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www.cppwind.comwww.cppwind.com
Probabilistic & Source 
Characterization Techniques 
in AERMOD Compliance
EUEC 2016 – San D...
www.cppwind.comwww.cppwind.com
Outline
• Building Downwash Limitations in BPIP/PRIME
• AERMOD’s Temporal Mismatch Limitati...
www.cppwind.comwww.cppwind.com
Limitations of Building Downwash 
in BPIP/PRIME
www.cppwind.comwww.cppwind.com
BPIPBuilding Geometry
Standard AERMOD Modeling Process
Meteorological Data
Terrain Data
AER...
www.cppwind.comwww.cppwind.com
Building Dimension Inputs & BPIP
• BPIP uses building footprints and tier heights 
• Combin...
www.cppwind.comwww.cppwind.com
PRIME 
AERMOD’s Building Downwash Algorithm
• Used EPA wind tunnel data 
base and past lite...
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 ...
www.cppwind.comwww.cppwind.com
Schuman, 2012‐ Building Length Issue
Likely due to
enhanced
turbulence up to
wake boundary:
www.cppwind.comwww.cppwind.com
BPIP Diagnostic Tool
http://bit.do/cppwind‐BPIPDiagnostic
Likely Overprediction Factor for ...
www.cppwind.comwww.cppwind.com
Complianc
e
Complianc
e
CPP’s EBDCPP’s EBD
BPIP iagnosticBPIP Diagnostic
ToolBuilding Geome...
www.cppwind.comwww.cppwind.com
• Equivalent Building Dimensions (EBDs) are the dimensions (height, width, length 
and loca...
www.cppwind.comwww.cppwind.com
Basic Wind Tunnel Modeling 
Methodology
Obtain source/site data
Construct scale model – 3D ...
www.cppwind.comwww.cppwind.com
Measure Ground‐level Concentrations
Data taken until good fit and max
obtained
Automated Ma...
www.cppwind.comwww.cppwind.com
Measure Ground‐level Concentrations With Site 
Structures Present
Tracer
from stack
Max gro...
www.cppwind.comwww.cppwind.com
Measure Ground‐level Concentrations with Various EBD 
in Place of Site Structures
Tracer
fr...
www.cppwind.comwww.cppwind.com
Measure Ground‐level Concentrations with no Structures
Tracer
from stack
Max ground-level c...
www.cppwind.comwww.cppwind.com
EBD Method
Specify Wind Tunnel Determined EBD that Matches 
Dispersion with Site Structures...
www.cppwind.comwww.cppwind.com
Summary of Approved Projects
• Studies conducted and approved using original guidance for I...
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 whe...
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 ...
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 ...
www.cppwind.comwww.cppwind.com
AERMOD’s Temporal Mismatch
www.cppwind.comwww.cppwind.com
Model’s Accuracy
Appendix W: 9.1.2 Studies of Model Accuracy 
a. A number of studies have b...
www.cppwind.comwww.cppwind.com
Perfect Model
24
MONITORED CONCENTRATIONS
AERMODCONCENTRATIONS
100
1000
-
-
www.cppwind.comwww.cppwind.com
Monitored vs Modeled Data:
Paired in Time and Space
AERMOD performance evaluation of three ...
www.cppwind.comwww.cppwind.com
Probability analyses of combining background concentrations with model-predicted concentrat...
www.cppwind.comwww.cppwind.com
Probability analyses of combining background concentrations with model-predicted concentrat...
www.cppwind.comwww.cppwind.com
AERMOD’s Evaluation
www.cppwind.comwww.cppwind.com
Are We Using the Model Correctly?
Temporal matching is not justifiable
Perfect model       ...
www.cppwind.comwww.cppwind.com
Pairing AERMOD and Monitored 
Values
www.cppwind.comwww.cppwind.com
Probability of Two Unusual Events 
Happening at the Same Time
www.cppwind.comwww.cppwind.com
Positively Skewed Distribution
http://www.agilegeoscience.com
www.cppwind.comwww.cppwind.com
24‐hr PM2.5 Observations 
Evaluation of the SO2 and NOX offset ratio method to account for ...
www.cppwind.comwww.cppwind.com
Combining 99th percentile AERMOD 
and BG 
P (AERMOD and BG) = P(AERMOD) * P(BG)
99% percent...
www.cppwind.comwww.cppwind.com
Combining 99th AERMOD and 50th BG 
P (AERMOD and BG) = P(AERMOD) * P(BG)
= (1‐0.99) * (1‐0....
www.cppwind.comwww.cppwind.com
Monte Carlo Approach
• Pioneered by the Manhattan Project scientists in 1940’s
• Technique ...
www.cppwind.comwww.cppwind.com
Emission Variability Processor
• Assuming fixed peak 1‐hour emissions on a continuous basis...
www.cppwind.comwww.cppwind.com
Solutions to AERMOD’s  Limitations
Advanced Modeling
Technique
Traditional Modeling Techniq...
www.cppwind.comwww.cppwind.com
Conclusion
• BPIP/PRIME commonly overestimate 
downwash effects 
• Temporal pairing of pred...
www.cppwind.comwww.cppwind.com
Ron Petersen, PhD, CCM Sergio A. Guerra, PhD
rpetersen@cppwind.com sguerra@cppwind.com
Mobi...
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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

  1. 1. www.cppwind.comwww.cppwind.com Probabilistic & Source  Characterization Techniques  in AERMOD Compliance EUEC 2016 – San Diego, CA February 4, 2016 June 24, 2015 Sergio A. Guerra, Ph.D. – CPP Inc. Ron Petersen, Ph.D., CCM – CPP Inc.
  2. 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 
  3. 3. www.cppwind.comwww.cppwind.com Limitations of Building Downwash  in BPIP/PRIME
  4. 4. www.cppwind.comwww.cppwind.com BPIPBuilding Geometry Standard AERMOD Modeling Process Meteorological Data Terrain Data AERMET AERMAP Operating ParametersOperating Parameters AERMOD Compliance
  5. 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. 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. 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. 8. www.cppwind.comwww.cppwind.com Schuman, 2012‐ Building Length Issue Likely due to enhanced turbulence up to wake boundary:
  9. 9. www.cppwind.comwww.cppwind.com BPIP Diagnostic Tool http://bit.do/cppwind‐BPIPDiagnostic Likely Overprediction Factor for each Flow Vector Source 1
  10. 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. 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?
  12. 12. www.cppwind.comwww.cppwind.com Basic Wind Tunnel Modeling  Methodology Obtain source/site data Construct scale model – 3D  Printing Install model in wind tunnel  and measure Cmax versus X
  13. 13. www.cppwind.comwww.cppwind.com Measure Ground‐level Concentrations Data taken until good fit and max obtained Automated Max GL Concentration Mapper
  14. 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. 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. 16. www.cppwind.comwww.cppwind.com Measure Ground‐level Concentrations with no Structures Tracer from stack Max ground-level concentrations measured versus x
  17. 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. 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. 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. 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. 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
  22. 22. www.cppwind.comwww.cppwind.com AERMOD’s Temporal Mismatch
  23. 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. 
  24. 24. www.cppwind.comwww.cppwind.com Perfect Model 24 MONITORED CONCENTRATIONS AERMODCONCENTRATIONS 100 1000 - -
  25. 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. 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. 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
  28. 28. www.cppwind.comwww.cppwind.com AERMOD’s Evaluation
  29. 29. www.cppwind.comwww.cppwind.com Are We Using the Model Correctly? Temporal matching is not justifiable Perfect model            AERMOD
  30. 30. www.cppwind.comwww.cppwind.com Pairing AERMOD and Monitored  Values
  31. 31. www.cppwind.comwww.cppwind.com Probability of Two Unusual Events  Happening at the Same Time
  32. 32. www.cppwind.comwww.cppwind.com Positively Skewed Distribution http://www.agilegeoscience.com
  33. 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!
  34. 34. www.cppwind.comwww.cppwind.com 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
  35. 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. 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. 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. 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. 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. 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!

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