Lecture 19
October 18, 2023
Text: VSRIKRISH to 22333
Decoupling of GDP and Air Pollution
Source: Resources for the Future
Last Class: Box (or “airshed”) model of air pollution
Today: Point sources and receptors (plume/puff models)
Concerned about dispersal from a point source:
Point Source/Receptor Schematic
At a given instant, flow from source to receptor follows a path:
Point Source Flow Traces
Averaging those traces over time gives us a “plume”:
Point Source Average Plume
What is the distribution of pollutant above the receptor?
Point Source Average Plume
We can reason that the concentration follows a “bell curve”:
Gaussian Plume
We can reason that the concentration follows a “bell curve”:
Gaussian Plume Distribution
Not illustrated, but this reasoning also applies cross-wind (\(y\)).
Gaussian Plume Distribution
\[\begin{equation} C(x,y,z) = \frac{Q}{2\pi u \sigma_y \sigma_z} \exp\left[-\frac{1}{2} \left(\frac{y^2}{\sigma_y^2} + \frac{(z - H)^2}{\sigma_z^2}\right)\right] \end{equation}\]
Variable | Meaning |
---|---|
\(C\) | Concentration (g/m\(^3\)) |
\(Q\) | Emissions Rate (g/s) |
\(H\) | Effective Source Height (m) |
\(u\) | Wind Speed (m/s) |
Use the advective-diffusion equation to describe the mass-balance of a small air parcel :
\[ \frac{\partial C}{\partial t} + \color{blue}\overbrace{[D + K] \nabla^2 C}^\text{diffusion} \color{black} - \color{red}\overbrace{\overrightarrow{u} \cdot \overrightarrow{\nabla} C}^\text{advection}\]
Concentration gradient + Diffusion ⇒ Flux
Fick’s law: mass transfer by diffusion
\[F_x = D \frac{dC}{dx}\]
Concentration gradient + Turbulent mixing ⇒ Flux
\[F_x = K_{xx}\frac{dC}{dx}\]
The dispersion coefficient \(K_{xx}\) depends on flow/eddy characteristics.
\[\frac{\partial C}{\partial t} + \vec{u} \cdot \vec{\nabla} C - [D + K] \nabla^2 C = 0\]
Assumptions:
\[ \frac{\partial C}{\partial t} = 0\]
\[\frac{\partial C}{\partial t} + \vec{u} \cdot \vec{\nabla} C - [D + K] \nabla^2 C = 0\]
Assumptions:
\[\vec{u} \cdot \vec{\nabla} C = u_x \frac{\partial C}{\partial x} + \cancel{u_y \frac{\partial C}{\partial y}} + \cancel{u_z \frac{\partial C}{\partial z}}\]
\[\frac{\partial C}{\partial t} + \vec{u} \cdot \vec{\nabla} C - [D + K] \nabla^2 C = 0\]
Assumptions:
\[-[\cancel{D} + K] \nabla^2 C = \cancel{K_{xx}} \frac{\partial^2 C}{\partial x^2} + K_{yy} \frac{\partial^2 C}{\partial y^2} + K_{zz} \frac{\partial^2 C}{\partial z^2}\]
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 \(Q\):
\[Q = \iint u C dy dz\]
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
\[\begin{aligned} \sigma_y^2 &= 2 K_{yy} t = 2 K_{yy} \frac{x}{u} \\ \sigma_z^2 &= 2 K_{zz} \frac{x}{u} \end{aligned}\]
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 \(z\).
Last piece: the ground dampens vertical dispersion.
Reflected Mass Off Ground
We can account for this extra term using a flipped “image” of the source.
\[\begin{aligned} C(x,y,z) = &\frac{Q}{2\pi u \sigma_y \sigma_z} \exp\left(\frac{-y^2}{2\sigma_y^2} \right) \times \\\\ & \quad \left[\exp\left(\frac{-(z-H)^2}{2\sigma_z^2}\right) + \exp\left(\frac{-(z+H)^2}{2\sigma_z^2}\right) \right] \end{aligned}\]
Assumptions:
Values of \(\sigma_y\) and \(\sigma_z\) matter substantially for modeling plume spread downwind. What influences them?
Main contribution: atmospheric stability
Greater stability ⇒ less vertical/cross-wind dispersion.
Pasquill (1961): Six stability classes.
Contributors to atmospheric stability:
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 |
\[\begin{aligned} \sigma_y &= ax^{0.894} \\ \sigma_z &= cx^d + f \end{aligned}\]
Dispersion Coefficients
Note: here \(x\) is in km, while in the plume equation \(y\), \(z\) are in m!
Then take estimates for \(\sigma_y\) and \(\sigma_z\) and plug into plume dispersion equation
\[\begin{aligned} C(x,y,z) = &\frac{Q}{2\pi u {\color{red}\sigma_y \sigma_z}} \exp\left(\frac{-y^2}{2{\color{red}\sigma_y^2}} \right) \times \\\\ & \quad \left[\exp\left(\frac{-(z-H)^2}{2{\color{red}\sigma_z^2}}\right) + \exp\left(\frac{-(z+H)^2}{2{\color{red}\sigma_z^2}}\right) \right] \end{aligned}\]
Friday: Managing multiple point sources of air pollution
Next Week: Mixed-Integer Linear Programming and Applications