AIR QUALITY MODELING AIR QUALITY MODELING AQM Predict

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 C 2 = C 1 (t 1/t 2) 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/m 3