Pairing aermod concentrations with the 50th percentile monitored value

PAIRING AERMOD CONCENTRATIONS WITH
THE 50TH PERCENTILE MONITORED VALUE
Background Concentrations Workgroup for Air Dispersion Modeling
Minnesota Pollution Control Agency
May 29, 2014
Sergio A. Guerra - Wenck Associates, Inc.

Road Map
• 1-Screening monitoring data
• 2-AERMOD’s time-space mismatch
• 3-Proposed 50th % Bkg Method
2

1. 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.
3

1. Exceptional Events
http://blogs.mprnews.org/updraft/2012/06/co_smoke_plume_now_visible_abo/
4

1. Example Tracer (SF6) Array
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

1. Summary of Tracer and SO2
Observed Outside 90° Downwind
Sector
Vol. 64, Iss. 3, 2014
8

1. 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
9

1. Positively Skewed Distribution
http://www.agilegeoscience.com
10

1. 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
Vol. 64, Iss. 3, 2014
11

2. 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.
12

2. Perfect Model
13
MONITORED CONCENTRATIONS
AERMOD CONCENTRATIONS

2. 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
14

2. Kincaid Power Station and 28 SO2 Monitors
Vol. 64, Iss. 3, 2014
15

2. SO2 Concentrations Paired in Time & Space
Vol. 64, Iss. 3, 2014
16

2. SO2 Concentrations Paired in Time Only
Vol. 64, Iss. 3, 2014
17

18
3. Current Practice for Pairing Bkg and
Mod
• Add maximum monitored concentration
• Add 98th (or 99th) monitored concentration
• Add 98th (or 99th) seasonal concentration

3. 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
19

3. Combining 99th percentile Pre and Bkg
(1-hr SO2)
= (1-0.99) * (1-0.99)
= (0.01) * (0.01)
= 0.0001 = 1 / 10,000
Equivalent to one exceedance every 27 years!
distribution
20

3. 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).
Vol. 64, Iss. 3, 2014
21

3. Combining 98th Pre and 50th Bkg
= (1-0.98) * (1-0.50)
= (0.02) * (0.50)
= 0.01 = 1 / 100
= 99th percentile of the combined
distribution
Vol. 64, Iss. 3, 2014
22

3. Combining 99th Pre and 50th Bkg
= (1-0.99) * (1-0.50)
= (0.01) * (0.50)
= 0.005 = 1 / 200
distribution
Vol. 64, Iss. 3, 2014
23

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3. Blaine ambient monitor location.

3. 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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3. Advantages
1. Simplicity and ease of use
2. Overcomes bias introduced by “exceptional” events
3. Provides a combined probability that is more
conservative than the form of the short-term standards
4. Not based on temporal pairing (e.g., paired sums,
seasonal pairing, etc.) that is inappropriate based on
AERMOD’s mismatch in time and space
5. Allows for flexibility to use higher percentile on a case-by-
case basis
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Conclusion
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• Use of 50th % monitored concentration is statistically
conservative when pairing it with the 98th (or 99th) %
predicted concentration
• Independence of Bkg and Mod distributions is evident
from accuracy evaluations showing lack of correlation
between Pred and Obs values
• Methods is protective of the NAAQS while still
providing a reasonable level of conservatism

QUESTIONS…
Sergio A. Guerra, PhD
Environmental Engineer
Phone: (952) 837-3340
sguerra@wenck.com
www.SergioAGuerra.com
28

Pairing aermod concentrations with the 50th percentile monitored value

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