Ambient air quality modelling, in conjunction with monitoring, plays an important role in assessing existing and potential risks to air quality, particularly as part of an initial assessment of new developments.
Air pollution models are the only method that quantifies the deterministic relationship between emissions and concentrations/depositions, including the consequences of past and future scenarios and the determination of the effectiveness of abatement strategies.
This makes air pollution models indispensable in regulatory, research, and forensic applications. The Gaussian Plume Model was developed for modelling point sources and is used in calculating the maximum ground level impact of plumes and the distance of maximum impact from the source.
Air pollution modelling at urban or larger scales are done using Lagrangian modeling and Eulerian modelling.
The first step in undertaking air quality modelling is to clearly define the objectives and expected outcomes. This can be done by addressing questions such as:
What is the reason for the air quality modelling?
What questions need to be answered by modelling work?
What pollutants or environmental indicators need to be modelled in order to provide the information required?
What data and information are already available and how can these help?
What considerations need to be made about background concentrations of pollutants?
What type of pollutant source/s need to be modelled?
What are the geographical features near the pollutant source/s?
How the modelled data is best utilized and reported to describe the issues under investigation?
Air Quality Modelling: Dispersion Modelling
When a pollutant is emitted into the air, it is transported and diluted by the atmosphere and may be transformed or removed before it reaches a receptor (site). It is often assumed that air quality is determined only by how much is emitted into the air.
While the amounts emitted into the air are very important to monitor, ambient concentrations are also a function of meteorology, topography, time, and the distance between sources and receptors. Because of this, the ambient concentrations are not related in a simple way to the emission amount.
Dispersion models take these influencing factors into account to predict ambient concentrations at specific sites. An air quality dispersion model is a system of science-based equations that mathematically describes how pollutants are dispersed and transformed in the atmosphere.
Concentrations of pollutants at specific receptors are estimated by placing sources (from an emission inventory) into a dispersion model which takes into account the interactions between sources, meteorology, and topography as the pollutants are transported and diluted by wind.
Dispersion models can help to provide a cause-effect link between emissions into the air and the resulting ambient concentrations. For example, large reductions in emissions from a stack located on a hill above a community may have a very small effect on the community‘s air quality since the plume is so high it seldom reaches ground level, where the emissions can be breathed in.
However, the air quality in a community downwind may be improved considerably as the emissions from the stack may have the greatest impact on that community‘s air quality. Dispersion models can help to determine the contribution of each source to ambient concentrations in an air shed.
Types of Dispersion Models
Different types of dispersion models can be used to assess the impact of pollutant sources on air quality, depending on the information required and the data available.
A Screening model can be used to provide a quick calculation of a worst case concentration that could occur from a source under different emissions and meteorological conditions.
Through screening, further modelling needs can be determined. Screening models are simple and quick to run because they require few inputs, since they use a built-in set of meteorological conditions. Example of screening model is SCREEN3.
A Refined model is more scientifically sound than a screening model and requires more input data and expertise to run.
These models require hourly meteorological data over a period of time (e.g., a year) and from the region of interest in order to make predictions that are site specific and more detailed as compared to a screening model.
The output consists of predicted concentrations for a given pollutant, for time averages from 1 hour to annually at specified receptor locations. The model output provides a rich dataset to understand the air quality impacts of meteorology on source emissions. An example of a refined model is AERMOD.
An Advanced model includes comprehensive treatments of the physics and chemistry of emissions in the atmosphere and thus requires considerable expertise and computer resources to set up, run, and interpret the results.
Advanced models are typically used to assess air quality impacts from large areas (such as cities) and over broad emission sectors for a selected time period (a few days is typical, but longer periods of time can also be modelled). CALPUFF is an example of an advanced modelling system.
The types of input data required can be categorized into the following types:
Emissions: Information on the type of pollutant and source characteristics are required, including the source type (point source such as a stack, an area source such as a sewage lagoon, a line source such as a highway), emission rates, and exit conditions (temperature, flow rates), and physical release characteristics, such as elevation and diameter.
Atmospheric Conditions: A dispersion model requires a description of the atmosphere, since the transport and mixing of the contaminant depends on atmospheric conditions.
Wind speed and direction as well as temperature and sometimes other data, such as clouds, precipitation, humidity and atmospheric stability, may be required.
Geophysical Description: The underlying topography and land characteristics must be specified.
Model Options/Switches: A model may have different ways in which the physics and chemistry are treated. The selection of a particular treatment is controlled by specifying options in the model; the correct selection of options can be important for realistic results.
Strengths of Dispersion Models
Dispersion models are widely accepted and utilized as an integral part of air management programs.
Dispersion modelling can help to assess the contribution of sources to ambient air quality levels by directly attributing sources and their contributions to ambient concentrations that are a consequence of emissions, meteorology, and location.
This can help identify high exposure scenarios (i.e., high exposure sites, meteorological conditions that favor high pollutant concentrations) as well as evaluate the effectiveness of scenarios for air quality-related interventions.
Limitations of Dispersion Models
Despite the value they provide, dispersion models may not be appropriate to use in all air quality assessments. Dispersion models can be quite complex and can require a large amount of input data.
If data are unavailable, incomplete or of poor quality, the usefulness of dispersion model results are limited.
Some of the more complicated models have large input data requirements, making them more difficult to run, restricting their use to circumstances where the input data are available.
Additionally, since dispersion models provide predictions, they may overestimate or underestimate the contribution of particular sources or pollutants to air quality, and must be validated by comparing the predictions with actual measurements.
Finally, dispersion modelling only predicts outdoor ambient concentrations and does not offer a way to account for the activity of individuals, which will affect their personal exposures (e.g., time spent in different locations, movement into buildings).
In summary, air quality modelling allows the estimation of the effect of a change in one or more sources that emit a pollutant of concern on ambient air quality.
This can be useful in predicting the impact of and potential risks of pollution. Models are thus indispensable regulatory tools.
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