Development, Maintenance an

Evaluation of CALPUFF 

Presented at:

9th Conference on Air Quality Modeling

October 9-10, 2008

by:

Joseph Scire

TRC Environmental Corporation
Model Development
Model Development

• CALPUFF system undergoes continual refinem

and development, with new features and produ

enhancements. EPA provides no funding for development or maintenance activities.

• TRC has developed graphical interfaces and 

visualization tools which are distributed to the without cost

• Technical developments are made within TRC 

with and without external funding

• EPRI PRIME downwash module • Flexible coordinate transformations• MMS updates for coastal applications• VISTAS enhancements• Forest Service enhancements• NASA and Forest Service sub-hourly Version 6
Model Development

• Technical developments are also contributed f

other users and researchers

• Hybrid puff-particle version - Switzerland• Large-particle settling (volcanic ash) - Italy• Solar radiation effects on canyon sidewalls and terra

shadowing - Italy

• TRC has implemented procedures to satisfy cu

regulatory needs without federal funding and distributes these codes to the public for free in

• EPA BART 98th percentile computations

• Proposed 2008 FLM (FLAG) visibility method

• Processors are continuously updated to accep

revised data formats

• TRC has develop interfaces to mesoscale prog

meteorological models such as MM5, WRF, RRAMS and Eta and distributes these codes to public for free.
Model Development

• Graphical User Interfaces (GUIs)

– CALPRO

• Geophysical processors (TERREL, CTGCOMP, CTGPROC, 

MAKEGEO)

• Surface meteorological data processor (SMERGE)• Upper air data processor (READ62)• Precipitation processors (PXTRACT, PMERGE)

– COORDS

• Converts to/from any of the following Geodetic, UTM, Lambe

Conformal, Tangential Transverse Mercator, Polar StereograEquatorial Mercator, Lambert Azimuthal Equal Area

• Datums (~150 different datums available worldwide, includin

NAD-27, NAD-83)

– BUOY

• Processes buoy data and creates SEA.DAT files

– UAMAKE

• Extracts data from a prognostic (e.g., MM5) file to create sou

replacements for missing upper air data
Model Development

• Graphical User Interfaces (GUIs)

– PRTMET

• Produces vector plots, contour plots of meteorological fields

• Time series files for plotting• Uses Surfer plotting package (Golden Software package)

– CALVIEW

• Displays vector/contour/3-D perspective plots• Animations

– CALWindRose

• Wind rose plotting software• Displays wind roses directly from SURF.DAT, UP.DAT, CAL

or 3D.DAT files

– SurfSizer

• Utility working with Surfer to quickly re-size plots

– SurfExporter

• Utility working with Surfer to quickly export plots in various fo

(BMP, JPG, TIF, etc.) and user-specified resolution
Model Development

• Graphical User Interfaces (GUIs)

– SIA Generator

• Develops a radius of concentration values above a threshold

• Useful for determining the Significant Impact Area (SIA)

– PICtoReport

• Imports image files into PowerPoint or Word• Automatic sizing and centering of each image• Works with PNG, BMP, JPG, TIF, GIF and BLN files

– CALPUFF Plot (currently in testing phase)

• Generates KML files from BPIP input files• Enables 3D viewing of building layout in Google Earth

• Useful for QA and display purposes• New: Ability to overlay concentration isopleths on base map
Copyrighted page/s removed. Copyrighted material is not available in 

Regulations.gov since it may not be reproduced without consent of 

the copyright holder. 

Contact the EPA Docket Center’s Public Reading Room to view 

or receive a copy of this document. 

Requests for copies may be made as follows: 

In person/writing: 

Environmental Protection Agency, Docket Center 

1301 Constitution Ave NW, 2822T, Room 3334 

Washington, DC. 20004 

Telephone: 

202-566-1744 

Fax: 

202-566- 9744 

Email: 

docket-customerservice@epa.gov 
Model Development

• Graphical User Interfaces (GUIs)

– FEPS2BAEM

• Fire Emission Production Simulator (FEPS) is a Forest Servi

that predicts fuel consumption and emissions from wildfires acontrolled burns 

• FEPS2BAEM converts FEPS output data to the CALPUFF b

area source emissions file format (BAEMARB.DAT)

– ScavCoeff

• Estimates size-dependent wet scavenging coefficients for liq

frozen precipitation

• For particulate matter < 20 

μm diameter

– AER2CAL 

• Converts AERMOD output concentration files into CALPUFF 

concentrations files

• Allows use of CALPUFF postprocessing tools with AERMOD 

• Contour plots, animations, base maps, etc.
Model Development

• Additional processor options and datasets

– Terrain data options include 30arcsec (900m), 3 arcsec (90 m), 1 

(30m) USGS DEMs, SRTM data format, generic data format

s

– Landuse – new option to process NLCD format

• Repartitioning of HNO

3/NO

3 (POSTUTIL)

– Source contribution analysis– Streamlined one-step ALM processing– Non-linear chemistry effects (NO

3

)

– Computation of total S and N deposition

• No-Observations version of CALMET

– Uses gridded 3-D data fields as initial guess fields– Adjusts winds for fine-scale terrain effects on CALMET grid– Allows forecast applications
Model Development - CALME

• New interface programs

– WRF – NCEP models: ETA and RUC2– RAMS– MM5 (Version 3)– enhanced output (3D.DAT, 2D.DAT files)

• SEA.DAT files created from MM5 model using high resolution se

temperature data

• COARE Algorithms• Convective mixing over water

– Maul-Carson (M-C) scheme and Batchvarova-Gryning (B-G) sc

(1994)
Model Development - CALPU

• Boundary condition module• Rain hat option on stacks• Mass balance/flux tracking options• Horizontal puff splitting• NH

3 as an active species

• New turbulence profile option using AERMOD prof

– Original CALPUFF turbulence parameterization– AERMOD turbulence profiles

• AERMOD met data options

– Option in CALPUFF to read AERMOD met data files– Utility to create AERMOD met files from 3-D CALMET out
Model Development - CALPU

• Time Step < 1 Hour (CALMET, CALPUFF)• Secondary organic aerosol module• Terrain enhancement of precipitation• Fogging and icing (cooling towers)

– Visible plume lengths– Frequency of plume-induced fogging and icing

• Turbulence advection• Platform downwash• Back trajectory analysis
Model Development – On-goi

• Nested grid option• Option to name receptor sets and process by receptor g• Meteorological evaluation software (METSERIES and 

METCOMP)

– Wind rose (graphics completed)– Time series plots– Scatter plots– Statistics– X-Z cross sections– Standard report

• Enhanced animation options

– Create, name and save animation files for future playing (comple– Display of puff movements and growth (on-going)
Model Maintenance
Model Maintenance

• With few exceptions, all model maintenance is 

without contractual funding support 

• Investigate all reported issues. Request detail

error reports and test datasets from user

• About 75% found to be due to data problems, 

hardware problems or input errors

• Bugs are isolated and fixed with detailed upda

in-code documentation and version/level journ

• Model Change Bulletin (MCB) updated• Updated code(s) run in EPA CALPUFF Update 

and results sent to EPA
Model Maintenance

CALPUFF MCB History

MCB-E February 14, 2008 No ActionMCB-E (Part 2) June 27, 2008 No Action

December 16, 2005 Accepted June 2May 1, 2007June 23, 2007

MCB-BMCB-C – VISTASMCB-DMCB-A July 16, 2004 Accepted June, 2

Official Version FR Notice April 15

CALPUFF EPA Actio

Note: MCB-C contains all fixes in VISTAS version released Augu
Model Maintenance – The Pla

• TRC

– provides developmental model version as a 

mechanism for quickly evaluating bug-fixes model enhancements

• EPA action at periodic intervals

– reviews bug fixes and considers new option

features

• accept/recommend new features• suggest changes/modifications

• Accepted features placed in regulatory recomm

option set

• Other features may be retained for additional t

or abandoned or not recommended for regulat
Model Maintenance - Reality

• Problems with Review Process

– Long delay in EPA reviewing bug fixes– EPA review of model enhancements on hol

• Example Enhancement

– Surface station data assigned to grid cells u

nearest-station in EPA-approved CALMET

– Surface station data interpolated to grid cel

1/R

2 weights in VISTAS CALMET

– Nearest-station technique leads to sharp gr– EPA criticizes sharp gradients yet has not 

considered this enhancement which was in VISTAS code and removed at EPA’s insistethe current EPA-approved code
Model Maintenance

• Earth Tech/TRC commitments have been 

scrupulously adhered to:

– Model maintenance continues to be done b

without EPA support

– Agreement to keep regulatory model uncha

on web site

– EPA determines “EPA-approved” version

• What has happened to process?

– Changes at EPA in management and staff– EPA Modeling Group – some loss of institu

memory and continuity?
Model Maintenance

• EPA presentations at 2007 and 2008 R/S/L Mo

Workshops contain misleading statements aboCALPUFF, and include examples that do not rgood modeling practice

• CALPUFF Regulatory Update (June 2008 R/S

Workshop)

– “Lack of adequate documentation”

• “New MREG option in CALMET not well-docume• “User’s guide last updated in 2000”• “Many important technical details are not docum

except in code”

– “serious unresolved technical concerns”
Model Maintenance

• Documentation?

– 885 pages of detailed technical documentation and 

guide updates in March 2006 describing MMS chang

– 853 pages of documentation in original user guides– Compares well in technical detail and content with A

documentation

• Why should this be known to EPA? MMS proje

changes were the subject of the EPA oversigh

– EPA represented on Scientific Review Board for MM

project (John Irwin, Modeling Group, OAQPS)

– MMS presentation was made at June 2007 EPA R/S

Modelers Workshop. Updated User’s Guides availaonline since early 2006 (listed in presentation)

– Multiple references made to MMS reports by TRC to 

during VISTAS model review
Model Maintenance

• Constructive criticism and helpful suggestions 

always welcome, but vague references to probwithout backup and data are not helpful or constructive.

• CALMET MREG Option?

– Option formulated by TRC with EPA review– Input file language presents option choices

• Unresolved Technical Concerns?

– Issue was default value for new CALMET in

variable. Value resolved to EPA satisfactio2007)
Model Maintenance

VISTAS CALMET technical enhancement

v5.8 awaiting EPA review

• Fully interpolated 2D fields for surface-layer 

temperature and density instead of nearest-staselection of surface meteorological data

• Use consistent surface-layer stability correctio

profiles throughout 

• Recognize convective over-water boundary lay

(instead of using neutral boundary layer formu

from OCD)

• Use Coupled Ocean Atmosphere Response 

Experiment (COARE) bulk flux model for overwfluxes in place of older OCD methods

• Updated convective boundary layer parameter

following Batchvarova and Gryning
Model Maintenance

• EPA CALPUFF studies used in EPA presentat

have not been open review by TRC or the pub

• Important for transparency that all modeling fil

which decisions/memos are based are made promptly public

– Results presented in public forums rather th

communicated directly

– Modeling and data files not available– Lack of constructive criticism with a focus o

resolving issues
Wind Field Bull’s Eye

From Bret Anderson (USEPA Region 7)’s presentation at the 2007 R

Local Modelers Workshop

“Illustration of meteorological issues – CALMET diagnostic meteorol
When MM5 does not match 

observations 

• Bull’s eye features is a result of MM5 wi

matching observations. Issue may be wMM5 or a non-representative observatio

• Solution:

– (1) Pure NOOBS mode : MM5 only fields– (2) Pure observation mode– (3) Hybrid mode with MM5 as initial guess fi

small R1-R2, matching the observations righ

stations and MM5 fields away from them

– (4) Hybrid mode with poorly selected large R

creating a bull’s eye, with station winds clasMM5 fields

So 3 good ways to run CALMET without creating a 

discontinuity and 1 bad way, creating a bulls’ e
• Sydney Harbor – Summer sea breeze

• Local observation at odds with mesoscale 

features

• MM5 at 1.33 km resolution • 5 surface stations, 1 buoy, 1 upper air sta

(not used in hybrid)

• Plots: 40km x 30km ; resolution: 1km

How to run CALMET when M

does not match the observat
Solution 1: Run in NOOB
Solution 2: run in OBS-on
Solution 3 : Hybrid with R1<<RMA

R1=1km – RMAX1=20km
4. EPA’s bull’s eye no-solution: hybri

R1>>RMAX1

R1=20km – RMAX1=3km
AERMOD

• And what would AERMOD do in this region?• An infinite radius bull’s eye, only matching one station and 

representing observations at any other sites....
Solution for Better EPA and 

Community Input?

Establish Science Advisory Committee• TRC-organized CALPUFF technical committee• Provide objective, unbiased advice• Identify model improvements• Possible Members:

– EPA– FLMs– MMS– PBL Expert– Dispersion Modeling Expert– 2-3 Active Users (Consultants or Industry)– One public meeting per year via web-link

• New web-based user group (or promote existing e-list a

Washington (Clint Bowman))
Model Applicability and 

Evaluation
Model Applicability – Near-Fie

AERMOD vs. CALPUFF

Overwater turbulence algorithm to treat coas

No coastal TIBL or fumigation algorithm

Coastal effects, fumigation

CALPUFF retains prevemissions within domaimpacts from them

Not treated. AERMOD has no memory of pollutants emitted during previous hours

Mass accumulation during stagnation. Memory?

Not treated – removed from the Calm winds treatedanalysis

Calm winds

Full variability consideCALMET meteorologic

None. Single station wind is applied over entire modeling domain

Horizontal wind variability

CALPUFF determines conditions in each grid it to a puff as it passes Downwind conditions 

every source are evaluby-puff basis

AERMOD looks upwind of met. 

station in a radius of 1-km to characterize turbulence up to 50-

km downwind of source

Policy on treating surface variability

Land use variability allowed in Full variability – uses 2wind sectors centered at met. station

Spatial variability of surface characteristicsCausality effects considered? No – plume extend to infinity Yes

Feature AERMOD CALPUFF
Model Applicability – Complex Ter

• EPA indicates AERMOD is suitable for 

complex terrain permitting applications

• Main Issues with AERMOD

– Use of a single meteorological field to chara

flow for facility + all background sources

– Use of surface characteristics upwind of 

meteorological station to characterize downturbulence of all sources

– Causality effects (plume go to infinity each – Use of straight-line trajectories 
Model Applicability – Complex Ter

• EPA’s argument that AERMOD is OK in 

complex flow regimes because only line sight impact matters is flawed for 2 majoreasons

– NAAQS and PSD increments are cumulati

standards, not single facility standards. Interacting sources matter in assessing compliance with the standards.

– Non-peak concentrations are important in 

assessing significant contribution above/beSILs.

• Facility contribution above the SIL to a violation i

considered a significant contribution
370 375 380 385 390 395 400 405 410

UTM East (km)

4730473547404745475047554760

UTM North (km)

 42.8N

 42.9N

112.5W 112.4W 112.3W 112.2W

112.1W

. 

UTM Zone: 12 Datum: WGS-84 Hemisphere: N Surface Meteorological Stations (used by Calmet) Upper air radiosonde station (used by Calme) Surface and Upper air Station (used by Aermod)

10m Calmet wind field 6 Jan (23h00) - 7 Jan (05h00) 1993

Wind Vectors (10m) – Complex Terrain with Channeled F
CALPUFF – Cumulative Impacts

370 375 380 385 390 395 400 405 410

UTM East (km)

4730473547404745475047554760

UTM North (km)

 42.8N

 42.9N

112.5W 112.4W 112.3W 112.2W

112.1W

. 

UTM Zone: 12Datum: WGS-84Hemisphere: N

INKOM

HOSP

FMC

CALPUFF shows channeling of flow. Backinteract to produce cumulative impacts in v
370 375 380 385 390 395 400 405 410

UTM East (km)

4730473547404745475047554760

UTM North (km)

 42.8N

 42.9N

112.5W 112.4W 112.3W 112.2W

112.1W

. 

UTM Zone: 12Datum: WGS-84Hemisphere: N

AERMOD Impacts – INKOM Source

AERMOD produces straight-line impacts using INKOM met. data. Note the lack of of the flow into the NW-SE oriented valle

INKOM
AERMOD – Cumulative Impacts

370 375 380 385 390 395 400 405 410

UTM East (km)

4730473547404745475047554760

UTM North (km)

 42.8N

 42.9N

112.5W 112.4W 112.3W 112.2W

112.1W

. 

UTM Zone: 12Datum: WGS-84Hemisphere: N AERMOD produces incorrect cross-valley 

valley impacts for HOSP and FMC sourceof plume interaction in AERMOD cumulat

FMC

HOSP

INKOM
Model Applicability

• Coastal case

– Sea breeze penetration over portion 

domain

– Relatively simple terrain– 3 sources– Most inland source considered as ma

“facility” source (met data based on it

• Demonstrates even in simple terra

spatial variability can be important
Sea Breeze Case – July 7, 1988 – 1:00pm LST

260 280 300 320 340 360 380 400 

UTM East (km)

4640466046804700472047404760

UTM North (km)

1

2

3

72.0W 71.5W 71.0W 70.5W 

LandBreeze

Sea Breeze
260 280 300 320 340 360 380 400 42

UTM East (km)

4640466046804700472047404760

UTM North (km)

1

2

3

42

42

43

72.0W 71.5W 71.0W 70.5W 70.0

Sea Breeze Case – July 7, 1988 – 1:00pm LST
1

2

3

Direction of AERMOD Plumes

Comparison of AERMOD Plume Trajectories (Blue Arr

with CALPUFF Trajectories (Filled Contours)

CALPUFF Plumes - filled contours
Model Applicability

• Random plume in AERMOD create

upwind halo around every source tresults in concentrations being predicted upwind

• Can be problematic for cumulative 

impact assessments and assessisignificant contributions to violation

• Upwind concentrations can even 

exceed downwind concentrations aSILs in AERMOD
-100 -80 -60 -40 -20 0 20 40 60 80 100

Distance (km)

-100

-80-60-40-20

0

20406080

100

Distance (km)

UMu* wZz0 

Source

Wind Direction

AERMOD Predicted Concentrations

Steady wind (4.5 m/s), Neutral stability
AERMOD Mass Fraction Removed Fro

Coherent Plume to Random Plume

0.00.10.20.30.40.50.60.7

0 10 20 30 40 50 60 70 80 90 

Distance (km)

Fraction Removed Light Wind, Convective Steady Wind, Neutral
-40 -30 -20 -10 0 10 20 30 40 

Distance (km)

2030405060708090

100110

Distance (km)

AERMOD

Stable Impingement Tests

WD=340 deg

Stack

050100150200250300350400450500550600650700

Terrain Elevation (m)
-20 -10 0 10 20

Distance (km)

50607080

Distance (km)

Stack

Wind Direction (340 de

Wide area of upwind conc. >>downwind concentrations

AERMOD – Problem: upwind concentratio

larger than downwind concentrations 
Spatial Land Use Variabilit

AERSURFACE Users Guide:

• “Determining effective surface characteristics for the pu

processing meteorological data for use with the AERMOmodel presents many challenges.”

• “AERMOD is a steady-state plume model which ass

spatially uniform meteorological conditions across 

modeling domain for each hour of meteorology, wh

cover across the domain is typically very heterogen

• “A sound understanding of the important physical proce

represented in the AERMOD model algorithms ... and thsensitivity of those algorithms to surface characteristics needed in order to properly interpret the available data amake an appropriate determination.”
Data Representativeness

AERMOD Implementation Guide (January 9, 200

• “... data representativeness can be thought of 

terms of constructing realistic planetary boundlayer (PBL) similarity profiles and adequately characterizing the dispersive capacity of the atmosphere.”

• “... the determination of representativeness of 

specific data for AERMOD applications should include an assessment of surface characteristthe measurement and source locations and cabe based solely on proximity.”
AERSURFACE Results (1km

AERSURFACE Roughness

Lengths (m) for Juneby Wind Direction Sector

.01

.34

.11

.10

Roughness lengths upwind of measurement location characterize local boundary layer

(Move location 1km East and smooth water disappears from character
AERMOD Dispersion

• Surface Roughness fo

Downwind of Each So

• Averaged Surface Roughness 

Upwind of Met. Measurements

1 km Up to 50 

km

Met Location

Source Location(s)

.01

.

.34

.11

.10 .1

.10

.34

“Upwind” roughness at measurement location 

determines downwind dispersion at source 

location in AERMOD
Downwind Roughness (m) fo

AERMOD Sources

.34 .11

.01

.10

Source B

.34 .11

.01

.10

Source A

Roughness distribution near Source A is not appropriate for modeling interacting Source B, nor is it appropriate for much of the 50km region around Source A
AERMOD Sensitivity to Surfac

Property Processing

• Current Guidance: 1 km search radius for surfac

roughness, with upwind sectors determined by t

Albedo and Bowen ratio determined for a 10km 

centered at location.

• Previous Guidance: 3 km search radius for surfa

roughness, albedo, and Bowen ratio with upwind determined by the user. 

• Significance of change characterized for actual 

application

– 4 stacks (2 at 231 m, 3 m and 27 m stack heights)– Nearly 3500 receptors– One full year of surface meteorological data (Louisville 

International Airport - SDF)
AERMOD Sensitivity to Surfac

Property Processing

-34% -43% -39% -3

AERSURFACE 1-km Method 

(airport – onsite location)

% Change in Peak Concentrations

89% 123% 103% -0.

AERSURFACE 1-km method 

– previous 3- km methodat Airport location

Averaging Time 1-hr 8-hr 24-hr Ann

• Switch to AERSURACE with 1-km radius for z0 increased mashort term concentration predictions from AERMOD by more thfactor of 2• Using airport roughness instead of onsite roughness changes reduces AERMOD predicted short term concentrations by up to 
AERMOD Sensitivity to AERM

DATUM Error

• EPA comment: Magnitude of changes in conce

due to CALPUFF v5.8 were larger than expecte

• All models will be sensitive to changes in the inp

parameters, including changes due to errors.

• Example: coordinate transformation corrected in 

AERMAP

• Significance of AERMAP changes characterized 

actual permitting application

– 2 tall stacks (231 m stack height)– 2 short stacks (3 m and 27 m stack height)– Nearly 3500 receptors
AERMOD Sensitivity to AERM

DATUM Error

Paired (correct location) -99 % -97 % -99 % -96 

% Change in Peak Concentrations [ (bad-corrected)/corr

Paired (bad location) 7307 % 10980 % 10246 % 178

Overall Maximum 48 % 7 % 52 % 43 

Averaging Time 1-hr 3-hr 8-hr 24

•Large differences frequently result from code changes•Differences at specific locations (paired in space) can be espsensitive
Model Sensitivity

• All dispersion models are sensitive 

changes in model inputs

• Skill that an experienced, compete

and objective modeler brings is abimake appropriate decisions on how model should be run

• Regulatory guidance has a role in 

model applications, but cookbook formulas do not work well in many cases
CALPUFF Model Evaluation

• Long-range transport

– CAPTEX field data for long-range transport distances

– Inel field data for intermediate transport distances

– Wyoming dataset (long and short range – cumulative impa

• Short-intermediate distances

– Kincaid SF

6: tracer releases from 187m stack in flat terrain

– Lovett SO

2: ambient monitoring on ridge near 145m stack 

Hudson river valley

– PRIME datasets (building downwash)

– Arkadelphia SF

6 : tracer releases from line source vents (

– Tennessee SO

2: 2 years of monitoring of emissions from 

and stacks

– Overwater and coastal datasets (5)

• Ventura, Carpinteria, Pismo Beach, Cameron (0.5 – 8 km)• Oresund (22 - 42 km)
AERMOD Model Evaluatio

9 used in developing/tuning model algorithms7 used to develop/test PRIME downwash module common to ISC-

AERMOD, and CALPUFF

(P(P(P(P

(PRIME)(PRIME)(PRIME)(PRIME)

Prairie Grass WestvacoMillstone TracyLovett Martin’s CreekKincaid SO2 Lee (wind tunnel)Kincaid SF6 EOCRIndianapolis Clifty CreekDAEC BaldwinBowline (subset) Bowline

Alaska AGA

Developmental Independent

17 AERMOD Evaluation Datasets
Model Evaluation

AERMOD (non-PRIME) Developmental Da

YesYesYesYesYesOnsitSigmaW

0.46 mrelease145 m Stack187 m Stack187 m Stack84 m StackSource Type(s)

Onsite SigmaTheta

Multiple Source Facilities

Land TerrainUse

Site

Prairie Grass Rural Flat None Yes(SO2)

Lovett Rural Complex None Yes(SO2)

Kincaid Rural Flat None Yes(SO2)

Kincaid Rural Flat None Yes(SF6)

Indianapolis Urban Flat None Yes(SF6)
Model Evaluation

AERMOD (non-PRIME) Independent Evaluation D

YesYesNoNoNoOnsite Sigma-W

183 m Stack91 m Stack122 m to 183 m StacksThree 208 m StacksThree 184 m StacksSource Type(s)

Onsite Sigma-Theta

Multiple Source Facilities

Land TerrainUse

Site

Westvaco Rural Complex None Yes(SO2)

Tracy Rural Complex None Yes(SF6)

Martin’s Rural Complex None YesCreek (SO2)

Half of None NoStack Ht.

Clifty Creek Rural(SO2)

Baldwin Rural Flat None No(SO2)
Model Evaluation

AERMOD (PRIME) Datasets

NoNoNoNoYesNoOnsite SigmaW

64.8 m Nonestack

Lee (wind “Rural” Flattunnel)

EOCR Rural Flat Rooftop? None Yes (SF6)

48m and 29m stacks1 m,24m & 46m rooftopsTwo 87 m Stacks39 m Stack10-25 m StackSource Type(s)

Onsite SigmaTheta

Multiple Source Facilities

Land TerrainUse

Site

Millstone Coastal Flat None No(SF6)

DAEC Rural Flat None Yes(SF6)

Bowline Point Rural Flat None No(SO2)

Alaska Rural Flat None Yes

(SF6)

AGA Rural Flat None No

(SF6)
Model Evaluation

Number of building downwash studies with long buildings

0

Number in coastal locations, but not a fumiscenario due to downwash

(PRIME building downwash dataset)

1

Number in complex terrain involving multi-fimpacts

0

Number involving cumulative impact asses

0

17 AERMOD Evaluation Datasets

•AERMOD has not been evaluated for use in assessing design concentration limits arising from interacting facilities, especially complex geographic areas•Evaluation datasets include evidence of underprediction
Model Evaluation

AERMOD Results for Kincaid SF6 Tracer Dataset• Arc-maximum results (best if samplers capture maximum)• Kincaid SF6 distributed in Model Validation Kit (MVK) used in 

“Harmonisation of Atmospheric Dispersion Modelling for RegulaPurposes” workshops

• MVK includes Quality Index (QI) values established for subset o

– QI=0: disregard value– QI=1: most probably not the maximum value– QI=2: identified value may be near a local maximu

m

– QI=3: identified value is a well-defined maximum

MVK Subset for QI=3 0.64 (underprediction)MVK Subset for QI=2 or 3 0.68 (underprediction)EPA (all data?) 0.77 (underprediction)Kincaid SF6 Dataset Modeled/ Observed RH
Model Evaluation

SO

2 Evaluation – Tennessee Smelter 

• SO

2 measured upwind and 300m downwind of facility

• Primary sources: 4 potrooms (line sources), 4 banks of sc

stacks (point sources) 

• Two years of data evaluated as part of 1981 BLP evaluati

resulted in BLP acceptance as a Guideline Model

• Simulations with BLP, AERMOD and CALPUFF

AERMOD 116.2 114.2

BLP 17.1 19.6

CALPUFF 15.8 18.5Observed

8 13

Units: (μg/m 1976 1977

3

)
Observed vs Predicted Q-Q Plots – 

(1-hr Average SO

2 Concentration

60 70 80 90 95 98 99 99.8 99.9 

Cumulative Frequency (%)

Observations

AERMOD

BLP

CALPUFF
Overwater Evaluation Datase

• OCD datasets (4 datasets)

– Ventura, Carpinteria, Pismo Beach, Cameron– Test of overwater dispersion over 4-8 km range exce

Carpinteria and Gaviota (<1km)

– Both stable and unstable conditions

• Oresund Experiment

– Tracer experiments across 20 km-wide strait of Ores

between Denmark and Sweden

– Tracer released at height of 95m or 115m– Transport distances to monitors: 22-44 km
Cameron, Carpinteria, Pismo Beach, Ventura

All CALMET Configurations with CALPUFF Configuration 

CALPUFF Configuration(Modeled Sigma-v > 0.37 m/s)

A -- Modeled Iy, CALPUFF Turbs, Draxler Fy

B -- Observed Iy, CALPUFF Turbs, Draxler FyE -- Modeled Iy, AERMOD Turbs, Draxler FyF -- Observed Iy, AERMOD Turbs, Draxler FyC -- Modeled Iy, CALPUFF Turbs, Variable TLyD -- Observed Iy, CALPUFF Turbs, Variable TLyG -- Modeled Iy, AERMOD Turbs, Variable TLyH -- Observed Iy, AERMOD Turbs, Variable TlyCALMET Configurationc0 – OCD overwater BL parameter modulec10d – COARE module (standard “deep water”)c10s – COARE module with shallow water adj.c11 – COARE module with wave option 1c12 – COARE module with wave option 2OCD5 with Modeled Iy (Sigma-v > 0.37 m/s)

O

CA
OCD5 0.54 

CALPUFF 0.62 

(AERMOD Turbulence Profile)

CALPUFF 0.60 

(CALPUFF Turbulence Profile)

Fraction within Co

factor of 2

Observed IyOCD5 0.54 CALPUFF 0.67 

(AERMOD Turbulence Profile)

CALPUFF 0.66 

(CALPUFF Turbulence Profile)

Fraction within Co

factor of 2

Modeled Iy

Factor of 2 and Correlation statis
310 320 330 340 350 360 370 380 390

UTM East (km) Zone 33N, Datum: EUR-M

614061506160617061806190620062106220

UTM North (km) Zone 33N, Datum: EUR-M Gladsaxe Barseback

L

. . 

Strait of Oresund

Denmark Sweden

T
Oresund

CALMET - CALPUFF Configuration-Zi1 – No Turb Advection, Maul-Carson 

Mixing Ht 

-Zi1OW – No Turb Advection, Maul-Carson

Mixing Ht , Obs Overwater

-Zi2 – No Turb Advection, BatchvarovaGryning Mixing Ht -Zi1OW – No Turb Advection,

Batchvarova-Gryning Mixing Ht , Obs Overwater

-800Zi1 –Turb Advection (800s), 

Maul-Carson Mixing Ht 

-800Zi1OW –Turb Advection (800s), 

Maul-Carson Mixing Ht , Obs Overwater

-800Zi2 –Turb Advection (800s), 

Batchvarova-Gryning Mixing Ht 

-800Zi1OW –Turb Advection (800s),

Batchvarova-Gryning Mixing Ht ,Obs Overwater

No 

ad

Wad
0

0.20.40.60.8

1

1.21.41.61.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Bridger IMPROVE [SO4]. Units: ug/m **3 of SO4

CALPUFF Predicted [SO4]. Units: ug/m**3 of SO4

CALPUFF PERFORMANCE PREDICTIN

W/ BCs and ALM (BRIDGER IMPROVE 

2 x overprediction

2 x unde

preobs
 IMPROVE Aerosol Observations

0

0.10.20.30.40.50.6

0 0.1 0.2 0.3 0.4 0.5 0.6

Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3

CALPUFF Predicted [NO3]. Units: ug/m**3 of NO3

CALPUFF PERFORMANCE PREDICTING 

W/ BCs and ALM (BRIDGER IMPROVE S

2 x overprediction

2x under

preob
OBSERVED AND PREDICTED S

NO

3 and SO

2 (ug/m

3

)

SO

2 .366 .585 .172 .207 

NO

3 .0978 .0922 .082 .081 

SO

4 .453 .492 .286 .248 

Pred

SD

Obs

SD

Pred

Mean

Obs

Mean

Species
OBSERVED AND PREDICTED Bext 

BRIDGER TRANSMISSOMETER 

Standard 4.6 5.5 7.2

-

Deviation

Mean 23.2 22.5 24.7 -0.017 

Fractiona

Bias

Method 2

CALPUFF Obs

Method 6

CALPUFF

Method 2

Variable
OBSERVED and PREDICTED ANNUAL W

and N DEPOSITION (kg/ha/yr) AT NADP S

S Gypsum 0.73 0.

N Gypsum 1.18 1.

S South Pass 0.97 0.

N South Pass 1.43 1.

S Sinks Canyon 1.11 0.

N Sinks Canyon 1.34 1.

S Pinedale 0.75 0.

N Pinedale 0.99 1.

Pred

Wet 

Observed

Wet Flux

Specie Site

s
Model Evaluation Summa

• AERMOD has been shown to perform well in c

circumstances but has shown underprediction several of the EPA datasets.

• No evaluation data presented by EPA for cum

impacts assessments or for upwind impacts prby random plume, or multi-source impacts

• Most evaluations method different from typical 

permitting study (i.e., observed turbulence rathpredicted turbulence)

• AERMOD model formulation makes it unsuitab

many non-steady-state situations
Consistency vs. Accurac

Paragraph 1(d) of the Guideline:

“The model that most accurately estimates concentrations in the are

is always sought. However, it is clear from the needs expressed by t

EPA Regional Offices, by many industries and trade associations, an

deliberations of Congress, that consistency in the selection and a

models and data based should be sought, even in case-by-case 

Consistency ensures that air quality control agencies and the general 

common basis for estimating pollutant concentrations, assessing con

and specifying emission limits. Such consistency is not, however, p

expense of model and data base accuracy. The Guideline provides a 

for selection of the model accurate models and data base

for use in air quality assessments.”
Summary

• AERMOD should not automatically be preferre

CALPUFF in applications involving non-steadyapplication

• AERMOD has significant issues with cumulativ

impacts

• AERMOD sensitive to specification of land use 

surface properties may involve substantial erroassociated with its inability to vary surface pro

appropriately for the facility source and especi

background sources.

• The intent of the GAQM should be followed to 

CALPUFF use wherever complex flow or non-state conditions are important