Background Concentrations and the Need for a New System to Update AERMOD

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Background Concentrations and the
Need for a New System to Update
AERMOD
EPA 11th Conference on Air Quality Modeling
August 13, 2015
Sergio A. Guerra, Ph.D. – CPP Inc.

Outline
• Preliminary evaluation of methods proposed
in Draft Guidance to account for background
concentrations.
• Alternative approach to account for
background.
• Appendix W: lessons learned and proposed
framework for new/advanced modeling
techniques.

Background Concentrations
In the draft Guidance EPA (Section 8.3) proposes the following:
1.Excluding the 90⁰ downwind sector from source in question.
2.Modifying ambient data record when monitor is impacted by
unusual events such as Canadian forest fires, construction, etc. This
is to be accomplished by:
1. Removing hourly or daily data,
2. Scaling or adjusting data from specific days or hours.
3.Pairing monitoring and modeled data on a temporal basis:
1. Season,
2. Hour of day or,
3. In rare cases of isolated sources, an hourly or daily pairing may be
recommended.
4.Using results from a regional-scale photochemical grid model.

Excluding the 90⁰ Downwind Sector
Probability Analyses of Combining Background Concentrations With Model-Predicted Concentrations
Douglas R. Murray and Michael B. Newman
Journal of the Air & Waste Management Association
Vol. 64, Iss. 3, 2014

Summary of Tracer and SO2 Observed
Outside 90° Downwind Sector
Probability Analyses of Combining Background Concentrations With Model-Predicted Concentrations
Douglas R. Murray and Michael B. Newman
Vol. 64, Iss. 3, 2014

Excluding Unusual Events
NASA’s Earth Observatory
http://earthobservatory.nasa.gov

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!

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.

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.

Excluding Unusual Events
Considerations:
• Meteorological data is necessary but seldom
collected at monitoring sites.
• Alternative methods are necessary when no
meteorological data are available.
• Data handling can be challenging.
• Difficult to identify all unusual events
impacting monitor.

Temporal Pairing of Bkg Values

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
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

AERMOD’s Evaluation

Temporal Matching is Not Justifiable
• AERMOD cannot accurately predict
concentrations on a temporal (or spatial) basis.
• Therefore, such pairing should be avoided.
Perfect model AERMOD

Bkg from Regional-Scale
Photochemical Grid Model
• Estimates from AERMOD and photochemical grid
models are not equivalent.
• Each calculates impacts in a very different way and
at different scales.
• EPA guidance states that absolute model output
from photochemical grid‐based models should be
used in a relative fashion due to the effects of
uneven performance and possible major bias in
predicting absolute concentrations of one or more
components (EPA, 2007a).
“Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and
Regional Haze.” EPA‐454/B‐07‐002, April 2007.

Alternative Pairing of Bkg and Pred

Combining 98th Percentile AERMOD and Bkg
P (AERMOD and Bkg) = P(AERMOD) * P(Bkg)
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 probability

Combining 99th percentile AERMOD and Bkg
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!

Combining 98th AERMOD and 50th Bkg
= (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 probability
Vol. 64, Iss. 3, 2014

Combining 99th AERMOD and 50th Bkg
= (1-0.99) * (1-0.50)
= (0.01) * (0.50)
= 0.005 = 1 of 200 days
Equivalent to 1.8 exceedances every year
Vol. 64, Iss. 3, 2014

Guideline on Air Quality Models
• Appendix W from 40 CFR Part 51: Guideline on Air Quality
Modeling.
• Originally published in 1978 and periodically revised to ensure
that new model developments or expanded regulatory
requirements are incorporated.
• Purpose is to streamline dispersion modeling techniques
across the country.
• Defines the accepted regulatory models.
• Critics stated that rigidity of rules would inhibit innovation
and would render Guidance obsolete as technology and
science advanced.

App. W Reset
• App. W needs to establish a Technical Review Advisory
Committee (TRAC) with the ability to evaluate, approve, and
incorporate new methods without the need to undergo a long
and infrequent rulemaking process.
• TRAC composed of leading experts from EPA, industry, and
academia.
• Purpose is to evaluate new dispersion modeling techniques and
incorporate scientifically valid methods to the regulatory model
in an expedient manner.
• APM Committee from AWMA can provide a good framework
for TRAC.

Why an App. W Reset?
Timing:
• Updates to guidance require long and complicated rulemaking
process.
• Current system results in a lengthy time gap between proposal
of new/advanced methods and their implementation for
widespread use.
• Current mechanism does not allow for an expedient update of
the model to incorporate “fixes” (e.g., AERMET’s adjusted u-star
option) and new techniques (e.g., ARM2) that science develops.

Rulemaking Process:
• To keep up with new methods and science, EPA was
supposed to update Guidance through rulemaking
process (i.e., formal public comment).
• Instead, EPA has issued “non binding” guidance (or TAD)
without formal evaluation process or public involvement.
• However, the courts have stated that:
If an agency acts as if a document issued at
headquarters is controlling in the field …if it
leads private parties or State permitting
authorities to believe that it will declare
permits invalid unless they comply with the
terms of the document, then the agency’s
document is for all practical purposes “binding.”
Appalachian Power Co. V. EPA, 208 F.3d 1015, 1021 (D.C. Cir., 2000)
• In reality, rulemaking /evaluation process has been
circumvented.

• Action:
– Science is constantly advancing new methods and refinements in
dispersion modeling.
– We must recognize that the current system needs to be more
efficient.
– Stakeholders need to change paradigm and embrace collaboration.

• Consistency:
– EPA has incorporated “Beta” options in AERMOD to add new methods
and refinements to the model.
– EPA has updated AERMOD and its pre-processors on a regular basis:
• AERMOD (11), AERSCREEN (5), AERMET(6), AERMAP (3),
AERMINUTE (3), AERSURFACE (1), BPIP (0)
– Types of updates include enhancements, bug fixes, and miscellaneous
changes (e.g., adding downwash above GEP height in #3 of MCB4).
– Updates and new modeling techniques originating outside of EPA
have to wait for App. W revisions before they can be available as
“default” options.
– It is not clear what changes can be made by EPA and what changes
need to wait until rulemaking to be effective.

App. W Makeover
• Technical Review Advisory Committee (TRAC) will
– Promote collaboration,
– Share responsibility,
– Result in a more efficient process,
– Improve timing of implementation of new science,
– Create consistency.
• APM committee from AWMA would be ideal framework- major
players are part of it already.
• As technology/science advance and evolve; so does our
professional framework.
• Let’s prove the critics of 1978 wrong, let’s update App. W so it
can work as efficiently as it was intended.

Summary of Comments
1. Excluding monitored values from the 90⁰ downwind sector does not
avoid double counting of ambient impacts.
2. Unusual events should be excluded from monitoring data but alternative
methods that do not depend on met data need to also be considered.
3. The pairing of modeled values with lower monitored percentiles (i.e.,
50th percentile) should be considered.
4. Statements about model accuracy for long and short term averages
should remain in the updated Guidance. Otherwise, evidence should be
provided that these statements are no longer valid.
5. Temporal matching is not justifiable because AERMOD’s accuracy is
suspect on a temporal basis.
6. Background values from photochemical grid modeling should be
reconsidered.
7. The formation of a Technical Review Advisory Committee with the ability
to evaluate and approve changes to the model is urgently needed.

Sergio A. Guerra, PhD
sguerra@cppwind.com
Direct: + 970 360 6020
www.SergioAGuerra.com
CPP, Inc.
2400 Midpoint Drive, Suite 190
Fort Collins, CO 80525
+ 970 221 3371
www.cppwind.com @CPPWindExperts
Thank You!

Background Concentrations and the Need for a New System to Update AERMOD

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Background Concentrations and the Need for a New System to Update AERMOD