Statistics of size distributions
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Statistics of Size distributions. Histogram. Continuous dist. Discrete distribution. Mean Diameter Standard Deviation Geometric Mean n th moment. The “moments” will come in when you do area, volume distributions We also define “effective areal” diameter and “effective volume” diameter.

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Statistics of size distributions
Statistics of Size distributions

Histogram

Continuous dist.

Discrete distribution

Mean Diameter

Standard Deviation

Geometric Mean

nth moment

The “moments” will come in when you do area, volume distributions

We also define “effective areal” diameter and “effective volume” diameter


Effective diameters
Effective Diameters

Consider Nt aerosol particles, each with Diameter D. This is a “monodisperse” distribution.

The Total surface area and volume will be:

Now consider the total surface area and volume of a polydisperse aerosol population

Substituting our definition for moments, we have


Effective diameters1
Effective Diameters

Substituting our definition for moments, we have

We now define an effective areal diamater, Da, and an effective volumetric diameter, Dv, which are the diameters that would produce the same surface area and volume if the distribution were monodisperse.

So…

Board Illustration: Consider a population of aerosols where 900 cm-3 are 0.1 mm, and 100 cm-3 are 1.0 mm. Compute Da, Dv.


Converting size distributions
Converting size distributions

Concentration:

Rule of thumb: Always use concentration, not number distribution, when converting from one type of size distribution to another

NOT


Converting size distributions1
Converting size distributions

Example:

Let n(D) = C = constant. What are the log-diameter and ln-diameter distributions?

Even though n(D) is constant w/ diameter, the log distributions are functions of diameter.

Problem:

Let n(D) = C = constant. What is the volumetric number distribution, dN/dv?


The power law junge size distribution
The Power-Law (Junge) Size Distribution

n(D)=C D-a

n(D)=1000 cm-3mm2 D-3

What is the total concenration, Nt?

What is volume distribution, dV/dD?

What is total volume?

What is log-number distribution?

Linear-linear plot

Major Points for Junge Distr.

Need lower+upper bound Diameters to constrain integral properties

Only accurate > 300 nm or so.

Linear in log-log space….

Log-Log plot

slope = -3


The log normal size distribution
The log-normal Size Distribution

Note that power-law is simply linear in log-log space, and was unbounded

Let’s make a distribution that is quadratic in log-log space (curvature down)


The log normal size distribution1
The Log-Normal Size Distribution

Nt=1000 cm-3, Dg = 1 mm, sg = 1.0

What is the total concenration, Nt?

What is volume distribution, dV/dD?

What is total volume?

What is log-number distribution?

Linear-linear plot

Major Points for Log-normal

3 parameters: Nt, Dg and sg

No need for upper/lower bound constraints  goes to zero both ways

Usually need multiple modes.

Log-Log plot



Medians modes moments and means from lognormal distributions
Medians, modes, moments, and means from lognormal distributions

Median – Divides population in half. i.e. median of # distribution is where half the particles are larger than that diameter. Median of area distribution means half of the area is above that size

Mode – peak in the distribution. Depends on which distribution you’re finding mode of (e.g. dN/dlogD or dN/dD). Set dn(D)/dD = 0

Secret to S+P 8.7… Get distributions in form where all dependence on D is in the form exp(-(lnD – lnDx)2). Complete the squares to find Dx. Then, the median and mode will be at Dx due to the symmetry of the distribution

The means and the moments are properties of the integral of the size distribution. In the form above, these will appear outside the exp() term. (i.e. what is leftover after completing the squares).





Vertical distributions
Vertical distributions distributions

Often aerosol comes in layers

Averaged over time, they form an exponentially decaying profile w/ scale height of ~1 to 2 km.


Particle aerodynamics s p chap 9
Particle Aerodynamics distributionsS+P Chap 9.

Need to consider two perspectives

  • Brownian diffusion – thermal motion of particle, similar to gas motions

  • Forces on the particle

    • Body forces: Gravity, electrostatic

    • Surface forces: Pressure, friction

      Relevant Scales

  • Diameter of particle vs. mean free path in the gas – Knudsen #

  • Inertial “forces” vs. viscous forces – Reynolds #


Knudsen
Knudsen # distributions

l = mean free path of air molecule

Dp = particle diameter

Gas molecule self-collision cross-section

Gas # concentration

  • Quantifies how much an aerosol particle influences its immediate environment

  • Kn Small – Particle is big, and “drags” the air nearby along with it

  • Kn Large – Particle is small, and air near particle has properties about the same as the gas far from the particle

Kn

Free Molecular

Regime

Transition

Regime

Continuum

Regime


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