Which atmospheric dispersion model is known as a similarity model?

The Britter-McQuaid model is known as a similarity model because it applies the principle of similarity in atmospheric dispersion. This model seeks to simplify the description of how pollutants disperse in the atmosphere by considering the influence of various atmospheric conditions, such as wind speed, stability class, and source strength. By relating the dispersion characteristics under different conditions to those of a well-studied scenario, the Britter-McQuaid model can effectively predict how pollutants will travel and spread in the atmosphere based on their similarity to previous cases.

The concept of similarity in dispersion modeling implies that the flow and dispersion behavior can be expressed as functions of certain dimensionless parameters, allowing for more generalized predictions applicable across various conditions. This makes the model particularly useful in practical applications where specific local measurements may not be available.

The other models mentioned do not fundamentally rely on the similarity principle in the same manner. For example, the Pasquill-Gifford model primarily categorizes atmospheric stability and uses lookup tables based on empirical data, while the Gaussian Dispersion model utilizes a mathematical representation of dispersion that assumes Gaussian distribution without harnessing the full principle of similarity that characterizes the Britter-McQuaid model. Meanwhile, the Computational Fluid Dynamics model involves complex simulations of fluid