neutral atmospheric conditions.     

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.     

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.