evaluation of aermod/prime for two sites with unusual structures

APPENDIX C

TECHNICAL PAPER –

“EVALUATION OF AERMOD/PRIME FOR TWO SITES WITH UNUSUAL STRUCTURES”

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Evaluation of AERMOD/PRIME For Two Sites with Unusual Structures Paper Number: 262 (A&WMA 99th Annual Conference, June 20-23, 2006)

Ronald L. Petersen Vice President, CPP, Inc., 1415 Blue Spruce Drive, Fort Collins, CO 80524

John J. Carter Associate, CPP, Inc., 1415 Blue Spruce Drive, Fort Collins, CO 80524

ABSTRACT This paper evaluates the performance of AERMOD/PRIME for modeling dispersion at two highly complicated sites. One site has a power plant with two 130 m hyperbolic cooling towers and various rectangular plant structures. The other site consists of two combustion turbines with associated multi-tiered, sloped, and porous structures. When simple rectangular structures are associated with a site, AERMOD should give reasonable concentrations estimates. When the structures become more complicated, porous and/or cylindrical, the model may not give accurate concentration estimates. To evaluate the performance of AERMOD/PRIME for these situations, wind-tunnel testing was conducted to obtain a data set of maximum ground-level concentrations versus downwind distance for 36 wind directions at each site. AERMOD/PRIME was then run using the BPIP determined building dimensions and the site specific Equivalent Building Dimensions (EBD) for AERMOD/PRIME input. EBD are the building dimensions that should be input in the model to accurately represent the ground-level concentrations due to the wakes and eddies created by the structures on the site. AERMOD/PRIME predicted concentrations, using both BPIP and EBD dimensions, were then compared with wind-tunnel measurements obtained using a scale model of the facility. ISC/PRIME and ISCST3 were also run for the same cases for comparative purposes. In general, the results showed that all models agreed best with the wind tunnel observations when EBD were used for the building dimension inputs. The study also showed that AERMOD/PRIME tended to underpredict the wind tunnel concentrations for these sites, apparently because AERMOD/PRIME does not adequately account for stack downwash effects. The paper also discusses some issues regarding AERMOD/PRIME inputs when comparing against wind tunnel databases.

INTRODUCTION In late 2005, EPA1 promulgated AERMOD2 as replacement for ISCST3.3 The new model includes state-of-the-art boundary layer parameterization techniques, convective dispersion, plume rise formulations, complex terrain/plume interactions as well as a recently developed building downwash algorithm. The development of the new building downwash algorithm was sponsored by EPRI and is referred to as the PRIME model.4 The PRIME model was initially implemented into the regulatory process through ISC/PRIME and is now included in AERMOD. The primary focus of this paper is on the

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PRIME component in AERMOD, referred to as AERMOD/PRIME in this paper.

The past performance evaluations of PRIME (and hence AERMOD/PRIME) considered field and wind-tunnel databases with the stack located near the building5,6,7 and at various distances from the simple building shapes. No evaluations have been presented to date, that the authors are aware of, showing how well AERMOD/PRIME performs for unusual building shapes. It seems this would be an important area to investigate, since many facilities using the model have unusually shaped and/or porous structures in close proximity to the stack (i.e., lattice support structures, architectural shrouds to hide the stacks, hyperbolic cooling towers, multi-tiered structures, etc.). In some cases, the porous structure may be used a solid structure by the Building Profile Input Program (BPIP) even though this structure may not be affecting plume downwash. In other cases it may be valid to neglect the effect of the porous feature. Alternately, AERMOD/PRIME may overestimate the downwash effect of streamlined structures such as hyperbolic cooling towers. Since PRIME provides good estimates for simple building shapes,6,7 the Equivalent Building Dimension (EBD) technique may be an appropriate method for improving the predictive capability of the PRIME model for complex sites, complex structures or porous structures for which PRIME was not designed. EBD are the building dimensions that should be input in the model to accurately predict the ground-level concentrations due to the wakes and eddies creased by complex structures on the site.

This paper evaluates the performance of AERMOD/PRIME for modeling dispersion at two highly complicated sites. One site has a power plant with two 130 m hyperbolic cooling towers and various rectangular plant structures. The other site consists of two combustion turbines with associated multi-tiered, sloped, and porous structures. To evaluate the performance of AERMOD/PRIME for these situations, wind-tunnel testing was conducted to obtain a data set of maximum ground-level concentrations versus downwind distance for 36 wind directions at each site. AERMOD/PRIME was then run using the BPIP determined building dimensions and the site specific building dimensions (EBD) for AERMOD/PRIME input. AERMOD/PRIME predicted concentrations, using both BPIP and EBD building dimension inputs, were then compared with wind-tunnel measurements obtained using a scale model of the facility. ISC/PRIME and ISCST3 were also run for the same cases for comparative purposes. The paper also discusses some issues regarding using AERMOD/PRIME when comparing against wind tunnel databases.

Why use wind tunnel modeling There are several reasons why wind-tunnel modeling should be used as the primary tool for evaluating the validity of dispersion models like AERMOD/PRIME: theoretical, dispersion comparability, controlled conditions and expense. The first and most important reason is theoretical. A wind-tunnel simulation is, in effect, a solution to the basic equations of motion. The basic equations are solved by simulating the flow at a reduced scale and then the desired quantity (i.e., concentration) is measured. This solution to the basic equations (i.e., the wind-tunnel simulation) is a steady state solution with a complete record of the time varying velocity and concentration fields. It should be noted that the Gaussian dispersion model also predicts steady-state average

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concentrations. Another way of looking at the wind tunnel is that it is an analog computer with near infinitesimal resolution and near infinite memory. As stated in an EPA report,8 “if a mathematical model cannot simulate the results of an idealized laboratory experiment, how can it possibly be applicable to the atmosphere?”

A second reason relates to dispersion comparability. With the passage of the EPA “good engineering practice” (GEP) stack height regulation, wind-tunnel modeling has been required to determine the GEP stack height for many facilities. As part of a GEP stack height evaluation, the wind-tunnel modeler must perform what is referred to as an “atmospheric dispersion comparability test.” For this test, wind profiles and dispersion measurements are made in the wind tunnel without the presence of structures. A flat, uniform, grassland type roughness is simulated. These tests have demonstrated that wind-tunnel velocity profiles match profile shapes observed in the atmosphere and that the profiles fit similarity theory. The tests have also shown that the horizontal and vertical dispersion coefficients are consistent with the default dispersion coefficients used in the AERMOD/PRIME model for urban and rural dispersion. The horizontal and vertical dispersion coefficients are also consistent with similarity theory and consequently reflect the character of the underlying surface roughness.

The third reason relates to the ability to control and monitor the meteorological and source conditions. When comparing dispersion models against field observations, the errors in the model inputs, as related to the site conditions at the time of the measurements, give one little hope in assessing the real validity of the model. In the field, wind profiles are frequently not available and the wind characteristics at the stack location are often assumed to be the same as at the anemometer. Quite often, the hourly source characteristics are also unknown in the field. You may get good agreement with field observations, but often the agreement is fortuitous. When using a wind-tunnel database the uncertainty in these control parameters is minimized. The wind direction and wind speed are set and remain constant during a given simulation. The source parameters are also fixed and known. Hence, model input errors are minimized and the true performance of the model can be assessed.

Another good reason for using the wind tunnel is the cost. A high quality data set can be obtained for wide variety of source and building configurations for a fraction of the cost for the same data set collected in the field.

Wind tunnel database A series of 36 wind-tunnel tests (i.e., 10 degree wind vector increments) were conducted to obtain profiles of maximum ground level concentrations versus downwind distance due to emissions for two separate sites. Figure 1 shows multi-tiered, porous, sloped structures that were located in an urban environment (referred to as “Urban Site”). Figure 2 shows stacks near hyperbolic cooling towers and other generally rectangular buildings in a rural environment (referred to as “Rural Site”).

Additional tests were conducted to obtain maximum ground level concentrations versus downwind distance for buildings with height/width/length ratios of 1:2:1 (i.e., the “equivalent buildings”) with the significant site structures removed, as shown in Figure 3.

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The simulated source parameters are provided in Table 1.

Figure 1. Close-up view of a wind tunnel model of the multi-tiered, sloped porous structures that were located in an urban environment (i.e., “Urban Site”).

Figure 2. Close-up view of exhaust stacks near hyperbolic cooling towers and other generally rectangular buildings in a rural environment (i.e., “Rural Site”)

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Figure 3. Close-up view of a typical equivalent building setup in the wind tunnel.

Table 1. Model Inputs

Exhaust Stack Parameters

Site Description

Urban Site

Combustion Turbines with Sloped-Porous Structures

Rural Site

Power Plant with Hyperbolic Cooling Towers

Exit Diameter 5.49 m 12.50 m

Stack Height 45.73 m 150.00 m

Exit Temperature 294.63 K 288.41 K

Volume Flow Rate 246.65 m3/s 1858.73 m3/s

Exit Velocity 10.43 m/s 15.15 m/s

Ambient Parameters

Wind vector Varies Varies

Stack Height Wind Speed 8.09 m/s 14.28 m/s

Approach Roughness (z0) Urban (1.0 m) Rural (0.5 m)

Ambient Temperature 291.48 285.91 K

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Wind-tunnel model operating conditions were set by matching the following parameters in model and full scale:

• momentum ratio, M0

Equation 1.

;22

0 ⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

rh

e

a

s

zd

UV

Mρρ

• buoyancy ratio, Β0

Equation 2.

( );

4 2

3

3

2

0 ⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

−=

rsa

s

har

sae

zd

FrR

UzVdg

Bρρ

ρρρ

• Reynolds number independence was ensured with a building Reynolds number in excess of 11,000;

• a neutral atmospheric boundary layer was established (Pasquill-Gifford C or D stability);

where

Equation 3.

( ) ;2

2

dgV

Frsa

ess ρρ

ρ−

=

and ρs = stack gas density (kg/m3);

ρa = ambient air density (kg/m3).

Ve = stack gas exit velocity (m/s);

Uh = wind velocity at stack top (m/s);

d = stack diameter (m);

zr = reference height (m); and

g = gravitational acceleration (m/s2).

Ground-level sampling taps were installed downwind of the stack so that up to 48 locations were sampled simultaneously for each simulation. A typical sampling grid

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pattern is shown in Figure 4 along with a schematic of the wind tunnel setup.

Figure 4. Wind-tunnel schematic showing a typical equivalent building and receptor grid setup in the wind tunnel.

The measured concentrations were converted to full-scale normalized concentrations (i.e., C/Q). The maximum concentration in each horizontal row was then selected to generate a database of maximum concentration versus downwind distance. The overall maximum concentration for each wind vector was also selected to generate a database of maximum concentration versus wind vector.

To determine “equivalent building dimensions” (EBD), the maximum ground level concentration profiles with the site structures in place are compared to those for the various simple geometry buildings.9,10 The criteria for defining whether or not two concentration profiles are similar is to determine the smallest simple geometry building which: 1) produces an overall maximum concentration exceeding 90 percent of the overall maximum concentration observed with all site structures in place; 2) at all other longitudinal distances, produces ground level concentrations which exceed the ground level concentration observed with all site structures in place less 20 percent of the overall maximum ground level concentration with all structures in place. A typical EBD plot is shown in Figure 5.

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Figure 5. Typical EBD selection plot showing the maximum concentration versus downwind distance for the actual site structures and the various equivalent buildings.

NUMERICAL VERSUS WIND TUNNEL OBSERVATIONS

Building Dimension Inputs Building dimensions for input into the numerical models were determined using both BPIP and the EBD method. For the urban site, the porous shroud surrounding the stack and the porous parts of each “wing” (see Figure 1) were assumed to be solid in the simplification. The building parameters generated by BPIP indicated that the main building was the dominant structure affecting plume downwash, not the nearby shroud.

EBD were determined by plotting the maximum observed C/Q in each receptor row versus downwind distance for the site structures as well as each equivalent building. Based on the criteria discussed above, an equivalent building was selected for each of the 36 wind vectors.

The variation in building height, H, versus wind vector for the various methods are shown in Figure 6 and Figure 7 for the urban and rural sites, respectively.

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Figure 6. Building height, H, determined using BPIP and EBD methods - urban site.

Figure 7. Building height, H, determined using BPIP and EBD methods - rural site.

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Note that for the urban site, the BPIP determined height is identical to the actual height of the main building for all wind vectors, not the taller shroud immediately surrounding the stack (see Figure 1). Several of the EBD determined building heights are slightly higher than the BPIP height. This is probably due to the criteria required for the EBD selection and does not suggest that the stack shroud is the dominant feature affecting plume dispersion. If the stack shroud were the dominant feature, the EBD heights would be greater than the main building height for all directions. For the rural site, the cooling towers are the dominant height for most directions using the BPIP method. Significant height reductions are noted for most wind directions when the EBD method is used.

Receptor Arrays For both the urban and the rural sites, polar receptor grids were evaluated with rings of receptors corresponding to each receptor row used in the wind tunnel. For example, downwind distances of 100 m, 144 m, 208 m, 300 m, 433 m, 624 m and 900 m were used for the urban site and distances of 700 m, 1100 m, 1500 m, 1900 m, 2300 m, 2700 m, 3100 m, 3500 m, 3900 m, 4700 m, 5100 m and 5500 m were used for the rural site.

Surface and Profile Inputs When running the ISCST3 and ISC/PRIME models, the urban and rural dispersion coefficient options were selected as appropriate. When running AERMOD/PRIME, the urban dispersion default of 1.0 m surface roughness was selected. However, in the authors understanding of how the Profile file (*.pfl) works, if non-null values are input for the vertical and lateral dispersion coefficients, these input coefficients over-ride the surface roughness-based calculated dispersion coefficients. Therefore, appropriate values for these coefficients, as measured during the wind tunnel tests, were used in the AERMOD/PRIME simulations, as shown in Table 2.

Table 2. Profile File Variables

Variable Description Urban Site

Combustion Turbines with Sloped-Porous Structures

Rural Site


Year 1967 1967

Month January January

Day 1 through 2 1 through 2

Hour 1 through 36

(37 through 48 contained null data)

1 through 36 (37 through 48 contained null

data)

Measurement Height Equals stack height from Table 1

Equals stack height from Table 1

Wind Direction [wind vector - 180] (degrees)

10 through 360 in 10 degree increments at each hourly

interval

10 through 360 in 10 degree increments at each hourly

interval

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Wind Speed (m/s) Stack height wind speed from Table 1

Stack height wind speed from Table 1

Temperature (C) Equal to Ambient Temperature from Table 1

Equal to Ambient Temperature from Table 1

Standard deviation of lateral wind direction fluctuations (degrees)

9.32 6.9

Standard deviation of vertical wind speed fluctuations (m/s)

0.878 0.92

In order to force AERMOD/PRIME to simulate a Pasquill-Gifford D stability for all hours of the day and to eliminate possible stability effects on plume rise that were not simulated in the wind tunnel, several variables in the Surface file (*.sfc) were also forced, as listed in Table 3.

Table 3. Surface File Variables

Site Description Combustion Turbines with Sloped-Porous Structures


Year 1967 1967

Month January January

Day 1 through 2 1 through 2

Hour 1 through 36

(37 through 48 contained null data)


data)

Sensible Heat Flux1 (W/m2) 0.0 0.0

Surface Friction Velocity2 (m/s) 0.876 0.552

Convective Velocity Scale1 (m/s) 0.001 0.001

Vertical Potential Temperature Gradient above PBL1

0.001 0.001

Height of Convectively Generated Boundary Layer1 (m)

1882 1882

Height of Mechanically Generated Boundary Layer1 (m)

1882 1882

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Monin-Obukhov Length1 (m) -1994.2 -1994.2

Surface Roughness Length1 (m) 1.0 0.5

Bowen Ratio1 not used by AERMOD not used by AERMOD

Albedo1 not used by AERMOD not used by AERMOD

Wind Speed1 (m/s)



Wind Direction1 (degrees)


data)


data)

Reference Height for Wind Speed and Wind Direction1 (m)



Temperature1 (K)



Reference Height for Temperature1 (m)



Notes: 1Forced by authors to match wind tunnel simulation. 2Calculated by AERMOD. 3Warning messages were noted for reference height in excess of 100 m, but concentration predictions appeared to be unaffected.

Concentration Results and Discussion The numerical models were run using building parameters generated using both the BPIP program and the EBD technique building dimension methods. The exhaust and primary ambient parameters simulated are listed in Table 1. Meteorological data files were simplified to duplicate the wind tunnel experiments as listed in Table 2 and Table 3.

As discussed above, in each of the models and the wind tunnel, the maximum concentration was determined for each of 36 wind vectors. The results for the rural site and the urban site are discussed in the following sections.

Rural Site Figure 8 and Figure 9 compare the model concentration predictions versus wind direction, for the BPIP and EBD input methods, to the concentrations observed in the wind tunnel at the rural site. In Figure 8, the predictions using the BPIP method follow the observed concentration trend for most wind directions using the more sophisticated models (ISC/PRIME and AERMOD/PRIME) but tend to be lower than observations. The presence of the hyperbolic cooling towers is obvious in the ISCST3 predictions.

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Figure 8. Maximum predicted concentration versus wind direction for the wind tunnel and model simulations using BPIP building dimensions - rural site.

Figure 9. Maximum predicted concentration versus wind direction for the wind tunnel and model simulations using EBD building dimensions - rural site.

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In Figure 9, the predictions using the EBD method follow the same trends as the observed concentrations, although the predicted values are lower for all directions using the more sophisticated models (ISC/PRIME and AERMOD/PRIME).

Figures 10 and 11 compare the model concentration predictions in rank order for the BPIP and EBD input methods, to the concentrations observed in the wind tunnel at the rural site. It was surmised that the reason all models are underpredicting is that they are not accounting properly for stack-tip downwash effects. It has been the authors experience that when the velocity ratio (i.e., ratio of exit velocity to wind speed) is less than 1.5, stack-tip downwash occurs and essentially the plume acts like a capped stack. For the Rural Site, the velocity ratio was 1.1, well into the stack-tip downwash regime. To assess this effect, AERMOD/PRIME was also run with extremely low exit velocity so that plume rise would be insignificant.

In Figure 10, using the BPIP inputs, the ISCST3 predictions appear to match the highest concentration but then continue at that value (i.e., overpredict) for many wind directions (see Figure 8). The ISC/PRIME and AERMOD/PRIME predictions are approximately equal, though both tend to underpredict the observed concentrations. When stack-tip downwash (i.e., low velocity) is included, AERMOD/PRIME tends to overpredict the highest seven concentrations, but match the observations quite well for the remaining values.

Figure 10. Maximum predicted concentration in rank order for the wind tunnel and model simulations using BPIP building dimensions - rural site.

In Figure 11, using the EBD building inputs, the ISCST3 model most closely

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approximated the observed results when using the stack parameters matching the wind tunnel experiments. However, the best agreement was achieved when using the AERMOD/PRIME model with a low exit velocity to simulate stack downwash.

Figure 11. Maximum predicted concentration in rank order for the wind tunnel and model simulations using EBD building dimensions - rural site.

Urban Site Figure 12 and Figure 13 compare the model concentration predictions versus wind direction, for the BPIP and EBD input methods, to the concentrations observed in the wind tunnel at the urban site. In Figure 12, the ISCST3 BPIP predictions are relatively flat and unaffected by the building geometry. The ISC/PRIME and AERMOD/PRIME predictions are roughly parallel, with the ISC/PRIME predictions more closely matching the observed concentrations for most wind directions. AERMOD/PRIME gives consistently lower concentrations than ISC/PRIME.

In Figure 13, the ISCST3 EBD predictions are relatively flat and in general do not follow the pattern of the observed concentrations, except from 160 to 200 degrees. The ISC/PRIME predictions reasonably match the observed concentrations from 60 degrees through 280 degrees, but follow nearly opposite trends from the observed data for the other wind directions. The AERMOD/PRIME predictions generally follow the ISC/PRIME predictions, but are lower in magnitude by a nearly constant factor.

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Figure 12. Maximum predicted concentration versus wind direction for the wind tunnel and model simulations using BPIP building dimensions - urban site.

Figure 13. Maximum predicted concentration versus wind direction for the wind tunnel and model simulations using EBD building dimensions - urban site.

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Figure 14 and Figure 15 compare the model concentration predictions in rank order, for the BPIP and EBD input methods, to the concentrations observed in the wind tunnel at the urban site. As discussed above, Figure 14 shows that the ISCST3 predictions, using the BPIP inputs, are relatively flat and therefore insensitive to the building geometry. The ISC/PRIME predictions reasonably match the observed concentrations and the AERMOD/PRIME predictions roughly parallel the ISC/PRIME predictions, though they are lower by a nearly constant factor. As for the rural site, a low exit velocity variation was also simulated using AERMOD/PRIME to investigate the model's ability to account for stack downwash. In this case, the low exit velocity simulation significantly over-predicted compared to the observed concentrations. Since the velocity ratio for this case was 1.3, stack-tip downwash effects are likely not as significant as for the rural case.

Figure 14. Maximum predicted concentration in rank order for the wind tunnel and model simulations using BPIP building dimensions - urban site.

In Figure 15, the three highest ISCST3 predictions match the observed concentrations, though the remaining predictions are flat and underpredict the observed concentrations. ISC/PRIME matches the observed concentrations quite well, while AERMOD/PRIME again underpredicts the observed concentrations, though the general pattern is similar to ISC/PRIME. As with the BPIP input model run, when a low exhaust velocity was used in AERMOD/PRIME, the model predictions were much greater than observations suggesting that stack downwash effects were not as significant for this case.

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Figure 15. Maximum predicted concentration in rank order for the wind tunnel and model simulations using EBD building dimensions - urban site.

CONCLUSIONS At least three conclusions can be drawn from the data presented above: 1) all of the model predictions were improved relative to the observed concentrations when EBD inputs were used for the building dimensions; 2) AERMOD/PRIME tends to underpredict the concentrations for these sites, possibly due to difficulty in accounting for stack downwash; and 3) ISC/PRIME tends to most accurately predict the observed concentrations, though inconsistencies are noted between the urban and rural sites.

One of the difficulties experienced by the authors in conducting the AERMOD simulations was in setting the model inputs to duplicate a neutrally stable atmosphere (as noted above “if a mathematical model cannot simulate the results of an idealized laboratory experiment, how can it possibly be applicable to the atmosphere?”). Default values for wind-speed cutoffs in the various atmospheric stability categories do not appear to be realistic, at least for categories D and E. In addition, many sensitivity tests were run in an attempt to isolate the effect of exhaust stack parameters and Profile and Surface parameters. At a minimum, AERMOD/PRIME appears to effectively over-ride the default dispersion parameters when surface roughness, lateral wind direction fluctuation and vertical wind speed fluctuation data are available. However, the proper implementation of these parameters, at least when the urban mode is selected, may be in question.

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REFERENCES 112345678910

1 US Environmental Protection Agency. Final Rulemaking on AERMOD. 70 Federal

Register 68218. see http://www.epa.gov/scram001/guidance/guide/appw_05.pdf . (2005).

2 Cimorelli, A.J., S.G. Perry, A. Venkatram, J.C. Weil, R.J. Paine, R.B. Wilson, R.F. Lee, W.D. Peters, and R.W. Brode. AERMOD: A Dispersion Model for Industrial Source Applications. Part I: General Model Formulation and Boundary Layer Characterization. JAM, 44, 682-693. American Meteorological Society, Boston, MA. (2005).

3 U. S. Environmental Protection Agency. User’s guide for the Industrial Source Complex (ISC3) dispersion models. Volume II: Description of model algorithms. EPA-454/B-95-003b, 120 pp. [NTIS PB95-222758.] (1995).

4 Schulman, L.L., D.G. Strimaitis, and J.S. Scire. Development and Evaluation of the PRIME Plume Rise and Downwash Model. J. Air & Waste Management Assoc., 50, 378-390. (2000).

5 Paine, R.J., R. Lew, “Project PRIME: Evaluation of Building Downwash Models Using Field and Wind Tunnel Data”, Preprint Volume for the Tenth Joint Conference on Applications of Air Pollution Meteorology with A&WMA, American Meteorological Society, Phoenix, Arizona, 1998.

6 Petersen, R.L., Cochran, B.C. and Carter, J.J., “Comparison of ISC and PRIME Model Predictions Versus Wind-tunnel Observations,” Paper 1113 presented at the Air and Waste Management Associations 93rd Annual Conference and Exhibition, Salt Lake City, Utah, June 18-22, 2000.

7 Petersen, R.L., “Evaluation of ISC-PRIME for Stacks at Various Distances From Buildings,” Paper 387 presented at the Air and Waste Management Associations 94th Annual Conference and Exhibition, Orlando, Florida, June 25-27, 2001.

8 EPA, Guideline for Use of Fluid Modeling of Atmospheric Diffusion. U.S. Environmental Protection Agency, Office of Air Quality, Planning and Standards, Research Triangle Park, North Carolina, EPA-600/8-81-009, April 1981.

9 Petersen, R.L., Cochran, B.C., Keen, D.E., and Walton, R.N., “Equivalent Building Dimensions for ISC2 Modeling Applications,” presented at the 88th Annual Meeting and Exhibition of the Air and Waste Management Association, San Antonio, Texas, June 18-23, 1995.

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10 Tikvart, J., “United States Environmental Protection Agency, NC, letter to Brenda

Johnson, Regional Modeling Contact, Region IV and Douglas Neeley, Chief Air Programs Branch, Region IV, July 25, 1994.

evaluation of aermod/prime for two sites with unusual structures

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