Advanced Modeling Techniques for Permit Modeling - Turning challenges into opportunities

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.

Outline
• AERMOD’s Temporal Mismatch Limitation
• Building Downwash Limitations in BPIP/PRIME
• Advanced Modeling Techniques to Overcome
these Limitations

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.

Perfect Model
MONITORED CONCENTRATIONS
AERMODCONCENTRATIONS
100
1000
-
-

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

SO2 Concentrations Paired in Time & Space
Probability analyses of combining background concentrations with model-predicted concentrations
Douglas R. Murray, Michael B. Newman
Vol. 64, Iss. 3, 2014

SO2 Concentrations Paired in Time Only
Probability analyses of combining background concentrations with model-predicted concentrations
Douglas R. Murray, Michael B. Newman
Vol. 64, Iss. 3, 2014

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
AERMOD Compliance

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

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
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

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

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)

Advanced Modeling Techniques to
Overcome AERMOD’s Limitations

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).

• 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?

Basic Wind Tunnel Modeling Methodology
•Obtain source/site data
•Construct scale model –
3D Printing
•Install model in wind
tunnel and measure Cmax
versus X

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
Typical AERMOD Predictions for Refinery
Structures with BPIP and EBD Inputs

0.00
0.25
0.50
0.75
1.00
BPIP EBD
Predicted
Concentrations
FACTOR of 4 to 8
Short building with a large foot print
Typical AERMOD Predictions for Buildings
with Large Footprint, BPIP and EBD Inputs

0.00
0.25
0.50
0.75
1.00
BPIP EBD
Predicted
Concentrations
FACTOR of 2 to 5
Very Wide/Narrow Buildings
Typical AERMOD Predictions for Very
Wide/Narrow Buildings with BPIP and EBD

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

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 industry
• EPA has approved this technique for risk assessments
• Used by EPA in the Guidance for 1-hour SO2
Nonattainment Area SIP Submissions (2014)

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

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.

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

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 A. Guerra, Shannon R. Olsen, Jared J. Anderson
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!

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.

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.

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

Combining 99th percentile AERMOD and 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

Combining 98th AERMOD and 50th 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
Vol. 64, Iss. 3, 2014

Combining 99th AERMOD and 50th 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
Vol. 64, Iss. 3, 2014

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

Conclusion
• Advanced modeling techniques can mitigate and
minimize limitations of the model
• EBD
• EMVAP
• ARM2
• 50th % bkg

Sergio A. Guerra, PhD
sguerra@cppwind.com
Direct: + 970 360 6020
www.SergioAGuerra.com
www.cppwind.com @CPPWindExperts
Thank You!

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