Probabilistic & Source Characterization Techniques in AERMOD Compliance

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Probabilistic & Source Characterization Techniques in AERMOD ComplianceEUEC 2016 – San Diego, CAFebruary 4, 2016June 24, 2015Sergio A. Guerra, Ph.D. – CPP Inc.Ron Petersen, Ph.D., CCM – CPP Inc.


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


Limitations of Building Downwash in BPIP/PRIME


BPIPBuilding Geometry

Standard AERMOD Modeling Process

Meteorological Data

Terrain Data

AERMET

AERMAP

Operating ParametersOperating Parameters AERMOD Compliance


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


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


Schulman, 2012‐ Building Width Issue

Hb=20mL = 45 mW = 220 mD = 2.5 mVe = 20 m/s

Error likely due to- Wake height calculation & large R- Start of enhanced turbulent at large R

W/H>4


Schuman, 2012‐ Building Length Issue

Likely due to enhanced

turbulence up to wake boundary:


BPIP Diagnostic Toolhttp://bit.do/cppwind‐BPIPDiagnostic

Likely Overprediction Factor for each Flow VectorSource 1


ComplianceCompliance

CPP’s EBDCPP’s EBD

BPIP iagnosticBPIP DiagnosticToolBuilding Geometry

Meteorological Data

Terrain Data

AERMET

AERMAP

Operating ParametersOperating Parameters AERMODOth

er In

puts

Bui

ldin

g In

puts

BPIP Diagnostic Tool


• 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 dataConstruct scale model – 3D PrintingInstall model in wind tunnel and measure Cmax versus X


Measure Ground‐level Concentrations

Data taken until good fit and max obtained

Automated Max GL Concentration Mapper


Measure Ground‐level Concentrations With Site Structures Present

Tracer from stack

Max ground-level concentrations measured versus x


Measure Ground‐level Concentrations with Various EBD in Place of Site Structures

Tracer from stack



Measure Ground‐level Concentrations with no Structures

Tracer from stack



EBD MethodSpecify 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


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


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


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


0.00

0.25

0.50

0.75

1.00

BPIP EBD


FACTOR of 2 to 5reduction when EBD used

Very Wide/Narrow Buildings

Typical AERMOD Predictions for Very Wide/Narrow Buildings with BPIP and EBD


AERMOD’s Temporal Mismatch


Model’s AccuracyAppendix 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

24

MONITORED CONCENTRATIONS

AE

RM

OD

CO

NC

EN

TRAT

ION

S 100

1000

-

-


Monitored vs Modeled Data:Paired in Time and Space

AERMOD performance evaluation of three coal-fired electrical generating units in Southwest IndianaKali D. Frost Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014


Probability analyses of combining background concentrations with model-predicted concentrationsDouglas R. Murray, Michael B. Newman 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 concentrationsDouglas R. Murray, Michael B. Newman Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014

SO2 Concentrations Paired in Time Only


AERMOD’s Evaluation


Are We Using the Model Correctly?

Temporal matching is not justifiable

Perfect model AERMOD


Pairing AERMOD and Monitored Values


Probability of Two Unusual Events Happening at the Same Time


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 formationSergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014

Percentile BGg/m3

Max. Availablebased on NAAQSg/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!


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


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 daysEquivalent 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 formationSergio A. Guerra, Shannon R. Olsen, Jared J. Anderson Journal of the Air & Waste Management Association Vol. 64, Iss. 3, 2014


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


Solutions to AERMOD’s LimitationsAdvanced Modeling Technique

Traditional Modeling Technique

Building Dimensions EBD Generated BPIP Generated

BackgroundConcentrations

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


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


Ron Petersen, PhD, CCM Sergio A. Guerra, [email protected] [email protected]: +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!

Probabilistic & Source Characterization Techniques in AERMOD Compliance

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