Presentation delivered at the Board meeting for the Upper Midwest section of the Air and Waste Management Association meeting on September 16, 2014. Innovative dispersion modeling techniques are presented including ARM2, EMVAP and the 50th percentile background concentration. Case study involves peaking engines that are used 250 hour per year. These intermittent sources are required to undergo a modeling evaluation in many states. Current modeling techniques grossly overestimate the emissions from these sporadic sources.

1. INNOVATIVE DISPERSION MODELING
PRACTICES TO ACHIEVE A REASONABLE
LEVEL OF CONSERVATISM IN AERMOD
MODELING DEMONSTRATIONS
CASE STUDY TO EVALUATE EMVAP, AMR2, AND BACKGROUND CONCENTRATIONS
Presentation to the Board of the Upper Midwest Section of the Air &
Waste Management Association
September 16, 2014
Sergio A. Guerra - Wenck Associates, Inc.

2. 2
Challenge of new short-term NAAQS

3. 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.
3

4. Perfect Model
4
MONITORED CONCENTRATIONS
AERMOD CONCENTRATIONS

5. 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
5

6. 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
6

7. 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
7

8. 8
Roadmap
• Case study based on 4 reciprocating internal combustion
engines (RICE) used for emergency purposes
• Engines are also part of a peaking shaving agreement
and may be required to operate 250 hour per year
• 3 Modeling techniques are presented
• EMVAP
• ARM2
• The use of the 50th percentile monitored concentration as Bkg

9. 9
EMVAP
• Problem: Currently assume continuous emissions from
proposed project or modification
• In this case study an applicant is requesting to load shave
250 hour per year.
• Current modeling practices prescribe that the engines be
modeled as if in continuous operation(i.e., 8760
hour/year).
• EMVAP assigns emission rates at random over numerous
iterations.
• The resulting distribution from EMVAP yields a more
representative approximation of actual impacts

10. 10
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.

11. Podrez, M. “Ambient Ratio Method Version 2 (ARM2) for use with ARMOD 1-hr NO2 Modeling”, 2013.
11

12. Four cases evaluated
Input parameter Case 1 Case 2 Case 3 Case 4
Description of
Dispersion
Modeling
Current
Modeling
Practices
EMVAP
(500 iterations)
ARM2 Method
EMVAP and
ARM2 Method
Maximum peak
shaving hours per
year
250 250 250 250
Hours of operation
assigned in the
model
8760 250 8760 250
NOx to NO2
Conversion
Assumed
100%
conversion
Assumed 100%
conversion
Calculated based
on the ARM2
equation
Calculated based
on the ARM2
equation
12

13. Engine Input Parameters
Input parameters per engine
Stack height (m) 10
NOx Emission rate (g/s) 5.0
Exit temperature
(degrees K)
700
Diameter (m) 0.305
Exit velocity (m/s) 50
13

14. 14
Results of 1-hour NO2 Concentrations
Case 1
(μg/m3)
Case 2
(μg/m3)
Case 3
(μg/m3)
Case 4
(μg/m3)
Case Description
Current
Modeling
Practices
EMVAP
(500 itr.)
ARM2
Method
EMVAP
and
ARM2
Method
H8H 2,455.6 577.8 491.1 157.7
Percent of
1,306% 307% 261% 84%
NAAQS

15. Background Concentrations
15

16. Sitting 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.
16

17. Exceptional Events
http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/
17

18. Exceptional Events
18

19. Exceptional Events
19

20. 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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21. Probability of two unusual events
21

22. Combining 98th percentile Pre and Bkg
(1-hr NO2 and 24-hr PM2.5)
P(Pre ∩ Bkg) = P(Pre) * P(Bkg)
= (1-0.98) * (1-0.98)
= (0.02) * (0.02)
= 0.0004 = 1 / 2,500
Equivalent to one exceedance every 6.8 years!
= 99.96th percentile of the combined
distribution
22

23. 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
23

24. Proposed Approach to Combine Modeled
and Monitored Concentrations
• Combining the 98th (or 99th for 1-hr SO2) % monitored
concentration with the 98th % 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
24

25. Combining 98th Pre and 50th Bkg
P(Pre ∩ Bkg) = P(Pre) * P(Bkg)
= (1-0.98) * (1-0.50)
= (0.02) * (0.50)
= 0.01 = 1 / 100
= 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
25

26. 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
26

27. Positively Skewed Distribution
http://www.agilegeoscience.com
27

28. 24-hr PM2.5 observations at Shakopee
2008-2010
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
28

29. Background concentrations
1) Bkg 1: Maximum 1-hour NO2 observations from the
Blaine monitor averaged over three years.
2) Bkg 2: Average of the annual 98th percentile daily
maximum 1-hour NO2 concentrations for years 2010-
2012.
3) Bkg 3: 50th percentile concentration from the 2010-
2012 hourly observations.
29

30. 30
Case 4 with three different backgrounds
Case 4 with
Bkg 1
(μg/m3)
Case 4 with
Bkg 2
(μg/m3)
Case 4 with
Bkg 3
(μg/m3)
Max. 98th% 50th %
H8H 157.7 157.7 157.7
Background 106.6 86.6 9.4
Total 264.3 244.2 167.1
Percent of
NAAQS
140.6% 130.0% 88.9%

31. Blaine ambient monitor location.
31

32. Histogram of 1-hour NO2 observations
Percentile g/m3
50th 9.4
60th 13.2
70th 16.9
80th 26.4
90th 39.5
95th 52.7
98th 67.7
99.9th 97.8
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33. Conclusion
33
• Use of EMVAP and ARM2 can help achieve more
realistic concentrations
• Use of 50th % monitored concentration is statistically
conservative when pairing it with the 98th (or 99th) %
predicted concentration
• 3 Methods are protective of the NAAQS while still
providing a reasonable level of conservatism

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