Plume Dispersion Models


Lecture 19

October 18, 2023

Review and Questions

Economic Dispatch

  • Second power systems LP
  • Incorporating ramping and minimum power constraints can lead to a deviation from simple “merit order” dispatch.

Questions

Poll Everywhere QR Code

Text: VSRIKRISH to 22333

URL: https://pollev.com/vsrikrish

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Plume Dispersion

Criteria Air Pollutants

  • Pollutants which have an ambient air quality standard.
  • Most common examples:
    • PM2.5, PM10
    • O3
    • CO
    • SO2
    • NO2

Impact of the Clean Air Act

Decoupling of GDP and Air Pollution

Some Approaches For Modeling Air Pollution

Last Class: Box (or “airshed”) model of air pollution

  • Looks at overall mass-balance.
  • Total inputs/outputs in a particular boundary.

Today: Point sources and receptors (plume/puff models)

Point Sources/Receptors

Concerned about dispersal from a point source:

Point Source/Receptor Schematic

Point Sources/Receptors

At a given instant, flow from source to receptor follows a path:

Point Source Flow Traces

Point Sources/Receptors

Averaging those traces over time gives us a “plume”:

Point Source Average Plume

Point Sources/Receptors

What is the distribution of pollutant above the receptor?

Point Source Average Plume

“Gaussian Plume”

We can reason that the concentration follows a “bell curve”:

Gaussian Plume

“Gaussian Plume”

We can reason that the concentration follows a “bell curve”:

Gaussian Plume Distribution

“Gaussian Plume”

Not illustrated, but this reasoning also applies cross-wind (\(y\)).

Gaussian Plume Distribution

Gaussian Plume Model

\[\begin{equation} C(x,y,z) = \frac{Q}{2\pi u \sigma_y \sigma_z} \exp\left[-\frac{1}{2} \left(\frac{y^2}{\sigma_y^2} + \frac{(z - H)^2}{\sigma_z^2}\right)\right] \end{equation}\]

Variable Meaning
\(C\) Concentration (g/m\(^3\))
\(Q\) Emissions Rate (g/s)
\(H\) Effective Source Height (m)
\(u\) Wind Speed (m/s)

Gaussian Plume Derivation

Use the advective-diffusion equation to describe the mass-balance of a small air parcel :

\[ \frac{\partial C}{\partial t} + \color{blue}\overbrace{[D + K] \nabla^2 C}^\text{diffusion} \color{black} - \color{red}\overbrace{\overrightarrow{u} \cdot \overrightarrow{\nabla} C}^\text{advection}\]

  • \(D\) is the diffusion coefficient
  • \(K\) is the dispersion coefficient (turbulent mixing)

Diffusive Flux

Diffusive Flux

Concentration gradient + Diffusion ⇒ Flux

Fick’s law: mass transfer by diffusion

\[F_x = D \frac{dC}{dx}\]

Turbulent Flux

Turbulent Flux

Concentration gradient + Turbulent mixing ⇒ Flux

\[F_x = K_{xx}\frac{dC}{dx}\]

The dispersion coefficient \(K_{xx}\) depends on flow/eddy characteristics.

Gaussian Plume Model Derivation

\[\frac{\partial C}{\partial t} + \vec{u} \cdot \vec{\nabla} C - [D + K] \nabla^2 C = 0\]

Assumptions:

  • Steady-state:

\[ \frac{\partial C}{\partial t} = 0\]

Gaussian Plume Model Derivation

\[\frac{\partial C}{\partial t} + \vec{u} \cdot \vec{\nabla} C - [D + K] \nabla^2 C = 0\]

Assumptions:

  • Wind only in \(x\)-direction:

\[\vec{u} \cdot \vec{\nabla} C = u_x \frac{\partial C}{\partial x} + \cancel{u_y \frac{\partial C}{\partial y}} + \cancel{u_z \frac{\partial C}{\partial z}}\]

Gaussian Plume Model Derivation

\[\frac{\partial C}{\partial t} + \vec{u} \cdot \vec{\nabla} C - [D + K] \nabla^2 C = 0\]

Assumptions:

  • Turbulence \(\gg\) diffusion, e.g. \(K \gg D\), and \(K\) is unimportant along \(x\)-direction:

\[-[\cancel{D} + K] \nabla^2 C = \cancel{K_{xx}} \frac{\partial^2 C}{\partial x^2} + K_{yy} \frac{\partial^2 C}{\partial y^2} + K_{zz} \frac{\partial^2 C}{\partial z^2}\]

Gaussian Plume Model Derivation

With these assumptions, the equation simplifies to:

\[u \frac{\partial C}{\partial x} = K_{yy} \frac{\partial^2 C}{\partial y^2} + K_{zz}\frac{\partial^2 C}{\partial z^2}\]

Assume mass flow through vertical plane downwind must equal emissions rate \(Q\):

\[Q = \iint u C dy dz\]

Boundary Condition for A-D Equation

Gaussian Plume Model Derivation

Solving this PDE:

\[C(x,y,z) = \frac{Q}{4\pi x \sqrt{K_{yy} + K_{zz}}} \exp\left[-\frac{u}{4x}\left(\frac{y^2}{K_{yy}} + \frac{(z-H)^2}{K_{zz}}\right)\right]\]

Now substitute

\[\begin{aligned} \sigma_y^2 &= 2 K_{yy} t = 2 K_{yy} \frac{x}{u} \\ \sigma_z^2 &= 2 K_{zz} \frac{x}{u} \end{aligned}\]

Gaussian Plume Model Derivation

This results in:

\[ C(x,y,z) = \frac{Q}{2\pi u \sigma_y \sigma_z} \exp\left[-\frac{1}{2}\left(\frac{y^2}{\sigma_y^2} + \frac{(z-H)^2}{\sigma_z^2}\right) \right]\]

which looks like a Gaussian distribution probability distribution if we restrict to \(y\) or \(z\).

Gaussian Plume Model with Reflection

Last piece: the ground dampens vertical dispersion.

Reflected Mass Off Ground

Gaussian Plume Model with Reflection

We can account for this extra term using a flipped “image” of the source.

Reflected Mass Off Ground

Final Model: Elevated Source with Reflection

\[\begin{aligned} C(x,y,z) = &\frac{Q}{2\pi u \sigma_y \sigma_z} \exp\left(\frac{-y^2}{2\sigma_y^2} \right) \times \\\\ & \quad \left[\exp\left(\frac{-(z-H)^2}{2\sigma_z^2}\right) + \exp\left(\frac{-(z+H)^2}{2\sigma_z^2}\right) \right] \end{aligned}\]

Final Model Assumptions

Assumptions:

  1. Steady-State
  2. Constant wind velocity and direction
  3. Wind >> dispersion in \(x\)-direction
  4. No reactions
  5. Smooth ground (avoids turbulent eddies and other reflections)

Dispersion and Atmospheric Stability

Estimating Dispersion “Spread”

Values of \(\sigma_y\) and \(\sigma_z\) matter substantially for modeling plume spread downwind. What influences them?

Estimating Dispersion “Spread”

Main contribution: atmospheric stability

  • Greater stability ⇒ less vertical/cross-wind dispersion.

  • Pasquill (1961): Six stability classes.

Estimating Dispersion “Spread”

Contributors to atmospheric stability:

  • Temperature gradient
  • Wind speed
  • Solar radiation
  • Cloud cover
  • Richardson number (buoyancy / flow shear)

Atmospheric Stability Classes

Class Stability Description
A Extremely unstable Sunny summer day
B Moderately unstable Sunny & warm
C Slightly unstable Partly cloudy day
D Neutral Cloudy day or night
E Slightly stable Partly cloudy night
F Moderately stable Clear night

Estimating Dispersion “Spread”

\[\begin{aligned} \sigma_y &= ax^{0.894} \\ \sigma_z &= cx^d + f \end{aligned}\]

Dispersion Coefficients

Note: here \(x\) is in km, while in the plume equation \(y\), \(z\) are in m!

Estimating Dispersion “Spread”

Then take estimates for \(\sigma_y\) and \(\sigma_z\) and plug into plume dispersion equation

\[\begin{aligned} C(x,y,z) = &\frac{Q}{2\pi u {\color{red}\sigma_y \sigma_z}} \exp\left(\frac{-y^2}{2{\color{red}\sigma_y^2}} \right) \times \\\\ & \quad \left[\exp\left(\frac{-(z-H)^2}{2{\color{red}\sigma_z^2}}\right) + \exp\left(\frac{-(z+H)^2}{2{\color{red}\sigma_z^2}}\right) \right] \end{aligned}\]

Key Takeaways

Key Takeaways

  • Plume models are commonly used for point sources and point receptors.
  • Gaussian plumes: continuous emissions from an elevated source.
  • F&T determined by advection and diffusion/turbulence.
  • However, number of critical assumptions.

Upcoming Schedule

Next Classes

Friday: Managing multiple point sources of air pollution

Next Week: Mixed-Integer Linear Programming and Applications