Transcript Air Quality Modeling • Overview of AQ Models • Gaussian Dispersion Model

Reading: Chap 7.3
Air Quality Modeling
• Overview of AQ Models
• Gaussian Dispersion Model
• Chemical Mass Balance (CMB) Models
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Overview
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Overview
What is the level of my exposure to these
emissions? Is my family safe? Where is safe?
How about the adverse impact on the environment
(plants, animals, buildings)? How to predict the
impact of emissions resulting from population
growth?
• Air Quality Models are mathematical formulations
that include parameters that affect pollutant
concentrations. They are used to
– Evaluate compliance with NAAQS and other
regulatory requirements
– Determine extent of emission reductions required
– Evaluate sources in permit applications
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Types of AQ Models
Meteorological
Model
Emission
Model
Chemical
Model
Temporal and spatial emission rates
Topography
Chemical Transformation
Pollutant Transport
Equilibrium between Particles and gases
Vertical Mixing
Source
Dispersion
Model
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Receptor
Model
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• Emission Model
– Estimates temporal and spatial emission rates
based on activity level, emission rate per unit
of activity and meteorology
• Meteorological Model
– Describes transport, dispersion, vertical
mixing and moisture in time and space
• Chemical Model
– Describes transformation of directly emitted
particles and gases to secondary particles
and gases; also estimates the equilibrium
between gas and particles for volatile species
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• Source Dispersion Model
– Uses the outputs from the previous models to
estimate concentrations measured at receptors;
includes mathematical simulations of transport,
dispersion, vertical mixing, deposition and
chemical models to represent transformation.
• Receptor Model
– Infers contributions from different primary
source emissions or precursors from
multivariate measurements taken at one ore
more receptor sites.
When are model applications required for regulatory
purposes?
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Regulatory Application of Models
• PSD: Prevention of Significant Deterioration of
Air Quality in relatively clean areas (e.g. National
Parks)
• SIP: State Implementation Plan revisions for
existing sources and to New Source Reviews
(NSR)
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Classifications of AQ Models
• Developed for a number of pollutant types
and time periods
– Short-term models – for a few hours to a few
days; worst case episode conditions
– Long-term models – to predict seasonal or
annual average concentrations; health effects
due to exposure
• Classified by
– Non-reactive models – pollutants such as
SO2 and CO
– Reactive models – pollutants such as O3,
NO2, etc.
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AQ Models
• Classified by coordinate system used
– Grid-based
• Region divided into an array of cells
• Used to determine compliance with NAAQS
– Trajectory
http://www.epa.gov/scram
001/images/grid4.jpg
• Follow plume as it moves downwind
• Classified by level of sophistication
– Screening: simple estimation use preset,
worst-case meteorological conditions to provide
conservative estimates. Purpose?
– Refined: more detailed treatment of physical
and chemical atmospheric processes; require
more detailed and precise input data. So what?http://www.epa.gov/scram001/i
mages/smokestacks.jpg
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USEPA AQ models
• Screening models available at:
http://www.epa.gov/scram001/dispersion_screening.htm
• Preferred models available at:
http://www.epa.gov/scram001/dispersion_prefrec.htm
– A single model found to outperform others
• Selected on the basis of other factors such as past use, public
familiarity, cost or resource requirements and availability
• No further evaluation of a preferred model is required
• Alternative models available at:
http://www.epa.gov/scram001/dispersion_alt.htm
– Need to be evaluated from both a theoretical and a performance
perspective before use
• Compared to measured air quality data, the results indicate the
alternative model performs better for the given application than a
comparable preferred model
• The preferred model is less appropriate for the specific application or
there is no preferred model
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USEPA AQ models
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Gaussian Dispersion Models
• Most widely used
• Based on the assumption
– plume spread results primarily by molecular diffusion
– horizontal and vertical pollutant concentrations in the plume are
normally distributed (double Gaussian distribution)
• Plume spread and shape vary in response to
meteorological conditions
Z
X
Q
u
Y
H
Fig 7.11
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Model Assumptions
• Gaussian dispersion modeling based on a
number of assumptions including
– Steady-state conditions (constant source emission
strength)
– Wind speed, direction and diffusion characteristics of
the plume are constant
– Mass transfer due to bulk motion in the x-direction far
outshadows the contribution due to mass diffusion
– Conservation of mass, i.e. no chemical
transformations take place
– Wind speeds are >1 m/sec.
– Limited to predicting concentrations > 50 m downwind
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Gaussian Dispersion Equation
 1  y 2 z  H 2 
Q

C  x, y , z  
exp   2 
2


2y  z u
2


  y
z

Why isn’t x in the equation?
Atmospheric Stability Classes
Table 7.4
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Dispersion Coefficients: Horizontal
Fig 7.12
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Dispersion Coefficients: Vertical
Fig 7.13
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Gaussian Dispersion Equation
If the emission source is at ground level with no
effective plume rise then
2
2 


Q
1 y
z 
C  x, y , z  
exp 
 2 
2
y  z u
 2   y  z 
Plume Rise
• H is the sum of the physical stack height and
plume rise.
H  h plume rise  hactual stack
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Plume Rise
Buoyant plume: Initial buoyancy >> initial momentum
Forced plume: Initial buoyancy ~ initial momentum
Jet:
Initial buoyancy << initial momentum
• For neutral and unstable atmospheric
conditions, buoyant rise can be calculated by
21.425F 0.75
hplumerise 
(F  55 m 4 / s 3 )
u
38.71F 0.6
hplume rise 
(F  55 m 4 / s 3 )
u
Vs: Stack exit velocity, m/s
where buoyancy flux is
d: top inside stack diameter, m
Ts: stack gas temperature, K
2
F  gVs d (Ts  Ta ) / 4TS Ta: ambient temperature, K
g: gravity, 9.8 m/s2
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Carson and Moses: vertical momentum & thermal
buoyancy, based on 615 observations involving 26 stacks.
Qh
Vs d
h plume rise  3.47
 5.15
u
u
Qh
Vs d
h plume rise  0.35
 2.64
u
u
Qh
Vs d
h plume rise  1.04
 2.24
u
u
Qh  m C p Ts  Ta 
(unstable)
(neutral)
(stable)
(heat emission rate, kJ/s)
d 2
P
m 
Vs
MW (stack gas mass flow rate. kg/s)
4
RTs
When pollutants are dispersed to the ground level,
how should we handle the situation?
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Wark & Warner, “Air Pollution: Its Origin & Control”
 y 2    z  H 2 
 z  H 2  
Q
C  x, y , z  
exp  2  exp 
  exp 

2
2
2y  z u
2 z 
2 z  
 2 y   

What if the surface is absorbing?
How should the concentration profile look like w/ reflection?
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Ground level concentration
 y2 
 H2 
Q
C
exp  2  exp  2 
y  z u
 2 y 
 2 z 
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Maximum Ground Level Concentration
Under moderately stable to near neutral conditions,
 y  k1 z
The ground level concentration at the center line is
 H2 
C x,0,0 
exp  2 
2
k1 z u
 2 z 
Q
The maximum occurs at
dC / d z  0
H
 z 
2
Once z is determined, x can be known and subsequently C.
Q
Q
C x,0,0 
exp  1  0.1171
y  z u
 yzu
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Example
• An industrial boiler is burning at 12 tons (10.9
mton) of 2.5% sulfur coal/hr with an emission
rate of 151 g/s. The following exist : H = 120 m,
u = 2 m/s, y = 0. It is one hour before sunrise,
and the sky is clear. Determine downwind
ground level concentration at 10 km.
Stability class =
y =
z =
C(10 km, 0, 0) =
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Exercise
• If emissions are from a ground level source with
H = 0, u = 4 m/s, Q = 100 g/s, and the stability
class = B, what is downwind concentration at
200 m?
At 200 m:
y =
z =
C(200 m, 0, 0) =
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Example
• Calculate H using plume rise equations for an 80 m high
source (h) with a stack diameter = 4 m, stack velocity =
14 m/s, stack gas temperature = 90o C (363 K), ambient
temperature = 25 oC (298 K), u at 10 m = 4m/s, and
stability class = B. Then determine MGLC at its location.
F=
h plume rise =
H=
z =
y =
Cmax =
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Residents around Florida Rock Cement Plant are complaining its emission
being violating its allowed level. The plant has its facility within 0.5 km
diameter. Its effective stack height is 60 m. You are a FLDEP environmental
specialist. Where are you going to locate your air quality monitors? Why?
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Chemical Mass Balance Model
• A receptor model for assessing source apportionment
using ambient data and source profile data.
• Available at EPA Support Center for Regulatory Air Models
- http://www.epa.gov/scram001/tt23.htm
PM10 emissions from permitted sources in
Alachua County (tons) (ACQ,2002)
2000 Values
1. GRU Deerhaven 144.2
2. Florida Rock cement plant 34.35
3. Florida Power UF cogen. plant 3.19
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10
1
8
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1997 Values
4. VA Medical Center incinerator 0.2
5. UF Vet. School incinerator 0.2
6. GRU Kelly 1.9
7. Bear Archery 9.5
8. VE Whitehurst asphalt plant 4.9
9. White Construction asphalt plant 0.7
10. Hipp Construction asphalt plant 0.3
11. Driltech equipment manufacturing 0.2
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3,4,5,12
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Receptor Sites
12. University of Florida
13. Gainesville Regional Airport
14. Gainesville Regional Utilities (MillHopper)
Principles
• Mass at a receptor site is a linear combination of the
mass contributed from each of a number of individual
sources;
• Mass and chemical compositions of source emissions
are conserved from the time of emission to the time the
sample is taken.
Cij = Σ(aik×Skj)
• Cij is the concentration of species ith in the sample jth
measured at the receptor site:
• aik is the mass fraction of the species in the emission
from source kth, and
• Skj is the total mass contribution from source kth in the jth
sample at the receptor site.
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Example
• Total Pb concentration (ng/m3) measured at the site: a
linear sum of contributions from independent source
types such as motor vehicles, incinerators, smelters, etc
PbT = Pbauto + Pb incin. + Pbsmelter +…
• Next consider further the concentration of airborne lead
contributed by a specific source. For example, from
automobiles in ng/m3, Pbauto, is the product of two
cofactors: the mass fraction (ng/mg) of lead in
automotive particulate emissions, aPb, auto, and the total
mass concentration (mg/m3) of automotive emission to
the atmosphere, Sauto
• Pbauto = aauto (ng/mg) × Sauto (mg/m3air)
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Assumptions
• Composition of source emissions is constant over
period of time,
• Chemicals do not react with each other,
• All sources have been identified and have had
their emission characterized, including linearly
independent of each other,
• The number of source category (j) is less than or
equal to the number of chemical species (i) for a
unique solution to these equations, and
• The measurement uncertainties are random,
uncorrelated, and normally distributed (EPA,
1990).
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Quick Reflection
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