This document discusses innovative dispersion modeling practices to achieve reasonable conservatism in regulatory modeling demonstrations. It presents a case study evaluating the Emissions and Meteorological Variability Processor (EMVAP) and approaches to establish background concentrations. The case study models SO2 concentrations from a power plant using 1) constant emissions, 2) variable emissions, and 3) EMVAP. EMVAP provides more realistic concentrations while accounting for emission variability. Using the 50th percentile monitored background concentration when combining with modeled values provides statistical conservatism compared to using high percentile values.

1. INNOVATIVE DISPERSION MODELING
PRACTICES TO ACHIEVE A REASONABLE
LEVEL OF CONSERVATISM IN AERMOD
MODELING DEMONSTRATIONS
CASE STUDY TO EVALUATE EMVAP, AND BACKGROUND CONCENTRATIONS
29th Annual Conference on the Environment-St. Paul, MN
November 19, 2014
Sergio A. Guerra - Wenck Associates, Inc.

2. 2
All truth passes through three stages.
First, it is ridiculed.
Second, it is violently opposed.
Third, it is accepted as being self-evident.
Arthur Schopenhauer

3. 3

4. 4
Challenge of new short-term NAAQS

5. AERMOD Model 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.
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6. Perfect Model
6
MONITORED CONCENTRATIONS
AERMOD CONCENTRATIONS

7. 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

8. 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

9. 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

10. 10
EMVAP
• Problem: Currently assume continuous emissions from
proposed project or modification
• Current modeling practices prescribe that an emission
source (e.g., power plant) be modeled as if in continuous
operation at maximum capacity.
• EMVAP assigns emission rates at random over numerous
iterations.
• The resulting distribution from EMVAP yields a more
representative approximation of actual impacts

11. Background Concentrations
11

12. Siting of Ambient Monitors
According to the Ambient Monitoring Guidelines for Prevention of
Significant Deterioration (PSD):
The existing monitoring data should be representative of three
types of area:
1) The location(s) of maximum concentration increase from
the proposed source or modification;
2) The location(s) of the maximum air pollutant
concentration from existing sources; and
3) The location(s) of the maximum impact area, i.e., where
the maximum pollutant concentration would hypothetically
occur based on the combined effect of existing sources and the
proposed source or modification. (EPA, 1987)
U.S. EPA. (1987). “Ambient Monitoring Guidelines for Prevention of Significant
Deterioration (PSD).”EPA‐450/4‐87‐007, Research Triangle Park, NC.
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13. Exceptional Events
http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/
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14. 14

15. Exceptional Events
15

16. 24-hr PM2.5 Santa Fe, NM Airport
Background Concentration and Methods to Establish Background Concentrations in Modeling.
Presented at the Guideline on Air Quality Models: The Path Forward. Raleigh, NC, 2013.
Bruce Nicholson
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17. Probability of two unusual events
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18. Combining 99th percentile Pre and Bkg
(1-hr SO2)
P(Pre ∩ Bkg) = P(Pre) * P(Bkg)
= (1-0.99) * (1-0.99)
= (0.01) * (0.01)
= 0.0001 = 1 / 10,000
Equivalent to one exceedance every 27 years!
= 99.99th percentile of the combined
distribution
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19. Proposed Approach to Combine Modeled
and Monitored Concentrations
• Combining the 99th (for 1-hr SO2) % monitored
concentration with the 99th % predicted concentration is
too conservative.
• A more reasonable approach is to use a monitored value
closer to the main distribution (i.e., the median).
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
19

20. Combining 99th Pre and 50th Bkg
P(Pre ∩ Bkg) = P(Pre) * P(Bkg)
= (1-0.99) * (1-0.50)
= (0.01) * (0.50)
= 0.005 = 1 / 200
= 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
20

21. Positively Skewed Distribution
http://www.agilegeoscience.com
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22. 22
Case Study: Three cases evaluated
1. Using AERMOD by assuming a constant maximum
emission rate (current modeling practice)
2. Using AERMOD by assuming a variable emission rate
3. Using EMVAP to account for emission variability

23. 23

24. 24
Three cases used to model the power plant
Input parameter Case 1 Case 2 Case 3
Description of
Dispersion
Modeling
Current
Modeling
Practices
AERMOD with
hourly emission
EMVAP
(500 iterations)
SO2 Emission rate
(g/s)
478.7
Actual
emission rates
from CEMS
data
Bin1: 478.7
(5.0% time)
Bin 2: 228.7
(95% time)
Stack height (m) 122
Exit temperature
416
(degrees K)
Diameter (m) 5.2
Exit velocity (m/s) 23

25. 25
Results of 1-hour SO2 concentrations for
the three cases
Case 1
(μg/m3)
Case 2
(μg/m3)
Case 3
(μg/m3)
Description
of
Dispersion
Modeling
Current
Modeling
Practices
AERMOD
with hourly
emission
EMVAP
(500
iterations)
H4H 229.9 78.6 179.3
Percent of
117% 40% 92%
NAAQS

26. 26
St. Paul Park 436 ambient monitor location

27. 27

28. 28
Concentrations at different percentiles for the
St. Paul Park 436 monitor (2011-2013)
Percentile g/m3
50th 2.6
60th 3.5
70th 5.2
80th 6.1
90th 9.6
95th 12.9
98th 20.1
99th 25.6
99.9th 69.5
99.99th 84.7
Max. 86.4

29. 29
Case 3 with three different background values
Case 3 with
Max. Bkg
(μg/m3)
Case 3 with
99th % Bkg
(μg/m3)
Case 3 with
50th % Bkg
(μg/m3)
H4H 179.3 179.3 179.3
Background 86.4 25.6 2.6
Total 265.7 204.9 181.9
Percent of NAAQS 135.6% 104.5% 92.8%

30. Conclusion
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• Use of EMVAP can help achieve more realistic
concentrations
• Use of 50th % monitored concentration is statistically
conservative when pairing it with the 99th % predicted
concentration
• Methods are protective of the NAAQS while still
providing a reasonable level of conservatism

31. QUESTIONS…
Sergio A. Guerra, PhD
Environmental Engineer
Phone: (952) 837-3340
sguerra@wenck.com
www.SergioAGuerra.com
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