Probabilistic & Source Characterization Techniques in AERMOD Compliance
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The short term NAAQS are more stringent and traditional techniques are not suitable anymore. The probabilistic nature of these standards also opens the door to modeling techniques based on probability. Source characterization studies can also be used to refine AERMOD’s inputs to be more accurate and achieve reductions of more than half. This presentation will cover these compliance methods. Currently, it is assumed that a given emission unit is in operation at its maximum capacity every hour of the year. However, assuming constant maximum emissions is overly conservative for facilities such as power plants that are not in operation all the time at full load. A better approach is the use of the Monte Carlo technique to account for emission variability. Another conservative assumption in NAAQS modeling relates to combining predicted concentrations from AERMOD with maximum or design concentrations from the monitor. A more reasonable approach is to combine the 50th percentile background concentration with AERMOD values. The inputs to AERMOD can be obtained by more accurate source characterization studies. Such is the case of building dimensions commonly calculated with BPIP. These dimensions tend to overstate the wake effects and produce significantly higher concentrations especially for lattice structures, elongated buildings, and streamlined structures. An Equivalent Building Dimensions (EBD) study can be used to inform AERMOD with more accurate downwash characteristics.
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Probabilistic & Source Characterization Techniques in AERMOD Compliance
- 2. www.cppwind.comwww.cppwind.com Outline • Building Downwash Limitations in BPIP/PRIME • AERMOD’s Temporal Mismatch Limitation • Advanced Modeling Techniques to Overcome these Limitations
- 4. www.cppwind.comwww.cppwind.com BPIPBuilding Geometry Standard AERMOD Modeling Process Meteorological Data Terrain Data AERMET AERMAP Operating ParametersOperating Parameters AERMOD Compliance
- 5. www.cppwind.comwww.cppwind.com Building Dimension Inputs & BPIP • BPIP uses building footprints and tier heights • Combines building/structures • All structures become one single solid rectangle for each wind direction and each stack • BPIP dimensions may not characterize the source accurately and may result in unreasonably high predictions
- 6. www.cppwind.comwww.cppwind.com PRIME AERMOD’s 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
- 7. www.cppwind.comwww.cppwind.com Schulman, 2012‐ Building Width Issue Hb=20m L = 45 m W = 220 m D = 2.5 m Ve = 20 m/s Error likely due to - Wake height calculation & large R - Start of enhanced turbulent at large R W/H>4
- 8. www.cppwind.comwww.cppwind.com Schuman, 2012‐ Building Length Issue Likely due to enhanced turbulence up to wake boundary:
- 9. www.cppwind.comwww.cppwind.com BPIP Diagnostic Tool http://bit.do/cppwind‐BPIPDiagnostic Likely Overprediction Factor for each Flow Vector Source 1
- 10. www.cppwind.comwww.cppwind.com Complianc e Complianc e CPP’s EBDCPP’s EBD BPIP iagnosticBPIP Diagnostic ToolBuilding Geometry Meteorological Data Terrain Data AERMET AERMAP Operating ParametersOperating Parameters AERMOD OtherInputs Building Inputs BPIP Diagnostic Tool
- 11. www.cppwind.comwww.cppwind.com • 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?
- 13. www.cppwind.comwww.cppwind.com Measure Ground‐level Concentrations Data taken until good fit and max obtained Automated Max GL Concentration Mapper
- 14. www.cppwind.comwww.cppwind.com Measure Ground‐level Concentrations With Site Structures Present Tracer from stack Max ground-level concentrations measured versus x
- 15. www.cppwind.comwww.cppwind.com Measure Ground‐level Concentrations with Various EBD in Place of Site Structures Tracer from stack Max ground-level concentrations measured versus x
- 16. www.cppwind.comwww.cppwind.com Measure Ground‐level Concentrations with no Structures Tracer from stack Max ground-level concentrations measured versus x
- 17. www.cppwind.comwww.cppwind.com EBD Method Specify Wind Tunnel Determined EBD that Matches Dispersion with Site Structures Present Wind Tunnel EBD much smaller than actual building No building works best for this case Site Structures in Wind TunnelEBD in Wind Tunnel
- 18. www.cppwind.comwww.cppwind.com Summary of Approved Projects • Studies conducted and approved using original guidance for ISC applications – Amoco Whiting Refinery, Region 5, 1990 – Public Service Electric & Gas, Region 2, 1993 – Cape Industries, Region 4, 1993 – Cambridge Electric Plant, Region 1, 1993 – District Energy, Region 5, 1993 – Hoechst Celanese Celco Plant, Region 3, 1994 – Pleasants Power, Region 3, 2002 • Studies conducted using original guidance for AERMOD/PRIME applications – Hawaiian Electric (Approved), Region 9, 1998 – Mirant Power Station (Approved), Region 3, 2006 – Cheswick Power Plant (Approved), Region 3, 2006 – Radback Energy (Protocol Approved), Region IX, 2010 • After 2011 EPA Clearinghouse Memo – Chevron 1 (Study Approved), Region 4, 2012 – Chevron 2 (Study Approved), Region 4, 2013 – Chevron 3 (In process), Region 4, 2015
- 19. www.cppwind.comwww.cppwind.com 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
- 20. www.cppwind.comwww.cppwind.com 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
- 21. www.cppwind.comwww.cppwind.com 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
- 23. www.cppwind.comwww.cppwind.com Model’s 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 EA–3074. 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. EPA–450/4–83–001. Office of Air Quality Planning & Standards, Research Triangle Park, NC.
- 25. www.cppwind.comwww.cppwind.com 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
- 26. www.cppwind.comwww.cppwind.com 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 SO2 Concentrations Paired in Time & Space
- 27. www.cppwind.comwww.cppwind.com 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 SO2 Concentrations Paired in Time Only
- 33. www.cppwind.comwww.cppwind.com 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 g/m3 Max. Available based on NAAQS g/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!
- 35. www.cppwind.comwww.cppwind.com 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
- 36. www.cppwind.comwww.cppwind.com Monte Carlo Approach • Pioneered by the Manhattan Project scientists in 1940’s • 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)
- 37. www.cppwind.comwww.cppwind.com Emission Variability Processor • 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 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
- 38. www.cppwind.comwww.cppwind.com Solutions to AERMOD’s Limitations Advanced Modeling Technique Traditional Modeling Technique Building Dimensions EBD Generated BPIP Generated Background Concentrations Combine AERMOD’s concentration with the 50th % observed Tier 1: Combine AERMOD’s concentration with max. or design value (e.g., 98th % observed for SO2) Tier 2: Combine predicted and observed values based on temporal matching (e.g., by season or hour of day). Variable emissions Use EMVAP to account for variability Assume continuous maximum emissions
- 39. www.cppwind.comwww.cppwind.com Conclusion • BPIP/PRIME commonly overestimate downwash effects • Temporal pairing of predicted and observed values is unjustified • Advanced methods can be used to overcome these limitations – 50th percentile bkg – EMVAP – ARM2
- 40. www.cppwind.comwww.cppwind.com Ron Petersen, PhD, CCM Sergio A. Guerra, PhD rpetersen@cppwind.com sguerra@cppwind.com Mobile: +1 970 690 1344 Mobile: + 612 584 9595 www.sergioaguerra.com CPP, Inc. 2400 Midpoint Drive, Suite 190 Fort Collins, CO 80525 + 970 221 3371 www.cppwind.com @CPPWindExperts Thanks!