EVOLUTION AND FUTURE OF DISPERSION MODELS IN AUSTRALIA AND NEW ZEALAND

assessments). While the ISC model has been superseded by AERMOD, it is still listed on the US EPA website as an alternative to the recommended models for certain circumstances.

In 1991, the US EPA recognised that a new dispersion model was needed to replace the ISC series of dispersion models. The new model needed to build on the assessment methodologies used for dispersion assessments in the US but also incorporate new state of the science understanding of plume and meteorological behaviour. To facilitate the development of this new model, a collaborative working group of scientists from the American Meteorological Society (AMS) and the US EPA was formed named “the AERMIC” (American Meteorological Society (AMS)/United States Environmental Protection Agency (EPA) Regulatory Model Improvement Committee). The AERMIC model development process resulted in the introduction of a dispersion model that:

 Considered different source types and elevations

 Allowed the assessment of different source locations (urban vs rural locations)

 Considers different terrain types, building wake effects, and deposition of particulates and gases

 uses a gaussian model to predict dispersion in horizontal and vertical directions under stable conditions and a non-gaussian model for vertical dispersion in unstable conditions.

 Accepts meteorology from multiple heights along with vertical profiles of wind turbulence and temperature.

AERMOD was promulgated on 9th November 2005 as the US EPA’s preferred regulatory model. AERMOD has been updated several times since its initial promulgation in 2005, with the latest US EPA accepted version being V22112, released on 27th June 2022.

AERMOD is accepted for use across all ANZ regulatory authorities, with Victoria listing AERMOD as its regulatory model which must be used unless otherwise justified to Victorian EPA.

2.3. Puff model development

While the development and use of Gaussian plume models has been constant since their early use in the 1940’s, well understood limitations to these models which are led to the development of alternative models for applications where the Gaussian plume models do not perform well. The key limitations of the Gaussian Plume models which led to the development of alternative models were as follows:

 Inaccuracies in the Gaussian Plume equation at low wind speeds.

 Limitations in accuracy of predictions for long distance transport of pollutants.

 Inaccuracies when resolving complex terrain features.

 Variability of meteorology over longer distance transport situations.

In response to these known limitations, an alternative model type was proposed. The model type was known as a Puff model.

This type of model traces its history back to the late 1960’s and early 1970’s, where puff models emerged as an alternative to the traditional grid models or trajectory-based models used for the simulation of plume movement and dispersion. Puff models worked by dividing the meteorological domain into grid cells through which pollutant concentrations were tracked. This type of model introduced the concept of discrete parcels (or puffs) of pollution that are emitted from a source. These puffs then undergo dispersion and diffusion as they move through the domain. Puff modelling allowed for the temporal and spatial variability of plumes to be addressed, more accurately capturing the effects of turbulence and mixing in the atmosphere. This allowed for a more accurate estimation of plume dispersion in complex and near field environments.

In response to the known limitations to Gaussian Plume models, an alternative model development team was formed, which included representatives from US EPA and from the Atmospheric Studies Group at the California Institute of Technology (CALTECH). The model developed from this team was known as CALPUFF, which consists of a hybrid lagrangian / gaussian puff model architecture.

CALPUFF was first released in 1989 and has undergone several upgrades to incorporate additional capabilities, with eth following being key aspects of the CALPUFF model:

 Ability to handle complex terrain.

 Ability to more accurately address long-range pollutant transport.

 Ability to predict the formation of secondary pollutants.

 Ability to use spatially variable meteorological data, including stable and turbulent conditions.

The most recent version of the model as at the time of this paper preparation is the CALPUFF v7 model, with the v5 model being the most recently US EPA approved version of the model.

While the US EPA has initially provided approval for the CALPUFF model, this approval has been withdrawn as of January 2017, meaning that

CALPUFF is no longer listed as a “Preferred and Recommended Model” in the US EPA Support Center for Regulatory Atmospheric Modelling (SCRAM). It is currently listed on the SCRAM website as an “Alternative model” which can be used in regulatory applications in the US with case-bycase justification to the Reviewing Authority.

In Australia CALPUFF is generally accepted by all state based regulatory authorities and is the model of choice for situation where there are complex environments, coastal sea breeze effects or the possibility of Thermal Internal Boundary Layers (TIBL’s), and situations where low wind speeds are critical e.g., odour impact assessments.

2.4. Building and obstacle wake effect handling

In parallel to the development of the dispersion modelling types discussed above, there was also the development of methods for dealing with the effects of buildings on the dispersion behaviour of stack emissions.

Early work on empirical plume behaviour recognised that there were a range of parameters affecting the shape and behaviour of a plume as it leaves a stack. The effect of buildings on the plume behaviour was not studied until the work done by Gary Briggs in the early 1970, which culminated in the development of the Briggs building wake model in 1975. This empirical model was the first to consider the alteration of wind patterns and turbulence near buildings.

The initial work undertaken by Briggs was further expanded by several groups working to further understand the effects of building wakes on short stacks in particular. In particular, work by Hosker, Schulman and Scire and Schulman and Hanna resulted in the adoption of building wake algorithms for use in the US for near and far field situations. Algorithms were adopted by the SCREEN3 model for near field applications and the ISC model for far field applications. This work was further refined in 2000 with the introduction of the Plume Rise Model Enhancements (PRIME), which was adopted by all major US developed dispersion models (AERMOD and CALPUFF).

Recent improvements with computational capabilities have led to a change in how building wakes are calculated. While gaussian and puff dispersion models calculate wake effects using empirically derived wake algorithms (such as PRIME, Briggs algorithm), newer complex environment models (such as GRAL or ADMS) calculate wake effects directly based on the effects of buildings on airflow patterns and pollutant dispersion (using a CFD-like approach).

A key consideration when understanding building wake effects and how dispersion models calculate their effects relates to the nature of the source. Building wake algorithms (PRIME, Briggs etc) only apply to short stack emissions, and are not activated when different source types (area source, volume source, road source) are included in the model. When wake effects are directly calculated, all source types are considered, as the pollution source releases pollution into a domain wide wind flow field accounting for all relevant buildings shapes or obstacles entered into the domain.

2.5. Computational fluid dynamics

Computational Fluid Dynamics (CFD) is a computational tool developed to aid in the analysis of fluid flow behaviour. While its application to dispersion modelling is limited (due to the computational intensiveness of the program itself), it is a useful tool as it can account for factors such as plume rise, atmospheric stability, wind patterns, turbulence, and the effects of nearby structures or obstacles. These models can provide detailed information on pollutant concentrations, dispersion patterns, and the impact of different meteorological and environmental conditions on plume behaviour.

CFD models have been in use since the early 1980’s with their use and complexity growing with the increase in computing capability since its first use. The most commonly used CFD model for the analysis of air pollution dispersion is the FLUENT CFD model.

While CFD itself is computationally intensive, prohibitively costly and difficult to set up, there are several models which have been developed that use CFD-like equations to estimate the flow of air within a modelling domain. These models (GRAL and ADMS) then use this complex air flow field to predict pollutant flow and pollutant dispersion.

2.6. Alternative models (GRAL)

There have been a wide range of bespoke dispersion models developed over the last 25 years with advancements in computational power and better understanding of physical and chemical processes underpinning the transportation of pollution. Of the models used within ANZ, the model which has garnered the most attention and use is the GRAL model.

GRAL is a Lagrangian particle model released in 1999 by the Institute for Internal Combustion Engines and Thermodynamics, Technical University Graz, Austria. It was initially developed to enable the assessment of pollution dispersion from roadways and tunnel portals. This model also included a meteorological pre-processor known as GRAMM which provided initial meteorology to the GRAL