Air Quality Modeling
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Transcript Air Quality Modeling
AIR QUALITY MODELING
AIR QUALITY MODELING (AQM)
Predict pollutant concentrations at various locations
around the source.
Identify source contribution to air quality problems.
Assess source impacts and design control strategies.
Predict future pollutant concentrations from sources after
implementation of new regulatory programs.
AREAS SURROUNDING THE SITE OF RELEASE
AIR QUALITY MODELING (AQM)
Mathematical and numerical techniques are used in AQM to
simulate the dispersion of air pollutants.
Modeling of the dispersion of pollutants
Toxic and odorous substances
Single or multiple points
Point, Area, or Volume sources
Input data required for Air Quality Modeling
Source characteristics
Meteorological conditions
Site and surrounding conditions
AMBIENT AIR CONCENTRATION MODELING
Types
Point Sources
•
e.g., stacks or vents
Area Sources
•
of Pollutant Sources
e.g., landfills, ponds, storage piles
Volume Sources
•
e.g., conveyors, structures with multiple vents
FACTORS AFFECTING DISPERSION OF POLLUTANTS
IN THE ATMOSPHERE
Source Characteristics
Emission rate of pollutant
Stack height
Exit velocity of the gas
Exit temperature of the gas
Stack diameter
Meteorological Conditions
Wind velocity
Wind direction
Ambient temperature
Atmospheric stability
GAUSSIAN MODELS
Advantages
Produce
results that match closely with experimental
data
Incorporate
Simple
in their mathematics
Quicker
Do
turbulence in an ad-hoc manner
than numerical models
not require super computers
GAUSSIAN MODELS
Disadvantages
Not
suitable if the pollutant is reactive in nature
Fails
to incorporate turbulence in comprehensive sense
Unable
to predict concentrations beyond radius of
approximately 20 Km
For
greater distances, wind variations, mixing depths and
temporal variations become predominant
SOURCES OF ERROR IN GAUSSIAN MODEL
NUMERICAL SOLUTIONS
Involves solving a system of partial differential equations
Equations mathematically represent the fate of pollutants
downwind concentration
The number of unknown parameters must be equal to
number of equations
System of equation is written in numerical form with
appropriate numerical scheme and solved using computer
codes
Classes of Numerical Models
Three Dimensional Equations (k-Theory) Model
Higher Order Closure Models (k- Type)
DIFFERENCE BETWEEN NUMERICAL MODELS AND
GAUSSIAN MODEL
The degree of completeness in the mathematical
description of the atmospheric dispersion processes
Type of releases i.e., stack, jet or area source are easy to
handle manually
The models are designed to handle, degree of completeness
in the description of non-transport processes like chemical
reactions
Terrain feature complexities for which the model is designed
METHODS TO INCORPORATE PLUME RISE
Effective Source Height Method
Variable Plume Model Method
METHODS TO INCORPORATE PLUME RISE
Effective source height method
Independent of downwind distance, x
Effective source height,
h = hs + ∆h – ht
where,
hs = Physical chimney height
ht = Maximum terrain height between the source and receptor
Variable plume method
Takes into account the tilt of the plume
PROBLEM
Calculate the nighttime concentration of nitrogen oxides
1 km downward of an open, burning dump if the dump
emits NOx at the rate of 4 g/sec. The wind speed is 4
m/sec at 10 m above ground level. The one-hour
average diffusion coefficients at 1 km are estimated as sy
= 70 m and sz = 50 m and the dump is assumed to be a
point source.
SOLUTION
Use Gaussian Model for ground level, center-line
concentration from a point source at ground level.
MODIFICATIONS IN GAUSSIAN PLUME MODEL
Simplified Equations for Maximum Ground Level
Concentration
Location of maximum concentration
Ground Level Concentration during Limited Mixing
Condition
Where,
L = Mixing Height
Concentration Estimate for Various Sampling Times
C2 = C1 (t1/t2) q
where,
q lies between 0.17 and 0.5
Average Time
Multiplying Factor
3 hours
0.9 (±0.1)
8 hours
0.7 (±0.1)
24 hours
0.4 (±0.1)
PLUME DISPERSION PARAMETERS
Different
Methods to Calculate Sigmas
Experimental data
Modified Experimental Curves
Lagrangian Auto Correlation Function
Moment-Concentration Method
Taylor's Statistical Theory
PLUME DISPERSION PARAMETERS
Factors
Considered while Calculating Sigmas
Nature of Release
Sampling Time
Release Height
Terrain Features
Velocity Field
PASQUILL CURVES
Curves are based on smoke plume elevation Hsp (visible
portion) and angular spread q using the relations
z= Hsp/2.14
y= qx/4.28
The numerical coefficient 2.14 is just the 10% ordinate
of the normal error curve
TVA DISPERSION COEFFICIENTS
Sigma’s are calculated as:
0.5]
=
Area
/
[C
*(2*p)
p
peak
Where,
Area = Base times the average height of Concentration Profile along the axis
Cpeak = Maximum concentrations in that profile
In a number of cases, sz is calculated using
Cmax = Q / [2*U* y* z*p]
and thus, the distribution is considered Gaussian i.e.,
C = Cmax exp[-0.5*(xg/s)2]
PROBLEM-1
For the following data, find the maximum ground level
concentration at 4.2 km from the following stack:
Effective stack height = 75 m
Emission rate = 2520 g/sec
Wind speed at stack height = 6 m/sec
y = 560 m
z = 535 m
PROBLEM-2
For the following data, find the maximum ground level
concentration.
Effective stack height = 150 m
Emission rate = 1260 g/sec
Wind speed at stack height = 6 m/sec
o
Answer: C = --------- g/m3