1. Air Quality Modeling

2. 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.

3. Areas Surrounding the Site of Release

4. 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

5. Ambient Air Concentration Modeling
Types of Pollutant Sources
Point Sources
e.g., stacks or vents
Area Sources
e.g., landfills, ponds, storage piles
Volume Sources
e.g., conveyors, structures with multiple vents

6. 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

7. Gaussian Models Advantages
Produce results that match closely with experimental data
Incorporate turbulence in an ad-hoc manner
Simple in their mathematics
Quicker than numerical models
Do not require super computers

8. 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

9. SOURCES OF ERROR IN GAUSSIAN MODEL

10. 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)

12. 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

17. Methods To Incorporate Plume Rise
Effective Source Height Method
Variable Plume Model Method

18. 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

21. 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.

22. Solution
Use Gaussian Model for ground level, center-line concentration from a point source at ground level.

23. 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

26. 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)

27. Plume Dispersion Parameters
Different Methods to Calculate Sigmas
Experimental data
Modified Experimental Curves
Lagrangian Auto Correlation Function
Moment-Concentration Method
Taylor's Statistical Theory

28. Plume Dispersion Parameters
Factors Considered while Calculating Sigmas
Nature of Release
Sampling Time
Release Height
Terrain Features
Velocity Field

29. 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

31. TVA Dispersion Coefficients
Sigma’s are calculated as:
p = Area / [Cpeak*(2*p)0.5]
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]

33. 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

34. 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
Answer: C = g/m3