Types of Models Marti Blad Northern Arizona University College of Engineering & Technology

Types of Models

Marti Blad

Northern Arizona UniversityCollege of Engineering & Technology

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Models Meteorological

Diagnostic Prognostic

Emissions Type of chemicals Rates of release Sources

Building impacts Surface

Terrain complexity Air turbulence

Viewing GUI to see pictures

Receptor Human Ecological impact

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EPA MODELS—Screening

COMPLEX1

RVD2

SHORTZ

VISCREENCTSCREEN

LONGZ

VALLEY

RTDM32

CTSCREEN

TSCREEN

SCREEN3

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EPA MODELS—Regulatory

ISC3

UAM

CTDMPLUS BLP

CALINE3

CDM2

OCD

RAM

EKMA

MPTER

CAL3QHC

CRSTER

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EPA Models—Other

CMB7

MOBILE5 DEGADIS

COMPDEP

RPM-IV

MESOPUFF

SDM

TOXST

PLUVUE2

FDM

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Models = Representations

Simplified representation of complex system

Used to study & understand the complex Numerical

Set of equations Describe = quantify

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Box Model ConceptTime= t

t, x

t, x, y

t, x, y, z

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1-D and 2-D Models

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3-Dimensional Models

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Types of Air Quality Models

Dispersion models Solves turbulent dispersion of

unreactive species based on Gaussian distributions

Chemical Tracer Models (CTMs) Lagrangian (trajectory) models Eulerian (grid) models

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Lagrangian Air Quality Models

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

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Lagrangian Model Strengths

Easy to code, run and analyze Explicit mechanisms easily modified Evaluate chemical effects

Isolate from the meteorology Facilitates evaluation of source-receptor Numerically efficient

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Eulerian Air Quality Models

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

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Eulerian Models (cont.) Plume in Grid (P in G) Simulates atmospheric chemistry

Gas phase & reactions photolysis

Transport Advection & diffusion

Deposition Particle modeling & visibility

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Eulerian Model Strengths

Contain detailed 4-D descriptions Meteorological and transport processes

Predicts species concentrations Defined geographical and temporal domain

Simulates multi-day scenarios

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What is a dispersion model?

Repetitious solution of dispersion equations Based on principles of transport, diffusion Computer-aided simulation of atmospheric

dispersion from emission Allows assessment of air quality problem in

spatial, temporal terms (i.e., space & time)

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Gaussian-Based Dispersion Models Plume dispersion in lateral &

horizontal planes characterized by a Gaussian distribution See picture next slide

Pollutant concentrations predicted are estimations

Uncertainty of input data values approximations used in the

mathematics intrinsic variability of dispersion

process

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

Gaussian Dispersion

h

hH

z

x

y

h = plume rise

h = stack height

H = effective stack heightH = h + h

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Gaussian Dispersion Concentration

( )

( )

( )

C

Qu

y

z H

z H

x y zy z y

z

z

, , exp

exp

exp

= −⎛

⎝⎜

⎞

⎠⎟

⎡

⎣⎢⎢

⎤

⎦⎥⎥

− −⎡

⎣⎢

⎤

⎦⎥

+

− +⎡

⎣⎢

⎤

⎦⎥

⎧

⎨

⎪⎪⎪

⎩

⎪⎪⎪

⎫

⎬

⎪⎪⎪

⎭

⎪⎪⎪

2 2

2

2

2

2

2

2

2

2

π σ σ σ

σ

σ

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Simple Gaussian Model Assumptions

Continuous pollutant emissions

Conservation of mass in atmosphere

Steady-state meteorological conditions

Concentration profiles represented by

Gaussian distribution – bell curve shape

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Model Considerations

Actual pattern of dispersion depends on atmospheric conditions prevailing during release

Major meteorological factors that influence dispersion of pollutants Atmospheric stability (& temperature) Mixing height Wind speed & direction

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Maximum Mixing Depth

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Review Atmospheric Effects

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Computer Model Input

Appropriate meteorological conditions Appropriate for the location Appropriate for the averaging time period

Stack or source emission data Pollutant emission data Stack or source specific data

Receptor data

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Model Considerations (cont.) Height of plume rise calculated

Momentum and buoyancy Can significantly alter dispersion & location

of downwind maximum ground-level concentration

Effects of nearby buildings estimated Downwash wake effects Can significantly alter dispersion & location

of downwind max. ground-level concentration

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Computer Model Input (cont.) Plume data

Source type Velocity of release Temperature of release

BPIP recommended Models downwash Multiple stacks and buildings

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Maximum Mixing Height (MMD)

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Coastal or Large Water Bodies

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Coastal Complexity

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Complex Terrain

Different math for flat or elevated terrain

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Types of Dispersion Models

Gaussian Plume Analytical approximation of dispersion

Numerical or CFDs Transport & diffusional flow fields

Statistical & Empirical Based on experimental or field data

Physical Flow visualization in wind tunnels, etc.

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Models Useful tools: right model for your needs Allows assessment of air quality problem

Space – different distances Time – different times of day Situations – change weather

Understand limitations Assumptions in science speak