Transcript Slide 1
Dispersion Modeling 101:
ISCST3 vs. AERMOD
Iowa Chapter AWMA
February 14, 2006
Mick Durham
Stanley Consultants, Inc.
What we are going to talk about
Brief History of Dispersion Modeling
Industrial Source Complex Model
AMS/EPA Regulatory Model (AERMOD)
Comparisons
The IDNR Connection
Questions & Answers
Brief History of Modeling
Earliest Studies Simulated the Movement of Air
– G.I. Taylor, 1915, Eddy Motion in the Atmosphere
– O.G. Sutton, 1932, A Theory of Eddy Diffusion
Dispersion of Pollutants (Mainly Particulate)
Followed WW II
– E.W. Hewson, 1945, Meteorological Control of
Atmospheric Pollutants by Heavy Industry
– E.W. Hewson, 1955, Stack Heights Required to
Minimize Ground Level Concentrations
– Gale, Stewart & Crooks, 1958, The Atmospheric
Diffusion of Gases Discharged from a Chimney
Brief History of Modeling
Birth of Dispersion Parameters
– F.A. Gifford, 1960, Atmospheric Dispersion Calculations
Using the Gaussian Plume Model
– F. Pasquill, 1961, The Estimation of the Dispersion of
Windborne Material
– D. Bruce Turner, 1967, Workbook on Atmospheric
Dispersion Estimates
– Briggs, Gary, 1969, Plume Rise
Brief History of Modeling
Modeling and the Computer Age
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PTMAX, PTMIN, PTMTP, 1972
Air Quality Display Model (AQDM), 1974
Single Source (CRSTER) Model, 1977
Complex Terrain (VALLEY) Model, 1977
Multiple Source (MPTER) Model, 1980
Pollutant and Environment Specific Models
– APRAC, CALINE, HIWAY Carbon Monoxide Models
– BLP (Bouyant Line and Point Sources); PAL (Point Area
and Line Source), 1979; TEM (Texas Episode for Urban
Areas)
– RPM (Reactive Plume Model) for Ozone, 1980
Brief History of Modeling
Guideline on Air Quality Models
– The Guidelines on Air Quality Models, 1978
– 40 CFR Part 58, Appendix W
Refined and More Complex Models
– Industrial Source Complex (ISC), 1979
Industrial Short-Term ST
Industrial Long-Term LT
– Complex Terrain (COMPLEX)
– Dense Gas (DEGADIS)
– Urban Airshed Model (UAM)
Brief History of Modeling
Refined and More Complex Models (cont.)
– Screening Model (SCREEN)
– California Line Source (CALINE) and Mobile
Source Emission Factors (MOBILE)
– Puff Models (INPUFF)
– Visibility (VISCREEN)
Brief History of Modeling
Advanced Models
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Industrial Source Complex Version 2 (ISC2), 1990
Industrial Source Complex Version 3 (ISC3), 1995
California Line Source (CAL3QH3)
Urban Airshed Model (UAM-V)
Complex Terrain Dispersion Model (CTDMPLUS)
Offshore and Coastal Dispersion Model (OCD)
Bouyant Line and Point Source (BLP)
Area Locations of Hazardous Atmospheres (ALOHA)
Dense Gas Dispersion Model (DEGADIS 2.1)
Brief History of Modeling
Today’s Models:
– AERMOD
Point, Area, Line Sources
Simple or Complex Terrain
Transport distance up to 50 km
– CALPUFF
Transport from 50 to hundreds of kilometers
Visibility, Regional Haze
Dispersion in Complex Terrain
– Complex Dispersion Model Plus Algorithms for Unstable
Conditions (CTDMPLUSS)
Dispersion in Complex Terrain
Brief History of Modeling
Today’s Models (Continued):
– Caline3 or CAL3QHC, MOBILE6
Highway Line sources
Simple Terrain
Carbon Monoxide
– Buoyant Line and Point Source (BLP)
Aluminum Reduction plants with buoyant line and point sources
Rural location
Simple Terrain
– Community Multi-scale Air Quality Model (CMAQ)
Ozone
Industrial Source Complex Model
Introduced in 1979
First adopted as Preferred Model in 1983
Major Revisions 4 times in 27 year history
Can remain acceptable as a preferred
model until November 9, 2006
Industrial Source Complex Model
Gaussian Plume Model
Building Downwash
Particulate Deposition
Point, Area, and Line Sources
Complex Terrain
Simple Meteorological Data Input
Industrial Source Complex Model
Has been primary model in Iowa for 27
years
Over 100 facilities have modeled
compliance with ISC
Generally the short-term standards have
caused greatest predicted non-compliance
Industrial Source Complex Model
Problems with ISCST3:
– Modeling of Plume Dispersion is Crude
– Only 6 possible states (Stability Classes)
– No variation in most meteorological variables with
height
– No use of observed turbulence data
– No information about surface characteristics
– Erroneous depiction of dispersion in convective
conditions
– Substantial overprediction in complex terrain
– Crude building downwash algorithm
AERMOD
AERMOD stands for American
Meteorological Society/ Environmental
Protection Agency Regulatory Model
Formally Proposed as replacement for ISC
in 2000
Adopted as Preferred Model November 9,
2005
AERMOD
3 COMPONENTS
– AERMET – THE METEOROLOGICAL PREPROCESOR
– AERMAP – THE TERRAIN DATA PREPROCESSOR
– AERMOD – THE DISPERSION MODEL
2 SUPPORT TOOLS
– AERSURFACE – PROCESSES SURFACE CHARACTERISTICS DATA
– AERSCREEN – PROVIDES A SCREENING TOOL
AERMOD
AERMOD IS SIMILAR TO ISC IN SETUP
– THE CONTROL FILE STRUCTURE IS THE
SAME
– VIRTUALLY ALL THE CONTROL KEYWORDS
AND OPTIONS ARE THE SAME
AERMOD
AERMOD IS DIFFERENT FROM ISC
– REQUIRES SURFACE CHARACTERISTICS (ALBEDO,
BOWEN RATIO, SURFACE ROUGHNESS) IN
AERMET
– HAS PRIME FOR BUILDING DOWNWASH AND THE
BUILDING PARAMETERS ARE MORE EXTENSIVE
– REQUIRES LONGER COMPUTER RUN TIMES (up to
5 times longer!)
Comparison of Dispersion Model Features:
Meteorological Data Input
– ISCST3:
• One level of data accepted
– AERMOD:
• An arbitrarily large number of data levels can be
accommodated
Comparison of Dispersion Model Features:
Plume Dispersion and Plume Growth Rates
ISCST3:
• Based upon six discrete stability classes only
• Dispersion curves are Pasquill-Gifford
• Choice of rural or urban surfaces only
AERMOD:
• Uses profiles of vertical and horizontal turbulence
variable with height
• Uses continuous growth function
• Uses many variations of surface characteristics
Comparison of Dispersion Model Features:
Complex Terrain Modeling
ISCST3:
• Elevation of each receptor point input
• Predictions are very conservative in complex
terrain
AERMOD:
• Controlling hill elevation and point elevation at
each receptor are input
• Predictions are nearly unbiased in complex
terrain
Comparisons ISC Vs AERMOD
CONSEQUENCE ANALYSIS - ratios of AERMOD predicted high concentrations
to ISCST3 predicted high concentrations:
flat and simple terrain
point, volume and area sources.
1hour
3hour
24hour
annual
average
high
low
1.04
4.25
0.32
1.09
2.82
0.26
1.14
3.15
0.24
1.33
3.89
0.30
Total
48
48
48
48
– AN OVERVIEW FOR THE 8TH MODELING CONFERENCE SEPTEMBER 22, 2005
Comparisons ISC Vs AERMOD
CONSEQUENCE ANALYSIS - ratios of AERMOD predicted high concentrations to ISCST3
(and PRIME) predicted high concentrations:
flat terrain
point sources with significant bldg downwash
ANNUAL
AER/ISC3 AER/ISCP
ave
1.08
1.05
max
1.35
1.29
min
0.69
0.79
No cases
6
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24 H2H
AER/ISC3 AER/ISCP
1.25
1.01
1.87
1.14
0.69
0.84
6
3 H2H
AER/ISC3 AER/ISCP
0.71
1.05
1.20
1.17
0.38
0.93
6
AN OVERVIEW FOR THE 8TH MODELING CONFERENCE SEPTEMBER 22, 2005
Comparisons ISC Vs AERMOD
– Duane Arnold Energy Center Data (Palo, IA)
– Ratio of Modeled Conc to Observed:
AERMOD: 0.69 (1-hr avg 46m release)
ISC-Prime: 0.76 (1-hr avg 46m release)
AERMOD: 0.25 (1-hr avg 24m release)
ISC-Prime: 0.29 (1-hr avg 24m release)
AERMOD: 0.51 (1-hr avg 1m release)
ISC-Prime: 0.38 (1-hr avg 1m release)
Comparisons ISC Vs AERMOD
Presentation at EUEC conference by Bob Paine,
TRC:
AERMOD consistently showed better or comparable
performance with ISCST3
In flat terrain, AERMOD and ISCST3 predictions are
comparable, but AERMOD has higher annual averages
In complex terrain, AERMOD predictions are markedly
lower
Building downwash predictions will often be lower,
especially for stacks located some distance from
controlling buildings
Overall, more confidence in accuracy of AERMOD results
Comparisons ISC Vs AERMOD
Our Recent Experience:
– Annual concentrations higher with AERMOD by
10-15%
– Short term concentrations similar without
downwash
– Short-term concentrations generally lower with
building downwash by 20%
The IDNR Connection
IDNR will allow use of either ISCST3 or
AERMOD until November 9, 2006
Meteorological Data will be provided by
IDNR for eight stations
Compliance with ISCST3 and noncompliance by AERMOD must be
addressed
Questions & Answers
AERMOD
Feature
Types of
Sources
Plume Rise
ISCST3
Point, Area, Volume
AERMOD
Point, Area, Volume
Uses Briggs
equations with
Stack-top wind
speed and vertical
temp gradient
In stable use Briggs
In convective uses
random convective
velocities
Met Data Input
One level of data
accepted
An arbitrarily large
number of data levels can
be accommodated
Profiling Met
Data
Only wind speed is
profiled
Plume
Dispersion
Gaussian treatment
in horizontal and
vertical
Urban
Treatment
Urban option either
on or off
Creates profiles for wind,
temperature and
turbulence
Same for stable only;
non-Gaussian probability
density in vertical for
unstable conditions
Population is specified so
treatment can consider a
variety of urban
conditions; sources can
individually be modeled
urban or rural
Comments
Models are
Comparable
AERMOD
superior in
accounting for
convective
updrafts and
downdrafts
AERMOD can
adapt multiple
levels of data to
various stack and
plume heights
More accurate
portrayal of actual
conditions
More accurate
portrayal of actual
conditions
More options to
depicts urban
characteristics
AERMOD
Feature
Surface
Characteristics
ISCST3
Choice of rural of
urban
AERMOD
Selection by direction
and month of roughness
length, albedo, and
Bowen ratio
Five update
methodologies for
improved boundary layer
interpretation
Boundary
Layer
Wind speed, mixing
height, and stability
class
Mixed layer
Height
Holzworth, based on
afternoon mixing ht
Has convective and
mechanical mixing layer
ht based upon sensible
heat flux
Terrain
Depiction
Elevation at each
receptor point
Controlling hill elevation
and point elevation at
each receptor using DEM
data
Comments
Provide
significantly more
options in selecting
sfc characteristics
Provides
parameters for use
with up-to-date
planetary boundary
layer
parameterization
Provides more
realistic sequence
of the diurnal
mixing height
changes
Uses digital data
for terrain heights
and preprocessor
(AERMAP)
advanced
streamline
algorithms
AERMOD
Feature
Plume Growth
Rates
Plume
Interaction with
Mixing Lid Convective
Plume
Interaction with
Mixing Lid Stable
Building
Downwash
ISCST3
Pasquill-Gifford
dispersion curves
and 6 stability
classes
If plume is above lid
zero concentration
on ground
Mechanical lid is
ignored; assumed
infinitely high
Combination HuberSnyder and ScireSchulman
algorithms; many
discontinuities
AERMOD
Uses profiles of vertical
and horizontal
turbulence; variable with
height;
Three plume
components: updrafts,
downdrafts, and stable
layer dispersion
Mechanical mixing layer
near surface; plume
reflection from elevated
lid
New PRIME downwash
algorithms
Comments
Turbulence- based
plume growth with
height superior to
6 classes
Avoids potential
under-prediction
due to all of
nothing approach
Advancement over
simplistic ISC
approach
AERMOD benefits
from tech advances
of PRIME