#### 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
Produce
results that match closely with experimental
data
Incorporate
Simple
in their mathematics
Quicker
Do
than numerical models
not require super computers
GAUSSIAN MODELS
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