Puff and plume models

Puff models

Quasi-instantaneous and short-term releases are frequently viewed as "puff" releases. A puff release scenario assumes that the release time and sampling times are very short compared to the travel time from the source to the receptor. In light of the limited data available to estimate the diffusion coefficients for puff diffusion, a number of models use the Pasquill-Gifford values. Since these coefficients were developed specifically for plumes, their use in puff models is questionable. In addition, most puff models assume a normal, or Gaussian concentration distribution within the plume. This assumption overlooks the in-puff fluctuations. Others, such as Högström11 have conducted field experiments to determine estimates of diffusion coefficients to support his theory of puff spread.

Plume models

Continuous releases are generally modeled as a plume. The assumption here is that the release time is much greater that the time of travel from the source to the receptor. There are a number of approaches to modeling plumes, each with its own focus, assumptions, and limitations. These approaches can be categorized as (Hanna [12], Barr and Clements [13]):

Gaussian plume models

Meandering plume models

Probability distribution function (pdf) models

K models

Statistical models

Similarity models

Second order closure and eddy simulation models.

Within these categories are a number of different models. Not all models using these approaches are at a developmental stage where they can be practicably applied, however. Some, like the statistical and second-order closure approaches, are too computer-intensive for routine use. Many of the other model types have little in the way of the field validation needed for application to real-world situations with confidence. Of the list, the most frequently used model types for odor modeling are the Gaussian model and the fluctuating plume-puff model.

Fluctuating plume-puff model

The fluctuating plume-puff model, first developed by Gifford (14), is a hybrid model which simulates the emissions from a source as a series of continuously emitting puffs. The model assumes that the dispersion is divided into two separate parts, one due to the instantaneous spreading of the Gaussian plume in the crosswind and vertical directions, and another due to the meandering, or fluctuation of the entire plume around its mean position.

Gifford visualized the fluctuating plume as an infinite series of overlapping disks. The model tries to follow the path of the puffs (or disks) of contaminant under the influence of varying wind fields and stability conditions and attempts to predict the peak concentration as a discrete puff passes a given receptor.

One problem with this type of model is that few data are available to help in the determination of the diffusion coefficients, [sigma]y and [sigma]z, needed to estimate the spread of the disks. Högström (15) developed a form of the fluctuating plume-puff model and also performed field experiments to determine the values of the diffusion coefficients. As with most of these types of models, it was assumed the concentration distribution within the instantaneous plume relative to the centerline to be constant and Gaussian; that is, fluctuations within the instantaneous plume are not considered.

The Gaussian model of diffusion is the most widely used model for plume dispersion. Its most attractive feature is that it fits what we see and experience in the real world for a range of conditions. In addition, the mathematics of the model are fairly straightforward. On the other hand, Gaussian models need significant empirical input to be used for practicable dispersion estimates, making the model results highly dependent on the conditions of the sampling used to derive the empirical values.

The basic assumptions of the Gaussian model are:

Conservation of mass

Continuous emissions

Steady-state conditions

Lateral and vertical concentration profiles follow normal distribution.

http://www.nywea.org/clearwaters/pre02fall/302142.html

Involving