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Session 8, Unit 15 ISC-PRIME and AERMOD. ISC-PRIME General info. PRIME - Plume Rise Model Enhancements Purpose - Enhance ISCST3 by addressing ISCST3’s.

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Presentation on theme: "Session 8, Unit 15 ISC-PRIME and AERMOD. ISC-PRIME General info. PRIME - Plume Rise Model Enhancements Purpose - Enhance ISCST3 by addressing ISCST3’s."— Presentation transcript:

1 Session 8, Unit 15 ISC-PRIME and AERMOD

2 ISC-PRIME General info. PRIME - Plume Rise Model Enhancements Purpose - Enhance ISCST3 by addressing ISCST3’s deficiency in building downwash Development work funded by Electric Power Research Institute (EPRI) in 1992 Algorithm developed, codified, and incorporated into ISCST3 by Earth Tech, Inc. The combined computer program is called ISC-PRIME

3 ISC-PRIME Deficiency of ISC3 model Reported over predictions under light wind, stable conditions Discontinuities in the vertical, alongwind, and crosswind directions Assumption that the source is always collocated with the structure causing down washing Streamline flow over a structure is not taken into account Plume rise is not adjusted due to the velocity deficit in the wake or due to vertical wind speed shear Concentrations in the cavity region are not linked to material capture

4 ISC-PRIME The features that ISC-PRIME has and ISCST3 does not: Stack location with respect to building Influence of streamline deflection on plume trajectory Effect of wind angle on wake structure Effects of plume buoyancy and vertical wind speed shear on plume rise near building Concentration in near wake (cavity)

5 ISC-PRIME PRIME Approach Trajectory of plume near building is determined by 2 factors:  Descent of the air containing the plume material  Rise of the plume relative to the streamlines due to buoyancy or momentum effects Mean streamlines near building  Initial ascending upwind of the building  location and maximum height of roof-top recirculation cavity  length of downwind recirculation cavity (near wake)  Building length scale

6 ISC-PRIME Running ISC-PRIME Same way to run ISCST3 with exception of the following three additional keyword in the “SO” pathway:  BUILDLEN - projected length of the building along the flow  XBADJ - along-flow distance from the stack to the center of the upwind face of the projected building  YBADJ - across-flow distance from the stack to the center of the upwind face of the projected building BPIP is modified (called BPIP-PRIME) to produce these parameters

7 ISC-PRIME Independent evaluation by ENSR Evaluation was based on 14 studies  8 tracer studies  3 long-term studies  3 wind tunnel studies

8 ISC-PRIME Evaluation results:  ISC-PRIME is generally unbiased or conservative (overpredicting)  Statistically ISC-PRIME performs better than ISCST3  Under stable conditions, ISCST3 is too conservative and ISC-PRIME is much better  Under neutral conditions, the two models are comparable and ISC-PRIME is slightly better.

9 ISC-PRIME Results of evaluation by EPA When no building data is included in the models, ISCST3 and ISC-PRIME produce the same results ISC-PRIME tend to be less conservative than ISCST3, but more conservative than observed values The results of the two model converge beyond 1 km, and become practically the same after 10 km Generally agree with ENSR’s evaluation and consider the objectives of PRIME have been met

10 AERMOD AERMIC – American Meteorological Society/Environmental Protection Agency Regulatory Model Improvement Committee AERMOD – AMS/EPA Regulatory Model Goals of AERMOD – To replace ISC3 (AERMOD has not incorporated the dry and wet deposition features of ISC3) AERMOD is still a steady-state model, but a more sophisticated one than ISC3

11 AERMOD New or improved algorithms: Dispersion in both the convective and stable boundary layers (separate procedures are used for CBL and SBL) Plume rise and buoyancy Plume penetration into elevated inversions Computation of vertical profiles of wind, turbulence, and temperature The urban boundary layer The treatment of receptors on all types of terrain from the surface up to and above the plume height.

12 AERMOD AERMOD is a modeling system consisting of: AERMOD - AERMIC Dispersion Model AERMAP – AERMOD Terrain Preprocessor AERMET - AERMOD Meteorological Preprocessor

13 AERMOD Data flow in AERMOD system

14 AERMOD AERMET Use met measurements to compute PBL parameters  Monin-Obukhov Length, L  Surface friction velocity, u *  Surface roughness length, z 0  Surface heat flux, H  Convective scaling velocity, w *  Convective and mechanical mixed layer heights, z ic and z im, respectively

15 AERMOD Met interface Compute vertical profiles of:  Wind direction  Wind speed  Temperature  Vertical potential temperature gradient  Vertical turbulence (  w )  Horizontal turbulence (  v ) Unlike ISC3, both  w and  v have more than 1 component Express inhomogeneous parameters in PBL as effective homogeneous values


17 AERMOD Treatment of terrain No distinction between simple terrain and complex terrain Plume either impacts the terrain or/and follows the flow



20 Calculation of concentrations Simulate 5 plume types  Direct (real source at the stack)  Indirect (imaginary source above CBL to account for slow downward dispersion)  Penetrated (the portion of the plume that has penetrated into the stable layer)  Injected  Stable.

21 AERMOD For CBL, contributions from 3 types of plume For SBL, similar to ISC3

22 AERMOD Dispersion coefficients Contributed by three factors:  ambient turbulence  Turbulence induced by a plume buoyancy  Enhancements from building wake effects Plume rise Source characterization Added feature – irregularly shaped area sources Adjustment for urban boundary layer For nighttime only

23 AERMOD Evaluation Scientifically AERMOD has an advantage over ISC3 Performance evaluation:  Data: 4 short-term tracer study 6 conventional long-term monitoring  Results (after minor revisions): Nearly unbiased Generally better than ISCST3 Recommended for regulatory applications (rule proposed)

24 Session 8, Unit 16 CALPUFF

25 CALPUFF ISC3, AERMOD Steady-sate Plume Local-scale CALPUFF Non-steady-state Puff Long-range (up to hundreds of kilometers) Can simulate ISC3

26 CALPUFF Recommended by IWAQM IWAQM – Interagency Workgroup on Air Quality Modeling EPA U.S. Forest Service National Park Service U.S. Fish and Wildlife Service

27 CALPUFF CALPUFF System CALMET CALPUFF CALPOST Prepare meteorological fields. It generates hourly wind and temperature fields on a 3-D gridded modeling domain. A Gaussian puff dispersion model with chemical removal, wet & dry deposition, complex terrain algorithm, building downwash, plume fumigation, and other effects Postprocessing programs for the output fields of met data, concentrations, deposition fluxes, and visibility data

28 CALPUFF CALMET process Step 1 – Initial guess wind field is adjusted for kinematic effects of terrain, slope flows, terrain blocking effects Step 2 – Introduce observational data into Step 1 wind field to produce final wind field

29 CALPUFF CALMET data requirements Surface met data (wind, temp, precipitation, etc.) Upper air data (e.g., observed vertical profiles of wind, temp, etc.) Overwater observed data (optional) Geophysical data (e.g., terrain, land use, etc.)

30 CALPUFF Example CALMET wind field

31 CALPUFF CALPUFF concept and solutions Plume is treated as series of puffs  Snapshot approach  Sampling time – time interval between snapshots  Concentrations at receptors are determined at the snapshot time. One receptors may receive contributions from more than 1 puff  Puffs may move and evolve in size between snapshots  Separation between puffs: <1-2 . Otherwise, results are not accurate Problems – too many puffs (e.g., thousands puffs/hr) Solutions 1. Radially symmetric puffs, OR 2. Non-circular puff (slug)

32 CALPUFF Other CALPUFF features Dispersion (dispersion coefficients, buoyancy- induced dispersion, puff splitting, etc.) Building downwash Plume rise Overwater and coastal dispersion Complex terrain Dry and wet deposition Chemical reaction Visibility modeling Odor modeling Graphic User Interface (GUI)

33 CALPUFF CALPUFF data and computer requirements Up to 16 input files (control, met, geophysical, source, etc.) Up to 9 output files Computer requirements:  Memory: typical case – 32 MB; more for more sources  Computing time: for a 500 MHz PC, 218 sources and 425 receptors 9 hours for CALMET 95 hours for CALPUFF

34 CALPUFF Summary Primarily for long range modeling, but can be used for local modeling A puff model Non-steady state Very sophisticated Resource intensive

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