Review of development and application of CRSTER and MPTER models

Atmospheric Environment Vol. 27B, No. 1, pp. 41 57, 1993. Printed in Great Britain.

0957-1272/93 $6.00+0.00 Pergamon Press Ltd

REVIEW OF DEVELOPMENT AND APPLICATION OF CRSTER AND MPTER MODELS ROBERT B. WILSON U.S. Environmental Protection Agency, Region 10, 1200 Sixth Avenue (ES-097), Seattle, WA 98101-3188, U.S.A. (First received 3 May 1991 and in final form 28 May 1992)

Abstract--The CRSTER and MPTER computer codes are two of many air quality dispersion models recommended for use in a regulatory context by the U.S. Environmental Protection Agency. CRSTER and MPTER are generally applicable to tall stack sources, such as coal-fired electrical utility power plants located in fiat or gently rolling terrain. This paper briefly reviews the developmental history, formulation, operation and application of the CRSTER and MPTER models. Also reviewed are performance evaluation studies which have included these two models. The paper concludes with a brief discussion of future directions for regulatory modeling of tall stack sources. Key word index: CRSTER, MPTER, EPA models. INTRODUCTION

With the advent of regulatory air pollution programs in the 1950s and 1960s came the need for practical tools for estimating transport and diffusion of pollutant plumes from continuously emitting industrial stack sources. The Gaussian plume model has become the widely accepted practical solution to this need. A computer code applicable to single sources, and known as CRSTER (EPA, 1977, 1980; Catalano, 1986), was developed in the 1970s, and became the benchmark regulatory model of the U.S. Environmental Protection Agency (EPA). A few years later, as the need became apparent for a model able to simulate stacks at multiple locations, the MPTER code (Pierce and Turner, 1980; Chico and Catalano, 1986) was developed. Since their development, the CRSTER and MPTER models have had widespread application for regulatory purposes, but particularly to tall stack sources, such as coal-fired electrical utility power plants. This paper briefly reviews the developmental history, formulation, operation and application of the CRSTER and MPTER models. Also reviewed are performance evaluation studies which have included these two models. The paper concludes with a brief discussion of future directions for regulatory modeling of tall stack sources.

DEVELOPMENTAL HISTORY

Early practical methods for estimating impacts (ground-level concentrations) from pollutant plumes Presented at the Workshop for the Development and Application of Air Quality Models, Taipei, Taiwan, 7-8 May 1991. 41

emitted from elevated point sources (stacks) were generally based on Gaussian diffusion formulae. Cramer (1957) and Hay and Pasquill (1957) were the first to use these formulae in mathematical diffusion models, correlated with empirical data (Gifford, 1975). Pasquill (1961), Gifford (1961, 1968) and Turner (1964) made further suggestions to develop the Gaussian plume model into a practical tool, applied with routinely available meteorological data. Increasing social awareness and environmental concern for air pollution problems in the late 1960s led to an emphasis on the establishment of regulatory programs. D. Bruce Turner's "Workbook of Atmospheric Dispersion Estimates", first published by the U.S. Public Health Service (Turner, 1969), and then in a slightly revised form by the then newly created U.S. Environmental Protection Agency (Turner, 1970), became a fundamental document upon which EPA's regulatory approach to atmospheric dispersion modeling was based. Turner's Workbook, as it is called, provided practical solutions to air pollution problems, such as transport and dispersion from industrial stack sources, based on the Gaussian plume model. Turner recommended the semi-empirical Pasquill-Gifford ( P - G ) dispersion coefficients, classified by atmospheric stability, for estimating plume spread. Turner suggested criteria for specifying stability class based on meteorological data typically available as hourly observations at hundreds of airport locations (Turner, 1961, 1964). While Turner's Workbook recommended plume rise estimation techniques suggested by Holland (1953), the work of Briggs (1969), published about the same time as Turner's Workbook, became the foundation for EPA regulatory models. Thus, in the early 1970s, Turner's Workbook, along with Briggs' plume rise algorithms, became the widely accepted regulatory approach for estimating concentrations due to

42

R.B. WILSON

emissions from industrial stack sources. The need to perform repetitively the mathematical computations associated with these techniques soon led to the development of computer codes. Three computer codes based on these techniques were developed by 1973 (Turner and Busse, 1973; Irwin and Cope, 1979). The three codes, written in the F O R T R A N computer language, were PTMAX, PTDIS and PTMTP. For a single stack source, PTMAX calculates, as a function of wind speed and stability class, the maximum ground-level concentration, along with the downwind distance to that maximum, and the effective plume centerline height (after achieving final rise). The user can select a specific wind speed/stability combination, or full range of wind speed/stability combinations. PTDIS calculates, for a specified wind speed and stability class, the plume centerline groundlevel concentration as a function of downwind distance from the source. One can also obtain lateral (crosswind) plume dimensions from PTDIS. With PTMTP, the user can estimate the concentration at selected receptor locations, due to emissions from multiple (spatially separated) sources, for a specified set of meteorological conditions (wind speed, wind direction and stability class). An issue that soon arose in applying the Turner Workbook (PT-model) approaches concerned what averaging time period for which the estimated concentrations should be considered applicable. The semiempirical P - G dispersion coefficients were developed based on field diffusion experiments in which the concentration measurements were made over periods of 3-10 min (Pasquill, 1961, 1976). However, as these techniques were applied for regulatory purposes, the concentration estimates were generally assumed to be appropriate for 1-h averaging periods. Underlying this assumption was the desire on the part of regulators to estimate maximum expected values. Because the P - G curves were based on ensemble averages of a relatively limited set of field experiments, it was hoped that one could protect against the possibility of underestimating maximum impacts, if the resulting concentrations were assumed to be 1-h averages. Limited performance evaluations, discussed below, reveal that this assumption apparently does not tend to bias model estimates toward overestimation. This "regulatory extension" of the P - G curves continues (with some controversy) to be general EPA practice today. Another aspect of regulatory air programs in the early 1970s which affected dispersion modeling was the development of ambient standards. National Ambient Air Quality Standards (NAAQS) were established by EPA for several pollutants as a result of requirements of the Clean Air Act, passed by the U.S. Congress in 1970. The approach adopted by EPA for setting NAAQS was one of establishing deterministic pollutant concentrations, not to be exceeded, or, for some short-term standards, not to be exceeded more than once per year at a given location. For example, the NAAQS for sulfur dioxide (SO2)

are: Averaging period 3h 24 h Annual

NAAQS for SO2 (#g m - 3) 1300 365 80

The annual NAAQS is never to be exceeded, while the 3- and 24-h NAAQS are not to be exceeded more than once per year at a given location. Typically, for continuous point sources of pollution, the short-term NAAQS are controlling with respect to the establishment of maximum allowable emission rates for that source. That is, if a source is in compliance with the 3- and 24-h NAAQS, for example, it likely will not have difficulty complying with the annual NAAQS. Thus, a source's maximum allowable emission limit is usually based on its largest impacts for a 3- or 24-h period. This deterministic approach to setting ambient standards results in the need for modeling methods which are able to predict the very upper end of the frequency distribution of concentrations at a particular location. This is a key forcing function which has driven the development of EPA modeling methodologies. As NAAQS were established in the early 1970s the focus of regulatory modeling rapidly became "worst-case conditions", that is, those extreme meteorological conditions which, for a given source, will produce the highest short-term concentrations during a year. An increased emphasis in the mid-1970s on the need for regulatory estimates of impacts from power plant emissions grew, in part, out of the Arab oil embargo of 1973. The shortage of oil prompted U.S. electric utilities to consider switching from relatively low-sulfur oil and gas fuels to coal. Much of the available coal was high in sulfur content, potentially leading to increases in SO 2 emissions. Thus, there was a rapidly developing need for regulatory tools to test SO 2 emission limits for power plants. EPA needed an objective, widely applicable dispersion model to estimate worst-case short-term SO 2 impacts from power plants. The worst-case conditions for a particular source will vary depending on characteristics of that source. Thus, it is necessary to evaluate source impacts under a variety of meteorological conditions, to determine those conditions which lead to maximum concentrations. The PT-models, and especially PTMAX, were very useful for quickly determining maximum 1-h average concentrations that might be expected from a particular stack source. However, our NAAQS are in many cases based on averaging times longer than 1 h, such as the above-mentioned SO 2 standards. How does one determine what the worst-case meteorological conditions are for longer time periods? Simply assuming persistence of the worst-case 1-h conditions for periods up to 24 h is unrealistic. The

Review of CRSTER and MPTER models variation in meteorology, from one location to another, makes it difficult to prescribe a priori reasonable worst-case conditions. At best, it is a subjective decision, not lending itself to the desired objectivity and consistency of regulatory programs. A possible solution to this problem would be, instead of making guesses (admittedly educated guesses) as to what the worst-case meteorological conditions are, one could simply (?) model all of the conditions over a long period of time, for example, a year or longer. One could take the relatively simple Gaussian plume model and exercise it for a given source sequentially for each set of measured, hourly-averaged meteorological variables for a 1-year period, and estimate ground-level concentrations at a large number of potential impact locations. From these 8760 1-h concentrations at each location, one could develop 2920 sequential 3-h average, and 365 sequential 24-h average concentrations. Having estimates for all of these periods, one could then determine the meteorological conditions which lead to the highest or secondhighest values. Thus, the model, rather than the modeler, determines the worst-case conditions. A degree of objectivity and consistency can be achieved in this manner to satisfy the desires of regulatory programs. Two problems subsequently arise with this "brute force" solution: (1) the modeler needs a long period of record of representative meteorological data and (2) the modeler must make a large number of calculations. The answer to the first problem is to use readily available hourly meteorological data collected at many airport locations, or install instrumentation and collect the necessary data. This issue of meteorological data collection and processing is covered in EPA guidance (EPA, 1987a). The answer to the second problem is computers. One could develop a computer code into which one could input source characteristics and hourly meteorological data for a 1-year period, and that code would calculate concentrations at many receptor locations to estimate the highest concentrations, or worst-case impacts, for averaging times of 1 h to 1 year. It was with this background that the EPA created the CRSTER code. Since CRSTER could only simulate sources from a single location, a code was needed that could estimate concentrations from several sources at multiple locations. Thus, the MPTER model was created. The original formulation of the CRSTER model (EPA, 1977) was included as a preferred (generically recommended) model in the first edition of EPA's Guideline on Air Quality Models (EPA, 1978). CRSTER was modified in 1980 (EPA, 1980) and again in 1986 (Catalano, 1986). The original MPTER model (Pierce and Turner, 1980) was also modified in 1986 (Chico and Catalano, 1986). The models de*Some material in this section is borrowed from the user's guides.

43

scribed in the remainder of this paper are the current versions of the models, CRSTER version 86211 and MPTER version 89142. Both CRSTER and MPTER are recommended as preferred models for stack sources in simple (fiat or gently rolling) terrain settings, in the latest version of EPA's air modeling guidance (EPA, 1986, 1987b). Originally, the CRSTER and MPTER codes were written in F O R T R A N for use on large mainframe computers. More recently, these codes have been modified for, and compiled with, IBM-compatible personal computer (PC) versions of FORTRAN. EPA is now distributing these codes to the public free of charge on an electronic bulletin board system called SCRAM (Support Center for Regulatory Air Models). These codes, many other models, documentation, modeling guidance and meteorological data are easily downloaded from SCRAM in compressed file format. The telephone number to access SCRAM is 919-541-5742. Or, the System Operator (currently Mr Russ Lee) can be reached at: SYSOP, SCRAM BBS, U.S. EPA, OAQPS (MD-14), Research Triangle Park, NC 27711, U.S.A., Tel. 919-541-5638. In addition, several private consulting firms have developed PC versions of CRSTER, MPTER and many other EPA regulatory air models. These models generally have a more user-friendly interface than EPA versions of the models, and are available commercially from the developers.

MODEL FORMULATION* The CRSTER model is based on a modified version of the Gaussian plume equation. This model assumes a continuous emission source, steady-state downwind plume and a Gaussian distribution for concentrations of pollutants within the plume, in both the crosswind and vertical directions. Plume rise is estimated using equations proposed by Briggs (1969, 1971, 1972, 1973, 1975) for hot, buoyant plumes and for momentumdominated plumes. As the plume expands due to eddy diffusion, it is diluted and transported downwind by the mean wind. The rate of expansion is characterized by a series of empirical dispersion coefficients which are dependent on the stability of the atmosphere, as determined in studies made by Pasquill (1974) and Gifford (1961), reported by Turner (1964, 1970), and by Briggs (1973) based on the observations of McElroy and Pooler (1968), which are included in Gifford (1976). The modifications made to the basic Gaussian plume equation include the following: • trapping of the plume between the top of the mixing layer and the ground surface, • uniform vertical mixing of the plume in the mixing layer beyond a critical distance, • exclusion of any ground-level impacts from plumes estimated to be above the mixing layer.

44

R.B. WILSON

The Gaussian plume equation The Gaussian plume equation for a continuous emission source gives the local concentration, X, of a gas or aerosol at a ground-level location (x, y) by the following expression: Z(x, y) =

Q

[

exp - ~

7~Cry~zU

exp

[ (1)

where the wind is advecting the plume at a speed, u, along the x-axis and dispersing it along the crosswind and vertical direction with dispersion coefficients a r and a=, respectively. The pollutant emission from the source is at a uniform rate, Q, and is assumed to be released at an effective stack height, H (stack height plus final plume rise). It is assumed that complete reflection of the plume takes place at the Earth's surface, that is, there is no deposition at the surface. The concentration Z is an average of the time interval represented by ay and az. CRSTER calculates shortterm concentrations and uses these directly as 1-h average concentrations without consideration of plume history, that is, each 1-h period is independent. Equation (1) is valid for any consistent set of units, however, those commonly employed are ~ in g m - 3, Q in g s - 1, u in m s - 1 and x, y, H, c% and or= in m.

Key assumptions The Gaussian plume equation (1) is a solution to the simplified conservation of mass equation assuming non-zero wind speed and constant eddy diffusivities along the principle axes. The use of a single wind speed and constant direction in the Gaussian plume equation reflects the assumption that the horizontal wind field is homogeneous, and that the effects of directional wind shear in the atmospheric boundary layer are not considered. The effects of surface friction

in reducing wind speeds near ground level are taken into account by CRSTER. The important assumptions incorporated into CRSTER can be summarized as follows: Steady-state conditions--ideal gas, continuous uniform emission rate, homogeneous horizontal wind field, representative hourly mean wind velocity, no directional wind shear in the vertical, instantaneous infinite downwind dispersion, no plume history. • Pollutant characteristics--the pollutant emitted is a stable gas or aerosol with no significant gravitational settling, which participates in the turbulent movement of the atmosphere, there is no chemical transformation or deposition. • Gaussian distribution--the pollutant material within the plume takes on a Gaussian distribution in both the horizontal crosswind and vertical directions, described by empirical dispersion parameters crr and az. •

Dispersion coefficients Both CRSTER and MPTER employ two sets of dispersion coefficients for a r and a=. The P - G curves reported by Turner (1970) are applicable to dispersion in rural areas; Briggs' presentation (Briggs, 1973; Gifford, 1976) of the McElroy-Pooler coefficients are applicable to dispersion in urban areas. (See section 8.2.8, p. 8-10, of EPA, 1986, 1987b, for objective urban/rural classification criteria based on Auer, 1978.) Figures 1-4 show these dispersion coefficients as a function of atmospheric stability class and downwind distance. Both CRSTER and MPTER allow the optional use of buoyancy-induced dispersion. This option, which is required for EPA regulatory applications, accounts for added plume growth during the plume rise phase due to the turbulent motions associated with the

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Review of CRSTER and MPTER models

45

interval in which the plume is dispersing. The wind at the stack elevation is commonly used as an approximation to that condition. Input wind speeds measured at other than stack-top height are adjusted by the models to stack-top elevation using a simple powerlaw relationship:

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Downwind distance (m) Fig. 3. Briggs' presentation of McEiroy-Pooler lateral dispersion coefficient, cry,for urban areas (Hanna et al., 1982).

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where u is hourly wind speed at h~ (m s- 1), u. is wind speed at za (m s- 1), za is anemometer height (m), hs is stack height (m) and p is wind-profile exponent. Separate urban and rural default wind-profile exponents are employed by CRSTER and MPTER as a function of stability class, as listed in Table 1. The rural exponents correspond to a surface roughness of about 0.1 m; the urban exponents result from a roughness of about 1 m, plus urban heat release influences (Irwin, 1979). The adjusted wind speed is used by the models to calculate plume rise and dilution. Plume rise

t

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Downwinddistance (m) Fig. 4. Briggs' presentation of McElroy-Pooler vertical dispersion coefficient, crz, for urban areas (Hanna et al., 1982).

conditions of plume release and the turbulent entrainment of air. Pasquill (1976) suggests that induced vertical dispersion, ~Tzo,can be approximated by the plume rise, Ah, divided by 3.5, and the effective dispersion, ~rze, can be determined by adding variances: crze =(azo 2 +c%2) 1/2.

(2)

Since, in the initial growth phases of release, the plume is nearly symmetrical about its centerline, buoyancy-induced dispersion in the horizontal direction is assumed in the models to be equal to the buoyancy-induced dispersion in the vertical direction, i.e. cryo=C%o=Ah/3.5. This expression is combined with that for dispersion due to ambient turbulence in the same manner as shown above for the vertical. In general, buoyancy-induced dispersion will have little effect upon maximum concentrations unless the stack height is small compared to the plume rise. Typically, the effects of buoyancy-induced dispersion are most important close to the source and at receptors located near plume centerline. Vertical wind speed profiles The wind speed required for input to the CRSTER and MPTER models is considered to be representative of the conditions throughout the vertical height

The effective stack height, H, used in Equation (1) is defined as the sum of the physical stack height (hs) and the plume rise (Ah). Buoyant and momentum plume rise in CRSTER and MPTER are estimated using equations proposed and later modified by Briggs (1969, 1971, 1972, 1973, 1975). These equations are based on the assumption that plume rise depends on the inverse of the mean wind speed and is directly proportional to the 2/3 power of the downwind distance from the source. Different equations are specified for the neutral-unstable conditions and the stable conditions. Optional features of the plume rise algorithm include stack-tip downwash, and gradual or final rise. Stack-tip downwash, when the stack effluent is drawn down immediately in the lee of the topmost portion of the stack structure, is assumed to occur when the stack gas exit velocity is less than 1.5 times the stack-top wind speed. Under these conditions, the modified stack height h's is (Briggs, 1973):

h', = hs, for

v~i> 1.5u,

where vs is stack gas exit velocity (m s - 1), D is stack

Table 1. Default wind-profile power-law exponents (p) Stability class A B C D E F

Rural exponent Urban exponent 0.07 0.07 0.10 0.15 0.35 0.55

0.15 0.15 0.20 0.25 0.30 0.30

46

R.B. WILSON

exit inside diameter (m) and u is wind speed at stack top (m s - 1). This typically minor adjustment to the stack height for stack-tip downwash is recommended for all regulatory applications of CRSTER and MPTER. For most plume rise situations, buoyant rise dominates momentum rise, and the value of Briggs' buoyancy flux parameter, F (in units ofm 4 s-3) is needed: s

rs,/'

(5)

where # is gravitational acceleration (9.8 m s - 2), T, is stack gas exit temperature (K) and Ta is ambient air temperature (K). For unstable and neutral conditions ( P - G classes A, B, C and D), the final buoyant plume rise is: Ah = 1.6 F l/a (3.5x*) 2/3,

(6)

U

where

x* = 14F 5/s when F < 5 5 m 4 s - 3

(7)

x* =34F 2/5 when F~>55 m4s-3; (8) x* (in m) is the downwind distance at which atmospheric turbulence begins to dominate entrainment. The downwind distance (in m) to final plume rise is 3.5x*. Neutral-unstable final momentum rise is calculated as"

Ah = 3D v-Ls.

(9)

U

Briggs (1969) suggests that this equation is most applicable when vs/u is greater than 4. Since momentum rise occurs quite close to the point of release, the distance to final rise is set equal to zero. If the unstable-neutral momentum rise is greater than the unstable-neutral buoyancy rise, momentum rise applies. For stable situations (P-G classes E and F), a stability parameter s is calculated as" g ao s=-- -T~ az

Ah=l.5 \

4r, u J S1/6

Gradual rise is not allowed to exceed final rise in the models. For regulatory applications, the gradual plume rise option is not used; the plume is assumed to disperse from a point above the stack, at the height of the final rise.

Limited mixing Turbulent mixing and vertical diffusion of a plume is often limited by the existence of an inversion aloft. The effects of limited mixing (or plume trapping) on plume dispersion are incorporated into CRSTER and MPTER by the assumption that the plume is completely reflected at the mixing height (L), as well as the ground surface. No partial penetration of the plume into the inversion layer aloft is simulated by these models. If the plume centerline height, H (after final rise is achieved), is above the mixing height, it is assumed that there is no ground-level impact of that plume, that is, ground-level concentrations are assumed to be zero for that condition. Trapping of the plume within the mixed layer is simulated using the method of multiple images proposed by Bierly and Hewson (1962). In this procedure, depicted in Fig. 5, each reflection is represented by an "image plume" from an imaginary source with a stack height equal to the vertical distance traveled by the plume edge to the point of ground reflection. The reflections between the mixing height (denoted by Hm in Fig. 5) and the ground can be represented by the convergent infinite series of Gaussian plume terms given in the following equation:

oxp[

x ~® expr

N=-~

I(H+2NL] 2]

L-2\

~

(12)

/ _1"

(14)

The series converges rapidly, and the summation is stopped when the contribution becomes small. Equation (14) applies when or= is less than 1.6L. Beyond this distance, a uniform vertical distribution of concentration is assumed: Z=

(v2D2Ta~ 1/3

(13)

U

(11)

and the distance Momentum rise under stable conditions is the lower of the unstable-neutral momentum rise, or:

';

Ah = 1.6 F1/ax2/3 .

(10)

where dO/dz is the vertical potential temperature gradient from stack top to plume top ( K m - 1). This value is often assumed by EPA, for screening purposes, to be 0.02 for stability class E, and 0.035 for stability class F. Buoyant plume rise for stable conditions is estimated by:

Ah=2.6( Fy/3 \us/ to final rise is nu/s ~/2.

For all stabilities, plume rise can be calculated for downwind distances less than the distance to final rise. (This is known as gradual rise.) This height is not calculated for momentum-dominated conditions. Gradual (buoyant) plume rise is estimated as:

v/~arL u exp

[

.

(15)

Terrain considerations The CRSTER and MPTER models are intended to be applied to sources located in flat or gently rolling terrain. Uneven terrain is accounted for in the models according to Fig. 6. For receptors above stack base

Review of CRSTER and M P T E R models /

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48

R.B. WILSON

elevation, the effective plume height above the receptor is reduced by an amount equal to the elevation difference between the receptor and the stack base, as depicted in Fig. 6 by receptors R 2, R 2 and R 3. For receptors below stack base (receptor R4), the effective plume height is increased by the elevation difference between the stack base and the receptor. The terrain adjustment made for any one receptor point does not affect concentrations at any other receptor point. In these models, receptor elevations are not allowed to exceed the elevation of the stack top. MPTER has other terrain treatment options, however, these are not recommended for regulatory applications, and are not discussed here. Note that mixing height is compared with the final plume height over the source when determining whether or not the plume is to be treated as within the mixed layer. As stated above, a plume whose centerline height at final rise is above L, is assumed to have zero ground-level impact. Receptors

The receptor array generated by the CRSTER and MPTER models consists of 180 receptors in a polar grid. The receptors are arranged in five rings (at five user-defined distances) of 36 receptors (at 10 ° intervals). A simple screening model, such as SCREEN (EPA, 1988) can be used to identify distances to maximum concentrations as a function of wind speed and stability, to assist the user in the specification of receptor ring distances. For tall stack sources in relatively flat terrain, maximum short-term ground-level concentrations are typically associated with unstable conditions (Irwin and Cope, 1979). Since maximum impacts occur relatively close in to the source, careful attention must be paid to receptor placement near the source for regulatory applications, to ensure that maximum concentrations are calculated. In the MPTER model, if less than five receptor rings are specified, discrete receptors can be located at user-specified Cartesian coordinates. Both models allow a maximum of 180 receptors. Both models allow the specification of receptor heights above local ground, so-called "flag-pole receptors". Source characterization

Both models can simulate multiple sources, however, in the CRSTER model all sources (up to 19) must be collocated. The models calculate concentrations for each source at each receptor; these concentrations are summed at each receptor to determine total concentrations. MPTER can simulate impacts from up to 250 spatially separated sources. Sources are characterized by stack location, height, diameter, exit velocity, exit temperature and emission rate. All source parameters are assumed constant during all periods modeled. CRSTER can optionally simulate monthly averaged variable emission rates and stack parameters. MPTER can optionally simulate emission rates which vary hourly, however, other stack parameters remain constant. CRSTER can simulate

one pollutant, while MPTER can simulate two pollutants. While deposition or chemical transformation are not treated by these models, the user can optionally specify a simple exponential "half-life" decay of the pollutant. Both models assume that the emitted plumes are not disturbed by building-wake-induced downwash.

MODEL OPERATION

The computer codes for the CRSTER and MPTER models are written in the FORTRAN computer language. While these codes were originally written for large mainframe computers, revised codes are now available, which can be compiled by FORTRAN compilers typically employed on personal computers. The codes are thoroughly documented, so that proper inputs to the models can be developed from information in the program listing. Input files can be developed with a simple text editor. Alternatively, model-specific data entry programs have been developed by a few U.S. consulting firms. These interactive, personal computer-based programs are very useful in reducing input development times and coding errors. Program options, source data and receptor data are input to the models by text files. The meteorological data are input by a binary file which is output by the meteorological pre-processor (RAMMET). MPTER can also take meteorological data as text input. One of the program options is a regulatory "switch", which sets program options to be consistent with current EPA regulatory policy. Output from the models optionally consists of printed output and concentration files for post-processing. CRSTER produces tables of: • average concentrations at each receptor for the period modeled (one year if one year of meteorological data is supplied); • highest and second-highest concentrations for 1-, 3and 24-hourly averages at each receptor; • highest and second-highest concentrations for a user-selected averaging period (2, 4, 6, 8 or 12 h) at each receptor; • highest 50 concentrations for each averaging period selected. MPTER produces the same, except that it produces the five highest values for each averaging period, except the annual average, at each receptor. In multiple source analyses, individual source impacts can optionally be output.

MODEL

APPLICABILITYAND LIMITATIONS

The CRSTER and MPTER models are intended to be applied to stacks with buoyant plume rise, located in fiat or gently rolling terrain. Source stack-tops

Review of CRSTER and MPTER models should be well above the surrounding terrain and local obstructions to the flow (e.g. nearby buildings). The models assume no horizontal or vertical deformation of plume trajectory due to uneven topography. The models accept hourly averaged meteorological data, and dispersion conditions, along with emission characteristics, are assumed to be steady state during the hour. With the assumption of steady-state conditions, the application of the models should normally be limited to downwind distances of 20 km. Occasionally, however, these models are applied to distances out to 50 km to obtain screening estimates for regulatory purposes. These models do not account for fumigation, stagnation, wind direction shear, plume impingement on elevated terrain, variable trajectories, intermittent sources, deposition, chemical transformation, negatively buoyant plume behavior or building-wake- or terrain-induced downwash. The CRSTER and MPTER models are most useful for estimation of maximum groundqevel concentrations in a regulatory context. The models perform adequately for estimating the highest, second-highest short-term concentrations expected due to emissions from relatively tall stacks of combustion sources, such as fossil-fuel-fired electric power generating plants. While the models are not particularly accurate for predicting concentrations at a specific location during a specific time, they do appear to have skill in predicting the highest measured concentrations which occur over a network of air quality monitors, during a long period of record. Employed in this manner, these models are able to determine the adequacy of emission limits to prevent violations of ambient air quality standards.

MODEL PERFORMANCE EVALUATIONS

Performance measures

In a regulatory context, a tendency exists to judge a model's performance in terms of its ability to predict concentrations which are comparable with ambient air quality standards. In the U.S.A., short-term ambient air quality standards are concentrations not to be exceeded more than once per year. Therefore, early model performance evaluations were comparisons of predicted and measured second-highest short-term (1- to 24-h average) concentrations. In September 1980, the American Meteorological Society organized a workshop (sponsored by the EPA) to consider the issue of model performance evaluation. The report of that workshop (Fox, 1981) contains an extensive array of recommended statistical procedures for comparing observed air quality with model predictions. These workshop recommendations were incorporated into an EPA guidance document called Interim Procedures for Evaluatin 0 Air Quality Models (EPA, 1984). Some early experience with that interim guidance was reported by EPA (EPA, 1985). This array of performance measures has

49

been distilled, and modifications have been suggested by Cox (1988) and more recently by Hanna (1988, 1989). Performance standards for air quality dispersion models have not been established by EPA. However, Cox (1988) recommends a screening procedure which may be useful for regulatory purposes. The fractional bias, correlation, fraction within a factor of two, and normalized mean square error (Hanna, 1989) currently appear to be the most useful statistics for measuring regulatory model performance. Early performance evaluations

Although the CRSTER model was not well documented (Hrenko et al., 1972) at that time, Lee et al. (1975) presented results of evaluations of the CRSTER model performance. These evaluations compared maximum CRSTER estimates of 1- and 24-h SO 2 concentrations to maximum measured ambient concentrations for four large power plants with tall stacks. Three of the four plants are located in gently rolling terrain, while the Philo plant has terrain at and above stack-top elevation within a few kilometers of the plant. Tables 2 and 3 (EPA, 1977) list, respectively, the high 1- and 24-h predicted and measured SO2 concentrations. These values are paired in space, but not in time. Table 4 (EPA, 1977) below lists the geometric mean ratios of predicted to measured second-highest concentrations. Note that an estimated background SO2 concentration has been subtracted from the measured values. The overprediction at the Philo plant is partially explained by it being located in relatively complex terrain. Elevations of two of the monitoring sites were within a few meters of stack-top elevation. This study concludes that the model predicts Upper ends of the frequency distributions of 1- and 24-h concentrations "acceptably well", while concentrations over the remainder of the frequency distributions are "significantly underpredicted." Tikvart and Mears (1976), in their summary of Lee et al. (1975) conclude that CRSTER is "generally accurate within a factor of two," but that it has a "tendency to underestimate, rather than overestimate" measured short-term concentrations. Evaluation of rural air quality simulation models

In 1980, EPA began a major program to evaluate systematically the performance of air quality dispersion models that might be used for regulatory purposes. Available models were grouped by application, with CRSTER/MPTER falling in the group of"rural" air quality simulation models, applicable to point sources located in a rural setting. (CRSTER and MPTER had not yet been modified to include urban dispersion coefficients at the time of program initiation.) The performance of several rural models has been documented through extensive tabulations of per-

R. B. WILSON

50

Table 2. One-hour concentration distribution statistics for measurements and CRSTER model (concentrations in /~gm -3)

Plant

Sampling station

95th percentile* O b s t CRSTER

99th percentile* Obs CRSTER

Second highest Obs CRSTER

Highest Obs CRSTER

Canal

1 2 3 4

25 14 18 15

< < < <

1 1 1 1

101 72 18 31

6 <1 2 <1

435 553 446 575

253 174 427 81

438 619 732 638

283 179 479 377

Stuart

! 2~ 3 4 5 6 7~

140 80 74 53 28 48 33

< 10 <10 26 < 10 < 10 < 10 < 10

270 445 200 180 80 135 102

400 180 240 130 < 10 120 30

685 685 1022 750 495 980 325

1372 814 565 515 823 595 976

857 1014 1153 883 565 1053 435

1393 948 1022 541 1219 693 1000

Muskingum River

1 2 3 4

27 57 130 72

< < < <

10 10 10 10

150 270 350 200

160 150 210 160

857 786 996 735

980 1304 873 465

925 786 1179 786

1083 1310 933 645

Philo

1 2 3 4 5 6

50 37 47 27 35 118

< < < <

I0 10 10 10 80 20

170 163 163 190 134 253

98 222 920 88 555 650

525 735 745 665 575 565

1295 945 4049 1945 1279 2369

893 891 917 695 675 595

1639 1059 4593 1981 1344 2482

* Percentile values given in terms of cumulative per cent of concentration less than given values. tObserved concentration minus background. ,Samplers were in operation for less than half the year. Data not included in subsequent analyses.

Table 3. Twenty-four-hour concentration distribution statistics for measurements and CRSTER model (concentrations in #g m - 3) 95th percentile* Sampling station

Obst

Canal

1 2 3 4

32 15 17 15

Stuart

1 2~ 3 4 5 6 7~

Muskingum River

Philo

Plant

CRSTER

99th percentile*

Second highest

Highest

Obs

CRSTER

Obs

CRSTER

Obs

CRSTER

4 <1 4 <1

52 28 46 44

14 6 18 2

66 36 77 63

16 9 38 4

75 46 83 75

29 11 39 16

83 46 50 40 31 42 45

55 28 36 24 5 21 23

245 160 110 63 52 135 69

128 52 75 41 50 46 60

259 63 181 79 63 147 69

149 75 91 45 57 69 73

277 159 225 83 77 195 77

161 98 102 49 75 83 120

1 2 3 4

32 55 98 52

32 32 24 29

100 100 130 95

69 80 58 41

133 131 165 109

81 82 73 45

170 137 227 115

97 91 74 47

1 2 3 4 5 6

45 35 44 41 23 65

29 39 143 47 81 107

134 60 92 60 78 121

139 69 368 111 207 217

132 67 127 62 87 121

133 86 471 165 .222 282

133 110 132 158 94 138

147 104 541 220 226 356

* Percentile values given in terms of cumulative per cent of concentration less than given values. tObserved concentration minus background. :~Samplers were in operation for less than half the year. Data not included in subsequent analyses.

Review of CRSTER and MPTER models

The performance of the models is displayed using the fractional bias statistic, in which model bias is normalized. The general expression for the fractional bias (FB) is given by:

Table 4. Geometric means of the ratios of predicted to measured (less background) second-highest concentrations Power plant

1-h

24-h

Canal Stuart Muskingum River Philo

0.60 0.95 1.01 2.79

0.20 0.59 0.51 2.06

All plants

1.23

0.68

51

FB=2(OB-PR)/(OB+PR)

formance statistics using field data collected around four rural power plants (Londergan et al., 1982; Cox and Moss, 1985; Cox et al., 1985, 1986, 1987a, b). The last of these reports presents graphically the composite performance of four rural models using the performance data assembled in the previous studies. M P T E R was a m o n g the four models. Although the results for the other three models are displayed in the figures, only the performance of the M P T E R model will be discussed here. The four power plants, around which the S O : databases were collected, had tall stacks and were located in flat or gently rolling terrain. The databases consisted of 2 years for the Clifty Creek plant (1975 and 1976), 2 years at the Muskingum River plant (1975 and 1976), 1 year at the Paradise plant (1976), and essentially 1 year of data at the Kincaid plant (1980 and portions of 1981), for a total of 6 years of data.

where OB is the average of the observed values, and P R is the average of the predicted values. The FB has the desirable traits of being dimensionless, symmetrical and bounded, with values ranging between - 2 . 0 (extreme overprediction by the model) and + 2.0 (extreme underprediction). An FB value between - 0 . 6 7 and +0.67 implies performance within a factor of two. The graphs in Fig. 7 display the FB of the average of the high 25 1-h concentrations and the FB of the standard deviation of the same values for four models including M P T E R . The high 25 values of the predictions and observations are unpaired in space and time. The upper left panel of Fig. 7 shows the performance of the M P T E R model for each of the 6 years of data. The bias of both the average and the standard deviation is within a factor of two for each of the databases except one. F o r Muskingum River (1976) the standard deviation of predicted values is larger than the standard deviation of the observed values by over a factor of two (FB less than -0.67). Figure 8 presents the composite performance of the models in which the mean FBs of the six databases are displayed. Separate panels are shown corresponding to 1-, 3- and 24-h averages. Note the shift from overprediction toward underprediction with increasing averaging time: M P T E R shows a slight bias toward

Fractional b i a s - - h i g h 25 concentrations Model = MPTER, averaging period=l

Fractional b i a s - - h i g h 25 concentrations Model = MPSDM, averaging period=l

2.0

2.0

1.5-

Databases

.;re 1.0 ~.~ o.5~-

+ = CC75 +I

x = CC76

...~

"7,

1.5~-

Databases + = CC75 x = CC76 o = MK75 0 = MK76 , = Paradise zx= Kincaid

1.0 k-

.~ 0.5~-

[] = M K 7 5

~-o.5 ~-

.~-1.oE

(16)

x o O

O = MK76

• = Paradise

4= Kincaid

m -1.5

~ -0.5 .3 -1.0 -1.5

+ t~O

x

o

I I 1 I I 1 -2.0 -2.0 -1.5 -1.0 -0.5 0.5 1.0 1.5 2.0 Bias of average

I I I I I I -2.0 -2.0 -1.5 -l.O -0.5 0.5 l.O 1.5 2.0 Bias of a v e r a g e

Fractional b i a s - - h i g h 25 concentrations Model =PPSP, averaging period=l

Fractional b i a s - - h i g h 25 concentrations M o d e l - - T E M - 8 A , averaging period= 1

2.0 ~ 1.5

1.0 .~ 0.5 N ~ ~O -0.5 4.~ -1.0 'tOo '~ -1.5 jx l I L L I -2.0 -2.0 -1.5 -1.0 -0.5 0.5 1.0 1.5 2.0 Bias of average

Databases + = CC75 x = CC76 [] = MK75 o = MK76 • = Paradise tx= Kincaid

2.0 1.5 l.O

Databases + = CC75 x = CC76 ,

G = MK75

o = MK76 -0.5 ~ = Paradise .~ -1.0 zx= Kincaid m -1.5 I -2.0 I I i i i -2.0 -1.5 -1.0 -0.5 0 0.5 1.0 1.5 2.0 Bias of average

Fig. 7. Fractional bias for MPTER and three other models using high 25 values for each database (Cox et al., 1987b). Averaging period is given in hours.

52

2.0 1.5 .~- 1.o ..~ 0.5 •~ 0

R.B. WILSON Fractional bias--high 25 concentrations Compostion means of rural databases Averaging ~eriod=l 2.0

g 1.5 b

.Models

~+

+ = MPTER x = MPSDM t3 = PPSP o =TEM-SA

~ -o.5 r~-1.o •.

Fractional bias--high 25 concentrations Compostion means of rural databases Averaging period=3

[]

t~ -1.5 I -2.0 I I I I -2.0 -1.5 -1.0 -0.5 0.5 1.0 Bias of average

.~ 1.0 "~ 0.5 "~ ~ 0 ,.6 -0.5 ~ -1.0

r'l

-1.5 I 1.5 2.0

Models + = MPTER x = MPSDM [] = PPSP o =TEM-8A

I I I F I I -2.0 -2.0-1.5 -1.0 -0.5 0.5 1.0 1.52.0 Biasofaverage

bias--high 25 concentrations Compostion means of rural databases Averaging eriod--24

Fractional

2.0 .L2 1.5 .~ 1.0 i>

,~ 0.5

~0 -0.5 [] .~ -1.0 -1.5 t I L I I -2.0 -2.0 -1.5 -1.0 -0.5 0 0.5 1.0 1.5 2.0 Bias of average

+ x o o

Models = MPTER = MPSDM = PPSP =TEM-8A

Fig. 8. Composite fractional bias for MPTER and three other models by averaging period (given in hours) using high 25 values (Cox et al., 1987b).

overprediction for the l-h period, essentially no bias for the 3-h period, and a slight bias toward underprediction for the 24-h period. The last two figures for this evaluation display similar FB information to contrast the biases for selected meteorological categories. The data consists of the 25 highest observed and predicted 1-h values within each of four selected stability categories (Fig. 9) a n d within each of three selected wind speed categories (Fig. 10). The stability categories were selected as very u n s t a b l e (class A or B), unstable (class C), neutral (Class D) a n d stable (class E or F). The wind speed categories were selected as low wind speed (less t h a n 2.5 m s - 1 ), m e d i u m (2.5-5.0 m s - 1 ) a n d high (greater t h a n 5.0 m s - 1 ). T h e u p p e r left panel of Fig. 9 indicates M P T E R ' s tendency to underpredict neutral a n d stable conditions, a n d slightly overpredict for m o r e unstable conditions. The same panel in Fig. 10 reveals n o a p p a r e n t trend for M P T E R ' s performance as a function of wind speed; slight overprediction for m e d i u m wind speeds is indicated. T h e fractional bias of M P T E R as a function of the h o u r of the day revealed a very distinct p a t t e r n t h a t was persistent a m o n g the six databases. O t h e r g r a p h s in Cox et al. (1987b, 1985), n o t s h o w n here, indicate M P T E R ' s clear tendency t o w a r d m a r k e d underprediction d u r i n g nighttime hours, a n d slight to moderate overprediction d u r i n g daytime hours. This is consistent with results in Fig. 9. L a x t o n (1988) drew overall conclusions from the E v a l u a t i o n of Rural Air Quality Simulation M o d e l s

P r o g r a m with respect to the performance of the M P T E R model: "... the highest estimated concentrations for point source models typically have an accuracy of + 10 to 40 percent, or well within the often quoted factor-of-two that has long been recognized for these models .... the MPTER model appears to be essentially unbiased in estimating highest concentrations for averaging periods consistent with existing SO 2 ambient standards. Actually, exact calculation of model bias, defined as the ratio of model predicted design concentrations to measurement based design concentrations, shows small departures from a true absence of bias, i.e., a ratio of 1.0. The composite data indicate that MPTER (1) slightly overestimates highest 3-hour average concentrations (by about 9 percent or a bias ratio of 1.09) and (2) slightly underestimates highest 24-hour average concentrations (by about 15 percent or a bias ratio of 0.85). More explicitly we can say that for 3-hour average concentrations the ratio of highest estimated to measured concentrations is 1.09, and the true value of this ratio lies between 0.97 and 1.23 with 95% confidence. For 24-hour average concentrations the ratio is 0.85 and the true value (with 95% confidence) lies between 0.74 and 0.98. However, it should be noted that these statements are properly limited to major isolated point sources where accuracy of estimates in 'space and time' is not an issue; they are not applicable to multiple-source situations. Nevertheless, we believe the statements to be typical of accuracy associated with a wide variety of point source applications." MPTER

vs H P D M

M o r e recently, H a n n a a n d Paine (1989) evaluated a n d c o m p a r e d the performance of M P T E R with a relatively new model called the H y b r i d P l u m e Dispersion M o d e l ( H P D M ) . H P D M was developed for

R e v i e w of C R S T E R a n d M P T E R m o d e l s Fractional b i a s - - h i g h 25 concentrations C o m p o s i t e means of rural databases Model = MPSDM, a v e r a g i n g p e r i o d = 1 2.0

Fractional b i a s - - h i g h 25 concentrations Composite means of rural databases Model = MPTER, a v e r a g i n g p e r i o d = l 2.0 1.5 "3 l.O

Stability V = very unstable U = unstable N = neutral S = stable

-

0.5

'~

U

0 -o.5 -

v

.~ - 1 . 0 m -1.5 -2.0 I I I I I -2.0 -1.5 -1.0 -0.5 0 0.5 1.0 Bias of a v e r a g e

I 1.5

1.5

~ -0.5 .~-1.o

~o -0.5 .~ -1.0 m -1.5 -2.0 I t [ I I -2.0 -1.5 -1.0 -0.5 0.5 1.0 Bias of a v e r a g e

V,, ON I 1.5

2.0

Fractional b i a s - - h i g h 25 concentrations C o m p o s i t e means o f rural databases Model =TEM-SA, a v e r a g i n g period =1 2.0 Stability V = very unstable U = unstable N = neutral S = stable

0.5 0

Stability V = very unstable U = unstable N = neutral S = stable

.~ o.5 •~ 0

Fractional b i a s - - h i g h 25 concentrations Composite means of rural databases Model =PPSP, a v e r a g i n g p e r i o d = l 2.0

'~

s

"= . . 1.o

m -1.5 -2.0 I 1 I [ I -2.0-1.5-l.0-0.5 0 0.5 l.o Bias of average

2.0

.o~. 1.5 .~. 1.0

53

1.5•= .~ 1 . o o~ 0.5 "~ ~ o ,,~ -0.5 .~-1.o -

½

V

V = very unstable U = unstable N = neutral S = stable

N

m -1.5 -2.0 I I I I I -2.0-1.5-1.0-0.5 0 0.5 1.0 Bias of average

I 1.5 2.0

Stability

S

[ "

I 1.5

2.0

Fig. 9. C o m p o s i t e fractional bias for M P T E R a n d three o t h e r m o d e l s using high 25 values for each stability class (Cox al., 1987b). A v e r a g i n g period is given in hours. Fractional b i a s - - h i g h 25 concentrations C o m p o s i t ~ means of rural databases Model = M P S D M , a v e r a g i n g p e r i o d = 1 2.0

Fractional b i a s - - h i g h 25 concentrations C o m p o s i t e means of rural databases Model = MPTER, a v e r a g i n g period = 1 2.0 1.5 ..~ 1.o 0.5 "~.~

Winds

0

L = low M = medium H = high

MH

-1.5 I 1.5

2.0

Fractional b i a s - - h i g h 25 concentrations C o m p o s i t e means of rural databases Model =PPSP, a v e r a g i n g p e r i o d = 1 2.0 1.5

-

-o.5

-

.~_ - 1 . 0 -

L

M

L = low M = medium H = high

k

,~ 0.5

Winds L = low M = medium H = high

H

~

0

M

I

1.5

2.0

Fractional b i a s - - h i g h 25 concentrations C o m p o s i t e means of rural databases Model =TEM-8A, a v e r a g i n g p e r i o d = 1 2.0 Winds

.ee 1 . o .~0.5 -~ 0

1.5.~ 1 . 0 -

,~ -o.5 L .~ - 1 . o m -1.5 -2.0 I I l I I -2.0 -1.5 -1.0 -0.5 0 0.5 1.0 Bias of average

-o.5

.3 -1.0 -2.0 I I I I I -2.0 -1.5 -1.0 -0.5 0 0.5 1.0 Bias of average

et

1.5.~ 1 . o ~ 0.5N 0 ~o -0.5 .~ - l . O -

Winds L = low M = medium H = high

- 1 . 5 --

-1.5 -

I I I I I -2.0 -1.5 -1.0 -0.5 0 0.5 1.0 Bias of average

-2.0

I 1.5

I I I I i -2.0 -1.5-1.0-0.5 0.5 1.0 Bias of a v e r a g e

-2.0

2.0

I 1.5

2.0

Fig. 10. C o m p o s i t e fractional bias for M P T E R a n d three o t h e r m o d e l s using h i g h 25 values for each wind speed class (Cox et al., 1987b). A v e r a g i n g period is given in hours. application to tall stack plumes dispersing over nearly

Journal of Climate and Applied Meteorology ( s e e W e l l ,

flat terrain. The meteorological and dispersion components of HPDM are based on recommendations in

1985, f o r e x a m p l e ) .

a number

of papers in the November

1985 i s s u e o f t h e

The performance of HPDM, based on modem planetary boundary layer theory and technology, and

54

R.B. WILSON

MPTER, based on traditional regulatory modeling practice, were compared to measurements from two extensive field tracer studies. These field programs were implemented at two large power plants: the Kincaid power plant, with a stack height of 187 m, is located in flat farmland in Illinois; the Bull Run power plant, with a stack height of 243 m is located in rolling 100-m forested hills in Tennessee. Both programs made extensive meteorological measurements and employed 200 tracer sampling stations. For model testing, about 200 hours of tracer (SF6) data were available from each site, and about 120 days of SO2 data were available from the Kincaid site. Several statistics were calculated to measure the models' performances. The concentrations were normalized by the source strength in all the calculations. The results for the maximum concentration (unpaired) and the average over the top 25 concentrations are given in Table 5 (Hanna and Paine, 1989). The authors conclude that for the SF6 Kincaid data, the maximum is overpredicted by MPTER by 8%. At Bull Run, where light-wind convective conditions prevailed, the MPTER predictions of the average top 25 are low by about a factor of two, while the maximum is underpredicted by 33%. This underprediction by MPTER "is probably due to the model's neglect of partial penetration of the capping inversion." The top 25 SO 2 data at Kincaid suggest that MPTER is accurate within about 10% for 1 h averages, but that MPTER's underprediction increases as the averaging time increases. MPTER underpredicts the 24-h values by about 40%. This result is consistent with previous evaluations. Other performance measures were calculated with the predictions and observations paired in time and in radial distance from the stack. The normalized mean square errors were high, the correlation coefficients

were low, and less than 50% were within a factor of two in these paired comparisons. MPTER and H P D M were also evaluated against high-wind (generally greater than 8 m s - l ) periods, when relatively high SO 2 concentrations were measured in the vicinity of five large power plants. Some of the performance statistics are given in Table 6 (Hanna and Paine, 1989). In these data sets MPTER underpredicts by a factor of about five, on average. The authors conclude from this evaluation that HPDM is an improvement over the standard regulatory model, MPTER, during light-wind convective conditions and high-wind neutral conditions. Hanna and Chang (1993) review another recent performance evaluation, in which an improved version of HPDM was compared to measurements at three field sites, along with estimates from two regulatory models, RAM and the Industrial Source Complex (ISC) model. RAM (Novak and Turner, 1976; Turner and Novak, 1978; Catalano et al., 1987) and ISC (Bowers et al., 1979; EPA, 1987c, 1992) are regulatory Gaussian plume models similar to CRSTER and MPTER, but with additional features. For example, RAM can simulate area sources as well as point sources, and ISC can simulate building-wakeinduced downwash. Some of the recent improvements to HPDM are modifications made to allow the model to simulate urban dispersion conditions (Hanna and Chang, 1991, 1992) One of the field data sets employed in the model performance evaluation consisted of 89 h of SF6 tracer data from an 83-m power plant stack located in urban Indianapolis, IN. After comparing estimates from HPDM and RAM to these measurements, the authors conclude that the "HPDM model is a significant improvement over the RAM model at the Indianapolis site." Since RAM and CRSTER and

Table 5. Results of evaluation of top 25 model predictions [z/Q (in sm -3) x

x/Q (max.) Data set

I

Kincaid, 1 h Bull Run, 1 h S O 2 Kincaid, 1 h SO 2 Kincaid, 3 h SO2 Kincaid, 24 h SF 6 SF 6

Obs 208 338 269 161 76

x/Q (avg. top 25)

MPTER HPDM 225 225 280 117 56

10 - 9 ]

202 383 264 129 55

Obs 136 197 160 83 38

MPTER HPDM 133 87 143 65 23

131 170 145 87 38

Table 6. Evaluation of HPDM and MPTER for high wind conditions (concentrations in/~gm-3) Data set

All hours Stable hours Unstable hours

Obs

1048 1045 1048

Max. predicted

~(p-Zo (avg. top 10) ;(p-Xo (all data)

HPDM

MPTER

HPDM

1198 863 1198

1109 193 1109

6 - 120 60

MPTER HPDM 17 - 741 83

-75 - 116 -49

MPTER -258 - 316 -224

Review of CRSTER and MPTER models MPTER would be expected to produce nearly identical concentration estimates for a source of this type and location, it can reasonably be inferred that HPDM would be a significant improvements over CRSTER and MPTER, had these two models been included in the evaluation.

FUTURE DIRECTIONS Problems with C R S T E R / M P T E R

Notwithstanding the problems of input database inaccuracies, the application of the CRSTER and MPTER models suffers from a few problems inherent to the models themselves. The parameterization of dispersion in the CRSTER and MPTER models is one area which could be improved. Based on the field programs used in their development, the P - G dispersion curves should not be applied to elevated point (tall stack) sources. Nor should the P - G curves be assumed to apply to a 1-h averaging period. The tracer experiments which produced the empirical data that Pasquill (1961) and Gifford (1961) used to develop the curves in Fig 1 and 2, involved tracer releases from near ground level, and the measurements were for periods of 3-10 min. Thus, the application of the P - G curves to elevated point sources for 1-h periods is an extrapolation of the P - G curves without real technical basis. Another problem with the dispersion parameterization supersedes the choice of dispersion curves. That is the use of a stability classification scheme. The atmosphere is turbulent in a continuous, not step-wise manner, so that it would seem more appropriate to characterize dispersion as a continuous function, rather than as classes of dispersion. The manner in which these models simulate the interaction of a rising, dispersing plume with an elevated inversion is oversimplified. Plumes partially penetrate elevated inversions and are not immediately and totally reflected. Furthermore, the assumption that the ground-level concentration is zero when the effective plume height is estimated to be greater than the height of the mixing layer has the dangerous attribute of potentially significantly underpredicting source impacts. Hanna and Paine (1989) make particular note of this attribute of MPTER. Potential changes

Much of the November 1985 edition (Vol. 24, No. 1l) of the Journal of Climate and Applied Meteorology was devoted to recommending improvements for regulatory modeling. Weil (1985) summarizes several recommendations. Models such as HPDM, employing new and better technology for characterizing the planetary boundary layer (especially for convective conditions), show promise for improving our ability to estimate impacts from tall stack sources. Another model, CTDMPLUS (EPA, 1989), develgped by AE(B) 27:1-E

55

EPA, incorporates modern boundary layer parameterizations to estimate impacts from elevated point sources located in mountainous topography. A joint committee of the American Meteorological Society and EPA (called AERMIC for AMS/EPA Regulatory Model Improvement Committee) has been formed to make recommendations for, and oversee implementation of, similar improvements to EPA's widely used ISC model (Weil, 1992). There now exists a need for development of guidance for operational monitoring of meteorological variables necessary to provide adequate input databases for these models. Disclaimer--This review article has not been subjected to review by the U.S. Environmental Protection Agency, and therefore does not necessarilyreflect the views of the Agency. No official endorsement by the Agency should be inferred.

REFERENCES

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