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Types of Models

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Marti Blad PhD PE

- Dispersion Models: Estimate pollutants at ground level receptors
- Photochemical Models: Estimate regional air quality, predicts chemical reactions
- Receptor Models: Estimate contribution of multiple sources to receptor location based on multiple measurements at receptor
- Screening Models: applied 1st , determines if further modeling needed
- Refined Models: req’d for SIP, NSR, and PSD
- Regulatory requirement for permits

- Numerical algorithms
- Sets of equations need inputs
- Describe = quantify movement
- Simplified representation of complex system
- Box or Mass Balance

- Used to study & understand the complex
- Physical, chemical, and spatial, interactions

- Gaussian Plume
- Analytical approximation of dispersion
- more later

- Statistical & Stochastic
- Based on probability
- Recall regression is linear model

- Empirical
- Based on experimental or field data
- Actual numbers

- Physical (scale models)
- Flow visualization in wind tunnels, etc.

- Plume dispersion in lateral & horizontal planes characterized by a Gaussian distribution
- Normal Distribution
- Mu is median
- Sigma is spread

- Pollutant concentrations are calculated estimations at receptor
- Uncertainty of input data values
- Data quality, completeness

- Steady state assumption
- No change in source emissions over time

- Screen3 will be end of the week

z

¤

Dh = plume rise

h = stack height

Dh

H = effective stack

height

H = h + Dh

H

h

x

C(x,y,z) Downwind at (x,y,z) ?

y

- This assumption allows us to calculate concentrations downwind of source using this equation

where

c(x,y,z) = contaminant concentration at the specified coordinate [ML-3], x = downwind distance [L], y = crosswind distance [L], z = vertical distance above ground [L], Q = contaminant emission rate [MT-1], sy = lateral dispersion coefficient function [L], sz = vertical dispersion coefficient function [L], u = wind velocity in downwind direction [L T-1], H = effective stack height [L].

- Predicted concentration map

- The shape of the curve = Bell shaped = Gaussian curve hence the model is called by that name.

- Gaussian = “normal” curve math
- Recall previous distribution picture
- Dispersion & diffusion dominates

- Eulerian
- Assumes uniform concentrations in box
- Assumes rapid vertical and horizontal mixing
- Plume in a grid
- Predicts species concentrations
- Multi day scenarios

AKA Plume in Grid

Figure from http://irina.colorado.edu/lectures/Lec29.htm

Variable is Time: t

Variable is Time and height: t, y

Variable is Time, height and length distance:

t, x, y

t, x, y, z

Depth of boxes discussed under meteorology

- “Puffs” of pollutants
- Trajectory models

- Follow the particle

Puff

W2

W1

S.S. Plume

From “INTERNATIONAL AIR QUALITY ADVISORY BOARD 1997-1999 PRIORITIES REPORT, the HYSPLIT Model” (http://www.ijc.org/boards/iaqab/pr9799/project.html)

- Physical conditions: Topography
- Locations: buildings, source, community, receptor
- Appropriate for the averaging time period

- Statistics & math
- Meteorology
- Stack or source emission data
- Pollutant emission data
- Plume rise, Stack or source specific data
- Location of source and receptors

CALPUFF

AERMOD