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Presentation includes some highlights from the dispersion modeling papers presented at the Annual AWMA conference in San Antonio, TX. Topics covered include: EMVAP, distance limitations of AERMOD, and two case studies comparing predicted and monitoring data,
Presented at the A&WMA UMS Board Meeting on August 21, 2012.

Published in: Education
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  1. 1. AIR DISPERSION MODELING HIGHLIGHTS FROM 2012 ACE Presentation for the A&WMA UMS Board Meeting August 21, 2012 Sergio Guerra Wenck Associates, Inc.
  2. 2. Outline • Introduction • EMVAP • Distance limitation for AERMOD use • Case studies • North Dakota • Gibson Station
  3. 3. Why do we use a model?
  4. 4. What is a model? • A Model is a way of expressing the relationship between the different variables of a system in mathematical terms
  5. 5. What is an Air Quality Model An attempt to predict or simulate the ambient concentrations of contaminants in an area of interest. An Air Quality Model can be as simple as an algebraic equation or more complex
  6. 6. AERMOD • AERMOD is a steady-state plume model that incorporates air dispersion based on planetary boundary layer turbulence structure and scaling concepts, including treatment of both surface and elevated sources, and both simple and complex terrain. • AERMOD replaced the Industrial Source Complex (ISCST3) model as EPA’s regulatory model on December 9, 2006 • Preprocessors include: AERMET,AERMINUTE,AERSURFACE,AERMAP,BPIP
  7. 7. What are the inputs of a dispersion model? • Source data • Building data • Receptor data • Site data • Meteorological data • Terrain data
  8. 8. ACE 2012 Highlights
  9. 9. Emissions Variability Processor (EMVAP) EMVAP an Emission Variability Processor for Modeling Applications Paper 2012-A-341-AWMA Richard P. Hamel, Robert J. Paine, David W. Heinold (AECOM) Naresh Kumar and Eladio Knipping (EPRI)
  10. 10. EMVAP • Large variation possible over the course of a year • Intermittent sources (e.g., emergency backup engines or bypass stacks) present modeling challenges • For these sources, assuming fixed peak 1‐hour emissions on a continuous basis will result in unrealistic modeled results • Better approach is to assume a prescribed distribution of emission rates • EMVAP uses this information to develop alternative ways to indicate modeled compliance using a range of emission rates instead of just one value
  11. 11. Hourly emission profile
  12. 12. Cumulative frequency distribution
  13. 13. Distance limitations of AERMOD Limitations of Steady-State Dispersion Models and Possible Advanced Approaches Paper 2012-500-AWMA Gary Moore, Robert Paine, and David Heinold (AECOM) Steve Hanna (Hanna Consultants)
  14. 14. Short range model distance applicability • Plumes are assumed to travel to infinite distances within 1 • • • • hour (“lighthouse beam” effect) Each hour, the previous hour’s emissions are replaced and forgotten Worst‐case conditions, especially associated with low winds, result in impossible distances Currently, though, US EPA considers these models to be applicable to a rather arbitrary distance of 50 km Equivalence between ISC and CALPUFF for 2 met data locations: • Salem, Oregon • Evansville, Indiana
  15. 15. Short range model distance applicability • 20‐30 km is the extent a single hour’s travel for most of the hours • Even after 4‐5 hours, more than half of air parcels followed with a 10‐m wind are still on the 50‐km modeling domain • Results suggest that a 20‐km limit seems more appropriate for steady‐state model (e.g., AERMOD) applicability rather than the current limit of 50 km
  16. 16. Case Study 1- North Dakota Comparison of AERMOD Modeled 1-hour SO2 Concentrations to Observations at Multiple Monitoring Stations in North Dakota Paper 2012-A-353-AWMA Mary M. Kaplan, Robert Paine (AECOM)
  17. 17. Evaluation Opportunity in North Dakota • Mercer County: Antelope Valley Station and Great Plains • • • • • Synfuels Plant Electrical generating unit sources dominate SO2 emissions – hourly data available Five SO2 monitors in area within about 10 km of two nearby “central” sources Site‐specific PSD quality meteorological data years available (10‐m tower) Major SO2 sources within 50 km were modeled Five recent years of data were used
  18. 18. Case Study 1- Dakota Gasification Co. • Allowable emissions used for all sources, assumed to be constantly at peak rates • Receptors placed at monitor sites only, using actual terrain (even though slopes are < 2%), except to characterize the spatial concentration pattern • Four of the five monitors were at elevations near local stack base, a fifth monitor was about 100 m higher
  19. 19. Test of Terrain Problem for Gentle Slope • Used generic tall stack buoyant source • Modeled both flat and very gentle terrain • Terrain case was uniformly sloped upward 1% in all directions • Modeled entire year of meteorology • Obtained peak concentration on each ring of receptors out to 50 km • Plots follow for flat and gently sloping terrain
  20. 20. Conclusions from Gentle Slope Test • AERMOD has unusual prediction result for very low wind, • • • • stable conditions and low slope Problem is, in part, caused by very low mixing height that leads to very compact plume Mixing height is below building obstacles, which the model does not know about Plume stays perfectly level; terrain should not be considered in these cases With terrain, result is an unexpected plume impact “bulge” at point of terrain impact
  21. 21. Case Study 2-Gibson Generating Station • Review of IDEM’s AERMOD Evaluation for the Gibson Generating Station • Robert Paine and Carlos Szembek (AECOM)
  22. 22. Case Study 2-Gibson Generating Station • The Indiana Department of Environmental Management (IDEM) conducted an evaluation of AERMOD • Gibson is an isolated source with 4 stacks and 3 nearby monitors • On-site met data and hourly SO2 emission data for 2010 • Comparison of monitored versus predicted concentrations
  23. 23. Case Study 2-Gibson Generating Station
  24. 24. Case Study 2-Gibson Generating Station • Low winds produced highest concentrations (~0.5m/s) • Plume travel distance within an hour is short of the distance needed to reach maximum receptors • Formulation problem or coding error related to sigma-z (used to calculate effective mixing lid)
  25. 25. Questions? Sergio A. Guerra Environmental Engineer Phone: (651) 395-5225