Air Pollution Control EENV 4313 Chapter 6 Air Pollutant Concentration Models

Why do we need them? To predict the ambient air concentrations that will result from any planned set of emissions for any specified meteorological conditions, at any location, for any time period, with total confidence in our prediction. The perfect model should match the reality, which is impossible. Therefore the model is a simplification of reality. The simpler the model, the less reliable it is. The more complex the model, the more reliable it is.

General Form of Models The models in this chapter are material balance models. The material under consideration is the pollutant of interest. The General material balance equation is: Notes: 1) we need to specify some set of boundaries. 2) The model will be applied to one air pollutant at a time. In other words, we cannot apply the model to air pollution in general. Accumulation rate = (all flow rates in) – (all flow rates out) + (creation rate) – (destruction rate)

Three types of Models (in this chapter) Fixed-Box Models Diffusion Models Multiple Cell Models These models are called source-oriented models. We use the best estimates of the emission rates of various sources and the best estimate of the meteorology to estimate the concentration of various pollutants at various downwind points.

1) Fixed-Box Models The city of interest is assumed to be rectangular. The goal is to compute the air pollutant concentration in this city using the general material balance equation. Fig. 6.1 De Nevers

1) Fixed-Box Models Assumptions: The city of interest is assumed to be rectangular. The goal is to compute the air pollutant concentration in this city using the general material balance equation. Assumptions: Rectangular city. W and L are the dimensions, with one side parallel to the wind direction. Complete mixing of pollutants up to the mixing height H. No mixing above this height. The pollutant concentration is uniform in the whole volume of air over the city (concentrations at the upwind and downwind edges of the city are the same). The wind blows in the x direction with velocity u , which is constant and independent of time, location, & elevation.

… Assumptions The concentration of pollutant in the air entering the city is constant and is equal to b (for background concentration). The air pollutant emission rate of the city is Q (g/s). The emission rate per unit area is q = Q/A (g/s.m2). A is the area of the city (W x L). This emission rate is assumed constant. No pollutant enters or leaves through the top of the box, nor through the sides. No destruction rate (pollutant is sufficiently long-lived)

Now, back to the general material balance eqn →Destruction rate = zero (from assumptions) →Accumulation rate = zero (since flows are independent of time and therefore steady state case since nothing is changing with time) → Q can be considered as a creation rate or as a flow into the box through its lower face. Let’s say a flow through lower face. Accumulation rate = (all flow rates in) – (all flow rates out) + (creation rate) – (destruction rate)

the general material balance eqn becomes: The equation indicates that the upwind concentration is added to the concentrations produced by the city. To find the worst case, you will need to know the wind speed, wind direction, mixing height, and upwind (background) concentration that corresponds to this worst case. 0 = (all flow rates in) – (all flow rates out) 0 = u W H b + q W L – u W H c Where c is the concentration in the entire city

Example 6.1 A city has the following description: W = 5 km, L = 15 km, u = 3 m/s, H = 1000 m. The upwind, or background, concentration of CO is b = 5 μg/m3. The emission rate per unit are is q = 4 x 10-6 g/s.m2. what is the concentration c of CO over the city? = 25 μg/m3

Comments on the simple fixed-box model 1) The third and the sixth assumptions are the worst (why?). 2) The fixed-box models does not distinguish between area sources and point sources. Area sources: small sources that are large in number and usually emit their pollutants at low elevations; such as autos, homes, small industries, etc. Point sources: large sources that are small in number and emit their pollutants at higher elevations; such as power plants, smelters, cement plants, etc. Both sources are combined in the q value. We know that raising the release point of the pollutant will decrease the ground-level concentration.

… Comments on the simple fixed-box model 3) If you are laying out a new city, how would you lay it? (page 125). In light of this, would it be preferable to put your city in a valley? 4) For an existing city, what actions would you take in order to minimize air pollutant concentrations? (answer in words that people can understand and act according to) 5) So far, the fixed-box model predicted concentrations for only one specific meteorological condition. We know that meteorological conditions vary over the year.

Modifications to improve the fixed-box model 1) Hanna (1971) suggested a modification that allows one to divide the city into subareas and apply a different value of q to each. (since variation of q from place to place can be obtained; q is low in suburbs and much higher in industrial areas). 2) Changes in meteorological conditions (comment #5) can be taken into account by a. determine the frequency distribution of various values of wind direction, u, and of H b. Compute the concentration for each value using the fixed-box model

…Modifications to improve the fixed-box model c. Multiply the concentrations obtained in step b by the frequency and sum to find the annual average

Example 6.2 For the city in example 6.1, the meteorological conditions described (u = 3 m/s, H = 1000 m) occur 40 percent of the time. For the remaining 60 percent, the wind blows at right angles to the direction shown in Fig. 6.1 at velocity 6 m/s and the same mixing height. What is the annual average concentration of carbon monoxide in this city? First we need to compute the concentration resulting from each meteorological condition and then compute the weighted average. For u = 3 m/s and H = 1000 m → c = 25 μg/m3

…example 6.2 cont. For u = 6 m/s and H = 1000 m → Note that L is now 5km, not 15km For u = 6 m/s and H = 1000 m →

Graphical Representation of the Fixed-Box Model Equation (Fig. 6 Graphical Representation of the Fixed-Box Model Equation (Fig. 6.2 in your textbook) Slope = (L/uH) Ambient air concentration, c Emission rate, q, g/s.km2

Example A pollutant concentration was calculated to be c1 with emission rate q1. If the Environmental Authority wishes to reduce the concentration to c2, compute the new allowable emission rate (q2) We can use graphical interpolation: OR Note this can be done only when the meteorological parameters are constant q1 q2 c2 c1 b

Example 6.3 (fractional reduction in emission rate) The ambient air quality standard for particulates (TSP) in the USA in 1971 was 75 μg/m3 annual average. In 1970 the annual average particulate concentration measured at one monitoring station in downtown Chicago was 190 μg/m3. The background concentration was estimated to be 20 μg/m3. By what percentage would the emission rate of particulates have to be reduced below the 1970 level in order to meet the 1971 ambient air quality standard? c1 = 190 μg/m3 , c2 = 75 μg/m3 q1 q2 c2 c1 b

Example 6.3 (fractional reduction in emission rate) c1 = 190 μg/m3 , c2 = 75 μg/m3 OR: you can use interpolation from the graph q1 q2 c2 c1 b

2) Diffusion Models Called as diffusion models. However, they are actually dispersion models. Such models usually use the Gaussian plume idea. Fig. 6.3 De Nevers

Problem Statement 2) Diffusion Models Point source (smoke stack) located at (0, 0, H) that steadily emits a pollutant at emission rate of Q (g/s) The wind blows in the x-direction with velocity u. The goal is to compute the concentration due to point source at any point (x, y, z) downwind.

Description of Situation in Fig. 6.3 2) Diffusion Models The origin of the coordinate system is (0, 0, 0), which is the base of the smoke stack. Plume is emitted form a point with coordinates (0, 0, H) H = h + Δh , h = physical stack height Δh = plume rise Plume rises vertically at the beginning (since it has higher temperature and a vertical velocity), then levels off to travel in the x-direction (wind direction). As the plume travels in the x-direction, it spreads in the y and z directions. The actual mixing mechanism is the turbulent mixing; not the molecular diffusion. What will happen if the molecular diffusion was the only mechanism?

… cont. Description of Situation in Fig. 6.3 2) Diffusion Models If we place a pollutant concentration meter at some fixed point in the plume, we would see the concentration oscillating in an irregular fashion about some average value (snapshot in Fig. 6.4). This is another evidence of the turbulent mixing. This average value is the value that the Gaussian plume model calculates The model does not calculate the instantaneous concentration value. It only calculates the average value. Therefore, results obtained by Gaussian plume calculations should be considered only as averages over periods of at least 10 minutes, and preferably one-half to one hour. The Gaussian plume approach calculates only this average value

The Basic Gaussian Plume Equation 2) Diffusion Models Where σy = horizontal dispersion coefficient (length units) σz = vertical dispersion coefficient (length units) The name “Gaussian” came from the similarity between the above equation and the Gauss normal distribution function used in statistics. The previous equation can also be written the following form:

Example 6.4 Q = 20 g/s of SO2 at Height H u = 3 m/s, At a distance of 1 km, σy = 30 m, σz = 20 m (given) Required: (at x = 1 km) SO2 concentration at the center line of the plume SO2 concentration at a point 60 m to the side of and 20 m below the centerline 2) Diffusion Models

… solution of example 6.4 2) Diffusion Models

What about σy and σz? (Dispersion coefficients) σy ≠ σz ►► Spreading in the two directions are not equal Most often σy > σz ►► Elliptical contour concentration at a given x . Symmetry is disturbed near the ground. To determine σy > σz , use figures 6.7 and 6.8 2) Diffusion Models

Horizontal dispersion coefficient Figure 6.7 De Nevers

Vertical dispersion coefficient Figure 6.8 De Nevers

Notes on Figures 6.7 and 6.8 Both σy & σz are experimental quantities. The derivations of equations 6.24 and 6.25 do not agree with reality. We will only use figures 6.7 and 6.8 to find σy & σz. Plotted from measurements over grasslands; i.e. not over cities However, we use them over cities as well since we have nothing better Measurements were made for x ≤ 1 km. Values beyond 1 km have been extrapolated. 2) Diffusion Models

What are the A to F categories? A to F are levels of atmospheric stability (table 6.1). Explanation: For a clear & hot summer morning with low wind speed, the sun heats the ground and the ground heats the air near it. Therefore air rises and mixes pollutants well. ►► Unstable atmosphere and large σy & σz values On a cloudless winter night, ground cools by radiation to outer space and therefore cools the air near it. Hence, air forms an inversion layer. ►► Stable atmosphere and inhibiting the dispersion of pollutants and therefore small σy & σz values 2) Diffusion Models

Stability Classes Table 3-1 Wark, Warner & Davis Table 6-1 de Nevers

Example 6.5

Some Modifications of the Basic Gaussian Plume Equation The effect of the ground Mixing height limits and one dimensional spreading

a) The Effect of the Ground Equation 6.27 assumes that the dispersion will continue vertically even below the ground level! The truth is that vertical spreading terminates at ground level. To account for this termination of spreading at the ground level, one can assume that a pollutant will reflect upward when it reaches the ground

… the Effect of the Ground This method is equivalent to assuming that a mirror-image plume exists below the ground. The added new concentration due to the image plume uses z+H instead of z – H . (draw the plume to check!)

Example 6.6 (effect of ground) Q = 20 g/s of SO2 at Height H u = 3 m/s, At a distance of 1 km, σy = 30 m, σz = 20 m (given) Required: (at x = 1 km) SO2 concentration at a point 60 m to the side of and 20 m below the centerline: a) for H = 20 m b) for H = 30 m 2) Diffusion Models

… example 6.6 a) For H = 20 m i.e. the concentration at the ground level itself (z = 0) (z – H)2 = (-H)2 = H2 (z + H)2 = (H)2 = H2 Therefore the answer will be exactly twice that in the 2nd part of example 6.4 c = (145 μg/m3) × 2 = 290 μg/m3 . b) For H = 30 m i.e. about 22 % greater than the basic plume equation (since the basic plume eqn does not take ground reflection into account.

Ground-Level Equation Set z = 0 in the equation accounting for the ground effect: This is the most widely used equation because it applies directly to the problem of greatest practical interest, which is the ground-level concentration. This is the ground-level modification of equation 6.27. It takes reflection into account.

Using Figure 6.9 to estimate Ground-Level concentration Figure 6.9 in your text book describes a way of finding the concentration at the line on the ground directly under the centerline of the plume. → z = 0 and y = 0 cu/Q can be plotted against “x” to obtain figure 6.9. Note that the right-hand side depends on H, σy, and σz . Therefore, we should have a group of H curves. Also figure 6.9 is for category C stability only.

Example 6.8 (using figure 6.9) Q = 100 g/s at Height H = 50 m u = 3 m/s, and stability category is C At a distance of 1 km, σy = 30 m, σz = 20 m (given) Required: Estimate the ground-level concentrations directly below the CL of the plume at distances of 0.2, 0.4, 0.5, 1, 5, 10 km downwind 2) Diffusion Models

… example 6.8 (using figure 6.9) Using figure 6.9, we can read the values of cu/Q at each distance The third column is obtained by multiplying the 2nd column by Q/u It is obvious that one of the benefits of figure 6.9 is that one can know the maximum ground level concentration & its distance downwind by inspection only Distance (km) cu/Q (m-2) c (μg/m3) 0.2 1.7×10-6 57 0.4 4.4×10-5 1467 0.5 5.3×10-5 1767 1 3.6×10-5 1200 5 2.7×10-6 83 10 7.8×10-7 24 2) Diffusion Models

Plume Rise This equation is only correct for the dimensions shown. Correction is needed for stability classes other than C: → For A and B classes: multiply the result by 1.1 or1.2 → For D, E, and F classes: multiply the result by 0.8 or 0.9 Δh = plum rise in m Vs = stack exit velocity in m/s D = stack diameter in m u = wind speed in m/s P = pressure in millibars Ts = stack gas temperature in K Ta = atmospheric temperate in K 2) Diffusion Models

Example 6.9 2) Diffusion Models

Multiple Cell Models Complex simultaneous reaction rate expressions (Figure 1.2) Multiple cell modeling is used. The Urban Airshed Model UAM is an example this modeling type. Model Description: The airspace above the city is divided into multiple cells. Each cell is normally from 2 to 5 km each way and is treated separately from the other. Four or six layers in the vertical direction, half below the mixing height and half above.

How does the Model Work? Mass balance for each cell. To start the simulation, we should have initial distribution of pollutants. The program calculates the change in concentration of the pollutant for a time step (typically 3 to 6 minutes) by numerically integrating the mass balance equation (eqn 6.1) Complex computations requiring data on: Wind velocity and direction Emissions of the ground-level cells Solar inputs

…How does the Model Work? The concentrations from the end of the previous time step are used to first compute the changes in concentration due to flows with the winds across the cell boundaries, and then compute the changes due to chemical reactions in the cell. These two results are combined to get the concentration in each cell at the end of the time step. Therefore, the model needs subprograms for the chemical transformations during the time step in any cell and subprograms for deposition of the pollutant from the ground-level cells.

…How does the Model Work? Complex computations requiring data on: Wind velocity and direction Emissions of the ground-level cells Solar inputs The previous data are needed to simulate a day or a few days in an urban area. What if such data are not available? →The program has ways of estimating them. The following is a common procedure: Choose a day on which the measured pollutant concentration was the maximum for the past year. The model is run using the historical record of the wind speeds and directions, solar inputs, and estimated emissions for that day. The model’s adjustable parameters are modified until the calculated concentrations match well with the observed ambient concentrations for that day.

…How does the Model Work? Then the model is re-run with different emission rates corresponding to proposed (or anticipated) future situations and the meteorology for that day. In this way the model performs a prediction of the worst day situation under the proposed future emission pattern.

Receptor-Oriented Models The previous models are called source-oriented models. We use the best estimates of the emission rates of various sources and the best estimate of the meteorology to estimate the concentration of various pollutants at various downwind points. In receptor-oriented models, one examines the pollutants collected at one or more monitoring sites, and from a detailed analysis of what is collected attempts to determine which sources contributed to the concentration at that receptor. This source differentiation is not an easy process; for example: If the pollutant is chemically uniform (e.g. CO, O3, SO2), then there is no way to distinguish between sources. If the pollutant is not chemically uniform; i.e. consisting of variety of chemicals within the pollutant itself (e.g. TSP, PM10, PM2.5), one can analyze their chemical composition and make some inferences about the sources. (Aluminum and silicon example in page 148)

Receptor-Oriented Models When results of both types disagree significantly, we tend to believe the receptor-oriented model because we have more confidence in chemical distribution data than we have in the meteorological data. If the goal is to estimate the effects of proposed new sources (e.g. for permitting issues), source oriented models are used. Receptor-oriented models cannot be used in such cases. Therefore receptor-oriented models are mostly used to test the estimates made by source-oriented models Simultaneously test the accuracy of the emissions estimates that are used in source-oriented models

Building Wakes & Aerodynamic Downwash When the wind flows over the building, a plume may get sucked and trapped into low-pressure wake behind the building. This will lead to high local concentration. A simple rule of thumb for avoiding this problem is to make the stack height at least 2.5 times the height of the tallest nearby building. Another simple rule of thumb:   downwash unlikely to be a problem if: hs  hb + 1.5 Lb   hs : stack height hb : building height Lb : the lesser of either building height or maximum projected building width.

… Building Wakes

… Building Wakes

… Building Wakes

Structure Influence Zone (SIZ): For downwash analyses with direction-specific building dimensions, wake effects are assumed to occur if the stack is within a rectangle composed of two lines perpendicular to the wind direction, one at 5L downwind of the building and the other at 2L upwind of the building, and by two lines parallel to the wind direction, each at 0.5L away from each side of the building, as shown below. L is the lesser of the height or projected width. This rectangular area has been termed a Structure Influence Zone (SIZ). Any stack within the SIZ for any wind direction shall be included in the modeling.

For US EPA regulatory applications, a building is considered sufficiently close to a stack to cause wake effects when the distance between the stack and the nearest part of the building is less than or equal to five (5) times the lesser of the building height or the projected width of the building. Distancestack-bldg <= 5L

Figure 4.6: GEP 360° 5L and Structure Influence Zone (SIZ) Areas of Influence (after U.S. EPA).