a ir q uality m odeling. a ir q uality m odeling (aqm) predict pollutant concentrations at various...
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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.
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 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
FACTORS AFFECTING DISPERSION OF POLLUTANTS IN THE ATMOSPHERESource 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 turbulence in an ad-hoc manner
Simple in their mathematics
Quicker than numerical models
Do not require super computers
GAUSSIAN MODELS
DisadvantagesNot 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
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
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 SigmasExperimental data
Modified Experimental Curves
Lagrangian Auto Correlation Function
Moment-Concentration Method
Taylor's Statistical Theory
PLUME DISPERSION PARAMETERS
Factors Considered while Calculating SigmasNature 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 relationsz= 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:
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]
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
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