EVOLUTION AND FUTURE OF DISPERSION MODELS IN AUSTRALIA AND NEW ZEALAND

CFD-like meteorological model which produced a 3dimensional grid of meteorology for use in dispersion modelling. The GRAL meteorology is then utilised by the particle model to make predictions of air pollution dispersion. The GRAL meteorological model enables a much higher resolution for buildings and obstacles (such as acoustic walls) allowing a much finer resolution for dispersion models, particularly when considering complex source or built environment configurations around a source or in areas with complex urban areas. Subsequent development of the GRAL model has included the ability to consider stack and area sources along with the original model’s ability to consider roads and portal sources.

There have been several updates to the GRAL model since its inception in 1999, with the latest update coming in November 2022.

Advantages to the GRAL model include:

 Ability to resolve fine scale buildings and obstructions

 Ability to consider fine-scale terrain

 Ability to consider several observational meteorological station when developing domain meteorology.

3. Factors influencing dispersion model development

Based on the brief historical discussion above, a list of qualitative factors affecting the development of dispersion models have been identified, which outlined below (in no particular order):

 Computer processing ability (increasing ability to process data / run models)

 Incorporation of AI / machine learning to improve speed and accuracy of dispersion models

 Increased resolution of results accuracy, e.g.: ability to investigate specific met conditions or times of day, use of GIS tools to better display results.

 Better understanding of meteorological processes and their implication to modelling results

 Better understanding of pollution dispersion science

 Better understanding of atmospheric chemistry

 Better ability to consider complex environments (buildings / walls etc)

 Better parameterisation of sources (e.g., buoyant areas, roads, portals)

 Better understanding of, and concern for, AQ health risks e.g., interest in finer particles

 Higher prevalence of brownfield site developments needing more complex assessments

 Increased urbanisation and increased complexity

 Need for regional scale modelling

 Higher backgrounds and lower criteria requiring more accurate assessments (less tolerant of overprediction / conservatism)

 Need to resolve / eliminate model limitations e.g., coastal / complex env, low wind speeds etc

 Interest in chemistry e.g., ozone, new particle formation

 Different types of sources becoming important (early modelling was industrial stacks, now looking at odour from ponds, dust from wind erosion, vehicles on roads, in tunnels etc.)

 Model improvement for its own sake (driven by academic research)

Given the large number of factors identified that lead to changes in or development of dispersion models, the factors identified above were examined from the perspective of broad drivers which resulted in the identified factors being placed into different quadrants. The broad drivers for model development included the following:

 Science driver vs Regulatory driver; and

 A desire for increased accuracy vs a broadening of scope for AQ studies.

Figure 2 shows a map of the above factors assigned to different sectors according to the drivers outlined above. It should be noted that this exercise is quite subjective with some of the factors spanning different quadrants or being placed differently depending on the perspective of the person assessing where they are best placed.

The results of the subjective mapping exercise in Figure 2 provide an indication of the distribution of the drivers showed the following:

 Most factors were identified as factors aiming to achieving improvements in accuracy.

 There were more factors in the Science Driver quadrant, although the number of factors in the regulatory driver quadrant was only slightly smaller.

 There were only a small number of drivers aimed at achieving a borader understanding of air quality assessment capability.

This suggests that the primary driver for the development of new models is a science driven process with an objective of increasing the accuracy of the findings. While the primary driver is based on the improvement of science, regulatory drivers are still shown to influence to influence model development. of the findings. While the primary driver is based on the improvement of science, regulatory drivers are still shown to influence to influence model development.

The two pathways for model development (Regulatory driven and Precedent driven) pathways have been described in Figure 3, and show the difference pathways followed to model acceptance and some of the linkages between the different pathways.

Historical drivers for change should also provide context for how model development is influenced. In the 1960’s and 1970’s, there were strong drivers for scientific change in the dispersion modelling field due to a regulatory desire for better tools to be developed for the assessment of air quality. These changes were driven through the involvement of bodies such as the US EPA, who have spearheaded all major US dispersion model development periods, from the development of ISC to AERMOD and the initial development of CALPUFF. Over the last 10-15 years however, this regulatory driven impetus to develop or improve models has been shifting from regulatory driven activity to science driven activity for the enhancement of dispersion modelling tools and methods. The role of regulators has changed how models are developed. Model advances driven by improvements in science require justification through comparison with existing “trusted” models. Over time this establishes a precedent whereby acceptance by regulators comes through the use models in a range of applications before the use of a model is more broadly accepted.

The two pathways for model development (Regulatory driven and Precedent driven) pathways have been described in Figure 3, and show the difference pathways followed to model acceptance and some of the linkages between the different pathways.

4. Future direction of dispersion models

Given that most of the identified model development factors seek a higher level of accuracy in new or improved models, it is clear future model development will follow this trend with models needing a higher degree of accuracy and the ability to process data quickly. The trend of precedent driven models is expected to continue with new models needing a significant period of time to gain acceptance in the modelling industry and be acceptable for use by regulators for impact assessments.

Historical drivers for change should also provide context for how model development is influenced. In the 1960’s and 1970’s, there were strong drivers for scientific change in the dispersion modelling field due to a regulatory desire for better tools to be developed for the assessment of air quality. These changes were driven through the involvement of bodies such as the US EPA, who have spearheaded all major US dispersion model development periods, from the development of ISC to AERMOD and the initial development of CALPUFF. Over the last 10-15 years however, this regulatory driven impetus to develop or improve models has been shifting from regulatory driven activity to science driven activity for the enhancement of dispersion modelling tools and methods. The role of regulators has changed how models are developed. Model advances driven by improvements in science require justification through comparison with existing “trusted” models. Over time this establishes a precedent whereby acceptance by regulators comes through the use models in a range of applications before the use of a model is more broadly accepted.

The two pathways for model development (Regulatory driven and Precedent driven) pathways have been described in Figure 3, and show the difference pathways followed to model acceptance and some of the linkages between the different pathways.

Given an increased reluctance in ANZ for involvement by regulatory authorities in the development of new models, it is considered unlikely that new model development would be driven by regulatory authorities. Regulatory authorities are more likely currently to rely on either the development of models by larger international regulatory authorities (such as US EPA) or through an informal precedent based approval process as discussed above and shown in Figure 3

4. Future direction of dispersion models

Given that most of the identified model development factors seek a higher level of accuracy in new or improved models, it is clear future model development will follow this trend with models needing a higher degree of accuracy and the ability to process data quickly. The trend of precedent driven models is expected to continue with new

While the use of common models such as AERMOD and CALPUFF are likely to remain in use in the short to medium term, increased complexity in the assessment requirements is expected to require either an update to the capability to these models or their replacement with alternative model(s) better suited to the modern regulatory assessment requirements. In the longer term, models able to consider fine scale changes in meteorology and pollution dispersion are expected to be the predominant tools for use in the modelling environment.

4. Future direction of dispersion models

Given that most of the identified model development factors seek a higher level of accuracy in new or improved models, it is clear future model development will follow this trend with models needing a higher degree of accuracy and the ability to process data quickly. The trend of precedent driven models is expected to continue with new models needing a significant period of time to gain acceptance in the modelling industry and be acceptable for use by regulators for impact assessments.

Given an increased reluctance in ANZ for involvement by regulatory authorities in the development of new models, it is considered unlikely that new model development would be driven by regulatory authorities. Regulatory authorities are more likely currently to rely on either the development of models by larger international regulatory authorities (such as US EPA) or through an informal precedent based approval process as discussed above and shown in Figure 3.

While the use of common models such as AERMOD and CALPUFF are likely to remain in use in the short to medium term, increased complexity in the assessment requirements is expected to require either an update to the capability to these models or their replacement with alternative model(s) better suited to the modern regulatory assessment requirements. In the longer term, models able to consider fine scale changes in meteorology and pollution dispersion are expected to be the predominant tools for use in the modelling environment.