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Advanced Modeling Techniques for
Permit Modeling
Turning challenges into opportunities
A&WMA’s 108th Annual Conference & E...
Outline
• AERMOD’s Temporal Mismatch Limitation
• Building Downwash Limitations in BPIP/PRIME
• Advanced Modeling Techniqu...
AERMOD’s Temporal Mismatch
Model’s Accuracy
Appendix W: 9.1.2 Studies of Model Accuracy
a. A number of studies have been conducted to examine model a...
Perfect Model
MONITORED CONCENTRATIONS
AERMODCONCENTRATIONS
100
1000
-
-
Monitored vs Modeled Data:
Paired in Time and Space
AERMOD performance evaluation of three coal-fired electrical generatin...
SO2 Concentrations Paired in Time & Space
Probability analyses of combining background concentrations with model-predicted...
SO2 Concentrations Paired in Time Only
Probability analyses of combining background concentrations with model-predicted co...
AERMOD’s Evaluation
Are We Using the Model Correctly?
Temporal matching is not justifiable
Perfect model AERMOD
Limitations of Building Downwash in
BPIP/PRIME
BPIPBuilding Geometry
Standard AERMOD Modeling Process
Meteorological Data
Terrain Data
AERMET
AERMAP
Operating Parameters...
Building Dimension Inputs & BPIP
• BPIP uses building footprints and tier heights
• Combines building/structures
• All str...
Refinery Structures Upwind
Solid BPIP Structure Upwind
No Structures
Streamlines for Lattice Structures
PRIME
AERMOD’s Building Downwash Algorithm
• Used EPA wind tunnel data
base and past literature
• Developed analytical
equ...
CPP’s Evaluation of BPIP/PRIME
1. Geometry of artificial building created by
BPIP
2. Theory/formulation
• Inconsistencies
...
BPIP Diagnostic
Are We Using the Model Correctly?
• BPIP/PRIME theory has limitations
• Theoretical/formulation limitations will
overpredi...
Advanced Modeling Techniques to
Overcome AERMOD’s Limitations
Solutions to AERMOD’s Limitations
Advanced Modeling
Technique
Traditional Modeling Technique
Building Dimensions EBD Gener...
• Equivalent Building Dimensions” (EBDs) are the dimensions (height, width,
length and location) that are input into AERMO...
Basic Wind Tunnel Modeling Methodology
•Obtain source/site data
•Construct scale model –
3D Printing
•Install model in win...
Measure Ground-level Concentrations
Tracer
from stack
Max ground-level concentrations measured versus x
Measure Ground-level Concentrations
Data taken until good fit and max
obtained
Automated Max GL Concentration Mapper
Why EBD Works
Very Long
Building
EBD
Building
Should not be enhanced here
More closely matches reality for Long
Building
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
T...
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 ...
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 Build...
GEP Stack Height
40 CFR 51.110 (ii) Defines GEP stack height to be
the greater of:
• 65 meters; the formula height; or
• T...
Results from past study
175m
100m
65m
75m
Monte Carlo Approach
• Pioneered by the Manhattan Project scientists in 1940’s
• Technique is widely used in science and i...
Emission Variability Processor
• Assuming fixed peak 1‐hour emissions on a continuous basis
will result in unrealistic mod...
Updated Ambient Ratio Method (ARM2)
• Emission sources emit mostly NOx that is gradually
converted to NO2
• Chemical react...
ARM2 Advantages
• Simplified way to model NO2
• No need for ozone hourly file
• No need for in-stack NO2 to NOx ratios
• B...
Pairing AERMOD and Monitored Values
Positively Skewed Distribution
http://www.agilegeoscience.com
24-hr PM2.5 Observations
Evaluation of the SO2 and NOX offset ratio method to account for secondary PM2.5 formation
Sergio...
Histogram of 1-hr NO2 Observations
Innovative Dispersion Modeling Practices to Achieve a Reasonable Level of Conservatism ...
Histogram of 1-hr SO2 Observations
Innovative Dispersion Modeling Practices to Achieve a Reasonable Level of Conservatism ...
Combining 98th Percentile AERMOD and BG
P (AERMOD and BG) = P(AERMOD) * P(BG)
98% percentile is 2 out of 100 days, or
= (0...
Combining 99th percentile AERMOD and BG
P (AERMOD and BG) = P(AERMOD) * P(BG)
99% percentile is 1 out of 100 days, or
= (0...
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...
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 = ...
Conclusion
• Temporal pairing of predicted and observed
values is unjustified
• BPIP/PRIME commonly overestimates
downwash...
Conclusion
• Advanced modeling techniques can mitigate and
minimize limitations of the model
• EBD
• EMVAP
• ARM2
• 50th %...
Sergio A. Guerra, PhD
sguerra@cppwind.com
Direct: + 970 360 6020
www.SergioAGuerra.com
www.cppwind.com @CPPWindExperts
Tha...
Advanced Modeling Techniques for Permit Modeling - Turning challenges into opportunities
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Advanced Modeling Techniques for Permit Modeling - Turning challenges into opportunities

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Advance modeling techniques can be used in AERMOD to refine the inputs that are entered in the model to get more accurate results. This presentation covers:
-AERMOD’s Temporal Mismatch Limitation
-Building Downwash Limitations in BPIP/PRIME
-Advanced Modeling Techniques to Overcome these Limitations

Solutions include:
Equivalent Building Dimensions (EBD)
Emission Variability Processor (EMVAP)
Updated ambient ratio method (ARM2)
Pairing AERMOD values with the 50th % background concentrations in cumulative analyses.

Published in: Engineering
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Advanced Modeling Techniques for Permit Modeling - Turning challenges into opportunities

  1. 1. Advanced Modeling Techniques for Permit Modeling Turning challenges into opportunities A&WMA’s 108th Annual Conference & Exhibition – Raleigh, NC June 24, 2015 Sergio A. Guerra, Ph.D. – CPP Inc. Ron Petersen, Ph.D., CCM – CPP Inc.
  2. 2. Outline • AERMOD’s Temporal Mismatch Limitation • Building Downwash Limitations in BPIP/PRIME • Advanced Modeling Techniques to Overcome these Limitations
  3. 3. AERMOD’s Temporal Mismatch
  4. 4. 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.
  5. 5. Perfect Model MONITORED CONCENTRATIONS AERMODCONCENTRATIONS 100 1000 - -
  6. 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. 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. 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. 9. AERMOD’s Evaluation
  10. 10. Are We Using the Model Correctly? Temporal matching is not justifiable Perfect model AERMOD
  11. 11. Limitations of Building Downwash in BPIP/PRIME
  12. 12. BPIPBuilding Geometry Standard AERMOD Modeling Process Meteorological Data Terrain Data AERMET AERMAP Operating Parameters AERMOD Compliance
  13. 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. 14. Refinery Structures Upwind Solid BPIP Structure Upwind No Structures Streamlines for Lattice Structures
  15. 15. 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
  16. 16. CPP’s 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. 17. BPIP Diagnostic
  18. 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 theory’s building ratios • Dealing with porous/lattice structures, elongated buildings, and streamlined structures (e.g., hyperbolic cooling towers or tanks)
  19. 19. Advanced Modeling Techniques to Overcome AERMOD’s Limitations
  20. 20. Solutions to AERMOD’s 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 AERMOD’s concentration with the 50th % observed Tier 1: Combine AERMOD’s 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. 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. 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. 23. Measure Ground-level Concentrations Tracer from stack Max ground-level concentrations measured versus x
  24. 24. Measure Ground-level Concentrations Data taken until good fit and max obtained Automated Max GL Concentration Mapper
  25. 25. Why EBD Works Very Long Building EBD Building Should not be enhanced here More closely matches reality for Long Building
  26. 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. 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. 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. 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. 30. Results from past study 175m 100m 65m 75m
  31. 31. 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)
  32. 32. 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
  33. 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. 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 • EPA’s 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. 35. Pairing AERMOD and Monitored Values
  36. 36. Positively Skewed Distribution http://www.agilegeoscience.com
  37. 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. 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. 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. 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. 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. 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. 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. 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. 45. Conclusion • Advanced modeling techniques can mitigate and minimize limitations of the model • EBD • EMVAP • ARM2 • 50th % bkg
  46. 46. Sergio A. Guerra, PhD sguerra@cppwind.com Direct: + 970 360 6020 www.SergioAGuerra.com www.cppwind.com @CPPWindExperts Thank You!

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