AirWare User Manual

AERMOD: A Dispersion Model
for Industrial Source Applications

See also: AERMOD: Description of Model Formulations   (PDF document)
Cimorelli et al., 1998 (DRaft Document Version 98314, USEPA).

Table of Contents

• Dispersion estimation
• Terrain handling
• Meteorological parameters
• MODEL FORMULATION
• Algorithms for the Convective Boundary Layer

INTRODUCTION

The basic approach in EPA's present regulatory platform for near-field modeling has, with very few exceptions, remained fundamentally unchanged since the beginning of the air programs some 20 years ago. During this time, significant scientific advances have been made which have yet to be incorporated into the basic approach. The Industrial Source Complex (ISC2) short-term model⁰ has been selected as the starting point for a new generation of regulatory models. ISC2 is the workhorse of current regulatory tools and, as a bonus, its recently restructured code is conducive to change.

AERMOD currently contains new or improved algorithms for:

• dispersion in both the convective and stable boundary layers;
• plume rise, buoyancy, and penetration into elevated inversions;
• treatment of elevated, near-surface, and surface level sources;
• computing vertical profiles of wind, turbulence, and temperature;
• and treatment of receptors on all terrain (from the surface up to and above the plume height).

Terrain handling is done with a simple approach including the dividing streamline concept in stably-stratified conditions. High priority for future efforts include upgrades or enhancements to the algorithms dealing with plume downwash.

The meteorological preprocessor (AERMET) uses both off-site and available on-site meteorological data and surface characteristics to calculate the boundary layer variables (e.g. mixing height, friction velocity, etc) needed by AERMOD. The AERMIC terrain preprocessor uses gridded terrain data for the modeling area to calculate a representative terrain-influence height associated with each receptor location selected by the user. The gridded data is either user supplied or preferably computed by the preprocessor from the U. S. Geological Survey's Digital Elevation Mapping (DEM) data. The terrain preprocessor can also be used to compute elevations for both discrete receptors and receptor grids.

In upgrading ISC2 into AERMOD, the workgroup has strived to follow certain design criteria (goals) to yield a model with desirable regulatory attributes. We felt that the model should:

1. be robust in estimating (regulatory) design concentrations (i.e. provide reasonable estimates under a wide variety of conditions with minimal discontinuities);
2. be easily implemented (user friendly, reasonable input requirements and computer resources), as is the current ISC2;
3. be based on state-of-the-art science that captures the essential physical processes while remaining fundamentally simple; and
4. accommodate modifications with ease as the science evolves.

Because AERMOD adopted the ISC2 computer architecture, the input and output structure of the two models are similar.

This paper contrasts the specific methods in ISC2 that have been replaced with analogous methods in AERMOD; describes the entire AERMIC modeling package that consists of the dispersion model (AERMOD), the meteorological preprocessor (AERMET), and the terrain preprocessor (yet unnamed).

TRANSITION FROM ISC2 TO AERMOD

Before describing the AERMOD algorithms in subsequent sections, we will outline here the specific areas in the model that represent improvements to the present ISC2 model. There remain other areas (e.g. building downwash), left unchanged in AERMOD, that we believe need improvement. It is our hope to address these areas in future revisions to AERMOD.

Although performance evaluations have shown models such as ISC2 to be relatively unbiased, these evaluations have not included all situations in which ISC2 is used. For those situations where the model has not been evaluated, confidence in its predictive abilities is related to how well its underlying scientific assumptions are satisfied. For example, as we discuss below, ISC2's reliance on the Pasquill-Gifford (PG) dispersion curves limits our confidence in applying the model to elevated releases. AERMOD's improved theoretical basis will greatly increase our confidence in its application, particularly in situations where the models have yet to be evaluated.

Next: Dispersion estimation