Have you ever looked at a stack and asked yourself the magnitude of the plume’s impact? Or you’re somewhere in traffic and wondered what the impact of all the vehicles there was and how far the impact was felt.
Air pollution modelling is a phrase that describes the use of mathematical theory to comprehend or predict the manner in which pollutants behave in the atmosphere. Modelling can be utilized to run various scenarios, test theories and understand environmental impact under various emission rates and development scenarios. There are other techniques, however, the objective is similar; come up with an assessment of the effects of a pollutant over a particular area. To achieve this, assumptions have to be made, and some data is added.
One assumption is that the atmosphere behaves in a predictable manner and plumes also behave in a recognized way. Rules used include:
Firstly, you can evaluate a hypothetical situation before it happens. For instance, an industrial operation wants to build a new facility. They might specify a stack of several meters high, emitting pollutants from a known process. A model can, therefore, assist to quantify the emissions.
These emissions can be fed into a particular model that can predict the emissions. The advantage of this is that the problem will be resolved before it even occurs.
Furthermore, a model can also be utilized to predict other situations. For example, the facility might opt to install more efficient scrubbers.
One major thing to note is that a model is as good as the data that it is fed, and models don't always manifest reality with absolute accuracy. Models depend on accurate input. Meteorological data needs to be treated with absolute care as wind speed plays a crucial role in the dispersion. There have been cases of models giving out terribly inaccurate results because of incorrect data that was used in setting up. This can be disastrous as models are used in the planning stages. This was recently the case, according to, Noosphere Ventures co-founder Max Polyakov.
There exist various types of air quality models, all utilized for different purposes. The most common ones are the ADM (Atmospheric Dispersion Models). They make use of mathematical assumptions on the manner in which the atmosphere behaves. These models are often utilized to model emissions from a set point, for instance, the stack of an industrial facility.
The most common ADM is the CALPUFF. It uses 3 inputs; a meteorological model, a post-processing package, and a dispersion model. The CALPUFF model is a very complex and powerful one when utilized correctly. It can be used at scales of up to several hundred kilometres. It can handle complex atmospheric chemistry processes and terrains.
Another common ADM is the AERMOD. It assumes a steady state, that is, constant emissions and environmental factors while the CALPUFF is non-steady and can be adjusted for changes in environmental conditions.
Land Use Regression (LUR) models are also some of the air models being used today, though these ones are different from ADMs.
Comprehending your model and its weaknesses are essential. Test your model's accuracy by gathering more data or deploying instruments and comparing the results.
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