1. Advanced Modeling Techniques for Permit Modeling Turning challenges into opportunities A&WMAs 108th Annual Conference & Exhibition Raleigh, NC June 24, 2015 Sergio A. Guerra, Ph.D. CPP Inc. Ron Petersen, Ph.D., CCM CPP Inc. 2. Outline AERMODs Temporal Mismatch Limitation Building Downwash Limitations in BPIP/PRIME Advanced Modeling Techniques to Overcome these Limitations 3. AERMODs Temporal Mismatch 4. Models Accuracy Appendix W: 9.1.2 Studies of Model Accuracy a. A number of studies have been conducted to examine model accuracy, particularly with respect to the reliability of short-term concentrations required for ambient standard and increment evaluations. The results of these studies are not surprising. Basically, they confirm what expert atmospheric scientists have said for some time: (1) Models are more reliable for estimating longer time-averaged concentrations than for estimating short-term concentrations at specific locations; and (2) the models are reasonably reliable in estimating the magnitude of highest concentrations occurring sometime, somewhere within an area. For example, errors in highest estimated concentrations of 10 to 40 percent are found to be typical, i.e., certainly well within the often quoted factor-of-two accuracy that has long been recognized for these models. However, estimates of concentrations that occur at a specific time and site, are poorly correlated with actually observed concentrations and are much less reliable. Bowne, N.E. and R.J. Londergan, 1983. Overview, Results, and Conclusions for the EPRI Plume Model Validation and Development Project: Plains Site. EPRI EA3074. Electric Power Research Institute, Palo Alto, CA. Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A Survey of Statistical Measures of Model Performance and Accuracy for Several Air Quality Models. Publication No. EPA450/483001. Office of Air Quality Planning & Standards, Research Triangle Park, NC. 5. Perfect Model MONITORED CONCENTRATIONS AERMODCONCENTRATIONS 100 1000 - - 6. Monitored vs Modeled Data: Paired in Time and Space AERMOD performance evaluation of three coal-fired electrical generating units in Southwest Indiana Kali D. Frost Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 7. SO2 Concentrations Paired in Time & Space Probability analyses of combining background concentrations with model-predicted concentrations Douglas R. Murray, Michael B. Newman Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 8. SO2 Concentrations Paired in Time Only Probability analyses of combining background concentrations with model-predicted concentrations Douglas R. Murray, Michael B. Newman Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 9. AERMODs Evaluation 10. Are We Using the Model Correctly? Temporal matching is not justifiable Perfect model AERMOD 11. Limitations of Building Downwash in BPIP/PRIME 12. BPIPBuilding Geometry Standard AERMOD Modeling Process Meteorological Data Terrain Data AERMET AERMAP Operating Parameters AERMOD Compliance 13. Building Dimension Inputs & BPIP BPIP uses building footprints and tier heights Combines building/structures All structures become one single rectangular solid for each wind direction and each source BPIP dimensions may not characterize the source accurately and may result in unreasonably high predictions 14. Refinery Structures Upwind Solid BPIP Structure Upwind No Structures Streamlines for Lattice Structures 15. PRIME AERMODs Building Downwash Algorithm Used EPA wind tunnel data base and past literature Developed analytical equations for cavity height, reattachment, streamline angle, wind speed and turbulence Developed for specific building dimensions When buildings outside of these dimensions, theory falls apart 16. CPPs Evaluation of BPIP/PRIME 1. Geometry of artificial building created by BPIP 2. Theory/formulation Inconsistencies Unverified assumptions Inaccuracies 3. Needed enhancements Turbulence estimated more accurately Wake boundary calculations updated for wider range of building shapes Streamline calculation for streamlined, porous, wide and elongated structures Correct BPIP building dimensions 17. BPIP Diagnostic 18. Are We Using the Model Correctly? BPIP/PRIME theory has limitations Theoretical/formulation limitations will overpredict downwash effects when: Building dimensions are outside of theorys building ratios Dealing with porous/lattice structures, elongated buildings, and streamlined structures (e.g., hyperbolic cooling towers or tanks) 19. Advanced Modeling Techniques to Overcome AERMODs Limitations 20. Solutions to AERMODs Limitations Advanced Modeling Technique Traditional Modeling Technique Building Dimensions EBD Generated BPIP Generated Variable emissions Use EMVAP to account for variability Assume continuous maximum emissions NOx to NO2 conversion ARM2 PVMRM and OLM Need: Hourly O3 data and In-stack NO2 to NOx ratios Based on temporal pairing of predicted and observed values Background Concentrations Combine AERMODs concentration with the 50th % observed Tier 1: Combine AERMODs concentration with max. or design value (e.g., 99th % observed for SO2) Tier 2: Combine predicted and observed values based on temporal matching (e.g., by season or hour of day). 21. Equivalent Building Dimensions (EBDs) are the dimensions (height, width, length and location) that are input into AERMOD in place of BPIP dimensions to more accurately predict building wake effects Guidance originally developed when ISC was the preferred model EPA, 1994. Wind Tunnel Modeling Demonstration to Determine Equivalent Building Dimensions for the Cape Industries Facility, Wilmington, North Carolina. Joseph A. Tikvart Memorandum, dated July 25, 1994. U.S. Environmental Protection Agency, Research Triangle Park, NC Determined using wind tunnel modeling What is EBD? 22. Basic Wind Tunnel Modeling Methodology Obtain source/site data Construct scale model 3D Printing Install model in wind tunnel and measure Cmax versus X 23. Measure Ground-level Concentrations Tracer from stack Max ground-level concentrations measured versus x 24. Measure Ground-level Concentrations Data taken until good fit and max obtained Automated Max GL Concentration Mapper 25. Why EBD Works Very Long Building EBD Building Should not be enhanced here More closely matches reality for Long Building 26. 0.00 0.25 0.50 0.75 1.00 BPIP EBD Predicted Concentrations FACTOR of 2 to 3.5 reduction when EBD used Lattice Structures Typical AERMOD Predictions for Refinery Structures with BPIP and EBD Inputs 27. 0.00 0.25 0.50 0.75 1.00 BPIP EBD Predicted Concentrations FACTOR of 4 to 8 reduction when EBD used Short building with a large foot print Typical AERMOD Predictions for Buildings with Large Footprint, BPIP and EBD Inputs 28. 0.00 0.25 0.50 0.75 1.00 BPIP EBD Predicted Concentrations FACTOR of 2 to 5 reduction when EBD used Very Wide/Narrow Buildings Typical AERMOD Predictions for Very Wide/Narrow Buildings with BPIP and EBD 29. GEP Stack Height 40 CFR 51.110 (ii) Defines GEP stack height to be the greater of: 65 meters; the formula height; or The height determined by a wind tunnel modeling study Can be taller than the formula!! Up to 3.25 times the building height versus 2.5 for the formula Typically 2 times the nearby terrain height 30. Results from past study 175m 100m 65m 75m 31. Monte Carlo Approach Pioneered by the Manhattan Project scientists in 1940s Technique is widely used in science and industry EPA has approved this technique for risk assessments Used by EPA in the Guidance for 1-hour SO2 Nonattainment Area SIP Submissions (2014) 32. Emission Variability Processor Assuming fixed peak 1hour emissions on a continuous basis will result in unrealistic modeled results Better approach is to assume a prescribed distribution of emission rates EMVAP assigns emission rates at random over numerous iterations The resulting distribution from EMVAP yields a more representative approximation of actual impacts Incorporate transient and variable emissions in modeling analysis EMVAP uses this information to develop alternative ways to indicate modeled compliance using a range of emission rates instead of just one value 33. Updated Ambient Ratio Method (ARM2) Emission sources emit mostly NOx that is gradually converted to NO2 Chemical reactions are based on plume entrapment and contact time Chu and Meyers* identified that higher NOx concentrations and lower NO2/NOx ambient ratios were present in the near proximity of the source, and lower NOx and higher NO2/NOx ratios occurred as distance increased * Chu and Meyers, Use of Ambient Ratios to Estimate Impact of NOx Sources on Annual NO2 Concentration, presented at the 1991 Air and Waste Management Association annual meeting. 34. ARM2 Advantages Simplified way to model NO2 No need for ozone hourly file No need for in-stack NO2 to NOx ratios Based on hard data from ambient monitors Not based on temporal pairing of hourly NOx and ozone values Added to AERMOD as a beta option since version 13350 EPAs testing and evaluation indicates that ARM2 may be appropriate in some cases.* *Clarification on the Use of AERMOD Dispersion Modeling for Demonstrating Compliance with the NO2 National Ambient Air Quality Standard, Memo from Chris Owen and Roger Brode, 9/30/2014 35. Pairing AERMOD and Monitored Values 36. Positively Skewed Distribution http://www.agilegeoscience.com 37. 24-hr PM2.5 Observations Evaluation of the SO2 and NOX offset ratio method to account for secondary PM2.5 formation Sergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 Percentile BG mg/m3 Max. Available based on NAAQS mg/m3 50th 7.6 27.4 60th 8.7 26.3 70th 10.3 24.7 80th 13.2 21.8 90th 16.9 18.1 95th 22.6 12.4 98th 29.9 5.1 99.9th 42.5 Exceeds! 38. Histogram of 1-hr NO2 Observations Innovative Dispersion Modeling Practices to Achieve a Reasonable Level of Conservatism in AERMOD Modeling Demonstrations. Sergio A. Guerra A&WMA 107th Annual Conference and Exhibition, June 26, 2014. 39. Histogram of 1-hr SO2 Observations Innovative Dispersion Modeling Practices to Achieve a Reasonable Level of Conservatism in AERMOD Modeling Demonstrations. Sergio A. Guerra EM Magazine, December 2014. 40. Combining 98th Percentile AERMOD and BG P (AERMOD and BG) = P(AERMOD) * P(BG) 98% percentile is 2 out of 100 days, or = (0.02) * (0.02) = 0.0004 = 1 out of 2,500 days Equivalent to one exceedance every 6.8 years! = 99.96th percentile of the combined distribution 41. Combining 99th percentile AERMOD and BG P (AERMOD and BG) = P(AERMOD) * P(BG) 99% percentile is 1 out of 100 days, or = (0.01) * (0.01) = 0.0001 = 1 out of 10,000 days Equivalent to one exceedance every 27 years! = 99.99th percentile of the combined distribution 42. Combining 98th AERMOD and 50th BG P (AERMOD and BG) = P(AERMOD) * P(BG) = (1-0.98) * (1-0.50) = (0.02) * (0.50) = 0.01 = 1 of 100 days Equivalent to 3.6 exceedances every year = 99th percentile of the combined distribution Evaluation of the SO2 and NOX offset ratio method to account for secondary PM2.5 formation Sergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 43. Combining 99th AERMOD and 50th BG P (AERMOD and BG) = P(AERMOD) * P(BG) = (1-0.99) * (1-0.50) = (0.01) * (0.50) = 0.005 = 1 of 200 days Equivalent to 1.8 exceedances every year = 99.5th percentile of the combined distribution Evaluation of the SO2 and NOX offset ratio method to account for secondary PM2.5 formation Sergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014 44. Conclusion Temporal pairing of predicted and observed values is unjustified BPIP/PRIME commonly overestimates downwash effects Advanced methods can be used to overcome these limitations Need to be based on sound science and A clear understanding of how AERMOD works 45. Conclusion Advanced modeling techniques can mitigate and minimize limitations of the model EBD EMVAP ARM2 50th % bkg 46. Sergio A. Guerra, PhD sguerra@cppwind.com Direct: + 970 360 6020 www.SergioAGuerra.com www.cppwind.com @CPPWindExperts Thank You!