Pollution Engineering - January 2009 - (Page 25)

cally in mg/m3), given a known or estimated air release rate (contaminant loading per second, g/s), airflow (wind speed in distance per second, m/s) and atmospheric stability conditions across the impact area (in m2). As such, any formula that can simulate emission dispersal under specified weather and topographic conditions can be a valuable tool to an air quality scientist, engineer or emergency response perso personnel. Modeling instru instruction and demonstration The stack simulator can b used to help stube dents more fully compreh comprehend the real world significance of the basic Gaussian plume co equation. Furthermore, combining a physical computatio model with a computational model such as the Gaussian plume equat can strengthen equation quantitat conceptual and quantitative understanding o of the complex reality of chemical dispersion in air. T To combine these two models for more bi h effective teaching/training, first, the instructor must introduce the general ideas of contaminant dispersion using the unifying formula presented above. Examples of sources and receptors, and key atmospheric dispersion components can be elicited from students. Alternatively, prior to revealing the unifying formula, students can be encouraged to construct a formula that represents the likely relationship among the three parts of the air pollution system. To have students construct a formula, assign three variables to represent each system component, e.g., S (source strength), D (dispersion from wind and stability), and R (concentration at receptor). If necessary, students may be prompted by being reminded to consider the units of each variable. R = S / D is an appropriate solution. Once the importance of the unifying formula, along with the relationships among its variables, has been thoroughly explored, the instructor can move on to introduce the basic Gaussian plume equation, and then compare and contrast the formula with the plume equation. For instance, note that the primary similarity between the formula and the equation is that both use “q” in the numerator to represent emission rate in g/s and “u” in the denominator to represent wind speed in m/s. (The difference here of course is between capitalization/non-capitalization of the variables.) One primary substantive difference between the formula and the equation is that the “S” stability term in the numerator of the formula is replaced by “π, y, z” in the denominator of the plume equation. Another primary difference is the introduction of the expression “exp (- H2 / 2 z2 )” to account for additional dispersion of emissions released from an elevated source such as a smokestack. When students have grasped the crucial features of the formulaic models of air dispersion, instructors can then introduce the artificial stack and its real life application to the numerical models. Now for the stack To begin, the source (smokestack) features can be examined. A hole punch is used to make multi-colored paper particles (the punched disks) that are inserted at the base of stack model. The known amount of “particulate loading” versus the time over which the particles are emitted out the stack provides the emission rate of the source, i.e., the particles-per-second rate. If students measure the mean mass of each paper particle, they can then determine the mass per second emission rate of particles exiting the stack. Additional stack parameters essential to computing downwind particle emission impact should be identified and then measured. These parameters include stack height and diameter, and exhaust temperature and velocity. The artificial stack stands 13 feet high from its base to its 4-inch exhaust opening. The stack can be separated to produce a 6.5-foot unit. The present unit consists of a two-speed blower for low- and highspeed flows that can be measured with, for example, a pitot tube anemometer through the stack’s sampling port. Using a simple liquid-in-glass thermometer, the stack gas temperature also can be measured through the port. Atmospheric conditions such as wind speed and stability are key to air dispersion and can be identified via visual observations (like the Beaufort Scale for wind speed and cloud cover for stability estimates). Or better, a meteorological tower that extends Figure 2. The SuperStack is shown on the Geneva College campus with optional La Crosse Technology wind equipment (near stack top) and 1 m2 foam board for identifying particle concentrations downwind of the stack. anemometer equipment to the stack top can be constructed so that students can measure wind direction and speed (see Figure 2). Experience has shown that an approximately 15-mph sustained wind is needed to properly disperse the particles emitted from the stack. (Note that precipitation is usually not desirable during students’ first demonstrations, since paper particles will be washed out of the air. However, precipitation can show the real world effects of rain and heavy snowfall on particulate matter in subsequent sessions.) Finally, the impact of the paper pollution particles on downwind receptors can be demonstrated by counting the portion of the total amount of particles that fall in a given area. To more readily accomplish this task, a 1-m2 foam board has been fabricated (see Figure 2). One way in which this board can be used is to locate the area of the 25 JANUARY2009 www.pollutionengineering.com http://www.pollutionengineering.com

Table of Contents Feed for the Digital Edition of Pollution Engineering - January 2009

Pollution Engineering - January 2009 Contents The Editor’s Desk EnviroNews PE Events Legal Lookout Green Connections Ten Top Technologies for 2009 Old Fashioned Chemistry Emitting Education NGWA Reports from Its Annual Meeting A Wood and a Pond Company Technical Profiles Filtration/Membrane Products Flow and Level Monitoring Equipment Classified Marketplace Advertisers Index State Rules

Pollution Engineering - January 2009

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