comparison of features and data requirements among the calpuff, aermod, and adms models

Modeling Software for EH&S Professionals

Comparison of Features and Data Requirements among the CALPUFF, AERMOD, and ADMS Models

Prepared By:

Russell F. Lee

BREEZE SOFTWARE 12770 Merit Drive

Suite 900 Dallas, TX 75251

+1 (972) 661-8881 breeze-software.com

Comparison of Features and Data Requirements among the CALPUFF,

AERMOD, and ADMS Models

Introduction to plume and puff models

CALPUFF, AERMOD, and ADMS are new advanced air quality models that are either being

used or are being proposed for use for air pollution regulatory applications. The physical

processes in the atmosphere that determine the transport and dispersion of air pollutants are very

complex, and are not all well understood. Furthermore, simplifying the physical processes in a

model that is sufficiently accurate for regulatory purpose, but not excessively costly and difficult

to use, is not an insignificant endeavor. Consequently, the development of air quality models is a

continuing process.

The Interagency Workgroup on Air Quality Modeling (IWAQM) has recently recommended the

use of the CALPUFF modeling system for “use in assessing air quality associated with

prevention of significant deterioration” of air quality in Federal Class 1 and Wilderness areas.

For such purposes, the ability of the model to adequately represent long-range transport of

pollutants, effect of pollution on visibility, and certain chemical reactions is critical. Once

CALPUFF was selected for their recommendation, IWAQM was involved in enhancing that

modeling system to better suit these purposes.

In 1991, the American Meteorological Society (AMS) and the U.S. Environmental Protection

Agency (EPA) cosponsored an initiative to bring plume models up to date. The committee

formed to accomplish this is the AMS/EPA Regulatory Model Improvement Committee

(AERMIC), and the resulting model is AERMOD (AERMIC Model). AERMOD is intended to

fill the niche currently occupied by the ISCST3 model. It is intended primarily to be used for

modeling non-reactive pollutants such as sulfur dioxide, carbon monoxide, and particulate

matter. These pollutants are characteristically emitted by local sources, resulting in the highest

concentrations occurring within a few kilometers of the contributing sources. After reviewing

existing models, AERMIC elected to develop a new model that was sufficiently similar to

ISCST3 that users would not have excessive difficulty in learning to apply it.

Concurrently with the development of AERMOD in the United States, a new plume model was

being developed in United Kingdom. ADMS (more accurately, UK-ADMS—UK-Atmospheric

Dispersion Modelling System) was being developed by the CERC (Cambridge Environmental

Research Consultants, Ltd.) with from industry and government organizations.

An overview of plume and puff models

Plume models have been the mainstay of regulatory near-field modeling of nonreactive

pollutants. The current regulatory model in the U.S., ISCST3, is based on work by Frank Pasquill

in the 1950’s, with modifications by Frank Gifford and adaptations for computer modeling by D.

Bruce Turner. In that model, atmospheric states are divided into six stability categories, and

plume growth is treated as a function of stability category and downwind distance. The

concentration distribution of pollutants through the plume is assumed to be Gaussian in all cases.

This is a reasonable assumption in the horizontal direction. For neutral and stable flow, when

there is no local boundary (such as the ground) affecting the turbulence profile, this is also a

reasonable assumption in the vertical direction. However, the presence of a ground surface skews

the shape of the pollutant distribution by suppressing turbulence (and, therefore, dispersion) on

the low side of a near-ground plume. During convective (i.e., unstable) conditions, things are

even more complex. Because there are very large turbulent eddies for this case, the presence of

the ground skews even elevated plumes. In addition, since the turbulent eddies are much larger

than the plume itself, parts of the plume get caught in updrafts while other parts get caught in

downdrafts. This results in a very non-Gaussian plume. These and many other problems with the

older models have been addressed in the newer plume models AERMOD and ADMS. These

newer models no longer characterize turbulence in terms of six discrete stability categories. They

recognize that turbulence is continuously variable, depending on height above ground, amount of

heating in the daytime or heat loss at night, and wind shear. Concerning the height above ground,

virtually all airflow near the ground is neutral, becoming more turbulent (daytime) or less

turbulent (nighttime) with height.

All plume models are, however, steady state by nature. This means that the models assume

steady or constant conditions between the time of emission and the time it reaches any receptor.

For downwind distances of a few kilometers, this is a reasonable assumption. However, consider

this. A plume model applied to a pollutant release during stable conditions at six o’clock in the

morning will make concentration calculations at a distance of 50 kilometers based on that one

wind direction under stable conditions. It will ignore the fact that the plume may have traveled

for six hours or more, changing direction due to changes in wind speed, and growing at varying

rates as it encounters a whole range of stabilities. It is clear that plume models cannot, in general,

be expected to give reasonable results for long range transport of pollutants.

This is where puff models are valuable. Puff models do not require the steady state assumption.

The emission that occurs from a source during the first time step (which may be a few minutes or

an hour) will be followed downwind. As the wind direction changes, the direction of motion of

the puff changes. It can even account for different winds at different locations. Unfortunately, to

give an accurate picture of the concentration distribution in a plume, these puffs must be

sufficiently close together to overlap. Close to the source, where little dispersion has occurred

and, thus, the puffs are small, it may be necessary to model a puff a second to avoid gaps

between the puffs. This would lead to extremely large runtimes for the model. For this reason, a

puff model is generally not a practical way to model concentrations near a point source, although

CALPUFF gets around the problem by some techniques described below. At large travel

distances, where the puffs have grown to considerable size, model runtimes are less severe. In

addition, the puff model can account for changes in wind and stability as the puff moves

downwind. Thus, a puff model can provide good results when long range transport is involved.

Discussion of the CALPUFF model

While many of the entries in Tables 1–3 are self-evident, several also require some discussion.

Under item 3b of Table 1, both puff and slug are indicated as options for CALPUFF. As noted

above, puff models are generally unable to represent a continuous plume near a point source, due

to the small sizes of the puffs. The plume ends up being represented by a sequence of puffs with

space between them, with resultant errors in calculated concentrations. CALPUFF solves this

problem with the optional use of a slug for such cases. A slug is an elongated puff. This allows

two separated puffs to be connected, to better represent the plume without having to generate an

excessive number of puffs. This may make it possible for CALPUFF to be used to estimate

concentrations nearer the source than is normally possible for puff models. CALPUFF also

allows an option to use a non-Gaussian probability density function for convective (unstable)

cases, as is done in AERMOD. There are also options allowing CALPUFF to emulate ISCST3

and CTDM in the near field. These should be used with caution at the present time, since there is

limited experience using CALPUFF to estimate near-field concentrations. It will be interesting to

learn how well it emulates plume models such as AERMOD and ADMS for these cases.

Since the main purpose of CALPUFF is to provide a model for long range transport of

pollutants, the meteorological data requirements can be substantial (see Tables 2 and 3).

Fortunately, for regulatory purposes, it will probably not be required to use hourly gridded wind

fields from the MM4, MM5, or CSUMM models. Nevertheless, it is a much more difficult model

to use than the traditional Gaussian models.

In general, CALPUFF is a technically good approach for long-range transport modeling, and can

account for some chemical reactions. Because of the introduction of the use of the concept of the

slug, as well as the non-Gaussian pdf, CALPUFF has potential for near-field analyses as well,

though this needs to be tested.

Discussion of the AERMOD model

AERMOD represents an improvement over traditional Gaussian models. The theory is much

more “solid,” and evaluation studies so far show substantial improvement over the traditional

models as well. AERMOD uses a non-Gaussian pdf during convective conditions, and accounts

for variations in wind, turbulence, and temperature gradient with height. The effect of wind shear

on transport is accounted for by estimating a characteristic wind speed and direction through the

thickness of the plume. AERMOD does not, however, deform the plume in response to the shear

as CALPUFF does.

AERMOD is a steady state plume model, and as such is not appropriate for long-range transport.

Although it will probably be approved for use for distances up to 50 kilometers, it is doubtful

that any plume model will retain its accuracy much beyond 20–30 kilometers. At the present

time, AERMOD does not include the capability of accounting for deposition, though there are

plans for adding this.

Discussion of the ADMS model

[TO BE ADDED]

Table 1. Comparison of Model Features

Category Feature CALPUFF AERMOD ADMS

1. Graphical User

Interface

Point & click model setup

and data input

YES Trinity

version: YES

EPA version:

NO

YES

Enhanced error checking YES Trinity

version: YES

EPA version:

NO

YES

Online help files YES Trinity

version: YES

EPA version:

NO

YES

2. Source Types

2a. Point sources YES YES YES ≤501

Variable emissions & stack

parameters

YES YES YES

Plume rise YES YES YES

Building downwash YES YES YES

Huber-Snyder YES YES NO

Schulman-Scire YES YES NO

Selection method User option Automatic N/A

Other YES

2b. Line sources YES NO YES ≤11

Variable emissions YES N/A YES

Plume rise YES N/A YES


2c. Volume sources YES YES YES ≤51

Variable emissions YES YES YES

Plume rise NO NO NO

2d. Area sources YES YES YES ≤51

Variable emissions YES YES YES

Uses Emissions

Production Model (U.S.

Forest Service) output

for controlled burns and

wildfires.

YES NO NO

Plume rise YES NO YES

2e. “Jet” sources

(non-buoyant, ejected

at any angle)

NO NO YES

2f. Treatment of

variable emissions

(all source types):

Hourly file of emissions

and stack parameters

User option User option User option

Factors by hour of day User option User option User option

Factors by season NO User option User option

Factors by month User option User option User option

Factors by hour &

season


Factors by wind speed

& stability


Factors by temperature User option NO NO


3. Plume/Puff

Characteristics

3a. Plume Rise

Buoyant rise YES YES YES

Momentum rise YES YES YES (?)

Stack tip effects YES YES YES

Partial penetration YES YES (?)

Vertical wind shear YES YES

(limited–

does not

deform

plume)

YES

(limited–

does not

deform

plume) (?)

3b. Plume/puff form

Steady-state plume NO YES YES

Puff User option NO Optional

puff for

short

releases

Slug User option NO NO

Non-Gaussian pdf User option3 YES YES

3c. Dispersion

coefficients y, z

based on:

Direct measurements of v,

w

User option2 User option (?)

Estimated v, w using

similarity theory

User option2 User option YES

PG (ISC3-Rural) User option NO NO

McElroy-Pooler (ISC3-

Urban)

User option NO NO

CTDM coefficients

(neutral/stable)

User option NO NO


3d. Overwater and

coastal effects

YES NO Overland

coastal only

Overwater boundary layer

parameters

YES NO NO

Change from overland to

over water conditions

modeled

YES NO NO

Plume fumigation YES NO YES (over

land)

TIBL included in subgrid

scale modeling

User option NO YES (on-

shore wind)

3e. Spatial variability

of meteorology

affecting plume or

puff

Gridded 3-D winds and

temperature

YES NO NO

2-D fields of zi, u*, w*, L,

precipitation rate

YES NO NO

Vertical variations in

turbulence

YES YES YES

Individual puffs split User option N/A N/A

Horizontal variation in

turbulence

YES NO NO

3f. Chemical

transformation

Exponential decay NO YES YES

Pseudo first-order reaction

for SO2, SO4=, NOX, HNO3

& NO3–

User

option2

(MESO-

PUFF II

method)

NO YES

Diurnal cycle of

transformation rate

User option NO NO (?)


3g. Dry deposition User option NO YES

Gas and/or particle Both

optional

NO Both

optional

Full space/time

variations of deposition

“resistance” values


Simpler diurnal cycle of

deposition “resistance”

values


3h. Wet deposition User option NO YES

Scavenging coefficient

approach

YES NO YES

Rate is function of precip.

intensity & type

YES NO YES

4. Outputs

Concentration (Max, 2nd

high, etc.)

YES YES YES

Averaging times < 1 hour YES NO YES

Running averages Requires

external

processing

Requires

external

processing

YES

Percentiles Requires

external

processing

Requires

external

processing

YES

Deposition YES NO YES

Visibility YES NO YES

Radioactive dose NO NO YES

1. Maximum number of sources or receptors permitted in the model.

2. A (or the) preferred option for this model.

3. For convective cases, CALPUFF has non-Gaussian pdf for near-source concentrations to

approximately emulate AERMOD

NOTE: CALPUFF features referenced above do not include those in the CALGRID

Photochemical Model and the KSP Particle Model. CALPUFF and KSP are part of the

CALPUFF modeling system, and allow additional model features, including complete

photochemical modeling and lagrangian particle modeling. These models, though included in the

CALPUFF system, are documented separately.

Table 2. Comparison of Data Requirements among the Meteorological Preprocessors

Category Feature CALMET1

(CALPUFF)

AERMET

(AERMOD)

ADMS

(Meteoro-

logical

input

module)

Surface data

Hourly observations of:

wind speed,

wind direction

temperature

cloud cover

ceiling height

surface pressure

relative humidity

precipitation type

precipitation rate

YES—

multiple sites

(Precipitation

type required

only for wet

deposition)

YES—single

site

(Precipitation

type, relative

humidity,

surface pressure

not needed)

YES—

single site

(Precipitation

type, relative

humidity,

surface

pressure not

needed)


(CALPUFF)

AERMET

(AERMOD)

ADMS

Upper air data

Daily maximum mixing

heights

NO NO YES

RAWINSONDES:

observed vertical profiles

of:

wind speed

wind direction

temperature

pressure

elevation

YES–twice

daily

soundings or

more

YES–one

early

morning

sounding

Requires

hourly

mixing

heights

Hourly gridded wind fields

from MM4/MM5

User option NO NO

Hourly gridded wind fields

from CSUMM

User option NO NO

Overwater observations

air-sea temperature

difference

air temperature

relative humidity

overwater mixing height

wind speed

wind direction

overwater temperature

gradients above and

below mixing height

User option NO NO


(CALPUFF)

AERMET

(AERMOD)

ADMS

Geophysical

data

Terrain elevations (gridded) YES YES YES

Land use categories YES-gridded NO (?)

Surface roughness User option-

gridded

YES-by

direction

YES

Albedo User option-

gridded

YES-by

direction

YES

Bowen ratio User option-

gridded

YES-by

direction

YES (?)

Soil heat flux constant User option-

gridded

NO NO (?)

anthropogenic heat flux User option-

gridded

Derived from

city size

NO (?)

leaf area indices YES NO NO

1. CALPUFF can optionally be run, with degradation of accuracy, using meteorological data

preprocessed for ISCST3. It requires the addition of hourly (not gridded) friction velocity,

Monin-Obukhov length, surface roughness, precipitation code and rate, potential temperature

gradient, wind speed profile power-law exponent, short-wave solar radiation, and relative

humidity.

An additional option allows the use of the surface and profile meteorological data files which

are used in CTDMPLUS and AERMOD, with the addition of precipitation code and rate,

short-wave solar radiation, and relative humidity to the surface file. The accuracy is expected

to be degraded from the normal CALPUFF mode, but should be better than that obtained

using ISCST3 meteorological data.

Table 3. Comparison of Other Input Data Requirements among the Models


Emissions data

Point source, constant or diurnal

emission pattern

YES (with

plume rise)

YES (with

plume rise)

YES (with

plume rise)

Point source, arbitrarily

varying emission pattern (i.e.,

a file of hour by hour

emission parameters)

YES (with

plume rise)

YES (with

plume rise)

YES (with

plume rise)

Line source, constant or diurnal

emission pattern

YES (with

plume rise)

NO YES (with

plume rise)

Line source, arbitrarily

varying emission pattern

YES (with

plume rise)

NO YES (with

plume rise)

Area source, constant or diurnal

emission pattern

YES (with

plume rise)

YES (no

plume rise)

YES (with

plume rise)

Area source, arbitrarily


YES (with

plume rise)

YES (no

plume rise)

YES (with

plume rise)

Volume source, constant or

diurnal emission pattern

YES (no

plume rise)

YES (no

plume rise)

YES (no

plume rise)

Volume source, arbitrarily


YES (no

plume rise)

YES (no

plume rise)

YES (no

plume rise)

Other data

Deposition velocity data YES NO YES (?)

Ozone monitoring data YES NO YES

Chemical transformation data YES NO (?)

Terrain data YES YES YES

USGS DEM data, or

equivalent

YES YES YES (UK

data)

Hill shape and height

parameters

YES (as in

CTDM+)

Derived in

model from

terrain data

Derived in

model from

terrain data

Receptor locations with

associated hill ID

YES Derived in

model from

terrain data

Derived in

model from

terrain data

Coastal boundary data YES NO YES