types of models marti blad northern arizona university college of engineering & technology
TRANSCRIPT
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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